Technology

Highlights Portfolio Strategy Synchronized global capex growth and higher interest rates are two key themes that will continue to dominate this year. Three high-conviction calls are levered to the former theme and two to the latter. A special situation completes our sextet. Reinstate the S&P construction machinery & heavy truck index to the high-conviction overweight list. We also reiterate our high-conviction underweight call in the newcomer S&P telecom services sector. Recent Changes S&P Construction Machinery & Heavy Truck - Add back to high-conviction overweight list. Table 1 Semblance Of Calm Semblance Of Calm Feature Chart 1Market Bounced Smartly Market Bounced Smartly Market Bounced Smartly Equities regained their footing last week, as volatility took a breather. There are high odds that the technical, mostly-sentiment driven, pullback that we have been flagging since January 22nd is nearly over, as the market smartly bounced off the 200-day moving average (top panel, Chart 1).1 A consolidation/absorption phase is looming and, according to our "buy the dip" cycle-on-cycle analysis, a retest of the recent lows is likely before the market gets out of the woods (please refer to Chart 1 from last week's publication). While inflation expectations, crude oil prices and financial conditions are all tightly linked with and weighing on the S&P 500 (second and third panels, Chart 1), a number of tactical high-frequency financial market indicators suggest that the cyclical SPX bull market remains intact. First, SPX e-mini futures positioning is an excellent leading indicator of market momentum, and the current message is positive (net speculative positions are advanced by 40 weeks, Chart 2). Second, bond market internal dynamics suggest that this mini "risk off" episode is an isolated one and not a precursor to a real tremor. The high yield bond ETF outperformed the long dated Treasury bond ETF (bottom panel, Chart 3). It would be unprecedented for an equity market downdraft to morph into a fully blown bear market without junk bonds sinking compared with the ultimate risk free asset. Even when adjusted for its lower duration, the high yield bond ETF remained resilient versus the 3-7 year Treasury bond ETF (top panel, Chart 3). Chart 2Futures Positioning... Futures Positioning... Futures Positioning... Chart 3...Junk Bonds... ...Junk Bonds... ...Junk Bonds... Third, the calmness in the TED spread corroborates the message from the bond market. Were a systemic risk to materialize, the TED spread should have widened and not come in as it did in the past two weeks (Chart 4). Put differently, quiet interbank markets are a healthy sign. Chart 4...And TED Spread All Flashing Green Semblance Of Calm Semblance Of Calm Finally, relative valuations have corrected not only on an absolute basis (please refer to the bottom panel of Chart 2A from last week's Report), but also controlled for equity market volatility. In fact, Chart 5 shows that both the VIX-adjusted Shiller P/E and the 12-month forward P/E have returned to the neutral zone. Meanwhile, two key macro indicators we track are also flashing green. Chart 6 shows momentum in money velocity or how fast "one unit of currency is used to purchase domestically-produced goods and services".2 Historically, velocity of M2 money stock has been positively correlated with stock market momentum. The recent spike in this indicator suggests that the longevity of the business cycle remains intact, and investors with a cyclical (9-12 month) investment horizon should start "buying the dip", as we suggested on February 8th.3 Another yield curve-type macro indicator confirms this buoyant business cycle message: real GDP growth is easily outpacing real interest rates, as per the 10-year TIPS market (Chart 7). In other words, real rates are not yet restrictive enough to choke off GDP growth, despite the recent 35bps increase. Were this spread to plunge below the zero line, it would predict recession. Thus, the recent widening underscores that recession is not imminent. Chart 5Valuations Return To Earth Valuations Return To Earth Valuations Return To Earth Chart 6Money Velocity... Money Velocity... Money Velocity... Chart 7...And Yield Curve Emit Bullish Signal ...And Yield Curve Emit Bullish Signal ...And Yield Curve Emit Bullish Signal Under such a backdrop, the upshot is that earnings will remain upbeat in 2018 and continue to underpin equity prices. This week we revisit our 2018 high-conviction call list and reinstate one sector to the overweight column. Chart 8Both Themes Remains Intact Both Themes Remains Intact Both Themes Remains Intact The Themes Two key BCA themes formed the cornerstone of our 2018 high conviction call list: Synchronized global capex upcycle Higher interest rates Last autumn, we started to articulate the synchronized global capital spending macro theme4 that, despite still flying under the radar, will likely dominate this year. Both advanced and emerging economies are simultaneously expanding gross fixed capital formation (middle panel, Chart 8). As a result, we reiterate our cyclical over defensive portfolio bent,5 and continue to tie three high-conviction overweight calls to this theme. Similarly, late last year we started to highlight BCA's U.S. Bond Strategy view of a higher 10-year yield on the back of rising inflation expectations for 2018 (bottom panel, Chart 8). Back in late-November we posited that if BCA's constructive crude oil view pans out then inflation and rates may get an added boost. Two high-conviction calls remain levered to this theme. Finally, a special situation rounds up our call this year. But before we update the call list and make a small tweak, a quick housekeeping note is in order. Taking The Tally Early this year, we added trailing stops to our high-conviction call list as a risk management tool. The goal was to help protect profits as a number of our calls were showing outsized gains for such a short time span. Our tactically souring view of the overall market also compelled us to introduce this risk management metric. As a result of the recent careening in the SPX, half of our calls got stopped out with lofty double digit gains since inception a mere two and a half months ago. Namely, our speculative underweights in the S&P semi equipment and S&P homebuilders registered gains of 20% and 10%, respectively. The high-conviction underweight in the S&P utilities sector got called at an 18% gain, and our high-conviction overweight call in the S&P construction machinery & heavy truck (CMHT) index got stopped out at the 10% mark. (Please refer to page 15 for the closed trades table). Last week we added the S&P telecom services sector as a high-conviction underweight replacing the S&P utilities sector, and now that the worst is likely behind us, we are reinstating the S&P CMHT index to the high-conviction overweight list. Anastasios Avgeriou, Vice President U.S. Equity Strategy anastasios@bcaresearch.com Construction Machinery & Heavy Truck (Overweight, Capex Theme) The capex upcycle is underpinning machinery stocks. Not only are expectations for overall capital outlays as good as they get (Chart 9), but there are also tentative signs that even the previously moribund mining and oil & gas complexes will be capex upcycle participants. While we are not calling for a return to the previous cycle's peak, even a modest renormalization of capital spending plans in these two key machinery client segments would rekindle industry sales growth. Recent news of oil majors accelerating their capex plans is a step in the right direction. This machinery end-demand improvement is not only a U.S. phenomenon, but also a global one. The middle panel of Chart 9 shows Caterpillar's global machinery sales to dealers hitting a decade high. Tack on the drubbing in the U.S. dollar and related commodity price inflation and the ingredients are in place for a global machinery export boom. While most of the countries we track enjoy a sizable rebound in machinery orders, Japan's machine tools orders have surged to an all-time high confirming that machinery global end demand is brisk (bottom panel, Chart 9). Finally, our machinery EPS model is firing on all cylinders, underscoring that the earnings-led recovery has more running room (fourth panel, Chart 9). Reinstate the S&P CMHT index to the high-conviction overweight list. The ticker symbols for the stocks in this index are: BLBG: S5CSTF - CAT, CMI, PCAR. Energy (Overweight, Capex Theme) The S&P energy sector is a key beneficiary of our synchronized global capex theme. The Dallas Fed manufacturing outlook survey is firing on all cylinders and, given the importance of oil to the state of Texas, it serves as an excellent gauge for oil activity. Importantly, the capital expenditures part of the survey hit its highest level in a decade, and capex intentions in the coming six months are also probing multi-year highs. The overall message is that the budding recovery in energy capital budgets will likely gain steam (second panel, Chart 10). Following the late-2015/early-2016 drubbing in oil prices, energy projects ground to a halt and only now are green shoots appearing (middle panel, Chart 10). Recent news that Exxon Mobil would bump domestic capital spending up to $50bn over the next five years is encouraging. New projects/investments comprise 70% of this figure. OECD oil stocks are receding steadily and so are U.S. crude oil inventories. OPEC 2.0 remains in place and will likely balance the oil market by continuing to constrain supply. Our Commodity & Energy Strategy service is still penciling in higher oil prices for 2018. On the demand side, emerging markets/Chinese demand is the key determinant of overall oil demand, and the news on this front is encouraging and consistent with BCA's synchronized global growth theme: following the recent lull, non-OECD demand is growing anew by roughly 1.5mn bbl/day. The upshot is that S&P energy relative revenues will climb out of the recent trough (bottom panel, Chart 10). The ticker symbols for the stocks in this index are: BLBG: S5ENRS - XLE: US. Chart 9Construction Machinery & Heavy Truck ##br##(Overweight, Capex Theme) Construction Machinery & Heavy Truck (Overweight, Capex Theme) Construction Machinery & Heavy Truck (Overweight, Capex Theme) Chart 10Energy (Overweight, Capex Theme) Energy (Overweight, Capex Theme) Energy (Overweight, Capex Theme) Software (Overweight, Capex Theme) The S&P software index is another clear capex upcycle beneficiary. If software commands a larger slice of the overall capital spending pie as we expect, then industry profits should enjoy a healthy rebound (second panel, Chart 11). Small business sector plans to expand keep on hitting fresh recovery highs, underscoring that software related outlays will likely follow them higher. Rebounding bank loan growth also corroborates the upbeat spending message and signals that businesses are beginning to loosen their purse strings (Chart 11). Reviving animal spirits suggest that demand for software upgrades will stay elevated. CEO confidence is pushing decade highs (middle panel, Chart 11). Such ebullience is positive for a pickup in software outlays. It has also rekindled software M&A activity, and pushed take out premia higher. Meanwhile, the structural pull from the proliferation of cloud computing and software-as-a-service has served as a catalyst to raise the profile of this more defensive and mature tech sub-sector. Tax reform is another bonus for this group that benefits from cash repatriation, which will likely result in increased shareholder friendly activities. The ticker symbols for the stocks in this index are: BLBG: S5SOFT-MSFT, ORCL, ADBE, CRM, ATVI, INTU, EA, ADSK, RHT, SYMC, SNPS, ANSS, CDNS, CTXS, CA. Banks (Overweight, Higher Interest Rates Theme) The S&P banks index remains a core overweight portfolio holding and there are high odds of additional relative gains in the coming quarters beyond the current 10% relative return mark since the November 27th, 2017 inception. All three key drivers of bank profits, namely price of credit, loan growth and credit quality, are simultaneously moving in the right direction. On the price front, BCA expects the 10-year yield will continue to rise more quickly than is discounted in the forward curve. Our U.S. bond strategists think that inflation expectations have more room to run, likely pushing the 10-year Treasury yield close to 3.25% (top panel, Chart 12). C&I and consumer loans, two large credit categories, are both forecast to reaccelerate in the coming months. The ISM remains squarely above the 50 boom/bust line and consumer confidence is still buoyant. Our credit growth model captures these positive forces and is sending an unambiguously positive message for loan reacceleration in the coming months (third panel, Chart 12). Finally, credit quality remains pristine despite some pockets of weakness in auto loans (especially subprime) and credit card debt. At this stage of the cycle, with a closed unemployment gap, NPLs will remain muted. The ticker symbols for the stocks in this index are: BLBG: S5BANKX - WFC, JPM, BAC, C, USB, PNC, BBT, STI, MTB, FITB, CFG, RF, KEY, HBAN, CMA, ZION, PBCT.  Chart 11Software (Overweight, Capex Theme) Software (Overweight, Capex Theme) Software (Overweight, Capex Theme) Chart 12Banks (Overweight, Higher Interest Rates Theme) Banks (Overweight, Higher Interest Rates Theme) Banks (Overweight, Higher Interest Rates Theme) Telecom Services (Underweight, Higher Interest Rates Theme) We downgraded the S&P telecom services index to underweight and added it to the high-conviction underweight list last week, filling the void left by the S&P utilities sector.6 Three main reasons are behind our dislike for this fixed income proxy sector: BCA's 2018 rising interest rate theme, both our Cyclical Macro Indicator (CMI) and our sales model send a distress signal, and a profit margin squeeze is looming. The top panel of Chart 13 shows that high dividend yielding telecom services stocks and the 10-year yield are nearly perfectly inversely correlated. In fact, telecom services stocks are prime beneficiaries of disinflation/deflation and vice versa. BCA's bond market view remains that the 10-year yield will continue to rise likely piercing through 3% and weigh heavily on this fixed income proxied sector. Our CMI has melted and relative consumer outlays on telecom services have also taken a nosedive (second & third panels, Chart 13), warning that revenue growth will be hard to come by for telecom carriers. In fact, while nearly all of the GICS1 sectors have come out of the top line growth lull of late-2015/early-2016, telecom services sales growth has relapsed. Worrisomely, our S&P telecom services revenue growth model remains deep in contractionary territory, waving a red flag (bottom panel, Chart 13). Finally, still steeply deflating selling prices are a major headwind for the sector's top and bottom line growth prospects and coupled with a still expanding wage bill, suggest that a profit margin squeeze is looming. The ticker symbols for the stocks in this index are: VZ, T, CTL. Pharmaceuticals (Underweight, Special Situation) Weak pricing power fundamentals, a soft spending backdrop, a depreciating U.S. dollar and deteriorating industry operating metrics will sustain downward pressure on pharma stocks. Industry selling prices remain soft (Chart 14). In the context of a bloated industry workforce, the profit margin outlook darkens significantly. If the Trump administration also manages to clamp down on the secular growth of pharma selling price inflation, as we expect, then industry margins will remain under chronic downward pressure. Our dual synchronized global economic and capex growth themes bode ill for this safe haven index. Nondiscretionary health care outlays jump in times of duress and underwhelm during expansions. Currently, the elevated ISM manufacturing index is signaling that pharma profits will underwhelm in the coming months as the most cyclical parts of the economy flex their muscles (the ISM survey is shown inverted, second panel, Chart 14). A depreciating currency is also synonymous with pharma profit sickness (bottom panel, Chart 14). While pharma exports should at least provide some top line growth relief during depreciating U.S. dollar phases, they are still contracting (middle panel, Chart 14), warning that global pharma demand is ill. Finally, even on the operating metric front, the outlook is dark. Pharma industrial production is nil and our productivity proxy remains muted, warning that the valuation derating phase is far from over. The ticker symbols for the stocks in this index are: BLBG: S5PHAR - JNJ, PFE, MRK, BMY, AGN, LLY, ZTS, MYL, PRGO. Chart 13Telecom Services ##br##(Underweight, Higher Interest Rates Theme) Telecom Services (Underweight, Higher Interest Rates Theme) Telecom Services (Underweight, Higher Interest Rates Theme) Chart 14Pharmaceuticals ##br##(Underweight, Special Situation) Pharmaceuticals (Underweight, Special Situation) Pharmaceuticals (Underweight, Special Situation) 1 Please see BCA U.S. Equity Strategy Weekly Report, "Too Good To Be True?" dated January 22, 2018, available at uses.bcaresearch.com. 2 https://fred.stlouisfed.org/series/M2V 3 Please see BCA U.S. Equity Strategy Insight, "Buy The Dip," dated February 8, 2018, available at uses.bcaresearch.com. 4 Please see BCA U.S. Equity Strategy Weekly Report, "Invincible," dated November 6, 2017, available at uses.bcaresearch.com. 5 Please see BCA U.S. Equity Strategy Special Report, "Top 5 Reasons To Favor Cyclicals Over Defensives," dated October 16, 2017, available at uses.bcaresearch.com. 6 Please see BCA U.S. Equity Strategy Weekly Report, "Manic Depressive?" dated February 12, 2018, available at uses.bcaresearch.com. Current Recommendations Current Trades Size And Style Views Favor value over growth. Stay neutral small over large caps (downgrade alert).

Highlights Despite having the largest negative return of major markets during the global equity market correction, China's investable stock selloff appears to be normal after controlling for its risk characteristics. Taken together, the association between the global correction and volatility/valuation should be viewed as a sharp reduction in complacency in the market. Several factors make us cautious about China's outsized tech sector exposure in a world of reduced complacency. We recommend that investors retain cyclical exposure to investable Chinese stocks while neutralizing exposure to the tech sector. Feature Chart 1An Average Size, But Very Rapid, ##br##Global Selloff An Average Size, But Very Rapid, Global Selloff An Average Size, But Very Rapid, Global Selloff Global equities have sold off quite sharply since the end of January, having declined a total of 9% in US$ terms from their January 26 high to last Friday's close (Chart 1). BCA addressed the rout in a Special Report last week,1 and noted that strong economic growth and positive earnings surprises are likely to keep the global equity bull market intact, a view largely supported by this week's stock market behavior. Still, the report also highlighted that investors need to adjust to the fact that realized volatility is likely to sustainably rise, even if forward-looking volatility measures (such as the VIX in the U.S.) are currently too elevated. More generally, we equate the return of volatility with a reduction in complacency, and in this week's report we explore the implications of lower complacency for investors with an overweight allocation towards Chinese equities. Our judgement is that the complacency risk for China's ex-tech equity market is low, but that the same cannot be said for China's technology stocks. We conclude by recommending two trades that investors can employ to retain cyclical exposure to investable Chinese stocks, but with a neutralized exposure to the tech sector. Normal Underperformance For China Chart 2At First China Appears To Be Among ##br##The Worst Performers... After The Selloff: A View From China After The Selloff: A View From China At first blush, China's investable stock market fared quite poorly during the global stock market correction. Chart 2 lists 21 major country stock markets by the magnitude of their decline in US$ terms and highlights that China's selloff ranks at the very top of the list. But a simple comparison of stock market performance is misleading, as it fails to adjust for the different degrees of riskiness that are normally observed across global equity markets. For example, it is well known that emerging market equities have tended to be high beta relative to global stocks over the past decade, and we noted in a recent Special Report that Chinese investable stocks have become high beta even relative to emerging markets. In order to properly compare the performance of these markets during the global stock market selloff, we rely on the concept of "abnormal return" that is often employed in event study analysis. This approach involves calculating a counterfactual "normal" return for each market based on its rolling 1-year alpha and beta versus global stocks prior to the selloff, and then comparing it to the actual return. This difference, the "abnormal return" of each market, is shown in Chart 3, which highlights that China's performance during the selloff was perfectly normal after controlling for its risk characteristics. In fact, Chart 3 shows that many equity markets outperformed on a risk-adjusted basis, highlighting that the magnitude of the selloff in global stocks could actually have been worse. As for the underlying cause of the selloff, we showed in last week's Special Report that a crowded "short volatility" trade was undoubtedly a driving force: Chart 4 highlights that net long speculative positions on the VIX had fallen to a new low over the past six months, a circumstance that has now completely reversed. But Chart 5 shows that valuation also appears to have been a factor contributing to the selloff, by presenting the abnormal returns shown in Chart 3 as a function of the difference between the market's 12-month forward P/E and that of the global benchmark. While the fit is somewhat loose, the chart confirms that markets with higher (lower) forward P/E ratios were more likely to have negative (positive) abnormal returns over the two-week period. Chart 3...But Not After Adjusting##br## For Riskiness After The Selloff: A View From China After The Selloff: A View From China Chart 4The Low-Vol Trade Contributed ##br##To The Speed Of The Selloff... The Low-Vol Trade Contributed To The Speed Of The Selloff... The Low-Vol Trade Contributed To The Speed Of The Selloff... Taken together, the association between the selloff and volatility/valuation should be viewed as a sharp reduction in complacency in the market. While this does not necessarily bode poorly for global equities over the coming 6-12 months, there are some potential implications to explore for China's investable stock market. Chart 5...But Valuation Was Also A Factor After The Selloff: A View From China After The Selloff: A View From China Complacency Risk And Chinese Stocks The sharp reversal in global markets raises the question of whether Chinese equities are complacent about some looming risk. The obvious candidate for complacency risk in China would be focused on its economy, and the potential for a more substantial economic slowdown than is currently expected by market participants. However, we are unconvinced that Chinese ex-tech stocks are somehow neglecting the risks facing China's economy over the coming year. First, we have noted in previous reports that Chinese investable ex-tech stocks are extremely cheap versus global ex-tech stocks, highlighting that investors have priced in a degree of structural risk. Second, recent economic data releases from China do not suggest that the pace of the ongoing economic slowdown is accelerating, suggesting that there is no basis to expect a severe downturn over the coming year. But we acknowledge that the same cannot be said for China's tech sector. While Chinese tech stocks are not stretched on a technical basis (either versus the investable benchmark or versus global tech stocks), several observations make us cautious about China's outsized tech exposure in a world of reduced complacency: First, the growth rates of IBES 12-month trailing and forward earnings growth for global technology stocks are currently at the 80th and 85th percentiles, respectively (Chart 6). This suggests that a substantial amount of fundamental improvement has already been priced in to global tech stocks, raising the risk of earnings disappointment over the coming year. Given that China's tech sector weight (42%) is considerably above that of the global benchmark (18%), a global tech selloff would cause China's investable stock market to underperform even if Chinese tech performance is in line with that of the global tech sector. Second, relative to global technology stocks, the growth rates of China's 12-month trailing and forward earnings growth are also quite elevated, at the 80th and 70th percentiles, respectively (Chart 6 panel 2). This suggests that the tech earnings exuberance observed globally is even worse in China. Third, Chart 7 highlights that China's tech sector has been responsible for pushing our relative composite valuation indicator for China into overvalued territory over the past year. Relative to global ex-tech, China's ex-tech stocks are still significantly cheap; relative to global tech, China's tech stocks are significantly overvalued. Last, we have noted in past reports that China's tech sector appears to be a domestic consumer play, and thus unlikely to significantly underperform over the coming year. However, we also noted in last week's report on China's housing market that the optimism of the consumer sector may be somewhat unfounded if it is based on expectations of future gains in employment and/or income.2 While we do not expect a broad-based retracement in China's consumer sector, even a moderate decline in consumer confidence could spark a non-trivial selloff in Chinese tech stocks given the stretched fundamental picture highlighted above. Chart 6Tech Earnings Growth##br## Is Significantly Stretched Tech Earnings Growth Is Significantly Stretched Tech Earnings Growth Is Significantly Stretched Chart 7Tech Stocks Have Pushed China ##br##Into Overvalued Territory Tech Stocks Have Pushed China Into Overvalued Territory Tech Stocks Have Pushed China Into Overvalued Territory Investment Recommendations Given our observations about the complacency risk facing Chinese tech sector stocks, we are making the following changes to our investment recommendations: We are closing our overweight MSCI China Free versus the emerging markets benchmark trade for a 31% relative return. This has been a core trade for BCA's China Investment Strategy service and has provided investors with significant outperformance since its initiation in May 2012. We are opening two new trades as a replacement for the closed China / EM position: 1) long MSCI China investable ex-technology / short MSCI All Country World ex-technology, and 2) long MSCI China investable value / short All Country World value. These two new trades are a slight variation of a single theme, which is to retain cyclical exposure to investable Chinese stocks while neutralizing exposure to the tech sector. While style indexes such as value and growth normally do not have such a stark sector orientation, Chart 8 highlights that the relative performance of China value vs global value looks very similar to our internally-calculated ex-technology indexes for both markets. This is because MSCI's China growth index is almost entirely made up of tech sector stocks, meaning that a relative value play effectively mimics an ex-tech position. As a final point, we noted above that it is difficult to see how Chinese ex-tech equities are complacent about the ongoing slowdown in China's economy. Chart 9 supports this view by presenting a model for China's investable ex-tech 12-month trailing earnings in US$ terms, based on the Li Keqiang index. The model fit has been tight over the past decade, and is currently forecasting roughly 10% earnings growth over the coming year. This would clearly represent a significant deceleration from current levels, but it is still a decent earnings result that signals Chinese ex-tech stocks are attractive on a risk/reward basis given the sizeable valuation discount that is levied on China relative to global stocks. Chart 8China Ex-Tech And Value:##br## Similar Performance Vs Global China Ex-Tech And Value: Similar Performance Vs Global China Ex-Tech And Value: Similar Performance Vs Global Chart 9Positive Ex-Tech Earnings Growth Likely, ##br##Even With A Slowing Economy Positive Ex-Tech Earnings Growth Likely, Even With A Slowing Economy Positive Ex-Tech Earnings Growth Likely, Even With A Slowing Economy We remain alert to the possibility of a further, more pronounced slowdown in China's economy, but barring that Chinese ex-tech stocks appear to be a solid buy over the coming 6-12 months. Jonathan LaBerge, CFA, Vice President Special Reports jonathanl@bcaresearch.com 1 Please see Global Investment Strategy Special Report, "The Return Of Vol", dated February 6, 2018, available at gis.bcaresearch.com. 2 Please see China Investment Strategy Weekly Report, "Is China's Housing Market Stabilizing?", dated February 8, 2018, available at cis.bcaresearch.com. Cyclical Investment Stance Equity Sector Recommendations

