Sectors

Special Report

Highlights We update our long-range forecasts of returns from a range of asset classes – equities, bonds, alternatives, and currencies – and make some refinements to the methodologies we used in our last report in November 2017. We add coverage of U.K., Australian, and Canadian assets, and include Emerging Markets debt, gold, and global Real Estate in our analysis for the first time. Generally, our forecasts are slightly higher than 18 months ago: we expect an annual return in nominal terms over the next 10-year years of 1.7% from global bonds, and 5.9% from global equities – up from 1.5% and 4.6% respectively in the last edition. Cheaper valuations in a number of equity markets, especially Japan, the euro zone, and Emerging Markets explain the higher return assumptions. Nonetheless, a balanced global portfolio is likely to return only 4.7% a year in the long run, compared to 6.3% over the past 20 years. That is lower than many investors are banking on. Feature Since we published our first attempt at projecting long-term returns for a range of asset classes in November 2017, clients have shown enormous interest in this work. They have also made numerous suggestions on how we could improve our methodologies and asked us to include additional asset classes. This Special Report updates the data, refines some of our assumptions, and adds coverage of U.K., Australian, and Canadian assets, as well as gold, global Real Estate, and global REITs. Our basic philosophy has not changed. Many of the methodologies are carried over from the November 2017 edition, and clients interested in more detailed explanations should also refer to that report.1 Our forecast time horizon is 10-15 years. We deliberately keep this vague, and avoid trying to forecast over a 3-7 year time horizon, as is common in many capital market assumptions reports. The reason is that we want to avoid predicting the timing and gravity of the next recession, but rather aim to forecast long-term trend growth irrespective of cycles. This type of analysis is, by nature, as much art as science. We start from the basis that historical returns, at least those from the past 10 or 20 years, are not very useful. Asset allocators should not use historical returns data in mean variance optimizers and other portfolio-construction models. For example, over the past 20 years global bonds have returned 5.3% a year. With many long-term government bonds currently yielding zero or less, it is mathematically almost impossible that returns will be this high over the coming decade or so. Our analysis points to a likely annual return from global bonds of only 1.7%. Our approach is based on building-blocks. There are some factors we know with a high degree of certainly: such as the return on U.S. 10-year Treasury yields over the next 10 years (to all intents and purposes, it is the current yield). Many fundamental drivers of return (credit spreads, the small-cap premium, the shape of the yield curve, profit margins, stock price multiples etc.) are either steady on average over the cycle, or mean revert. For less certain factors, such as economic growth, inflation, or equilibrium short-term interest rates, we can make sensible assumptions. Most of the analysis in this report is based on the 20-year history of these factors. We used 20 years because data is available for almost all the asset classes we cover for this length of time (there are some exceptions, for example corporate bond data for Australia and Emerging Markets go back only to 2004-5, and global REITs start only in 2008). The period from May 1999 to April 2019 is also reasonable since it covers two recessions and two expansions, and started at a point in the cycle that is arguably similar to where we are today. Some will argue that it includes the Technology bubble of 1999-2000, when stock valuations were high, and that we should use a longer period. But the lack of data for many assets classes before the 1990s (though admittedly not for equities) makes this problematic. Also, note that the historical returns data for the 20 years starting in May 1999 are quite low – 5.8% for U.S. equities, for example. This is because the starting-point was quite late in the cycle, as we probably also are now.   We make the following additions and refinements to our analysis: Add coverage of the U.K., Australia, and Canada for both fixed income and equities. Add coverage of Emerging Markets debt: U.S. dollar and local-currency sovereign bonds, and dollar-denominated corporate credit. Among alternative assets, add coverage of gold, global Direct Real Estate, and global REITs. Improve the methodology for many alt asset classes, shifting from reliance on historical returns to an approach based on building blocks – for example, current yield plus an estimation of future capital appreciation – similar to our analysis of other asset classes. In our discussion of currencies, add for easy reference of readers a table of assumed returns for all the main asset classes expressed in USD, EUR, JPY, GBP, AUD, and CAD (using our forecasts of long-run movements in these currencies). Added Sharpe ratios to our main table of assumptions. The summary of our results is shown in Table 1. The results are all average annual nominal total returns, in local currency terms (except for global indexes, which are in U.S. dollars). Table 1BCA Assumed Returns Unsurprisingly, given the long-term nature of this exercise, our return projections have in general not moved much compared to those in November 2017. Indeed, markets look rather similar today to 18 months ago: the U.S. 10-year Treasury yield was 2.4% at end-April (our data cut-off point), compared to 2.3%, and the trailing PE for U.S. stocks 21.0, compared to 21.6. If anything, the overall assumption for a balanced portfolio (of 50% equities, 30% bonds, and 20% equal-weighted alts) has risen slightly compared to the 2017 edition: to 4.7% from 4.1% for a global portfolio, and to 4.9% from 4.6% for a purely U.S. one. That is partly because we include specific forecasts for the U.K., Australia, and Canada, where returns are expected to be slightly higher than for the markets we limited our forecasts to previously, the U.S, euro zone, Japan, and Emerging Markets (EM). Equity returns are also forecast to be higher than 18 months ago, mainly because several markets now are cheaper: trailing PE for Japan has fallen to 13.1x from 17.6x, for the euro zone to 15.5x from 18.0x, and for Emerging Markets to 13.6x from 15.4x (and more sophisticated valuation measures show the same trend). The long-term picture for global growth remains poor, based on our analysis, but valuation at the starting-point, as we have often argued, is a powerful indicator of future returns. We include Sharpe ratios in Table 1 for the first time. We calculate them as expected return/expected volatility to allow for comparison between different asset classes, rather than as excess return over cash/volatility as is strictly correct, and as should be used in mean variance optimizers. Chart 1Volatility Is Easier To Forecast Than Returns For volatility assumptions, we mostly use the 20-year average volatility of each asset class. As discussed above, historical returns should not be used to forecast future returns. But volatility does not trend much over the long-term (Chart 1). We looked carefully at volatility trends for all the asset classes we cover, but did not find a strong example of a trend decline or rise in any. We do, however, adjust the historic volatility of the illiquid, appraisal-based alternative assets, such as Private Equity, Real Estate, and Farmland. The reported volatility is too low, for example 2.6% in the case of U.S. Direct Real Estate. Even using statistical techniques to desmooth the return produces a volatility of only around 7%. We choose, therefore, to be conservative, and use the historic volatility on REITs (21%) and apply this to Direct Real Estate too. For Private Equity (historic volatility 5.9%), we use the volatility on U.S. listed small-cap stocks (18.6%). Looking at the forecast Sharpe ratios, the risk-adjusted return on global bonds (0.55) is somewhat higher than that of global equities (0.33). Credit continues to look better than equities: Sharpe ratio of 0.70 for U.S. investment grade debt and 0.62 for high-yield bonds. Nonetheless, our overall conclusion is that future returns are still likely to be below those of the past decade or two, and below many investors’ expectations. Over the past 20 years a global balanced portfolio (defined as above) returned 6.3% and a similar U.S. portfolio 7.0%. We expect 4.7% and 4.9% respectively in future. Investors working on the assumption of a 7-8% nominal return – as is typical among U.S. pension funds, for example – need to become realistic. Below follow detailed descriptions of how we came up with our assumptions for each asset class (fixed income, equities, and alternatives), followed by our forecasts of long-term currency movements, and a brief discussion of correlations. 1. Fixed Income We carry over from the previous edition our building-block approach to estimating returns from fixed income. One element we know with a relatively high degree of certainty is the return over the next 10 years from 10-year government bonds in developed economies: one can safely assume that it will be the same as the current 10-year yield. It is not mathematical identical, of course, since this calculation does not take into account reinvestment of coupons, or default risk, but it is a fair assumption. We can make some reasonable assumptions for returns from cash, based on likely inflation and the real equilibrium cash rate in different countries. After this, our methodology is to assume that other historic relationships (corporate bond spreads, default and recovery rates, the shape of the yield curve etc.) hold over the long run and that, therefore, the current level reverts to its historic mean. The results of our analysis, and the assumptions we use, are shown in Table 2. Full details of the methodology follow below. Table 2Fixed Income Return Calculations Projected returns have not changed significantly from the 2017 edition of this report. In the U.S., for the current 10-year Treasury bond yield we used 2.4% (the three-month average to end-April), very similar to the 2.3% on which we based our analysis in 2017. In the euro zone and Japan, yields have fallen a little since then, with the 10-year German Bund now yielding roughly 0%, compared to 0.5% in 2017, and the Japanese Government Bond -0.1% compared to zero. Overall, we expect the Bloomberg Barclays Global Index to give an annual nominal return of 1.7% over the coming 10-15 years, slightly up from the assumption of 1.5% in the previous edition. This small rise is due to the slight increase in the U.S. long-term risk-free rate, and to the inclusion for the first time of specific estimates for returns in the U.K., Australia, and Canada. Fixed Income Methodologies Cash. We forecast the long-run rate on 3-month government bills by generating assumptions for inflation and the real equilibrium cash rate. For inflation, in most countries we use the 20-year average of CPI inflation, for example 2.2% in the U.S. and 1.7% in the euro zone. This suggests that both the Fed and the ECB will slightly miss their inflation targets on the downside over the coming decade (the Fed targets 2% PCE inflation, but the PCE measure is on average about 0.5% below CPI inflation). Of course, this assumes that the current inflation environment will continue. BCA’s view is that inflation risks are significantly higher than this, driven by structural factors such as demographics, populism, and the advent of ultra-unorthodox monetary policy.2 But we see this as an alternative scenario rather than one that we should use in our return assumptions for now. Japan’s inflation has averaged 0.1% over the past 20 years, but we used 1% on the grounds that the Bank of Japan (BoJ) should eventually see some success from its quantitative easing. For the equilibrium real rate we use the New York Fed’s calculation based on the Laubach-Williams model for the U.S., euro zone, U.K., and Canada. For Japan, we use the BoJ’s estimate, and for Australia (in the absence of an official forecast of the equilibrium