Asset Allocation

Highlights The economic performance of Sweden, which did not have a lockdown, has been almost as bad as Denmark, which did have a lockdown. This proves that the current recession is not ‘man-made’, it is ‘pandemic-made’. While the pandemic remains in play, investors should maintain a defensive bias to their portfolios: favouring US T-bonds in bond portfolios, and technology and healthcare in equity portfolios. The technology sector has become defensive, largely because it has flipped from hardware dominance to software dominance. A new recommendation is to overweight technology-heavy Netherlands. Fractal trade: short AUD/CHF. Feature Chart I-IASweden: Avoiding A Lockdown Did Not Prevent A Slump In Consumption... Sweden: Avoiding A Lockdown Did Not Prevent A Slump In Consumption... Sweden: Avoiding A Lockdown Did Not Prevent A Slump In Consumption... Chart I-1B...But Led To Many More ##br##Infections ...But Led To Many More Infections ...But Led To Many More Infections Sweden and Denmark are neighbours. They speak near-identical languages and share a broadly similar culture and demographic. Yet the two countries have followed completely different strategies to halt the coronavirus pandemic. Sweden chose not to impose a lockdown. Instead, it opted for a ‘trust based’ approach, relying on its citizens to act sensibly and appropriately. Whereas Denmark imposed one of Europe’s earliest and most draconian lockdowns. The contrasting approaches of Sweden and neighbouring Denmark provide us with the closest thing to a controlled experiment on pandemic strategies. The Recession Is Not ‘Man-Made’, It Is ‘Pandemic-Made’ The surprising thing is that the economic performance of Sweden, which did not have a lockdown, has been almost as bad as Denmark, which did. This year, the unemployment rates in both economies have surged by 2 percentage points (albeit the latest data is for May in Sweden and April in Denmark). Furthermore, high-frequency measures of consumption show that Sweden suffered almost as severe a contraction as Denmark (Chart of the Week and Chart I-2). Chart I-2Unemployment Has Surged In Both No-Lockdown Sweden And Lockdown Denmark Unemployment Has Surged In Both No-Lockdown Sweden And Lockdown Denmark Unemployment Has Surged In Both No-Lockdown Sweden And Lockdown Denmark This surprising result challenges the popular view that this global recession is man-made. This view argues that without the government-imposed lockdowns, the global economy would not have entered a tailspin. But if this view is right, then why did consumption crash in Sweden? The simple answer is that in a pandemic, most people will change their behaviour to avoid catching the virus. The cautious behaviour is voluntary, irrespective of whether there is no lockdown, as in Sweden, or there is a lockdown, as in Denmark. People will shun public transport, shopping, and other crowded places, and even think twice about letting their children go to school. In a pandemic, the majority of people will change their behaviour even without a lockdown. But if the cautious behaviour is voluntary, then why impose a lockdown? The answer is that without a lockdown, the majority will behave sensibly to avoid catching the virus, but a minority will take a ‘devil may care’ attitude. In the pandemic, this is critical because less than 10 percent of infected people are responsible for creating 90 percent of all coronavirus infections. If this tiny minority of so-called ‘super-spreaders’ is left unchecked, then the pandemic will let rip. All of which brings us back to Sweden versus Denmark.  As a result of not imposing a mandatory lockdown to rein in its super-spreaders, Sweden now has one of the world’s worst coronavirus infection and mortality rates, four times higher than Denmark (Chart I-3, Chart I-4, Chart I-5). Chart I-3No-Lockdown Sweden Has Suffered Many More Deaths Than Lockdown Denmark No-Lockdown Sweden Has Suffered Many More Deaths Than Lockdown Denmark No-Lockdown Sweden Has Suffered Many More Deaths Than Lockdown Denmark Chart I-4Avoiding A Lockdown Meant More Infections… Who’s Right On The Pandemic – Sweden Or Denmark? Who’s Right On The Pandemic – Sweden Or Denmark? Chart I-5…And More ##br##Deaths Who’s Right On The Pandemic – Sweden Or Denmark? Who’s Right On The Pandemic – Sweden Or Denmark? Put simply, containing the pandemic depends on reining in a minority of super-spreaders. Which explains why no-lockdown Sweden suffered a much worse outbreak of the disease than lockdown Denmark. In contrast, the economy depends on the behaviour of the majority. In a pandemic the majority will voluntarily exercise caution. Which explains why no-lockdown Sweden and lockdown Denmark suffered similar contractions in consumption. Looking ahead, will the widespread adoption of face masks and plexiglass screens change the public’s cautious behaviour? To a certain extent, yes – it will permit essential activities and let people take calculated risks. That said, if you are forced to wear a mask on public transport and in the shops, and you have to spread out in restaurants while being served by a masked waiter, then – rightly or wrongly – you are getting a strong signal: the danger is still out there. Meaning that many people will continue to shun discretionary activities and spending. The upshot is that while the pandemic remains in play, investors should maintain a defensive bias to their portfolios. Explaining Why Technology Is Now Defensive A defensive bias to your portfolio now requires an exposure to technology – because in 2020 the tech sector is behaving like a classic defensive. Its relative performance is correlating positively with the bond price, like other classic defensive sectors such as healthcare (Chart I-6 and Chart I-7). Chart I-6In 2020, Tech Is Behaving Like A Defensive... In 2020, Tech Is Behaving Like A Defensive... In 2020, Tech Is Behaving Like A Defensive... Chart I-7...Like Healthcare ...Like Healthcare ...Like Healthcare The behaviour of the technology sector in the current recession contrasts with its performance in the global financial crisis of 2008. Back then, it behaved like a classic cyclical – its relative performance correlated negatively with the bond price (Chart I-8). Begging the question: why has the tech sector’s behaviour flipped from cyclical to defensive? Chart I-8In 2008, Tech Behaved Like A Cyclical In 2008, Tech Behaved Like A Cyclical In 2008, Tech Behaved Like A Cyclical The main reason is that the tech sector’s composition has flipped from hardware dominance to software dominance. In 2008, the sector market cap had a 65:35 tilt to technology hardware. But today, it is the mirror-image: a 65:35 tilt to computer and software services (Chart I-9). Chart I-9Tech Is More Defensive Now Because It Is Dominated By Software Tech Is More Defensive Now Because It Is Dominated By Software Tech Is More Defensive Now Because It Is Dominated By Software Computer and software services have many defensive characteristics suited to the current environment: For many companies, enterprise software is now business critical. It is a must-have rather than a like-to-have. Computer and software services use a subscription-based revenue model, minimising the dependency on discretionary spending. Computer and software services are helping firms to cut costs through automation and back-office efficiencies as well as facilitating the boom in ‘working from home’. The sector is cash rich. Despite these defensive characteristics, there remains a lingering worry: is the tech sector overvalued? The Rally In Growth Defensives Is Not A Mania  Some people fear that the recent run-up in stock markets does not make sense, other than as a ‘Robin Hood’ day-trader fuelled mania. After all, the pandemic is still very much in play, and so are other geopolitical risks, so how can some stock prices be near all-time highs? Yet the recent run-up in growth defensives such as tech and healthcare does make sense. Their valuations have moved in near-perfect lockstep with the bond yield, implying that the rally is based on fundamentals (Chart I-10). Chart I-10Tech And Healthcare Valuations Are Tracking The Bond Yield Tech And Healthcare Valuations Are Tracking The Bond Yield Tech And Healthcare Valuations Are Tracking The Bond Yield Simply put, if the 10-year T-bond is going to deliver a pitiful 0.7 percent a year over the next decade, then the prospective return from growth defensives must also compress. It would be absurd to expect these stocks to be priced for high single digit returns. Since late 2018, the decline in growth defensives’ forward earnings yield has broadly tracked the 250bps decline in the 10-year T-bond yield. Given that the forward earnings yield correlates well with the 10-year prospective return, the depressed bond yield is depressing the prospective return from growth defensives – as it should. Tech and healthcare valuations have moved in near-perfect lockstep with the bond yield. But with the pandemic and geopolitical risks menacing in the background, shouldn’t the gap between the prospective return on stocks and bonds – the equity risk premium – be larger? This is open to debate. When bond yields approach the lower bound, the appeal of owning bonds also diminishes because bond prices have limited upside. Nevertheless, the gap between the tech and healthcare forward earnings yield and the bond yield has gone up this year and is much larger than in 2018 (Chart I-11). This suggests that valuations are taking some account of the pandemic and other risks. Moreover, in a longer-term perspective the current gap between the tech and healthcare forward earnings yield and the bond yield, at +4 percent, hardly indicates a mania. In the true mania of 2000, the gap stood at -4 percent! (Chart I-12) Chart I-11The Equity Risk Premium Has Risen In 2020 The Equity Risk Premium Has Risen In 2020 The Equity Risk Premium Has Risen In 2020 Chart I-12Tech And Health Care Valuations Are Not In A Mania Tech And Health Care Valuations Are Not In A Mania Tech And Health Care Valuations Are Not In A Mania In summary, until the pandemic is conquered, investors should maintain a defensive bias to their portfolios. Bond investors should overweight US T-bonds versus core European bonds. Equity investors should overweight the growth defensives, technology and healthcare, which implies overweighting the technology-heavy US versus Europe. A new recommendation is to overweight technology-heavy Netherlands. Stay overweight healthcare-heavy Switzerland, and bank-light France and Germany (albeit expect a technical 5 percent underperformance of Germany versus the UK in the coming weeks). And stay underweight bank-heavy Austria. Fractal Trading System* The AUD is technically overbought and vulnerable to a tactical reversal. Accordingly, this week’s recommended trade is short AUD/CHF, with a profit target and symmetrical stop-loss set at 4.2 percent. The rolling 1-year win ratio now stands at 63 percent. Chart I-13AUD/CHF AUD/CHF AUD/CHF 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. * 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.   Dhaval Joshi Chief European Investment Strategist dhaval@bcaresearch.com Fractal Trading System   Cyclical Recommendations Structural Recommendations 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 - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-6Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-7Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-8Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations  

Highlights We test the out-of-sample performance of Mean-Variance Optimization (MVO) portfolios. We find that MVO portfolios based on historical estimates have historically underperformed static-weight portfolios. However, MVO portfolios using momentum-based return estimates and a “shrunk” correlation matrix dramatically improved performance, allowing them to outperform our benchmarks. Additionally, MVO portfolios where the asset weights could not deviate more than 10% from the benchmark still added value, making them a more attractive option for benchmarked investors. Feature “Let every man divide his money into three parts, and invest a third in land, a third in business, and a third let him keep in reserve” – Rabbi Isaac bar Aha, Babylonian Talmud, 200 C.E.   Diversification is the main pillar of asset allocation. Its advantages are clear: By spreading out funds to different assets, a portfolio can remain resilient to the failings of individual investments, provided they do not occur at the same time. But, while it is universally accepted that “you should not put all your eggs in one basket”,1 putting this concept into practice remains challenging: After all, how exactly do you diversify? The first attempt to answer this question dates back to the Babylonian Talmud, which suggested that an equally weighted portfolio between a growth asset, a real asset, and a safe asset was the best way to allocate one’s funds. After that, it took almost 2000 years for the first formal theory on portfolio construction to emerge, with the derivation of the mean-variance solution by Harry Markowitz in the 1950s. Today, while other methods such as risk parity, factor-based diversification, and the Kelly criterion have also emerged, mean-variance optimization remains the standard theoretical framework for portfolio construction. However, mean-variance optimization (MVO) has not proven to be the final solution to the asset allocation puzzle. Far from it. With time, investors have realized that, while MVO might provide the optimal allocation in theory, it has several drawbacks when used to construct real world portfolios. Specifically, MVO portfolios have become notorious due to their inability to deal with estimation error, as well as for their large concentrated positions on a single asset. In this report we examine the strengths and weaknesses of MVO portfolios from an empirical standpoint, and we suggest three practical solutions to solve some of the problems described above. The report is structured as follows: We first describe our data and methodology in our Methodology section. We then provide a summary of our main findings in the Summary Of Results section. For readers who wish to read a more detailed description of our analysis, please refer the Results In Full section. Methodology Data In order to build optimized portfolios, we use monthly returns for seven assets from the perspective of a US-based investor: US equities, international equities (developed markets ex-US), US Treasurys, US investment-grade corporate bonds, commodity futures, US REITs, and US cash. We use a sample starting in 1973 and ending in April 2020.2 Optimization We build three types of mean-variance optimized portfolios: A conservative portfolio, with a target volatility of 6%, a moderate portfolio, with a target volatility of 9%, and an aggressive portfolio, with a target volatility of 12%.3 In the optimization procedure, each portfolio seeks to maximize returns subject to the risk constraint. In addition to this risk constraint, we also do not allow for leverage or short positions. The optimization and rebalancing are done on a monthly basis. Benchmarking As a benchmark we build three portfolios (an aggressive benchmark, a moderate benchmark, and a conservative benchmark) with constant weights. The portfolios are rebalanced on a monthly basis. Be advised that this is not a completely fair comparison, as these benchmarks are constructed ex-post, which means that in contrast to the MVO portfolios, these benchmark portfolios have the benefit of hindsight to achieve their target volatility. However, they will prove useful to evaluate whether MVO portfolios are doing a good job at maximizing returns. Table 1 describes the benchmark portfolios. Table 1Benchmark Portfolio Weights The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization Turnover limits In order to get a better picture of how MVO performs under realistic conditions, we assess the performance of MVO portfolios with turnover limits. These turnover limits are put in place to take into account that changing portfolio weights by large amounts from month to month is not feasible for most asset managers. We limit the month-to-month change in the weight of each asset to 5%. As an example, if the current weight in US Treasurys is 20%, and the MVO finds that the optimal weight for the following month is 40%, the new weight will only be 25%. In a few months this results in buying and selling not being equal. In those cases, turnover for certain assets might be limited further to ensure that all of the weights add up to 100%. Transaction costs Table 2One-Way Transaction Costs The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization To calculate transaction costs, we multiply the absolute value of the month-on-month change in the weight of each asset by the transaction costs shown in Table 2. Note that this approach does not take into account drift. However, this will not be important when comparing portfolios, given that drift will also not be considered for the benchmarks (since weights stay static from month to month, transaction costs for the benchmarks are zero in our analysis).   Summary Of Results After conducting our analysis, our main findings were the following (please see full details below in the following sections): Historical performance MVO portfolios using historical estimates as inputs have historically underperformed static-weight benchmarks in both raw return and risk-adjusted terms (Table 3). The realized volatility of MVO portfolios was relatively close to their target risk over the sample. However, this was not true when looking at shorter periods. Table 3Summary Of Results The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization Solution #1: Momentum-based return estimates Expected return estimates can be improved by taking into account that prices usually trend (price momentum). These momentum-based return estimates improve both the return and the Sharpe ratio. Additionally, the volatility of MVO portfolios using momentum-based return estimates remains below the target risk more consistently. Solution #2: Shrinking correlation matrix  Historical correlation estimates are often too noisy and lead to estimation error.  “Shrinking” cross-asset correlations to a pre-specified value often results in improvements in both raw returns and risk-adjusted returns.   