Dear Client, This Special Report is the full transcript and slides of a presentation I recently gave at the London School of Economics symposium: 'Will I Work For AI, Or Will AI Work For Me?' The presentation pulls together several years of research analyzing the impact of current technological advances on work, the economy and society. I hope you find the presentation insightful and provocative, especially the narrative surrounding Slide 12. Dhaval Joshi Slide 2 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Feature Good afternoon Thank you very much for the invitation to speak here at the London School of Economics. The specific question you asked me was: will we be able to work in the future? (Slide 1). To which my answer is yes, an emphatic yes. I'm very optimistic that we will be able to work in the future. And one reason I'm saying this is, imagine that we had this symposium 100 years ago. I suspect we might have had exactly the same fears that we have right now (Slide 2). Slide 1 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 2 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Specifically, at the start of the 20th century, about 35% of all jobs were on farms and another 6% were domestic servants. At the time, you could probably also have said, "Well, these jobs aren't going to exist." More or less half of the jobs that existed at that time were going to disappear - and disappear they did. So we'd have thought there would be mass unemployment. Of course, there wasn't mass unemployment, because just as jobs were destroyed, we had an equivalent job creation (Slide 3). For example, at the start of the 20th century, less than 5% of people worked in professional and technical jobs. But by the end of the century, these jobs employed a quarter of the workforce. I guess what I'm saying is that we're very conscious of job destruction because we can see existing jobs being destroyed. But we're not very conscious of job creation, because in real time, it's difficult to visualize or imagine where these new jobs will be. In essence, what we saw in the 20th century was one major segment of employment basically collapsed from very significant to insignificant. While another segment surged from insignificant to very significant (Slide 4). Slide 3 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 4 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? As you all know, there is an economic thesis that underlies this. It's called Say's Law, derived by French economist Jean-Baptiste Say in 1803. In simple terms, it says that new supply creates new demand. Think about it like this: why would you replace a human with a machine? You would only do that if it increases your productivity, right? Otherwise, it does not make sense to replace a human with any sort of machine, including AI. But because you have increased productivity, you then have extra income to spend on new goods and services. Now if those goods and services are being supplied by a machine, then you can redeploy humans to satiate new desires, desires that do not even exist at the time. In economic terms, the producer of X - as long as his products are demanded - is able to buy Y (Slide 5). The question is, what is Y? Y is the new product or service. Let me give you some examples (Slide 6). In the 19th century, we had the advent of railways. And then someone thought. "Hang on a minute. We have this way of moving things around much faster, and we've got all these people who live hundreds of miles from the coast who might want to eat fresh fish." So this was the birth of the frozen food industry. But you could not have the frozen food industry without railways. What I'm saying is that entrepreneurs will seize the new technology to satiate a desire. Or even create a new desire because maybe the people in the middle of the country never thought they could eat fresh sea fish. Until someone came along and said, "you can eat fresh fish now." Slide 5 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 6 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Another example is, as technology improved the health and longevity of your teeth someone thought. "Well, hang on a minute. Maybe there's a desire to make teeth look beautiful." And we created this whole new industry called the dental cosmetics industry. We know this because prior to the 1960s, there was no job called dental technician or dental hygienist. A third example is, let's say that we have more advanced healthcare and pharmaceuticals, so humans are living longer and healthier lives. Well, then you can sort of ask. "Hang on a minute. Don't you want your dog to live the same long and healthy life that you're living?" And this is behind the explosion of the pet care industry that we're seeing at the moment. So while one segment of the economy will employ less, a new segment will come along to replace it. In the 20th century we saw farm work disappearing but professional work rising. Today, we are seeing manufacturing and driving jobs disappearing but healthcare work rising (Slide 7). Which does raise a pretty obvious question (Slide 8). Is there anything really different this time around? Slide 7 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 8 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Well, the answer is yes, there is a subtle but crucial difference this time around. To see the difference, we have to look more closely at where jobs are being destroyed, and where they are being created. As you can see, the mega-sectors losing a lot of jobs are manufacturing, the auto industry, and finance (Slide 9). While on the other side of the ledger, we have job creation in health, social work and education. But now, let's look in a little more detail. Where, specifically, are the jobs being created? For this we have to look at the United States data which is much more granular than in Europe. Here are the top five subsectors of job creation this decade (Slide 10). At the top of the list is food services and drinking places, which is just a euphemistic way of describing bartenders, waitresses, and pizza delivery boys. We also have a lot of new administrative jobs and care workers. What is the common link in this job creation? Answer: these are predominantly low-income jobs. Slide 9 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 10 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? So it is true that we have an enormous amount of job creation in the last decade or so, and the policymakers keep boasting about it, they say, "Well look, the unemployment rate in the U.S. is at a record low, the unemployment rate in the UK is at a record low, the unemployment rate in Germany is at a record low. We're creating loads and loads of jobs." The trouble is that these are predominantly low-income jobs. Meanwhile the job destruction is in middle-income jobs in manufacturing and finance. This means what we're seeing in the labour market is called a 'negative composition effect' - a hollowing out of middle incomes. So while we're getting loads and loads of job creation, it is not translating into wage inflation at an aggregate level. I think one of the reasons is a concept called Moravec's paradox. Professor Hans Moravec is an expert in robotics and Artificial Intelligence, and he noticed this paradox (Slide 11). He said, "Look. For AI, the things that we think are difficult are actually easy." By easy, he means they're doable. Let me give you some specific examples. Say someone could speak five languages fluently and translate between them at ease. We would think that person is a genius, a real rare specimen, and the economy would value this person extremely highly, probably pay that person hundreds of thousands of pounds at a minimum. But actually, AI can translate across five languages quite easily, and even something like Google Translate, which we all use, does a reasonably good first stab at translating from one language to another. Slide 11 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Or consider something like insurance underwriting. Pricing an insurance premium from lots of data on a risk. AI can do that extremely well, much better than a human can. Or medical diagnosis. Figuring out what's wrong with a patient from very detailed medical data. Again, AI beats humans hands down on that. What I'm saying is, these skills that we thought were difficult transpired not to be that difficult for AI, because they just amount to narrow-frame pattern recognition and repetition of algorithms. Whereas, the second part of Moravec's paradox is that AI finds the easy things very hard. Things that we think are really innate, we don't even give them a second thought like walking up some stairs, cleaning a table, moving objects around, and cleaning around them. Actually, AI finds these things incredibly difficult, almost impossible. We have a false sense of what is difficult and what is easy. The main reason is that the things that we find innate took millions and millions of years of human brain evolution for us to find them innate. And as AI is in essence trying to replicate the human brain, only now are we recognizing that things that we find innate are actually incredibly complex. If it took millions and millions of years to evolve the sensorimotor skills that allow us to walk up some stairs, recognize subtle emotional signals, and respond appropriately, then obviously AI is going to find it very, very difficult to replicate those innate human skills. Conversely, the brain's ability to do calculus, construct a grammatical structure for a language, or play chess only evolved relatively recently. So AI can do them very easily. Which brings me to quite a profound thought. If there's one thing that I want you to remember from this presentation it is this (Slide 12). Might we have completely misvalued the human brain? Might we have grossly overvalued things that are actually quite easy? And might we have undervalued things which are actually very, very difficult? And what AI is now doing is correcting this huge error. In which case, the next decade could be extremely disruptive as AI corrects this economic misvaluation of our skills. Slide 12 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? This might also explain the mystery as to why there is no wage inflation when the Phillips curve says there should be. The Phillips curve makes a simple relationship between the unemployment rate and wage pressures. And the folks at the Federal Reserve and Bank of England, they're sort of getting really perplexed. They're saying, "Look, unemployment is so low. Where is this wage inflation? It's going to kick in any time now." In fact, there's a bit of a paradox going on. For the people who are continuously employed in the same job, there has been pretty good wage inflation - at sort of three, four percent (Slide 13). But when you take the negative composition effect into account, then suddenly there's this big gap because what's happening is that the well-paid jobs are disappearing to be replaced by lower-paid jobs. So even if you give the bartender making thirty thousand a big pay rise to thirty-five thousand. Even if you hire two of them, but you're losing a finance job paying over a hundred thousand, then at the aggregate level, you won't see much wage inflation. And this problem, I think, continues for the next few years, minimum. It means that you will not get the wage pressures that a lot of economists think you're going to get from the low unemployment rate. Because you have to look at the quality of the jobs as well as the quantity. I think there is another disturbing impact from a societal perspective. Look again at where the jobs are being lost and where they're being created, and look at the percentage of male employees (Slide 14). Job destruction is occurring in sectors that are male-dominated, whereas job creation is occurring in sectors that are female-dominated. Slide 13 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 14 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? AI is good at narrow-frame pattern recognition and repetition of algorithms and functions - jobs like driving, which are typically male-dominated. Whereas jobs that require emotional input, emotional understanding, and empathy in the 'caring sectors' are typically female-dominated. So if you're a male, you're in trouble. You're in a lot of trouble. Obviously, there'll be re-training, so all the guys who were driving trucks will have to retrain as nurses, or as essential carers. But if you're a female, things are looking okay. You can see that in the data (Slide 15). Female labour force participation is in a very clear uptrend. Male participation is flat to down. This varies by country by country, and in the U.S., it's catastrophic for males, especially young males. Young male participation in the U.S. is really falling off a cliff at the moment. I think the other thing to say from a societal perspective is that the so-called 'Superstar Economy' is booming - both superstar individuals and superstar firms. One way of seeing this is in this index called 'the cost of living extremely well' calculated every year by Forbes (Slide 16). Whereas the ordinary CPI includes the cost of bread and milk, the CPI index for the extremely rich includes the cost of Petrossian caviar and Dom Perignon champagne. And a Learjet 70, a Sikorsky S-76D helicopter. I think there's a pedigree racehorse in there too. Anyway, we're seeing the CPI for the extremely rich rising at a dramatically faster pace than the CPI for society as a whole. So it would seem that superstar individuals and superstar firms are really thriving. Slide 15 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 16 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Let's explain this dynamic in terms of a superstar we all recognise - Roger Federer. Roger Federer was unknown initially, but as he went up the tennis rankings and became a superstar, his income grew exponentially. The other aspect is, how long can he stay a superstar? Because all superstars are eventually displaced by a new superstar. So there's two aspects to the dynamics of superstar incomes (Slide 17). First, how exponential is your income growth? And second, how long do you stay a superstar? What I'm saying is that the rise of AI, by hollowing out the middle jobs, actually allows a few superstars to have this exponential rise in their income. Let's think about it in terms of the legal profession. The top lawyer will be in huge demand. Technology really boosts him. Not just AI, but things like the internet, the fact that social media will reinforce his position, whereby everyone will know who he is. Even if he can't service you directly, he will have a team with his brand on it. And he can stay there for longer before he is displaced. So this is the mechanism by which technology can increase income inequality by hollowing out the middle. In the legal profession, the assistant lawyer who just checks a document for simple legal principle, well the machine can do that. But the guy who knows all the oddities, who knows all the loopholes that can win you the case, the machine won't be able to do that. Essentially what I'm saying is that the technological revolution - it's not just AI, it's technology in aggregate, including the internet and social media, and so on - it increases the rate of income growth for a few superstar individuals and firms. And it increases their longevity (Slide 18). And these are the two drivers for the Pareto distribution of incomes. You can actually go through the mathematics of this to show that it does increase the polarization of incomes. Slide 17 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 18 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Let's sum up (Slide 19). First of all, yes, we will be able to work in the future. I don't think there's any doubt about that because there will be new jobs created, the nature of which we can only guess because we're going to get new industries to satiate our new desires. However, in the coming years, middle-income work will suffer high disruption because of Moravec's Paradox. Some things that we thought were difficult are actually quite easy for AI. But things like gardening, plumbing, nursing, and childcare are very difficult for machines to replicate. Which means that low-income work will suffer much less disruption and, of course, low-income work will get paid better over time - though the gap is so large at the moment that it's preventing overall wage inflation from kicking in. And that, I think, will persist for the next few years at a minimum. Slide 19 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Men are going to suffer much more disruption than women because of the nature of the job destruction versus the job creation. And the final point is that superstars will thrive. All of this has a lot of implications for how we respond as a society, and maybe we will need some support mechanisms in this period of disruption. I think the most intense disruption will be in the next decade. After that we will reach a new equilibrium once we have actually corrected this misvaluation of the brain, this misvaluation of what it is that makes us truly human. Thank you very much. Dhaval Joshi, Senior Vice President Chief European Investment Strategist dhaval@bcaresearch.com

Risk management is important in tumultuous times. Our long held strategy of how to navigate choppy waters during a tactical correction has been to book gains in pair trades and thus de-risk the portfolio, and institute trailing stops to the high-flyers in our high-conviction call list. Two additional high-conviction underweight calls got stopped out recently with hefty gains for our portfolio: 10% for our underweight call on homebuilders and 20% for our underweight call in semi equipment stocks. We are obeying both stops and taking profits by removing them from the high-conviction underweight list. Nevertheless, the spiking lumber prices, surging interest rates and tax reform trifecta is still, at the margin, weighing on homebuilders. Therefore, we continue to recommend an underweight stance in this niche consumer discretionary industry. Similarly, while our underweight conviction level is not as high for semiconductor equipment stocks as on November 27, 2017, we continue to recommend a below benchmark allocation to this highly cyclical industry. Rising interest rates, a key BCA theme for 2018 is working against last year's stellar performers with growth stocks (semi equipment equities included) suffering a valuation derating. Bottom Line: Crystalize profits of 20% and 10% in chip equipment and homebuilding stocks, respectively, and remove from the high-conviction underweight list. We continue to recommend a below benchmark allocation in both indexes. The ticker symbols for the stocks in the S&P semi equipment and S&P homebuilders indexes are: AMAT, LRCX, KLAC, and LEN, DHI, PHM, respectively. Housekeeping In Turbulent Times Housekeeping In Turbulent Times

Dear Client, In addition to this Special Report written by my colleagues Mark McClellan and Brian Piccioni, we are sending you an abbreviated weekly report. Best regards, Peter Berezin, Chief Global Strategist Global Investment Strategy Highlights Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. Technological advance in the past has not prevented improving living standards or led to ever rising joblessness over the decades, but pessimists argue that recent advances are different. The issue is important for financial markets. If structural factors such as automation are holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. We see no compelling evidence that the displacement effect of emerging technologies is any stronger than in the past. Robot usage has had a modest positive impact on overall productivity. Despite this contribution, overall productivity growth has been dismal over the past decade. If automation is increasing 'exponentially' and displacing workers on a broad scale as some claim, one would expect to see accelerating productivity growth, robust capital spending and more violent shifts in occupational shares. Exactly the opposite has occurred. Periods of strong growth in automation have historically been associated with robust, not lackluster, wage gains, contrary to the consensus view. The Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. This and other evidence suggest that it is difficult to make the case that robots will make it tougher for central banks to reach their inflation goals than did previous technological breakthroughs. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. Feature Recent breakthroughs in technology are awe-inspiring and unsettling. These advances are viewed with great trepidation by many because of the potential to replace humans in the production process. Hype over robots is particularly shrill. Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. In the first in our series of Special Reports focusing on the structural factors that might be preventing central banks from reaching their inflation targets, we demonstrated that the impact of Amazon is overstated in the press. We estimated that E-commerce is depressing inflation in the U.S. by a mere 0.1 to 0.2 percentage points. This Special Report tackles the impact of automation. We are optimistic that robot technology and artificial intelligence will significantly boost future productivity, and thus reduce costs. But, is there any evidence at the macro level that robot usage has been more deflationary than technological breakthroughs in the past and is, thus, a major driver of the low inflation rates we observe today across the major countries? The question matters, especially for the outlook for central bank policy and the bond market. If structural factors are indeed holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. However, if low inflation simply reflects long lags between wages and the tightening labor market, then inflation may suddenly lurch to life as it has at the end of past cycles. The bond market is not priced for that scenario. Are Robots Different? A Special Report from BCA's Technology Sector Strategy service suggested that the "robot revolution" could be as transformative as previous General Purpose Technologies (GPT), including the steam engine, electricity and the microchip.1 GPTs are technologies that radically alter the economy's production process and make a major contribution to living standards over time. The term "robot" can have different meanings. The most basic definition is "a device that automatically performs complicated and often repetitive tasks," and this encompasses a broad range of machines: From the Jacquard Loom, which was invented over 200 years ago, on to Numerically Controlled (NC) mills and lathes, pick and place machines used in the manufacture of electronics, Autonomous Vehicles (AVs), and even homicidal robots from the future such as the Terminator. Our Technology Sector report made the case that there is nothing particularly sinister about robots. They are just another chapter in a long history of automation. Nor is the displacement of workers unprecedented. The industrial revolution was about replacing human craft labor with capital (machines), which did high-volume work with better quality and productivity. This freed humans for work which had not yet been automated, along with designing, producing and maintaining the machinery. Agriculture offers a good example. This sector involved over 50% of the U.S. labor force until the late 1800s. Steam and then internal combustion-powered tractors, which can be viewed as "robotic horses," contributed to a massive rise in output-per-man hour. The number of hours worked to produce a bushel of wheat fell by almost 98% from the mid-1800s to 1955. This put a lot of farm hands out of work, but these laborers were absorbed over time in other growing areas of the economy. It is the same story for all other historical technological breakthroughs. Change is stressful for those directly affected, but rising productivity ultimately lifts average living standards. Robots will be no different. As we discuss below, however, the increasing use of robots and AI may have a deeper and longer-lasting impact on inequality. Strong Tailwinds Chart 1Robots Are Getting Cheaper Robots Are Getting Cheaper Robots Are Getting Cheaper Factory robots have improved immensely due to cheaper and more capable control and vision systems. As these systems evolve, the abilities of robots to move around their environment while avoiding obstacles will improve, as will their ability to perform increasingly complex tasks. Most importantly, robots are already able to do more than just routine tasks, thus enabling them to replace or aid humans in higher-skilled processes. Robot prices are also falling fast, especially after quality-adjusting the data (Chart 1). Units are becoming easier to install, program and operate. These trends will help to reduce the barriers-to-entry for the large, untapped, market of small and medium sized enterprises. Robots also offer the ability to do low-volume "customized" production and still keep unit costs low. In the future, self-learning robots will be able to optimize their own performance by analyzing the production of other robots around the world. Robot usage is growing quickly according to data collected by the International Federation of Robotics (IFR) that covers 23 countries. Industrial robot sales worldwide increased to