rate) we take the average real cash rate over the past 20 years. Finally, we assume that the cash yield will move from its current level to the equilibrium over 10 years. Government Bonds. Using the 10-year bond yield as an anchor, we calculate the return for the government bond index by assuming that the spread between 7- and 10-year bonds, and between 3-month bills and 10-year bonds will average the same over the next 10 years as over the past 20. While the shape of the yield curve swings around significantly over the cycle, there is no sign that is has trended in either direction (Chart 2). The average maturity of government bonds included in the index varies between countries: we use the five-year historic average for each, for example, 5.8 years for the U.S., and 10.2 years for Japan. Spread Product. Like government bonds, spreads and default rates are highly cyclical, but fairly stable in the long run (Chart 3). We use the 20-year average of these to derive the returns for investment-grade bonds, high-yield (HY) bonds, government-related securities (e.g. bonds issued by state-owned entities, or provincial governments), and securitized bonds (e.g. asset-backed or mortgage-backed securities). For example, for U.S. high-yield we use the average spread of 550 basis points over Treasuries, default rate of 3.8%, and recovery rate of 45%. For many countries, default and recovery rates are not available and so we, for example, use the data from the U.S. (but local spreads) to calculate the return for high-yield bonds in the euro zone and the U.K. Inflation-Linked Bonds. We use the average yield over the past 10 years (not 20, since for many countries data does not go back that far and, moreover, TIPs and their equivalents have been widely used for only a relatively short period.) We calculate the return as the average real yield plus forecast inflation. Chart 2Yield Curves Chart 3Credit Spreads & Default Rates     Bloomberg Barclays Aggregate Bond Indexes. We use the weights of each category and country (from among those we forecast) to derive the likely return from the index. The composition of each country’s index varies widely: for example, in the euro zone (27% of the global bond index), government bonds comprise 66% of the index, but in the U.S. only 37%. Only the U.S. and Canada have significant weightings in corporate bonds: 29% and 50% respectively. This can influence the overall return for each country’s index. Table 3Emerging Market Debt Emerging Market Debt. We add coverage of EMD: sovereign bonds in both local currency and U.S. dollars, and USD-denominated EM corporate debt. Again, we take the 20-year average spread over 10-year U.S. Treasuries for each category. A detailed history of default and recovery is not available, so for EM corporate debt we assume similar rates to those for U.S. HY bonds. For sovereign bonds, we make a simple assumption of 0.5% of losses per year – although in practice this is likely to be very lumpy, with few defaults for years, followed by a rush during an EM crisis. For EM local currency debt, we assume that EM currencies will depreciate on average each year in line with the difference between U.S. inflation and EM inflation (using the IMF forecast for both – please see the Currency section below for further discussion on this). After these calculations, we conclude that EM USD sovereign bonds will produce an annual return of 4.7%, and EM USD corporate bonds 4.5% – in both cases a little below the 5.6% return assumption we have for U.S. high-yield debt (Table 3).   2. Equities Our equity methodologies are largely unchanged from the previous edition. We continue to use the return forecast from six different methodologies to produce an average assumed return. Table 4 shows the results and a summary of the calculation for each methodology. The explanation for the six methodologies follows below. Table 4Equity Return Calculations The results suggest slightly higher returns than our projections in 2017. We forecast global equities to produce a nominal annual total return in USD of 5.9%, compared to 4.6% previously. The difference is partly due to the inclusion for the first time of specific forecasts for the U.K., Australia and Canada, which are projected to see 8.0%, 7.4% and 6.0% returns respectively. The projection for the U.S. is fairly similar to 2017, rising slightly to 5.6% from 5.0% (mainly due to a slightly higher assumption for productivity growth in future, which boosts the nominal GDP growth assumption). Japan, however, does come out looking significantly more attractive than previously, with an assumed return of 6.2%, compared to 3.5% previously. This is mostly due to cheaper valuations, since the growth outlook has not improved meaningfully. Japan now trades on a trailing PE of 13.1x, compared to 17.6x in 2017. This helps improve the return indicated by a number of the methodologies, including earnings yield and Shiller PE. The forecast for euro zone equities remains stable at 4.7%. EM assumptions range more widely, depending on the methodology used, than do those for DM. On valuation-based measures (Shiller PE, earnings yield etc.), EM generally shows strong return assumptions. However, on a growth-based model it looks less attractive. We continue to use two different assumptions for GDP growth in EM. Growth Model (1) is based on structural reform taking place in Emerging Markets, which would allow productivity growth to rebound from its current level of 3.2% to the 20-year average of 4.1%; Growth Model (2) assumes no reform and that productivity growth will continue to decline, converging with the DM average, 1.1%, over the next 10 years. In both cases, the return assumption is dragged down by net issuance, which we assume will continue at the 10-year average of 4.9% a year. Our composite projection for EM equity returns (in local currencies) comes out at 6.6%, a touch higher than 6.0% in 2017. Equity Methodologies Equity Risk Premium (ERP). This is the simplest methodology, based on the concept that equities in the long run outperform the long-term risk-free rate (we use the 10-year U.S. Treasury yield) by a margin that is fairly stable over time. We continue to use 3.5% as the ERP for the U.S., based on analysis by Dimson, Marsh and Staunton of the average ERP for developed markets since 1900. We have, however, tweaked the methodology this time to take into account the differing volatility of equity markets, which should translate into higher returns over time. Thus we use a beta of 1.2 for the euro zone, 0.8 for Japan, 0.9 for the U.K., 1.1 for both Australia and Canada, and 1.3 for Emerging Markets. The long-term picture for global growth remains poor, but valuation at the starting-point, as we have often argued, is a powerful indicator of future returns. Growth Model. This is based on a Gordon growth model framework that postulates that equity returns are a function of dividend yield at the starting point, plus the growth of earnings in future (we assume that the dividend payout ratio stays constant). We base earnings growth off assumptions of nominal GDP growth (see Box 1 for how we calculate these). But historically there is strong evidence that large listed company earnings underperform nominal GDP growth by around 1 percentage point a year (largely because small, unlisted companies tend to show stronger growth than the mature companies that dominate the index) and so we deduct this 1% to reach the earnings growth forecast. We also need to adjust dividend yield for share buybacks which in the U.S., for tax reasons, have added 0.5% to shareholder returns over the past 10 years (net of new share issuance). In other countries, however, equity issuance is significantly larger than buybacks; this directly impacts shareholders’ returns via dilution. For developed markets, the impact of net equity issuance deducts 0.7%-2.7% from shareholder returns annually. But the impact is much bigger in Emerging Markets, where dilution has reduced returns by an average of 4.9% over the past 10 years. Table 5 shows that China is by far the biggest culprit, especially Chinese banks. Table 5Dilution In Emerging Markets BOX 1 Estimating GDP Growth We estimate nominal GDP growth for the countries and regions in our analysis as the sum of: annual growth in the working-age population, productivity growth, and inflation (we assume that capital deepening remains stable over the period). Results are shown in Table 6. Table 6Calculations Of Trend GDP Growth For population growth, we use the United Nations’ median scenario for annual growth in the population aged 25-64 between 2015 and 2030. This shows that the euro zone and Japan will see significant declines in the working population. The U.S. and U.K. look slightly better, with the working population projected to grow by 0.3% and 0.1% respectively. There are some uncertainties in these estimates. Stricter immigration policies would reduce the growth. Conversely, greater female participation, a later retirement age, longer working hours, or a rise in the participation rate would increase it. For emerging markets we used the UN estimate for “less developed regions, excluding least developed countries”. These countries have, on average, better demographics. However, the average number hides the decline in the working-age population in a number of important EM countries, for example China (where the working-age population is set to shrink by 0.2% a year), Korea (-0.4%), and Russia (-1.1%). By contrast, working population will grow by 1.7% a year in Mexico and 1.6% in India. For productivity growth, we assume – perhaps somewhat optimistically – that the decline in productivity since the Global Financial Crisis will reverse and that each country will return to the average annual productivity growth of the past 20 years (Chart 4). Our argument is that the cyclical factors that depressed productivity since the GFC (for example, companies’ reluctance to spend on capex, and shareholders’ preference for companies to pay out profits rather than to invest) should eventually fade, and that structural and technical factors (tight labor markets, increasing automation, technological breakthroughs in fields such as artificial intelligence, big data, and robotics) should boost productivity. Based on this assumption, U.S. productivity growth would average 2.0% over the next 10-15 years, compared to 0.5% since 1999. Note that this is a little higher than the Congressional Budgetary Office’s assumption for labor productivity growth of 1.8% a year. Chart 4AProductivity Growth (I) Chart 4BProductivity Growth (II) Our assumptions for inflation are as described above in the section on Fixed Income. The overall results suggest that Japan will see the lowest nominal GDP growth, at 0.9% a year, with the U.S. growing at 4.4%. The U.K. and Australia come out only a little lower than the U.S. For emerging markets, as described in the main text, we use two scenarios: one where productivity grow continues to slow in the absence of reforms, especially in China, from the current 3.2% to converge with the average in DM (1.1%) over the next 10-15 years; and an alternative scenario where reforms boost productivity back to the 20-year average of 4.1%.   Growth Plus Reversion To Mean For Margins And Profits. There is logic in arguing that profit margins and multiples tend to revert to the mean over the long term. If margins are particularly high currently, profit growth will be significantly lower than the above methodology would suggest; multiple contraction would also lower returns. Here we add to the Growth Model above an assumption that net profit margin and trailing PE will steadily revert to the 20-year average for each country over the 10-15 years. For most countries, margins are quite high currently compared to history: 9.2% in the U.S., for example, compared to a 20-year average of 7.7%. Multiples, however, are not especially high. Even in the U.S. the trailing PE of 21.0x, compares to a 20-year average of 20.8x (although that admittedly is skewed by the ultra-high valuations in 1999-2000, and coming out of the 2007-9 recession – we would get a rather lower number if we used the 40-year average). Indeed, in all the other countries and regions, the PE is currently lower than the 20-year average. Note that for Japan, we assumed that the PE would revert to the 20-year average of the U.S. and the euro zone (19.2), rather than that of Japan itself (distorted by long periods of negative earnings, and periods of PE above 50x in the 1990s and 2000s).  