Very high shrink factors had the best performance. Solution #3: Constraining weights MVO portfolios result in large concentrations in one or two assets. Such an allocation is not practical for most asset managers, particularly if they are benchmarked. However, MVO can improve performance even when asset weights are constrained. Specifically, the information ratio can be improved when using the two solutions described above. We remind clients that MVO should be used prudently. Specifically, even though many of the measurements we have suggested here concentrate in making MVO more robust to noisy estimates, MVO is always vulnerable to the risk of estimation error. Other inputs should be considered when making final asset allocation decisions. Results In Full Historical performance The theory underpinning mean-variance optimizations assumes perfect knowledge of expected returns, volatility, and correlation. In practice, however, this is never the case. Instead, these inputs need to be estimated – a process which unavoidably carries error with it. This error in the estimated inputs can lead to significant deviations from the optimal weights, resulting in lower performance. But to what extent does performance suffer when using imperfect inputs? To answer this question, we construct MVO portfolios as they are often built in practice: using historical estimates. The portfolios are built as follows: Each month we use the historical mean return, historical volatility, and historical correlation matrix up to that point in time, as inputs for every asset with the exception of cash.4 To ensure that the historical inputs are robust enough, we start building the portfolios when we have at least 10 years of historical data (since our data begins in 1973, we start building the portfolios in 1983). Table 4 shows several key metrics for these MVO portfolios with the different risk targets as well as for their benchmarks. Overall, MVO portfolios underperformed their respective benchmarks in every single category regardless of the risk level. This result is in line with previous research, which has shown that MVO usually underperforms equally weighted or static-weight portfolios.5 Table 4MVO Versus Benchmarks The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization The one positive trait of the MVO portfolios was that the volatility of returns over the entire sample stayed relatively close to the risk target. This happened because, over the very long term, volatility remains fairly stable within assets, in stark contrast to returns or correlations, which can undergo dramatic changes even over very long horizons. (Chart 1). However, it is important to point out that, while the volatility of the MVO portfolios stayed relatively close to the risk target throughout the almost 40-year sample, the same was not true if we look at subperiods. Volatility of MVO portfolios was often significantly higher than the risk constraint in the medium term (Chart 2). This is a problem, given that asset managers are usually evaluated in these shorter time frames. Chart 1Volatility Is Relatively Stable Over The Long-Term Volatility Is Relatively Stable Over The Long-Term Volatility Is Relatively Stable Over The Long-Term Chart 2MVO Volatility Can Stray Well Above Target MVO Volatility Can Stray Well Above Target MVO Volatility Can Stray Well Above Target   Solution #1: Momentum-based return estimates Expected returns are the most important input for mean-variance optimization. In general, there are many ways to improve on the dismal track record of historical estimates. However, in this report we will focus on a simple way to improve them. Specifically, we take into account the fact that prices usually trend. As we discussed in our July 2019 report, momentum in asset prices has been one of the most persistent forces in the history of financial markets.6 The propensity of returns to trend, was first discussed by the academic literature in the 1990s but has possibly been known amongst practitioners since the primitive financial markets of the 17th century. We use this simple stylized fact about returns for a couple of reasons: First, there is extensive literature arguing that there are structural forces which causes prices to trend. This gives us some assurance that this tendency is not a random trait of the data, but rather a result of an internal mechanism in the market. Second, the existence of price momentum has been known for a very long time, which means that any sophisticated investor could have realistically used this fact during our sample to improve his or her estimate of expected returns. To take price momentum into account, we estimate expected returns as follows for each asset except for cash: Each month we compute the average return of the asset up to that point, following periods when price was above the 12-month moving average (uptrend average). We also compute the average return of the asset up to that point, following periods when price was below the 12-month moving average (downtrend average). If the asset’s price is above its 12-month moving average, we use the uptrend average as our expected return estimate. If the asset’s price is below its 12-month moving average, we use the downtrend average as our expected return estimate. Much like with our historical estimates, we wait until we have 10 years of data to begin constructing the portfolios. Table 5 shows the result of the MVO portfolios using these momentum-based expected return inputs, as well as the MVO portfolios using historical estimates and the benchmarks. Overall, using momentum-based expected returns significantly improved the performance of all of the MVO portfolios. Additionally, the volatility of the enhanced MVO portfolios was much better behaved, staying much closer to the risk target than the volatility of the MVO that used historical return estimates (Chart 3). Table 5Momentum-Based Return Estimates Enhance Performance The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization Chart 3Momentum-Based Return Estimates Lead To Better Behaved Volatility Momentum-Based Return Estimates Lead To Better Behaved Volatility Momentum-Based Return Estimates Lead To Better Behaved Volatility Solution #2: Shrinking correlation matrix What about improving correlation estimates? Correlations are notoriously noisy and difficult to forecast since they often change depending on the regime. Moreover, while the number of estimates necessary for expected returns and volatility increases linearly with the number of assets in consideration, the number of estimates necessary for the correlation matrix increases at a much higher rate.7 One alternative solution proposed by practitioners to deal with this noise and limit the amount of estimation error is to anchor correlations to some pre-specified value (also known as “shrinking” the correlation matrix). This reduces the noise of the correlation estimate, making for a much more robust input. Shrinking the correlation matrix can be done as follows: Choose shrinkage target: The anchor value or the shrinkage target, is the number to which cross-asset correlations are anchored. Often the average cross-asset correlation between all assets is chosen as the shrinkage target.8 Choose a shrinkage factor: The shrinkage factor is the weight we put on the anchor versus our estimate. The weighting is done with the formula below: Shrunk correlation = (Shrinkage factor) * (Shrinkage target) + (1-Shrinkage factor)* (Correlation estimate) But what exactly should the shrinkage factor be? To answer this question, we test the performance of MVO portfolios that use historical estimates for expected returns and variance, but where the correlation matrix is shrunk. Additionally, we examine how varying the shrinkage factor affected historical performance for different types of investors. Our results indicate that correlation matrices with high levels of shrinkage (70%+ shrink factor) have invariably improved Sharpe ratios and reduced volatility, while also improving returns in almost all cases (Table 6). Table 6High Shrinkage Factors Lead To Better Risk-Adjusted Returns The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization This result was also robust to using other types of expected return estimates. Table 7 shows that shrinking the correlation continued to either maintain or improve performance when using the momentum-based expected return inputs from the section above. Importantly, MVO portfolios using momentum-based returns and shrunk correlation with high shrinkage factors were able to beat the benchmarks at various metrics regardless of the type of portfolio. Table 7Momentum-Based Return Estimates Enhance Performance The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization Solution #3: Constraining weights MVO often results in portfolios that have highly concentrated positions in one or two assets (Chart 4). Additionally, asset weights can experience dramatic changes, even when turnover limits are imposed. For several reasons this is not a desirable trait for most asset managers. Benchmarked managers in particular are often constrained in how much they can deviate from their benchmarks. Moreover, even if they do not have explicit deviation limits, benchmarked managers are often limited implicitly since they are evaluated on their tracking error. Chart 4MVO Weights Are Highly Concentrated The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization One solution to the extreme weights problem is to limit the amount by which the weights can deviate from the benchmark. To test this hypothesis, we perform the optimization as described previously, using momentum-based return estimates and a shrunk correlation matrix, but we limit the amount by which weights can deviate from the benchmark weights by 10%. Table 8A shows the result of MVO portfolios with weight limits, as well the benchmark. Overall, constraining the amount of deviation from the benchmark weights resulted in significant performance improvements across every metric we tested on. Additionally, while the limited MVO portfolios did not always outperform the unconstrained MVO portfolios in terms of return, they were able to have much lower tracking risk and better information ratios, making them a better option for benchmarked investors (Table 8B). Finally, the constrained portfolios had much better-behaved asset weights than the unconstrained ones9 (Chart 5). Table 8APortfolios With Shrunk Correlation (80% Shrinkage Factor) & Momentum-Based Return Estimates The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization Table 8BPortfolios With Shrunk Correlation (80% Shrinkage Factor) & Momentum-Based Return Estimates The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization Chart 5Limited MVO Leads To More Realistic Assets Weights For Benchmarked Investors The User Manual On Portfolio Construction: Mean-Variance Optimization The User Manual On Portfolio Construction: Mean-Variance Optimization   Juan Correa Ossa, CFA  Associate Editor juanc@bcaresearch.com Footnotes 1  The origin of this phrase is often attributed to Don Quijote, the main character of the famous 17th century Spanish novel of the same name by Miguel de Cervantes. 2 Our sources are MSCI Inc (Please see copyright declaration), Bloomberg /Barclays Indices, National Association Of Real Estate Investment Trusts and Goldman Sachs via Datastream. 3 Our approach is loosely based on the work from Bessler, Opfer and Wolff. For more details, please see Wolfgang Bessler, Heiko Opfer, and Dominik Woff, “Multi-Asset Portfolio Optimization and Out-of-Sample Performance: An Evaluation of Black-Litterman, Mean-Variance, and Naïve Diversification Approaches”, European Journal of Finance, Vol. 23, no.1, 2017. 4 For the expected return of cash we just use the current cash yield, since this will be the return of cash with certainty. Volatility and correlation are calculated using historical estimates. 5 Please see De Miguel, Garlappi, Uppal, “Optimal Versus Naïve Diversification: How Inefficient is the 1/N Portfolio Strategy?”, The Review of Financial Studies, Vol. 22, no. 5, May 2009. 6 Please see Global Asset Allocation Report “Swimming With The Tide: Momentum Strategies In Financial Markets,” dated July 23 2019, available at gaa.bcaresearch.com 7 Specifically, if you need to build a portfolio of n assets, you need n estimates of expected returns, n estimates of variances but (n*(n-1))/2 estimates of asset correlations. 8 We use this method to compute the shrinkage target in our analysis since we have a balanced variety of assets (two risk assets, two fixed income assets, two real assets and cash). However, be advised that this shrinkage target might not always be appropriate. In general, judgement should be used to choose an appropriate shrinkage target. 9 In general, there might be other benefits to constraining asset weights. A recent paper by Pedersen et al showed that MVO tends to suffer due to taking large exposure in problematic portfolios that arise due to noise. Constraining MVO weights is a solution to control for this noise and keep the optimization from overweighting this problem portfolios. For more details please see Pedersen, Lasse Heje and Babu, Abhilash and Levine, Ari, “Enhanced Portfolio Optimization”, NYU Stern School of Business, 2020.

Highlights Treasuries: Keep portfolio duration close to benchmark on a 6-12 month horizon, but continue to hold tactical overlay positions that will profit from modestly higher bond yields: Overweight TIPS versus nominal Treasuries, hold duration-neutral nominal curve steepeners, hold real yield curve steepeners. IG Tech: Given our positive outlook for investment grade corporate bond spreads, the Technology sector’s high credit rating and defensive characteristics make it decidedly un-compelling. However, Tech spreads are attractive compared to other A-rated corporate bonds. HY Tech: We want to focus our high-yield allocation on defensive sectors where a large proportion of issuers are able to benefit from Fed support. The high-yield Technology sector checks both of those boxes and offers attractive risk-adjusted compensation to boot. Feature Chart 1Three Treasury Trades Three Treasury Trades Three Treasury Trades As we have previously written, bond yields should move modestly higher over the course of the summer as the US economy re-opens.1 However, there are enough potential medium-term pitfalls related to US politics and COVID transmission that we aren’t yet comfortable with below-benchmark portfolio duration. Instead, we recommend that investors keep portfolio duration close to benchmark on a 6-12 month horizon, but add three tactical overlay positions that will profit from higher bond yields: Overweight TIPS versus nominal Treasuries Duration-neutral nominal Treasury curve steepeners Real yield curve steepeners All three of these positions have performed well during the past couple of months (Chart 1), and in the first section of this report we assess whether they have further to run. The remaining two sections of this week’s report consider the outlooks for investment grade and high-yield Technology bonds, respectively. Three Trades To Profit From Higher Yields 1) Overweight TIPS Versus Nominals Chart 2Adaptive Expectations Model Adaptive Expectations Model Adaptive Expectations Model TIPS breakeven inflation rates have moved up considerably since mid-March. Back then, the 10-year TIPS breakeven rate troughed at 0.50%. It currently sits at 1.31%. Despite the large move, TIPS breakeven inflation rates still have a considerable amount of upside. One way to assess how much is through the lens of our Adaptive Expectations Model (Chart 2).2 This model considers several different measures of inflation expectations (based on realized CPI inflation and surveys) and uses the difference between those measures of inflation expectations and the 10-year TIPS breakeven inflation rate to forecast the future 12-month change in the 10-year TIPS breakeven. At present, the model forecasts that the 10-year TIPS breakeven inflation rate will rise 23 bps during the next 12 months, bringing it to 1.54%. It’s important to note that our model is biased towards measures of longer-run inflation expectations. As a result, it can be surprised from time to time by large fluctuations in drivers of short-term inflation expectations, like the oil price. This year’s massive drop in oil – and concurrent decline in headline inflation – were the main factors that caused the 10-year TIPS breakeven inflation rate to fall so far below our model’s fair value. However, as we discussed in last week’s report, the oil price looks to have troughed and there is preliminary evidence that we might also be past the lowest point for headline CPI.3 Profit from rising bond yields by entering a duration-neutral yield curve steepener. We see TIPS continuing to outperform nominal Treasuries over both short- and long-run horizons. 2) Duration-Neutral Yield Curve Steepeners Chart 3Stick With Steepeners Stick With Steepeners Stick With Steepeners Another way to profit from rising bond yields without taking a large duration bet is via a duration-neutral yield curve steepener. One example would be a long position in the 5-year note and a short position in a duration-matched barbell consisting of the 2-year and 10-year notes. Alternatively, you could use the 2-year note and 30-year bond as the two legs of the barbell. These sorts of duration-matched trades where you take a long position in a bullet maturity near the middle of the curve and go short the wings are designed to perform well in periods of yield curve steepening.4  In the current environment, where dovish Fed guidance has dampened volatility at the front-end of the yield curve, any bond sell-off will be felt disproportionately at the long-end, leading to a steeper curve. The only problem with this proposed trade is that it is no longer cheap. The spread between the 5-year bullet and 2/10 barbell is -6 bps and the spread relative to the 2/30 barbell is -3 bps (Chart 3). What’s more, the 5-year bullet trades expensive relative to the 2/10 and 2/30 barbells, according to our fair value models (Chart 3, bottom panel). However, for the time being we are inclined to overlook stretched valuations. The 5-year bullet does appear expensive but it has been more expensive in the past, most notably during the last zero-lower-bound episode from 2010 to 2013. Similar to then, the market is now priced for an extended period of a zero fed funds rate. We would not be surprised to see bullets become much more expensive in that sort of environment, and possibly even return to extended 2010-2013 valuations.   