almost 300,000 units in 2016, up 16% from the year before (Chart 2). The stock of industrial robots globally has grown at an annual average pace of 10% since 2010, reaching slightly more than 1.8 million units in 2016.2 Robot usage is far from evenly distributed across industries. The automotive industry is the major consumer of industrial robots, holding 45% of the total stock in 2016 (Chart 3). The computer & electronics industry is a distant second at 17%. Metals, chemicals and electrical/electronic appliances comprise the bulk of the remaining stock. Chart 2Global Robot Usage Global Robot Usage Global Robot Usage Chart 3Global Robot Usage By Industry (2016) The Impact Of Robots On Inflation The Impact Of Robots On Inflation As far as countries go, Japan has traditionally been the largest market for robots in the world. However, sales have been in a long-term downtrend and the stock of robots has recently been surpassed by China, which has ramped up robot purchases in recent years (Chart 4). Robot density, which is the stock of robots per 10 thousand employed in manufacturing, makes it easier to compare robot usage across countries (Chart 5, panel 2). By this measure, China is not a heavy user of robots compared to other countries. South Korea stands at the top, well above the second-place finishers (Germany and Japan). Large automobile sectors in these three countries explain their high relative robot densities. Chart 4Stock Of Robots By Country (I) Stock Of Robots By Country (I) Stock Of Robots By Country (I) Chart 5Stock Of Robots By Country (II) (2016) The Impact Of Robots On Inflation The Impact Of Robots On Inflation While the growth rate of robot usage is impressive, it is from a very low base (outside of the automotive industry). The average number of robots per 10,000 employees is only 74 for the 23 countries in the IFR database. Robot use is tiny compared to total man hours worked. In the U.S., spending on robots is only about 5% of total business spending on equipment and software (Chart 6). To put this into perspective, U.S. spending on information, communication and technology (ICT) equipment represented 35-40% of total capital equipment spending during the tech boom in the 1990s and early 2000s.3 Chart 6U.S. Investment In Robots U.S. Investment In Robots U.S. Investment In Robots The bottom line is that there is a lot of hype in the press, but robots are not yet widely used across countries or industries. It will be many years before business spending on robots approaches the scale of the 1990s/2000s IT boom. A Deflationary Impact? As noted above, we view robotics as another chapter in a long history of technological advancements. Pessimists suggest that the latest advances are different because they are inherently more threatening to the overall job market and wage share of total income. If the pessimists are right, what are the theoretical channels though which this would have a greater disinflationary effect relative to previous GPT technologies? Faster Productivity Gains: Enhanced productivity drives down unit labor costs, which may be passed along to other industries (as cheaper inputs) and to the end consumer. More Human Displacement: The jobs created in other areas may be insufficient to replace the jobs displaced by robots, leading to lower aggregate income and spending. The loss of income for labor will simply go to the owners of capital, but the point is that the labor share of income might decline. Deflationary pressures could build as aggregate demand falls short of supply. Even in industries that are slow to automate, just the threat of being replaced by robots may curtail wage demands. Inequality: Some have argued that rising inequality is partly because the spoils of new technologies over the past 20 years have largely gone to the owners of capital. This shift may have undermined aggregate demand because upper income households tend to have a high saving rate, thereby depressing overall aggregate demand and inflationary pressures. The human displacement effect, described above, would exacerbate the inequality effect by transferring income from labor to the owners of capital. 1. Productivity It is difficult to see the benefits of robots on productivity at the economy-wide level. Productivity growth has been abysmal across the major developed countries since the Great Recession, but the productivity slowdown was evident long before Lehman collapsed (Chart 7). The productivity slowdown continued even as automation using robots accelerated after 2010. Chart 7Productivity Collapsed Despite Automation Productivity Collapsed Despite Automation Productivity Collapsed Despite Automation Some analysts argue that lackluster productivity is simply a statistical mirage because of the difficulties in measuring output in today's economy. We will not get into the details of the mismeasurement debate here. We encourage interested clients to read a Special Report by the BCA Global Investment Strategy service entitled "Weak Productivity Growth: Don't Blame The Statisticians." 4 Our colleague Peter Berezin makes the case that the unmeasured utility accruing from free internet services is large, but so was the unmeasured utility from antibiotics, radio, indoor plumbing and air conditioning. He argues that the real reason that productivity growth has slowed is that educational attainment has decelerated and businesses have plucked many of the low-hanging fruit made possible by the IT revolution. Cyclical factors stemming from the Great Recession and financial crisis are also to blame, as capital spending has been slow to recover in most of the advanced economies. Some other factors that help to explain the decline in aggregate productivity are provided in Appendix 1. Nonetheless, the poor aggregate productivity performance does not mean that there are no benefits to using robots. The benefits are evident at the industrial level, where measurement issues are presumably less vexing for statisticians (i.e., it is easier to measure the output of the auto industry, for example, than for the economy as a whole). Chart 8 plots the level of robot density in 2016 with average annual productivity growth since 2004 for 10 U.S. manufacturing industries (robot density is presented in deciles). A loose positive relationship is apparent. Chart 8U.S.: Productivity Vs. Robot Density The Impact Of Robots On Inflation The Impact Of Robots On Inflation Academic studies estimate that robots have contributed importantly to economy-wide productivity growth. The Centre for Economic and Business Research (CEBR) estimated that labor productivity growth rises by 0.07 to 0.08 percentage points for every 1% rise in the rate of robot density.5 This implies that robots accounted for roughly 10% of the productivity growth experienced since the early 1990s in the major economies. Another study of 14 industries across 17 countries by the Centre for Economic Performance (CEP) found that robots boosted annual productivity growth by 0.36 percentage points over the 1993-2007 period.6 This is impressive because, if this estimate holds true for the U.S., robots' contribution to the 2½% average annual U.S. total productivity growth over the period was 14%. To put the importance of robotics into historical context, its contribution to productivity so far is roughly on par with that of the steam engine (Chart 9). It falls well short of the 0.6 percentage point annual productivity contribution from the IT revolution. The implication is that, while the overall productivity performance has been dismal since 2007, it would have been even worse in the absence of robots. What does this mean for inflation? According to the "cost push" model of the inflation process, an increase in productivity of 0.36% that is not accompanied by associated wage gains would reduce unit labor costs (ULC) by the same amount. This should trim inflation if the cost savings are passed on to the end consumer, although by less than 0.36% because robots can only depress variable costs, not fixed costs. There indeed appears to be a slight negative relationship between robot density and unit labor costs at the industrial level in the U.S., although the relationship is loose at best (Chart 10). Chart 9GPT Contribution To Productivity The Impact Of Robots On Inflation The Impact Of Robots On Inflation Chart 10U.S.: Unit Labor Costs Vs. Robot Density The Impact Of Robots On Inflation The Impact Of Robots On Inflation In theory, divergences in productivity across industries should only generate shifts in relative prices, and "cost push" inflation dynamics should only operate in the short term. Most economists believe that inflation is a purely monetary phenomenon in the long run, which means that central banks should be able to offset positive productivity shocks by lowering interest rates enough that aggregate demand keeps up with supply. Indeed, the Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. Also, note that inflation is currently low across the major advanced economies, irrespective of the level of robot intensity (Chart 11). From this perspective, it is hard to see that robots should take much of the credit for today's low inflation backdrop. Chart 11Inflation Vs. Robot Density The Impact Of Robots On Inflation The Impact Of Robots On Inflation 2. Human Displacement A key question is whether robots and humans are perfect substitutes. If new technologies introduced in the past were perfect substitutes, then it would have led to massive underemployment and all of the income in the economy would eventually have migrated to the owners of capital. The fact that average real household incomes have risen over time, and that there has been no secular upward trend in unemployment rates over the centuries, means that new technologies were at least partly complementary with labor (i.e., the jobs lost as a direct result of productivity gains were more than replaced in other areas of the economy over time). Rather than replacing workers, in many cases tech made humans more productive in their jobs. Rising productivity lifted income and thereby led to the creation of new jobs in other areas. The capital that workers bring to the production process - the skills, know-how and special talents - became more valuable as interaction with technology increased. Like today, there were concerns in the 1950s and 1960s that computerization would displace many types of jobs and lead to widespread idleness and falling household income. With hindsight, there was little to worry about. Some argue that this time is different. Futurists frequently assert that the pace of innovation is not just accelerating, it is accelerating 'exponentially'. Robots can now, or will soon be able to, replace humans in tasks that require cognitive skills. This means that they will be far less complementary to humans than in the past. The displacement effect could thus be much larger, especially given the impressive advances in artificial intelligence. However, Box 1 discusses why the threat to workers posed by AI is also heavily overblown in the media. The CEP multi-country study cited above did not find a large displacement effect; robot usage did not affect the overall number of hours worked in the 23 countries studied (although it found distributional effects - see below). In other words, rather than suppressing overall labor input, robot usage has led to more output, higher productivity, more jobs and stronger wage and income growth. A report by the Economic Policy Institute (EPI)7 takes a broader look at automation, using productivity growth and capital spending as proxies. Automation is what occurs as the implementation of new technologies is incorporated along with new capital equipment or software to replace human labor in the workplace. If automation is increasing 'exponentially' and displacing workers on a broad scale, one would expect to see accelerating productivity growth, robust capital spending, and more violent shifts in occupational shares. Exactly the opposite has occurred. Indeed, the report demonstrates that occupational employment shifts were far slower in the 2000-2015 period than in any decade in the 1900s (Chart 12). Box 1 The Threat From AI Is Overblown Media coverage of AI/Deep Learning has established a consensus view that we believe is well off the mark. A recent Special Report from BCA's Technology Sector Strategy service dispels the myths surrounding AI.8 We believe the consensus, in conjunction with warnings from a variety of sources, is leading to predictions, policy discussions, and even career choices based on a flawed premise. It is worth noting that the most vocal proponents of AI as a threat to jobs and even humanity are not AI experts. At the root of this consensus is the false view that emerging AI technology is anything like true intelligence. Modern AI is not remotely comparable in function to a biological brain. Scientists have a limited understanding of how brains work, and it is unlikely that a poorly understood system can be modeled on a computer. The misconception of intelligence is amplified by headlines claiming an AI "taught itself" a particular task. No AI has ever "taught itself" anything: All AI results have come about after careful programming by often PhD-level experts, who then supplied the system with vast amounts of high quality data to train it. Often these systems have been iterated a number of times and we only hear of successes, not the failures. The need for careful preparation of the AI system and the requirement for high quality data limits the applicability of AI to specific classes of problems where the application justifies the investment in development and where sufficient high-quality data exists. There may be numerous such applications but doubtless many more where AI would not be suitable. Similarly, an AI system is highly adapted to a single problem, or type of problem, and becomes less useful when its application set is expanded. In other words, unlike a human whose abilities improve as they learn more things, an AI's performance on a particular task declines as it does more things. There is a popular misconception that increased computing power will somehow lead to ever improving AI. It is the algorithm which determines the outcome, not the computer performance: Increased computing power leads to faster results, not different results. Advanced computers might lead to more advanced algorithms, but it is pointless to speculate where that may lead: A spreadsheet from 2001 may work faster today but it still gives the same answer. In any event, it is worth noting that a tool ceases to be a tool when it starts having an opinion: there is little reason to develop a machine capable of cognition even if that were possible. Chart 12U.S. Job Rotation Has Slowed The Impact Of Robots On Inflation The Impact Of Robots On Inflation The EPI report also notes that these indicators of automation increased rapidly in the late 1990s and early 2000s, a period that saw solid wage growth for American workers. These indicators weakened in the two periods of stagnant wage growth: from 1973 to 1995 and from 2002 to the present. Thus, there is no historical correlation between increases in automation and wage stagnation. Rather than automation, the report argues that it was China's entry into the global trading system that was largely responsible for the hollowing out of the U.S. manufacturing sector. We have also made this argument in previous research. The fact that the major advanced economies are all at, or close to, full employment supports the view that automation has not been an overwhelming headwind for job creation. Chart 13 demonstrates that there has been no relationship between the change in robot density and the loss of manufacturing jobs since 1993. Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. Interestingly, despite a worsening labor shortage, robot density among Japanese firms is falling. Moreover, the Japanese data show that the industries that have a high robot usage tend to be more, not less, generous with wages than the robot laggard industries. Please see Appendix 2 for more details. Chart 13Global Manufacturing Jobs Vs. Robot Density The Impact Of Robots On Inflation The Impact Of Robots On Inflation The bottom line is that it does not appear that labor displacement related to automation has been responsible in any meaningful way for the lackluster average real income growth in the advanced economies since 2007. 3. Inequality That said, there is evidence suggesting that robots are having important distributional effects. The CEP study found that robot use has reduced hours for low-skilled and (to a lesser extent) middle-skilled workers relative to the highly skilled. This finding makes sense conceptually. Technological change can exacerbate inequality by either increasing the relative demand for skilled over unskilled workers (so-called "skill-biased" technological change), or by inducing companies to substitute machinery and other forms of physical capital for workers (so-called "capital-biased" technological change). The former affects the distribution of labor income, while the latter affects the share of income in GDP that labor receives. A Special Report appearing in this publication in 2014 focused on the relationship between technology and inequality.9 The report highlighted that much of the recent technological change has been skill-biased, which heavily favors workers with the talent and education to perform cognitively-demanding tasks, even as it reduces demand for workers with only rudimentary skills. Moreover, technological innovations and globalization increasingly allow the most talented individuals to market their skills to a much larger audience, thus bidding up their wages. The evidence suggests that faster productivity growth leads to higher average real wages and improved living standards, at least over reasonably long horizons. Nonetheless, technological change can, and in the future almost certainly will, increase income inequality. The poor will gain, but not as much as the rich. The fact that higher-income households tend to maintain a higher savings rate than low-income households means that the shift in the distribution of income toward the higher-income households will continue to modestly weigh on aggregate demand. Can the distribution effect be large enough to have a meaningful depressing impact on inflation? We believe that it has played some role in the lackluster recovery since the Great Recession, with the result that an extended period of underemployment has delivered a persistent deflationary impulse in the major developed economies. However, as discussed above, stimulative monetary policy has managed to overcome the impact of inequality and other headwinds on aggregate demand, and has returned the major countries roughly to full employment. Indeed, this year will be the first since 2007 that the G20 economies as a group will be operating slightly above a full employment level. Inflation should respond to excess demand conditions, irrespective of any ongoing demand headwind stemming from inequality. Conclusions Technological change has led to rising living standards over the decades. It did not lead to widespread joblessness and did not prevent central banks from meeting their inflation targets over time. The pessimists argue that this time is different because robots/AI have a much larger displacement effect. Perhaps it will be 20 years before we will know the answer. But our main point is that we have found no evidence that recent advances in robotics and AI, while very impressive, will be any different in their macro impact. There is little evidence that the modern economy is less capable in replacing the jobs lost to automation, although the nature of new technologies may be affecting the distribution of income more than in the past. Real incomes for the middle- and lower-income classes have been stagnant for some time, but this is partly due to productivity growth that is too low, not too high. Moreover, it is not at all clear that positive productivity shocks are disinflationary beyond the near term. The link between robot usage and unit labor costs over the past couple of decades is loose at best at the industry level, and is non-existent when looking across the major countries. The Fed was able to roughly meet its 2% inflation target in the 1990s and the first half of the 2000s, despite IT's impressive contribution to productivity growth during that period. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. The global output gap will shift into positive territory this year for the first time since the Great Recession. Any resulting rise in inflation will come as a shock since the bond market has discounted continued low inflation for as far as the eye can see. We expect bond yields and implied volatility to rise this year, which may undermine risk assets in the second half. Mark McClellan Senior Vice President The Bank Credit Analyst Brian Piccioni Vice President Technology Sector Strategy Appendix 1 Why Is Productivity So Low? A recent study by the OECD10 reveals that, while frontier firms are charging ahead, there is a widening gap between these firms and the laggards. The study analyzed firm-level data on labor productivity and total factor productivity for 24 countries. "Frontier" firms are defined to be those with productivity in the top 5%. These firms are 3-4 times as productive as the remaining 95%. The authors argue that the underlying cause of this yawning gap is that the diffusion rate of new technologies from the frontier firms to the laggards has slowed within industries. This could be due to rising barriers to entry, which has reduced contestability in markets. Curtailing the creative-destruction process means that there is less pressure to innovate. Barriers to entry may have increased because "...the importance of tacit knowledge as a source of competitive advantage for frontier firms may have risen if increasingly complex technologies were to increase the amount and sophistication of complementary investments required for technological adoption." 11 The bottom line is that aggregate productivity is low because the robust productivity gains for the tech-savvy frontier companies are offset by the long tail of firms that have been slow to adopt the latest technology. Indeed, business spending has been especially weak in this expansion. Chart 14 highlights that the slowdown in U.S. productivity growth has mirrored that of the capital stock. Chart 14U.S. Capex Shortfall Partly To Blame For Poor Productivity U.S. Capex Shortfall Partly To Blame For Poor Productivity U.S. Capex Shortfall Partly To Blame For Poor Productivity Appendix 2 Japan - The Leading Edge Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. The popular press is full of stories of how robots are taking over. If the stories are to be believed, robots are the answer to the country's shrinking workforce. Robots now serve as helpers for the elderly, priests for weddings and funerals, concierges for hotels and even sexual partners (don't ask). Prime Minister Abe's government has launched a 5-year push to deepen the use of intelligent machines in manufacturing, supply chains, construction and health care. Indeed, Japan was the leader in robotics use for decades. Nonetheless, despite all the hype, Japan's stock of industrial robots has actually been eroding since the late 1990s (Chart 4). Numerous surveys show that firms plan to use robots more in the future because of the difficulty in hiring humans. And there is huge potential: 90% of Japanese firms are small- and medium-sized (SME) and most are not currently using robots. Yet, there has been no wave of robot purchases as of 2016. One problem is the cost; most sophisticated robots are simply too expensive for SMEs to consider. This suggests that one cannot blame robots for Japan's lack of wage growth. The labor shortage has become so acute that there are examples of companies that have turned down sales due to insufficient manpower. Possible reasons why these companies do not offer higher wages to entice workers are beyond the scope of this report. But the fact that the stock of robots has been in decline since the late 1990s does not support the view that Japanese firms are using automation on a broad scale to avoid handing out pay hikes. Indeed, Chart 15 highlights that wage deflation has been the greatest in industries that use almost no robots. Highly automated industries, such as Transportation Equipment and Electronics, have been among the most generous. This supports the view that the productivity afforded by increased robot usage encourages firms to pay their workers more. Looking ahead, it seems implausible that robots can replace all the retiring Japanese workers in the years to come. The workforce will shrink at an annual average pace of 0.33% between 2020 and 2030, according to the Japan Institute for Labour Policy and Training. Productivity growth would have to rise by the same amount to fully offset the dwindling number of workers. But that would require a surge in robot density of 4.1, assuming that each rise in robot density of one adds 0.08% to the level of productivity (Chart 16). The level of robot sales would have to jump by a whopping 2½ times in the first year and continue to rise at the same pace each year thereafter to make this happen. Of course, the productivity afforded by new robots may accelerate in the coming years, but the point is that robot usage would likely have to rise astronomically to offset the impact of the shrinking population. Chart 15Japan: Earnings Vs. Robot Density The Impact Of Robots On Inflation The Impact Of Robots On Inflation Chart 16Japan: Where Is The Flood Of Robots? Japan: Where Is The Flood Of Robots? Japan: Where Is The Flood Of Robots? The implication is that, as long as the Japanese economy continues to grow above roughly 1%, the labor market will continue to tighten and wage rates will eventually begin to rise. 1 Please see Technology Sector Strategy Special Report "The Coming Robotics Revolution," dated May 16, 2017, available at tech.bcaresearch.com 2 Note that this includes only robots used in manufacturing industry, and thus excludes robots used in the service sector and households. However, robot usage in services is quite limited and those used in households do not add to GDP. 3 Note that ICT investment and capital stock data includes robots. 4 Please see BCA Global Investment Strategy Special Report "Weak Productivity Growth: Don't Blame The Statisticians," dated March 25, 2016, available at gis.bcaresearch.com 5 Centre for Economic and Business Research (January 2017) "The Impact of Automation." A Report for Redwood. In this report, robot density is defined to be the number of robots per million hours worked. 6 Graetz, G., and Michaels, G. (2015): "Robots At Work." CEP Discussion Paper No 1335. 7 Mishel, L., and Bivens, J. (2017): "The Zombie Robot Argument Lurches On," Economic Policy Institute. 8 Please see BCA Technology Sector Strategy Special Report "Bad Information - Why Misreporting Deep Learning Advances Is A Problem," dated January 9, 2018, available at tech.bcaresearch.com 9 Please see The Bank Credit Analyst, "Rage Against the Machines: Is Technology Exacerbating Inequality?" dated June 2014, available at bca.bcaresearch.com. 10 OECD Productivity Working Papers, No. 05 (2016) "The Best Versus the Rest: The Global Productivity Slowdown, Divergence Across Firms and the Role of Public Policy." 11 Please refer to page 8.