Earnings Yield. This is intuitively a neat way of thinking about future returns. Investors are rewarded for owning equity, either by the company paying a dividend, or by reinvesting its earnings and paying a dividend in future. If one assumes that future return on capital will be similar to ROC today (admittedly a rash assumption in the case of fast-growing companies which might be tempted to invest too aggressively in the belief that they can continue to generate rapid growth) it should be immaterial to the investor which the company chooses. Historically, there has been a strong correlation between the earnings yield (the inverse of the trailing PE) and subsequent equity returns, although in the past two decades the return has been somewhat higher that the EY suggested, and so in future might be somewhat lower. This methodology produces an assumed return for U.S. equities of 4.8% a year. Shiller PE. BCA’s longstanding view is that valuation is not a good timing tool for equity investment, but that it is crucial to forecasting long-term returns. Chart 5 shows that there is a good correlation in most markets between the Shiller PE (current share price divided by 10-year average inflation-adjusted earnings) and subsequent 10-year equity returns. We use a regression of these two series to derive the assumptions. This points to returns ranging from 5.4% in the case of the U.S. to 12.5% for the U.K. Composite Valuation Indicator. There are some issues that make the Shiller PE problematical. It uses a fixed 10-year period, whereas cycles vary in length. It tends to make countries look cheap when they have experienced a trend decline in earnings (which may continue, and not mean revert) and vice versa. So we also use a proprietary valuation indicator comprising a range of standard parameters (including price/book, price/cash, market cap/GDP, Tobin’s Q etc.), and regress this against 10-year returns. The results are generally similar to those using the Shiller PE, except that Japan shows significantly higher assumed returns, and the U.K. and EM significantly lower ones (Chart 6). Chart 5Shiller PE Vs. 10-Year Return Chart 6Composite Valuation Vs. 10-Year Return     3. Alternative Investments We continue to forecast each illiquid alternative investment separately, but we have made a number of changes to our methodologies. Mostly these involve moving away from using historical returns as a basis for our forecasts, and shifting to an approach based on current yield plus projected future capital appreciation. In direct real estate, for example, in 2017 we relied on a regression of historical returns against U.S. nominal GDP growth. We move in this edition to an approach based on the current cap rate, plus capital appreciation (based on forecasts of nominal GDP growth), and taking into account maintenance costs (details below). We also add coverage of some additional asset classes: global ex-U.S. direct real estate, global ex-U.S. REITs, and gold. Table 7 summarizes our assumptions, and provides details of historic returns and volatility. Table 7Alternatives Return Calculations It is worth emphasizing here that manager selection is far more important for many alternative investment classes than it is for public securities (Chart 7). There is likely to be, therefore, much greater dispersion of returns around our assumptions than would be the case for, say, large-cap U.S. equities. Chart 7For Alts, Manager Selection Is Key Hedge Funds Chart 8Hedge Fund Return Over Cash Hedge fund returns have trended down over time (Chart 8). Long gone is the period when hedge funds returned over 20% per year (as they did in the early 1990s). Over the past 10 years, the Composite Hedge Fund Index has returned annually 3.3% more than 3-month U.S. Treasury bills. But that was entirely during an economic expansion and so we think it is prudent to cut last edition’s assumption of future returns of cash-plus-3.5%, to cash-plus-3% going forward. Direct Real Estate Our new methodology for real estate breaks down the return, in a similar way to equities, into the current cash yield (cap rate) plus an assumption of future capital growth. For the cap rate, we use the average, weighted by transaction volumes, of the cap rates for apartments, office buildings, retail, industrial real estate, and hotels in major cities (for example, Chicago, Los Angeles, Manhattan, and San Francisco for the U.S., or Osaka and Tokyo for Japan). We assume that capital values grow in line with each’s country’s nominal GDP growth (using the IMF’s five-year forecasts for this). We deduct a 0.5% annual charge for maintenance, in line with industry practice. Results are shown in Table 8. Our assumptions point to better returns from real estate in the U.S. than in the rest of the world. Not only is the cap rate in the U.S. higher, but nominal GDP growth is projected to be higher too. Table 8Direct Real Estate Return Calculations REITs We switch to a similar approach for REITs. Previously we used a regression of REITs against U.S. equity returns (since REITs tend to be more closely correlated with equities than with direct real estate). This produced a rather high assumption for U.S. REITs of 10.1%. We now use the current dividend yield on REITs plus an assumption that capital values will grow in line with nominal GDP growth forecasts. REITs’ dividend yields range fairly narrowly from 2.9% in Japan to 4.7% in Canada. We do not exclude maintenance costs since these should already be subtracted from dividends. The result of using this methodology is that the assumed return for U.S. REITs falls to a more plausible 8.5%, and for global REITs is 6.2%. Private Equity & Venture Capital Chart 9Private Equity Premium Has Shrunk Around It makes sense that Private Equity returns are correlated with returns from listed equities. Most academic studies have shown a premium over time for PE of 5-6 percentage points (due to leverage, a tilt towards small-cap stocks, management intervention, and other factors). However, this premium has swung around dramatically over time (Chart 9). Over the past 10 years, for example, annual returns from Private Equity and listed U.S. equities have been identical: 12%. However, there appears to be no constant downtrend and so we think it advisable to use the 30-year average premium: 3.4%. This produces a return assumption for U.S. Private Equity of 8.9% per year. Over the same period, Venture Capital has returned around 0.5% more than PE (albeit with much higher volatility) and we assume the same will happen going forward.   Structured Products In the context of alternative asset classes, Structured Products refers to mortgage-backed and other asset-backed securities. We use the projected return on U.S. Treasuries plus the average 20-year spread of 60 basis points. Assumed return is 2.7%. Farmland & Timberland Chart 10Farm Prices Grow More Slowly Than GDP As with Real Estate and REITs, we move to a methodology using current cash yield (after costs) plus an assumption for capital appreciation linked to nominal GDP forecasts. The yield on U.S. Farmland is currently 4.4% and on Timberland 3.2%. Both have seen long-run prices grow significantly more slowly than nominal GDP growth. Since 1980, for example, farm prices have risen at a compound rate of 3.9% per acre, compared to U.S. nominal GDP growth of 5.2% and global GDP growth of 5.5% (Chart 10). We assume that this trend will continue, and so project farm prices to grow 1.5 percentage points a year more slowly than global GDP (using global, not U.S., economic growth makes sense since demand for food is driven by global factors). This produces a total return assumption of 6%. For timberland, we did not find a consistent relationship with nominal GDP growth and so assumed that prices would continue to grow at their historic rate over the past 20 years (the longest period for which data is available). We project timberland to produce an annual return of 4.8%. Commodities & Gold For commodities we use a very different methodology (which we also used in the previous edition): the concept that commodities prices consistently over time have gone through supercycles, lasting around 10 years, followed by bear markets that have lasted an average of 17 years (Chart 11). The most recent super-cycle was 2002-2012. In the period since the supercycle ended, the CRB Index has fallen by 42%. Comparing that to the average drop in the past three bear markets, we conclude that there is about 8% left to fall over the next nine years, implying an annual decline of about 1%. Our overall conclusion is that future returns are still likely to be below those of the past decade or two, and below many investors’ expectations. We add gold to our assumptions, since it is an asset often held by investors. However, it is not easy to project long-term returns for the metal. Since the U.S. dollar was depegged from gold in 1968, gold too has gone through supercycles, in the 1970s and 2002-11 (Chart 12). We find that change in real long-term interest rates negatively affects gold (logically since higher rates increase the opportunity cost of owning a non-income-generating asset). We use, therefore, a regression incorporating global nominal GDP growth and a projection of the annual change in real 10-year U.S. Treasury yields (based on the equilibrium cash rate plus the average spread between 10-year yields and cash). This produces an assumption of an annual return from gold of 4.7% a year. We continue to see this asset class more as a hedge in a portfolio (it has historically had a correlation of only 0.1 with global equities and 0.24 with global bonds) rather than a source of return per se.  Chart 11Commodities Still In A Bear Market Chart 12Gold Also Has Supercycles   4. Currencies Chart 13Currencies Tend To Revert To PPP All the return projections in this report are in local currency terms. That is a problem for investors who need an assumption for returns in their home currency. It is also close to impossible to hedge FX exposure over as long a period as 10-15 years. Even for investors capable of putting in place rolling currency hedges, GAA has shown previously that the optimal hedge ratio varies enormously depending on the home currency, and that dynamic hedges (i.e. using a simple currency forecasting model) produce better risk-adjust returns than a static hedge.3  Fortunately, there is an answer: it turns out that long-term currency forecasting is relatively easy due to the consistent tendency of currencies, in developed economies at least, to revert to Purchasing Power Parity (PPP) over the long-run, even though they can diverge from it for periods as long as five years or more (Chart 13). We calculate likely currency movements relative to the U.S. dollar based on: 1) the current divergence of the currency from PPP, using IMF estimates of the latter; 2) the likely change in PPP over the next 10 years, based on inflation differentials between the country and the U.S. going forward (using IMF estimates of average CPI inflation for 2019-2024 and assuming the same for the rest of the period). The results are shown in Table 9. All DM currencies, except the Australian dollar, look cheap relative to the U.S. dollar, and all of them, again excluding Australia, are forecast to run lower inflation that the U.S. implying that their PPPs will rise further. This means that both the euro and Japanese yen would be expected to appreciate by a little more than 1% a year against the U.S. dollar over the next 10 years or so. Table 9Currency Return Calculations PPP does not work, however, for EM currencies. They are all very cheap relative to PPP, but show no clear trend of moving towards it. The example of Japan in the 1970s and 1980s suggests that reversion to PPP happens only when an economy becomes fully developed (and is pressured by trading partners to allow its currency to appreciate). One could imagine that happening to China over the next 10-20 years, but the RMB is currently 48% undervalued relative to PPP, not so different from its undervaluation 15 years ago. For EM currencies, therefore, we