Sectors

Highlights Corporate Spreads: The Fed’s dovish pivot prolongs the period of time before the yield curve inverts, thus extending the window for corporate bond outperformance. Investors should remain overweight corporate bonds, with a preference for securities rated Baa and below, where spreads remain wide relative to our fair value estimates. Yield Curve: Investors should barbell their U.S. bond portfolios, favoring long-maturity (> 10 years) and short-maturity (< 2 years) securities while avoiding the 5-year and 7-year notes. This positioning will boost average portfolio yield and will benefit from any future hawkish re-assessment of Fed policy. MBS: Lower mortgage rates have led to a jump in mortgage refinancings and wider MBS spreads. However, MBS spreads remain quite low compared to history. Maintain a neutral allocation to MBS in U.S. bond portfolios. Feature Last December, we laid out our key fixed income themes for 2019 in a Special Report.1 In that report we also introduced a framework for splitting the economic cycle into three phases based on the slope of the yield curve. Specifically, we use the 3-year/10-year Treasury slope to divide each cycle into the following three phases:2 Phase 1 runs from the end of the last recession until the 3/10 slope flattens to below 50 bps. Phase 2 encompasses the period when the 3/10 slope is between 0 bps and 50 bps. Phase 3 begins after the 3/10 slope inverts and ends at the start of the next recession. Clearly, as is illustrated in Chart 1, we are smack dab in the middle of a Phase 2 environment. This has implications for how we should think about positioning a U.S. bond portfolio. Chart 1Firmly In Phase 2 What Makes The Middle Phase Awkward? Table 1 shows annualized excess returns for Treasuries and corporate bonds (both investment grade and high-yield) in each phase of every cycle stretching back to the mid-1970s. Treasury excess returns are calculated relative to cash, as a proxy for the returns from taking duration risk. Corporate excess returns are relative to a duration-matched position in Treasury securities. Table 1Bond Performance In Different Yield Curve Regimes A look at Table 1 reveals why we call Phase 2 the “awkward” middle phase of the cycle. The excess returns earned from taking both duration and corporate spread risk tend to be underwhelming. On duration, we observe that in three of the four complete cycles in our sample, Treasury excess returns are lowest in Phase 2. This lines up well with intuition. The flatter yield curve means that Treasuries offer a lower term premium in Phase 2 than in Phase 1. Meanwhile, Phase 3 periods tend to coincide with rapid Fed rate cuts, and thus large capital gains. Phase 2 periods, in contrast, often contain Fed tightening cycles. On corporate credit, we observe that excess returns tend to be lower in Phase 2 than in Phase 1, but are usually still positive. Returns tend not to turn consistently negative until after the 3/10 slope inverts and we enter Phase 3. Overall, if we know nothing other than that we are in Phase 2 of the cycle, our results suggest that we should take less duration risk in our portfolio than in Phases 1 or 3. Overall, if we know nothing other than that we are in Phase 2 of the cycle, our results suggest that we should take less duration risk in our portfolio than in Phases 1 or 3. The results also suggest that we should prefer corporate credit over Treasuries, though to a lesser extent than in Phase 1. What Makes The Middle Phase Long? In last December’s Special Report, we argued that the U.S. economy would remain in a Phase 2 environment for a long time, at least until late 2019. Our reasoning was that, in the absence of inflationary pressures, the Fed would be reluctant to tighten policy enough to invert the 3/10 curve. The Fed’s recent dovish pivot, and the resultant steepening of the curve (see Chart 1), only prolongs the current Phase 2 environment. We now think it will be well into 2020, and possibly later, before the 3/10 slope inverts and the economy enters Phase 3. One obvious investment implication of an extended Phase 2 environment is that we should remain overweight corporate bonds relative to duration-matched Treasuries. However, we also need to consider valuation before drawing too firm of a conclusion. Charts 2A and 2B show spreads for each corporate credit tier, encompassing both investment grade and high-yield, along with our spread targets. The spread targets are the median levels observed in prior Phase 2 environments, adjusted for changes in the average duration of the bond indexes over time.3 The charts reveal that Aaa-rated bonds already look expensive, while Aa and A-rated bonds are close to fairly valued. Baa-rated bonds are 13 bps cheap relative to our target, while the high-yield credit tiers offer significantly more value. Chart 2AInvestment Grade Spread Targets Chart 2BHigh-Yield Spread Targets As discussed in last week’s report, the Fed’s dovish pivot will cause corporate spreads to tighten in the near-term, but it will take longer before Treasury yields respond by moving higher.4 For Treasury yields to move higher, investors must first become convinced that the Fed’s reflationary efforts are translating into stronger global economic growth. Ultimately, we expect this will occur in the second half of this year and Treasury yields will be higher 12 months from now, as the Fed will fail to deliver the 92 bps of rate cuts that are currently priced. The flat yield curve means that the yield give-up is small, and we expect global growth to improve in the second half of the year. Bottom Line: The Fed’s dovish pivot prolongs the period of time before the yield curve inverts, thus extending the window for corporate bond outperformance. Investors should remain overweight corporate bonds, with a preference for securities rated Baa and below, where spreads remain wide relative to our fair value estimates. Investors should also keep portfolio duration low. The flat yield curve means that the yield give-up is small, and we expect global growth to improve in the second half of the year. Barbell Your Portfolio Chart 3Barbell Your Portfolio For those unwilling or unable to deviate portfolio duration significantly from benchmark, there is another way to bet on the Fed delivering fewer cuts than are currently priced into the market. Investors can run a barbelled portfolio, favoring short-maturity (< 2 years) and long-maturity (> 10 years) securities, while avoiding the belly (5-year/7-year) of the curve. This sort of positioning has a few advantages. First, since the financial crisis, the yield curve has tended to steepen out to the 5-year/7-year point and flatten beyond that point whenever our 12-month Fed Funds Discounter rises (Chart 3). Conversely, whenever the market prices in more cuts/fewer hikes and our discounter falls, the yield curve has flattened out to the 5-year/7-year maturity point and steepened beyond that point. This correlation has been very consistent during the past few years, and continued to hold during the most recent decline in rate expectations. Notice that the 5-year yield has fallen by more than either the 2-year or 10-year yields since our Discounter's early-November peak (Table 2). Table 2The Belly Of The Curve Is Most Sensitive To Rate Expectations The upshot is that, if rate expectations rise during the next 12 months, as we expect, the 5-year and 7-year notes will endure the most damage. The second reason why a barbelled portfolio makes sense is that valuation is very attractive. Chart 4 shows that the 5-year yield is below the yield on a duration-matched 2/10 barbell. It also shows that this 2/5/10 butterfly spread is very low relative to our model’s fair value.5  Chart 42/10 Barbell Is Attractive Versus 5-Year Bullet We run similar fair value models for every possible bullet/barbell combination along the yield curve, and barbells appear universally cheap (see Appendix). Bottom Line: Investors should barbell their U.S. bond portfolios, favoring long-maturity (> 10 years) and short-maturity (< 2 years) securities while avoiding the 5-year and 7-year notes. This positioning will boost average portfolio yield and will benefit from any future hawkish re-assessment of Fed policy.   