We recommend holding onto duration-neutral yield curve steepeners, despite unattractive valuations. Specifically, we favor going long the 5-year bullet and short a duration-matched 2/10 barbell. 3) Real Yield Curve Steepeners Chart 4Higher Inflation Means Steeper Real Yield Curve Higher Inflation Means Steeper Real Yield Curve Higher Inflation Means Steeper Real Yield Curve The final position we recommend is a steepener along the real yield curve. We first recommended this trade on April 28 when a plunge in oil (and spike in deflationary sentiment) caused the real 2-year yield to jump to 0.28% compared to a real 10-year yield of -0.70%.5 Since then, the real 2-year yield has collapsed to -1% compared to a real 10-year yield of -0.87%. Although the real 2-year/10-year slope is once again positive, it has typically been higher during the past few years (Chart 4). We therefore expect further steepening as long as the oil price and headline inflation continue to recover from April’s lows. Much like during the 2008/09 financial crisis, the combination of the Fed’s zero-lower-bound forward guidance and a massive drop in both oil and headline inflation caused short-dated real yields to jump. Subsequently, this led to a massive steepening of the real yield curve, once the oil price and headline inflation started to recover. We believe that same dynamic is playing out today. Investors should continue to hold real yield curve steepeners, at least until rebounding oil and headline CPI return short-dated inflation expectations to more reasonable levels. Investment Grade Tech Risk Profile Technology accounts for 9% of the overall Bloomberg Barclays investment grade corporate index, which makes it the second biggest industry group, after Banking. Its large index weight is due to the presence of three tech giants: Microsoft (Aaa-rated), Apple (Aa-rated) and Oracle (A-rated) which, combined, constitute 38% of the Tech sector.  Investment grade Technology is a highly defensive corporate bond sector. In sharp contrast with the equity market, Technology is a highly defensive corporate bond sector. That is, it tends to outperform the overall corporate bond index during periods of spread widening and underperform during periods of spread tightening. This largely comes down to the fact that Tech has a higher credit rating than the overall corporate index. Twenty five percent of the Tech sector’s market cap carries a Aaa or Aa rating compared to just 9% for the overall index (Chart 5). Further, of the high-flying FAANG stocks that garner a lot of attention from equity analysts, only Apple is a significant presence in the Technology bond index.6 Chart 5Investment Grade Credit Rating Distributions* Take A Look At High-Yield Technology Bonds Take A Look At High-Yield Technology Bonds Chart 6IG Technology Risk ##br##Profile IG Technology Risk Profile IG Technology Risk Profile The Tech sector’s defensive nature is confirmed by looking at its duration-times-spread (DTS) ratio and historical excess returns (Chart 6).7 The sector’s DTS ratio is consistently below 1.0, and its excess returns show a clear pattern of outperformance during periods of spread widening and underperformance during periods of spread tightening. Valuation In terms of valuation, although the Tech sector does not offer a spread advantage over the corporate index – which should be expected given its higher credit rating – we find that it trades cheap relative to its comparable credit tier (Table 1). Tech offers an option-adjusted spread of 115 bps versus 111 bps for the A-rated corporate index, and the sector still appears attractive after controlling for duration differences by looking at the 12-month breakeven spread. In absolute terms, Tech sector spreads are just above their median since 2010. The A-rated corporate index spread currently sits right on top of its post-2010 median. Table 1IG Technology Valuation Take A Look At High-Yield Technology Bonds Take A Look At High-Yield Technology Bonds Balance Sheet Health Chart 7IG Technology Debt Growth IG Technology Debt Growth IG Technology Debt Growth The Technology sector added a large amount of debt during the last recovery. The par value of the Tech index’s outstanding debt has grown 5.2 times since 2010 compared to 2.4 times for the benchmark. As a result, Tech’s weight in the corporate index has more than doubled, from 4% to 9% (Chart 7). However, earnings have done a pretty good job of keeping pace with the large increase in debt. The market cap-weighted net debt-to-EBITDA ratio for the investment grade Tech index is only 2.4, and the sector’s average credit rating has been stable since 2010. At the individual issuer level, there are 58 issuers in the Tech index and only 4 currently have a negative ratings outlook from Moody’s (Appendix B). What’s more, of the 16 Tech sector ratings that Moody’s has reviewed this year, 12 have been affirmed with a stable outlook, 1 was assigned a positive outlook and only 3 were assigned negative outlooks. Macro Considerations Chart 8Technology Sector Macro Drivers Technology Sector Macro Drivers Technology Sector Macro Drivers The Tech sector can be split into three major segments that have distinct macro drivers: Software (26% of Tech index market cap, includes Microsoft and Oracle) Hardware (29% of Tech index market cap, includes Apple, IBM and Dell) Semiconductors (24% of Tech index market cap, includes Intel and Avago Technologies) Software investment has been in a structural bull market for many years, and should remain resilient during the COVID recession as demand for remote working solutions increases. While we only have data through the end of March, software investment did not see the same collapse as other sectors during the first quarter (Chart 8). The Hardware and Semiconductor segments are more cyclical and geared toward manufacturing. As such, their macro outlooks were already challenged pre-COVID, due to the US/China trade war and manufacturing downturn of 2019. Both US computer exports and global semiconductor sales were showing signs of life near the end of last year, but were decimated when the pandemic struck in 2020 (Chart 8, panels 3 & 4). A revival in this space is contingent upon continued gradual re-opening and a return to economic growth. More optimistically, US consumer spending on personal computers and peripheral equipment has not fallen as much as broad consumer spending during the past few months (Chart 8, bottom panel). In the long-run, the 5G smartphone rollout is a significant structural tailwind for both semiconductor issuers and Apple. Meanwhile, the threat of significant regulatory crackdown on Tech firms remains a long-run risk. Our sense is that any push toward stricter regulations won’t have that much impact on Technology bond returns. This is because the subjects of most lawmaker scrutiny – Facebook, Amazon and Google – are largely absent from the Bloomberg Barclays Tech index. Investment Conclusions We expect that investment grade corporate bond spreads will tighten during the next 6-12 months. Against this positive back-drop, investors should focus exposure on cyclical (lower-rated) sectors that offer greater expected returns. With that in mind, the Tech sector’s high credit rating and defensive characteristics make it decidedly un-compelling. However, Tech does offer a slight spread advantage compared to other A-rated bonds and the macro back-drop is reasonably supportive. We would therefore recommend Tech bonds to investors looking for some A-rated corporate bond exposure. But in general, we prefer the greater spreads on offer from sectors that occupy the high-quality Baa space, such as subordinate bank debt.8 High-Yield Tech Risk Profile High-Yield Technology’s credit rating profile is similar to that of the overall benchmark, but with a slightly larger presence of low-rated (Caa & below) issuers (Chart 9). The largest issuers in the space are Dell (5.7% of Tech index market cap, Ba-rated), MSCI Inc. (5.1% of Tech index market cap, Ba-rated, see copyright declaration) and CommScope (8.1% of Tech index market cap, B-rated). High-yield Tech recently transitioned from being a cyclical sector to a defensive one. Interestingly, the high-yield Tech sector recently transitioned from being a cyclical sector to a defensive one. The sector behaved cyclically during the 2008 recession, underperforming the index when spreads widened and outperforming when they tightened. But Tech then outperformed the High-Yield index during the spread widening episodes of 2015 and 2020. Based on the sector’s low DTS ratio, this defensive behavior should persist for the next 12 months (Chart 10). Chart 9High-Yield Credit Rating Distributions* Take A Look At High-Yield Technology Bonds Take A Look At High-Yield Technology Bonds Chart 10HY Technology Risk Profile HY Technology Risk Profile HY Technology Risk Profile Valuation The High-Yield Technology option-adjusted spread (OAS) is significantly lower than the average OAS for the benchmark High-Yield index. However, it offers a spread premium compared to other Ba-rated issuers (Table 2). Adjusting for duration differences by looking at the 12-month breakeven spread makes high-yield Tech look significantly more attractive. The high-yield Tech spread would have to widen by 146 bps for the sector to underperform duration-matched Treasuries during the next 12 months. This compares to 96 bps for other Ba-rated issuers and 152 bps for the overall junk index. Table 2HY Technology Valuation Take A Look At High-Yield Technology Bonds Take A Look At High-Yield Technology Bonds It is apparent that the Tech sector’s low average duration (Chart 10, bottom panel) is a major reason for its relatively tight OAS. On a risk-adjusted basis, high-yield Tech valuation actually appears quite compelling, with a 12-month breakeven spread only 6 bps below that of the overall index. Balance Sheet Health Chart 11HY Technology Debt Growth HY Technology Debt Growth HY Technology Debt Growth The amount of outstanding high-yield Technology debt has grown a bit more rapidly than overall junk index debt since 2010 (Chart 11). As a result, Technology’s weight in the index has increased from 5% in 2010 to 6% today. At the issuer level, the Tech sector should benefit from having a large number of issuers that will be able to take advantage of the Fed’s Main Street Lending facilities. To be eligible for the Main Street facilities, issuers must have less than 15000 employees or less than $5 billion in 2019 revenue. Also, the issuers must be able to keep their Debt-to-EBITDA ratios below 6.0, including any new debt added through the Main Street programs. Of the 43 high-yield Tech issuers with available data, we estimate that 30 are eligible to receive support from the Main Street facilities (Appendix C). This even includes 11 out of the 16 B-rated issuers. Typically, we don’t expect that many B-rated issuers will be eligible for the Main Street facilities, which makes this result encouraging for Tech sector spreads. Investment Conclusions We recommend an overweight allocation to high-yield Technology bonds. As we wrote last week, high-yield spreads appear too tight if we ignore the impact of the Fed’s emergency lending facilities and consider only the fundamental credit back-drop.9 With that in mind, we want to focus our high-yield allocation on defensive sectors where a large proportion of issuers able to benefit from Fed support. The Technology sector checks both of those boxes and offers attractive risk-adjusted compensation to boot. Appendix A: Buy What The Fed Is Buying The Fed rolled out a number of aggressive lending facilities on March 23. These facilities focused on different specific sectors of the US bond market. The fact that the Fed has decided to support some parts of the market and not others has caused some traditional bond market correlations to break down. It has also led us to adopt of a strategy of “Buy What The Fed Is Buying”. That is, we favor those sectors that offer attractive spreads and that benefit from Fed support. The below Table tracks the performance of different bond sectors since the March 23 announcement. We will use this to monitor bond market correlations and evaluate our strategy’s success. Table 3Performance Since March 23 Announcement Of Emergency Fed Facilities Take A Look At High-Yield Technology Bonds Take A Look At High-Yield Technology Bonds Appendix B Table 4Investment Grade Technology Issuers Take A Look At High-Yield Technology Bonds Take A Look At High-Yield Technology Bonds Appendix C Table 5High-Yield Technology Issuers Take A Look At High-Yield Technology Bonds Take A Look At High-Yield Technology Bonds   Ryan Swift US Bond Strategist rswift@bcaresearch.com Jeremie Peloso Senior Analyst jeremiep@bcaresearch.com Footnotes 1 Please see US Bond Strategy Weekly Report, “Bonds Vulnerable As North America Re-Opens”, dated May 26, 2020, available at usbs.bcaresearch.com 2 For more details on our Adaptive Expectations Model please see US Bond Strategy Weekly Report, “How Are Inflation Expectations Adapting?”, dated February 11, 2020, available at usbs.bcaresearch.com 3 Please see US Bond Strategy Weekly Report, “No Holding Back”, dated June 16, 2020, available at usbs.bcaresearch.com 4 For an explanation of why this works please see US Bond Strategy Special Report, “Bullets, Barbells And Butterflies”, dated July 25, 2017, available at usbs.bcaresearch.com 5 Please see US Bond Strategy Weekly Report, “Negative Oil, The Zero Lower Bound And The Fisher Equation”, dated April 28, 2020, available at usbs.bcaresearch.com 6 Of the other FAANG stocks: Google accounts for just 0.5% of Tech bond sector market cap, Facebook has close to no debt, Amazon is included in the Consumer Cyclical corporate bond index and Netflix is included in the Media: Entertainment sector of the High-Yield index. 7 Duration-Times-Spread (DTS) is a simple measure that is highly correlated with excess return volatility for corporate bonds. The DTS ratio is the ratio of a sector’s DTS to that of the benchmark index. It can be thought of like the beta of a stock. A DTS ratio above 1.0 signals that the sector is cyclical (or “high beta”), a DTS ratio below 1.0 signals that the sector is defensive or (“low beta”). For more details on the DTS measure please see: Arik Ben Dor, Lev Dynkin, Jay Hyman, Patrick Houweling, Erik van Leeuwen & Olaf Penninga, “DTS (Duration-Times-Spread)”, Journal of Portfolio Management 33(2), January 2007. 8 For more details on our recommendation to overweight subordinate bank bonds please see US Bond Strategy Weekly Report, “Negative Oil, The Zero Lower Bound And The Fisher Equation”, dated April 28, 2020, available at usbs.bcaresearch.com 9 Please see US Bond Strategy Weekly Report, “No Holding Back”, dated June 16, 2020, available at usbs.bcaresearch.com Fixed Income Sector Performance Recommended Portfolio Specification

Highlights We conservatively estimate lost output from shutdowns and social distancing will equal $10 trillion, and we expect the jobs market to be permanently scarred. Inflation, even at 2 percent, is a pipe dream, which leads to three investment conclusions on a 1-year horizon: Overweight US T-bonds and Spanish Bonos versus German Bunds and French OATs. Any high-quality bond yield that can decline will decline. Overweight CHF/USD. The tightening yield spread will structurally favour the CHF, while the haven status of the CHF should prevent it from underperforming in periods of market stress. Overweight defensive equities (technology and healthcare) versus cyclical equities (banks and energy). This implies underweight European equities versus other markets. Fractal trade: Short Germany versus the UK. The recent outperformance of German equities is technically extended. Feature Chart of the WeekCredit Impulses Are Large, But The Hole In Output Is Much Larger Credit Impulses Are Large, But The Hole In Output Is Much Larger Credit Impulses Are Large, But The Hole In Output Is Much Larger Big numbers befuddle us. Hardly a day passes without someone listing the unprecedented global stimulus unleashed to counter the coronavirus forced shutdowns – the trillions in government spending promises, tax relief, loan guarantees, money supply growth, and central bank asset-purchases. The most optimistic estimates quantify the total stimulus at $15 trillion. This includes $7 trillion of loan guarantees plus increases in central bank balance sheets which do not directly boost demand. So the direct stimulus is closer to $7 trillion.1 Yet the size of the stimulus is meaningless until we quantify the massive hole in economic output that needs to be filled. Assuming no further large-scale shutdowns, we conservatively estimate that the hole will amount to 12 percent of world output, or $10 trillion. A $10 Trillion Hole In Output Last week, the UK’s Office for National Statistics (ONS) helped us to estimate the hole in output, because unusually the ONS calculates UK GDP on a monthly basis. Between February and April, when the UK economy went from fully open to full shutdown, UK GDP collapsed by 25 percent. This despite the UK having an outsized number of jobs suitable for ‘working from home.’ For a more typical economy, we estimate that a full shutdown collapses output by 30 percent (Chart I-2). Chart I-2A Full Shutdown Collapses Output By 30 Percent A Full Shutdown Collapses Output By 30 Percent A Full Shutdown Collapses Output By 30 Percent The next question is: how long does the full shutdown last? Assuming it lasts for three months, output would suffer a hole amounting to 7.5 percent of annual GDP.2  But in practice, the economy will not fully re-open after three months. Social distancing will persist until people feel confident that the pandemic is under control. An effective vaccine against Covid-19 is unlikely to be available for a year. So, even without government policy to enforce social distancing, many people will choose to avoid crowds and congregations for fear of catching the virus. The size of the stimulus is meaningless until we quantify the massive hole in economic output. This means that the sectors that rely on crowds and congregations – leisure and hospitality and retail trade – will be operating at half-capacity, at best. Given that these sectors generate 9 percent of GDP, operating at half-capacity will create an additional hole amounting to 4.5 percent of output. More worryingly, these two sectors employ 21 percent of all workers, so operating at sub-par will leave the jobs market permanently scarred.3 Combining the 7.5 percent existing hole with the 4.5 percent future hole, the full hole in economic output will amount to around 12 percent of annual GDP. As global GDP is worth around $85 trillion, this equates to $10 trillion.  