Highlights U.S. equities 'melted up' in January as tax cuts made the robust growth/low inflation sweet spot even sweeter. Ominously, recent market action is beginning to resemble a classic late cycle blow-off phase. The fundamentals supporting the market will persist through most of the year, before an economic downturn in the U.S. takes hold in 2019. The repatriation of overseas corporate cash will also flatter EPS growth this year via buyback and M&A activity. The S&P 500 could return 14% or more this year. Unfortunately, the consensus now shares our upbeat view for 2018. Valuation is stretched and many indicators suggest that investors have become downright giddy. This month we compare valuation across the major asset classes. U.S. equities are the most overvalued, followed by gold, raw industrials and EM assets. Oil is still close to fair value. Long-term investors should already be scaling back on risk assets. Investors with a 6-12 month horizon should stay overweight equities versus bonds for now, but a risk management approach means that they should not try to squeeze out the last few percentage points of return. In terms of the sequencing of the exit from risk, the most consistent lead/lag relationship relative to previous tops in the equity market is provided by U.S. corporate bonds. For this reason, we are likely to take profits on corporates before equities. EM assets are already at underweight. We still see a window for the U.S. dollar to appreciate, although by only about 5%. A lot of good news is discounted in the euro, peripheral core inflation is slowing and ECB policymakers are getting nervous. Monetary policy remains the main risk to a pro-cyclical investment stance, although not because of the coming change in the makeup of the FOMC. The economy and inflation should justify four Fed rate hikes in 2018 no matter the makeup. The bond bear phase will continue. Feature Chart I-1Investors Are Giddy Investors Are Giddy Investors Are Giddy U.S. equities 'melted up' in January as tax cuts made the robust growth/low inflation sweet spot even sweeter. Ominously, though, recent market action is beginning to resemble the classic late cycle blow-off phase. Such blow-offs can be highly profitable, but also make it more difficult to properly time the market top. Our base case is that the fundamentals supporting the market will persist through most of the year, before an economic downturn in the U.S. takes hold in 2019. Unfortunately, the consensus now shares our upbeat view for 2018 and many indicators suggest that investors have become downright giddy (Chart I-1). These indicators include investor sentiment, our speculation index, and the bull-to-bear ratio. Net S&P earnings revisions and the U.S. economic surprise index are also extremely elevated, while equity and bond implied volatility are near all-time lows. From a contrarian perspective, these observations suggest that a lot of good news is discounted and that the market is vulnerable to even slight disappointments. It is also a bad sign that our Revealed Preference Indicator moved off of its bullish equity signal in January (see Section III for more details). Meanwhile, central banks are beginning to take away the punchbowl as global economic slack dissipates. This is all late-cycle stuff. Equity valuation does not help investors time the peak in markets, but it does tell us something about downside risk and medium-term expected returns. The Shiller P/E ratio has surged above 30 (Chart I-2). Chart I-3 highlights that, historically, average total returns were negligible over the subsequent 10-year period when the Shiller P/E was in the 30-40 range. Granted, the Shiller P/E will likely fall mechanically later this year as the collapse of earnings in 2008 begins to drop out of the 10-year EPS calculation. Nonetheless, even the BCA Composite Valuation indicator, which includes some metrics that account for extremely low bond yields, surpassed +1 standard deviations in January (our threshold for overvaluation; Chart I-2, bottom panel). An overvaluation signal means that investors should be biased to take profits early. Chart I-2BCA Valuation Indicator Surpasses One Sigma BCA Valuation Indicator Surpasses One Sigma BCA Valuation Indicator Surpasses One Sigma Chart I-3Expected Returns Given Starting Point Shiller P/E February 2018 February 2018 As we highlighted in our 2018 Outlook Report, long-term investors should already be scaling back on risk assets. We recommend that investors with a 6-12 month horizon should stay overweight equities versus bonds for now, but we need to be vigilant in terms of scouring for signals to take profits. A risk management approach means that investors should not try to get the last few percentage points of return before the peak. U.S. Earnings And Repatriation Before we turn to the timing and sequence of our exit from risk assets, we will first update our thoughts on the earnings cycle. Fourth quarter U.S. earnings season is still in its early innings, but the banking sector has set an upbeat tone. S&P 500 profits are slated to register a 12% growth rate for both Q4/2017 and calendar 2017. Current year EPS growth estimates have been aggressively ratcheted higher (from 12% growth to 16%) in a mere three weeks on the back of Congress' cut to the corporate tax rate.1 U.S. margins fell slightly in the fourth quarter, but remain at a high level on the back of decent corporate pricing power. A pick-up in productivity growth into year-end helped as well. Our short-term profit model remains extremely upbeat (Chart I-4). The positive profit outlook for the first half of the year is broadly based across sectors as well, according to the recently updated EPS forecast models from BCA's U.S. Equity Sector Strategy service.2 The repatriation of overseas corporate cash will also flatter EPS growth this year via buyback and M&A activity. Studies of the 2004 repatriation legislation show that most of the funds "brought home" were paid out to shareholders, mostly in the form of buybacks. A NBER report estimated that for every dollar repatriated, 92 cents was subsequently paid out to shareholders in one form or another. The surge in buybacks occurred in 2005, according to the U.S. Flow of Funds accounts and a proxy using EPS growth less total dollar earnings growth for the S&P 500 (Chart I-5). The contribution to EPS growth from buybacks rose to more than 3 percentage points at the peak in 2005. Chart I-4Profit Growth Still Accelerating Profit Growth Still Accelerating Profit Growth Still Accelerating Chart I-5U.S. Buybacks To Lift EPS U.S. Buybacks To Lift EPS U.S. Buybacks To Lift EPS We expect that most of the repatriated funds will again flow through to shareholders, rather than be used to pay down debt or spent on capital goods. Cash has not been a constraint to capital spending in recent years outside of perhaps the small business sector, which has much less to gain from the tax holiday. A revival in animal spirits and capital spending is underway, but this has more to do with the overall tax package and global growth than the ability of U.S. companies to repatriate overseas earnings. Estimates of how much the repatriation could boost EPS vary widely. Most of it will occur in the Tech and Health Care sectors. Buybacks appear to have lifted EPS growth by roughly one percentage point over the past year. We would not be surprised to see this accelerate by 1-2 percentage points, although the timing could be delayed by a year if the 2004 tax holiday provides the correct timeline. This is certainly positive for the equity market, but much of the impact could already be discounted in prices. Organic earnings growth, and the economic and policy outlook will be the main drivers of equity market returns over the next year. We expect some profit margin contraction later this year, but our 5% EPS growth forecast is beginning to look too conservative. This is especially the case because it does not include the corporate tax cuts. The amount by which the tax cuts will boost earnings on an after-tax basis is difficult to estimate, but we are using 5% as a conservative estimate. Adding 2% for buybacks and 2% for dividends, the S&P 500 could provide an attractive 14% total return this year (assuming no multiple expansion). Timing The Exit Chart I-6Timing The Exit (I) Timing The Exit (I) Timing The Exit (I) That said, we noted in last month's Report and in BCA's 2018 Outlook that this will be a transition year. We expect a recession in the U.S. sometime in 2019 as the Fed lifts rates into restrictive territory. Equities and other risk assets will sniff out the recession about six months in advance, which means that investors should be preparing to take profits sometime during the next 12 months. Last month we discussed some of the indicators we will watch to help us time the exit. The 2/10 Treasury yield curve has been a reliable recession indicator in the past. However, the lead time on the peak in stocks was quite extended at times (Chart I-6). A shift in the 10-year TIPS breakeven rate above 2.4% would be consistent with the Fed's 2% target for the PCE measure of inflation. This would be a signal that the FOMC will have to step-up the pace of rate hikes and aggressively slow economic growth. We expect the Fed to tighten four times in 2018. We are likely to take some money off the table if core inflation is rising, even if it is still below 2%, at the time that the TIPS breakeven reaches 2.4%. We will also be watching seven indicators that we have found to be useful in heralding market tops, which are summarized in our Scorecard Indicator (Chart I-7). At the moment, four out of the seven indicators are positive (Chart I-8): State of the Business Cycle: As early signals that the economy is softening, watch for the ISM new orders minus inventories indicator to slip below zero, or the 3-month growth rate of unemployment claims to rise above zero. Monetary and Financial Conditions: Using interest rates to judge the stance of monetary policy has been complicated by central banks' use of their balance sheet as a policy tool. Thus, it is better to use two of our proprietary indicators: the BCA Monetary Indicator (MI) and the Financial Conditions Indictor. The S&P 500 index has historically rallied strongly when the MI is above its long-term average. Similarly, equities tend to perform well when the FCI is above its 250-day moving average. The MI is sending a negative signal because interest rates have increased and credit growth has slowed. However, the broader FCI remains well in 'bullish' territory. Price Momentum: We simply use the S&P 500 relative to its 200-day moving average to measure momentum. Currently, the index is well above that level, providing a bullish signal for the Scorecard. Sentiment: Our research shows that stock returns have tended to be highest following periods when sentiment is bearish but improving. In contrast, returns have tended to be lowest following periods when sentiment is bullish but deteriorating. The Scorecard includes the BCA Speculation Indicator to capture sentiment, but virtually all measures of sentiment are very high. The next major move has to be down by definition. Thus, sentiment is assigned a negative value in the Scorecard. Value: As discussed above, value is poor based on the Shiller P/E and the BCA Composite Valuation indicator. Valuation may not help with timing, but we include it in our Scorecard because an overvalued signal means investors should err on the side of getting out early. Chart I-7Equity ScoreCard: Watch For A Dip Below 3 Equity ScoreCard: Watch For A Dip Below 3 Equity ScoreCard: Watch For A Dip Below 3 Chart I-8Timing The Exit (II) Timing The Exit (II) Timing The Exit (II) We demonstrated in previous research that a Scorecard reading of three or above was historically associated with positive equity total returns in subsequent months. A drop below three this year would signal the time to de-risk. Table I-1Exit Checklist February 2018 February 2018 To our Checklist we add the U.S. Leading Economic index, which has a good track record of calling recessions. However, we will use the LEI excluding the equity market, since we are using it as an indicator for the stock market. It is bullish at the moment. Our Global LEI is also flashing green. Table I-1 provides a summary checklist for trimming equity exposure. At the moment, 2 out of 9 indicators are bearish. Cross Asset Valuation Comparison Clients have asked our view on the appropriate order in which to scale out of risk assets. One way to approach the question is to compare valuation across asset classes. Presumably, the ones that are most overvalued are at greatest risk, and thus profits should be taken the earliest. It is difficult to compare valuation across asset classes. Should one use fitted values from models or simple deviations from moving averages? Over what time period? Since there is no widely accepted approach, we include multiple measures. More than one time period was used in some cases to capture regime changes. Table I-2 provides out 'best guestimate' for nine asset classes. The approaches range from sophisticated methods developed over many years (i.e. our equity valuation indicators), to regression analysis on the fundamentals (oil), to simple deviations from a time trend (real raw industrial commodity prices and gold). Table I-2Valuation Levels For Major Asset Classes February 2018 February 2018 We averaged the valuation readings in cases where there are multiple estimates for a single asset class. The results are shown in Chart I-9. Chart I-9Valuation Levels For Major Asset Classes February 2018 February 2018 U.S. equities stand out as the most expensive by far, at 1.8 standard deviations above fair value. Gold, raw industrials and EM equities are next at one standard deviation overvalued. EM sovereign bond spreads come next at 0.7, followed closely by U.S. Treasurys (real yield levels) and investment-grade corporate (IG) bonds (expressed as a spread). High-yield (HY) is only about 0.3 sigma expensive, based on default-adjusted spreads over the Treasury curve. That said, both IG and HY are quite expensive in absolute terms based on the fact that government bonds are expensive. Oil is sitting very close to fair value, despite the rapid price run up over the past couple of months. This makes oil exposure doubly attractive at the moment because the fundamentals point to higher prices at a time when the underlying asset is not expensive. Sequencing Around Past S&P 500 Peaks Historical analysis around equity market peaks provides an alternative approach to the sequencing question. Table I-3 presents the number of days that various asset classes peaked before or after the past major five tops in the S&P 500. A negative number indicates that the asset class peaked before U.S. equities, and a positive number means that it peaked after. Table I-3Asset Class Leads & Lags Vs. Peak In S&P 500 February 2018 February 2018 Unfortunately, there is no consistent pattern observed for EM equities, raw industrials, U.S. cyclical stocks, Tech stocks, or small-cap versus large-cap relative returns. Sometimes they peaked before the S&P 500, and sometime after. The EM sovereign bond excess return index peaked about 130 days in advance of the 1998 and 2007 U.S. equity market tops, although we only have three episodes to analyse due to data limitations. Oil is a mixed bag. A peak in the price of gold led the equity market in four out of five episodes, but the lead time is long and variable. The most consistent lead/lag relationship is given by the U.S. corporate bond market. Both investment- and speculative-grade excess returns relative to government bonds peaked in advance of U.S. stocks in four of the five episodes. High-yield excess returns provided the most lead time, peaking on average 154 days in advance. Excess returns to high-yield were a better signal than total returns. This leading relationship is one reason why we plan to trim exposure to corporate bonds within our bond portfolio in advance of scaling back on equities. But the 'return of vol' that we expect to occur later this year will take a toll on carry trades more generally. We are already underweight EM equities and bonds. This EM recommendation has not gone in our favor, but it would make little sense to upgrade them now given our positive views on volatility and the dollar. An unwinding of carry trades will also hit the high-yielding currencies outside of the EM space, such as the Kiwi and Aussie dollar. Base metal prices will be hit particularly hard if the 2019 U.S. recession spills over to the EM economies as we expect. We may downgrade base metals from neutral to underweight around the time that we downgrade equities, but much depends on the evolution of the Chinese economy in the coming months. Oil is a different story. OPEC 2.0 is likely to cut back on supply in the face of an economic downturn, helping to keep prices elevated. We therefore may not trim energy exposure this year. As for equity sectors, our recommended portfolio is still overweight cyclicals for now. Our synchronized global capex boom, rising bond yield, and firm oil price themes keep us overweight the Industrials, Energy and Financial sectors. Utilities and Homebuilders are underweight. Tech is part of the cyclical sector, but poor valuation keeps us underweight. That said, our sector specialists are already beginning a gradual shift away from cyclicals toward defensives for risk management purposes. This transition will continue in the coming months as we de-risk. We are also shifting small caps to neutral on earnings disappointments and elevated debt levels. The Dollar Pain Trade Market shifts since our last publication have largely gone in our favor; stocks have surged, corporate bonds spreads have tightened, oil prices have spiked, bonds have sold off and cyclical stocks have outperformed defensives. One area that has gone against us is the U.S. dollar. Relative interest rate expectations have moved in favor of the dollar as we expected at both the short- and long-ends of the curve. Nonetheless, the dollar has not tracked its historical relationship versus both the yen and euro. The Greenback did not even get a short-term boost from the passage of the tax plan and holiday on overseas earnings. Perhaps this is because the lion's share of "overseas" earnings are already held in U.S. dollars. Reportedly, a large fraction is even held in U.S. banks on U.S. territory. Currency conversion is thus not a major bullish factor for the U.S. dollar. The recent bout of dollar weakness began around the time of the release of the ECB Minutes in January which were interpreted as hawkish because they appeared to be preparing markets for changes in monetary policy. The European debt crisis and economic recession were the reasons for the ECB's asset purchases and negative interest rate policy. Neither of these conditions are in place now. The ECB is meeting as we go to press, and we expect some small adjustments in the Statement that remove references to the need for "crisis" level accommodations. Subsequent steps will be to prepare markets for a complete end to QE, perhaps in September, and then for rates hikes likely in 2019. The key point is that European monetary policy has moved beyond 'peak stimulus' and the normalization process will continue. Perhaps this is partly to blame for euro strength although, as mentioned above, interest rate differentials have moved in favor of the dollar. Does this mean that the dollar has peaked and has entered a cyclical bear phase that will persist over the next 6-12 months? The answer is 'no', although we are less bullish than in the past. We believe there is still a window for the dollar to appreciate against the euro and in broader trade-weighted terms by about 5%. First, a lot of euro-bullish news has been discounted (Chart I-10). Positive economic surprises heavily outstripped that in the U.S. last year, but that phase is now over. The euro appears expensive based on interest rate differentials, and euro sentiment is close to a bullish extreme. This all suggests that market positioning has become a negative factor for the currency. Chart I-10Euro: A Lot Of Bullish News Is Discounted EURO: A Lot Of Bullish News Is Discounted EURO: A Lot Of Bullish News Is Discounted Second, the chorus of complaints against the euro's strength is growing among European central bankers, including Ewald Nowotny, the rather hawkish Austrian central banker. Policymakers' concerns may partly reflect the fact that peripheral inflation excluding food and energy has already weakened to 0.6% from a high of 1.3% in April last year (Chart I-10, fourth panel). Third, U.S. consumer price and wage inflation have yet to pick up meaningfully. The dollar should receive a lift if core U.S. inflation clearly moves toward the Fed's 2% target, as we expect. The FOMC would suddenly appear to have fallen behind the curve and U.S. rate expectations would ratchet higher. Chart I-10, bottom panel, highlights that the euro will weaken if U.S. core inflation rises versus that in the Eurozone. The implication is that the Euro's appreciation has progressed too far and is due for a pullback. As for the yen, the currency surged in January when the Bank of Japan (BoJ) announced a reduction in long-dated JGB purchases. This simply acknowledged what has already occurred. It was always going to be impossible to target both the quantity of bond purchases and the level of 10-year yield simultaneously. Keeping yields near the target required less purchases than they thought. The market interpreted the BoJ's move as a possible prelude to lifting the 10-year yield target. It is perhaps not surprising that the market took the news this way. The economy is performing extremely well; our model that incorporates high-frequency economic data suggests that real GDP growth will move above 3% in the coming quarters. The Japanese economy is benefiting from the end of a fiscal drag and from a rebound in EM growth. Nonetheless, following January's BoJ policy meeting, Kuroda poured cold water on speculation that the BoJ may soon end or adjust the YCC. Recent speeches by BoJ officials reinforce the view that the MPC wants to see an overshoot of actual inflation that will lower real interest rates and thereby reinforce the strong economic activity that is driving higher inflation. Only then will officials be convinced that their job is done. Given that inflation excluding food and energy only stands at 0.3%, the BoJ is still a long way from the overshoot it desires. On the positive side, Japan's large current account surplus and yen undervaluation provide underlying support for the currency. Balancing the offsetting positive and negative forces, our foreign exchange strategists have shifted to neutral on the yen. The Euro remains underweight while the dollar is overweight. Similar to our dollar view, we still see a window for U.S. Treasurys to underperform the global hedged fixed-income benchmark as world bond yields shift higher this year. European government bonds will also sell off, but should outperform Treasurys. JGBs will provide the best refuge for bondholders during the global bond bear phase, since the BoJ will prevent a rise in yields inside of the 10-year maturity. Our global bond strategists upgraded U.K. gilts to overweight in January. Momentum in the U.K. economy is slowing, as a weaker consumer, slower housing activity, and softer capital spending are offsetting a pickup in exports. With the inflationary impulse from the 2016 plunge in the Pound now fading, and with Brexit uncertainty weighing on business confidence, the Bank of England will struggle to raise rates in 2018. FOMC Transition Monetary policy remains the main risk to a pro-cyclical investment stance, although not because of the coming change in the makeup of the FOMC. An abrupt shift in policy is unlikely. There was some support at the December 2017 FOMC meeting to study the use of nominal GDP or price level targeting as a policy framework, but this has been an ongoing debate that will likely continue for years to come. The Fed will remain committed to its current monetary policy framework once Powell takes over. Table I-4 provides a summary of who will be on the FOMC next year, including their policy bias. Chart I-11 compares the recent FOMC makeup with the coming Powell FOMC (voting members only). The hawk/dove ratio will not change much under Powell, unless Trump stacks the vacant spots with hawks. Table I-4Composition Of The FOMC February 2018 February 2018 Chart I-11Composition Of Voting FOMC Members 2017 Vs. 2018 February 2018 February 2018 In any event, history shows that the FOMC strives to avoid major shifts in policy around changeovers in the Fed Chair. In previous transitions, the previous path for rates was maintained by an average of 13 months. Moreover, Powell has shown that he is not one to rock the boat during his time on the FOMC. It will be the evolution of the economy and inflation, not the composition of the FOMC, that will have the biggest impact on markets at the end of the day. Recent speeches reveal that policymakers across the hawk/dove spectrum are moving modesty toward the hawkish side because growth has accelerated at a time when unemployment is already considered to be below full-employment by many policymakers. The melt-up in equity indexes in January did little to calm worries about financial excesses either. The Fed is struggling to understand the strength of the structural factors that could be holding down inflation. This month's Special Report, beginning on page 21, focusses on the impact of robot automation. While advances on this front are impressive, we conclude that it is difficult to find evidence that robots are more deflationary than previous technological breakthroughs. Thus, increased robot usage should not prevent inflation from rising as the labor market continues to tighten. The macro backdrop will likely justify the FOMC hiking at least as fast as the dots currently forecast. The risks are skewed to the upside. The median Fed dot calls for an unemployment rate of 3.9% by end-2018, only marginally lower than today's rate of 4.1%. This is inconsistent with real GDP growth well in excess of its supply-side potential. The unemployment rate is more likely to reach a 49-year low of 3.5% by the end of this year. As highlighted in last month's Report, a key risk to the bull market in risk assets is the end of the 'low vol/low rate' world. The selloff in the bond market in January may mark the start of this process. Conclusions We covered a lot of ground in this month's Overview of the markets, so we will keep the conclusions brief and focused on the risks. Our key point is that the fundamentals remain positive for risk assets, but that a lot of good news is discounted and it appears that we have entered a classic blow-off phase. This will be a transition year to a recession in the U.S. in 2019. Given that valuation for most risk assets is quite stretched, and given that the monetary taps are starting to close, investors must plan for the exit and keep an eye on our timing checklist. The main risk to our pro-cyclical portfolio is a rise in U.S. inflation and the Fed's response, which we believe will end the sweet spot for risk assets. Apart from this, our geopolitical strategists point to several other items that could upset the applecart this year:3 1. Trade China has cooperated with the U.S. in trying to tame North Korea. Nonetheless, President Trump is committed to an "America First" trade policy and he may need to show some muscle against China ahead of the midterm elections in November in order to rally his base. It is politically embarrassing to the Administration that China racked up its largest trade surplus ever with the U.S. in Trump's first year in office. A key question is whether the President goes after China via a series of administrative rulings - such as the recently announced tariffs on solar panels and white goods - or whether he applies an across-the-board tariff and/or fine. The latter would have larger negative macroeconomic implications. 2. Iran On January 12, President Trump threatened not to waive sanctions against Iran the next time they come due (May 12), unless some new demands are met. Pressure from the U.S. President comes at a delicate time for Iran. Domestic unrest has been ongoing since December 28. Although protests have largely fizzled out, they have reopened the rift between the clerical regime, led by Supreme Leader Ayatollah Ali Khamenei, and moderate President Hassan Rouhani. Iranian hardliners, who control part of the armed forces, could lash out in the Persian Gulf, either by threatening to close the Straits of Hormuz or by boarding foreign vessels in international waters. The domestic political calculus in both Iran and the U.S. make further Tehran-Washington tensions likely. For the time being, however, we expect only a minor geopolitical risk premium to seep into the energy markets, supporting our bullish House View on oil prices. 3. China Last month's Special Report highlighted that significant structural reforms are on the way in China, now that President Xi has amassed significant political support for his reform agenda. The reforms should be growth-positive in the long term, but could be a net negative for growth in the near term depending on how deftly the authorities handle the monetary and fiscal policy dials. The risk is that the authorities make a policy mistake by staying too tight, as occurred in 2015. We are monitoring a number of indicators that should warn if a policy mistake is unfolding. On this front, January brought some worrying economic data. The latest figures for both nominal imports and money growth slowed. Given that M2 and M3 are components of BCA's Li Keqiang Leading Indicator, and that nominal imports directly impact China's contribution to global growth, this raises the question of whether December's economic data suggest that China is slowing at a more aggressive pace than we expect. For now, our answer is no. First, China's trade numbers are highly volatile; nominal import growth remains elevated after smoothing the data. Second, China's export growth remains buoyant, consistent with a solid December PMI reading. The bottom line is that we are sticking with our view that China will experience a benign deceleration in terms of its impact on DM risk assets, but we will continue to monitor the situation closely. Mark McClellan Senior Vice President The Bank Credit Analyst January 25, 2018 Next Report: February 22, 2018 1 According to Thomson Reuters/IBES. 2 Please see U.S. Equity Sector Strategy Special Report "White Paper: Introducing Our U.S. Equity Sector Earnings Models," dated January 16, 2018, available at uses.bcaresearch.com 3 For more information, please see BCA Geopolitical Strategy Weekly Report "Upside Risks In U.S., Downside Risks In China," dated January 17, 2018, available at gps.bcaresearch.com. Also see "Watching Five Risks," dated January 24, 2018. II. The Impact Of Robots On Inflation Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. Technological advance in the past has not prevented improving living standards or led to ever rising joblessness over the decades, but pessimists argue that recent advances are different. The issue is important for financial markets. If structural factors such as automation are holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. We see no compelling evidence that the displacement effect of emerging technologies is any stronger than in the past. Robot usage has had a modest positive impact on overall productivity. Despite this contribution, overall productivity growth has been dismal over the past decade. If automation is increasing 'exponentially' and displacing workers on a broad scale as some claim, one would expect to see accelerating productivity growth, robust capital spending and more violent shifts in occupational shares. Exactly the opposite has occurred. Periods of strong growth in automation have historically been associated with robust, not lackluster, wage gains, contrary to the consensus view. The Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. This and other evidence suggest that it is difficult to make the case that robots will make it tougher for central banks to reach their inflation goals than did previous technological breakthroughs. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. Recent breakthroughs in technology are awe-inspiring and unsettling. These advances are viewed with great trepidation by many because of the potential to replace humans in the production process. Hype over robots is particularly shrill. Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. In the first in our series of Special Reports focusing on the structural factors that might be preventing central banks from reaching their inflation targets, we demonstrated that the impact of Amazon is overstated in the press. We estimated that E-commerce is depressing inflation in the U.S. by a mere 0.1 to 0.2 percentage points. This Special Report tackles the impact of automation. We are optimistic that robot technology and artificial intelligence will significantly boost future productivity, and thus reduce costs. But, is there any evidence at the macro level that robot usage has been more deflationary than technological breakthroughs in the past and is, thus, a major driver of the low inflation rates we observe today across the major countries? The question matters, especially for the outlook for central bank policy and the bond market. If structural factors are indeed holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. However, if low inflation simply reflects long lags between wages and the tightening labor market, then inflation may suddenly lurch to life as it has at the end of past cycles. The bond market is not priced for that scenario. Are Robots Different? A Special Report from BCA's Technology Sector Strategy service suggested that the "robot revolution" could be as transformative as previous General Purpose Technologies (GPT), including the steam engine, electricity and the microchip.1 GPTs are technologies that radically alter the economy's production process and make a major contribution to living standards over time. The term "robot" can have different meanings. The most basic definition is "a device that automatically performs complicated and often repetitive tasks," and this encompasses a broad range of machines: From the Jacquard Loom, which was invented over 200 years ago, on to Numerically Controlled (NC) mills and lathes, pick and place machines used in the manufacture of electronics, Autonomous Vehicles (AVs), and even homicidal robots from the future such as the Terminator. Our Technology Sector report made the case that there is nothing particularly sinister about robots. They are just another chapter in a long history of automation. Nor is the displacement of workers unprecedented. The industrial revolution was about replacing human craft labor with capital (machines), which did high-volume work with better quality and productivity. This freed humans for work which had not yet been automated, along with designing, producing and maintaining the machinery. Agriculture offers a good example. This sector involved over 50% of the U.S. labor force until the late 1800s. Steam and then internal combustion-powered tractors, which can be viewed as "robotic horses," contributed to a massive rise in output-per-man hour. The number of hours worked to produce a bushel of wheat fell by almost 98% from the mid-1800s to 1955. This put a lot of farm hands out of work, but these laborers were absorbed over time in other growing areas of the economy. It is the same story for all other historical technological breakthroughs. Change is stressful for those directly affected, but rising productivity ultimately lifts average living standards. Robots will be no different. As we discuss below, however, the increasing use of robots and AI may have a deeper and longer-lasting impact on inequality. Strong Tailwinds Chart II-1Robots Are Getting Cheaper Robots Are Getting Cheaper Robots Are Getting Cheaper Factory robots have improved immensely due to cheaper and more capable control and vision systems. As these systems evolve, the abilities of robots to move around their environment while avoiding obstacles will improve, as will their ability to perform increasingly complex tasks. Most importantly, robots are already able to do more than just routine tasks, thus enabling them to replace or aid humans in higher-skilled processes. Robot prices are also falling fast, especially after quality-adjusting the data (Chart II-1). Units are becoming easier to install, program and operate. These trends will help to reduce the barriers-to-entry for the large, untapped, market of small and medium sized enterprises. Robots also offer the ability to do low-volume "customized" production and still keep unit costs low. In the future, self-learning robots will be able to optimize their own performance by analyzing the production of other robots around the world. Robot usage is growing quickly according to data collected by the International Federation of Robotics (IFR) that covers 23 countries. Industrial robot sales worldwide increased to almost 300,000 units in 2016, up 16% from the year before (Chart II-2). The stock of industrial robots globally has grown at an annual average pace of 10% since 2010, reaching slightly more than 1.8 million units in 2016.2 Robot usage is far from evenly distributed across industries. The automotive industry is the major consumer of industrial robots, holding 45% of the total stock in 2016 (Chart II-3). The computer & electronics industry is a distant second at 17%. Metals, chemicals and electrical/electronic appliances comprise the bulk of the remaining stock. Chart II-2Global Robot Usage Global Robot Usage Global Robot Usage Chart II-3Global Robot Usage By Industry (2016) February 2018 February 2018 As far as countries go, Japan has traditionally been the largest market for robots in the world. However, sales have been in a long-term downtrend and the stock of robots has recently been surpassed by China, which has ramped up robot purchases in recent years (Chart II-4). Robot density, which is the stock of robots per 10 thousand employed in manufacturing, makes it easier to compare robot usage across countries (Chart II-5, panel 2). By this measure, China is not a heavy user of robots compared to other countries. South Korea stands at the top, well above the second-place finishers (Germany and Japan). Large automobile sectors in these three countries explain their high relative robot densities. Chart II-4Stock Of Robots By Country (I) Stock Of Robots By Country (I) Stock Of Robots By Country (I) Chart II-5Stock Of Robots By Country (II) (2016) February 2018 February 2018 While the growth rate of robot usage is impressive, it is from a very low base (outside of the automotive industry). The average number of robots per 10,000 employees is only 74 for the 23 countries in the IFR database. Robot use is tiny compared to total man hours worked. Chart II-6U.S. Investment In Robots U.S. Investment in Robots U.S. Investment in Robots In the U.S., spending on robots is only about 5% of total business spending on equipment and software (Chart II-6). To put this into perspective, U.S. spending on information, communication and technology (ICT) equipment represented 35-40% of total capital equipment spending during the tech boom in the 1990s and early 2000s.3 The bottom line is that there is a lot of hype in the press, but robots are not yet widely used across countries or industries. It will be many years before business spending on robots approaches the scale of the 1990s/2000s IT boom. A Deflationary Impact? As noted above, we view robotics as another chapter in a long history of technological advancements. Pessimists suggest that the latest advances are different because they are inherently more threatening to the overall job market and wage share of total income. If the pessimists are right, what are the theoretical channels though which this would have a greater disinflationary effect relative to previous GPT technologies? Faster Productivity Gains: Enhanced productivity drives down unit labor costs, which may be passed along to other industries (as cheaper inputs) and to the end consumer. More Human Displacement: The jobs created in other areas may be insufficient to replace the jobs displaced by robots, leading to lower aggregate income and spending. The loss of income for labor will simply go to the owners of capital, but the point is that the labor share of income might decline. Deflationary pressures could build as aggregate demand falls short of supply. Even in industries that are slow to automate, just the threat of being replaced by robots may curtail wage demands. Inequality: Some have argued that rising inequality is partly because the spoils of new technologies over the past 20 years have largely gone to the owners of capital. This shift may have undermined aggregate demand because upper income households tend to have a high saving rate, thereby depressing overall aggregate demand and inflationary pressures. The human displacement effect, described above, would exacerbate the inequality effect by transferring income from labor to the owners of capital. 1. Productivity It is difficult to see the benefits of robots on productivity at the economy-wide level. Productivity growth has been abysmal across the major developed countries since the Great Recession, but the productivity slowdown was evident long before Lehman collapsed (Chart II-7). The productivity slowdown continued even as automation using robots accelerated after 2010. Chart II-7Productivity Collapsed Despite Automation Productivity Collapsed Despite Automation Productivity Collapsed Despite Automation Some analysts argue that lackluster productivity is simply a statistical mirage because of the difficulties in measuring output in today's economy. We will not get into the details of the mismeasurement debate here. We encourage interested clients to read a Special Report by the BCA Global Investment Strategy service entitled "Weak Productivity Growth: Don't Blame The Statisticians." 4 Our colleague Peter Berezin makes the case that the unmeasured utility accruing from free internet services is large, but so was the unmeasured utility from antibiotics, radio, indoor plumbing and air conditioning. He argues that the real reason that productivity growth has slowed is that educational attainment has decelerated and businesses have plucked many of the low-hanging fruit made possible by the IT revolution. Cyclical factors stemming from the Great Recession and financial crisis are also to blame, as capital spending has been slow to recover in most of the advanced economies. Some other factors that help to explain the decline in aggregate productivity are provided in Appendix II-1. Nonetheless, the poor aggregate productivity performance does not mean that there are no benefits to using robots. The benefits are evident at the industrial level, where measurement issues are presumably less vexing for statisticians (i.e., it is easier to measure the output of the auto industry, for example, than for the economy as a whole). Chart II-8 plots the level of robot density in 2016 with average annual productivity growth since 2004 for 10 U.S. manufacturing industries (robot density is presented in deciles). A loose positive relationship is apparent. Chart II-8U.S.: Productivity Vs. Robot Density February 2018 February 2018 Academic studies estimate that robots have contributed importantly to economy-wide productivity growth. The Centre for Economic and Business Research (CEBR) estimated that labor productivity growth rises by 0.07 to 0.08 percentage points for every 1% rise in the rate of robot density.5 This implies that robots accounted for roughly 10% of the productivity growth experienced since the early 1990s in the major economies. Another study of 14 industries across 17 countries by the Centre for Economic Performance (CEP) found that robots boosted annual productivity growth by 0.36 percentage points over the 1993-2007 period.6 This is impressive because, if this estimate holds true for the U.S., robots' contribution to the 2½% average annual U.S. total productivity growth over the period was 14%. To put the importance of robotics into historical context, its contribution to productivity so far is roughly on par with that of the steam engine (Chart II-9). It falls well short of the 0.6 percentage point annual productivity contribution from the IT revolution. The implication is that, while the overall productivity performance has been dismal since 2007, it would have been even worse in the absence of robots. What does this mean for inflation? According to the "cost push" model of the inflation process, an increase in productivity of 0.36% that is not accompanied by associated wage gains would reduce unit labor costs (ULC) by the same amount. This should trim inflation if the cost savings are passed on to the end consumer, although by less than 0.36% because robots can only depress variable costs, not fixed costs. There indeed appears to be a slight negative relationship between robot density and unit labor costs at the industrial level in the U.S., although the relationship is loose at best (Chart II-10). Chart II-9GPT Contribution To Productivity February 2018 February 2018 Chart II-10U.S.: Unit Labor Costs Vs. Robot Density February 2018 February 2018 In theory, divergences in productivity across industries should only generate shifts in relative prices, and "cost push" inflation dynamics should only operate in the short term. Most economists believe that inflation is a purely monetary phenomenon in the long run, which means that central banks should be able to offset positive productivity shocks by lowering interest rates enough that aggregate demand keeps up with supply. Indeed, the Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. Also, note that inflation is currently low across the major advanced economies, irrespective of the level of robot intensity (Chart II-11). From this perspective, it is hard to see that robots should take much of the credit for today's low inflation backdrop. Chart II-11Inflation Vs. Robot Density February 2018 February 2018 2. Human Displacement A key question is whether robots and humans are perfect substitutes. If new technologies introduced in the past were perfect substitutes, then it would have led to massive underemployment and all of the income in the economy would eventually have migrated to the owners of capital. The fact that average real household incomes have risen over time, and that there has been no secular upward trend in unemployment rates over the centuries, means that new technologies were at least partly complementary with labor (i.e., the jobs lost as a direct result of productivity gains were more than replaced in other areas of the economy over time). Rather than replacing workers, in many cases tech made humans more productive in their jobs. Rising productivity lifted income and thereby led to the creation of new jobs in other areas. The capital that workers bring to the production process - the skills, know-how and special talents - became more valuable as interaction with technology increased. Like today, there were concerns in the 1950s and 1960s that computerization would displace many types of jobs and lead to widespread idleness and falling household income. With hindsight, there was little to worry about. Some argue that this time is different. Futurists frequently assert that the pace of innovation is not just accelerating, it is accelerating 'exponentially'. Robots can now, or will soon be able to, replace humans in tasks that require cognitive skills. This means that they will be far less complementary to humans than in the past. The displacement effect could thus be much larger, especially given the impressive advances in artificial intelligence. However, Box II-1 discusses why the threat to workers posed by AI is also heavily overblown in the media. The CEP multi-country study cited above did not find a large displacement effect; robot usage did not affect the overall number of hours worked in the 23 countries studied (although it found distributional effects - see below). In other words, rather than suppressing overall labor input, robot usage has led to more output, higher productivity, more jobs and stronger wage and income growth. A report by the Economic Policy Institute (EPI)7 takes a broader look at automation, using productivity growth and capital spending as proxies. Automation is what occurs as the implementation of new technologies is incorporated along with new capital equipment or software to replace human labor in the workplace. If automation is increasing 'exponentially' and displacing workers on a broad scale, one would expect to see accelerating productivity growth, robust capital spending, and more violent shifts in occupational shares. Exactly the opposite has occurred. Indeed, the report demonstrates that occupational employment shifts were far slower in the 2000-2015 period than in any decade in the 1900s (Chart II-12). Box II-1 The Threat From AI Is Overblown Media coverage of AI/Deep Learning has established a consensus view that we believe is well off the mark. A recent Special Report from BCA's Technology Sector Strategy service dispels the myths surrounding AI.8 We believe the consensus, in conjunction with warnings from a variety of sources, is leading to predictions, policy discussions, and even career choices based on a flawed premise. It is worth noting that the most vocal proponents of AI as a threat to jobs and even humanity are not AI experts. At the root of this consensus is the false view that emerging AI technology is anything like true intelligence. Modern AI is not remotely comparable in function to a biological brain. Scientists have a limited understanding of how brains work, and it is unlikely that a poorly understood system can be modeled on a computer. The misconception of intelligence is amplified by headlines claiming an AI "taught itself" a particular task. No AI has ever "taught itself" anything: All AI results have come about after careful programming by often PhD-level experts, who then supplied the system with vast amounts of high quality data to train it. Often these systems have been iterated a number of times and we only hear of successes, not the failures. The need for careful preparation of the AI system and the requirement for high quality data limits the applicability of AI to specific classes of problems where the application justifies the investment in development and where sufficient high-quality data exists. There may be numerous such applications but doubtless many more where AI would not be suitable. Similarly, an AI system is highly adapted to a single problem, or type of problem, and becomes less useful when its application set is expanded. In other words, unlike a human whose abilities improve as they learn more things, an AI's performance on a particular task declines as it does more things. There is a popular misconception that increased computing power will somehow lead to ever improving AI. It is the algorithm which determines the outcome, not the computer performance: Increased computing power leads to faster results, not different results. Advanced computers might lead to more advanced algorithms, but it is pointless to speculate where that may lead: A spreadsheet from 2001 may work faster today but it still gives the same answer. In any event, it is worth noting that a tool ceases to be a tool when it starts having an opinion: there is little reason to develop a machine capable of cognition even if that were possible. Chart II-12U.S. Job Rotation Has Slowed February 2018 February 2018 The EPI report also notes that these indicators of automation increased rapidly in the late 1990s and early 2000s, a period that saw solid wage growth for American workers. These indicators weakened in the two periods of stagnant wage growth: from 1973 to 1995 and from 2002 to the present. Thus, there is no historical correlation between increases in automation and wage stagnation. Rather than automation, the report argues that it was China's entry into the global trading system that was largely responsible for the hollowing out of the U.S. manufacturing sector. We have also made this argument in previous research. The fact that the major advanced economies are all at, or close to, full employment supports the view that automation has not been an overwhelming headwind for job creation. Chart II-13 demonstrates that there has been no relationship between the change in robot density and the loss of manufacturing jobs since 1993. Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. Interestingly, despite a worsening labor shortage, robot density among Japanese firms is falling. Moreover, the Japanese data show that the industries that have a high robot usage tend to be more, not less, generous with wages than the robot laggard industries. Please see Appendix II-2 for more details. Chart II-13Global Manufacturing Jobs Vs. Robot Density February 2018 February 2018 The bottom line is that it does not appear that labor displacement related to automation has been responsible in any meaningful way for the lackluster average real income growth in the advanced economies since 2007. 3. Inequality That said, there is evidence suggesting that robots are having important distributional effects. The CEP study found that robot use has reduced hours for low-skilled and (to a lesser extent) middle-skilled workers relative to the highly skilled. This finding makes sense conceptually. Technological change can exacerbate inequality by either increasing the relative demand for skilled over unskilled workers (so-called "skill-biased" technological change), or by inducing companies to substitute machinery and other forms of physical capital for workers (so-called "capital-biased" technological change). The former affects the distribution of labor income, while the latter affects the share of income in GDP that labor receives. A Special Report appearing in this publication in 2014 focused on the relationship between technology and inequality.9 The report highlighted that much of the recent technological change has been skill-biased, which heavily favors workers with the talent and education to perform cognitively-demanding tasks, even as it reduces demand for workers with only rudimentary skills. Moreover, technological innovations and globalization increasingly allow the most talented individuals to market their skills to a much larger audience, thus bidding up their wages. The evidence suggests that faster productivity growth leads to higher average real wages and improved living standards, at least over reasonably long horizons. Nonetheless, technological change can, and in the future almost certainly will, increase income inequality. The poor will gain, but not as much as the rich. The fact that higher-income households tend to maintain a higher savings rate than low-income households means that the shift in the distribution of income toward the higher-income households will continue to modestly weigh on aggregate demand. Can the distribution effect be large enough to have a meaningful depressing impact on inflation? We believe that it has played some role in the lackluster recovery since the Great Recession, with the result that an extended period of underemployment has delivered a persistent deflationary impulse in the major developed economies. However, as discussed above, stimulative monetary policy has managed to overcome the impact of inequality and other headwinds on aggregate demand, and has returned the major countries roughly to full employment. Indeed, this year will be the first since 2007 that the G20 economies as a group will be operating slightly above a full employment level. Inflation should respond to excess demand conditions, irrespective of any ongoing demand headwind stemming from inequality. Conclusions Technological change has led to rising living standards over the decades. It did not lead to widespread joblessness and did not prevent central banks from meeting their inflation targets over time. The pessimists argue that this time is different because robots/AI have a much larger displacement effect. Perhaps it will be 20 years before we will know the answer. But our main point is that we have found no evidence that recent advances in robotics and AI, while very impressive, will be any different in their macro impact. There is little evidence that the modern economy is less capable in replacing the jobs lost to automation, although the nature of new technologies may be affecting the distribution of income more than in the past. Real incomes for the middle- and lower-income classes have been stagnant for some time, but this is partly due to productivity growth that is too low, not too high. Moreover, it is not at all clear that positive productivity shocks are disinflationary beyond the near term. The link between robot usage and unit labor costs over the past couple of decades is loose at best at the industry level, and is non-existent when looking across the major countries. The Fed was able to roughly meet its 2% inflation target in the 1990s and the first half of the 2000s, despite IT's impressive contribution to productivity growth during that period. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. The global output gap will shift into positive territory this year for the first time since the Great Recession. Any resulting rise in inflation will come as a shock since the bond market has discounted continued low inflation for as far as the eye can see. We expect bond yields and implied volatility to rise this year, which may undermine risk assets in the second half. Mark McClellan Senior Vice President The Bank Credit Analyst Brian Piccioni Vice President Technology Sector Strategy Appendix II-1 Why Is Productivity So Low? A recent study by the OECD10 reveals that, while frontier firms are charging ahead, there is a widening gap between these firms and the laggards. The study analyzed firm-level data on labor productivity and total factor productivity for 24 countries. "Frontier" firms are defined to be those with productivity in the top 5%. These firms are 3-4 times as productive as the remaining 95%. The authors argue that the underlying cause of this yawning gap is that the diffusion rate of new technologies from the frontier firms to the laggards has slowed within industries. This could be due to rising barriers to entry, which has reduced contestability in markets. Curtailing the creative-destruction process means that there is less pressure to innovate. Barriers to entry may have increased because "...the importance of tacit knowledge as a source of competitive advantage for frontier firms may have risen if increasingly complex technologies were to increase the amount and sophistication of complementary investments required for technological adoption." 11 The bottom line is that aggregate productivity is low because the robust productivity gains for the tech-savvy frontier companies are offset by the long tail of firms that have been slow to adopt the latest technology. Indeed, business spending has been especially weak in this expansion. Chart II-14 highlights that the slowdown in U.S. productivity growth has mirrored that of the capital stock. Chart II-14U.S. Capex Shortfall Partly To Blame For Poor Productivity U.S. Capex Shortfall Partly To Blame For Poor Productivity U.S. Capex Shortfall Partly To Blame For Poor Productivity Appendix II-2 Japan - The Leading Edge Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. The popular press is full of stories of how robots are taking over. If the stories are to be believed, robots are the answer to the country's shrinking workforce. Robots now serve as helpers for the elderly, priests for weddings and funerals, concierges for hotels and even sexual partners (don't ask). Prime Minister Abe's government has launched a 5-year push to deepen the use of intelligent machines in manufacturing, supply chains, construction and health care. Indeed, Japan was the leader in robotics use for decades. Nonetheless, despite all the hype, Japan's stock of industrial robots has actually been eroding since the late 1990s (Chart II-4). Numerous surveys show that firms plan to use robots more in the future because of the difficulty in hiring humans. And there is huge potential: 90% of Japanese firms are small- and medium-sized (SME) and most are not currently using robots. Yet, there has been no wave of robot purchases as of 2016. One problem is the cost; most sophisticated robots are simply too expensive for SMEs to consider. This suggests that one cannot blame robots for Japan's lack of wage growth. The labor shortage has become so acute that there are examples of companies that have turned down sales due to insufficient manpower. Possible reasons why these companies do not offer higher wages to entice workers are beyond the scope of this report. But the fact that the stock of robots has been in decline since the late 1990s does not support the view that Japanese firms are using automation on a broad scale to avoid handing out pay hikes. Indeed, Chart II-15 highlights that wage deflation has been the greatest in industries that use almost no robots. Highly automated industries, such as Transportation Equipment and Electronics, have been among the most generous. This supports the view that the productivity afforded by increased robot usage encourages firms to pay their workers more. Looking ahead, it seems implausible that robots can replace all the retiring Japanese workers in the years to come. The workforce will shrink at an annual average pace of 0.33% between 2020 and 2030, according to the Japan Institute for Labour Policy and Training. Productivity growth would have to rise by the same amount to fully offset the dwindling number of workers. But that would require a surge in robot density of 4.1, assuming that each rise in robot density of one adds 0.08% to the level of productivity (Chart II-16). The level of robot sales would have to jump by a whopping 2½ times in the first year and continue to rise at the same pace each year thereafter to make this happen. Of course, the productivity afforded by new robots may accelerate in the coming years, but the point is that robot usage would likely have to rise astronomically to offset the impact of the shrinking population. Chart II-15Japan: Earnings Vs. Robot Density February 2018 February 2018 Chart II-16Japan: Where Is The Flood Of Robots? Japan: Where Is The Flood OF Robots? Japan: Where Is The Flood OF Robots? The implication is that, as long as the Japanese economy continues to grow above roughly 1%, the labor market will continue to tighten and wage rates will eventually begin to rise. 1 Please see Technology Sector Strategy Special Report "The Coming Robotics Revolution," dated May 16, 2017, available at tech.bcaresearch.com 2 Note that this includes only robots used in manufacturing industry, and thus excludes robots used in the service sector and households. However, robot usage in services is quite limited and those used in households do not add to GDP. 3 Note that ICT investment and capital stock data includes robots. 4 Please see BCA Global Investment Strategy Special Report "Weak Productivity Growth: Don't Blame The Statisticians," dated March 25, 2016, available at gis.bcaresearch.com 5 Centre for Economic and Business Research (January 2017): "The Impact of Automation." A Report for Redwood. In this report, robot density is defined to be the number of robots per million hours worked. 6 Graetz, G., and Michaels, G. (2015): "Robots At Work." CEP Discussion Paper No 1335. 7 Mishel, L., and Bivens, J. (2017): "The Zombie Robot Argument Lurches On," Economic Policy Institute. 8 Please see BCA Technology Sector Strategy Special Report "Bad Information - Why Misreporting Deep Learning Advances Is A Problem," dated January 9, 2018, available at tech.bcaresearch.com 9 Please see The Bank Credit Analyst, "Rage Against The Machines: Is Technology Exacerbating Inequality?" dated June 2014, available at bca.bcaresearch.com 10 OECD Productivity Working Papers, No. 05 (2016): "The Best Versus the Rest: The Global Productivity Slowdown, Divergence Across Firms and the Role of Public Policy." 11 Please refer to page 27. III. Indicators And Reference Charts As we highlight in the Overview section, the earnings backdrop for the U.S. equity market remains very upbeat, as highlighted by the rise in the net earnings revisions and net earnings surprises indexes. Bottom-up analysts will likely continue to boost after-tax earnings estimates for the year as they adjust to the U.S. tax cut news. Our main concern is that a lot of good news is now discounted. Our Technical Indicator remains bullish, but our composite valuation indicator surpassed one sigma in January, which is our threshold of overvaluation. From these levels of overvaluation, the medium-term outlook for equity total returns is negligible. Our speculation index is at all-time highs and implied volatility is low, underscoring that investors are extremely bullish. From a contrary perspective, this is a warning sign for the equity market. Our Monetary Indicator has also moved further into 'bearish' territory for equities, although overall financial conditions remain positive for growth. It is also disconcerting that our Revealed Preference Indicator (RPI) shifted to a 'sell' signal for stocks, following five straight months on a 'buy' signal. This occurred because investors may be buying based on speculation rather than on a firm belief in the staying power of the underlying fundamentals. For now, though, our Willingness-to-Pay indicator for the U.S. rose sharply in January, highlighting that investor equity inflows are very strong and are favoring U.S. equities relative to Japan and the Eurozone. This is perhaps not surprising given the U.S. tax cuts just passed by Congress. The RPI indicators track flows, and thus provide information on what investors are actually doing, as opposed to sentiment indexes that track how investors are feeling. Our U.S. bond technical indicator shows that Treasurys are close to oversold territory, suggesting that we may be in store for a consolidation period following January's surge in yields. Treasurys are slightly cheap on our valuation metric, although not by enough to justify closing short duration positions. The U.S. dollar is oversold and due for a bounce. EQUITIES: Chart III-1U.S. Equity Indicators U.S. Equity Indicators U.S. Equity Indicators Chart III-2Willingness To Pay For Risk Willingness To Pay For Risk Willingness To Pay For Risk Chart III-3U.S. Equity Sentiment Indicators U.S. Equity Sentiment Indicators U.S. Equity Sentiment Indicators Chart III-4Revealed Preference Indicator Revealed Preference Indicator Revealed Preference Indicator Chart III-5U.S. Stock Market Valuation U.S. Stock Market Valuation U.S. Stock Market Valuation Chart III-6U.S. Earnings U.S. Earnings U.S. Earnings Chart III-7Global Stock Market And Earnings: ##br##Relative Performance Global Stock Market And Earnings: Relative Performance Global Stock Market And Earnings: Relative Performance Chart III-8Global Stock Market And Earnings: ##br##Relative Performance Global Stock Market And Earnings: Relative Performance Global Stock Market And Earnings: Relative Performance FIXED INCOME: Chart III-9U.S. Treasurys And Valuations U.S. Treasurys and Valuations U.S. Treasurys and Valuations Chart III-10U.S. Treasury Indicators U.S. Treasury Indicators U.S. Treasury Indicators Chart III-11Selected U.S. Bond Yields Selected U.S. Bond Yields Selected U.S. Bond Yields Chart III-1210-Year Treasury Yield Components 10-Year Treasury Yield Components 10-Year Treasury Yield Components Chart III-13U.S. Corporate Bonds And Health Monitor U.S. Corporate Bonds And Health Monitor U.S. Corporate Bonds And Health Monitor Chart III-14Global Bonds: Developed Markets Global Bonds: Developed Markets Global Bonds: Developed Markets Chart III-15Global Bonds: Emerging Markets Global Bonds: Emerging Markets Global Bonds: Emerging Markets CURRENCIES: Chart III-16U.S. Dollar And PPP U.S. Dollar And PPP U.S. Dollar And PPP Chart III-17U.S. Dollar And Indicator U.S. Dollar And Indicator U.S. Dollar And Indicator Chart III-18U.S. Dollar Fundamentals U.S. Dollar Fundamentals U.S. Dollar Fundamentals Chart III-19Japanese Yen Technicals Japanese Yen Technicals Japanese Yen Technicals Chart III-20Euro Technicals Euro Technicals Euro Technicals Chart III-21Euro/Yen Technicals Euro/Yen Technicals Euro/Yen Technicals Chart III-22Euro/Pound Technicals Euro/Pound Technicals Euro/Pound Technicals COMMODITIES: Chart III-23Broad Commodity Indicators Broad Commodity Indicators Broad Commodity Indicators Chart III-24Commodity Prices Commodity Prices Commodity Prices Chart III-25Commodity Prices Commodity Prices Commodity Prices Chart III-26Commodity Sentiment Commodity Sentiment Commodity Sentiment Chart III-27Speculative Positioning Speculative Positioning Speculative Positioning ECONOMY: Chart III-28U.S. And Global Macro Backdrop U.S. And Global Macro Backdrop U.S. And Global Macro Backdrop Chart III-29U.S. Macro Snapshot U.S. Macro Snapshot U.S. Macro Snapshot Chart III-30U.S. Growth Outlook U.S. Growth Outlook U.S. Growth Outlook Chart III-31U.S. Cyclical Spending U.S. Cyclical Spending U.S. Cyclical Spending Chart III-32U.S. Labor Market U.S. Labor Market U.S. Labor Market Chart III-33U.S. Consumption U.S. Consumption U.S. Consumption Chart III-34U.S. Housing U.S. Housing U.S. Housing Chart III-35U.S. Debt And Deleveraging U.S. Debt And Deleveraging U.S. Debt And Deleveraging Chart III-36U.S. Financial Conditions U.S. Financial Conditions U.S. Financial Conditions Chart III-37Global Economic Snapshot: Europe Global Economic Snapshot: Europe Global Economic Snapshot: Europe Chart III-38Global Economic Snapshot: China Global Economic Snapshot: China Global Economic Snapshot: China Mark McClellan Senior Vice President The Bank Credit Analyst

Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. Technological advance in the past has not prevented improving living standards or led to ever rising joblessness over the decades, but pessimists argue that recent advances are different. The issue is important for financial markets. If structural factors such as automation are holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. We see no compelling evidence that the displacement effect of emerging technologies is any stronger than in the past. Robot usage has had a modest positive impact on overall productivity. Despite this contribution, overall productivity growth has been dismal over the past decade. If automation is increasing 'exponentially' and displacing workers on a broad scale as some claim, one would expect to see accelerating productivity growth, robust capital spending and more violent shifts in occupational shares. Exactly the opposite has occurred. Periods of strong growth in automation have historically been associated with robust, not lackluster, wage gains, contrary to the consensus view. The Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. This and other evidence suggest that it is difficult to make the case that robots will make it tougher for central banks to reach their inflation goals than did previous technological breakthroughs. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. Recent breakthroughs in technology are awe-inspiring and unsettling. These advances are viewed with great trepidation by many because of the potential to replace humans in the production process. Hype over robots is particularly shrill. Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. In the first in our series of Special Reports focusing on the structural factors that might be preventing central banks from reaching their inflation targets, we demonstrated that the impact of Amazon is overstated in the press. We estimated that E-commerce is depressing inflation in the U.S. by a mere 0.1 to 0.2 percentage points. This Special Report tackles the impact of automation. We are optimistic that robot technology and artificial intelligence will significantly boost future productivity, and thus reduce costs. But, is there any evidence at the macro level that robot usage has been more deflationary than technological breakthroughs in the past and is, thus, a major driver of the low inflation rates we observe today across the major countries? The question matters, especially for the outlook for central bank policy and the bond market. If structural factors are indeed holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. However, if low inflation simply reflects long lags between wages and the tightening labor market, then inflation may suddenly lurch to life as it has at the end of past cycles. The bond market is not priced for that scenario. Are Robots Different? A Special Report from BCA's Technology Sector Strategy service suggested that the "robot revolution" could be as transformative as previous General Purpose Technologies (GPT), including the steam engine, electricity and the microchip.1 GPTs are technologies that radically alter the economy's production process and make a major contribution to living standards over time. The term "robot" can have different meanings. The most basic definition is "a device that automatically performs complicated and often repetitive tasks," and this encompasses a broad range of machines: From the Jacquard Loom, which was invented over 200 years ago, on to Numerically Controlled (NC) mills and lathes, pick and place machines used in the manufacture of electronics, Autonomous Vehicles (AVs), and even homicidal robots from the future such as the Terminator. Our Technology Sector report made the case that there is nothing particularly sinister about robots. They are just another chapter in a long history of automation. Nor is the displacement of workers unprecedented. The industrial revolution was about replacing human craft labor with capital (machines), which did high-volume work with better quality and productivity. This freed humans for work which had not yet been automated, along with designing, producing and maintaining the machinery. Agriculture offers a good example. This sector involved over 50% of the U.S. labor force until the late 1800s. Steam and then internal combustion-powered tractors, which can be viewed as "robotic horses," contributed to a massive rise in output-per-man hour. The number of hours worked to produce a bushel of wheat fell by almost 98% from the mid-1800s to 1955. This put a lot of farm hands out of work, but these laborers were absorbed over time in other growing areas of the economy. It is the same story for all other historical technological breakthroughs. Change is stressful for those directly affected, but rising productivity ultimately lifts average living standards. Robots will be no different. As we discuss below, however, the increasing use of robots and AI may have a deeper and longer-lasting impact on inequality. Strong Tailwinds Chart II-1Robots Are Getting Cheaper Robots Are Getting Cheaper Robots Are Getting Cheaper Factory robots have improved immensely due to cheaper and more capable control and vision systems. As these systems evolve, the abilities of robots to move around their environment while avoiding obstacles will improve, as will their ability to perform increasingly complex tasks. Most importantly, robots are already able to do more than just routine tasks, thus enabling them to replace or aid humans in higher-skilled processes. Robot prices are also falling fast, especially after quality-adjusting the data (Chart II-1). Units are becoming easier to install, program and operate. These trends will help to reduce the barriers-to-entry for the large, untapped, market of small and medium sized enterprises. Robots also offer the ability to do low-volume "customized" production and still keep unit costs low. In the future, self-learning robots will be able to optimize their own performance by analyzing the production of other robots around the world. Robot usage is growing quickly according to data collected by the International Federation of Robotics (IFR) that covers 23 countries. Industrial robot sales worldwide increased to almost 300,000 units in 2016, up 16% from the year before (Chart II-2). The stock of industrial robots globally has grown at an annual average pace of 10% since 2010, reaching slightly more than 1.8 million units in 2016.2 Robot usage is far from evenly distributed across industries. The automotive industry is the major consumer of industrial robots, holding 45% of the total stock in 2016 (Chart II-3). The computer & electronics industry is a distant second at 17%. Metals, chemicals and electrical/electronic appliances comprise the bulk of the remaining stock. Chart II-2Global Robot Usage Global Robot Usage Global Robot Usage Chart II-3Global Robot Usage By Industry (2016) February 2018 February 2018 As far as countries go, Japan has traditionally been the largest market for robots in the world. However, sales have been in a long-term downtrend and the stock of robots has recently been surpassed by China, which has ramped up robot purchases in recent years (Chart II-4). Robot density, which is the stock of robots per 10 thousand employed in manufacturing, makes it easier to compare robot usage across countries (Chart II-5, panel 2). By this measure, China is not a heavy user of robots compared to other countries. South Korea stands at the top, well above the second-place finishers (Germany and Japan). Large automobile sectors in these three countries explain their high relative robot densities. Chart II-4Stock Of Robots By Country (I) Stock Of Robots By Country (I) Stock Of Robots By Country (I) Chart II-5Stock Of Robots By Country (II) (2016) February 2018 February 2018 While the growth rate of robot usage is impressive, it is from a very low base (outside of the automotive industry). The average number of robots per 10,000 employees is only 74 for the 23 countries in the IFR database. Robot use is tiny compared to total man hours worked. Chart II-6U.S. Investment In Robots U.S. Investment in Robots U.S. Investment in Robots In the U.S., spending on robots is only about 5% of total business spending on equipment and software (Chart II-6). To put this into perspective, U.S. spending on information, communication and technology (ICT) equipment represented 35-40% of total capital equipment spending during the tech boom in the 1990s and early 2000s.3 The bottom line is that there is a lot of hype in the press, but robots are not yet widely used across countries or industries. It will be many years before business spending on robots approaches the scale of the 1990s/2000s IT boom. A Deflationary Impact? As noted above, we view robotics as another chapter in a long history of technological advancements. Pessimists suggest that the latest advances are different because they are inherently more threatening to the overall job market and wage share of total income. If the pessimists are right, what are the theoretical channels though which this would have a greater disinflationary effect relative to previous GPT technologies? Faster Productivity Gains: Enhanced productivity drives down unit labor costs, which may be passed along to other industries (as cheaper inputs) and to the end consumer. More Human Displacement: The jobs created in other areas may be insufficient to replace the jobs displaced by robots, leading to lower aggregate income and spending. The loss of income for labor will simply go to the owners of capital, but the point is that the labor share of income might decline. Deflationary pressures could build as aggregate demand falls short of supply. Even in industries that are slow to automate, just the threat of being replaced by robots may curtail wage demands. Inequality: Some have argued that rising inequality is partly because the spoils of new technologies over the past 20 years have largely gone to the owners of capital. This shift may have undermined aggregate demand because upper income households tend to have a high saving rate, thereby depressing overall aggregate demand and inflationary pressures. The human displacement effect, described above, would exacerbate the inequality effect by transferring income from labor to the owners of capital. 1. Productivity It is difficult to see the benefits of robots on productivity at the economy-wide level. Productivity growth has been abysmal across the major developed countries since the Great Recession, but the productivity slowdown was evident long before Lehman collapsed (Chart II-7). The productivity slowdown continued even as automation using robots accelerated after 2010. Chart II-7Productivity Collapsed Despite Automation Productivity Collapsed Despite Automation Productivity Collapsed Despite Automation Some analysts argue that lackluster productivity is simply a statistical mirage because of the difficulties in measuring output in today's economy. We will not get into the details of the mismeasurement debate here. We encourage interested clients to read a Special Report by the BCA Global Investment Strategy service entitled "Weak Productivity Growth: Don't Blame The Statisticians." 4 Our colleague Peter Berezin makes the case that the unmeasured utility accruing from free internet services is large, but so was the unmeasured utility from antibiotics, radio, indoor plumbing and air conditioning. He argues that the real reason that productivity growth has slowed is that educational attainment has decelerated and businesses have plucked many of the low-hanging fruit made possible by the IT revolution. Cyclical factors stemming from the Great Recession and financial crisis are also to blame, as capital spending has been slow to recover in most of the advanced economies. Some other factors that help to explain the decline in aggregate productivity are provided in Appendix II-1. Nonetheless, the poor aggregate productivity performance does not mean that there are no benefits to using robots. The benefits are evident at the industrial level, where measurement issues are presumably less vexing for statisticians (i.e., it is easier to measure the output of the auto industry, for example, than for the economy as a whole). Chart II-8 plots the level of robot density in 2016 with average annual productivity growth since 2004 for 10 U.S. manufacturing industries (robot density is presented in deciles). A loose positive relationship is apparent. Chart II-8U.S.: Productivity Vs. Robot Density February 2018 February 2018 Academic studies estimate that robots have contributed importantly to economy-wide productivity growth. The Centre for Economic and Business Research (CEBR) estimated that labor productivity growth rises by 0.07 to 0.08 percentage points for every 1% rise in the rate of robot density.5 This implies that robots accounted for roughly 10% of the productivity growth experienced since the early 1990s in the major economies. Another study of 14 industries across 17 countries by the Centre for Economic Performance (CEP) found that robots boosted annual productivity growth by 0.36 percentage points over the 1993-2007 period.6 This is impressive because, if this estimate holds true for the U.S., robots' contribution to the 2½% average annual U.S. total productivity growth over the period was 14%. To put the importance of robotics into historical context, its contribution to productivity so far is roughly on par with that of the steam engine (Chart II-9). It falls well short of the 0.6 percentage point annual productivity contribution from the IT revolution. The implication is that, while the overall productivity performance has been dismal since 2007, it would have been even worse in the absence of robots. What does this mean for inflation? According to the "cost push" model of the inflation process, an increase in productivity of 0.36% that is not accompanied by associated wage gains would reduce unit labor costs (ULC) by the same amount. This should trim inflation if the cost savings are passed on to the end consumer, although by less than 0.36% because robots can only depress variable costs, not fixed costs. There indeed appears to be a slight negative relationship between robot density and unit labor costs at the industrial level in the U.S., although the relationship is loose at best (Chart II-10). Chart II-9GPT Contribution To Productivity February 2018 February 2018 Chart II-10U.S.: Unit Labor Costs Vs. Robot Density February 2018 February 2018 In theory, divergences in productivity across industries should only generate shifts in relative prices, and "cost push" inflation dynamics should only operate in the short term. Most economists believe that inflation is a purely monetary phenomenon in the long run, which means that central banks should be able to offset positive productivity shocks by lowering interest rates enough that aggregate demand keeps up with supply. Indeed, the Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. Also, note that inflation is currently low across the major advanced economies, irrespective of the level of robot intensity (Chart II-11). From this perspective, it is hard to see that robots should take much of the credit for today's low inflation backdrop. Chart II-11Inflation Vs. Robot Density February 2018 February 2018 2. Human Displacement A key question is whether robots and humans are perfect substitutes. If new technologies introduced in the past were perfect substitutes, then it would have led to massive underemployment and all of the income in the economy would eventually have migrated to the owners of capital. The fact that average real household incomes have risen over time, and that there has been no secular upward trend in unemployment rates over the centuries, means that new technologies were at least partly complementary with labor (i.e., the jobs lost as a direct result of productivity gains were more than replaced in other areas of the economy over time). Rather than replacing workers, in many cases tech made humans more productive in their jobs. Rising productivity lifted income and thereby led to the creation of new jobs in other areas. The capital that workers bring to the production process - the skills, know-how and special talents - became more valuable as interaction with technology increased. Like today, there were concerns in the 1950s and 1960s that computerization would displace many types of jobs and lead to widespread idleness and falling household income. With hindsight, there was little to worry about. Some argue that this time is different. Futurists frequently assert that the pace of innovation is not just accelerating, it is accelerating 'exponentially'. Robots can now, or will soon be able to, replace humans in tasks that require cognitive skills. This means that they will be far less complementary to humans than in the past. The displacement effect could thus be much larger, especially given the impressive advances in artificial intelligence. However, Box II-1 discusses why the threat to workers posed by AI is also heavily overblown in the media. The CEP multi-country study cited above did not find a large displacement effect; robot usage did not affect the overall number of hours worked in the 23 countries studied (although it found distributional effects - see below). In other words, rather than suppressing overall labor input, robot usage has led to more output, higher productivity, more jobs and stronger wage and income growth. A report by the Economic Policy Institute (EPI)7 takes a broader look at automation, using productivity growth and capital spending as proxies. Automation is what occurs as the implementation of new technologies is incorporated along with new capital equipment or software to replace human labor in the workplace. If automation is increasing 'exponentially' and displacing workers on a broad scale, one would expect to see accelerating productivity growth, robust capital spending, and more violent shifts in occupational shares. Exactly the opposite has occurred. Indeed, the report demonstrates that occupational employment shifts were far slower in the 2000-2015 period than in any decade in the 1900s (Chart II-12). Box II-1 The Threat From AI Is Overblown Media coverage of AI/Deep Learning has established a consensus view that we believe is well off the mark. A recent Special Report from BCA's Technology Sector Strategy service dispels the myths surrounding AI.8 We believe the consensus, in conjunction with warnings from a variety of sources, is leading to predictions, policy discussions, and even career choices based on a flawed premise. It is worth noting that the most vocal proponents of AI as a threat to jobs and even humanity are not AI experts. At the root of this consensus is the false view that emerging AI technology is anything like true intelligence. Modern AI is not remotely comparable in function to a biological brain. Scientists have a limited understanding of how brains work, and it is unlikely that a poorly understood system can be modeled on a computer. The misconception of intelligence is amplified by headlines claiming an AI "taught itself" a particular task. No AI has ever "taught itself" anything: All AI results have come about after careful programming by often PhD-level experts, who then supplied the system with vast amounts of high quality data to train it. Often these systems have been iterated a number of times and we only hear of successes, not the failures. The need for careful preparation of the AI system and the requirement for high quality data limits the applicability of AI to specific classes of problems where the application justifies the investment in development and where sufficient high-quality data exists. There may be numerous such applications but doubtless many more where AI would not be suitable. Similarly, an AI system is highly adapted to a single problem, or type of problem, and becomes less useful when its application set is expanded. In other words, unlike a human whose abilities improve as they learn more things, an AI's performance on a particular task declines as it does more things. There is a popular misconception that increased computing power will somehow lead to ever improving AI. It is the algorithm which determines the outcome, not the computer performance: Increased computing power leads to faster results, not different results. Advanced computers might lead to more advanced algorithms, but it is pointless to speculate where that may lead: A spreadsheet from 2001 may work faster today but it still gives the same answer. In any event, it is worth noting that a tool ceases to be a tool when it starts having an opinion: there is little reason to develop a machine capable of cognition even if that were possible. Chart II-12U.S. Job Rotation Has Slowed February 2018 February 2018 The EPI report also notes that these indicators of automation increased rapidly in the late 1990s and early 2000s, a period that saw solid wage growth for American workers. These indicators weakened in the two periods of stagnant wage growth: from 1973 to 1995 and from 2002 to the present. Thus, there is no historical correlation between increases in automation and wage stagnation. Rather than automation, the report argues that it was China's entry into the global trading system that was largely responsible for the hollowing out of the U.S. manufacturing sector. We have also made this argument in previous research. The fact that the major advanced economies are all at, or close to, full employment supports the view that automation has not been an overwhelming headwind for job creation. Chart II-13 demonstrates that there has been no relationship between the change in robot density and the loss of manufacturing jobs since 1993. Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. Interestingly, despite a worsening labor shortage, robot density among Japanese firms is falling. Moreover, the Japanese data show that the industries that have a high robot usage tend to be more, not less, generous with wages than the robot laggard industries. Please see Appendix II-2 for more details. Chart II-13Global Manufacturing Jobs Vs. Robot Density February 2018 February 2018 The bottom line is that it does not appear that labor displacement related to automation has been responsible in any meaningful way for the lackluster average real income growth in the advanced economies since 2007. 3. Inequality That said, there is evidence suggesting that robots are having important distributional effects. The CEP study found that robot use has reduced hours for low-skilled and (to a lesser extent) middle-skilled workers relative to the highly skilled. This finding makes sense conceptually. Technological change can exacerbate inequality by either increasing the relative demand for skilled over unskilled workers (so-called "skill-biased" technological change), or by inducing companies to substitute machinery and other forms of physical capital for workers (so-called "capital-biased" technological change). The former affects the distribution of labor income, while the latter affects the share of income in GDP that labor receives. A Special Report appearing in this publication in 2014 focused on the relationship between technology and inequality.9 The report highlighted that much of the recent technological change has been skill-biased, which heavily favors workers with the talent and education to perform cognitively-demanding tasks, even as it reduces demand for workers with only rudimentary skills. Moreover, technological innovations and globalization increasingly allow the most talented individuals to market their skills to a much larger audience, thus bidding up their wages. The evidence suggests that faster productivity growth leads to higher average real wages and improved living standards, at least over reasonably long horizons. Nonetheless, technological change can, and in the future almost certainly will, increase income inequality. The poor will gain, but not as much as the rich. The fact that higher-income households tend to maintain a higher savings rate than low-income households means that the shift in the distribution of income toward the higher-income households will continue to modestly weigh on aggregate demand. Can the distribution effect be large enough to have a meaningful depressing impact on inflation? We believe that it has played some role in the lackluster recovery since the Great Recession, with the result that an extended period of underemployment has delivered a persistent deflationary impulse in the major developed economies. However, as discussed above, stimulative monetary policy has managed to overcome the impact of inequality and other headwinds on aggregate demand, and has returned the major countries roughly to full employment. Indeed, this year will be the first since 2007 that the G20 economies as a group will be operating slightly above a full employment level. Inflation should respond to excess demand conditions, irrespective of any ongoing demand headwind stemming from inequality. Conclusions Technological change has led to rising living standards over the decades. It did not lead to widespread joblessness and did not prevent central banks from meeting their inflation targets over time. The pessimists argue that this time is different because robots/AI have a much larger displacement effect. Perhaps it will be 20 years before we will know the answer. But our main point is that we have found no evidence that recent advances in robotics and AI, while very impressive, will be any different in their macro impact. There is little evidence that the modern economy is less capable in replacing the jobs lost to automation, although the nature of new technologies may be affecting the distribution of income more than in the past. Real incomes for the middle- and lower-income classes have been stagnant for some time, but this is partly due to productivity growth that is too low, not too high. Moreover, it is not at all clear that positive productivity shocks are disinflationary beyond the near term. The link between robot usage and unit labor costs over the past couple of decades is loose at best at the industry level, and is non-existent when looking across the major countries. The Fed was able to roughly meet its 2% inflation target in the 1990s and the first half of the 2000s, despite IT's impressive contribution to productivity growth during that period. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. The global output gap will shift into positive territory this year for the first time since the Great Recession. Any resulting rise in inflation will come as a shock since the bond market has discounted continued low inflation for as far as the eye can see. We expect bond yields and implied volatility to rise this year, which may undermine risk assets in the second half. Mark McClellan Senior Vice President The Bank Credit Analyst Brian Piccioni Vice President Technology Sector Strategy Appendix II-1 Why Is Productivity So Low? A recent study by the OECD10 reveals that, while frontier firms are charging ahead, there is a widening gap between these firms and the laggards. The study analyzed firm-level data on labor productivity and total factor productivity for 24 countries. "Frontier" firms are defined to be those with productivity in the top 5%. These firms are 3-4 times as productive as the remaining 95%. The authors argue that the underlying cause of this yawning gap is that the diffusion rate of new technologies from the frontier firms to the laggards has slowed within industries. This could be due to rising barriers to entry, which has reduced contestability in markets. Curtailing the creative-destruction process means that there is less pressure to innovate. Barriers to entry may have increased because "...the importance of tacit knowledge as a source of competitive advantage for frontier firms may have risen if increasingly complex technologies were to increase the amount and sophistication of complementary investments required for technological adoption." 11 The bottom line is that aggregate productivity is low because the robust productivity gains for the tech-savvy frontier companies are offset by the long tail of firms that have been slow to adopt the latest technology. Indeed, business spending has been especially weak in this expansion. Chart II-14 highlights that the slowdown in U.S. productivity growth has mirrored that of the capital stock. Chart II-14U.S. Capex Shortfall Partly To Blame For Poor Productivity U.S. Capex Shortfall Partly To Blame For Poor Productivity U.S. Capex Shortfall Partly To Blame For Poor Productivity Appendix II-2 Japan - The Leading Edge Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. The popular press is full of stories of how robots are taking over. If the stories are to be believed, robots are the answer to the country's shrinking workforce. Robots now serve as helpers for the elderly, priests for weddings and funerals, concierges for hotels and even sexual partners (don't ask). Prime Minister Abe's government has launched a 5-year push to deepen the use of intelligent machines in manufacturing, supply chains, construction and health care. Indeed, Japan was the leader in robotics use for decades. Nonetheless, despite all the hype, Japan's stock of industrial robots has actually been eroding since the late 1990s (Chart II-4). Numerous surveys show that firms plan to use robots more in the future because of the difficulty in hiring humans. And there is huge potential: 90% of Japanese firms are small- and medium-sized (SME) and most are not currently using robots. Yet, there has been no wave of robot purchases as of 2016. One problem is the cost; most sophisticated robots are simply too expensive for SMEs to consider. This suggests that one cannot blame robots for Japan's lack of wage growth. The labor shortage has become so acute that there are examples of companies that have turned down sales due to insufficient manpower. Possible reasons why these companies do not offer higher wages to entice workers are beyond the scope of this report. But the fact that the stock of robots has been in decline since the late 1990s does not support the view that Japanese firms are using automation on a broad scale to avoid handing out pay hikes. Indeed, Chart II-15 highlights that wage deflation has been the greatest in industries that use almost no robots. Highly automated industries, such as Transportation Equipment and Electronics, have been among the most generous. This supports the view that the productivity afforded by increased robot usage encourages firms to pay their workers more. Looking ahead, it seems implausible that robots can replace all the retiring Japanese workers in the years to come. The workforce will shrink at an annual average pace of 0.33% between 2020 and 2030, according to the Japan Institute for Labour Policy and Training. Productivity growth would have to rise by the same amount to fully offset the dwindling number of workers. But that would require a surge in robot density of 4.1, assuming that each rise in robot density of one adds 0.08% to the level of productivity (Chart II-16). The level of robot sales would have to jump by a whopping 2½ times in the first year and continue to rise at the same pace each year thereafter to make this happen. Of course, the productivity afforded by new robots may accelerate in the coming years, but the point is that robot usage would likely have to rise astronomically to offset the impact of the shrinking population. Chart II-15Japan: Earnings Vs. Robot Density February 2018 February 2018 Chart II-16Japan: Where Is The Flood Of Robots? Japan: Where Is The Flood OF Robots? Japan: Where Is The Flood OF Robots? The implication is that, as long as the Japanese economy continues to grow above roughly 1%, the labor market will continue to tighten and wage rates will eventually begin to rise. 1 Please see Technology Sector Strategy Special Report "The Coming Robotics Revolution," dated May 16, 2017, available at tech.bcaresearch.com 2 Note that this includes only robots used in manufacturing industry, and thus excludes robots used in the service sector and households. However, robot usage in services is quite limited and those used in households do not add to GDP. 3 Note that ICT investment and capital stock data includes robots. 4 Please see BCA Global Investment Strategy Special Report "Weak Productivity Growth: Don't Blame The Statisticians," dated March 25, 2016, available at gis.bcaresearch.com 5 Centre for Economic and Business Research (January 2017): "The Impact of Automation." A Report for Redwood. In this report, robot density is defined to be the number of robots per million hours worked. 6 Graetz, G., and Michaels, G. (2015): "Robots At Work." CEP Discussion Paper No 1335. 7 Mishel, L., and Bivens, J. (2017): "The Zombie Robot Argument Lurches On," Economic Policy Institute. 8 Please see BCA Technology Sector Strategy Special Report "Bad Information - Why Misreporting Deep Learning Advances Is A Problem," dated January 9, 2018, available at tech.bcaresearch.com 9 Please see The Bank Credit Analyst, "Rage Against The Machines: Is Technology Exacerbating Inequality?" dated June 2014, available at bca.bcaresearch.com 10 OECD Productivity Working Papers, No. 05 (2016): "The Best Versus the Rest: The Global Productivity Slowdown, Divergence Across Firms and the Role of Public Policy." 11 Please refer to page 27.