MBS & Housing: The Implications Of Lower Mortgage Rates Alongside bond yields, mortgage rates have fallen sharply during the past few months, a trend that has important implications for both MBS spreads and future housing data. We consider the outlook for both. MBS Spreads Lower mortgage rates encourage homeowners to refinance their loans, and any increase in refinancing activity puts upward pressure on MBS spreads. Not surprisingly, as mortgage rates have declined we have seen a jump in the MBA Refinance Index and a widening of nominal MBS spreads (Chart 5). Chart 5MBS Spreads Still Historically Tight While spreads have widened somewhat, they remain low compared to history (Chart 5, top panel). As such, we do not see a compelling buying opportunity in MBS. This is especially true relative to corporate credit where spreads are more attractive. Chart 6Limited Upside For Refis With the mortgage rate now below 4%, our rough calculation suggests that approximately 44% of the Bloomberg Barclays Conventional 30-year MBS index is refinanceable. A regression of the MBA Refi Index versus the refinanceable share suggests a fair value of 2014 for the Refi Index, slightly above its actual level of 1950 (Chart 6). We also calculate that a further drop in the mortgage rate to below 3.5%, where it troughed in mid-2016, would increase the refinanceable share to 77%. Our regression translates this 77% share to a level of 3309 on the Refi Index. It should be noted that when the refinanceable share rose to 77% in 2016, the MBA Refi Index peaked at 2870. This means that our simple regression analysis probably overstates the surge in refis that would occur if mortgage rates fell another 50 bps. In addition, we think it’s unlikely that mortgage rates will actually fall back to 3.5%, as they did in 2016, and as such, we are hesitant to position for further MBS spread widening. The improvement in housing actitivty is not uniform across all indicators. We recommend maintaining a neutral allocation to MBS for now. If mortgage rates drop and spreads widen further in the near-term, then a buying opportunity may present itself. Housing Activity Chart 7Housing Activity: A Mixed Picture The drop in mortgage rates will also have a significant impact on housing activity data. This is important because, as we have demonstrated in prior reports, housing activity data – particularly single-family housing starts and new homes sales – are reliable indicators of U.S. recessions and interest rates.6 By all measures, housing activity weakened significantly as mortgage rates surged in 2018. But it has improved somewhat now that mortgage rates have declined. However, the improvement is not uniform across all indicators (Chart 7): New home sales jumped sharply early this year, then fell back more recently. The current trend is neutral, with the latest monthly print very close to the 12-month moving average (Chart 7, top panel). Housing starts and permits are both trending below their respective 12-month moving averages, though by less than in 2018 (Chart 7, panel 2 & 3). Existing home sales have popped, and are now exerting upward pressure on the 12-month average (Chart 7, panel 4). Likewise for mortgage purchase applications (Chart 7, panel 5). Homebuilders also report that lower mortgage rates have led to a jump in sales activity (Chart 7, bottom panel).  With mortgage rates still low, the tentative rebound in housing activity data should continue in the coming months. Looking further out, we see significantly more upside in single-family housing starts and new home sales as builders shift construction toward lower-priced properties. The Bifurcated Housing Market Beyond the large swings in mortgage rates, another trend has significantly influenced housing activity in recent years. For the past few years, homebuilders have focused their attention on higher priced homes, and that segment of the market now looks oversupplied. Data from the American Enterprise Institute Housing Center show that the recent deceleration in home prices has been driven by falling prices for the most expensive homes. Homes in the lowest price tier have seen prices accelerate (Chart 8).7 The divergence is also evident in the supply data. New home inventories are roughly consistent with average historical levels, while existing home inventories are incredibly low (Chart 9). In fact, new home inventories now represent 6.4 months of demand while existing home inventories represent 4.3 months of demand (Chart 9, panel 3). Such a wide divergence is historically rare. Chart 8An Oversupply Of High ##br##Priced Homes... Chart 9...And An Undersupply Of Low Priced Homes   The divergence between an oversupply of new homes and an undersupply of existing homes is a result of new construction having focused on higher priced homes in recent years. The median price for a new home used to be only slightly above the median price for an existing home, but the difference shot up to above 75k during the past few years (Chart 9, bottom panel). More recently, the price differential between new and existing homes has started to fall, as builders are starting to recognize that the greater growth opportunity lies at the low-end of the market where demand is strong relative to supply. As this supply-side adjustment plays out, it will provide an additional boost to new homes sales and housing starts going forward. Appendix The following tables present the current read-outs from our butterfly spread models. We use these models to identify opportunities to take duration-neutral positions across the Treasury curve. The following two Special Reports explain the models in more detail: U.S. Bond Strategy Special Report, “Bullets, Barbells And Butterflies”, dated July 25, 2017, available at usbs.bcaresearch.com U.S. Bond Strategy Special Report, “More Bullets, Barbells And Butterflies”, dated May 15, 2018, available at usbs.bcaresearch.com Table 3 shows the raw residuals from each model. A positive value indicates that the bullet is cheap relative to the duration-matched barbell. A negative value indicates that the barbell is cheap relative to the bullet. Table 3Butterfly Strategy Valuation: Raw Residuals In Basis Points (As of June 27, 2019) Table 4 scales the raw residuals in Table 3 by their historical means and standard deviations. This facilitates comparison between the different butterfly spreads. Table 4Butterfly Strategy Valuation: Standardized Residuals (As of June 27 2019) Ryan Swift, U.S. Bond Strategist rswift@bcaresearch.com Footnotes 1 Please see U.S. Bond Strategy Special Report, “2019 Key Views: Implications For U.S. Fixed Income”, dated December 11, 2018, available at usbs.bcaresearch.com 2 We use the 3/10 Treasury slope in place of the more commonly referenced 2/10 slope because it is a close proxy that provides an additional 14 years of historical data. 3 For more details on how we arrive at our spread targets please see U.S. Bond Strategy Weekly Report, “The Value In Corporate Bonds”, dated February 19, 2019, available at usbs.bcaresearch.com 4 Please see U.S. Bond Strategy / Global Fixed Income Strategy Weekly Report, “The Fed’s Got Your Back”, dated June 25, 2019, available at usbs.bcaresearch.com 5 For more details on our yield curve models please see U.S. Bond Strategy Special Report, “Bullets, Barbells And Butterflies”, dated July 25, 2017, available at usbs.bcaresearch.com 6 Please see U.S. Bond Strategy Weekly Report, “More Than One Reason To Own Steepeners”, dated September 25, 2018, available at usbs.bcaresearch.com 7 Low-tier homes are those in the bottom 40% of the price distribution in each metro area. High-tier homes are those that are both in the top 20% of the price distribution and exceed the GSE loan limit by more than 25%. For further details: http://www.aei.org/wp-content/uploads/2019/06/HPA_market_conditions_report_June_2019.pdf Fixed Income Sector Performance Recommended Portfolio Specification

GAA DM Equity Country Allocation Model Update The GAA DM Equity Country Allocation model is updated as of June 28, 2019.  The quant model has upgraded Sweden to the second largest overweight (from a slight underweight) mainly due to sharp improvement in the liquidity indicator. This is financed by reductions in the overweight of Germany, Italy and the downgrade of Switzerland to a slight underweight (from overweight), as shown in Table 1.  Table 1Model Allocation Vs. Benchmark Weights As shown in Table 2 and Charts 1, 2 and 3, the overall model outperformed the MSCI world benchmark by 39 bps in June, largely driven by 104 bps of outperformance from Level 2 model, offset by 10 bps of underperformance from Level 1.  Directionally, six out of the 12 choices generated positive alpha. The largest contributions to the outperformance in June came from the overweight in Italy and the underweight in Japan.  Since going live, the overall model has outperformed by 238 bps, with 511 bps of outperformance by the Level 2 model, offset by 2 bps of underperformance from Level 1. Table 2Performance (Total Returns In USD %) Chart 1GAA DM Model Vs. MSCI World Chart 2GAA U.S. Vs. Non U.S. Model (Level 1)   Chart 3GAA Non U.S. Model (Level 2) Please see also the website http://gaa.bcaresearch.com/trades/allocation_performance. For more details on the models, please see Special Report, “Global Equity Allocation: Introducing The Developed Markets Country Allocation Model,” dated January 29, 2016, available at https://gaa.bcaresearch.com. Please note that the overall country and sector recommendations published in our Monthly Portfolio Update and Quarterly Portfolio Outlook use the results of these quantitative models as one input, but do not stick slavishly to them. We believe that models are a useful check, but structural changes and unquantifiable factors need to be considered too in making overall recommendations.   GAA Equity Sector Selection Model Chart 4Overall Model Performance The GAA Equity Sector Model (Chart 4) is updated as of June 28, 2019. The model’s relative tilts between cyclicals and defensives have changed compared to last month. The model increased its cyclical exposure by overweighting Materials on the backdrop of improvement in its momentum component. The model is therefore overweight two cyclical and two defensive sectors – Industrials, Materials, Consumer Staples and Utilities. The valuation component remains muted across all sectors. The growth component continues to favor defensive sectors so far, as an improvement in global growth hard data has not yet materialized. For more details on the model, please see the Special Report “Introducing the GAA Equity Sector Selection Model,” dated July 27, 2016, as well as the Sector Selection Model section in the Special Alert “GAA Quant Model Updates,” dated March 1, 2019 available at https://gaa.bcaresearch.com. Table 3Model’s Performance (March 1, 2019 - Current) Table 4Current Model Allocations   Xiaoli Tang, Associate Vice President xiaoliT@bcaresearch.com Amr Hanafy, Research Associate amrh@bcaresearch.com    

Highlights Portfolio Strategy Business sector selling price inflation is sinking like a stone following the bond market’s melting inflation expectations, at a time when wage inflation continues to expand smartly. There are good odds that profit margins have already peaked for the cycle, and we reiterate our cyclically cautious overall equity market view. The souring global macro backdrop, rising policy uncertainty, melting real yields and a stampede into bonds all signal that it still pays to hold global gold miners as a portfolio hedge. Three key defense manufacturers’ demand drivers – global rearmament, a space race and cyber security – remain upbeat and will continue to underpin relative industry profitability. Recent Changes There are no changes to the portfolio this week. Table 1 Feature The SPX fell from all-time highs last week on the eve of the G20 Trump-Xi meeting, the outcome of which will dominate trading this week. The “three hopes” rally, as we have coined it predicated upon a U.S./China trade deal, Chinese massive reflation and a fresh Fed easing cycle, is at risk of disappointment as all the good news is likely already priced into stocks. Stocks may suffer a buy the rumor sell the news setback as they did back in early-December right after the Argentina G20 meeting. Following up from last week’s charts 3-6 that generated higher-than-usual responses from clients, we were encouraged to broaden out these eighteen indicators and try to include some positive ones as it appeared that we may be cherry picking the data.1 Put differently, there must be some economic data series that would offset the grim U.S. macro backdrop we painted and likely aid the Fed in its looming easing cycle. This week we update our corporate pricing power table, highlight a safe haven materials subgroup, and an industrials bulletproof subindex. With regard to the 2018 stock market related fiscal easing boost, neither corporate tax rates would drop further in 2019 nor would buybacks hit the $1tn mark this year. Already, the Standard & Poor’s reported preliminary data that showed buybacks contracted sequentially by 7.7% in Q1/2019 (top panel, Chart 1).2 Retail sales and personal consumption expenditures (PCE) are indeed expanding, however retail sales have decelerated lately (top & second panels, Chart 2). In contrast, consumer sentiment and consumer confidence are contracting on a year-over-year (yoy) basis and the U.S. leading economic indicator is steeply decelerating near 2%/annum from almost 7% at the beginning of the year (middle, fourth & bottom panels, Chart 2). Chart 1Buybacks Are Decelerating Chart 2Retail Sales And PCE Are Expanding   The mortgage application purchase index is gaining momentum courtesy of the 125bps drop in interest rates over the past eight months. But, equity market internals suggest that some of these applications may not convert into home sales: relative homebuilders share price momentum is contracting (Chart 3). As a reminder we recently monetized relative gains of 10% in the S&P homebuilding index, since inception.3  Sticking with housing, new median single family home prices remain 10% below their 2017 zenith, and the Case-Shiller 20-city index growth rate hit the zero line recently on a month-over-month basis. New home sales are in contraction territory (Chart 4). Chart 3Are Cracks Forming… Chart 4…In The Housing Market?   On the labor front, while the unemployment rate and unemployment insurance claims are both at generationally low levels, it will be extremely difficult for either of these labor market series to fall significantly from current levels. In contrast, there are rising odds that the deteriorating credit quality backdrop will soon infect the labor market (top & second panels, Chart 5). Already, “jobs are hard to get” confirming that the unemployment rate cannot fall much further from current levels (middle panel, Chart 5). Not only is credit quality deteriorating at the margin, but also loan growth is decelerating with our credit impulse diffusion indicator falling below the boom/bust line (fourth & bottom panels, Chart 5). U.S. manufacturing, the most cyclical part of the U.S. economy, is under intense pressure. The U.S./China trade tussle is the culprit. Industrial production and capacity utilization petered out last year in September and November, respectively (top & second panels, Chart 6). Chart 5Could The Labor Market Sour Next? Chart 6Manufacturing Has No…   Chart 7…Pulse Durable goods orders are not showing any signs of a turnaround with overall orders flirting with the zero line and core orders contracting (third panel, Chart 6). Total business sales-to-inventories are stuck in the contraction zone (bottom panel, Chart 6). Manufacturing survey data series are all in a synchronous meltdown. Seven regional Fed manufacturing surveys are all sinking (Chart 7). Such broad-based weakness bodes ill for the upcoming ISM manufacturing survey print (we went to print on Friday after the market close, and as a reminder we observed Canada Day yesterday).   The ISM manufacturing new orders-to-inventories ratio sits right at one, warning that more profit trouble looms for the SPX (bottom panel, Chart 1). Keep in mind that typically the ISM manufacturing survey pulls down the ISM services one, as the former represents the most cyclical parts of the U.S. economy. Both are currently contracting on a yoy basis (Chart 8). Adding it all up, the negative economic data clearly dominate and only a handful of data series remain standing. The final tally on these indicators is fifteen negative and five positive (Chart 9). We are still awaiting a turn in the majority of the data to confirm the economy is on a solid footing. Chart 8ISM Services Survey Is Contracting Chart 10Heed The Message From The GS Current Activity Indicator Goldman Sachs’ Current Activity Indicator (GSCAI, a first principal component of 37 weekly and monthly data series) does an excellent job in capturing all these forces. Currently, the GSCAI is steeply decelerating, warning that SPX profit growth will surprise to the downside in coming quarters (top panel, Chart 10).  Thus, we reiterate that a cyclically (3-12 month horizon) cautious equity market stance is still warranted. This is U.S. Equity Strategy’s view, which stands in contrast to the sanguine equity BCA House View. This week we update our corporate pricing power table, highlight a safe haven materials subgroup, and an industrials bulletproof subindex. Corporate Pricing Power Update U.S. Equity Strategy’s corporate sector pricing power proxy has sunk further since our last update three months ago, and is now deflating 1.1%/annum. Chart 11 shows that the last time the business sector was mired in deflation was during the 2015/16 manufacturing recession. Chart 11Profit Margin Trouble To Persist However, the big difference between now and 2015/16 is that wages are currently expanding at a healthy clip, warning that the corporate sector margin squeeze will not abate any time soon. Granted, unit labor costs are indeed contracting on the back of a surge in productivity, and may thus provide a partial offset. SPX margins have been contracting for two consecutive quarters and sell-side analysts forecast that they will contract for another two. Our margin proxy corroborates this grim sell-side profit margin expectation, and similar to the 2015/2016 episode is firing a margin squeeze warning shot (bottom panel, Chart 11). Digging beneath the surface, our corporate pricing power proxy is revealing. As a reminder, we calculate industry group pricing power from the relevant CPI, PPI, PCE and commodity growth rates for each of the 60 industry groups we track. Table 2 also highlights shorter term pricing power trends and each industry's spread to overall inflation. Two thirds of the industries we cover are lifting selling prices, but only a quarter are raising prices at a faster clip than overall inflation. On a selling price inflation trend basis, 81% of the industries we cover are either flat or in a downtrend (Table 2). Table 2Industry Group Pricing Power There is only one commodity-related industry in the top ten, a sea change from our late-March update when the commodity complex