use a different methodology: a regression of inflation relative to the U.S. against historic currency movements. This implies that EM currencies are driven by the relative inflation, but that they do not trend towards PPP. Based on IMF inflation forecasts, many Emerging Markets are expected to experience higher inflation than the U.S. (Table 10). On this basis, the Turkish lira would be expected to decline by 7% a year against the U.S. dollar and the Brazilian real by 2% a year. However, the average for EM, which we calculated based on weights in the MSCI EM equity index, is pulled down by China (29% of that index), Korea (15%) and Taiwan (12%). China’s inflation is forecast to be barely above that in the U.S, and Korean and Taiwanese inflation significantly below it. MSCI-weighted EM currencies, consequently, are forecast to move roughly in line with the USD over the forecast horizon. One warning, though: the IMF’s inflation forecasts in some Emerging Markets look rather optimistic compared to history: will Mexico, for example, see only 3.2% inflation in future, compared to an average of 5.7% over the past 20 years? Higher inflation than the IMF forecasts would translate into weaker currency performance. Table 10EM Currencies In Table 11, we have restated the main return assumptions from this report in USD, EUR, JPY, GBP, AUD, and CAD terms for the convenience of clients with different home currencies. As one would expect from covered interest-rate parity theory, the returns cluster more closely together when expressed in the individual currencies. For example, U.S. government bonds are expected to return only 0.8% a year in EUR terms (versus 2.1% in USD terms) bringing their return closer to that expected from euro zone government bonds, -0.4%. Convergence to PPP does not, however, explain all the difference between the yields in different countries. Table 11Returns In Different Base Currencies 5. Correlations Chart 14Correlations Are Hard To Forecast We have not tried to forecast correlations in this Special Report. As discussed, historical returns from different asset classes are not a reliable guide to future returns, but it is possible to come up with sensible assumptions about the likely long-run returns going forward. Volatility does not trend much over the long term, so we think it is not unreasonable to use historic volatility data in an optimizer. But correlation is a different matter. As is well known, the correlation of equities and bonds has moved from positive to negative over the past 40 years (mainly driven by a shift in the inflation environment). But the correlation between major equity markets has also swung around (Chart 14). Asset allocators should preferably use rough, conservative assumptions for correlations – for example, 0.1 or 0.2 for the equity/bond correlation, rather than the average -0.1 of the past 20 years. We plan to do further work to forecast correlations in a future edition of this report.  But for readers who would like to see – and perhaps use – historic correlation data, we publish below a simplified correlation matrix of the main asset classes that we cover in this report (Table 12). We would be happy to provide any client with the full spreadsheet of all asset classes . Table 12Correlation Matrix Garry Evans Chief Global Asset Allocation Strategist garry@bcaresearch.com Footnotes 1      Please see Global Asset Allocation Special Report, “What Returns Can You Expect?”, dated 15 November 2017, available at gaa.bcaresearch.com 2      Please see Global Asset Allocation Special Report, “Investors’ Guide To Inflation Hedging: How To Invest When Inflation Rises,” dated 22 May 2019, available at gaa.bcaresearch.com 3      Please see GAA Special Report, “Currency Hedging: Dynamic Or Static? A Practical Guide For Global Equity Investors,” dated 29 September 2017, available at gaa.bcaresearch.com  

Highlights Portfolio Strategy Melting inflation expectations, widening relative indebtedness, expensive adjusted relative valuations, high odds of a further drop in relative profit margins and the high-octane small cap status all signal that large caps continue to have the upper hand versus small caps. Modest deterioration in credit quality, weakening prospects for loan growth and falling inflation expectations, compel us to put the S&P bank index on downgrade alert. Recent Changes We got stopped out on the long S&P managed health care/short S&P semis trade on June 10 for a gain of 10% since inception. We got stopped out on the long S&P homebuilders/short S&P home improvement retailers trade on June 14 for a gain of 10% since inception. Table 1 Feature Equities surged to all-time highs last week, as investors cheered the Fed’s dovish stance and increasing likelihood of a late-July interest rate cut. The addiction to low interest rates and global dependence on QE are evident and simultaneously very worrisome signs. We are nervous that the U.S. economy is in a soft-patch, thus vulnerable to a shock (maybe sustained trade hawkishness is the negative catalyst) that can tilt the economy in recession. The risk/reward tradeoff on the overall equity market remains to the downside on a cyclical (3-12 month) time horizon as we first posited two weeks ago (this is U.S. Equity Strategy’s view and is going against BCA’s cyclically constructive equity market House View). In fact, using the NY Fed’s probability of a recession in the coming 12 months data series signals that there’s ample downside for stocks from current levels (recession probability shown inverted, Chart 1).1 We heed this message and reiterate our cautious equity market stance. Chart 1Watch Out Down Below Importantly, drilling deeper with regard to the excesses we are witnessing this cycle, Chart 2 is instructive and an unintended consequence of QE and zero interest rate policy. In previous research we highlighted the cumulative equity buybacks corporations have completed this cycle near the $5tn mark. Chart 2Financial Engineering What is worrying is that this “accomplishment” has come about at a great cost: a massive change in the capital structure of the firm. In other words, all of the buybacks are reflected in debt origination from the non-financial business sector (using the Fed’s flow of funds data), confirming our claim that the excesses this cycle are not in the financial or household sectors, but rather in the non-financial business sector (please refer to Chart 4A from the June 10 Weekly Report). One likely trigger of a jumpstart to a default cycle, other than a U.S./China trade dispute re-escalation, is dwindling demand. On that front, we are bemused on how much weight market participants place on the Fed’s shoulders bailing out the economy and the stock market. Chart 3 is a vivid reminder of this narrative. On the one side of the seesaw is the mighty Fed with its forecast interest rate cuts and on the other a slew of slipping indicators. Our sense is that these eighteen indicators will more than offset the Fed’s about-to-commence easing cycle and eventually tilt the U.S. economy in recession, especially if the Sino-American trade talks falter. S&P 500 quarterly earnings are contracting on a year-over-year basis and the semi down-cycle points to additional profit pain for the rest of the year (top panel, Chart 4). On the trade front, exports are below the zero line and imports are flirting with the boom/bust line (second panel, Chart 4). Overall rail freight, including intermodal (retail segment) freight is plunging and so is the CASS freight shipments index at a time when the broad commodity complex is also deflating (third & bottom panels, Chart 4). The latest Q2 update of CEO confidence was disconcerting, weighing on the broad equity market’s prospects (top panel, Chart 5). Non-residential capital outlays have petered out and private construction is sinking like a stone. In fact, the latter have never contracted at such a steep rate during expansions over the past five decades (second panel, Chart 5). Real residential investment has clocked its fifth consecutive quarter of negative growth during an expansion, for the first time since the mid-1950s. Single family housing starts and permits are contracting (third panel, Chart 5). Chart 4Cracks… Chart 5…Are… Light vehicle sales are ailing (bottom panel, Chart 5) and the latest senior loan officer survey continued to show that there is feeble demand for credit across nearly all the categories the Fed tracks (bottom panel, Chart 6). Non-farm payrolls fell to 75K on a month-over-month basis last month and layoff announcements are gaining steam signaling that the labor market, a notoriously lagging indicator, is also showing some signs of strain (layoffs shown inverted, third panel, Chart 6). The latest update of the U.S. Equity Strategy’s corporate pricing power gauge is contracting (please look forward to reading a more in-depth analysis on our quarterly update on July 2) following down the path of the market’s dwindling inflation expectations. Finally, the yield curve remains inverted (top and second panels, Chart 6). Chart 6…Forming Chart 7The “Hope" RallyAdding it all up, we deem that the equity market remains divorced from the economic reality and too much faith is placed on the Fed’s shoulders to save the day. Thus, we refrain from positioning the portfolio on “three hopes”: first that the Fed will engineer a soft landing, second that the U.S./China trade tussle will get resolved swiftly, and finally that the Chinese authorities will inject massive amounts of liquidity and reflate their economy (Chart 7). This week we are putting a key financials sub-sector on downgrade alert and update our view on the size bias. Large Cap Refuge While small caps shielded investors from the U.S./China trade dispute that heated up in 2018 (owing to their domestic focus), this year small caps have failed to live up to their trade war-proof expectations and have lagged their large cap brethren by the widest of margins. In fact, the relative share price ratio sits at multi-year lows giving back all the gains since the Trump election, and then some (Chart 8). Chart 8Stick With A Large Cap Bias As a reminder, our large cap preference has netted our portfolio 14% gains since the May 10 2018 cyclical inception and this size bias is also up 9% since our high-conviction call inclusion in early December 2018. Five key reasons underpin our large/mega cap preference in the size bias. Bearishness toward small vs. large caps has been pervasive raising the question: does it still pay to prefer large caps to small caps? The short answer is yes. Five key reasons underpin our large/mega cap preference in the size bias. First, melting inflation expectations have been positively correlated with the relative share price ratio, and the current message is to expect more downside (Chart 8). While the SPX has a higher energy weight than the S&P 600, financials and industrials dominate small cap indexes and likely explain the tight positive correlation with inflation expectations (Table 2). Table 2S&P 600/S&P 500 Sector Comparison Table Second, relative indebtedness has been widening. Debt saddled small caps have been issuing debt at an accelerating pace at a time when cash flow growth has not been forthcoming. Small cap net debt-to-EBITDA is now almost three times as high as large cap net debt-to-EBITDA. Investors have finally realized that rising indebtedness is worrisome, especially at the late stages of the business cycle, and that is why small caps have failed to insulate investors from the re-escalating trade dispute (top & middle panels, Chart 9). Third, a large number of small cap companies (100 in the S&P 600 and 600 in the Russell 2000) have no forward EPS. Very few S&P 500 companies have negative projected profits. Thus, while, relative valuations have been receding, the relative forward P/E trading at par is masking the relative value proposition of the indexes. Were the S&P or Russell to adjust for this, small caps would trade at a significant forward P/E premium to large caps (bottom panel, Chart 9). Chart 9Mind The Debt Gap Fourth, a small cap margin squeeze has been underway since the 2012 cyclical peak and the relative margin outlook is even grimmer. Simply put, small business labor costs are rising at a faster clip than overall wage