Equities have melted up in recent weeks, celebrating the tax bill passage, synchronized upswing in global economic data, still quiescent inflation and near vanishing tail risk. On July 10th when we penned the "SPX 3,000?" report, the S&P 500 was close to 2400.1 Over the past six months stocks have been in an uninterrupted upleg, moving to within 10% of our SPX 3,000 target. Table 1 White Paper: Introducing Our U.S. Equity Sector Earnings Models White Paper: Introducing Our U.S. Equity Sector Earnings Models Stocks have run "too far too fast" for our liking and there are increasing odds of a healthy pullback, especially now that no pundits are talking of a correction. In addition, were the selloff in the bond markets to accelerate in a short time frame, at some point it will cause equity market consternation. But, bonds still remain extremely overvalued versus stocks (Chart 1). Late last year, we began to modestly de-risk the portfolio via booking impressive gains in tactical market-neutral trades, as our upbeat cyclical view remains intact.2 Our cyclical strategy is to "buy the dip", as we do not foresee a recession in the coming 9-12 months. Importantly, profits will dictate the S&P 500's direction and the cyclical path of least resistance is higher still. Our SPX profit model continues to forecast healthy EPS growth in 2018 (Chart 2) and as we posited in the last report of 2017, earnings will do the heavy lifting at the current juncture with the forward P/E multiple likely moving laterally (Chart 3). Chart 1Simple Bond Valuation Metric Says:##br## Bonds Are Overvalued Vs. Stocks Simple Bond Valuation Metric Says: Bonds Are Overvalued Vs. Stocks Simple Bond Valuation Metric Says: Bonds Are Overvalued Vs. Stocks Chart 2All ##br##Clear All Clear All Clear Chart 3EPS Will Do The##br## Heavy Lifting In 2018 EPS Will Do The Heavy Lifting In 2018 EPS Will Do The Heavy Lifting In 2018 A simple decomposition shows that equity returns could reasonably reach a low-to-mid double digit level this year. Our assumptions are the following: nominal GDP can grow near 5% (3% real plus 2% inflation) and thus we estimate organic EPS growth that typically mimics GDP at this stage of the cycle of ~5%, ~2% dividend yield, ~2% buyback yield, ~5% tax related boost to EPS and no multiple expansion. The above assumptions are based on four key drivers: energy and financials will command a larger slice of the earnings pie,3 synchronized global capex upcycle will boost EPS,4 delayed positive translation effects from the U.S. dollar will lift profits5 and easy fiscal policy will also act as a tonic to EPS.6 On this note, this White Paper officially introduces the U.S. Equity Strategy earnings models for the eleven GICS1 equity sectors. We have identified key macro earnings drivers for each sector and incorporated them into individual sector models. The objective is to forecast the direction of earnings growth. Beyond introducing our EPS models, the purpose of this White Paper is to also compare and contrast the cyclical readings of our equity sector models with sell-side analysts' profit growth (Charts 4 & 5) and margin expectations and help clients position portfolios for the rest of 2018. The earnings models carry the most weight in determining our sector positioning, with our macro overlay and our valuation and technical indicators rounding out our methodology. Currently, our earnings models are consistent with maintaining a mostly cyclically biased portfolio structure (top panel, Chart 6), and thus participating in the broad market's overshoot. Chart 4What EPS Are Priced In... What EPS Are Priced In... What EPS Are Priced In... Chart 5...Per Sector For 2018 ...Per Sector For 2018 ...Per Sector For 2018 Chart 6Continue To Prefer Cyclicals Over Defensives Continue To Prefer Cyclicals Over Defensives Continue To Prefer Cyclicals Over Defensives Encouragingly, an equal weight of the 10 GICS1 sector model outputs (we are excluding real estate due to lack of history), accurately forecasts the S&P 500's profit growth (bottom panel, Chart 6), and currently also confirms the broad market's upbeat four factor macro EPS model (Chart 2). Anastasios Avgeriou, Vice President U.S. Equity Strategy anastasios@bcaresearch.com Financials (Overweight) Our financials earnings growth model comprises bank credit growth, the U.S. dollar index and net earnings revisions. The U.S. credit impulse is gaining traction, indicating that the market has digested the almost doubling in long-term rates over the past 18 months. Bankers are willing extenders of C&I credit and, with the economy humming north of 3% in real GDP terms, the outlook for loan growth is excellent. Loosening U.S. banking regulatory requirements, and pent up demand for shareholder friendly activities are all welcome news for financials profitability. Tack on BCA's higher interest rate view in 2018 and net interest margins will also get a bump, further adding to the sector's EPS euphoria. Credit quality is the third key profit driver for bank profitability and pristine credit quality is a harbinger of increased profits. The unemployment rate is plumbing generational lows and suggests that non-performing loans as a percentage of total loans will remain on a downward trajectory. Our profit model is expanding at twice the current profit growth rate (second panel, Chart 7) and 10 percentage points above the Street's 12-month forward estimates (top panel, Chart 5). In fact, the latter have gone vertical of late playing catch up to our model's estimates. The S&P financials sector remains a core portfolio overweight and we reiterate our high-conviction overweight status in the heavyweight S&P banks index. Chart 7Financials (Overweight) Financials (Overweight) Financials (Overweight) Energy (Overweight) The three drivers behind the S&P energy sector EPS growth model are oil-related currencies, the U.S. oil & gas rig count and WTI crude oil prices. A depreciating greenback, whittling down OECD oil stocks and rising global oil demand are all boosting energy profitability. OPEC 2.0 cutbacks have not only helped stabilize oil markets, but also paved the way for a breakout in oil prices above the $62.50/bbl stiff resistance level. Sustained OPEC output restraint will counterbalance U.S. shale oil production increases and coupled with rising global demand likely continue to underpin oil prices. Our synchronized global capex upcycle theme included the basic resources following a multi-year drubbing in outlays. Energy capex cannot contract at double digit rates indefinitely. Already a V-shaped capex momentum recovery is in store, as 2018 capital spending budgets are on track to at least match 2017. Our EPS growth model (second panel, Chart 8) matches sell-side analyst optimism (third panel, Chart 5). Keep in mind that only recently did the energy space become profit positive, making a solid recovery from an extremely low base. Margins are only now renormalizing above the zero line and breakneck pace EPS growth should continue in 2018. Following a negative 2017 return, the S&P energy sector is the best performing sector year-to-date, and we reiterate the high-conviction overweight stance. Chart 8Energy (Overweight) Energy (Overweight) Energy (Overweight) Industrials (Overweight) Our S&P industrials EPS model comprises the ISM manufacturing survey, raw industrials commodity prices and interest rates. It has an excellent track record in forecasting industrials EPS momentum, and sports one of the highest explanatory powers amongst all sector EPS models. While industrials EPS growth has been bouncing off the zero line for the better part of the past five years, our profit model has spoken: forecast EPS are in a V-shaped recovery since the end of the recent manufacturing recession (second panel, Chart 9). Commodity prices are recovering and increasing final demand, coupled with a soft U.S. dollar suggest that more gains are in store. Tack on the global virtuous capex upcycle, and the stars are aligned for this deep cyclical sector to break out of its multi-year trading range funk on the back of a surge in profits. China is a wild card, but signs of stability are enough to sustain the upward trajectory in the commodity-levered complex, including industrials stocks. Our industrials sector EPS model suggests that industrials profits will easily surpass the low (and below the overall market) analysts' EPS growth hurdle (third panel, Chart 4). The late-cyclical S&P industrials sector remains an overweight. Chart 9Industrials (Overweight) Industrials (Overweight) Industrials (Overweight) Consumer Staples (Overweight) The S&P consumer staples EPS growth model key drivers are: food exports, non-discretionary retail sales and analysts' net earnings revision ratio. Overall industry exports are expanding at a healthy clip as a consequence of a softening U.S. dollar and robust European and rebounding emerging markets demand. Deflating raw food commodity prices are offsetting rising energy and labor input costs, heralding a sideways move to margins. Sell side analysts are also currently penciling in a lateral profit margin move (middle panel, Chart 10). Our model is expanding at a near double digit rate, and is in line with 12-month forward EPS growth estimates (second panel, Chart 4). Investors have been vehemently avoiding staples stocks during the board market's uninterrupted run up, and have put out positioning offside. However, in the context of our cyclical over defensive portfolio bent we refrain from putting all our eggs in one basket, and prefer to keep consumer staples as our sole defensive sector overweight. This small hedge will serve our portfolio well if we do indeed get a healthy Q1/2018 pullback, as we expect. Chart 10Consumer Staples (Overweight) Consumer Staples (Overweight) Consumer Staples (Overweight) Consumer Discretionary (Neutral - Downgrade Alert) Measures of consumer confidence, consumer discretionary exports and the net earnings revisions ratio comprise BCA's global consumer discretionary EPS growth model, which has an excellent track record in forecasting the path of consumer discretionary profits. Consumer confidence is rolling over, albeit from a nose-bleed level, signaling that, at the margin, discretionary consumer outlays will remain tame. Worrisomely, rising interest rates coupled with a breakout in crude oil prices are net negatives for consumer spending. Our consumer drag indicator captures these consumer headwinds and warns that the sector is not out of the woods yet (bottom panel, Chart 11). The Fed is on track to raise rate three more times in 2018 and continue to mop up liquidity via renormalizing its balance sheet. This dual tightening backdrop bodes ill for early cyclical discretionary stocks as we highlighted in the September 25th Weekly Report. Our consumer discretionary EPS growth model is making an effort to bounce, signaling that contracting earnings will likely reverse course and come out of their recent funk (second panel). But, analysts are overly optimistic penciling in a near double-digit profit growth backdrop for the consumer discretionary sector (fourth panel, Chart 5). Netting it all out, the anemic message from our profit model along with the ongoing Fed tightening cycle and spiking energy prices warrant a downgrade alert. Stay tuned. Chart 11Consumer Discretionary (Neutral-Downgrade Alert) Consumer Discretionary (Neutral-Downgrade Alert) Consumer Discretionary (Neutral-Downgrade Alert) Telecom Services (Neutral) Telecom pricing power and capital expenditures expectations comprise our S&P telecom services EPS growth model. Telecom capital expenditures have bounced off the zero line and are growing at 4% per annum while sector sales growth has been nil. This capital-intensive industry must continually invest to stay relevant. A push by telecom carriers into TV offerings as part of a quad-play (internet, wireline, wireless and TV) has rekindled an M&A boom, and capex is slated to increase. However, margins will suffer if increased investment fails to translate into new sales (bottom panel, Chart 12). Steeply contracting pricing power is a bad omen both for top and bottom line growth prospects (fourth panel). Hopefully, industry consolidation will lead to a better pricing backdrop, but the jury is still out. Our EPS model has sunk into the contraction zone (second panel). Analysts are a little bit more sanguine, penciling in low single-digit profit growth (bottom panel, Chart 4). Industry deflation is not alone as a headwind as the bond market selloff is weighing on the high dividend yielding telecom services stocks. Despite all the bearish news, near all-time lows in relative valuation and washed out technicals are keeping us on the sidelines. Chart 12Telecom Services (Neutral) Telecom Services (Neutral) Telecom Services (Neutral) Materials (Neutral) Materials EPS growth is a far cry from the near 100% year-over-year mark hit during the commodity super-cycle the mid-2000s and the reflex rebound following the Great Recession (second panel, Chart 13). Our S&P materials EPS model inputs include the U.S. currency, metals commodity prices and a measure of borrowing costs. The model has been steadily decelerating recently, and moving in the opposite direction compared with sell-side analysts' optimistic estimates (bottom panel, Chart 5). Consequently, there is scope for downward revisions. Materials stocks are reflationary beneficiaries and also high fixed cost high operating leverage deep cyclicals that benefit most during the later stages of the business cycle when a virtuous capex/EPS upcycle takes root. A number of both developed and developing central banks have recently embarked on tightening monetary policy following in the Fed's footsteps. Global liquidity is on the verge of getting mopped up as even the ECB and the BoJ have started to hint that they would remove some of their ultra-accommodative and unconventional policy measures. These opposing forces keep us at bay and we continue to recommend a benchmark allocation in the S&P materials index. Chart 13Materials (Neutral) Materials (Neutral) Materials (Neutral) Real Estate (Neutral) Commercial real estate loan demand, a labor market measure and the EUR/USD comprise our S&P real estate profit growth model (second panel, Chart 14). The 10-year Treasury yield and real estate relative performance have been nearly perfectly inversely correlated since the GFC as REITs sport a hefty dividend yield and thus are considered a fixed income proxy. BCA's higher interest rate 2018 theme suggests that more downside looms for this rate-sensitive sector. Similarly, a firming EUR/USD reflecting the nearly 100% domestic exposure of the sector weighs on real estate relative performance. Our EPS model has recently sunk into the contraction zone and is in sync with sell-side analysts' negative profit growth figures for calendar 2018 (second panel, Chart 5). While all this signals that an underweight stance is appropriate, we would rather stay on the sidelines for three reasons: First, sector pricing power (mostly rents) has not eroded yet, despite the surge in multi-family housing construction. Second, most of the bad news is likely already discounted in sinking valuations and extremely oversold technicals. Finally, we would rather concentrate our interest rate related underweight in the pure play fixed income proxy, the utilities sector (please see page 15). Stick with a benchmark allocation in the S&P real estate index. Chart 14Real Estate (Neutral) Real Estate (Neutral) Real Estate (Neutral) Health Care (Underweight) Our S&P health care EPS growth model consists of health care pricing power, labor costs and a measure of health care outlays. Health care demand is fairly inelastic, signaling that health care spending prospects remain upbeat, especially given the aging population. However, the industry's up-to-recently structurally robust pricing power backdrop is under intense scrutiny. Medical commodity cost inflation is melting and drug pricing power has nearly halved since early 2016. Democrats and Republicans alike, despise the pharmaceutical/biotech industry's pricing tactics and drug price containment is on nearly every legislator's agenda. Add on the generic drug inroads, and Big Pharma/biotech resilient profits appear vulnerable, weighing heavily on the sector's relative performance. From a secular perspective, there is scope for health care sector profit gains. Developing countries are only just starting to institute social "safety nets" that the developed world already has in place. Our profit model is decelerating (second panel, Chart 15) and forecasting single digit EPS growth, in line with the Street's 12-month forward profit estimates (fourth panel, Chart 4). The S&P health care sector is a core underweight portfolio holding and we reiterate the high-conviction underweight status in the heavy weight S&P pharma sub index. Chart 15Health Care (Underweight) Health Care (Underweight) Health Care (Underweight) Utilities (Underweight) Utilities pricing power, the yield curve and analysts' net earnings revisions are the key inputs in our S&P utilities EPS growth model (second panel, Chart 16). While natgas prices, the industry's marginal price setter, have been stuck in a trading range between $2.6 and $3.4/mmbtu over the past 18 months, they are currently contracting and weighing heavily on industry pricing power. The U.S. economy is firing on all cylinders (bottom panel, Chart 16) and a selloff in the 10-year Treasury market near 3% is BCA's base-case scenario for 2018. Under such a backdrop, fixed income proxied defensive equities lose their luster, and thus utilities stocks will likely remain under intense downward pressure, Our S&P utilities EPS growth model is expanding at a mid-single digit growth rate, broadly in line with sell-side analysts' forecasts (fifth panel, Chart 4) and roughly 700bps below the broad market. The S&P utilities sector is a high-conviction underweight. Chart 16Utilities (Underweight) Utilities (Underweight) Utilities (Underweight) Technology (Underweight - Upgrade Alert) Our three-factor global technology EPS growth model includes capex intentions, the trade-weighted U.S. dollar and sell-side analysts' net earnings revision ratio. While the tech sector is still largely considered a deep cyclical, we view it as more defensive. The majority of large capitalization tech companies are mature, cash rich, cash flow generating, dividend paying and high margin. Tech firms thrive in a deflationary backdrop as business models have been built to withstand the inherently disinflationary "creative destruction" process. BCA's interest rate view calls for an inflationary driven sell off in bonds for 2018, suggesting that investors avoid high-flying tech stocks. Weakness in basic resources explains most of the delta in cyclical capital outlays. Encouragingly, technology's share of the U.S. capex pie is making inroads rising to roughly 10% (bottom panel, Chart 17). Tech investment has been so abysmal for so long that it is hard to get any worse. In fact, it has started to improve both on an absolute and relative basis, as pent-up tech demand is being unleashed. Our synchronized global capex upcycle theme is gaining traction and the tech sector will continue to make gains at the expense of resource-related spending. Our global tech EPS model is forecasting modest double-digit growth in the coming quarters (second panel, Chart 17), largely aligned with sell-side analysts' profit growth expectations (fifth panel, Chart 5). On balance, we are putting the S&P tech sector on upgrade alert reflecting the capex tailwind offsetting the rising interest rate backdrop, and reiterate our capex-related high-conviction overweight in the S&P software sub-index. Chart 17Technology (Underweight-Upgrade Alert) Technology (Underweight-Upgrade Alert) Technology (Underweight-Upgrade Alert) 1 Please see BCA U.S. Equity Strategy Weekly Report, "SPX 3,000?," dated July 10, 2017, available at uses.bcaresearch.com. 2 Please see BCA U.S. Equity Strategy Weekly Report, "EPS And "Nothing Else Matters"," dated December 18, 2017, available at uses.bcaresearch.com. 3 Please see BCA U.S. Equity Strategy Weekly Report, "Dissecting Profit Composition," dated July 24, 2017, available at uses.bcaresearch.com. 4 Please see BCA U.S. Equity Strategy Weekly Report, "Invincible," dated November 6, 2017, available at uses.bcaresearch.com. 5 Please see BCA U.S. Equity Strategy Weekly Report, "Dollar The Great Reflator," dated September 18, 2017, available at uses.bcaresearch.com. 6 Please see BCA U.S. Equity Strategy Weekly Report, "Can Easy Fiscal Offset Tighter Monetary Policy?," dated October 9, 2017, available at uses.bcaresearch.com.