Crucially though, our estimate assumes that a second wave of the pandemic will not force a new cycle of shutdowns. If it does, the hole will become even bigger. Don’t Be Fooled By Money Supply Growth The recent growth in broad money supply seems a big number. Since the start of the year, the outstanding stock of bank loans has increased by around $0.7 trillion in the euro area, and by $1 trillion in both the US and China (Chart I-3 and Chart I-4). This has boosted the 6-month credit impulses in all three economies. Indeed, the US 6-month credit impulse recently hit its highest value of all time, and the combined 6-month impulse across all three blocs equals around $2 trillion (Chart of the Week). Chart I-3Don't Be Fooled By Money Supply Growth In The Euro Area And The US... Don't Be Fooled By Money Supply Growth In The Euro Area And The US... Don't Be Fooled By Money Supply Growth In The Euro Area And The US... Chart I-4...And In ##br##China ...And In China ...And In China This 6-month credit impulse quantifies the additional borrowing in the most recent six-month period compared to the previous period. Ordinarily, a $2 trillion impulse would create a huge boost to demand. After all, the private sector does not usually borrow just to hold the cash in a bank. Yet in the coronavirus crisis this is precisely what has happened. While the shutdowns lasted, firms drew on existing bank credit lines to build up emergency cash buffers. Therefore, much of the money growth will not generate new demand. While the shutdowns lasted, firms drew on existing bank credit lines to build up emergency cash buffers.  To the extent that this cash is sitting idly in a firm’s bank account, the monetary velocity will decline. Meaning there will be a much-reduced transmission from credit impulses to spending growth. Furthermore, when the economy re-opens, many firms will relinquish the precautionary credit lines. There is no point holding cash in the bank when there are few investment opportunities. Hence, credit impulses will fall back – as seems to be the case right now in the US. QE: The Great Misunderstanding To repeat, big numbers befuddle us. They must always be put into context. No truer is this than when it comes to central bank asset-purchases. The great misunderstanding is that the act of central banks buying assets, per se, drives up those asset prices. Central banks act as lenders of last resort to solvent but illiquid banks and sovereigns. If there is ample liquidity in these markets – as is the case now – then the primary function of central bank asset-purchases is to set the term-structure of interest rates. In turn, the term-structure of global interest rates establishes the prices of $500 trillion of global assets. The prices of these assets are inextricably inter-connected and inter-dependent4 (Chart I-5). Chart I-5The Prices Of $500 Trillion Of Assets Are Inextricably Inter-Connected The Prices Of $500 Trillion Of Assets Are Inextricably Inter-Connected The Prices Of $500 Trillion Of Assets Are Inextricably Inter-Connected The great misunderstanding is that the act of central banks buying assets, per se, drives up those asset prices. Yet central banks set no price target for their asset-purchases. They leave that to the market. Moreover, in the context of the $500 trillion of inter-dependent asset prices, the $10-15 trillion or so of central bank asset-purchases to date constitutes chicken feed (Chart I-6). Hence, the mechanism by which asset-purchases work is through the signal they give to the $500 trillion market on the likely course of interest rate policy. This sets the term-structure of interest rates, which in turn sets the required return on all the $500 trillion of assets (Chart I-7). Chart I-6$10-15 Trillion Of QE Is Chicken Feed... $10-15 Trillion Of QE Is Chicken Feed... $10-15 Trillion Of QE Is Chicken Feed... Chart I-7...Compared To $500 Trillion Of Assets Priced By The Term-Structure Of Interest Rates ...Compared To $500 Trillion Of Assets Priced By The Term-Structure Of Interest Rates ...Compared To $500 Trillion Of Assets Priced By The Term-Structure Of Interest Rates As the ECB’s former Chief Economist, Peter Praet, explains: “There is a signalling channel inherent in asset purchases, which reinforces the credibility of forward guidance on policy rates. This credibility of promises to follow a certain course for policy rates in the future is enhanced by the asset purchases, as these asset purchases are a concrete demonstration of our desire (to keep policy rates at the lower bound.)” The credible commitment to keep policy rates near the lower bound for an extended period depresses bond yields towards the lower bound too. But once bond yields have reached their lower bound the effectiveness of central bank asset-purchases becomes exhausted. Three Investment Conclusions The main purpose of this report was to put the $7 trillion of direct stimulus dollars unleashed into the economy into a proper context. With lost output estimated at $10 trillion and the jobs market permanently scarred, inflation – even at 2 percent – is a pipe dream. Moreover, a second wave of the pandemic and a new cycle of shutdowns would inject a further disinflationary impulse. This leads to three investment conclusions on a 1-year horizon: Any high-quality bond yield that can decline – because it is not already near the -1 percent lower bound to yields – will decline. An excellent relative value trade is to overweight US T-bonds and Spanish Bonos versus German Bunds and French OATs (Chart I-8). Long CHF/USD is a win-win. The tightening yield spread will structurally favour the CHF, while the haven status of the CHF should prevent it from underperforming in periods of market stress. Overweight defensive equities versus cyclical equities, with technology correctly defined as defensive, not cyclical. The performance of cyclicals (banks and energy) versus defensives (technology and healthcare) is now joined at the hip to the bond yield (Chart I-9). This implies underweight European equities versus other markets. Chart I-8Bond Yields That Can Decline Will Decline Bond Yields That Can Decline Will Decline Bond Yields That Can Decline Will Decline Chart I-9The Performance Of Cyclicals Versus Defensives Is Joined At The Hip To The Bond Yield The Performance Of Cyclicals Versus Defensives Is Joined At The Hip To The Bond Yield The Performance Of Cyclicals Versus Defensives Is Joined At The Hip To The Bond Yield Fractal Trading System* The recent outperformance of German equities is technically extended. Accordingly, this week’s recommended trade is to go short Germany versus the UK, expressed through the MSCI dollar indexes. Set the profit target and symmetrical stop-loss at 5 percent. MSCI: Germany Vs. UK MSCI: Germany Vs. UK In other trades, long euro area personal products versus healthcare achieved its 7 percent profit target at which it was closed. The rolling 1-year win ratio now stands at 65 percent. 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. * 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. Footnotes 1 Source: Reuters estimate. 2 A 30 percent loss in output for a quarter of a year (3 months) amounts to a 30*0.25 = 7.5 percent loss in annual output. 3 Using the weights of leisure and hospitality and retail trade in the US economy as a proxy for the global weights. 4 The $500 trillion of assets comprises: real estate $300 trillion, public and private equity $100 trillion, corporate bonds and EM debt $50 trillion, and high-quality government bonds $50 trillion.   Dhaval Joshi Chief European Investment Strategist dhaval@bcaresearch.com Fractal Trading System   Cyclical Recommendations Structural Recommendations Closed Fractal Trades Trades Closed Trades Asset Performance Currency & Bond Equity Sector Country Equity Indicators Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Indicators To Watch - Bond Yields Interest Rate Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations

Dear client, It was my pleasure to join Dhaval Joshi, BCA’s Chief European Investment Strategist, this past Friday June 12, 2020 on a webcast he hosted titled: “Sectors To Own, And Sectors To Avoid In The Post-Covid World”. You can access the replay of the lively webcast here, where Dhaval and I debate how investors should be positioned in different time horizons. I hope you will find it both insightful and informative. Kind Regards, Anastasios Highlights Portfolio Strategy While we cannot time the exact equity market top, our sense is that we are more than fairly valued at the current juncture and the equity market has entered a speculative phase; thus the risk/reward tradeoff is poor in the near-term. We are compelled to put the S&P home improvement retailers index (HIR) on our downgrade watch list and institute a stop at the 10% return mark in order to reflect softness in our HIR macro model, a hook down in existing home sales and a high profit growth bar that sell-side analysts have set for the coming year. Recent Changes Our rolling 10% stop got hit last Tuesday and we monetized 32% gains since the reinstatement of the long S&P oil & gas exploration & production / short global gold miners pair trade.1 Feature Equities briefly erased all losses for the year early last week, but the Fed’s June meeting lacked any additional easing measures and served as a catalyst for a much needed breather – the fifth 5.3-7.3% pullback since the March 23 bottom – as the week drew to a close. While extremely easy monetary and fiscal policies remain the key macro drivers for the SPX, any hiccups in passing a new fiscal spending bill once the money runs out on July 31, carry enough risk to short circuit the equity market’s momentum and result in a shakeout phase. Importantly, given the recent speculative overshoot in equities, the cyclical return potential has diminished, and that is cause for concern. The ongoing COVID-19 catalyzed recession that the NBER last week confirmed commenced in February, the “second wave” risk, a flare up in the US/Sino trade war and more recently, civil unrest have dominated the news flow. However in all this chaos, the November election has slowly moved into the background, especially the SPX return implications during the 4th year of a Presidency. Chart 1 shows the profile of the S&P 500 during Presidential Election calendar years, going back to the 1950s. The solid green line shows the historical mean, and shaded areas denote the 10th and 90th percentiles of SPX performance. If history rhymes, the average profile of these 17 iterations suggests that more cyclical gains are in store for the S&P 500. Chart 1Do Not Ignore… Do Not Ignore… Do Not Ignore… Nevertheless, before getting carried away, a word of caution is in order. As we highlighted last week, a Biden win represents a risk to the SPX’s euphoric rise from the March lows, and could serve as a catalyst for a much needed pullback (Chart 2).2 Thus, according to our analysis if the 90th percentile proves accurate, then the SPX could trace this lower bound and fall 640 points or 20% (Chart 1). This is a key tail risk to our cyclically sanguine equity market view. Chart 2…(Geo)Political Risks Exit Stage Right Exit Stage Right Turning over to the reopening of the economy, while the SPX has now discounted a near fully functioning economy for the rest of the year and beyond (bottom panel, Chart 3), fixed income investors are not in total agreement. In fact, the missing ingredient in giving the green light for equities is a selloff in the bond market, which financials/banks are currently sniffing out on the back of the reopening of the economy. Until fixed income investors get on the same page as equity investors, the SPX will remain on shaky ground (top panel, Chart 3). We first turned positive on the cyclical prospects of the equity market in mid-March3 and cemented our conviction in our March 23 report presenting 20 reasons to buy stocks.4 Since then, the SPX has rocketed higher by 1000 points and overshot our 3,000 SPX target that we recently derived from three methods.5 While we cannot time the exact top and equities may have a bit more upside, our sense is that today, stocks are more than fairly valued and they have entered a speculative phase (Chart 4). Thus the risk/reward tradeoff in the near-term has shifted to the downside. Once these (geo)political risks get appropriately repriced via a higher risk premium, then the broad equity market will resume its cyclical upside march. Chart 3Bond Market Is Not Buying Stock Market’s Euphoria Bond Market Is Not Buying Stock Market’s Euphoria Bond Market Is Not Buying Stock Market’s Euphoria Chart 4Lots Of Good News Is Priced In Lots Of Good News Is Priced In Lots Of Good News Is Priced In This week we update one consumer discretionary subgroup and put it on our downgrade watch list. Put Home Improvement Retailers On Downgrade Alert We are putting the S&P home improvement retailers index (HIR) on downgrade alert and setting a stop at the 10% return mark in order to protect handsome gains for our portfolio since the mid-April overweight inception. HIR have catapulted to all-time highs both in absolute terms and relative to the broad market. Granted, this has been an earnings-led propulsion (top panel, Chart 5), however, we are uneasy that HD is a top ten holding in the S&P growth index (middle panel, Chart 5).6 Importantly, the first print in the real GDP release for Q1/2020 in late-April made for grim reading, with one notable exception: real residential investment. Business capex took it to the chin, but housing related outlays spiked over 20% on a quarter-over-quarter annualized basis, and signal that DIY same-store retail sales will likely prove resilient this summer (bottom panel, Chart 6). Chart 5An Earnings-Led Advance… An Earnings-Led Advance… An Earnings-Led Advance… Chart 6…Buttressed By Resilient Residential Investment… …Buttressed By Resilient Residential Investment… …Buttressed By Resilient Residential Investment… As a reminder, these Big Box retailers are highly levered to the ebbs and flows of residential investment and the latest GDP print should sustain the recent bid under S&P HIR prices (top & middle panels, Chart 6). Tack on the roughly $75/tbf jump in lumber prices since the early-April trough (not shown), and profits benefit from a dual lift: rising volumes and firming selling prices. The DIY avalanche is real and not likely to dissipate any time soon as a consequence of the coronavirus-induced working from home pervasiveness. Yet, HIR has run too far too fast and is due for a consolidation phase. One yellow flag is the recent fall in existing home sales, despite the all-time lows in mortgage rates brought back by the Fed’s ZIRP. The middle panel of Chart 7 shows that if the home sales decline continues in the summer months, then HIR sales will face stiff headwinds as remodeling activity suffers a setback. In addition, in previous recessions the inventory of homes for sale has surged, but at the current juncture only a small jump in inventories is visible (inventories shown inverted, top panel, Chart 7). Were that trend to gain steam, it could put downward pressure to high-flying HIR equities. Chart 7…But Soft Home Sales Are An Issue… …But Soft Home Sales Are An Issue… …But Soft Home Sales Are An Issue… Chart 8…And The Tick Down In Our HIR Model Is A Yellow Flag …And The Tick Down In Our HIR Model Is A Yellow Flag …And The Tick Down In Our HIR Model Is A Yellow Flag The industry’s net earnings revision ratio has climbed to multi-year highs and warns that analyst optimism is excessive, which is contrarily negative (bottom panel, Chart 7). Our macro driven HIR model does an excellent job in encapsulating all the moving parts and its recent tick down is worrisome (Chart 8). Nevertheless, given that this has been a profit-led advance, HIR have a large valuation cushion. The relative forward P/E is trading near a market multiple and below the historical mean (bottom panel, Chart 5). Netting it all out, we are compelled to put the S&P HIR index on our downgrade watch list and institute a stop at the 10% return mark in order to reflect softness in our HIR macro model, a hook down in existing home sales and a high profit growth bar that sell-side analysts have set for the coming year (middle panel, Chart 5). Bottom Line: While we remain overweight the S&P HIR index it is now on downgrade alert. We also set a stop at the 10% return mark in order to protect profits for our portfolio. Stay tuned. The ticker symbols for the stocks in this index are: BLBG: S5HOMI – HD, LOW.   Anastasios Avgeriou US Equity Strategist anastasios@bcaresearch.com     Footnotes 1     Please see BCA US Equity Strategy Insight Report, “Pocketing Gains In Oil/Gold Pair Trade” dated June 10, 2020, available at uses.bcaresearch.com. 2     Please see BCA US Equity Strategy Weekly Report, “Don’t Turn A Blind Eye To Geopolitical Risks” dated June 8, 2020, available at uses.bcaresearch.com. 3    Please see BCA US Equity Strategy Weekly Report, “Inflection Point” dated March 16, 2020, available at uses.bcaresearch.com. 4    Please see BCA US Equity Strategy Weekly Report, “The Darkest Hour Is Just Before The Dawn” dated March 23, 2020, available at uses.bcaresearch.com 5    Please see BCA US Equity Strategy Weekly Report, “New SPX Target” dated April 20, 2020, and “Gauging Fair Value” dated April 27, 2020, available at uses.bcaresearch.com. 6    https://us.spindices.com/indices/equity/sp-500-growth#data-constituents   Current Recommendations Current Trades Strategic (10-Year) Trade Recommendations Exit Stage Right Exit Stage Right Size And Style Views June 3, 2019 Stay neutral cyclicals over defensives (downgrade alert)  January 22, 2018 Favor value over growth April 28, 2020  Stay neutral large over small caps  June 11, 2018 Long the BCA Millennial basket  The ticker symbols are: (AAPL, AMZN, UBER, HD, LEN, MSFT, NFLX, SPOT, TSLA, V).

Highlights Historically, when global growth picks up, the yen weakens. But this is less likely in an environment where global yields remain anchored at low levels. Meanwhile, there is rising risk that consumption in Japan will remain muted. This will limit any pickup in domestic inflation. A modest rise in real rates will lead to a self-reinforcing upward spiral for the yen. That said, cheap yen valuations will buffet Japanese exports. Go short USD/JPY with an initial target of 100. Feature Chart I-1Higher Volatility, Higher Yen An Update On The Yen An Update On The Yen The powerful bounce in global markets since the March lows is at risk of a bigger technical correction. As we enter the volatile summer months, it may only require a small shift in market sentiment to trigger this reversal. The yen has tended to strengthen when market volatility rises (Chart I-1). Should this happen, it will provide the necessary catalyst for established long yen positions. On the other hand, if risk sentiment stays ebullient, the yen will surely weaken on its crosses but can still strengthen vis-à-vis the dollar. This places short USD/JPY bets in an enviable “heads I win, tails I do not lose too much” position. Growth And Monetary Policy Like most other economies, Japan entered a recession in the first quarter of this year, with GDP contracting at a 2.2% annualized pace. For the private sector, this is the worst growth rate since the Fukushima crisis in 2011. This is particularly significant, since the structural growth rate of the economy has fallen below interest rates. Going back to Japan’s lost decades, where private sector GDP growth averaged well below nominal rates (due to the zero bound), it is particularly imperative that Japan exits this liquidity trap in fast order (Chart I-2). A strong yen back then, on the back of deficient domestic demand, led to a self-fulfilling deflationary spiral. Chart I-2The Story Of Japan In One Chart The Story Of Japan In One Chart The Story Of Japan In One Chart The Bank of Japan began to acknowledge this problem with the end of the Heisei era1  last year. For example, with the BoJ owning almost 50% of outstanding JGBs, the supply side puts a serious limitation on how much more stimulus the BoJ can provide. The yen has become extremely sensitive to shifts in the relative balance sheets between the Federal Reserve and the BoJ. If the BoJ continues to purchase securities at the current pace, then the rate of expansion in its balance sheet will severely lag behind the Fed, and could trigger a knee-jerk rally in the yen (Chart I-3). Chart I-3The Yen And QE The Yen And QE The Yen And QE Inflation And The 2% Target The US is a much more closed economy than Japan, and has not been able to maintain a 2% inflation rate since the Global Financial Crisis. This makes the BoJ’s target of 2% a pipe dream for any timeline in the near future. There are three key variables the authorities pay attention to for inflation: Core CPI, the GDP deflator and the output gap. All three indicators point towards deflationary pressures, with the recent slowdown in the global economy exacerbating the trend. In fact, since the financial crisis, prices in Japan have only been able to really rise during a tax hike (Chart I-4). Always forgotten is that the overarching theme for prices in Japan is a rapidly falling (and ageing) population, leading to deficient demand. The overarching theme for prices in Japan is a rapidly falling (and ageing) population, leading to deficient demand. More importantly, almost 50% of the Japanese consumption basket is in tradeable goods, meaning domestic inflation is as much driven by the influence of the BoJ as it is by globalization. Even for domestically-driven prices, an ageing demographic that has a strong preference for falling prices is a powerful conflicting force. For example, over the years, a strong voting lobby has been able to advocate for lower telecom prices, which makes it difficult for the BoJ to re-anchor inflation expectations upward (Chart I-5). Chart I-4Japan CPI At A Glance Japan CPI At A Glance Japan CPI At A Glance Chart I-5Strong Deflationary Pressures In Japan Strong Deflationary Pressures In Japan Strong Deflationary Pressures In Japan Meanwhile, the BoJ understands that it needs domestic banks to expand the credit intermediation process if any inflation is to take hold. Unfortunately, the yield curve control strategy and negative interest rates have been anathema for Japanese net interest margins and share prices (Chart I-6). This puts the BoJ in a precarious balance between trying to stimulate the economy further and biting the hand that will feed a pickup in inflation. Chart I-6Point Of No Return For Japanese Banks? Point Of No Return For Japanese Banks? Point Of No Return For Japanese Banks? Japanese Consumption And Fiscal Policy The consumption tax hike last year delivered a severe punch to aggregate demand in Japan. COVID-19 has dealt a fatal blow. In prior episodes of the tax hikes, it took around three to four quarters for growth to eventually bottom. This suggests that a protracted slowdown in Japanese consumption is a fait accompli (Chart I-7). Foreign and domestic machinery orders are slowing, employment growth has gone from over 2% to free fall and the availability of jobs relative to applicants has reversed a decade-long rising trend. The Abe government has passed an additional 117 trillion yen of fiscal stimulus. With overall fiscal announcements near 40% of GDP, could this fully plug the spending gap? Not quite. The consumption tax hike last year delivered a severe punch to aggregate demand in Japan.  First, as is usually the case with Japanese stimulus announcements, the timeframe is uncertain for when the funds will be deployed. It could be one year or ten years. Chart I-7A V-Shaped Recovery Might Stall A V-Shaped Recovery Might Stall A V-Shaped Recovery Might Stall Chart I-8More Jobs, More Savings More Jobs, More Savings More Jobs, More Savings Second, Japanese consumption has been quite weak for some time. Despite relatively robust economic conditions since the Fukushima disaster, Japanese consumption has trended downward. The reason is that government spending triggered a rise in private savings, because of expectations of higher taxes. In other words, the savings ratio for workers has surged. If consumers were not willing to spend prior to COVID-19 due to Ricardian equivalence,2  they are unlikely to do so with much higher fiscal deficits (Chart I-8). Some of the government’s outlays will certainly go a long way to boosting aggregate demand, since the fiscal multiplier tends to be much larger in a liquidity trap. This will especially be the case for increased social security spending such as child education, construction activity or the move towards promoting cashless transactions (with a tax rebate). However, there are important near-term offsets. In particular, the postponement of the Olympics will continue to be a drag on Japanese construction activity, and the labor (and income) dividend from immigration has practically vanished. The important tourism industry that faced sudden death will only recover slowly. This suggests a much more protracted recovery in many nuggets of Japanese activity. The Yen As A Safe Haven Real interest rates are already higher in Japan, well before any of the above factors began to meaningfully generate a deflationary impulse. As such, the starting point for yen long positions is already favorable (Chart I-9). Real interest rates are already higher in Japan, well before any of the above factors began to meaningfully generate a deflationary impulse. With global growth bottoming, a continued rise in global equity markets is a key risk to our scenario. However, if inflows into Japan accelerate on cheap equity valuations, the propensity of investors to hedge these purchases will be much less today, given how cheap the yen has become. This is especially important since in an era of rising budget deficits, balance of payments dynamics can resurface as the key driver of currencies. This suggests the negative yen/Nikkei correlation will continue to weaken, as has been the case in recent quarters. Chart I-9Real Rates And The Yen Real Rates And The Yen Real Rates And The Yen Chart I-10USD/JPY And DXY Are Positively Correlated USD/JPY And DXY Are Positively Correlated USD/JPY And DXY Are Positively Correlated As a low-beta currency, our contention is that the yen will surely weaken on its crosses, but could strengthen versus the dollar. The yen rises versus the dollar not only during recessions, but during most episodes of broad dollar weakness (Chart I-10). This places short USD/JPY trades in an envious “heads I win, tails I do not lose too much” position.   Chester Ntonifor Foreign Exchange Strategist chestern@bcaresearch.com Footnotes 1 The Heisei era refers to the period of Japanese history corresponding to the reign of Emperor Akihito from 8 January 8th, 1989 until his abdication on April 30th, 2019. 2 Ricardian equivalence suggests in simple terms that public sector dissaving will encourage private sector savings. Currencies U.S. Dollar Chart II-1USD Technicals 1 USD Technicals 1 USD Technicals 1 Chart II-2USD Technicals 2 USD Technicals 2 USD Technicals 2 Recent data in the US have been robust: Nonfarm payrolls increased by 2.5 million in May after declining by a record 20.7 million in April. This was better than expectations of an 8 million job loss. The unemployment rate fell from 14.7% to 13.3%. The NFIB business optimism index increased from 90.9 to 94.4 in May. Headline consumer price inflation fell from 0.3% to 0.1% year-on-year in May. Core inflation fell from 1.4% to 1.2%. Initial jobless claims increased by 1542K for the week ended June 5th. The DXY index fell by 1.3% this week. On Wednesday, the Fed left interest rates unchanged, with a signal that rates might not be increased before the end of 2022. The Fed also stated that it will maintain the current pace of Treasuries and mortgage-backed securities purchases, at minimum. Report Links: DXY: False Breakdown Or Cyclical Bear Market? - June 5, 2020 Cycles And The US Dollar - May 15, 2020 Capitulation? - April 3, 2020 The Euro Chart II-3EUR Technicals 1 EUR Technicals 1 EUR Technicals 1 Chart II-4EUR Technicals 2 EUR Technicals 2 EUR Technicals 2 Recent data in the euro area have been improving: The Sentix investor confidence index improved from -41.8 to -24.8 in June. Employment increased by 0.4% year-on-year in Q1. GDP contracted by 3.1% year-on-year in Q1. The euro appreciated by 1.2% against the US dollar this week. At an online seminar held this week, Isabel Schnabel, member of the executive board of the ECB, noted that "evidence is increasingly pointing towards a protracted impact of the crisis on both demand and supply conditions in the euro area and beyond" and that the current PEPP remains appropriate in de aling with the global recession. Report Links: On The DXY Breakout, Euro, And Swiss Franc - February 21, 2020 Updating Our Balance Of Payments Monitor - November 29, 2019 On Money Velocity, EUR/USD And Silver - October 11, 2019 Japanese Yen Chart II-5JPY Technicals 1 JPY Technicals 1 JPY Technicals 1 Chart II-6JPY Technicals 2 JPY Technicals 2 JPY Technicals 2 Recent data in Japan have been negative: The coincident index fell from 88.8 to 81.5 in April. The leading economic index also decreased from 85.1 to 76.2. The current account surplus shrank from ¥1971 billion to ¥262.7 billion in April. Annualized GDP fell by 2.2% year-on-year in Q1. Machine tool orders plunged by 52.8% year-on-year in May, following a 48.3% decrease the previous month. The Japanese yen appreciated by 2.6% against the US dollar this week. According to a Bloomberg survey, the majority of economists believe that the BoJ has done enough to cushion the economy, and expect the BoJ to leave current monetary policy unchanged next week. We continue to recommend the yen as a safe-haven hedge, especially given a possible second wave of COVID-19. Report Links: The Near-Term Bull Case For The Dollar - February 28, 2020 Building A Protector Currency Portfolio - February 7, 2020 Currency Market Signals From Gold, Equities And Flows - January 31, 2020 British Pound Chart II-7GBP Technicals 1 GBP Technicals 1 GBP Technicals 1 Chart II-8GBP Technicals 2 GBP Technicals 2 GBP Technicals 2 Recent data in the UK have been positive: Halifax house prices increased by 2.6% year-on-year in May. Retail sales surged by 7.9% year-on-year in May, up from 5.7% the previous month. GfK consumer confidence was little changed at -36 in May. The British pound rose by 1% against the US dollar this week. On Wednesday, BoE governor Andrew Bailey noted that easing lockdown restrictions has been fueling a recovery in the UK, which could be faster than previously anticipated. Our long GBP/USD and short EUR/GBP positions are 4% and 0.2% in the money, respectively. Report Links: Updating Our Balance Of Payments Monitor - November 29, 2019 A Few Trade Ideas - Sept. 27, 2019 United Kingdom: Cyclical Slowdown Or Structural Malaise? - Sept. 20, 2019 Australian Dollar Chart II-9AUD Technicals 1 AUD Technicals 1 AUD Technicals 1 Chart II-10AUD Technicals 2 AUD Technicals 2 AUD Technicals 2 Recent data in Australia have been mixed: The NAB business confidence index increased from -45 to -20 in May. The business conditions index also ticked up from -34 to -24. The Westpac consumer confidence index increased from 88.1 to 93.7 in June. Home loans declined by 4.8% month-on-month in April, down from a 0.3% increase the previous month. That said, expectations were for a fall of 10%. AUD/USD was flat this week. While the RBA has other options in its policy toolkit to combat the global recession, negative interest rates is still on the table and hasn't been totally ruled out. We remain positive on the Australian dollar both against the US dollar and the New Zealand dollar due to cheap valuations and increasing Chinese stimulus. Report Links: On AUD And CNY - January 17, 2020 Updating Our Balance Of Payments Monitor - November 29, 2019 A Contrarian View On The Australian Dollar - May 24, 2019 New Zealand Dollar Chart II-11NZD Technicals 1 NZD Technicals 1 NZD Technicals 1 Chart II-12NZD Technicals 2 NZD Technicals 2 NZD Technicals 2 Recent data in New Zealand have been mixed: Manufacturing sales declined by 1.7% quarter-on-quarter in Q1, down from a 2.8% increase the previous quarter. ANZ business confidence increased from -41.8 to -33 in June. The activity outlook index also ticked up from -38.7 to -29.1. The New Zealand dollar appreciated by 0.8% against the US dollar this week. RBNZ's Deputy Governor Geoff Bascand said that house prices in New Zealand could fall by 9-10% or even worse. Besides disrupting exports and imports for a trade-reliant country like New Zealand, the global health crisis is also likely to further reduce immigration to New Zealand, curbing housing demand. Report Links: Updating Our Balance Of Payments Monitor - November 29, 2019 Place A Limit Sell On DXY At 100 - November 15, 2019 USD/CNY And Market Turbulence - August 9, 2019 Canadian Dollar Chart II-13CAD Technicals 1 CAD Technicals 1 CAD Technicals 1 Chart II-14CAD Technicals 2 CAD Technicals 2 CAD Technicals 2 Recent data in Canada have been positive: The unemployment rate ticked up from 13% to 13.7% in May, versus expectations of a rise to 15%, but this was due to a  rise in the participation rate from 59.8% to 61.4%. Average hourly wages increased by 10% year-on-year in May. Net employment increased by 289.6K, up from a 1994K job loss the previous month. Housing starts increased by 193.5K in May, up from 166.5K the previous month.  The Canadian dollar fell by 0.2% against the US dollar this week. The labor market has seen some recovery in May with the gradual easing of COVID-19 restrictions and re-opening of the economy. Employment rebounded and absences from work dropped. Notably, Quebec accounts for nearly 80% of overall employment gains in May. Report Links: More On Competitive Devaluations, The CAD And The SEK - May 1, 2020 A New Paradigm For Petrocurrencies - April 10, 2020 The Loonie: Upside Versus The Dollar, But Downside At The Crosses Swiss Franc Chart II-15CHF Technicals 1 CHF Technicals 1 CHF Technicals 1 Chart II-16CHF Technicals 2 CHF Technicals 2 CHF Technicals 2 There was scant data out of Switzerland this week: FX reserves increased from CHF 801 billion to CHF 816 billion in May.  The unemployment rate increased from 3.1% to 3.4% in May, lower than the expected 3.7%. The Swiss franc appreciated by 2.3% against the US dollar this week, reflecting a flight back to safety amid concerns over political risks and a second wave of COVID-19. While the euro has been strong recently and EUR/CHF touched 1.09, the franc has lost most of those gains. We are lifting our limit buy on EUR/CHF to 1.055 on expectations we are in a run-of-the-mill correction.  Report Links: On The DXY Breakout, Euro, And Swiss Franc - February 21, 2020 Currency Market Signals From Gold, Equities And Flows - January 31, 2020 Portfolio Tweaks Before The Chinese New Year - January 24, 2020 Norwegian Krone Chart II-17NOK Technicals 1 NOK Technicals 1 NOK Technicals 1 Chart II-18NOK Technicals 2 NOK Technicals 2 NOK Technicals 2 Recent data in Norway have been mixed: Manufacturing output shrank by 1.6% month-on-month in April.  PPI fell by 17.5% year-on-year in May. Headline consumer prices increased by 1.3% year-on-year in May, up from 0.8% the previous month. Core inflation also increased from 2.8% to 3% in May. The Norwegian krone fell by 1.5% against the US dollar this week. The recent OPEC meeting over the weekend concluded that all members agreed to the extension to curb oil production. We believe that oil prices will continue to recover, and recommend to stay long the Norwegian krone. Report Links: A New Paradigm For Petrocurrencies - April 10, 2020 Building A Protector Currency Portfolio - February 7, 2020 On Oil, Growth And The Dollar - January 10, 2020 Swedish Krona Chart II-19SEK Technicals 1 SEK Technicals 1 SEK Technicals 1 Chart II-20SEK Technicals 2 SEK Technicals 2 SEK Technicals 2 Recent data in Sweden have been mixed: Household consumption plunged by 10% year-on-year in April. The current account surplus increased from SEK 43.2 billion to SEK 80.6 billion in Q1. Headline consumer prices recovered from a 0.4% year-on-year decline to flat in May. The Swedish krona increased by 0.6% against the US dollar this week. Sweden is benefitting economically from a less stringent Covid-19 agenda. With very cheap valuations, we remain short EUR/SEK and USD/SEK. Report Links: Updating Our Balance Of Payments Monitor - November 29, 2019 Where To Next For The US Dollar? - June 7, 2019 Balance Of Payments Across The G10 - February 15, 2019 Trades & Forecasts Forecast Summary Core Portfolio Tactical Trades Limit Orders Closed Trades

Please note that yesterday we published Special Report on Egypt recommending buying domestic bonds while hedging currency risk. Today we are enclosing analysis on Hungary, Poland and Colombia. I will present our latest thoughts on the global macro outlook and implications for EM during today’s webcast at 10 am EST. You can access the webcast by clicking here. Yours sincerely, Arthur Budaghyan Hungary Versus Poland: Mind The Reversal Conditions are set for the Hungarian forint to outperform the Polish zloty over the coming months. We recommend going long the HUF against the PLN. Hungarian opposition parties criticized the government about the considerable depreciation in the forint. As a result, we suspect that political pressure from Prime Minister Viktor Orban led monetary authorities to alter their stance since April. Critically, the main architect of super-dovish monetary policy Marton Nagy resigned from the board of the central bank on May 28. In line with tighter liquidity, interbank rates have risen above the policy rate. This is marginally positive for the forint. The Hungarian central bank (NBH) tweaked its monetary policy in April after the currency had plunged to new lows against the euro, underperforming its Central European counterparts. The NBH widened its policy rate corridor by hiking the upper interest band to 1.85% and keeping the policy rate at 0.90%. The wider interest rate corridor makes it more costly for commercial banks to borrow reserves from the central bank. Hence, such liquidity tightening is positive for the forint. For years, Hungary was pursuing a super-easy monetary policy and consumer price inflation rose to 4% (Chart I-1). With the NBH keeping interest rates close to zero, real rates have plunged well into negative territory (Chart I-2, top panel). Chart I-1Hungary: Inflation Could Pause For Now Hungary: Inflation Could Pause For Now Hungary: Inflation Could Pause For Now Chart I-2Hungary Vs. Poland: Real Rates Reversal Is Coming Hungary Vs. Poland: Real Rates Reversal Is Coming Hungary Vs. Poland: Real Rates Reversal Is Coming     In brief, the central bank has been behind the inflation curve. As a result, the forint has been depreciating against both the euro and its central European peers. In such a situation, the key to reversal in the exchange rate trend would be the monetary authority’s readiness to raise real interest rates. The NBH has made a small step in this direction. Going forward, the central bank will be restrained in its quantitative easing (QE) program and will not augment it any further. So far, QE uptake has been slow: around half out of the available HUF 1,500 billion has been tapped by commercial banks and corporates. Importantly, the NBH announced its intention to sterilize its government and corporate bond purchases. Already, the commercial banks excess reserves at the central bank have fallen to zero, which suggests that liquidity is no longer abundant in the banking system (Chart I-3). In line with tighter liquidity, interbank rates have risen above the policy rate. This is marginally positive for the forint. Hungarian authorities have become more cognizant of the economic and financial risks associated with their ultra-accommodative policies. For instance, they initiated a clampdown on real estate speculation, which is leading to dwindling real estate prices. This will lead to a decline in overall inflation expectations and, thereby, lift expected real interest rates. The open nature of Hungary’s economy – whereby exports of goods and services constitute 85% of GDP - makes it much more sensitive to pan-European tourism and manufacturing cycles. With the collapse in its manufacturing and tourism revenues, wage growth in Hungary is bound to decelerate rapidly (Chart I-4). Chart I-3Hungary: Central Bank Has Drained Liquidity Hungary: Central Bank Has Drained Liquidity Hungary: Central Bank Has Drained Liquidity Chart I-4Economic Growth: Hungary Is More Vulnerable Than Poland Economic Growth: Hungary Is More Vulnerable Than Poland Economic Growth: Hungary Is More Vulnerable Than Poland   Rapidly deteriorating wage and employment dynamics reduces the odds of an inflation breakout anytime soon. This will cool down inflation and, thereby, increase real rates on the margin. The central bank in Poland will stay super accommodative while the National Bank of Hungary will be a bit less aggressive. Bottom Line: Although this monetary policy adjustment does not entail the end of easy policy in Hungary, generally, it does signal restraint on the part of monetary authorities resulting from a much reduced tolerance for currency depreciation. This creates conditions for the forint to outperform. Poland In the meantime, Polish monetary authorities have switched into an ultra-accommodative mode. Recent policy announcements by the National Bank of Poland (NBP) represent the most dramatic example of policy easing in Central Europe. Such a policy stance in Poland will produce lower real rates than in Hungary, which is negative for the Polish zloty against the forint. The NBP is set to finance the majority of a new 11% of GDP fiscal spending program enacted by the government amid the COVID-19 lockdowns. This amounts to de-facto public debt and fiscal deficit monetization. The latter will not be sterilized unlike in Hungary and will therefore lead to an excess liquidity overflow in the banking system. The Polish central bank has cut interest rates by 140 bps to 10 bps since March. Pushing nominal rates down close to zero has produced more negative real policy rates than in Hungary (Chart I-2, top panel on page 2). Also, Polish prime lending rates in real terms have fallen below those in Hungary (Chart I-2, bottom panel). Chances are that inflation in Poland will also prove to be stickier than in Hungary due to the minimum wage raise at the beginning of the year and very aggressive fiscal and monetary stimulus since the pandemics has erupted (Chart I-5). Critically, the Polish economy is much less open than Hungary’s, and it is therefore less vulnerable to the collapse of pan-European manufacturing and tourism. This will ensure better employment and wage conditions in Poland. All in all, Poland’s final demand outperformance, versus Hungary, will contribute to a higher rate of inflation there. Bottom Line: The central bank in Poland will stay super accommodative while the National Bank of Hungary will be a bit less aggressive. This is producing a U-turn in both countries’ nominal and relative real interest rates, which heralds a reversal in the HUF / PLN cross rate (Chart I-6). Chart I-5Polish Inflation Will Be Sticker Than In Hungary Polish Inflation Will Be Sticker Than In Hungary Polish Inflation Will Be Sticker Than In Hungary Chart I-6Go Long HUF / Short PLN Go Long HUF / Short PLN Go Long HUF / Short PLN   Investment Strategy For Central Europe A new trade: go long the HUF versus the PLN. Take a 3% profit on the short HUF and PLN / long CZK trade. Close the short IDR / long PLN trade with a 20% loss. Downgrade central European bourses (Polish, Czech and Hungarian) from an overweight to a neutral allocation within the EM equity benchmark. Lower for longer European interest rates disfavor bank stocks that dominate central European bourses. Andrija Vesic Associate Editor andrijav@bcaresearch.com Colombia: Continue Betting On Lower Rates Colombia has been badly hit by two shocks: the precipitous fall in oil prices and the strict quarantine measures to constrain the spread of the COVID-19 outbreak. An underwhelming fiscal stimulus in response to the lockdowns will further weigh on private demand. An underwhelming fiscal stimulus in response to the lockdowns will further weigh on private demand. We have been recommending receiving 10-year swap rates in Colombia since April 23rd and this strategy remains unchanged: While oil prices seem to have rebounded sharply, they will remain structurally low (Chart II-1). The Emerging Markets Strategy team's view is that oil prices will average $40 per barrel this year and next.1 After the recent rally, chances of further upside in crude prices are limited. Chart II-1A Long-Term Perspective On Oil Prices A Long-Term Perspective On Oil Prices A Long-Term Perspective On Oil Prices Table II-1Colombia’s Fiscal Package Is The Lowest In The Region Hungary Versus Poland; Colombia Hungary Versus Poland; Colombia Colombia's high sensitivity to oil prices is particularly visible via its current account balance. Indeed, Colombia’s net crude exports cover as much as 50% of the current account deficit, such that low oil prices severely affect the currency and produce a negative income shock for the economy. Fiscal policy remains unreasonably tight, especially in the face of the global pandemic. The government’s fiscal response plan amounts to only a meagre 1.5% of GDP. This is low not only compared to advanced economies but also to the rest of Latin America (Table II-1). Moreover, President Duque’s administration has been running the tightest fiscal budget in almost a decade, with the primary fiscal balance reaching 1% of GDP before the pandemic. The country’s COVID-19 response has been fast and effective. Colombia has managed to achieve the lowest amount of infections and deaths among major economies in Latin America (Chart II-2). Chart II-2COVID-19 Casualties Across Latin America COVID-19 Casualties Across Latin America COVID-19 Casualties Across Latin America Duque’s administration has taken a pragmatic approach to handling the pandemic by enforcing strict lockdowns and banning international and inter-municipal travel since late March, only three days after the country’s first casualty. Further, the nationwide confinement measures have been extended until July 1st, with particularly stringent rules applying to major cities. These have helped the country avoid a nation-wide health crisis, but they will engender prolonged economic pain. Regarding monetary stimulus, the central bank (Banrep) has cut interest rates by 150 basis points since March of this year. It also embarked on the first and largest QE program in the region. Banrep has committed to purchase 12 trillion pesos worth of government and corporate securities (amounting to a whopping 8% of GDP). Consumer price inflation is falling across various core measures and will drop below the low end of Banrep’s target range (Chart II-3). This will push the central bank to continue cutting rates. Despite the monetary easing, nominal lending rates are still restrictive. Real lending rates (deflated by core CPI) remain elevated at 7% (Chart II-4). Chart II-3Colombia: Inflation Will Fall Below Target Colombia: Inflation Will Fall Below Target Colombia: Inflation Will Fall Below Target Chart II-4Colombia: Real Lending Rates Are Still High Colombia: Real Lending Rates Are Still High Colombia: Real Lending Rates Are Still High Chart II-5The Colombian Economy Was Already Under Pressure The Colombian Economy Was Already Under Pressure The Colombian Economy Was Already Under Pressure Importantly, there has not been an appropriate amount of credit support and debt waving programs for SMEs, as there has been in many other countries. Given that SMEs employ a large share of the workforce, and that household spending accounts for about 70% of GDP, consumer spending and overall economic growth will contract substantially and be slow to recover. Employment rates had already been contracting, and wage growth downshifting, before the pandemic started (Chart II-5). Household income is now certainly in decline as major cities are in full lockdown and economic activity is frozen. Investment Recommendations Even though we are structurally positive on the country due to its orthodox macroeconomic policies, positive structural reforms, and low levels of debt among both households and companies, we maintain a neutral allocation on Colombian stocks within an EM equity portfolio. This bourse is dominated by banks and energy stocks. The lack of both fiscal support and bank loan guarantees amid the recession means that banks will carry the burden of ultimate losses. They will suffer materially due to loan restructuring and defaults. For fixed income investors, we reiterate our call to receive 10-year swap rates and recommend overweighting local currency government bonds versus the EM domestic bond benchmark. The yield curve is steep and real bond yields are elevated (Chart II-6). Hence, long-term interest rates offer great value. Additional monetary easing, including quantitative easing, will suppress yields much further. Chart II-6A Great Opportunity In Colombian Rates A Great Opportunity In Colombian Rates A Great Opportunity In Colombian Rates Chart II-7The COP Has Depreciated Considerably The COP Has Depreciated Considerably The COP Has Depreciated Considerably   We are upgrading Colombia sovereign credit from neutral to overweight within an EM credit portfolio. General public debt (including the central and state governments) stands at 59% of GDP. Conservative fiscal policy and the central bank’s large purchases of local bonds will allow the government to finance itself locally. Presently, 40% of public debt is foreign currency and 60% local currency denominated. As a result, sovereign credit will outperform the EM credit benchmark. In terms of the currency, we recommend investors to be cautious for now. Even though the peso is cheap (Chart II-7), another relapse in oil prices or a potential flare up in social protests could cause further downfall in the currency. Juan Egaña Research Associate juane@bcaresearch.com   1 This differs from the view of BCA’s Commodities and Energy Strategy service. We believe structural forces such as the lasting decline in air travel and commuting will impede a recovery in oil demand while, at the same time, US shale production will rise again considerably if crude prices rise and remain well above $40   Equities Recommendations Currencies, Credit And Fixed-Income Recommendations

  In a webcast this Friday I will be joined by our Chief US Equity Strategist, Anastasios Avgeriou to debate ‘Sectors To Own, And Sectors To Avoid In The Post-Covid World’. Today’s report preludes five of the points that we will debate. Please join us for the full discussion and conclusions on Friday, June 12, at 8:00 AM EDT (1:00 PM BST, 2:00 PM CEST, 8.00 PM HKT).   Highlights Technology is behaving like a Defensive. Defensive versus Cyclical = Growth versus Value. Growth stocks are not a bubble if bond yields stay ultra-low. The post-Covid world will reinforce existing sector mega-trends. Sectors are driving regional and country relative performance. Fractal trade: Long ZAR/CLP.   Chart of the WeekSector Defensiveness/Cyclicality = Positive/Negative Sensitivity To The Bond Price Sector Defensiveness/Cyclicality = Positive/Negative Sensitivity To The Bond Price Sector Defensiveness/Cyclicality = Positive/Negative Sensitivity To The Bond Price 1. Technology Is Behaving Like A Defensive How do we judge an equity sector’s sensitivity to the post-Covid economy, so that we can define it as cyclical or defensive? One approach is to compare the sector’s relative performance with the bond price. According to this approach, the more negatively sensitive to the bond price, the more cyclical is the sector. And the more positively sensitive to the bond price, the more defensive is the sector (Chart I-1).   On this basis the most cyclical sectors in the post-Covid economy are, unsurprisingly: energy, banks, and materials. Healthcare is unsurprisingly defensive. Meanwhile, the industrials sector sits closest to neutral between cyclical and defensive, showing the least sensitivity to the bond price. The tech sector’s vulnerability to economic cyclicality appears to have greatly reduced. The big surprise is technology, whose high positive sensitivity to the bond price during the 2020 crisis qualifies it as even more defensive than healthcare. This contrasts sharply with its behaviour during the 2008 crisis. Back then, tech’s relative performance was negatively correlated with the bond price, defining it as classically cyclical. But over the past year, tech’s relative performance has been positively correlated with the bond price, defining it as classically defensive (Chart I-2 and Chart I-3). Chart I-2In 2008, Tech Behaved Like ##br##A Cyclical... In 2008, Tech Behaved Like A Cyclical... In 2008, Tech Behaved Like A Cyclical... Chart I-3...But In 2020, Tech Is Behaving Like A Defensive ...But In 2020, Tech Is Behaving Like A Defensive ...But In 2020, Tech Is Behaving Like A Defensive This is not to say that the big tech companies cannot suffer shocks. They can. For example, from new superior technologies, or from anti-oligopoly legislation. However, the tech sector’s vulnerability to economic cyclicality appears to have greatly reduced over the past decade. 2. Defensive Versus Cyclical = Growth Versus Value If we reclassify the tech sector as defensive in the 2020s economy, then the post mid-March rebound in stocks was first led by defensives. Cyclicals took over leadership of the rally only in May. Moreover, with the reclassification of tech as defensive, the two dominant defensive sectors become tech and healthcare. But tech and healthcare are also the dominant ‘growth’ sectors. The upshot is that growth versus value has now become precisely the same decision as defensive versus cyclical (Chart I-4). Chart I-4Defensive Versus Cyclical = Growth Versus Value Defensive Versus Cyclical = Growth Versus Value Defensive Versus Cyclical = Growth Versus Value 3. Growth Stocks Are Not A Bubble If Bond Yields Stay Ultra-Low Some people fear that growth stocks have become dangerously overvalued. There is even mention of the B-word. Let’s address these fears. Yes, valuations have become richer. For example, the forward earnings yield for healthcare is down to 5 percent; and for big tech it is down to just over 4 percent. This valuation starting point has proved to be an excellent guide to prospective 10-year returns, and now implies an expected annualised return from big tech in the mid-single digits. Yet this modest positive return is well above the extremes of the negative 10-year returns implied and delivered from the dot com bubble (Chart I-5). Chart I-5Big Tech Is Priced To Deliver A Positive Return, Unlike In 2000 Big Tech Is Priced To Deliver A Positive Return, Unlike In 2000 Big Tech Is Priced To Deliver A Positive Return, Unlike In 2000 Moreover, we must judge the implied returns from growth stocks against those available from competing long-duration assets – specifically, against the benchmark of high-quality government bond yields. If bond yields are ultra-low, then they must depress the implied returns on growth stocks too. Meaning higher absolute valuations (Chart I-6 and Chart I-7). Chart I-6Tech's Forward Earnings Yield Is Above The Bond Yield, Unlike In 2000 Tech's Forward Earnings Yield Is Above The Bond Yield, Unlike In 2000 Tech's Forward Earnings Yield Is Above The Bond Yield, Unlike In 2000 Chart I-7Healthcare's Forward Earnings Yield Is Above The Bond Yield, Unlike In 2000 Healthcare's Forward Earnings Yield Is Above The Bond Yield, Unlike In 2000 Healthcare's Forward Earnings Yield Is Above The Bond Yield, Unlike In 2000 In the real bubble of 2000, big tech was priced to return 12 percent (per annum) less than the 10-year T-bond. Whereas today, the implied return from big tech – though low in absolute terms – is above the ultra-low yield on the 10-year T-bond. If bond yields are ultra-low, then they must depress the implied returns on growth stocks too. The upshot is that high absolute valuations of growth stocks are contingent on bond yields remaining at ultra-low levels. And that the biggest threat to growth stock valuations would be a sustained rise in bond yields. 4. The Post-Covid World Will Reinforce Existing Sector Mega-Trends If a sector maintains a structural uptrend in sales and profits, then a big drop in the share price provides an excellent buying opportunity for long-term investors. This is because the lower share price stretches the elastic between the price and the up-trending profits, resulting in an eventual catch-up. However, if sales and profits are in terminal decline, then the sell-off is not a buying opportunity other than on a tactical basis. This is because the elastic will lose its tension as profits drift down towards the lower price. In fact, despite the sell-off, if the profit downtrend continues, the price may be forced ultimately to catch-down. This leads to a somewhat counterintuitive conclusion. After a big drop in the stock market, long-term investors should not buy everything that has dropped. And they should not buy the stocks and sectors that have dropped the most if their profits are in major downtrends. In this regard, the post-Covid world is likely to reinforce the existing mega-trends. The profits of oil and gas, and of European banks will remain in major structural downtrends (Chart I-8 and Chart I-9). Conversely, the profits of healthcare, and of European personal products will remain in major structural uptrends (Chart I-10 and Chart I-11). Chart I-8Oil And Gas Profits In A Major ##br##Downtrend Oil And Gas Profits In A Major Downtrend Oil And Gas Profits In A Major Downtrend Chart I-9Bank Profits In A Major ##br##Downtrend European Banks Profits In A Major Downtrend Bank Profits In A Major Downtrend European Banks Profits In A Major Downtrend Bank Profits In A Major Downtrend Chart I-10Healthcare Profits In A Major Uptrend Healthcare Profits In A Major Uptrend Healthcare Profits In A Major Uptrend Chart I-11Personal Products Profits In A Major Uptrend Personal Products Profits In A Major Uptrend Personal Products Profits In A Major Uptrend   5. Sectors Are Driving Regional And Country Relative Performance Finally, sector winners and losers determine regional and country equity market winners and losers. Nowadays, a stock market’s relative performance is predominantly a play on its distinguishing overweight and underweight ‘sector fingerprint’. This is because major stock markets are dominated by multinational corporations which are plays on their global sectors, rather than the region or country in which they have a stock market listing. It follows that when tech and healthcare outperform, the tech-heavy and healthcare-heavy US stock market must outperform, while healthcare-lite emerging markets (EM) must underperform. It also follows that the tech-heavy Netherlands and healthcare-heavy Denmark stock markets must outperform. Sector mega-trends will shape the mega-trends in regional and country relative performance. Equally, when energy and banks underperform, the energy-heavy Norway and bank-heavy Spain stock markets must underperform. (Chart I-12 and Chart I-13). These are just a few examples. Every stock market is defined by a sector fingerprint which drives its relative performance.  Chart I-12Sector Relative Performance Drives... Sector Relative Performance Drives... Sector Relative Performance Drives... Chart I-13...Regional And Country Relative Performance ...Regional And Country Relative Performance ...Regional And Country Relative Performance If sector mega-trends continue, they will also shape the mega-trends in regional and country relative performance – favouring those stock markets that are heavy in growth stocks and light in old-fashioned cyclicals. Please join the webcast to hear the full debate and conclusions. Fractal Trading System*  This week’s recommended trade is to go long the South African rand versus the Chilean peso. Set the profit target and symmetrical stop-loss at 5 percent. In other trades, long Spanish 10-year bonds versus New Zealand 10-year bonds achieved its 3.5 percent profit target at which it was closed. And long Australia versus New Zealand equities is approaching its 12 percent profit target. The rolling 1-year win ratio now stands at 63 percent. Chart I-14ZAR/CLP ZAR/CLP ZAR/CLP   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. * 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.   Dhaval Joshi Chief European Investment Strategist dhaval@bcaresearch.com Fractal Trading System   Cyclical Recommendations Structural Recommendations 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 - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-6Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-7Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-8Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations  

Highlights Volatility strategies are a useful tool for asset allocators. They can be used for both alpha generation and risk mitigation, but they have to be managed properly within a fund’s total risk management framework. Dedicated tail-risk hedging can reduce volatility, but can be very costly depending on the holding period. Short volatility strategies can generate alpha, but can also incur large losses when volatility spikes. Long volatility and also relative-value volatility strategies are much better alpha generators. A simple and easy-to-implement rule-based dynamic hedging strategy using short-term VIX futures reduces equity portfolio risk significantly without sacrificing return. The Sensational Headlines The COVID-19 pandemic-induced financial market volatility has put two major pension funds in the proverbial spotlight. First, CalPERS was questioned about its October 2019 decision to unwind its tail-risk hedging program that would have generated a payoff of more than US$1 billion during the March equity market selloff.1 Then, AIMCo was said to have lost over C$3 billion in its short volatility program, and was also forced to shut the program down.2 With such high-profile stories making the rounds, it is not surprising that we have received questions about tail-risk hedging and volatility strategies from many clients: Should long-term investors hedge tail risk? Is short volatility not a suitable strategy for pension funds? What are the efficient ways to manage large drawdowns? Chart 1The High Profile Failures: Not Uncommon The High Profile Failures: Not Uncommon The High Profile Failures: Not Uncommon Before we attempt to answer these questions, we want to first point out that tail-risk hedging and short-volatility strategies are negatively correlated, as shown in Chart 1, panel 1. It is normal for short-volatility strategies to suffer large drawdowns when tail-hedging strategies make handsome gains in periods of extreme financial market stress. This is largely due to the nature of volatility. As shown in panel 2 in Chart 1, VIX futures curves are normally in contango (the far-month contract is higher than the near-month contract), so a plain-vanilla short position in VIX futures benefits from positive rolling yields, while a plain-vanilla long position suffers from negative rolling yields. When VIX spikes, however, the futures curve turns into large backwardation (the far-month contract is lower than the near-month contract) in a fast and furious fashion, hence the large insurance-like payoff. The short-volatility and tail-hedge indexes in Chart 1 are from CBOE Eurekahedge, which has a suite of volatility indexes. As shown in Table 1, these indexes track the average performance of hedge funds that employ various volatility strategies, including tail-risk volatility, long volatility, short volatility and relative-value volatility. Table 1CBOE Eurekahedge Volatility Hedge Fund Indexes* Demystifying Tail-Risk Hedging And Volatility Strategies Demystifying Tail-Risk Hedging And Volatility Strategies   The performance statistics of these indexes are shown in Table 2. It is clear that not all volatility strategies are created equal. Below, we explore in more detail how these strategies should be used. Table 2CBOE Eurekahedge Volatility Index Performance Statistics Demystifying Tail-Risk Hedging And Volatility Strategies Demystifying Tail-Risk Hedging And Volatility Strategies Tail-Risk Hedging Is Not Free Tail-risk hedging has been in the news of late, given the unprecedently sharp drop in equities in February and March and also the untimely decision by CalPERS to unwind its tail-risk hedging program last October. So, what is tail-risk-hedging exactly? How does it work? Tail-risk hedging strategies aim to profit from large drawdowns in risky assets. Unlike the traditional approach of diversification that reduces the weighting of risky assets (for example, a 60-40 equity-bond portfolio is less risky than a 100% equity portfolio), tail-risk hedging attempts to allocate a small percentage of capital, say 3-5%, to a specially designed insurance-like payoff, while maintaining exposure to the risky asset. As such, tail-risk hedging is like buying an insurance policy against a catastrophic event. The premiums paid may or may not be recouped, depending on how likely it is that a catastrophic event may occur and how long one has held the insurance policy. The Universa Tail Fund is one of the two tail-risk funds that CalPERS made the untimely decision to redeem. The fund returned 3,600% in March alone, and 4,440% in the first quarter of 2020. As well, according to reports, a portfolio with 96.7% in the S&P 500 and 3.3% in Universa’s tail-risk fund would effectively have mitigated the S&P 500’s large loss in March, and would have also produced a compounded return of 11.5% since March 2008 versus 7.9% for the S&P 500.3  The performance of the Universa Tail Fund seems to be very different from the average hedge fund in this category, as shown in Table 2 and Chart 1. The CBOE Eurekahedge Tail Risk Hedge Fund index is an average of eight hedge funds that employ tail-risk strategies to achieve capital appreciation during periods of market stress. Since December 2007, when the index started, it has had two outsized monthly gains: 37.5% in March 2020 and 27.5% in August 2011, when MSCI US equities lost 12.7 and 5.5%. However, such benefit is very costly from a long-term perspective because the index has generated an annualized loss of 2.5%, even through April 2020. Its arithmetic average during the period is about -1.6%. To better understand why Universa has been doing so much better than the “average” tail risk hedge fund, we replicate a stylized exercise by Universa published in October 2017.4 The only difference is that we use the MSCI US equity total return index instead of the S&P 500 index. The payoff structure of 9 to 1 means that when the MSCI US calendar year return is less than -15%, the hedge would generate a return of 900%. In other years, insurance premium is not recouped at all, i.e. there is a loss of 100%. The original exercise by Universa designed such a payoff structure because it aimed to have an average payoff of zero in the period from 1996 to 2016. As shown in Chart 2, the biggest advantage of the tail-hedged portfolio (97% MSCI US + 3% Insurance) is its much smoother return stream, with a standard deviation of 12.9% compared to 17.7% for the unhedged MSCI US equity portfolio based on calendar year returns from 1970 to 2020 (as of March for 2020). Also, the skew is improved to -0.1 from -0.7. In terms of return, however, it is highly variable depending on the period chosen. The hedged portfolio outperformed the MSCI US total return index by about 70 basis points annualized from 1996 to 2016, consistent with the result from the original exercise by Universa.5 Outside this period, however, the average return of the payoff stream really depends on how often US equities fall below -15% yearly. In the 50-year period from December 1969 to December 2019, the average return of the insurance payoff was -20%, and the tail-hedged program underperformed MSCI US by 26 basis points annualized. Chart 2Universa Exercise Replica* For 12/1969 - 3/2020 Demystifying Tail-Risk Hedging And Volatility Strategies Demystifying Tail-Risk Hedging And Volatility Strategies This simple stylized exercise shows that both the starting point to initiate the tail-risk hedge and the length of time to hold the hedge are very important for a tail-risk hedge to work, not to mention generate spectacular results. Like a catastrophic insurance policy, a tail hedge should not be considered as a stand-alone strategy but as a hedge to the underlying portfolio. It is critical to design the right payoff structure, which in turn requires a view on how often a large drawdown will likely happen in the forecast period. It also takes special skill to find the right instruments to implement such a payoff structure and manage it accordingly. As we will show in the section on page 9, a dynamic approach is needed to ensure the hedge is on only when it’s needed to reduce cost. In fact, Universa did mention about using extreme valuation as one indicator to identify periods with high likelihood of downside risks.6 It also locked in a massive gain in March 2020,7 another indication of the “dynamic nature” of tail-hedging management. Bottom Line: From a long-term perspective, tail-risk hedge does not significantly improve compound returns, but it does reduce volatility significantly. Unless an investor has the skill to dynamically manage a hedge program, passively holding a tail-risk hedge can be costly in terms of return, even though it does improve risk-adjusted returns. Is A Short-Volatility Strategy Suitable For Pension Funds? The CBOE Eurekhedge Short Volatility index lost 20.8% in the first four months of 2020, in which March was the worst month in its history since December 2004, with a loss of 15.8%, while April was the best month with a gain of 9.3%. The annualized return since December 2004, however, has been 5.4%, and 73% of monthly returns have been in positive territory (Table 2). On the other hand, AIMCo had to shut down its volatility trading program in March because of its large $3 billion loss, or about 2.5% of its $119 billion of AUM. It is not known why a small volatility program was allowed to lose more than the fund’s total full-year value-add target. Chart 3Volatility Measures: Implied Vs. Realized VOLATILITIES: IMPILED VS REALIZED VOLATILITIES: IMPILED VS REALIZED There are different ways to short volatility. One is to sell options on the underlying assets. This approach, however, is also impacted by the price level of the underlying assets. VIX futures, as shown in Chart 1, panel 2, are a way to bet on the change in implied volatility. Another way to short volatility is via variance swaps, which bet on the change between realized variance at the expiry of the swap and the strike variance, which is set according to both historical variance and implied variance.8 Because variance is the square of volatility, the payoff of a variance swap is convex, i.e. when volatility spikes up, a short seller loses more money than when volatility decreases. As shown in Chart 3, VIX, the implied volatility, peaked on March 16, and realized volatility peaked on March 27. However, the difference between realized and implied volatility did not peak until April 6, and remained positive through the end of April. As such, a short volatility program via variance swaps would have experienced severe mark-to-market losses daily from mid-March to early April, even though equities bottomed on March 23.   