inflation, warning that small cap profit margins have further to fall compared with large caps margins (Chart 10). Finally, small cap stocks are higher beta stocks and typically rise when volatility gets suppressed. As such, they also tend to outperform large caps when emerging markets outperform the SPX and vice versa. Tack on the recent yield curve inversion, and the odds are high that the size bias has entered a prolonged period of sustained small cap underperformance. Netting it all out, melting inflation expectations, widening relative indebtedness, expensive adjusted relative valuations, high odds of a further drop in relative profit margins and the high-octane small cap status all signal that large caps continue to have the upper hand versus small caps (Chart 11). Chart 10Relative Margin Trouble Chart 11Shay Away From Small Caps Bottom Line: Small cap underperformance has staying power. Continue to prefer large/mega caps to their small cap brethren. Put Banks On Downgrade Alert In the context of de-risking our portfolio we are taking the step and adding the S&P banks index on our downgrade watch list. The Fed’s signal of a cut in the upcoming July meeting steepened the yield curve last week. While the yield curve has put in higher lows in the past eight months, relative bank performance has been facing stiff resistance and has failed to follow the yield curve’s lead (Chart 12). One of the reasons for the Fed’s dovishness is melting inflation expectations. The latter are joined at the hip with relative bank performance and signal that downside risks are rising especially if the Fed fails to arrest the lower anchoring of inflation expectations (Chart 13). Chart 12Banks Are Not Participating Chart 13Melting Inflation Expectations Are Anchoring Banks With regard to credit demand, the latest Fed Senior Loan Officer survey remained subdued confirming the anemic reading from our Economic Impulse Indicator (a second derivative gauge of six parts of the U.S. economy, bottom panel, Chart 14). Lack of credit demand translates into lack of credit growth, despite the fact that bankers are, for the most part, willing extenders of credit. U.S. Equity Strategy’s overall loans & leases growth model has crested (second panel, Chart 15). Chart 14Anemic Loan Demand… Chart 15…Will Weigh On Loan Origination Similarly, the recent softness in a number of manufacturing surveys signal that C&I loan growth in particular – the largest credit category in bank loan books – is at risk of flirting with the contraction zone (third panel, Chart 15). Worrisomely, not only is the overall U.S. credit impulse contracting, but also U.S. Equity Strategy’s bank credit diffusion index is collapsing (second panel, Chart 16). Such broad breadth of loan growth deterioration warns that loan growth and thus bank earnings are at risk of underwhelming still optimistic sell-side analysts’ expectations (not shown). On the credit quality front there are now two loan categories that are starting to show some modest signs of stress. Credit card net chargeoffs and non-current loans are spiking and now C&I delinquent loans have ticked up for the first time since the manufacturing recession (third & bottom panel, Chart 16). Our bank EPS growth model does an excellent job in capturing all these forces and signals that bank EPS euphoria is misplaced (bottom panel, Chart 15). Nevertheless, despite these softening bank sector drivers there are four significant offsets. First the drubbing in the 10-year yield has been reflected nearly one-to-one on the 30-year fixed mortgage rate and the recent surge in mortgage applications signals that residential real estate loans (second largest bank loan category) may reaccelerate in the back half of the year (top panel, Chart 17). Chart 16Deteriorating Credit Quality Chart 17Some Significant… Second, while there have been credit card and C&I loan credit quality issues, as a percentage of total loans they just ticked higher and remain near cyclical lows, at a time when banks have been putting more money aside to cover for these potential loan losses (bottom panel, Chart 17). Third, bank source of funding remains very cheap as depositors have not been enjoying higher short term interest rates, at least not at the big money center banks. In other words, banks have not been passing higher interest rates to depositors sustaining relatively high NIMs (not shown). Finally, banks are one of the few sectors with pent up equity buyback demand. The upcoming release of the Fed’s stress test will likely continue to allow banks to pursue shareholder friendly activities, that they have been deprived from for so long, and raise dividend payments and increase share buybacks (Chart 18). Chart 18…Offsets In sum, melting inflation expectations, modest deterioration in credit quality, and weakening prospects for loan growth compel us to put the S&P bank index on downgrade alert. Bottom Line: We remain overweight the S&P banks index, but have put it on downgrade alert and are looking for an opportunity to downgrade to neutral. The ticker symbols for the stocks in this index are: BLBG: S5BANKX – WFC, JPM, BaAC, C, USB, PNC, BBT, STI, MTB, FITB, CFG, RF, KEY, HBAN, CMA, ZION, PBCT, SIVB, FRC.   Anastasios Avgeriou, U.S. Equity Strategist anastasios@bcaresearch.com Footnotes 1      https://www.newyorkfed.org/research/capital_markets/ycfaq.html Current Recommendations Current Trades Size And Style Views Favor value over growth Favor large over small caps

​​​​​​​Overweight, High-Conviction On June 10th we tightened our stops on the overweight call in the S&P software index, as a risk management measure in the context of our cautious broad equity market stance. Our bullish software thesis has not changed, and we reiterate that the only way to monetize gains in these highflying stocks is via tightening stops. Yesterday’s ultra-dovish Fed meeting boosted the appeal of high growth stocks, including software, as the Fed is seriously considering a cut in the late-July meeting. Moreover, software investment is the last pillar keeping overall U.S. capital outlays in positive territory. Not only is software investment rising, but it is also garnering a larger slice of the overall capex pie (middle & bottom panels). Another source of support is that software is a service-based industry and, at the margin, mostly insulated from the U.S./China trade dispute, so investors have been finding refuge in these equities. Adobe’s and Oracle’s recent healthy earnings reports and upbeat guidance confirm that software profits will remain upbeat and will likely continue to outpace the broad market (bottom panel). Bottom Line: We remain cyclically overweight the S&P software index (it is also a high-conviction overweight), but we will obey our stops in case a riot point materializes in the broad equity market. The ticker symbols for the stocks in this index are: BLBG: S5SOFT – MSFT, ORCL, ADBE, CRM, INTU, ADSK, RHT, CDNS, SNPS, ANSS, SYMC, CTXS, FTNT.  

Global smartphone sales, which drive 29% of global semiconductor revenues, are currently contracting. According to the International Data Corporation (IDC), in Q1/2019 global smartphone shipments declined 6.6% year-on-year (yoy) in volume terms. The slowdown is also picking up pace, as last year’s drop was 4.4% (see chart). We also expect smartphone shipments to continue contracting in the second half of this year. Major markets such as mainland China and advanced economies have entered the saturation phase of mobile-phone demand. For example, U.S. shipments were down 15% yoy in Q1 due to near-full market penetration. Investors are also mistakenly betting on 5G technology. Although Samsung, Huawei, OnePlus, Xiaomi, Motorola, LG, and ZTE have either released or will release their 5G phones this year, the sales growth from 5G phones will not be able to offset the loss in 2G, 3G and 4G phone sales, at least not in 2019. IDC estimated that 5G phones would only reach 0.5% of the global mobile-phone market share this year. 5G phones will likely only begin boosting overall semiconductor demand next year, when they will garner a larger slice of the global smartphone market. Bottom Line: Global semiconductor stocks are still facing considerable downside. Our Emerging Markets Strategy service remains negative on Asian semiconductor share prices in absolute terms. A continued contraction in global semiconductor sales will further squeeze their profits. For additional details, please see this past Monday’s Special Report authored by Ellen JingYuan He, Associate Vice President of Emerging Markets Strategy. ​​​​​​​​​​​​​​

The odds of a cyclical upturn in global semiconductors over the next three-to-six months are low as global demand remains feeble and is contracting 15%/annum (top panel). Drilling deeper into global demand reveals that the slowdown is structural, affecting a number of geographical areas (bottom five panels). Chip company revenues have so far contracted by 24% since the October 2018 peak, which is disproportionally more than the decline in share prices. The global semiconductor equity index is only 14% below its March 2018 high. It appears as though the market is expecting a quick recovery in semi sales. However, as we highlighted in our most recent Special Report authored by Ellen JingYuan He, Associate Vice President of Emerging Markets Strategy, there are structural problems in each of the key segments that drive global semiconductor sales, warning that the odds of an upturn are low. Please see the next Insight where we discuss the major demand driver that accounts for 29% of the world’s total semiconductor sales. ​​​​​​​

Special Report

Highlights The report reviews our framework for predicting broad market earnings in China based on the experience of the past decade, and documents the relationship between sector earnings and broad market earnings for both the investable and domestic market. We also review the cyclicality of earnings in each sector, and highlight the sectors where relative earnings have been successful at predicting relative performance. Energy and consumer discretionary in both markets, along with real estate and financials in the domestic market, have historically been the best candidates for a classic top-down fundamental “sector rotation” strategy. Compared with these sectors, investable telecom stocks have exhibited a weaker link between sector and index earnings, but this has occurred because of relatively steady, low volatility earnings growth. As such, telecom stocks are reliably defensive, but only in the investable market. We conclude by noting the extreme nature of long-term de/re-rating trends that have occurred for several of China’s equity sectors, and argue that the strength of the relationship between earnings and stock prices for these sectors is set to rise over a secular time horizon. Over the coming few years, investors should focus nearly exclusively on the earnings outlook for high flying and beaten down sectors, as further multiple expansion/contraction is unlikely to drive future returns (without an earnings catalyst). Feature Last week’s joint report with our Geopolitical Strategy service provided investors with an update on the trade war in the lead up to the G20 meeting in Osaka.1 While a new tariff ceasefire may emerge from the meeting, the report underscored why the odds are skewed against a positive outcome over the coming 18 months. Our bet is that investors are unlikely to assume that a deal will occur merely in response to a new timetable for talks, implying that any near-term boost to stock prices will be minimal until negotiators provide market participants with evidence (rather than hope) that a deal is achievable. This means that a financial market riot point remains likely over the coming few months, and that a cyclically bullish stance towards Chinese stocks rests on the likelihood of a major policy response in China to counter the likely shock to its export sector. During times of high policy uncertainty, we often take the opportunity to review and update our framework for key asset drivers. In today’s report we review our framework for predicting broad market earnings in China based on the experience of the past decade, and then document the relationship between sector earnings and broad market earnings for both the investable and domestic market. We review the cyclicality of earnings in each sector, and highlight the sectors where relative earnings have been successful at predicting relative performance. We conclude with a summary of what our results would imply over the tactical and cyclical investment horizons given our view of China’s likely growth trajectory, and highlight why several sectors may see a stronger relationship between their earnings and stock prices over the secular horizon. The report illustrates our key conclusions in the body of the text, but reference charts for each sector/industry group in both the investable and domestic market are provided as a convenience on pages 12 - 23. Predicting Chinese Equity Index Earnings Our framework for predicting index EPS is straightforward but reliable. Chart 1Stronger Economic Activity = Stronger Investable Earnings Chart 1 presents the first element of our framework for predicting Chinese investable earnings per share (EPS) growth. The chart illustrates the strong leading relationship between our BCA China Activity Indicator and the year-over-year growth rate of investable EPS, which underscores that the fundamental performance of Chinese equities is still predominantly driven by China’s “old economy”. The leading nature of our activity index partly reflects the fact that earnings per share are measured on a trailing basis; the key point for investors is that indicators such as our Activity Index have been more successful at capturing the coincident trend in China’s economy than, for example, real GDP growth has over the past several years. Chart 2illustrates that the earnings cycle for the investable and domestic equity markets is the same, with the magnitude of a given cycle accounting for the difference between the two markets. This means that investors exposed to the Chinese equity market should be focused heavily on predicting the coincident trend in the economy, as doing so will lead investors to the same conclusion about the trend in H- and A-share EPS growth. Chart 2Same Earnings Cycle In The Investable And Domestic Markets Chart 3Our Leading Indicator Reliably Predicts Economic Activity In turn, Chart 3 presents our framework for predicting Chinese economic activity, which we originally laid out in our November 30, 2017 Special Report.2 The chart shows that our leading activity indicator has reliably predicted inflection points in actual activity over the past several years, including the slowdown of the past two years (the leading indicator peaked in Q1 2017). As detailed in the report, our indicator is based on monetary conditions and money & credit growth. Panel 2 of Chart 3 shows that monetary conditions are very easy and credit growth is picking up, though it needs to continue to improve alongside a forceful pickup in money growth in order for the economy to strengthen. The key takeaway for investors is that the overall earnings cycle in China is strongly linked to “old economy” economic activity, which in turn appears to reliably predicted by our indicator. This provides us with a stable platform from which we can examine (and ultimately predict) equity sector EPS. Sector Earnings: Predictability And Cyclicality Given the strong link between Chinese economic activity and equity market EPS that we noted above, the question for equity-oriented investors is then to identify the relationship between sector and overall index EPS. In other words, to what degree are sector EPS in China linked to the overall earnings trend (versus being driven by idiosyncratic factors), and is this relationship pro- or counter-cyclical in nature? Charts 4 and 5 present the answers to these questions, based on the 2011 – 2018 period.3 The charts present the highest R-squared value resulting from a regression of detrended sector EPS versus broad market EPS for both the investable and domestic markets, after accounting for any leading/lagging relationships. The color/shading of each bar denotes whether the beta of the relationship for each sector or industry group is above or below 1. The charts present a mix of surprising and unsurprising results. Among the latter in the investable market, the cyclicality of typically high-beta sectors such as energy, materials, industrials, consumer discretionary, and technology would be readily accepted by most investors, as would the defensive characteristics of financials, telecom services, health care, utilities, and consumer staples. Investable consumer staples, health care, and utilities EPS are driven by either bottom-up/industry-specific factors or macro factors that are not fully captured by the trend in China’s business cycle. However, there were several less-intuitive results that emerged from our analysis, related to both the investable and domestic markets: Within the investable market, the low predictability of health care, utilities, and consumer staples EPS is somewhat difficult to explain. A weak relationship would easily be explained if EPS growth for these sectors were somewhat constant in the face of fluctuations in overall index EPS, but Chart 6 shows that the volatility in EPS growth for these sectors are not bottom-ranked (see also pages 16, 17 and 22). In fact, utilities EPS growth vol has been relatively high, and it is higher for health care and consumer staples than it is for financials and banks, whose EPS growth are highly linked to the overall earnings cycle. This result suggests that the determinants of earnings for these sectors are driven by either bottom-up/industry-specific factors or macro factors that are not fully captured by the trend in China’s business cycle. The low predictability of consumer staples and utilities EPS observed in the investable market is also evident in the domestic market, suggesting that this finding is not the result of quirky data. We noted earlier that overall index earnings are highly correlated with our BCA China Activity Index, and we have noted in past reports that China’s business cycle continues to be subject to its “old” growth model centered on investment and exports rather than the services and consumer sectors.4 This may explain the relatively idiosyncratic EPS profile for consumer staples, although it still fails to explain the low predictability and relatively high volatility of utilities earnings. Telecom services and technology earnings also have a very low correlation with overall earnings in the domestic market, which is similar to the investable market but more extreme. On the tech front, this is explained by the fact that Alibaba and Tencent, China’s tech giants, are not listed in the domestic market, underscoring that investable tech and domestic tech should be considered by investors to be distinctly separate sectors. In the investable market the low predictability and defensive characteristic of telecom services EPS can be explained by stable, low-volatility growth, but this is not true in the domestic market. In fact, over the past several years the volatility of domestic telecom EPS growth has been among the highest of any of China’s domestic equity sectors, and it has been cyclical rather than defensive in nature. These findings are difficult to explain from a top-down perspective. Finally, while Charts 4 and 5 show a difference in the cyclicality of real estate earnings between the investable and domestic markets, the difference is not substantial: the beta of the former is 1.03 versus 0.94 for the latter. The truly surprising result from real estate stocks is that their EPS growth is not considerably high-beta, given the boom & bust nature of Chinese property prices and the enormous amount of activity that has occurred in Chinese real estate over the past decade. Given that beta is determined relative to the overall index, this is emblematic (and an important reminder) of the underlying cyclicality of China’s economy and its financial markets relative to its global counterparts. Sector Earnings: Relevance For Stock Prices Following our review of the predictability and cyclicality of Chinese sector EPS, Charts 7 and 8 illustrate the relationship between relative EPS and relative stock price performance for these sectors. The charts highlight several notable points: In both the investable and domestic markets, the relative performance of energy and consumer discretionary stocks have been highly explained by the trend in relative EPS. Both of these sectors have also shown reasonably high EPS predictability (based on overall index EPS), suggesting that these two sectors have historically been the best candidates for a classic top-down fundamental “sector rotation” strategy. The relative re-rating of consumer staples and de-rating of banks reflects the existence of a long consumer economy / short industrial economy trade. Chart 9Multiples Have Been More Important In Driving The Returns Of These Sectors Within the investable market, relative EPS has not been successful at predicting relative stock price performance for financials/banks, health care, consumer staples, and industrials. This means that multiple expansion/contraction has been a relatively more important factor in driving returns, which can clearly be seen in Chart 9. The chart shows that investable banks, health care, and industrials have been meaningfully de-rated over the past several years, whereas the relative P/E ratio for consumer staples stocks has risen (albeit in a choppy fashion). Domestic consumer staples have also benefited from re-rating, although it has occurred entirely within the past three years and has merely made up for the substantial de-rating that took place in 2012 (Chart 9, panel 2). Taken together, the relative re-rating of consumer staples and de-rating of banks and industrials reflects, at least in part, the existence of a long consumer economy / short industrial economy trade. The relative EPS trend of utilities in both markets and that of telecom services stocks in the investable market have done a decent-to-good job of predicting relative stock price performance. We noted earlier that investable telecom services earnings appear to have a weak relationship with overall index earnings because of their low variability, meaning that they have also been a good top-down rotation candidate on the defensive side of the spectrum. The high responsiveness of the relative equity performance of Chinese utilities to relative EPS raises the importance of predicting the latter, which is likely to be a topic of future reports for BCA’s China Investment Strategy service. Finally, Chart 7 shows that the most important sector trend in the investable market over the past several years, the outperformance of information technology, has been strongly explained by the trend in relative EPS. This is good news for investors, as it suggests that relative tech returns can be reasonably predicted by accurate earnings analysis. From a top-down perspective, we noted earlier that the relationship between tech and overall index EPS has not been extremely high, which raises the bar for investors to understand the idiosyncratic drivers of earnings for the BAT (Baidu, Alibaba, and Tencent) stocks. Chinese consumer spending remains the most important macro factor for these stocks, but our understanding of this relationship is not complete and is an area of ongoing research at BCA. Investment Conclusions Chart 10 summarizes the results of Charts 4-5 and 7-8, by grouping investable and domestic equity sectors into four quadrants based on top-down EPS predictability (x-axis) and the impact of the trend in relative EPS on relative stock price performance (y-axis): Over a multi-year time horizon, the relationship between relative earnings and relative stock prices is likely to rise for several sectors. As we noted above, energy and consumer discretionary in both markets along with real estate and financials in the domestic market have had the strongest relationship across both dimensions (top-right quadrant). The EPS relationship is cyclical in both markets in the case of energy and consumer discretionary, whereas it is modestly cyclical for domestic real estate and defensive for domestic financials. Sectors in the top-left quadrant have shown a strong link between earnings and stock price performance, but a weaker link between sector and index earnings. This is the