Dear Client, This is our final publication for the year. We will be back on January 5th. On behalf of the entire Global Investment Strategy team, I would like to wish you a Merry Christmas, Happy Holidays, and a Prosperous New Year! Best regards, Peter Berezin, Chief Global Strategist Highlights Global bonds have sold off in recent days, but the spread between long-term and short-term Treasury yields remains well below where it was at the start of the year. A flatter Treasury yield curve suggests that the ongoing U.S. business-cycle expansion is getting long in the tooth. Nevertheless, three factors dilute the potentially bearish message from the curve. First, the yield curve has flattened largely because short-term rate expectations have risen thanks to better economic data. Second, both the 10-year/2-year and 10-year/3-month spreads are still above levels that have foreshadowed poor returns for risk assets in the past. This is particularly true for equities. Third, a structurally low term premium has distorted the signal from the yield curve. The U.S. yield curve is likely to steepen over the next six months, before flattening again in the lead-up to a recession in late-2019. We reveal the One Number that will kill bitcoin. Feature A Harbinger Of Recession? The U.S. yield curve has steepened in recent days, but is still much flatter than it was at the start of the year. The 10-year/3-month spread currently stands at 113 bps, down 84 bps year-to-date. The 10-year/2-year spread has fallen from 125 bps to 62 bps. Numerous academic studies have highlighted the importance of the yield curve as a leading indicator of recessions.1 In fact, every U.S. recession over the past 50 years has been preceded by an inverted yield curve (Chart 1). Chart 1An Inverted Yield Curve Has Often Been A Harbinger Of A Recession An Inverted Yield Curve Has Often Been A Harbinger Of A Recession An Inverted Yield Curve Has Often Been A Harbinger Of A Recession The converse has generally been true as well: Most inversions in the yield curve have coincided with a recession. The only two exceptions were in 1967 - when credit conditions tightened and industrial production decelerated, but the U.S. still managed to avoid succumbing to a recession - and in 1998, when the yield curve briefly inverted during the LTCM crisis. Considering that recessions and equity bear markets typically overlap (Chart 2), it is not surprising that investors have begun to fret about what a flatter yield curve may mean for their portfolios. Chart 2Recessions And Bear Markets Usually Overlap Recessions And Bear Markets Usually Overlap Recessions And Bear Markets Usually Overlap Don't Worry... Yet Chart 3U.S. Growth Expectations Revised Higher U.S. Growth Expectations Revised Higher U.S. Growth Expectations Revised Higher We would not be as dismissive of a flatter yield curve as Fed Chair Yellen was during her December press conference. Policymakers and investors alike have been too quick to downplay the signal from the yield curve in the past. In 2006, they blamed the "global savings glut" for dragging down long-term yields. In 2000, they argued that the federal government's budget surplus was reducing the supply of long-term bonds. In both cases, the bond market turned out to be seeing something more ominous than they were. That said, there are three reasons why we would discount some of the more bearish interpretations of what a flatter yield curve is telling us. First, the flattening of the yield curve has occurred mainly because of an increase in short-term rate expectations, rather than a decrease in long-term bond yields. The increase in rate expectations has been largely driven by stronger growth data. The economic surprise index has surged far into positive territory and analysts are now scrambling to revise up their 2018 and 2019 U.S. GDP growth projections (Chart 3). The Fed now sees growth of 2.5% in 2018 and an unemployment rate of 3.9% by the end of next year. Back in September, the Fed expected growth of 2.1% and an unemployment rate of 4.1%. Second, our research suggests that the slope of the yield curve only becomes worrisome for the economy when it falls to extremely low levels. This conclusion is reinforced by the New York Fed's Yield Curve Recession Model, which uses the difference between 10-year and 3-month Treasury rates to estimate the probability of a U.S. recession twelve months ahead.2 The model's current recession probability stands at a modest 11% (Chart 4). The last three recessions all began when the implied probability was over 25%. Chart 4NY Fed's Yield Curve Model Suggests That The Probability Of A Recession Is Still Quite Low NY Fed's Yield Curve Model Suggests That The Probability Of A Recession Is Still Quite Low NY Fed's Yield Curve Model Suggests That The Probability Of A Recession Is Still Quite Low Third, the slope of the yield curve is weighed down by a structurally low term premium. The term premium measures the additional return investors can expect to receive by locking in their money in a 10-year Treasury note instead of rolling over a short-term Treasury bill for an entire decade. Historically, the term premium has been positive. Over the past few years, however, it has often been negative - meaning that investors have been willing to pay a premium to take on duration risk. Many commentators have attributed this peculiar state of affairs to central bank asset purchases, which they claim have artificially depressed long-term bond yields. There is some truth to this, but we think there is an even more important reason: Bonds today provide a good hedge against bad economic news. When fears of an economic slowdown mount, equities tend to sell off, while bond prices rise. This differs from the circumstances that existed in the 1970s and 1980s, when bad economic news usually meant higher inflation. To the extent that long-term bonds now serve as insurance policies against recessions, investors are more willing to accept the lower yields that they offer. Empirically, one can see this in the shift of the correlation between equity returns and bond yields. It was strongly negative up until the mid-1990s. Now it is strongly positive (Chart 5). A low term premium implies that the slope of the yield curve should be structurally flatter. That is exactly what we see today. Chart 6 shows that the 10-year/3-month spread would be well above its long-term average if the term premium were removed from the picture. This implies that investors have little to fear from the shape of today's yield curve, at least over the next six-to-twelve months. Chart 5Bond Prices Now Tend To Rise When Equity Prices Go Down Bond Prices Now Tend to Rise When Equity Prices Go Down Bond Prices Now Tend to Rise When Equity Prices Go Down Chart 6Stripping Out The Term Premium,##BR##The Yield Curve Is Not So Flat Stripping Out The Term Premium, The Yield Curve Is Not So Flat Stripping Out The Term Premium, The Yield Curve Is Not So Flat Rising Odds Of A Recession In Late-2019 Beyond then, things start to get dicey. The Fed's end-2018 unemployment rate projection of 3.9% is 0.7 percentage points below its long-term estimate of the unemployment rate. This means that at some point in the future, the Fed will need to lift interest rates above their "neutral" level in order to push the unemployment rate up to its equilibrium level. That's a risky gambit. There has never been a case in the post-war era where the unemployment rate has risen by more than one-third of a percentage point without a recession ensuing (Chart 7). Modern economies are subject to feedback loops. Once economic conditions begin to deteriorate, households cut back on spending. This leads to less hiring and even less spending. Bad economic news begets worse news. Chart 7Even A Small Uptick In The Unemployment Rate Is Bad News For The Business Cycle Even A Small Uptick In The Unemployment Rate Is Bad News For The Business Cycle Even A Small Uptick In The Unemployment Rate Is Bad News For The Business Cycle Implications For Equities And Credit A flatter Treasury yield curve suggests that the U.S. business cycle is entering the home stretch. Nevertheless, as we pointed out two weeks ago, the 7th-to-8th innings of business-cycle expansions are often the juiciest for equity investors (Table 1).3 Table 1Too Soon To Get Out Don't Fear A Flatter Yield Curve Don't Fear A Flatter Yield Curve Chart 8 shows that the term spread today is still at levels that have signaled positive equity returns in the past. In fact, today's term spread is close to levels that prevailed in the second half of the 1990s, a period that coincided with the greatest bull market in American history. This message is echoed by our forthcoming MacroQuant model, which continues to flag upside risks for stocks over the next 6-to-12 months (Chart 9). Chart 8Current Term Spread Is Still Pointing##BR##To Positive Equity Returns Don't Fear A Flatter Yield Curve Don't Fear A Flatter Yield Curve Chart 9MacroQuant Still Positive##BR##On The Stock Market Don't Fear A Flatter Yield Curve Don't Fear A Flatter Yield Curve Globally, we favor euro area and Japanese equities (in local-currency terms) in the developed market sphere due to our expectation that the euro and yen will depreciate somewhat next year. Both the euro area and Japan also have greater exposure to cyclical sectors. This fits with our bias towards owning cyclicals over defensive stocks. Today's term spread is a bit more worrying for corporate credit. As our bond strategists have noted, a flatter yield curve is consistent with lower, though still positive, monthly excess returns for high-yield bonds (Chart 10).4 Again, the second half of the 1990s provides a potentially useful template: Despite a sizzling stock market, high-yield spreads actually widened as corporations loaded up on debt (Chart 11). The deterioration in our Corporate Health Monitor over the past five years suggests that a similar dynamic may be afoot (Chart 12). Chart 10Junk Monthly Excess Returns##BR##And The Yield Curve Don't Fear A Flatter Yield Curve Don't Fear A Flatter Yield Curve Chart 11Second Half Of 1990s: When High-Yield Spreads##BR##Rose With Stock Prices Second Half Of 1990s: When High-Yield Spreads Rose With Stock Prices Second Half Of 1990s: When High-Yield Spreads Rose With Stock Prices Chart 12Corporate Health Has##BR##Been Deteriorating Corporate Health Has Been Deteriorating Corporate Health Has Been Deteriorating Yield Curve Should Steepen Over The Coming Months Of course, much depends on what happens to the yield curve going forward. We suspect that it will flatten again towards the end of next year. However, it is likely to steepen over the next six months. U.S. GDP growth will remain above trend next year, as wages start to rise more briskly and firms boost capital spending to meet rising demand for their products. Fiscal policy should also help. Tax cuts will lift growth by 0.2%-to-0.3% in 2018. Higher disaster relief efforts following the hurricanes and a pending agreement to raise caps on discretionary spending will also translate into increased federal government spending. Investors have largely overlooked this source of fiscal stimulus, but increased spending will contribute almost as much to growth next year as lower taxes. Unfortunately, all this additional growth, coming at a time when the output gap is all but closed, is likely to stoke inflationary pressures. Our Pipeline Inflation Pressure Index has risen sharply since early 2016, while the ISM prices paid index has shot up. The New York Fed's Underlying Inflation Gauge has accelerated to an 11-year high of 3% (Chart 13). Historically, rising inflation expectations have led to a steeper yield curve (Chart 14). The implication is that investors should favor inflation-linked securities over government bonds. Chart 13U.S. Inflation Pressure Are Building U.S. Inflation Pressure Are Building U.S. Inflation Pressure Are Building Chart 14Rising Inflation Expectations Lead To A Steeper Yield Curve Don't Fear A Flatter Yield Curve Don't Fear A Flatter Yield Curve The One Number That Will Kill Bitcoin In a normal world, most reasonable people would regard a flatter yield curve and continued weak inflation readings as evidence that fiat money was, if anything, doing too good a job as a store of value. However, nothing is normal or reasonable about bitcoin.5 Chart 15Governments Will Want Their Cut:##BR##U.S. Seigniorage Revenue Governments Will Want Their Cut: U.S. Seigniorage Revenue Governments Will Want Their Cut: U.S. Seigniorage Revenue No one knows when the bitcoin bubble will burst. Only a tiny fraction of the public owns the virtual currency. The value of all bitcoin in circulation represents 0.35% of global GDP. At its peak in 1996, the value of all pyramid scheme assets in Albania amounted to almost half of GDP. Never underestimate the lure of easy money. While we do not know where the price of bitcoin will be ten months from now, we do have a good guess of where it will be ten years from today. And that price is zero, or thereabouts. When the U.S. Treasury issues a $100 bill, it gains the ability to buy $100 of goods and services with it. The government's cost is whatever it pays to print the bill, which is next to nothing. This so-called "seigniorage revenue" is set to reach $100 billion this year (Chart 15). That is the number that will kill bitcoin. There is no way the U.S. government will forsake this revenue in order to make room for bitcoin and other cryptocurrencies. Not when there are entitlements to pay and gaping budget deficits to finance. A variety of other countries have a love-hate relationship with bitcoin, partly because of their "the enemy of my enemy is my friend" attitude towards the dollar. But that will change when they see their tax bases eroding as more commerce gets done in the anonymous world of cryptocurrencies. Bitcoin's days are numbered. The only question is who will be holding the bag when the party ends. Peter Berezin, Chief Global Strategist peterb@bcaresearch.com 1 Please see Jonathan H. Wright, "The Yield Curve And Predicting Recessions," FEDs Working Paper No. 2006-7, May 3, 2006; Michael Owyang, "Is the Yield Curve Signaling a Recession?"Federal Reserve Bank Of St. Louis, March 24, 2016; and Arturo Estrella and Mishkin, Frederic S., "The Yield Curve as a Predictor of U.S. Recessions," Federal Reserve Bank Of New York, (2:7), June 1996. 2 Please see "The Yield Curve As A Leading Indicator: Probability of U.S. Recession Charts," Federal Reserve Bank Of New York. 3 Please see Global Investment Strategy Weekly Report, "When To Get Out," dated December 8, 2017. 4 Please see U.S. Bond Strategy, "Proactive, Reactive Or Right?" dated December 12, 2017. 5 Please see European Investment Strategy Weekly Report, "Bitcoins And Fractals," dated December 21, 2017; Technology Sector Strategy Special Report, "Cyber Currencies: Actual Currencies Or Just Speculative Assets?" dated December 12, 2017; Global Investment Strategy Special Report, "Bitcoin's Macro Impact," dated September 15, 2017; and Technology Sector Strategy Special Report, "Blockchain And Cryptocurrencies," dated May 5, 2017. 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Highlights As bitcoin has developed into a fledgling form of money, the best valuation framework for it is the quantity theory of money. This states that the bitcoin money supply (in dollars) times bitcoin's velocity of circulation = the amount of world GDP carried out in bitcoin. In the short term, excessive herding signals a likely countertrend reversal, and implies that the bitcoin price will retest $12,750 at some point in the next 130 days. In the long term, the wholesale acceptance of cryptocurrencies in the global economy will be deflationary. Feature Bitcoin's near-vertical price ascent to $19,000 has left many commentators crying "bubble!" The problem with this is that you cannot define an asset bubble simply from the behaviour of a price. You need to assess fundamental value, and the extent of deviation above this fundamental value. Conceivably, bitcoin's near-vertical price ascent could be a correction from an "anti-bubble", in which the price was a long way below its fundamental value and rapidly corrected upwards. Which begs the question: what is the best way to assess the fundamental value of bitcoin and other cryptocurrencies? Chart of the WeekCryptocurrencies Will Prevent Inflation, Just Like The Gold Standard Cryptocurrencies Will Prevent Inflation, Just Like The Gold Standard Cryptocurrencies Will Prevent Inflation, Just Like The Gold Standard A Valuation Framework For Bitcoin As bitcoin has developed into a fledgling form of money, one potential valuation framework is the quantity theory of money. This states that the money supply times its velocity of circulation equals nominal GDP. Given that the supply of bitcoin will not exceed an upper limit of 21 million coins, we can say that the bitcoin money supply (in dollars) is the bitcoin price times 21 million. We can then use the quantity theory to deduce: Bitcoin price times 21 million times bitcoin's velocity of circulation = Amount of world GDP carried out in bitcoin. If we additionally assume that bitcoin's velocity is similar to that of the stock of broad fiat money, 1.5, then we can rearrange and simplify the equation to approximately: Bitcoin price = Amount of world GDP carried out in bitcoin divided by 30 million So if the market was discounting that $0.5 trillion of world GDP would be carried out in bitcoin, then its price should be $16,700. Given the purported nefarious uses of cryptocurrencies at the moment, and an estimated size of the world's shadow economy at around $16 trillion, an assumption of $0.5 trillion of bitcoin use in the world economy does not seem excessive. On the other hand, nefarious use might make bitcoin's velocity of circulation a lot higher than conventional money. Which would pull bitcoin's fair price much lower. Suffice to say, the above assumptions are broad-brush and open to challenge. Nevertheless, despite the many caveats, the above framework is probably the most valid for valuing a cryptocurrency once it gains acceptance as a fledgling form of money. Putting Bitcoin Through Fractal Analysis The behaviour of price alone cannot gauge an asset bubble. But the behaviour of price alone can gauge a shortage of liquidity in the asset which implies a potential countertrend reversal. Liquidity is plentiful when the market is split between short-term momentum traders and longer-term value investors. This is because the two herds generally disagree with each other. If the price fluctuates up, the momentum trader wants to buy while the value investor wants to sell; and vice-versa. So the herds trade with each other with plentiful liquidity and little movement in price. This raises an obvious question. Can there really be any value investors in cryptocurrencies? The answer is potentially yes, if these investors believe that cryptocurrency acceptance will increase over time. And if they apply the aforementioned valuation framework from the quantity theory of money. Still, liquidity will periodically evaporate if too many value investors join the short-term momentum herd. Instead of dispassionately investing on the basis of a valuation framework, value investors get lured into participating in a strong rally, and their buy orders add fuel to the rally. A tipping point comes when all the value investors have joined the momentum herd. If a value investor then suddenly reverts to type and puts in a sell order, he will find that there are no buyers left. Liquidity has evaporated, and finding new liquidity might require a substantial reversal in the price to attract a buy order from an ultra-long-term deep value investor. As regular readers know, fractal analysis measures whether the herding behaviour in any financial instrument has reached its tipping point, signalling a likely end of its price trend. Today, the 130-day herding indicator for bitcoin is at a level which has indicated three previous countertrend reversals of at least one fifth of the preceding 130-day move (Chart I-2, Chart I-3, Chart I-4). Chart I-2Bitcoin: The 130 Day Fractal Dimension Signalled A Reversal In 2015 Bitcoin: The 130 Day Fractal Dimension Signalled A Reversal In 2015 Bitcoin: The 130 Day Fractal Dimension Signalled A Reversal In 2015 Chart I-3Bitcoin: The 130 Day Fractal Dimension Signalled Two Reversals In 2017 Bitcoin: The 130 Day Fractal Dimension Signalled Two Reversals In 2017 Bitcoin: The 130 Day Fractal Dimension Signalled Two Reversals In 2017 Chart I-4Bitcoin: The 65 Day Fractal Dimension Also Signalled Two Previous Reversals Bitcoin: The 65 Day Fractal Dimension Also Signalled Two Previous Reversals Bitcoin: The 65 Day Fractal Dimension Also Signalled Two Previous Reversals If this herding indicator signals a fourth countertrend reversal, it implies that the bitcoin price will retest $12,750 at some point in the next 130 days. Are Cryptocurrencies Inflationary Or Deflationary? On the face of it, the emergence of cryptocurrencies sounds inflationary. After all, if the general acceptance of cryptocurrencies for commercial transactions increases, there will be new money supply. And this new money supply will increase the nominal demand for goods and services. However, the truth is more nuanced. Unlike fiat money supply - which can expand without limit - each cryptocurrency has a defined limit to its supply. Although new cryptocurrencies can emerge, there seems to be a limit to the aggregate amount of cryptocurrency supply. The limiting factor is that it takes energy to create cryptocurrency through so-called 'mining'. Miners must compete to validate transactions that occur in a cryptocurrency. The competition takes the form of solving a mathematical problem - for example, finding the prime factors of a very large number. And the computational demands are energy sapping. Furthermore, the computational demands - known as 'proof of work' - get progressively more difficult for each additional new coin mined. Given that the computational resources in the world are finite and growing at a gentle and predictable rate, the implication is that the growth in the total amount of cryptocurrency is also limited. So while the emergence of cryptocurrencies does increase the money supply in the near-term (Chart I-5), a large-scale rejection of fiat money would make it impossible for uncouth policymakers to spike the overall money supply over the longer-term. Chart I-5Cryptocurrencies: Market Cap Is Now Non-Trivial Bitcoins And Fractals Bitcoins And Fractals Here's a further thought. Imagine if the proof of work computations, instead of being random mathematical calculations, solved useful problems that expanded the envelope of knowledge. This could boost real productivity, which is ultimately just a function of the stock of human ingenuity. In which case, any increase in money supply would be matched by an increase in potential real output. Interestingly, a recent paper from the Bank of Canada proposes that a wholesale acceptance of cryptocurrencies in the global economy could act as a new gold standard, whose effect would be mildly deflationary1 (Chart of the Week) and Table I-1). We fully agree with the Bank of Canada analysis. Table I-1No Persistent Inflation For 700 Years! Bitcoins And Fractals Bitcoins And Fractals The sting in the tail is that the analysis describes prices denominated in cryptocurrency terms. In fiat currency terms, the quantity theory of money implies that prices would rise2 - unless central banks reacted to the emergence of cryptocurrencies by shrinking the supply of fiat money. Would they? Very likely yes. If they didn't, the demise of fiat money would accelerate as people voted with their wallets and switched to superior stores of purchasing power. Nevertheless, we suspect that any central bank response would just delay the inevitable. As Larry Summers puts it: I am much more confident that the world of payments will look very different 20 years from now than I am about how it will look. And with that observation, I am signing off for 2017. I do hope you have enjoyed our provocative and counterintuitive insights this year. In the vast majority of cases, these insights have led to highly profitable investment recommendations. We promise to continue the success in 2018! Early next year, we will also unveil a major enhancement to our proprietary fractal trading strategy. So stay tuned. It just remains for me to wish you all a very enjoyable Festive Season and a prosperous 2018. Dhaval Joshi, Senior Vice President Chief European Investment Strategist dhaval@bcaresearch.com 1 Bank of Canada Staff Working Paper, A Bitcoin Standard: Lessons from the Gold Standard https://www.bankofcanada.ca/2016/03/staff-working-paper-2016-14/ 2 Please see the Global Investment Strategy Special Report titled "Bitcoin's Macro Impact", dated September 15, 2017 available at gis.bcaresearch.com and Technology Sector Strategy Special Report titled "Cyber Currencies: Actual Currencies Or Just Speculative Assets?", dated December 12, 2017 available at tech.bcaresearch.com. Fractal Trading Model* As discussed in the main body of this report, this week's trade is to expect a countertrend reversal in bitcoin. Go short with a profit target at $12750 and stop-loss at $28000. In other trades, long silver has had a strong 1-week bounce while long U.K. personal products / short U.K. food and beverages reached the end of its 65 day maximum holding period and closed with a small profit. For any investment, excessive trend following and groupthink can reach a natural point of instability, at which point the established trend is highly likely to break down with or without an external catalyst. An early warning sign is the investment's fractal dimension approaching its natural lower bound. Encouragingly, this trigger has consistently identified countertrend moves of various magnitudes across all asset classes. Chart I-6 Long Silver Long Silver The post-June 9, 2016 fractal trading model rules are: When the fractal dimension approaches the lower limit after an investment has been in an established trend it is a potential trigger for a liquidity-triggered trend reversal. Therefore, open a countertrend position. The profit target is a one-third reversal of the preceding 13-week move. Apply a symmetrical stop-loss. Close the position at the profit target or stop-loss. Otherwise close the position after 13 weeks. Use the position size multiple to control risk. The position size will be smaller for more risky positions. * For more details please see the European Investment Strategy Special Report "Fractals, Liquidity & A Trading Model," dated December 11, 2014, available at eis.bcaresearch.com Fractal Trading Model Recommendations Equities Bond & Interest Rates Currency & Other Positions Closed Fractal Trades Trades Closed Trades Asset Performance Currency & Bond Equity Sector Country Equity Indicators Bond Yields Chart II-1Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Chart II-2Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Chart II-3Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Chart II-4Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Interest Rate Chart II-5Indicators To Watch##br## - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-6Indicators To Watch ##br##- Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-7Indicators To Watch ##br##- Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-7Indicators To Watch ##br##- Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations