Developed Countries

Neutral – Downgrade Alert Banks hit all-time lows again this week on the back of mixed profit results. While Q3 loan loss reserves will rise albeit at a slower pace than H1/2020, net interest income ails and difficulty in growing revenues are significant offsets. This backdrop makes banks hostage to the 10-year US Treasury yield (top panel). With regard to fiscal stimulus and economic uncertainty, Jamie Dimon recently warned that “If the double-dip (recession) happens, we would be under-reserved by $20 billion.” Worrisomely, the longer the new stimulus checks take to arrive, the longer it will take banks to rebound. Banks have been semi-sheltered from the recession courtesy of eviction/foreclosure moratorium as well as mortgage forbearance agreements. Absent a fresh stimulus package, the unemployment rate will remain elevated, warning that lagging non-performing loans will skyrocket. Bottom Line: Stay neutral the S&P banks index, but keep it on the downgrade watchlist. The ticker symbols for the stocks in this index are: BLBG: S5BANKX – JPM, BAC, C, WFC, USB, TFC, PNC, FRC, FITB, MTB, KEY, SIVB, RF, CFG, HBAN, ZION, CMA, PBCT. For more details, please refer to this Monday’s Weekly Report.  

Special Report

Highlights The US Senate election is as important as the presidency for US politics and markets. Our quantitative Senate election model suggests Democrats will win control – as we have long argued – but there is a 49% chance that they do not, which is higher than consensus. A Republican Senate under a Biden presidency is positive for US stocks relative to global. Corporate taxes will stay put. However, fiscal reflation will have to be earned through tough budget battles, which will raise hurdles for markets. The Democratic sweep scenario is generating excessive enthusiasm in the media, as taxes will rise, but it is ultimately reflationary. It will benefit global stocks more than US stocks. Feature Chart 1Democratic Sweep Favors Global Stocks Versus US Throughout the year we have argued that, as a base case for the US election, investors should expect that the pandemic, recession, and widespread social unrest in the United States would culminate in an anti-incumbent movement among voters. President Trump and the Republicans would lose the White House and Senate in a Democratic sweep. The implication for markets was that, after election volatility, global equities would rally in expectation of less hawkish US foreign and trade policy, while US equities would underperform on the expectation of higher taxes and regulation at home. This view has now become the market consensus (Chart 1). However, our quantitative US election model – which does not rely on head-to-head opinion polling – has recently given President Trump a 49% chance of winning in the latest reading. It is flagging a Biden victory but is essentially “too close to call” (Chart 2). The rapid snapback in the economy provides a basis for Trump to make an eleventh-hour comeback, contrary to optics. Chart 2Quant Model Shows Trump Loss, But 49% Odds Of Winning In this report we present our quant model for the US Senate election, updated for the 2020 cycle. The Senate model is constructed with similar variables, though not exactly the same, and the result is that Democrats are favored to win control but only slightly. The implication is that Democrats are currently overrated by markets and that the election could still go either way. Uncertainty will go up for the remainder of the month. Ultimately we are sticking with our original forecast unless Trump and the Republicans regain momentum in opinion polling, but our models are flagging major risk. Investors should expect volatility to rise in the short term. We will maintain our tactical risk-off trades, since the risk of a contested election and/or a Trump re-election (and hence renewed global trade war) is rising. The Foundations Of Our Senate Model BCA Geopolitical Strategy developed a US Senate election model in September 2018 which quantified the margin of victory for the GOP among several Senate races during the 2018 mid-term election. The beta model focused on modeling individual Senate races, those deemed competitive by BCA’s Geopolitical Strategy at the time, by combining state and national level economic and political variables as well as the latest polling data applicable to each race.1 We are now re-introducing this model, but with a twist: this time we are adopting the same methodology as per our US presidential election model. The result is a state-by-state model that predicts the number of seats the incumbent party will win in the Senate election on November 3, 2020. Like our US election model, our Senate model is based off a probit model that produces a probability that each state will remain under the control of the incumbent party. The dependent variable (classified as “elected”) is stated as 1 = incumbent party wins the Senate election in each state; or 0 = incumbent party does not win the Senate election in each state. This method allows us to measure the probability that a state with certain characteristics will fall into one of these two categories. Therefore we can predict the probability of the incumbent party winning all the Senate seats in each of the 50 states (though, of course, this is only relevant to the one-third of the states that have a Senate seat up for election in 2020). Our model would have predicted the past five Senate election outcomes correctly on an in-sample basis and the past four Senate elections on an out-sample basis. Unlike our presidential election model, which sampled nine elections (1984 to 2016), our sample size for the Senate model is notably larger. That is, our sample consists of 18 Senate elections (1984 to 2018), across 50 states, amounting to 900 observations. While midterm Senate elections are different from those held during a presidential election year, we would not want to exclude the information they provide. The 2018 Senate race has a bearing on our 2020 prediction and this is appropriate. The Senate Model’s Variables Our Senate model includes six explanatory variables: 1. The Federal Reserve Bank of Philadelphia State Coincident Index. The coincident index for each state combines four of the state’s indicators to summarize current economic conditions in a single statistic. The four indicators are nonfarm payroll employment; average hours worked in manufacturing by production workers; the unemployment rate; and wage and salary disbursements plus proprietors' income deflated by the consumer price index (U.S. city average). Like in our US Presidential model, we applied several transformations to the data to obtain meaningful results in the modeling process. We found that using a three-month change of the state coincident index in our Senate model provided the most statistically significant result. Our Senate model suggests that Republican odds of winning are underrated by online betting markets, as with our presidential model. The three-month change of all the monthly state coincident indexes are given heavier weight as we approach the Senate election early in November. However, we only include the preceding year of a Senate election up until September of the election year (i.e. the last data release in October prior to the election itself). Senate elections occur every two years, and we excluded data that has been accounted for in previous elections. As we highlighted in the update of our US Election model we assume that prevailing economic conditions matter most to voters (as future expectations inevitably affect people’s assessment of their current situation), and this bolsters our rationale in using a 3-month change of the state coincident index. 2. The incumbent party’s margin of victory in previous Senate elections in each state Senate race. This is measured as the incumbent party’s share of the popular vote minus the non-incumbent party’s share. If the incumbent party failed to secure a solid win in each state in the previous Senate election, the probability of securing a solid win in the current election becomes smaller. Moreover, the larger the margin of victory in a previous Senate election race, the more likely that incumbent party will win re-election in said state. 3. Net average approval level of the incumbent president in a Senate election year. This is the difference between the incumbent president’s approval and disapproval levels in a Senate election year, from the start of the year up until the end of October of that year – taken as an average. 4. Generic congressional ballot (net support rate). The generic congressional ballot asks people which party they are likely to vote for in Congress. We take the average net support rate in a Senate election year (that being whichever party leads the other in congressional ballot polling). Democrats are usually favored in congressional generic ballot voting, so the net rate is more predictive than the gross rate 5. Dummy variable for congressional ballot. A dummy variable is assigned to variable number four. For example, dummy takes the value of 1 when Democrats have a positive net support rate in generic congressional ballot voting, and 0 when Republicans have a net positive support rate. We assign only one dummy variable to avoid a dummy variable trap.2 6. A “time for change” variable, a categorical variable indicating whether the incumbent party has controlled the Senate for three or more terms (six or more years). If the Senate has been controlled by the incumbent party for three or more terms, the model will “punish” the incumbent party, as we would expect to see a change in control of the Senate the longer one incumbent party controls it. Estimating The Model Since this is a probit model, the coefficients cannot be directly interpreted like in an ordinary regression.3 In Table 1, the sign of the coefficient corresponds to the direction of change in probability. An increase in the State Coincident Index, the incumbent’s margin of victory in previous Senate races, net approval of the incumbent president and generic congressional support ballot, all increase the probability of the incumbent winning a Senate election in a state. Table 1Senate Model Regression Coefficients Meanwhile occupying the Senate for more than three terms serves as a “punishment” and would decrease the probability of winning a Senate election in a state. The output of our model is the probability of an incumbent win in each state. As in our US Presidential election model, there are two ways of aggregating these probabilities to produce a national-level outcome: Proportional: Allocate the number of Senate seats won by the incumbent proportionally to their probability of victory in each state, and then sum them up across all states. Winner Takes All: As we do in our US Presidential election model, assume a probability threshold of 50%: any state with an incumbent win that is at least 50% likely is fully assigned to the incumbent. The latter, winner takes all, is the aggregation method we base our Senate prediction on. Senate Election Model Prediction Table 2 shows our 2020 prediction. Overall, the Republican Party is expected to win 49 Senate seats, a decrease of four seats from its current 53-seat majority. This means that the Democrats are expected to control the Senate with 51 seats (this includes Independents that caucus with Democrats). Moreover, the model suggests that Republicans have a 49% chance of retaining Senate control. Table 2Predicted 2020 Senate Balance Of Power This is substantially higher than consensus, which has put Republicans at 42% throughout the past month and currently has them at 37% (Chart 3). As with our presidential model, the rapid recovery in the state economic indicators is providing the Republicans with a last-minute boost that contradicts the gloomier picture painted by opinion polls. We do not think they will retain the Senate, but our conviction level is now lower. Chart 3Betting Market Overrates Democratic Odds Of Winning Senate In terms of Senate seats, our model expects Republicans to lose Arizona, Colorado, Maine, Montana, and North Carolina. This is enough for Democrats to obtain 51 seats, a majority, assuming that they lose Alabama. The full list of states that have Senate races in 2020 and the probabilities of a Republican win according to the model are shown in Chart 4. Chart 4Quant Model Shows Democrats Win Senate, But GOP Odds 49% Three Senate races are classified as toss-ups, which we define as having a probability between 45% and 55% according to the model. These states are Iowa (54%), Maine (48%) and North Carolina (49%). Montana is close to a toss-up, with a 44% chance of a Republican win. We expect Democrats to win control of the Senate with 51 votes. They need 50, plus the White House, to have a majority. Of these states, if Republicans retain any two, then they will retain their majority, so control of the Senate is on a knife’s edge. Chart 5 shows the odds for each of the 12 swing states in this election. Chart 5Our Senate Odds Compared With The Bookies Bear in mind that only 50 seats are needed for the party that wins the White House, since the Vice President is also the President of the Senate and casts the tiebreaking vote. Senate Races Of Interest Our results show that the consensus is underestimating the Republicans, except in Michigan and Montana. The latter could affect overall control of the Senate. The same can be said for Maine, where the Republican challenger may be underrated (Chart 6). The trend of opinion polling in Chart 6 generally shows closer races than the betting markets expect. Our model supports the betting markets on the unlikelihood that Democrats will prevail in several deep red states. Chart 6US Senate Polling And The Betting Odds The presidential race should be the decisive factor. If voters in swing states are sufficiently motivated to vote out the sitting president that they chose only four years ago, which is uncommon in modern US history, then they will likely repudiate the senators who carried that president’s water through a whirlwind of scandals and controversies. Yet with the races so precariously balanced, small or local factors could also decide the outcome. This is an important limitation on our macro method. For example, it is not at all clear that Democrats will win Maine. Our model gives Republicans a 48% chance, while online gamblers put it at 27%. Susan Collins is well-entrenched, having survived again and again since 1996. If Democrats do poach Maine, it is still not clear that they will carry Iowa and Montana, which are more conservative yet saw Democratic victories in 2018. Our model suggests Montana will go Democratic and Iowa will stay Republican. Democrats must win one of these two states (or North Carolina) or they will not take the Senate. A feather could tip the scales. A feather may already be doing that in North Carolina, the other key toss-up state. Democratic candidate Cal Cunningham’s sex scandal has roiled the race. It is not yet clear that voters will abandon Cunningham (see Chart 6, panel 1), but that is likely unless there is an unstoppable Democratic wave.4 If North Carolina stays Republican as a result, then, according to our model, the US Senate would tie at 50-50 and the winner of the White House would turn the balance. Some Democrats have argued that deeper red states may be in contention, such as Georgia, South Carolina, Alaska, Kansas, or Kentucky. Of these, Kansas is notable since no candidate has an incumbent advantage. However, our model rules these races out of play and we tend to agree. Bottom Line: Our model suggests that Democrats will narrowly win control of the Senate as things stand today. With several races extremely close, a trivial event in a single state could turn the balance of power in the US Senate and hence the policy consequences of the entire US election. However, the close contest implies that the party that wins the White House will also win the Senate. Back Testing Our Model Our Senate model performs at an acceptable level during in-sample and out-sample back testing. For in-sample testing, we test our model over our entire sample period (1984 – 2018) and find that 72% of Senate elections (control of the Senate) are correctly predicted, with the model predicting the outcome of the last five Senate elections correctly (Chart 7). Chart 7In-Sample Back Testing Results During out-sample back testing, we look at a sample period of 2000 – 2018, comprising of ten Senate elections, where our model correctly predicts 69% of actual outcomes. The previous four Senate elections are predicted correctly (Chart 8). There is still a roughly 50/50 chance of divided US government in 2021-22. Chart 8Out-Sample Back Testing Results Investment Takeaways Our US Senate model is based off a similar methodology as our US Presidential election model. There are however some minor differences. First, we use a weighted maximum likelihood estimate as opposed to a traditional maximum likelihood estimate. This is because of unbalanced binary outcomes in our dependent variable (see Appendix). Chart 9Fair Chance Of Divided Government Still Secondly, not all our explanatory variables are the same. While we maintain using the State Coincident Index as our one and only economic variable, our suite of political variables has changed to be more geared towards predicting the Senate outcome. Our Senate model predicts Republicans will retain only 49 seats and lose control of the Senate. The Democrats will take control with 51 seats. And yet Republicans have a 49% chance of retaining Senate control. This is equivalent to saying that the race is “too close to call” – which is similar to our presidential model results. The reason is the rapid snapback in the economy. Subjectively, the risk is to the downside for Republicans given the President’s poor polling, particularly on his handling of the pandemic, and the high unemployment rate. The Senate outcome should be determined by the White House race, but obviously there is a fair chance that the winner of the White House still loses control of the Senate (Chart 9). Chart 10Wall Street Expects Divided Government Chart 11Trump Protectionism Good For The Dollar The stock market is behaving like it expects gridlock, rather than a Democratic sweep – the latter offering greater downside and lesser upside, at least judging by history (Chart 10). So let’s boil this all down to what we know with reasonable certainty: If Trump wins with a Republican Senate, he will still face opposition from House Democrats, so he will be driven to foreign and trade policy in his second term. Protectionism will affect not only China but also Europe and other economies. This is broadly positive for the dollar and US equities relative to global stocks and commodities (Chart 11). Government bond yields would be volatile due to the risk to the cyclical recovery from global trade war. If Biden wins in a Democratic sweep, economies other than China will benefit from lower trade risk and the US will benefit from higher odds of unfettered fiscal stimulus in 2021. But financial markets will simultaneously have to adjust for the negative shock to US corporate earnings from higher taxes and regulation. This outcome is broadly negative for the dollar and US equities relative to global equities and commodities. Government bond yields would rise on the generally reflationary agenda. If Biden wins without the Senate, the market has the most positive outcome of all: less trade war yet no new tax hikes. Both US and global equities would benefit. Bond prices and the dollar would trend downward over time, but not during the occasional fiscal battles that would ensue between the Democratic president and Republican senators.   Guy Russell Research Analyst GuyR@bcaresearch.com Matt Gertken Vice President Geopolitical Strategy mattg@bcaresearch.com Statistical Appendix A notable property in our dependent variable data requires a brief discussion. Our dependent variable classified as “elected” takes the form of a binary outcome. This data, however, is what’s called “unbalanced,” since incumbent Senators are re-elected approximately 80% of the time. This means that most outcomes in our dependent variable are coded as “1,” with fewer “0’s” because of the strong incumbency effect in Senate races. There are many data sets that exhibit this type of property, such as events like wars, vetoes, cases of political activism, or epidemiological infections, where non-events occur rarely. To alleviate this statistical property in the data, we estimate our model using a weighted maximum likelihood estimate as opposed to the ordinary maximum likelihood estimate usually used in a probit regression.5 This method assigns more weighting to the unbalanced data, or what is known theoretically as “rare event” data, to aid the probit regression in assigning higher probabilities to “0” outcomes. Through this process, we effectively deal with our unbalanced dependent variable data. That said, in developing our quantitative US Senate Election Model, we estimated a suite of probit regressions with several other variables that were theoretically assumed to be relevant and subsequently tested empirically. In Appendix Table 1 below, we only include variables 1, 2, 3 and 6 from our listed variables (we excluded the generic congressional ballot and its corresponding dummy variable). This model suggests that Republicans will hold control of the Senate with 51 seats. Back testing this model revealed that 71% of past Senate elections were correctly predicted, while 67% were correctly predicted in out-sample testing. This is only slightly worse of a track record than our final model. If this model proves more accurate in the event, the implication is that the generic congressional ballot is an unreliable poll. Americans could be shy about stating their support for the Republican Party in the era of Trump. For this outcome, Republicans would only lose Arizona and Colorado. Critical swing states here are Montana (53%) and Arizona (45%). Appendix Table 1Alternative Senate Model Predictions A re-work of the above model, but with a variable that punishes Republicans for holding the Senate for six years or more on average, suggests that Republicans will only win 47 seats in the Senate, giving up six seats (Appendix Table 2). Forecast accuracy is slightly worse off, giving just 68% and 67% predictive accuracy during in and out-sample forecasting of previous Senate elections, respectively. Compared to our primary model, Republicans would lose Arizona, Colorado, Iowa, Maine, Montana, North Carolina and Alabama. Alabama (45%) is the only critical swing state. Appendix Table 2Alternative Senate Model Predictions Note: This report has been corrected since publication due to errors in charting. Charts 7 and 8 showed the correct majority party in historical Senate elections but mistakenly attributed to that party the minority party’s number of seats. The changes do not affect the text or the substance of the report: our quantitative model’s accuracy levels remain unchanged, as does the model’s performance relative to historical election results. Footnotes 1 The model was able to predict 14 out of 18 (77%) Senate races flagged as competitive by BCA’s Geopolitical Strategy. Florida, North Dakota, Indiana and Missouri were flagged as Democratic by our model but were won by Republican candidates. 2 A dummy variable trap is a scenario in which the independent variables are multicollinear — a scenario in which two or more variables are highly correlated; or, in simple terms, in which one variable can be predicted from the others. To avoid such a trap, we must exclude one of the categorical variables. Since there are two categorical variables that can be represented here (Republican or Democrat), we use k-1 (where k = the number of categorical variables). 3 The coefficients in a probit regression model measure the change in the Z-score associated to each independent variable for a one-unit change in that variable. 4 See Evie Fordham, "NC Democrat Cal Cunningham faces FEC complaint over California trip amid affair," Fox News, October 13, 2020, foxnews.com. 5 Weighted maximum likelihood estimation is a reasonable approach in dealing with dependent variables that show significant imbalance in their data set. See: King, G. and Zeng, L., 2001. Logistic regression in rare events data. Political analysis, 9(2), pp.137-163.

  Following up from yesterday’s US Equity Strategy’s sector insight, today we take a closer look at VIX and e-mini futures positioning, again from a contrarian perspective. Using CFTC weekly data, VIX non-commercial speculative positions are net short. In fact, as a percentage of total open interest, net shorts are more extended than the months both prior to “Volmageddon” and to the Q4/2018 20% SPX drawdown. With regard to this year’s equity market carnage, net shorts are almost as extended as in late-2019/early 2020 (VIX net positioning shown inverted, top panel). Similarly, non-commercial speculative positions in S&P 500 e-mini futures are net long on a par with readings recorded in early 2020 (bottom panel). The implication is that speculators are betting on a dying down in volatility and fresh SPX all-time highs. While this will likely materialize post the November election, in the near-term our fear is that speculators will get caught offside, as elevated election and fiscal policy uncertainties will sustain downward pressure on stocks. Bottom Line: Our view remains that the SPX could glide lower into the November election before rallying into year-end.  

  Chart Of The WeekInvestor Consensus Is Bearish On Dollar Today we are releasing another issue from our series Charts That Matter. Going forward, this publication will become a regular monthly deliverable to our clients. This is a charts-only report with minimal wording. It presents the key charts, indicators, and relationships that we monitor at the time of publication. Needless to say, the importance of different indicators and factors varies over time. Thus, each issue of Charts That Matter will present different charts, indicators and relationships. Presently, global assets are experiencing a tug-of-war. On the one hand, equity and credit markets are overbought and have elevated valuations. On the other hand, expectations of a large US fiscal stimulus package are sustaining prospects of continued US and global economic recoveries. We have been expecting a pullback in risk assets before year-end due to a delay in significant US fiscal stimulus, potential volatility around the US elections as well as overbought conditions in risk assets. In addition, since April commodities prices have benefited from China’s growth recovery as well as inventory restocking (see Charts on page 11). Given that the latter is likely to be followed by a destocking phase, we believe resource prices are at a risk of experiencing a setback. This will weigh on commodity-producing emerging markets.   The correction in September has been short circuited. It seems the prospects of an eventual large US fiscal stimulus package, even if it is next year, and the ongoing recovery in China (Charts on pages 8-9) are sustaining a bid under risk assets. Besides, cash on the sidelines has not been fully exhausted (Charts on page 6). Consistently, we illustrate on pages 3 that various US equity indexes are presently trying to break out and that the US equity market breadth has recently been strong. In contrast, EM equity breadth has been very weak (Chart on page 4). The latest rebound in the EM equity index has been again narrow, led by mega-cap new economy stocks in China, Korea and Taiwan. Provided such poor EM equity breadth in both absolute terms and relative to the US, we are reluctant to upgrade EM equities from neutral to overweight in a global equity portfolio. As to absolute performance, the Charts on pages 12-18 illustrate that many market-based indicators are flagging yellow or red lights for EM risk assets. Even though we turned structurally bearish on the US dollar in early July, we currently expect a tactical rebound in the greenback. Investor sentiment on the greenback is very depressed, which is positive for the US dollar from a contrarian perspective (Chart of the Week on page 1). In short, global financial markets are due to reset, which will not be long-lasting but will be meaningful and produce a better entry point. For now, we maintain a neutral allocation to EM stocks and credit markets within global equity and credit portfolios, respectively.  In the currency space, we are short several EM currencies – BRL, CLP, ZAR, TRY, KRW and IDR – versus a basket of the euro, CHF and JPY. As to local rates, we are long duration – receiving 10-year swap rates in several countries – but are reluctant to take on currency risk at the moment. Arthur Budaghyan Chief Emerging Markets Strategist arthurb@bcaresearch.com US Equities Have Been Trading Well Various US equity indexes have broken out to new cyclical highs. This is a sign of a broad-based rally. Chart I-1US Equities Have Been Trading Well Chart I-2US Equities Have Been Trading Well   Equity Market Breadth Is Strong In The US But Poor In EM The advance-decline line for the US equity market has rebounded from the neutral level of 0.5. On the contrary, the same measure for EM stocks remains below the 0.5 line, signaling poor breadth despite the rebound in the EM equity index. Chart I-3Equity Market Breadth Is Strong In The US But Poor In EM The World Economy And Global Trade Are Reviving Economic data for September continue to register a sequential revival in business activity in most parts of the world. Chart I-4The World Economy And Global Trade Are Reviving Chart I-5The World Economy And Global Trade Are Reviving The US: Cash On The Sidelines Has Declined But Is Not Exhausted US institutional