case for telecom services because of relatively steady, low volatility earnings growth, meaning that telecom stocks are reliably defensive. Fluctuations in the growth of index EPS do not explain the majority of changes in investable tech EPS, but it is an important driver in a cyclical relationship. Sectors in the bottom-right quadrant have a predominantly strong and defensive relationship with index earnings growth (with the exception of domestic industrials), but have experienced significant changes in multiples over the past several years that have materially impacted their relative stock price performance. We showed in Chart 9 that banks have been meaningfully de-rated over the past several years; this process appears to have halted at the end of 2017, suggesting that the relationship between relative earnings and relative stock prices may be stronger going forward. Chart 11Investable Real Estate And Materials Stocks Trade At A Huge Discount Finally, sectors in the bottom left quadrant have had relatively idiosyncratic earnings trends, and relative EPS have not explained a majority of the trend in relative performance. We would draw a distinction between investable industrials, real estate, and materials and the rest of the sectors shown, as they are on the cusp of being in the top-right or bottom-right quadrants, and all three appear to have suffered from meaningful de-rating. Investable real estate and materials now trade at over a 40% discount to the overall index (Chart 11), raising a serious question as to whether relative P/Es can continue to compress and explain the majority of relative equity performance. However, investable consumer staples and health care, along with domestic technology and telecom services stocks, do appear to be legitimately idiosyncratic, suggesting that an equity beta approach (regressing sector returns against index returns) is the best top-down method available to investors when allocating to these sectors. For investable staples and health care their equity return betas are clearly defensive, whereas domestic tech and telecom services stocks are market neutral. What does this all mean for investors? Our findings above lead us to some specific conclusions over the tactical (0-3 months), cyclical (6-12 months), and secular (multi-year) horizons: Over the cyclical horizon, we expect Chinese co-incident economic activity to pick up and for overall index EPS to improve, suggesting that global investors have a fundamental basis to be overweight investable energy, consumer discretionary, materials, media & entertainment (within the new communication services sector) and industrial stocks, at the expense of telecom services and financials.5 Investable health care, consumer staples, and utilities stocks are also likely to underperform, although this view is based on a statistical/empirical relationship rather than a fundamental one. In the domestic market, our findings support substituting real estate for technology in comparison to the investable sectors we listed above, but we are concerned that policymakers may crack down more heavily on the property sector if they allow overall credit growth to rise meaningfully as part of a stimulative response. For now, we would not recommend aggressive bets in favor of the domestic real estate sector. Chart 12Flagging Earnings Growth Heightens Tactical Risks To Chinese Stocks Over the tactical horizon, however, we would advise either the opposite stance, or a benchmark sector allocation. In addition to our view that a financial market riot point remains likely over the coming few months to force policymakers to address the economic weakness that an escalated tariff scenario would entail, broad-market Chinese EPS growth continues to decelerate (Chart 12). We see this continued slowdown as a lagged response to past economic weakness, which we expect will be reversed over the coming year due to stronger money & credit growth. However, sectors with pro-cyclical earnings growth may fare poorly in the near term until investors gain confidence that the (inevitable) policy response will stabilize the earnings outlook. Over the secular horizon, the most important conclusion is that there have been several long-term sectoral de/re-rating trends within China’s equity market. In the investable market, health care, consumer staples, and consumer discretionary (of which Alibaba is heavily represented) trade at 100-200% of a premium relative to the broad equity market on a trailing earnings basis, whereas financials, materials, and real estate stocks trade at a 40-60% discount. These divergences also exist in the domestic market, although the range is somewhat less extreme. A simple contrarian instinct might be to strategically overweight/underweight expensive/cheap sectors, but to us the simpler conclusion is that the extreme nature of these trends means that the strength of the relationship between EPS and stock prices for these sectors is set to rise. Over the coming few years, investors should focus nearly exclusively on the earnings outlook for high flying and beaten down sectors, a question that is very likely to be the topic of additional China Investment Strategy reports this year. Stay tuned!   Jonathan LaBerge, CFA, Vice President Special Reports jonathanl@bcaresearch.com     Reference Charts Energy Chart 13 Chart 14 Materials Chart 15 Chart 16   Industrials Chart 17 Chart 18   Consumer Discretionary Chart 19 Chart 20   Consumer Staples Chart 21 Chart 22   Health Care Chart 23 Chart 24   Financials Chart 25 Chart 26   Banking Chart 27 Chart 28   Information Technology Chart 29 Chart 30   Telecom Services Chart 31 Chart 32   Utilities Chart 33 Chart 34   Real Estate Chart 35 Chart 36   Footnotes 1      Please see Geopolitical Strategy and China Investment Strategy Special Report, “Another Phony G20? And A Word On Hong Kong”, dated June 14, 2019, available at cis.bcaresearch.com. 2      Please see China Investment Strategy Special Report, “The Data Lab: Testing The Predictability Of China’s Business Cycle”, dated November 30, 2017, available at cis.bcaresearch.com. 3      S&P Dow Jones and MSCI Inc. implemented major structural changes to the Global Industry Classification Standard (GICS) in Q4 2018 that substantially altered the sector composition of the MSCI China Investable index. The weight of the information technology sector in the investable index dropped dramatically after the GICS changes occurred. Investors should note that we used Q3 2018 as the end date of our analysis in order to remove any impact from the GICS sector change; the reference charts shown on pages 12 – 23 provide all data since 2011. 4     Please see China Investment Strategy Weekly Report, “The Three Pillars Of China’s Economy”, dated May 16, 2018, available at cis.bcaresearch.com. 5      Due to the changes to the GICS classification structure noted in footnote 3, the tech sector relationships that we highlighted above now apply to the consumer discretionary sector (level 1) and media & entertainment industry-group (level 2, within the new level 1 communication services sector. Cyclical Investment Stance Equity Sector Recommendations

Since early March, when we first turned tactically cautious on the prospects of the broad equity market, we started applying risk metrics to our portfolio in order to protect profits. In recent weeks as our cautiousness morphed from a tactical into a cyclical time horizon, we have both added more stops and also tightened existing trailing stops to our portfolio. As a result, our long S&P homebuilding/short S&P home improvement retail market- and sector-neutral trade got stopped out last week at the 10% return mark since the January 22nd, 2019 inception. Bottom Line: We have been increasingly using risk management metrics to protect gains in our U.S. equity portfolio and we are obeying the trailing stop on the long S&P homebuilders/short S&P home improvement retailers pair trade for a gain of 10% since inception. 

Special Report

Highlights The odds of a cyclical upturn in the global semiconductor sector over the next three to six months are low. Global semiconductor demand will continue to decline due to contracting demand for smartphones, automobiles, personal computers (PCs), and servers. Global semiconductor stocks are still facing considerable downside in absolute terms. We recommend going long Asian semiconductor stocks versus the U.S. S&P 500 semiconductor index. Dedicated EM equity portfolios should stay neutral on the Taiwanese bourse and Korean technology sector relative to the overall EM benchmark. Feature Chart 1 shows share prices of the global semiconductor sector and global semiconductor sales in the past two decades. Chart 1Global Semiconductor Market: Sales & Share Prices Was last December’s trough in global semiconductor equity prices the ultimate bottom in this cycle? The odds are in favor of a continued contraction in global semiconductor sales and further downside in semiconductor share prices over the next three to six months. Cycle-On-Cycle Analysis Semiconductor sales experienced five recessions over the past 20 years. Table 1 illustrates the peak-to-bottom percentage decline in nominal global semiconductor sales and the magnitude of the drop in global semiconductor share prices in U.S. dollar terms during these five cyclical downturns in this industry. It also indicates the duration of each downturn and the number of months that semiconductor stocks led the bottom in global semiconductor sales. Table 1Key Statistics Of Five Cyclical Downturns In Global Semiconductor Market The current shrinkage of semiconductor sales is worse than the 2011-12 and 2015 downturns. Yet, it is still smaller than the magnitude during the 2008 Great Financial Crisis and the 2001 tech bubble bust. The revenue of semiconductor companies has so far contracted by 24%, which is disproportionally more than the decline in share prices of these companies. The global semiconductor equity index is only 14% below its March 2018 high. It appears as though the market is expecting a quick recovery in semiconductor sales. As per Table 1, in the downturns of 2008, 2011 and 2015, global semiconductor stocks all bottomed before the bottom of global semiconductor sales. Only in the 2001 episode, stock prices bottomed eight months after the bottom in sales. In the current cyclical downturn, global semiconductor sales have so far had only four months of growth contraction,1 far less than the 13-16 months experienced in all the past four cycles. All in all, we would lean against the market’s expectation of an imminent recovery in the semiconductor cycle. The demand downturn will last another three to six months and share prices are facing major headwinds. Global Semiconductor Demand Semiconductor sales are in contraction across countries and regions (Chart 2). In April – before President Trump’s tweet on imposing new import tariff on China, global semiconductor sales growth sank to a negative 15% year-on-year. The short-term (three-to-six month) outlook for global semiconductor demand remains dismal. Chart 3 shows global semiconductor revenue breakdown in terms of end usage. Mobile phones account for the largest share (29%) of the market, followed by PCs (12%), miscellaneous consumer products (12%), and servers (11%). All of these major demand sources are under downward pressure: Smartphone Sales Global smartphone sales are shrinking (Chart 4). According to the International Data Corporation (IDC), global smartphone shipments declined 6.6% year-on-year in volume terms in the first quarter of this year, worse than last year’s 4.4% drop. Chart 2Semiconductor Sales Are In Contraction Across Countries  In the current cyclical downturn, global semiconductor sales have so far had only four months of growth contraction, far less than the 13-16 months experienced in all the past four cycles. Chart 4Global Smartphone Sales: Contracting We expect smartphone shipments to continue contracting in the second half of this year. Major markets such as mainland China and advanced economies have entered the saturation phase of mobile-phone demand. For example, U.S. shipments were down 15% year-on-year in the first quarter due to near-full market penetration. In China, smartphone sales have shown signs of stabilization (Chart 5). However, this is probably temporary and has been driven by the boom in Huawei smartphone sales in China since early this year. The incredible 50% year-on-year growth of Huawei smartphone sales in the first quarter is not sustainable. While global sales of Huawei smartphones increased by 20 million units, total global smartphone sales of all brands fell by 22 million units (Chart 6). The U.S. punitive actions towards China and Huawei have also instigated nationalism in China. This has triggered a Chinese buying-spree of the Huawei smartphone. Chart 5Chinese Smartphone Sales: Temporary Stabilization Yet, this has probably reduced the number of potential Chinese smartphones buyers in the near future. After all, many buyers likely made the purchase earlier than otherwise planned in the absence of a trade war. Although Samsung, Huawei, OnePlus, Xiaomi, Motorola, LG, and ZTE have either released or will release their 5G phones this year, the sales growth from 5G phones will not be able to offset the loss in 2G, 3G and 4G phone sales, at least not in 2019. The IDC estimated that 5G phones would only account for about 0.5% of the market share this year. 5G will likely only begin affecting overall semiconductor demand next year, when they account for a larger share of smartphone sales.   Huawei is the market leader in 5G technology. The U.S. boycott of Huawei will likely continue. This will only slow the pace of 5G phone adoption and the development of 5G networks worldwide. On balance, global smartphone demand may only recover next year. Server Demand Global server shipments also experienced a 5% contraction in volume terms in the first quarter of this year, according to IDC (Chart 7). The outlook for the rest of 2019 does not look promising. Global server demand will likely remain in contraction in the second half of this year. Many hyperscale data centers have already purchased considerable amounts of servers in advance of the trade war to avoid tariffs.2 Meanwhile, the escalation in the U.S.-China confrontation has increased economic uncertainties. This may delay potential datacenter investments. Decelerating 5G network development worldwide due to the U.S. ban on Huawei will also tend to discourage new datacenter and cloud services projects. This is because the 5G technology enables datacenter and cloud services to experience a huge improvement in terms of data transfer speeds, latency, connectivity, capacity, reliability and mobility. Chart 7Global Server Shipment: Are In Contraction Personal Computers (PC) PCs sales are also in contraction (Chart 8). PC demand has entered into the deep-maturation phase while facing strong competition from tablets and laptops. Auto Sales Global auto sales also sank by 5% in April from a year ago, registering the worst contraction since 2009 (Chart 9). Chart 8Global PCs Sales: Deeply Saturated Chart 9Global Auto Sales Are In Contraction As Well Regarding auto demand, the Chinese government may continue to implement more supportive policies to stimulate car sales in China. However, we believe the recovery will be delayed. The government has already implemented a number of policies to lift domestic car sales since late January, including providing subsidies to encourage new energy vehicle sales, to promote auto sales in rural areas, and to increase auto replacement. The central government recently loosened auto sales restrictions in the first tier cities of Guangzhou and Shenzhen that have restrictive auto sales policies. However, all of these policies have failed to lift Chinese domestic car sales out of deep contraction. The key reason has been a diminishing willingness to spend among Chinese consumers, as suggested by falling households’ marginal propensity to consume (Chart 10). Bottom Line: Global semiconductor demand growth will likely remain weak and will fail to recover in the second half of this year. The basis is that its major upstream markets (smartphone, servers, PCs and automobiles) are all facing cyclically declining demand. Chart 10Chinese Consumers: Diminishing Willingness To Consume Inventories And Prices Chart 11 shows the semiconductor supply chain illustrating the process of manufacturing semiconductors starting with silicon wafers and up to final electronic products. Box 1 explains the role of key segments and players along the supply chain. Box 1 A Brief Explanation Of The Key Segments/Players Of The Supply Chain Both integrated device manufacturers (IDM) and foundries are at the center of the supply chain, responsible for chip manufacturing. In terms of semiconductor sales revenue, Samsung, Intel and SK Hynix are the world’s top three IDM companies and TSMC, Global Foundries and United Microelectronics Corp (UMC) are the world’s top three foundries. While IDMs cover most of the process from IC design, chip fabrication, assembly, testing and packaging, IDM companies still have to purchase raw materials and equipment for the chip-making process. Foundry companies receive orders from IC designing companies like Qualcomm, Nvidia, and Huawei HiSilicon, then purchase needed raw materials and equipment to proceed in the chip-manufacturing process. Both IDMs and foundries can either outsource the tasks of semiconductor assembly and testing or perform them on their own. The final semiconductor products will be used in electronics products, such as smartphones, computers, home appliances, automobiles, etc. Global semiconductor demand growth will likely remain weak and will fail to recover in the second half of this year. The basis is that its major upstream markets (smartphone, servers, PCs and automobiles) are all facing cyclically declining demand. In a typical business cycle, a cyclical downturn begins with a slump in demand for final electronic products (upstream demand). This leads to falling semiconductor sales. As a result, inventory buildup will occur across most of the segments along the semiconductor supply chain. Chipmakers: Producers’ semiconductor inventory in both Taiwan and Korea have reached either a record high or a near-record high (Chart 12). The installed wafer capacities at these two countries are the world’s largest, together accounting for 43% of total global wafer capacity. In addition, the inventory of some major electronic parts and components have also increased considerably in Taiwan (Chart 13). This also implies weaker demand for semiconductor raw materials. Chart 12Chipmakers: A Rapid Buildup In Inventory Chart 13Rising Inventory Of Some Major Electronic Parts And Components Raw material suppliers: Silicon wafer is the indispensable raw material required in the chip manufacturing process. Japanese companies account for over half of global silicon wafer supply.Chart 14 shows that silicon wafer inventory in Japan has had a significant buildup in volume terms since late last year. Importantly, it is not declining yet. Chart 14Silicon Wafer Inventory: A Significant Buildup As Well Outsourced semiconductor assembly and test (OSAT) providers: Both Singapore and Thailand are OSAT providers while they also manufacture, assemble and export electronic products. Both countries are closer to the downstream side of the semiconductor supply chain. Semiconductor inventory at these two countries has also jumped to a record high (Chart 15).   Chart 15Singapore and Thailand: Record High Semiconductor inventory   Importantly, a marginal improvement in demand will tend to support spot prices. For example, in the memory chip market, falling prices denote weak demand relative to excess supply. When prices of DRAM and NAND start to form a bottom or decisively move up, this may indicate the arrival of a cyclical upturn. So far, both DRAM and NAND prices are continuing to fall (Chart 16). In addition, the prices of silicon wafer – the most important raw material used in the chip-making process – have declined in the first half of this year.3  Chart 16Still Falling Memory Chip Prices Chart 17Deflating DRAM Prices Suggest Downside Risks To Korean Tech StocksIn short, prices are the most important variable to monitor. Chart 17 exhibits the high correlation between DRAM prices and the Korean technology sector stock prices. Falling DRAM prices suggest downside risks to technology stocks in Korea. Samsung accounts for about 65% of Korea’s tech index and 27% of the overall Korean equity index. Memory chips accounted for 68% of Samsung’s operating profits in the first quarter of this year.   Bottom Line: There has been involuntary inventory accumulation along the entire supply chain of semiconductors. This and ongoing price deflation among various types of semiconductors foreshadow a downbeat near-term outlook. The Interpretation Of Some Positive Developments There have been some positive developments in the past several months. Taiwanese PMI new orders diffusion index in the electronics sector jumped out of deep contraction to reach zero, and Chinese semiconductor imports halted their decline in both volume and value terms (Chart 18). The improvement in the aforementioned data was probably mainly due to large semiconductor purchases by China to hedge the rising risk of U.S. blocking China’s technological development (Chart 19). Chart 18Some Positive Development Chart 19China: More Semiconductors Purchases Before The Tariff And U.S. Huawei Ban? Besides, Huawei smartphone sales have been booming, which we deliberated on page 5, could have been responsible for the improvement in these data. This one-off surge will likely dwindle going forward. Investment Conclusions We remain negative on Asian semiconductor share prices in absolute terms. A continued contraction in global semiconductor sales will further squeeze their profits. In relative terms, we are neutral on the Asian semiconductor sector: we continue recommending market-weight allocation to Taiwan’s overall market and the Korean technology sector within the EM equity benchmark. As a new trade, we recommend going long Asian semiconductor stocks and short the S&P 500 semiconductor index over the next three to six months (Chart 20). The Bloomberg Asia Pacific semiconductor index has nine stocks. Samsung and TSMC account for 42% and 38% of the index, respectively. There has been involuntary inventory accumulation along the entire supply chain of semiconductors. This and ongoing price deflation among various types of semiconductors foreshadow the downbeat near-term outlook. Samsung will likely benefit from the U.S. ban on Huawei in the smartphone sector outside of China. In addition, Samsung will win some market share from Apple as the latter does not have a 5G phone to release this year. These positive factors may partially offset the negative impact from falling memory prices and demand on Samsung. The S&P 500 semiconductor index has 13 stocks. Intel, Broadcom, Texas Instruments and Qualcomm are the top five constituents, together accounting for nearly 70% of the index. Most of these companies are IC designing companies, which will likely suffer as Chinese demand for their products shrink due to the U.S. administration's ban on Huawei. This position will also benefit from U.S. dollar appreciation. A firm dollar will hurt profits of U.S. semiconductor stocks. In turn, currency depreciation in Korea and Taiwan will on the margin benefit Asian semiconductor stocks. Chart 20Recommend Long Asia Pacific Semiconductor Stock Vs. S&P 500 Semiconductor Index Chart 21The 2015 Experience Chart 21 shows that global foundry companies outperformed global IC designing companies during the final phase of the 2015 cyclical downturn. Odds are that these dynamics will play out in this downturn as well. Finally, the relative performance of Asian semiconductor stocks versus U.S. ones is oversold and might stage some sort of mean reversion (Chart 20). Ellen JingYuan He, Associate Vice President ellenj@bcaresearch.com Footnotes 1      Please note that here the calculation for “the number of months of the growth contraction” is different from the one for the “peak-to-bottom duration” in Table 1. “The number of months of the growth contraction” equals the number of months when semiconductor sales year-on-year growth is negative. 2      https://marketrealist.com/2019/05/nvidias-data-center-revenue-inference-rendering-and-edge/ 3      http://www.sohu.com/a/300386061_132567, http://news.moore.ren/industry/104958.htm