dominated the top ranks occupying six spots (Table 2). Interestingly, industrials have a healthy showing in the top sixteen spots with five entries. On the flip side, energy-related industries continue to populate the bottom of the ranks as WTI crude oil is still deflating from the October 2018 peak. In sum, business sector selling price inflation is sinking like a stone following the bond market’s melting inflation expectations, at a time when wage inflation continues to expand smartly. There are good odds that profit margins have already peaked for the cycle, and we reiterate our cyclically cautious overall equity market view. In sum, business sector selling price inflation is sinking like a stone following the bond market’s melting inflation expectations, at a time when wage inflation continues to expand smartly. There are good odds that profit margins have already peaked for the cycle, and we reiterate our cyclically cautious overall equity market view. Glittering Gold On March 4th, 2019 we reiterated our view that it still made sense to hold an above benchmark allocation to gold equities as a portfolio hedge.4 While our overweight position is in the red since inception, it has recouped 15% versus the broad market since our early-March update, and more gains are in store in the coming months. When global growth is in retreat investors bid up the price of the safe-haven shiny metal which in turn pulls global gold miners higher. The opposite is also true. Chart 12 shows this inverse relationship gold mining equities have with global growth. In more detail, relative share prices move inversely with the global manufacturing PMI (PMI shown inverted, Chart 12). Chart 12Gold Miners Benefit From… Currently, economists, tracked by Bloomberg, have been aggressively decreasing their estimates for 2019 global real GDP growth, down 50bps year-to-date to 3.3% (bottom panel, Chart 13). Similarly, the global ZEW economic sentiment survey has collapsed to levels last hit during the great recession (top panel, Chart 14). Chart 13…Global Growth… Chart 14…Slowdown   Tack on the sustained increase in global policy uncertainty with trade wars, Iranian sanctions, Brexit and Italian politics to name a few, and global gold miners are in the pole position (top panel, Chart 13). As a result, global equity risk premia have come out of hibernation and signal that the gold mining rally has more legs (middle panel, Chart 14). This souring global macro backdrop has dealt a blow to global real yields that are melting. Given that gold equities sport a low dividend yield, they are primary beneficiaries of this disinflationary global economic backdrop (real yield shown inverted, middle panel, Chart 13). Chart 15Negative Yielding Bonds Boost Global Gold Miners Meanwhile, investors have been piling into global bonds and currently negative yielding bonds have surpassed the $13tn mark. Such a stampede into negative yielding bonds has been a boon to global gold mining stocks (Chart 15). This investor risk aversion is also evident in the total return stock-to-bond (S/B) ratio: bonds have been outperforming equities since late-September 2018. Since the early 1990s, relative share prices have been moving in the opposite direction of the S/B ratio, and the current message is to expect more gains in the former (S/B ratio shown inverted, Chart 16). Chart 16When Bonds Outperform Stocks, Buy Gold Miners Chart 17A Tad Overbought, But Still Cheap Meanwhile, the Fed is about to embark on an easing cycle courtesy of a softening economic backdrop and any insurance interest rate cuts will likely put a further dent in the dollar. The upshot is that gold is priced in U.S. dollars similar to the broad commodity complex and tends to rise in price when the greenback depreciates and vice versa. A lower trade-weighted dollar will also boost relative share prices (U.S. dollar shown inverted, bottom panel, Chart 14). Finally, while relative share prices are slightly overbought, relative valuations remain in the neutral zone (Chart 17). In sum, the souring global macro backdrop, rising policy uncertainty, melting real yields and a stampede into bonds all signal that it still pays to hold global gold miners as a portfolio hedge. Bottom Line: We remain overweight the global gold mining index. The ticker symbol for the global gold mining exchange traded fund is: GDX: US. Defense Delivers Recent M&A news in the aerospace & defense sector with UTX bidding for RTN was initially cheered by investors, but President Trump signaled that such a deal would decrease competition in the sector and U.S. regulators would block it. Irrespective of the outcome of this deal, we remain overweight the pure-play BCA Defense Index on a structural basis and also reiterate its high-conviction overweight status. Three key pillars will sustain the upbeat sales and profit backdrop for defense stocks. In sum, the souring global macro backdrop, rising policy uncertainty, melting real yields and a stampede into bonds all signal that it still pays to hold global gold miners as a portfolio hedge. First, the global arms race is alive and well and any governments seeking to augment their defense capabilities have to solicit the U.S. defense manufacturers. U.S. defense spending is rising at a healthy clip representing the major source of revenue growth for the industry (Chart 18). Defense capital goods orders have taken off and backlogs are at the highest level since 2012. The industry’s shipments-to-inventories ratio is also probing decade highs and weapons exports are near all-time highs (Chart 19). Chart 18Defense Spending Remains Upbeat Chart 19Healthy Operating Metrics   Second, there is a space race going on with China and India working on manned missions to the moon, but recently President Trump signaled that he would like to beat both of these countries to the moon and in outer space. The defense industry also benefits when global space related demand is on the rise. Finally, cyber security remains a global threat and governments are serious about fighting it off decisively given the sensitivity of the data that cyber criminals are after. While defense stocks are not pure-play software outfits combating cyber criminals, recent industry tuck in acquisitions include such software companies in order for defense contractors to offer one-stop shop solutions to governments. Netting it all up, three key defense manufacturers’ demand drivers – global rearmament, a space race and cyber security – remain upbeat and will continue to underpin relative industry profitability. With regard to the financial health of the sector, balance sheets are pristine with net debt-to-EBITDA registering below the broad non-financial equity market and below 2x. Interest coverage is sky high at over 10x, again trumping the broad market. On the return on equity (ROE) front, defense stocks have the upper hand trading at an all-time high ROE of 39% or more than twice the broad market ROE (Chart 20). Looking at the valuation backdrop, relative valuations have corrected recently and defense equities no longer command a premium versus the overall market on both an EV/EBITDA and P/E basis (second & bottom panels, Chart 21). Chart 20Excellent Financial Standing Chart 21Valuations Have Corrected   Netting it all up, three key defense manufacturers’ demand drivers – global rearmament, a space race and cyber security – remain upbeat and will continue to underpin relative industry profitability. Bottom Line: The BCA Defense Index remains a secular overweight and a high-conviction overweight. The ticker symbols for the stocks in the BCA Defense Index are: LLL, LMT, NOC, GD and RTN. Anastasios Avgeriou, U.S. Equity Strategist anastasios@bcaresearch.com          Footnotes 1      Please see BCA U.S. Equity Strategy Weekly Report, “Cracks Forming” dated June 24, 2019, available at uses.bcaresearch.com. 2      https://us.spindices.com/documents/index-news-and-announcements/2019062… 3      Please see BCA U.S. Equity Strategy Insight Report, “Locking In Homebuilder Gains” dated May 22, 2019, available at uses.bcaresearch.com. 4      Please see BCA U.S. Equity Strategy Report, “The Good, The Bad And The Ugly,” dated March 4, 2019, available at uses.bcaresearch.com. Current Recommendations Current Trades Size And Style Views Favor value over growth Favor large over small caps

In the context of de-risking our portfolio, this past Monday we added the S&P banks index on our downgrade watch list. The Fed’s signal of a cut in the upcoming July meeting has steepened the yield curve. 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 (top panel). 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). Worrisomely, not only is the overall U.S. credit impulse contracting, but also U.S. Equity Strategy’s bank credit diffusion index is collapsing (middle panel). Such broad breadth of loan growth deterioration warns that bank earnings are at risk of underwhelming still optimistic sell-side analysts’ expectations (not shown). 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. For additional details please refer to this Monday’s Weekly Report. 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.