and money market funds presently amount to 8.5% of the value of the US equity market cap plus all US-dollar denominated bonds available to investors. The Fed and commercial banks hold $11 trillion of debt securities. This amount of securities has been withdrawn from the market and is not available to non-bank investors. Chart I-6The US: Cash On The Sidelines Has Declined But Is Not Exhausted Chart I-7The US: Cash On The Sidelines Has Declined But Is Not Exhausted   A Delay In The US Fiscal Stimulus Package Is A Risk to The US Economy US fiscal transfers have produced a surge in household disposable income, which through consumer spending have contributed to the global recovery via a widening trade deficit. In the absence of large fiscal transfers to consumers, the opposite dynamics will prevail. Chart I-8A Delay In The US Fiscal Stimulus Package Is A Risk to The US Economy Chart I-9A Delay In The US Fiscal Stimulus Package Is A Risk to The US Economy   The Business Cycle In China Is Recovering China’s domestic demand and production are recovering but labor market improvements are still timid. Chart I-10The Business Cycle In China Is Recovering Chart I-11The Business Cycle In China Is Recovering   China: The Stimulus Is Working Its Way Into The Economy In China, the credit and fiscal stimulus leads the business cycle by about nine months. Thereby, China’s recovery will continue until the end of Q2 2021. Chart I-12China: The Stimulus Is Working Its Way Into The Economy Chart I-13China: The Stimulus Is Working Its Way Into The Economy   China: Liquidity Tightening Has Not Yet Affected Money And Credit Growth The PBoC has withdrawn liquidity, pushing up the policy rate and bond yields. With a time lag, money and credit growth will eventually roll over. But for now, China is enjoying another period of credit splurge and the credit excesses are getting larger. Chart I-14China: Liquidity Tightening Has Not Yet Affected Money And Credit Growth Chart I-15China: Liquidity Tightening Has Not Yet Affected Money And Credit Growth   China: From Commodities Restocking To Destocking? Chinese imports of many commodities have been super strong since April. However, they have substantially outpaced their final demand. This suggests there has been an inventory restocking phase. This will likely soon be followed by a period of destocking when Chinese imports of resources dwindle for several months. Chart I-16China: From Commodities Restocking To Destocking? Chart I-17China: From Commodities Restocking To Destocking?   Red Flags For EM Currencies The rollover in platinum prices and pick-up in EM currency volatility (shown inverted on the bottom panel) point to a rebound in the US dollar and a relapse in EM exchange rates. Chart I-18Red Flags For EM Currencies Yellow Flags For EM Equities The new cyclical high in EM share prices has not been confirmed by a new low in EM equity volatility (the latter shown inverted in the top panel). Moreover, our Risk-On/Safe-Haven Currency ratio has been trending lower since June, flagging risks to EM assets. Finally, global ex-TMT stocks are struggling to break above their June highs. Chart I-19Yellow Flags For EM Equities EM Sovereign And Corporate Spreads, Currencies, Equities And Commodities Commodities prices and EM currencies drive EM sovereign and corporate spreads while EM corporate bond yields (shown inverted in the bottom panel) correlate with EM share prices. Chart I-20EM Sovereign And Corporate Spreads, Currencies, Equities And Commodities Many Currencies Against The US Dollar Are At Critical Resistances If these currencies break out of these technical resistance levels, they will experience a lasting appreciation versus the US dollar. However, in our view, they will initially weaken before breaking out next year. Chart I-21Many Currencies Against The US Dollar Are At Critical Resistances Chart I-22Many Currencies Against The US Dollar Are At Critical Resistances   Are Global Defensive Equity Sectors On A Cusp Of Outperformance? Many defensive equity sectors have reached or are close to their technical support lines. Their outperformance will likely occur during a risk-off period. Chart I-23Are Global Defensive Equity Sectors On A Cusp Of Outperformance? Chart I-24Are Global Defensive Equity Sectors On A Cusp Of Outperformance?   These Markets Have Not Yet Entered A Bull Market  These markets have rebounded to their technical resistance lines but have so far failed to break out. This gives us comfort to remain neutral on EM by expecting a pullback. Chart I-25These Markets Have Not Yet Entered A Bull Market Chart I-26These Markets Have Not Yet Entered A Bull Market   Risk Measures Signal Modest Investor Complacency The SKEW index for the S&P 500 is low, entailing that investors are not hedging tail risks. The put-call ratio is not elevated despite many investors hedging against the US election uncertainty. Critically, the Nasdaq’s volatility is in a bull market. Chart I-27Risk Measures Signal Modest Investor Complacency Chart I-28Risk Measures Signal Modest Investor Complacency   EM (ex-China, Korea And Taiwan): The Recovery Is Sluggish And Subdued Outside China, Korea and Taiwan, EM domestic demand recovery is very slow and tame. In these economies, the fiscal stimulus has been small, the banking system is unhealthy and the monetary transmission mechanism is broken, i.e. banks are failing to properly transmit monetary easing into the real economy. Chart I-29EM (ex-China, Korea And Taiwan): The Recovery Is Sluggish And Subdued Chart I-30EM (ex-China, Korea And Taiwan): The Recovery Is Sluggish And Subdued   Footnotes Equities Recommendations Currencies, Credit And Fixed-Income Recommendations

Highlights Long-term investors should seek companies and sectors that facilitate and support a new way of doing things: specifically, a way of life and business that is more de-centralised and de-urbanised… …and a way of life in which we live, work, and interact more online, remotely, and digitally. The long-term winners will be technology, biotechnology, healthcare, and communications: the growth defensives. The long-term losers will be banks, oil and gas, and resources: the value cyclicals. The European stock market’s substantial underweighting to the growth defensives will weigh on its relative performance, both in the short term and in the long term. Fractal trade: Overweight the US 30-year T-bond versus the French 30-year OAT. Also, we have closed our tactical underweight to equities versus bonds. Feature Chart of the WeekYield Chasers Get A Rude Awakening As Dividends Collapse For the world’s yield chasers, 2020 has been a rude awakening. What seemed to be safe and attractive dividend yields have vanished into smoke, as blue-chip company after blue-chip company has slashed its dividend. To name just a few, HSBC has cut its dividend to zero for the first time ever; Barclays has cut its dividend to zero for the first time since 2009; and Royal Dutch Shell has slashed its dividend by 34 percent, taking it back to where it was in 2009. More generally, the high-yielding sectors have slashed their dividends: the world oil and gas sector by 60 percent (Chart of the Week) and the world bank sector by 33 percent (Chart I-2). The basic resources sector has cut its dividend by a more modest 15 percent, but the dividend now stands at the same level as it was in 2009 (Chart I-3). Chart I-2Dividend Cuts From High-Yielding Banks... Chart I-3...And High-Yielding Resource Companies In contrast, the low-yielding technology and healthcare sectors have managed to grow their dividends consistently over the past decades, and then maintain the dividends during the current crisis (Chart I-4 and Chart I-5). Chart I-4Dividend Growth And Continuity From ##br##Low-Yielding Healthcare... Chart I-5…And Low-Yielding ##br##Tech The world’s yield chasers have had a rude awakening because they often confuse yield with return. One reason for this confusion is that for cash and for high-quality government bonds held to redemption, yield and return are broadly the same.1  But for an equity, yield and return are not the same. As we have seen with the oil and gas sector and banks, an equity could start with a seemingly safe and attractive dividend yield yet end up generating a deeply negative return.2 The lesson is that long-term investors should never search for yield, they should always search for return. Mental Accounting Bias, And The Irrational Search For Yield The confusion between yield and return is not just an issue of semantics. It is a well-known phenomenon in behavioural finance known as mental accounting bias.3 This psychological bias describes the tendency to group financial gains and losses into separate mental accounts or buckets. This causes people to treat money differently according to the bucket that the money occupies. One version of this bias is a distinction between the return that an investment provides from yield and that which it provides from capital appreciation. The distinction between yield and capital appreciation is irrational. Assuming an equal tax treatment, the money that comes from yield and the money that comes from capital appreciation is perfectly fungible. Yet psychologically, the distinction is very stark. Behavioural finance postulates that because of fears about self-control, some people tend to categorize an investment’s yield as spending money, and its capital as saving money. Long-term investors should never search for yield, they should always search for return. Hence, those people who want their assets to generate spending money – say, retirees – have an irrational bias towards investments that generate yield. Whereas those people that are saving for the long term have a bias towards investments that generate capital growth. To reiterate, these biases are completely irrational.  Under normal circumstances, the irrational biases are not a problem because there are enough investments available for both buckets. But in today’s world of zero and negative interest rates, the assets that would normally generate the safe income for the spending bucket – cash and government bonds – are no longer doing so (Chart I-6). In the ensuing ‘search for yield’, income focussed investors have flocked to the dwindling number of investments that appear to generate the required income, such as high-yielding equities. But in irrationally focussing on yield rather than on expected return, the world’s yield chasers have lost a lot of money. Chart I-6Equities Are The Only Yield-Generating Mainstream Asset-Class The Halo Effect, And The Shattered Halo The matter is made worse by a second phenomenon in behavioural finance known as the halo effect. This is the tendency to worship – place a halo – on someone or something based on some narrow criteria. For a company, the narrow criteria can mean its dividend history. The dividend is one of the few financial metrics over which the company has substantial control, giving it totemic significance with the company’s investors. Investors place a halo on companies with dividend continuity, a lengthy absence of a dividend cut. The distinction between yield and capital appreciation is irrational. However, if the company cuts its dividend, even slightly, then the halo shatters. Given this stigma, companies try very hard not to cut the dividend until it is unavoidable. But when they do cut, they usually cut big, and for an extended period – because the halo is shattered anyway (Chart I-7 and Chart I-8). Chart I-7When Firms Cut Their Dividends, They Usually Cut Big... Chart I-8...And For An Extended ##br##Period Realising this, investors flip the company from saint to sinner, meaning that they demand a higher cost of capital. The upshot is that even after the dividend cut, the stock can suffer a prolonged period of underperformance. Low Yield To Deliver High Return To repeat, long-term investors should never search for yield, they should always search for return. Today, this search for return boils down to two questions: Which companies will be able to grow or, at the very least, maintain their dividends in the post-pandemic world? What is the likely direction of bond yields, and specifically the long-duration T-bond yield, given its pivotal role in setting the discount rate on all investments? To the first question, the winning companies will be the ones that facilitate and support a new way of doing things: specifically, a way of life and business that is more de-centralised and de-urbanised. And one in which the way we live, work, and interact – both socially and economically – is more remote, online, and digital. The pandemic is the accelerant, and not the cause, of the structural shift in our way of life. Crucially, this means that when a credible treatment for Covid-19 eventually arrives, it will not reverse the major changes that our way of life is now undergoing. To the second question, the Federal Reserve’s recent strategic review has made its reaction function blatantly asymmetric, especially to the labour market. The central bank has told us that it will be thick-skinned to reflationary shocks or lower unemployment, but trigger-happy to the slightest further deflationary shock or higher unemployment. The pandemic is the accelerant, and not the cause, of the structural shift in our way of life. Hence, when the slightest further deflationary shock comes – and sooner or later it will – the Fed will either follow the Bank of England in a volte-face about adding negative interest rate policy into its toolbox. Or more likely, the Fed will follow the Bank of Japan in formally implementing yield curve control. Either way, US long-duration bond yields will eventually converge with those in the UK and Japan at zero. The result of our two answers is that long-term investors should seek companies that can thrive off the major changes in the way we live, work, and interact; and investors should seek companies with long-duration cashflows that benefit most from a further compression in the long-duration T-bond yield. In combination, the long-term winners will be technology, biotechnology, healthcare, and communications: the growth defensives (Chart I-9). And the long-term losers will be banks, oil and gas, and resources: the value cyclicals (Chart I-10). Chart I-9Growth Defensives Are The Long-Term Winners Chart I-10Value Cyclicals Are The Long-Term##br## Losers For the European stock market, the unfortunate consequence is that its substantial underweighting to the growth defensives sectors will weigh on its relative performance, both in the short term and in the long term. Fractal Trading System* This week’s recommended trade is to go long the US 30-year T-bond versus the French 30-year OAT. Set the profit target and symmetrical stop-loss at 3.2 percent. The tactical underweight to equities versus bonds (short DAX versus 10-year T-bond) reached the end of its holding period. Although it closed in slight loss, the fractal signal correctly identified that the majority of the strong rally in the DAX was over by mid-July after which the DAX has traded broadly sideways. The countertrend move in the Italian BTP’s rally versus the German bund did not materialise, so this trade was closed at its stop-loss. The rolling 1-year win ratio now stands at 57 percent. Chart I-1130-Year Govt. Bonds: US Vs. France When the fractal dimension approaches the lower limit after an investment has been in an established trend it is a potential trigger for a liquidity-triggered trend reversal. Therefore, open a countertrend position. The profit target is a one-third reversal of the preceding 13-week move. Apply a symmetrical stop-loss. Close the position at the profit target or stop-loss. Otherwise close the position after 13 weeks. * For more details please see the European Investment Strategy Special Report “Fractals, Liquidity & A Trading Model,” dated  December 11, 2014, available at eis.bcaresearch.com.   Dhaval Joshi Chief European Investment Strategist dhaval@bcaresearch.com Footnotes 1 Assuming no reinvestment risk on the bond’s income. 2 This is because unlike the government bond, the equity does not generate a predetermined stream of cash flows. 3 See Rational Choice and the Framing of Decisions by Amos Tversky and Daniel Kahneman. 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