However, such a spike happened in 2008 as well. Any back-test would have included such an occurrence in 2008. Granted, the magnitude of the current spike is larger than that in 2008, but it reversed quickly down to the 2008 level. We may never know why AIMCo’s short volatility program suffered such outsized losses. The only guess is that it may have used variance swaps, and the embedded leverage made the size of the program not appropriate for the total fund. Bottom Line: Short volatility can be a useful tool for alpha generation. The key, however, is risk management. It should be properly sized within the overall risk management framework of the total fund. Volatility As An Asset Class? Tail-risk hedging using volatility is too costly in general, while shorting volatility outright can be disastrous. Some argue that investors should not have anything to do with volatility strategies. On the other hand, other investors treat volatility as an asset class for both alpha generation and risk mitigation. Chart 4 shows the CBOE Eurekahedge Relative-Value Volatility index and the Long-Volatility index together with the MSCI US equity index, and Bloomberg Barclays US aggregate bond index and US Treasury index. The relative-value volatility index can be long, short, or neutral on volatility (Table 1). As shown in Table 2, it has achieved an annualized return of 7.6%, only 60 basis points less than MSCI US equity return of 8.2%, but much higher than the 4.3% and 4.5% respective return from Bloomberg Barclays US Treasury index and aggregate bond index in the period from December 2004 to April 2020. Its standard deviation of 3.9% is much lower than the MSCI US (14.7%) and very close to Treasurys (4.1%) and aggregate bonds (3.2%). For this specific period, in fact, this index even has a much better risk-return profile than a typical 60/40 US equity/aggregate-bond portfolio, which scores a 7.1% annualized return with 8.9% standard deviation. With almost zero correlation to both stocks and bonds, this index serves as an ideal addition to a balanced equity-bond portfolio (Chart 5). Chart 4Volatility As An Asset Class VOLATILITY AS AN ASSET CLASS VOLATILITY AS AN ASSET CLASS Chart 5Relative-Value Vol Strategy Improves The Performance Of A 60/40 Equity/Bond Portfolio Demystifying Tail-Risk Hedging And Volatility Strategies Demystifying Tail-Risk Hedging And Volatility Strategies The challenge, however, is that this index is an average of 35 hedge funds that employ relative-value or opportunistic-volatility strategies that can be long, short, or neutral on implied volatility.9 Because of this, capacity constraints for investors to get into those funds may exist, which could produce diverging performances. Even the long-volatility strategy (Chart 4, panel 2), which in theory suffers negative rolling yields when the VIX is in a normal range, has generated a 5% annualized return. It has a negative correlation of 0.46 with MSCI US equities, comparable to the negative correlation of 0.5 between the Tail-Risk index and MSCI US. Given the much better statistics of this index compared to the Tail-Risk index, it should be a less costly alternative to the Tail-Risk Hedge index (Table 2). To illustrate how these two strategies work to mitigate downside risk in the MSCI US equities, we compare a series of portfolios that allocate from 0-100% of capital to MSCI US and 100-0% to the two volatility strategies, respectively. As shown in Chart 6, the long-volatility strategy is a much better risk mitigator to the MSCI US equities index than the tail-hedge strategy at all levels of allocations for the period from January 2008 to April 2020.  Chart 6Risk Mitigation Using Long Vol Vs. Tail-Risk Hedge Demystifying Tail-Risk Hedging And Volatility Strategies Demystifying Tail-Risk Hedging And Volatility Strategies Dynamic Hedging Using VIX Futures The CBOE Eurekahedge volatility indexes are based on average returns of the funds in each index. They are not investable. Also, hedge funds in these indexes may have capacity issues to accommodate large investors. In this section we run a simple rule-based hedging strategy using VIX futures to illustrate how investors can use volatility strategies in-house as an alternative tool to mitigate risk. We use the S&P VIX short-term futures index for this exercise, because it can be easily replicated in-house. This index is constructed based on rolling daily 5% of the front-month contract to the second-month contract. This means the index always has one month to expiry. It also means that daily rolling averages out the rolling yield for any given month.  The rule is simple: invest in the short-term volatility futures only when the VIX is outside its normal range. Since its inception in 1990, the VIX average is about 20. To test how different thresholds and rebalancing frequencies work, we test four different VIX thresholds: 25, 30, 35 and 40 with both weekly and monthly rebalances. The rebalance rule is: if the VIX is greater than a threshold at the end of one period, then in the next period, 5% of the fund is allocated to the S&P short-term VIX futures index and 95% is allocated to MSCI US. Otherwise 100% goes to MSCI US equities. For comparison, we also run a static hedge that has 5% in VIX futures and 95% in the MSCI US index.  The monthly rebalanced results are quite interesting, as shown in Table 3 and Chart 7: Table 3Dynamic Hedging Using VIX Futures Demystifying Tail-Risk Hedging And Volatility Strategies Demystifying Tail-Risk Hedging And Volatility Strategies Chart 7Dynamic Hedging Works DYNAMIC HEDGING WITH VIX FUTURES DYNAMIC HEDGING WITH VIX FUTURES Despite a terrible risk-return profile on its own, VIX futures can be a good risk mitigator when the hedge is put on only when the VIX is above a certain threshold.  Even though the 60-40 wins in terms of risk-adjusted return, dynamically hedged portfolios have better returns than both the 60-40 and US equities. The results are also robust when we do a weekly rebalance. Three conclusions can be drawn from Charts 8A and 8B, and Chart 9: Chart 8ADynamic Hedging – Monthly Rebalance DYNAMIC HEDGE-MONTHLY REBALNCE DYNAMIC HEDGE-MONTHLY REBALNCE Chart 8BDynamic Hedging – Weekly Rebalance DYNAMIC HEDGE-WEEKLY REBALNCE DYNAMIC HEDGE-WEEKLY REBALNCE     Chart 9Simple But Robust Dynamic Hedging Demystifying Tail-Risk Hedging And Volatility Strategies Demystifying Tail-Risk Hedging And Volatility Strategies   Hedging reduces volatility significantly. The lower the VIX threshold is, the larger the volatility reduction in the hedged portfolio compared to the unhedged. Hedging also improves average returns, albeit at a smaller scale compared to the reductions in volatility. Depending on the rebalancing frequency, the return improvement differs. For the monthly rebalance, the best VIX threshold lies between 30-35; for the weekly rebalance, the best is when the VIX threshold is at 30. Hedging is not needed all the time because volatility is within a normal range most of the time. Even when it spikes, it does not stay high for an extended period of time. Bottom Line: A simple rule-based dynamic hedging approach using VIX futures can substantially improve an equity portfolio’s risk-return profile by decreasing volatility significantly without sacrificing return. In a low interest rate environment, dynamic hedging using VIX futures can be a good alternative to a 60-40 equity-bond mix.   Xiaoli Tang Associate Vice President xiaoliT@bcaresearch.com   Footnotes 1  https://www.institutionalinvestor.com/article/b1l65mvpw5xpts/The-Inside-Story-of-CalPERS-Untimely-Tail-Hedge-Unwind 2 https://www.institutionalinvestor.com/article/b1l9c8n9lgdj1r/AIMCo-s-3-Billion-Volatility-Trading-Blunder 3 https://www.bloomberg.com/news/articles/2020-04-08/taleb-advised-universa-tail-risk-fund-returned-3-600-in-march 4 https://www.universa.net/UniversaResearch_SafeHavenPart1_RiskMitigation.pdf 5  https://www.universa.net/UniversaResearch_SafeHavenPart1_RiskMitigation.pdf 6 https://www.universa.net/UniversaResearch_SafeHavenPart2_NotAllRisk.pdf 7 https://www.bloomberg.com/news/articles/2020-04-08/taleb-advised-universa-tail-risk-fund-returned-3-600-in-march 8  https://en.wikipedia.org/wiki/Variance_swap 9 https://www.eurekahedge.com/Indices/CBOE-Eurekahedge-Volatility-Indexes-Methodology  

Highlights Social distancing must persist to prevent dangerous super-spreading of COVID-19. The jobs recovery will be much weaker than the output recovery, because the sectors most hurt by social distancing have a very high labour intensity. This will force a prolonged period of ultra-accommodative monetary policy… …structurally favour T-bonds and Bonos over Bunds and OATs… …growth defensives such as tech and healthcare… …and the S&P 500 over the Euro Stoxx 50. Stay overweight Animal Care (PAWZ). Working from home has generated a puppy boom. Fractal trade: short gold, long lead. Feature As economies reopen, economists and strategists are quibbling about the shape of the output recovery: U, V, W, square root, or even ‘swoosh’. But for the furloughed or displaced worker, the more urgent question is, what will be the shape of the jobs recovery? Unfortunately, the jobs recovery will be much weaker than the output recovery – because the sectors most hurt by social distancing have a very high labour intensity (Chart Of The Week). Chart Of The Week 1ALeisure And Hospitality Makes A Large Contribution To Jobs Relative To Output A Jobless V-Shape Recovery, And A Puppy Boom A Jobless V-Shape Recovery, And A Puppy Boom Chart Of The Week 1BFinance Makes A Small Contribution To Jobs Relative To Output A Jobless V-Shape Recovery, And A Puppy Boom A Jobless V-Shape Recovery, And A Puppy Boom Output Might Snap Back, But Jobs Will Not The sectors most hurt by social distancing make a huge contribution to employment but a much smaller contribution to economic output. This is true for Europe and all advanced economies, though the following uses US data given its superior granularity and timeliness. The leisure and hospitality sector generates 11 percent of jobs, but just 4 percent of output. Retail trade generates 10 percent of jobs, but just 5 percent of output. It follows that if both sectors are operating at half their pre-coronavirus capacity, output will be down by 4.5 percent, but employment will collapse by 10.5 percent. Conversely, sectors which are relatively unaffected by social distancing make a small contribution to employment but a much bigger contribution to economic output. Financial activities generate just 6 percent of jobs, but 19 percent of economic output. Information technology generates just 2 percent of jobs, but 5 percent of output (Table I-1). Table I-1Sectors Hurt By Social Distancing Have A Very High Labour Intensity A Jobless V-Shape Recovery, And A Puppy Boom A Jobless V-Shape Recovery, And A Puppy Boom If economies are reopened but social distancing persists – either via government policy or personal choice – then output can rebound in a V-shape, but employment cannot (Chart I-2). Forcing a prolonged period of ultra-accommodative monetary policy, with all its ramifications for financial markets. Chart I-2UK Unemployment Is Set To Surge If The US Is Any Guide UK Unemployment Is Set To Surge If The US Is Any Guide UK Unemployment Is Set To Surge If The US Is Any Guide This raises a key question. Must social distancing persist? To answer, we need to pull together our latest understanding of COVID-19. COVID-19: What We Know So Far Many people argue that coronavirus fears are disproportionate. The mortality rate seems comfortingly low, at well below 0.5 percent (Chart 3). Yet this argument misses the point. Chart I-3The COVID-19 Mortality Rate Is Not High A Jobless V-Shape Recovery, And A Puppy Boom A Jobless V-Shape Recovery, And A Puppy Boom COVID-19 is dangerous not because it kills, but because it makes a lot of people seriously ill. It has a low mortality rate, but a high morbidity rate. According to the World Health Organisation, around one in six that gets infected “develops difficulty in breathing”. Moreover, The Lancet points out that many recovered COVID-19 patients suffer pulmonary fibrosis, a permanent scarring of the lungs that impairs their breathing for the rest of their lives. Hence, while COVID-19 is highly unlikely to kill you, it could damage your health forever1 (Figure I-1). Figure 1COVID-19 Is Unlikely To Kill You, But It Could Permanently Damage Your Lungs A Jobless V-Shape Recovery, And A Puppy Boom A Jobless V-Shape Recovery, And A Puppy Boom The most famous COVID-19 victim to date is British Prime Minister Boris Johnson who spent several days recovering in intensive care. By his own admission, Johnson’s only pre-existing conditions are that he is overweight and “drinks an awful lot”. But those pre-existing conditions could apply to a large swathe of the population. COVID-19 is virulent. But we now know that most infections are the result of so-called ‘super-spreaders’ – a small minority of virus carriers who infect tens or hundreds of other people. We also know that talking loudly, singing, or chanting tends to eject higher doses of the virus, and in an aerosol form that can linger in enclosed spaces. This creates the perfect conditions for one infected person to infect scores of others very quickly.  Based on this latest knowledge, the good news is that economies can reopen. The bad news is that, until an effective vaccine is developed, social distancing must persist. Specifically, people must avoid forming the crowds, congregations, and loud gatherings that can generate very dangerous super-spreading events. Hence, the sectors that are most hurt by social distancing – leisure and hospitality and retail trade – will continue to operate well below capacity for many months, at a minimum. And as these sectors have a very high labour intensity, there will be no V-shape recovery in jobs. Without Higher Bond Yields, European Equities Struggle To Outperform Social distancing is set to persist, which will create heaps of slack in advanced economy labour markets. This will force central banks to push the monetary easing ‘pedal to the metal’ – though in many cases, the pedal is already at the metal. In turn, this will force bond yields to stay ultra-low and, where they can, go even lower. One immediate takeaway is to stay overweight positively yielding US T-bonds and Spanish Bonos versus negatively yielding German Bunds and French OATs. Depressed bond yields must also compress the discount rate on competing long-duration investments that generate safely growing cashflows. Meaning, growth defensive equities such as technology and healthcare. Now comes the part that is conceptually difficult to grasp because it is novel to this unprecedented era of ultra-low bond yields. Take some time to absorb the following few paragraphs. For growth defensives, both components of the discount rate – the bond yield and the equity risk premium (ERP) – compress together. This is because the ERP is a tight function of the difference in equity and bond price ‘negative asymmetries’, defined as the potential price downside versus upside. When bond yields converge to their lower limit, bond prices converge to their upper limit, which increases the potential price downside versus upside. The result is that the difference in equity and bond negative asymmetries converges to zero, forcing the ERP to converge to zero. As the discount rate on growth defensives such as tech and healthcare collapses towards zero, the net present value must increase exponentially. This exponentially higher valuation of tech and healthcare is a mathematical consequence of the novel risk relationship between growth defensive equities and bonds at ultra-low bond yields. The unprecedented phenomenon has a major implication for European equity relative performance. The Euro Stoxx 50 is heavily underweight technology and healthcare, and this defining sector fingerprint is the key structural driver of European equity market relative performance (Chart I-4). Meanwhile, the relative performance of technology and healthcare is just an inverse exponential function of the bond yield (Chart I-5). The upshot is that European equities tend to outperform other regions only when bond yields are heading higher and the growth defensives are underperforming (Chart I-6). Chart I-4The Euro Stoxx 50's Underweight In Tech Drives Its Relative Performance The Euro Stoxx 50's Underweight In Tech Drives Its Relative Performance The Euro Stoxx 50's Underweight In Tech Drives Its Relative Performance Chart I-5Tech Outperforms When The Bond Yield Declines... Tech Outperforms When The Bond Yield Declines... Tech Outperforms When The Bond Yield Declines... Chart I-6...Hence, Without Higher Bond Yields The Euro Stoxx 50 Struggles To Outperform ...Hence, Without Higher Bond Yields The Euro Stoxx 50 Struggles To Outperform ...Hence, Without Higher Bond Yields The Euro Stoxx 50 Struggles To Outperform Some commentators are calling the higher valuations in tech and healthcare a new bubble. But it is a bubble only to the extent that bond yields are in a ‘negative bubble’, meaning that ultra-low yields are unsustainable. However, with social distancing set to leave heaps of slack in the advanced economy labour markets, ultra-low bond yields are here to stay and could go even lower. Moreover, as shown earlier, tech and healthcare demand and output are immune to social distancing. They may even benefit from social distancing. Hence, on a one-year horizon and beyond, stay overweight the growth defensive tech and healthcare sectors. And stay overweight the tech and healthcare heavy S&P 500 versus Euro Stoxx 50. A Puppy Boom We finish on a very positive note for animal lovers. The shift to working from home has generated a puppy boom. The Association of German Dogs claims that “the demand for puppies is endless” and the UK Kennel Club says that “there is unprecedented demand.” In the era of social distancing, the waiting list for puppies has quadrupled, and prices of easy to look after crossbreeds such as cockapoos have more than doubled. The demand for pet food and equipment is also very strong. Dogs make excellent companions for the socially isolated, which describes how many people are now feeling. Furthermore, with millions of people now working from home or on extended furlough, a growing number of households can fulfil the dream of owning a dog. We have recommended a structural overweight to the Animal Care sector based on the ‘humanisation’ of pets and the structural uptrend in spend per pet, especially on veterinary costs (Chart I-7). Animal Care has outperformed by 50 percent in the past two and a half years, but the shift to working from home will add impetus to the structural uptrend (Chart I-8). Chart I-7Animal Care Prices Are Rising... Animal Care Prices Are Rising... Animal Care Prices Are Rising... Chart I-8...And The Animal Care Sector Is Strongly Outperforming ...And The Animal Care Sector Is Strongly Outperforming ...And The Animal Care Sector Is Strongly Outperforming Stay overweight Animal Care. The ETF ticker, appropriately enough, is called PAWZ.  Fractal Trading System This week’s recommended trade is to short gold versus lead, given that the relative performance recently reached a fractal resistance point that has successfully identified four previous turning points. Set the profit target and symmetrical stop-loss at 13 percent. In our other open trades, five are in profit and one is in loss. The rolling 1-year win ratio now stands at 64 percent. Gold Vs. Lead Gold Vs. Lead 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. * 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.   Dhaval Joshi Chief European Investment Strategist dhaval@bcaresearch.com Footnotes 1    https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30222-8/fulltext Fractal Trading System   Cyclical Recommendations Structural Recommendations 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 - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-6Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations     Chart II-7Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Chart II-8Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations Indicators To Watch - Interest Rate Expectations