Special Report

Highlights Like in any currency board, Hong Kong dollar money supply is not fully backed by foreign currency (FX) reserves. Yet, the Hong Kong authorities have large FX reserves to defend the currency peg for now. Regardless, mounting capital outflows and the ensuing currency defense will lead to higher interest rates. Contrary to Hong Kong, Singapore has a flexible exchange rate regime and will begin easing monetary policy soon. Interest rates in Singapore will drop relative to Hong Kong. We are therefore reiterating our short Hong Kong / long Singaporean property stocks strategy. Feature The recent popular protests in Hong Kong against the extradition bill will likely mark a regime shift – not only in the territory’s socio-political dynamics but also in its financial outlook. It seems the local authorities are still considering an adoption of the extradition bill. For now, the bill has been suspended, but it has not been withdrawn outright. In light of elevated political uncertainty over the one-country, two-systems model, it is reasonable to assume that capital outflows from Hong Kong will rise in the coming year or so.  In light of elevated political uncertainty over the one-country, two-systems model, it is reasonable to assume that capital outflows from Hong Kong will rise in the coming year or so. The question therefore becomes whether or not the Hong Kong Monetary Authority (HKMA) has sufficient foreign currency (FX) reserves to defend the Hong Kong dollar’s peg. Even though Hong Kong's broad money supply is not fully backed by FX reserves, we see no major risk to the currency peg at the moment. That said, mounting capital outflows will necessitate higher interest rates, as least relative to U.S. ones, to defend the peg. This is negative for Hong Kong’s property market and share prices. Are Hong Kong Dollars Fully Backed By FX Reserves? Hong Kong operates a linked-exchange rate system, which stipulates that its monetary base must be fully backed by FX reserves. The monetary base includes (Table I-1): The balance of the clearing accounts of banks kept with the HKMA (called the Aggregate Balance, which represents commercial banks’ excess reserves). Exchange Fund bills and notes – securities issued by the Exchange Fund to manage excess reserves/liquidity in the interbank market. Certificates of Indebtedness which are equivalent to currency in circulation. These certificates are held by note-issuing banks in exchange for their FX deposits at the Exchange Fund. The Exchange Fund is a balance sheet vehicle of the HKMA. Government-issued coins in circulation. Presently, Hong Kong’s FX reserves-to-monetary base ratio is 2.2 (Chart I-1on page 1). This ratio is well above the stipulated currency board rule of one: a unit of monetary base can be issued only when it is backed by an equivalent foreign currency asset. Chart I-1HK: FX Coverage Of Monetary Base Is Well Above 1 The reason the ratio is currently more than double where it technically should be is because the HKMA’s foreign exchange reserves also include the fiscal authorities’ foreign currency deposits at the Exchange Fund. Hence, the large pool of fiscal assets converted into foreign currency and sitting in the Exchange Fund has pushed the monetary base’s coverage ratio above two. As of December 31, 2018, the Exchange Fund’s foreign currency assets consisted of HK$743 billion of its own foreign currency reserves (net FX reserves), HK$1.17 trillion of the fiscal authorities’ foreign currency deposits, and HK$485 billion of foreign currency deposits by money issuing commercial banks (Table I-1). However, broad money supply in Hong Kong is not fully backed by foreign currency reserves (Chart I-2). At 0.45, this coverage ratio entails that each HK dollar of broad money supply is backed by 0.45 USD foreign currency reserves within the Exchange Fund. Broad money supply includes currency in circulation, demand, savings and time deposits, and negotiable certificates of deposits (NCDs) issued by licensed banks. Chart I-2HK: FX Coverage Of HK Dollars Is Only 0.45 Crucially, broad money supply does not include commercial banks’ reserves at the central bank in any economy, including Hong Kong. The pertinent measure of any exchange rate backing is the ratio of FX reserves to broad money supply (all local currency deposits plus cash in circulation). The motive is that households and companies can use not only cash in circulation but also their deposits to acquire foreign currency. With the ratio standing at 0.45, the Hong Kong monetary authorities do not have sufficient amounts of U.S. dollars to guarantee the exchange of each unit of local currency (cash in circulation and all deposits) into U.S. dollars in the event of a full-blown flight out of HK dollars. It is essential to clarify that the monetary authorities in Hong Kong have not deviated from the original framework of the currency board. This exchange rate mechanism was devised in 1983 in such a way that only the monetary base – not broad money supply – was supposed to be backed by foreign currency. In short, any currency board entails that only the monetary base – not broad money supply - is backed by FX reserves. Hong Kong is not an exception. Nevertheless, there is widespread perception in the financial community and among economists that all Hong Kong dollars are backed by foreign currency reserves, which is incorrect. Like in any banking system, when commercial banks in Hong Kong grant loans or buy assets from non-banks, they create local currency deposits “out of thin air.” These deposits are not backed by foreign currency, and commercial banks that create these deposits are not obliged to deposit FX reserves at the Exchange Fund. The credit boom in Hong Kong has accelerated since 2009 (Chart I-3, top panel). Consistently, since that time, the amount of local currency deposits has mushroomed – these deposits are not backed by foreign currency (Chart I-3, bottom panel).  Chart I-3Banks' Loans And Deposit Growth Go Hand-In-Hand On the whole, the currency board system in Hong Kong and elsewhere cannot guarantee full convertibility of broad money supply (all types of deposits). Therefore, these currency regimes are ultimately based on confidence. If and when confidence in the exchange rate plummets and economic agents rush to exchange a large share of their local currency cash in circulation and deposits into foreign currency, the monetary authorities’ FX reserves will not be sufficient. That said, there is presently no basis to argue that close to 45% of Hong Kong broad money supply (cash and coins in circulation and deposits of all types) is poised to panic-flood the currency market. Hence, we do not foresee a de-pegging of the HKD exchange rate for now. The currency will continue to trade within its HKD/USD 7.75-7.85 band. Bottom Line: Like in any currency board, the Hong Kong dollars are not fully backed by its FX reserves. However, the Hong Kong authorities have large FX reserves to defend the currency peg for some time. Liquidity Strains? According to the Impossible Trinity thesis, in an economy with an open capital account, the monetary authorities can control either interest rates or the exchange rate, but not both simultaneously. Provided Hong Kong has both an open capital account and a fixed exchange rate, the monetary authorities have little control over interest rates. Balance-of-payment (BoP) dynamics determine whether the HKMA has to buy or sell foreign currency to preserve the exchange rate peg. When the BoP is in surplus, the HKMA accumulates FX reserves, and vice versa.  The odds are rising that Hong Kong will begin experiencing capital outflows due to heightening political uncertainty over the one-country, two-systems model. Consistently, the BoP will swing from recurring surpluses to deficits and the HKMA will have to finance them by selling FX reserves (Chart I-4). By doing so, the monetary authorities will drain banks’ excess reserves, thereby tightening interbank liquidity. Chart I-4Balance Of Payments And FX Reserves Chart I-5Falling Excess Reserves = Higher Interbank Rates Notably, the HKMA’s FX reserves have plateaued, commercial banks’ excess reserves (the Aggregate Balance at the HKMA) have shrunk and money market rates have risen since 2016 (Chart I-5). Importantly, the latter has continued, even as U.S. interest rates have dropped over the past six months (Chart I-5, bottom panel). These dynamics are set to continue. To defend the HKD’s fixed exchange rate, interest rates in Hong Kong should rise and stay above those in the U.S. This will be the equivalent of pricing in a risk premium in Hong Kong rates due to higher political uncertainty in domestic politics as well as the ongoing U.S.-China trade confrontation. To defend the HKD’s fixed exchange rate, interest rates in Hong Kong should rise and stay above those in the U.S. On a positive note, the HKMA has ample room to mitigate liquidity strains resulting from FX interventions. In years when the BoP was in surplus, to prevent HKD appreciation the authorities purchased substantial amounts of U.S. dollars. As a result, the aggregate balance/excess reserves swelled, and Exchange Fund bills and notes were issued to absorb excess reserves (Chart I-6). Chart I-6HK Authorities Have Large Liquidity Firepower Going forward, with capital outflows causing tightening liquidity, the HKMA can redeem its own bills and notes to replenish the Aggregate Balance. This will ease interbank liquidity and preclude interest rates from shooting up dramatically. The HKMA’s liquidity firepower is sizable: the amount of Exchange Fund bills and notes is more than HK$1 trillion. This compares with aggregate balance (excess reserves) of HK$55 billion. Hence, potential interbank liquidity is HK$1.1 trillion (the Aggregate Balance plus the Exchange Fund’s bills and notes) (Chart I-6, top panel). There is no way to guesstimate potential capital outflows from Hong Kong. Hence, it is difficult to know what the equilibrium level of the interest rate spread over U.S. rates will be. The market will be re-balancing continuously, and the interest rate differential will fluctuate – i.e., it will be a moving target that ensures the fixed value of the currency. Bottom Line: Odds are that market-based interest rates in Hong Kong have to rise and stay above the U.S. ones for now. Heading Into Recession? With non-financial private sector debt close to 300% of GDP (Chart I-7) and property/construction and financial services sectors accounting for a large share of the economy, the Hong Kong economy is extremely sensitive to interest rates. Chart I-7Hong Kong: Leverage And Debt Servicing Chart I-8HK Economy Is In A Cyclical Downtrend Economic conditions have already been worsening, and any further rise in interest rates will escalate the economic downtrend: Private credit growth has decelerated and is probably heading into contraction (Chart I-8, top panel). The property market is one of the most expensive in the world. Property transactions have plunged and real estate prices will likely deflate (Chart I-8, middle panels). China’s weakening economy and subsiding Hong Kong business and investor confidence will hurt domestic demand. Retail sales volumes are already contracting (Chart I-8, bottom panel). Investment Implications The interest rate differential between Hong Kong and the U.S. has recently become positive after two and a half years of lingering below zero (Chart I-9). Odds are that it will remain positive at least over the next couple years. Therefore, even if U.S. interest rates decline further, Hong Kong rates will not. This has major investment ramifications: Hong Kong stocks will likely underperform U.S. and EM equity benchmarks, as its interest rate differential with the U.S. stays on the positive side and widens further (Chart I-10).  Chart I-9HK Interest Rate Spread Over U.S. Will Rise And Stay Positive Chart I-10Higher HK Interest Rates Herald HK Equity Underperformance The MSCI Hong Kong stock index is composed of financials (36% of market cap) and property stocks (26% of market cap). Therefore, domestic stocks are very sensitive to interest rates. Hong Kong companies are also very exposed to mainland growth. A recovery in the latter is not yet imminent. As a market neutral trade, we are reiterating our short Hong Kong property / long Singapore property stocks strategy. Chart I-11Favor Singapore Stocks Versus Hong Kong Ones All of this leads us to maintain our underweight stance on Hong Kong domestic stocks versus U.S. and EM equity indexes (Chart I-10). As a market neutral trade, we are reiterating our short Hong Kong property / long Singapore property stocks strategy. Hong Kong interest rates will rise above Singapore’s, leading to the former’s equity underperformance versus the latter across property, banks and probably the overall stock index (Chart I-11). For a more detailed discussion of Singapore, please see below.   Arthur Budaghyan Chief Emerging Markets Strategist arthurb@bcaresearch.com Lin Xiang, Research Analyst linx@bcaresearch.com           Singapore: Monetary Easing Is Imminent Singapore’s stock market is at risk of selling off in absolute terms. However, the monetary authorities (MAS) will soon commence policy easing. This will differentiate Singapore from Hong Kong. While both Singapore and Hong Kong suffer from property and credit excesses and are facing a cyclical downtrend, the former – unlike the latter – can and will lower interest rates and allow its currency to depreciate to reflate the system. As a result, we are reiterating our short Hong Kong / long Singaporean property stocks strategy. Cyclical Headwinds Persist While both Singapore and Hong Kong suffer from property and credit excesses and are facing a cyclical downtrend, the former – unlike the latter – can and will lower interest rates and allow its currency to depreciate to reflate the system. Singapore’s cyclical growth outlook is worsening: Chart II-1 shows that the narrow money impulse is in deep contraction and the private domestic banks loans impulse is dipping into negative territory anew. The property sector – which is an important driver of Singapore’s economy – is depressed. Residential units sold has dipped, the high-end condominium market is virtually frozen and housing mortgage growth has stalled. These create formidable risks for Singapore’s real estate stocks’ absolute performance (Chart II-2). The latter account for 15% of the Singaporean stock market. Chart II-1Singapore: Money / Credit Impulses Chart II-2Singapore: Real Estate Stocks Are At Risk Meanwhile, there has been no signs of improvement in both domestic demand and exports. The top panel of Chart II-3 shows that the marginal propensity to spend among both consumers and non-financial businesses is diminishing. Specifically, the impulse for overall consumer loans is negative, while retail sales are contracting (Chart II-3, bottom panel). As for the business sector, it is also slowing down. Manufacturing PMI and new orders are in a contraction zone (Chart II-4). Chart II-3Private Consumption Is Weakening Chart II-4Business Sector Is Hit Hard Finally, corporate profitability of listed non-financial and non-property firms has massively deteriorated in the last decade. Chart II-5 illustrates that both return on assets (ROA) and return-on-equity (ROE) have been in a downward trend and have lately plunged. Shrinking profit margins have been the result of escalating unit labor costs (Chart II-6). In other words, productivity gains among listed non-financial companies have lagged behind wage increases. Chart II-5Corporate Profitability Is At 20-Year Low Chart II-6Rising Unit Labor Costs = Shrinking Profit Margins Monetary Policy Will Be Relaxed Chart II-7The Central Bank Has Been Withdrawing Liquidity The Monetary Authority of Singapore (MAS) conducts monetary policy by controlling the currency and by default allowing domestic interest rates to find their own equilibrium. Currently, the MAS’s monetary policy setting is restrictive – i.e. it is aiming to gradually appreciate the trade-weighted Singaporean dollar by withdrawing excess reserve from the banking system (Chart II-7, top panel). This in turn, is causing commercial banks to bid interbank rates higher (Chart II-7, bottom panel). Nevertheless, with the domestic growth deceleration intensifying and the private sector highly leveraged, the MAS will soon opt for policy easing. It will guide the trade-weighted exchange rate lower by injecting liquidity into the banking system and lowering interest rates. Bottom Line: The Singaporean economy needs lower rates and the MAS is not constrained by the currency peg as the HKMA is. Consequently, interest rates in Singapore will decline both in absolute terms and relative to Hong Kong ones. Investment Conclusion The cyclical downturn will deepen and Singapore share prices will drop in absolute U.S. dollar terms. Relative to the EM or the Asian benchmarks, we continue to recommend a neutral position on overall Singaporean equities for now. Importantly, Singapore is better positioned than Hong Kong because the former’s monetary authorities can lower interest rates and allow the currency to depreciate. Hong Kong monetary authorities cannot tolerate lower interest rates due to their peg to the U.S. dollar and budding capital outflows. Interest rates in Singapore will drop relative to Hong Kong. We are therefore reiterating our short Hong Kong / long Singaporean property stocks strategy (Chart I-11 on page 10).   Ayman Kawtharani, Editor/Strategist ayman@bcaresearch.com Footnotes Fixed-Income, Credit And Currency Recommendations Equity Recommendations  

Special Report

Productivity growth is experiencing a cyclical rebound, but remains structurally weak. The end of the deepening of globalization, statistical hurdles, and the possibility that today’s technological advances may not be as revolutionary as past ones all hamper productivity. On the back of rising market power and concentration, companies are increasing markups instead of production. This is depressing productivity and lowering the neutral rate of interest. For now, investors can generate alpha by focusing on consolidating industries. Growing market power cannot last forever and will meet a political wall. Structurally, this will hurt asset prices.   “We don’t have a free market; don’t kid yourself. (…) Businesspeople are enemies of free markets, not friends (…) businesspeople are all in favor of freedom for everybody else (…) but when it comes to their own business, they want to go to Washington to protect their businesses.” Milton Friedman, January 1991. Despite the explosion of applications of growing computing power, U.S. productivity growth has been lacking this cycle. This incapacity to do more with less has weighed on trend growth and on the neutral rate of interest, and has been a powerful force behind the low level of yields at home and abroad. In this report, we look at the different factors and theories advanced to explain the structural decline in productivity. Among them, a steady increase in corporate market power not only goes a long way in explaining the lack of productivity in the U.S., but also the high level of profit margins along with the depressed level of investment and real neutral rates. A Simple Cyclical Explanation The decline in productivity growth is both a structural and cyclical story. Historically, productivity growth has followed economic activity. When demand is strong, businesses can generate more revenue and therefore produce more. The historical correlation between U.S. nonfarm business productivity and the ISM manufacturing index illustrates this relationship (Chart II-1). Chart II-1The Cyclical Behavior Of Productivity Chart II-2Deleveraging Hurts Productivity Since 2008, as households worked off their previous over-indebtedness, the U.S. private sector has experienced its longest deleveraging period since the Great Depression. This frugality has depressed demand and contributed to lower growth this cycle. Since productivity is measured as output generated by unit of input, weak demand growth has depressed productivity statistics. On this dimension, the brief deleveraging experience of the early 1990s is instructive: productivity picked up only after 1993, once the private sector began to accumulate debt faster than the pace of GDP growth (Chart II-2). The recent pick-up in productivity reflects these debt dynamics. Since 2009, the U.S. non-financial private sector has stopped deleveraging, removing one anchor on demand, allowing productivity to blossom. Moreover, the pick-up in capex from 2017 to present is also helping productivity by raising the capital-to-workers ratio. While this is a positive development for the U.S. economy, the decline in productivity nonetheless seems structural, as the five-year moving average of labor productivity growth remains near its early 1980s nadir (Chart II-3). Something else is at play. The Usual Suspects Three major forces are often used to explain why observed productivity growth is currently in decline: A slowdown in global trade penetration, the fact that statisticians do not have a good grasp on productivity growth in a service-based economy, and innovation that simply isn’t what it used to be. Slowdown In Global Trade Penetration Two hundred years ago, David Ricardo argued that due to competitive advantages, countries should always engage in trade to increase their economic welfare. This insight has laid the foundation of the argument that exchanges between nations maximizes the utilization of resources domestically and around the world. The collapse in new business formation in the U.S. is another fascinating development. Rarely was this argument more relevant than over the past 40 years. On the heels of the supply-side revolution of the early 1980s and the fall of the Berlin Wall, globalization took off. The share of the world's population participating in the global capitalist system rose from 30% in 1985 to nearly 100% today. Generating elevated productivity gains is simpler when a country’s capital stock is underdeveloped: each unit of investment grows the capital-to-labor ratio by a greater proportion. As a result, productivity – which reflects the capital-to-worker ratio – can grow quickly. As more poor countries have joined the global economy and benefitted from FDI and other capital inflows, their productivity has flourished. Consequently, even if productivity growth has been poor in advanced economies over the past 10 years, global productivity has remained high and has tracked the share of exports in global GDP (Chart II-4). Chart II-4The Apex Of Globalization Represented The Summit Of Global Productivity Growth This globalization tailwind to global productivity growth is dissipating. First, following an investment boom where poor decisions were made, EM productivity growth has been declining. Second, with nearly 100% of the world’s labor supply already participating in the global economy, it is increasingly difficult to expand the share of global trade in global GDP and increase the benefit of cross-border specialization. Finally, the popular backlash in advanced economies against globalization could force global trade into reverse. As economic nationalism takes hold, cross-border investments could decline, moving the world economy further away from an optimal allocation of capital. These forces may explain why global productivity peaked earlier this decade. Productivity Is Mismeasured Recently deceased luminary Martin Feldstein argued that the structural decline in productivity is an illusion. As the argument goes, productivity is not weak; it is only underestimated. A parallel with the introduction of electricity in the late 19th century often comes to mind. Back then, U.S. statistical agencies found it difficult to disentangle price changes from quantity changes in the quickly growing revenues of electrical utilities. As a result, the Bureau Of Labor Statistics overestimated price changes in the early 20th century, which depressed the estimated output growth of utilities by a similar factor. Since productivity is measured as output per unit of labor, this also understated actual productivity growth – not just for utilities but for the economy as a whole. Ultimately, overall productivity growth was revised upward. Chart II-5Plenty Of Room To Mismeasure Real Output Growth In today’s economy, this could be a larger problem, as 70% of output is generated in the service sector. Estimating productivity growth is much harder in the service sector than in the manufacturing sector, as there is no actual countable output to measure. Thus, distinguishing price increases from quantity or quality improvements is challenging. Adding to this difficulty, the service sector is one of the main beneficiaries of the increase in computational power currently disrupting industries around the world. The growing share of components of the consumer price index subject to hedonic adjustments highlight this challenge (Chart II-5). Estimating quality changes is hard and may bias the increase in prices in the economy. If prices are unreliably measured, so will output and productivity. Pushing The Production Frontier Is Increasingly Hard Chart II-6A Multifaceted Decline In Productivity Another school of thought simply accepts that productivity growth has declined in a structural fashion. It is far from clear that the current technological revolution is much more productivity-enhancing than the introduction of electricity 140 years ago, the development of the internal combustion engine in the late 19th century, the adoption of indoor plumbing, or the discovery of penicillin in 1928. It is easy to overestimate the economic impact of new technologies. At first, like their predecessors, the microprocessor and the internet created entirely new industries. But this is not the case anymore. For all its virtues, e-commerce is only a new method of selling goods and services. Cloud computing is mainly a way to outsource hardware spending. Social media’s main economic value has been to gather more information on consumers, allowing sellers to reach potential buyers in a more targeted way. Without creating entirely new industries, spending on new technologies often ends up cannibalizing spending on older technologies. For example, while Google captures 32.4% of global ad revenues, similar revenues for the print industry have fallen by 70% since their apex in 2000. If new technologies are not as accretive to production as the introduction of previous ones were, productivity growth remains constrained by the same old economic forces of capex, human capital growth and resource utilization. And as Chart II-6 shows, labor input, the utilization of capital and multifactor productivity have all weakened. Some key drivers help understand why productivity growth has downshifted structurally. Let’s look at human capital. It is much easier to grow human capital when very few people have a high-school diploma: just make a larger share of your population finish high school, or even better, complete a university degree. But once the share of university-educated citizens has risen, building human capital further becomes increasingly difficult. Chart II-7 illustrates this problem. Growth in educational achievement has been slowing since 1995 in both advanced and developing economies. This means that the growth of human capital is slowing. This is without even wading into whether or not the quality of education has remained constant. This is pure market power, and it helps explain the gap between wages and productivity. Human capital is also negatively impacted by demographic trends. Workers in their forties tend to be at the peak of their careers, with the highest accumulated job know-how. Problematically, these workers represent a shrinking share of the labor force, which is hurting productivity trends (Chart II-8). Chart II-8Demographics Are Hurting Productivity   The capital stock too is experiencing its own headwinds. While Moore’s Law seems more or less intact, the decline in the cost of storing information is clearly decelerating (Chart II-9). Today, quality adjusted IT prices are contracting at a pace of 2.3% per annum, compared to annual declines of 14% at the turn of the millennium. Thus, even if nominal spending in IT investment had remained constant, real investment growth would have sharply decelerated (Chart II-10). But since nominal spending has decelerated greatly from its late 1990s pace, real investment in IT has fallen substantially. The growth of the capital stock is therefore lagging its previous pace, which is hurting productivity growth. Chart II-10The Impact Of Slowing IT Deflation Chart II-11A Dearth Of New Businesses   The collapse in new business formation in the U.S. is another fascinating development (Chart II-11). New businesses are a large source of productivity gains. Ultimately, 20% of productivity gains have come from small businesses becoming large ones. Think Apple in 1977 versus Apple today. A large decline in the pace of new business formation suggests that fewer seeds have been planted over the past 20 years to generate those enormous productivity explosions than was the case in the previous 50 years.   The X Factor: Growing Market Concentration The three aforementioned explanations for the decline in productivity are all appealing, but they generally leave investors looking for more. Why are companies investing less, especially when profit margins are near record highs? Why is inflation low? Why has the pace of new business formation collapsed? These are all somewhat paradoxical. Chart II-12Wide Profit Margins: A Testament To The Weakness Of Labor This is where a growing body of works comes in. Our economy is moving away from the Adam Smith idea of perfect competition. Industry concentration has progressively risen, and few companies dominate their line of business and control both their selling prices and input costs. They behave as monopolies and monopsonies, all at once.1 This helps explain why selling prices have been able to rise relative to unit labor costs, raising margins in the process (Chart II-12). Let’s start by looking at the concept of market concentration. According to Grullon, Larkin and Michaely, sales of the median publicly traded firms, expressed in constant dollars, have nearly tripled since the mid-1990s, while real GDP has only increased 70% (Chart II-13).2 The escalation in market concentration is also vividly demonstrated in Chart II-14. The top panel shows that since 1997, most U.S. industries have experienced sharp increases in their Herfindahl-Hirshman Index (HHI),3 a measure of concentration. In fact, more than half of U.S. industries have experienced concentration increases of more than 40%, and as a corollary, more than 75% of industries have seen the number of firms decline by more than 40%. The last panel of the chart also highlights that this increase in concentration has been top-heavy, with a third of industries seeing the market share of their four biggest players rise by more than 40%. Rising market concentration is therefore a broad phenomenon – not one unique to the tech sector.     This rising market concentration has also happened on the employment front. In 1995, less than 24% of U.S. private sector employees worked for firms with 10,000 or more employees, versus nearly 28% today. This does not seem particularly dramatic. However, at the local level, the number of regions where employment is concentrated with one or two large employers has risen. Azar, Marinescu and Steinbaum developed Map II-1, which shows that 75% of non-metropolitan areas now have high or extreme levels of employment concentration.4 Chart II-15The Owners Of Capital Are Keeping The Proceeds Of The Meagre Productivity Gains This growing market power of companies on employment can have a large impact on wages. Chart II-15 shows that real wages have lagged productivity since the turn of the millennium. Meanwhile, Chart II-16 plots real wages on the y-axis versus the HHI of applications (top panel) and vacancies (bottom panel). This chart shows that for any given industry, if applicants in a geographical area do not have many options where to apply – i.e. a few dominant employers provide most of the jobs in the region – real wages lag the national average. The more concentrated vacancies as well as applications are with one employer, the greater the discount to national wages in that industry.5 This is pure market power, and it helps explain the gap between wages and productivity as well as the widening gap between metropolitan and non-metropolitan household incomes. Growing market power and concentration do not only compress labor costs, they also result in higher prices for consumers. This seems paradoxical in a world of low inflation. But inflation could have been even lower if market concentration had remained at pre-2000s levels. In 2009, Matthew Weinberg showed that over the previous 22 years, horizontal mergers within an industry resulted in higher prices.6 In a 2014 meta-study conducted by Weinberg along with Orley Ashenfelter and Daniel Hosken, the authors showed that across 49 studies ranging across 21 industries, 36 showed that horizontal mergers resulted in higher prices for consumers.7 While today’s technology may be enhancing the productive potential of our economies, this is not benefiting output and measured productivity. Instead, it is boosting profit margins. In a low-inflation environment, the only way for companies to garner pricing power is to decrease competition, and M&As are the quickest way to achieve this goal. After examining nearly 50 merger and antitrust studies spanning more than 3,000 merger cases, John Kwoka found that, following mergers that augmented an industry’s concentration, prices increased in 95% of cases, and on average by 4.5%.8 In no industry is this effect more vividly demonstrated than in the healthcare field, an industry that has undergone a massive wave of consolidation – from hospitals, to pharmacies to drug manufacturers. As Chart II-17 illustrates, between 1980 and 2016, healthcare costs have increased at a much faster pace in the U.S. than in the rest of the world. However, life expectancy increased much less than in other advanced economies. In this context of growing market concentration, it is easy to see why, as De Loecker and Eeckhout have argued, markups have been rising steadily since the 1980s (Chart II-18, top panel) and have tracked M&A activity (Chart II-18, bottom panel).9 In essence, mergers and acquisitions have been the main tool used by firms to increase their concentration. Another tool at their disposal has been the increase in patents. The top panel of Chart II-19 shows that the total number of patent applications in the U.S. has increased by 3.6-fold since the 1980s, but most interestingly, the share of patents coming from large, dominant players within each industry has risen by 10% over the same timeframe (Chart II-19, bottom panel). To use Warren Buffet’s terminology, M&A and patents have been how firms build large “moats” to limit competition and protect their businesses. Chart II-18Markups Rise Along With Growing M&A Activity Chart II-19How To Build A Moat?   Why is this rise in market concentration affecting productivity? First, from an empirical perspective, rising markups and concentration tend to lead to lower levels of capex. A recent IMF study shows that the more concentrated industries become, the higher the corporate savings rate goes (Chart II-20, top panel).10 These elevated savings reflect wider markups, but also firms with markups in the top decile of the distribution display significantly lower investment rates (Chart II-20, bottom panel). If more of the U.S. output is generated by larger, more concentrated firms, this leads to a lower pace of increase in the capital stock, which hurts productivity. Second, downward pressure on real wages is also linked to a drag on productivity. Monopolies and oligopolies are not incentivized to maximize output. In fact, for any market, a monopoly should lead to lower production than perfect competition would. Diagram II-I from De Loecker and Eeckhout shows that moving from perfect competition to a monopoly results in a steeper labor demand curve as the monopolist produces less. As a result, real wages move downward and the labor participation force declines. Does this sound familiar?   The rise of market power might mean that in some way Martin Feldstein was right about productivity being mismeasured – just not the way he anticipated. In a June 2017 Bank Credit Analyst Special Report, Peter Berezin showed that labor-saving technologies like AI and robotics, which are increasingly being deployed today, could lead to lower wages (Chart II-21).11 For a given level of technology in the economy, productivity is positively linked to real wages but inversely linked to markups – especially if the technology is of the labor-saving kind. So, if markups rise on the back of firms’ growing market power, the ensuing labor savings will not be used to increase actual input. Rather, corporate savings will rise. Thus, while today’s technology may be enhancing the productive potential of our economies, this is not benefiting output and measured productivity. Instead, it is boosting profit margins.12 Unsurprisingly, return on assets and market concentration are positively correlated (Chart II-22).     Finally, market power and concentration weighing on capex, wages and productivity are fully consistent with higher returns of cash to shareholders and lower interest rates. The higher profits and lower capex liberate cash flows available to be redistributed to shareholders. Moreover, lower capex also depresses demand for savings in the economy, while weak wages depress middle-class incomes, which hurts aggregate demand. Additionally, higher corporate savings increases the wealth of the richest households, who have a high marginal propensity to save. This results in higher savings for the economy. With a greater supply of savings and lower demand for those savings, the neutral rate of interest has been depressed. Investment Implications First, in an environment of low inflation, investors should continue to favor businesses that can generate higher markups via pricing power. Equity investors should therefore continue to prefer industries where horizontal mergers are still increasing market concentration. Second, so long as the status quo continues, wages will have a natural cap, and so will the neutral rate of interest. This does not mean that wage growth cannot increase further on a cyclical basis, but it means that wages are unlikely to blossom as they did in the late 1960s, even within a very tight labor market. Without too-severe an inflation push from wages, the business cycle could remain intact even longer, keeping a window open for risk assets to rise further on a cyclical basis. Third, long-term investors need to keep a keen eye on the political sphere. A much more laissez-faire approach to regulation, a push toward self-regulation, and a much laxer enforcement of antitrust laws and merger rules were behind the rise in market power and concentration.13 The particularly sharp ascent of populism in Anglo-Saxon economies, where market power increased by the greatest extent, is not surprising. So far, populists have not blamed the corporate sector, but if the recent antitrust noise toward the Silicon Valley behemoths is any indication, the clock is ticking. On a structural basis, this could be very negative for asset prices. An end to this rise in market power would force profit margins to mean-revert toward their long-term trend, which is 4.7 percentage-points below current levels. This will require discounting much lower cash flows in the future. Additionally, by raising wages and capex, more competition would increase aggregate demand and lift real interest rates. Higher wages and aggregate demand could also structurally lift inflation. Thus, not only will investors need to discount lower cash flows, they will have to do so at higher discount rates. As a result, this cycle will likely witness both a generational peak in equity valuations as well as structural lows in bond yields. As we mentioned, these changes are political in nature. We will look forward to studying the political angle of this thesis to get a better handle on when these turning points will likely emerge. Mathieu Savary Vice President The Bank Credit Analyst   Footnotes 1       A monopsony is a firm that controls the price of its input because it is the dominant, if not unique, buyer of said input. 2       G. Grullon, Y. Larkin and R. Michaely, “Are Us Industries Becoming More Concentrated?,” April 2017. 3       The Herfindahl-Hirschman Index (HHI) is calculated by taking the market share of each firm in the industry, squaring them, and summing the result. Consider a hypothetical industry with four total firm where firm1, firm2, firm3 and firm4 has 40%, 30%, 15% and 15% of market share, respectively. Then HHI is 402+302+152+152 = 2,950. 4       J. Azar, I. Marinescu, M. Steinbaum, “Labor Market Concentration,” December 2017. 5     J. Azar, I. Marinescu, M. Steinbaum, “Labor Market Concentration,” December 2017. 6     M. Weinberg, “The Price Effects Of Horizontal Mergers”, Journal of Competition Law & Economics, Volume 4, Issue 2, June 2008, Pages 433–447. 7     O. Ashenfelter, D. Hosken, M. Weinberg, "Did Robert Bork Understate the Competitive Impact of Mergers? Evidence from Consummated Mergers," Journal of Law and Economics, University of Chicago Press, vol. 57(S3), pages S67 - S100. 8    J. Kwoka, “Mergers, Merger Control, and Remedies: A Retrospective Analysis of U.S. Policy,” MIT Press, 2015. 9     J. De Loecker, J. Eeckhout, G. Unger, "The Rise Of Market Power And The Macroeconomic Implications," Mimeo 2018. 10     “Chapter 2: The Rise of Corporate Market Power and Its Macroeconomic Effects,” World Economic Outlook, April 2019. 11     Please see The Bank Credit Analyst Special Report "Is Slow Productivity Growth Good Or Bad For Bonds?"dated May 31, 2017, available at bca.bcaresearch.com. 12     Productivity can be written as: 13     J. Tepper, D. Hearn, “The Myth of Capitalism: Monopolies and the Death of Competition,” Wiley, November 2018.

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