Economy

Highlights The sharp drop in Chinese lending over the past year is highly likely to weigh on (non-oil) commodity demand and prices through the remainder of 2021. Commodity demand shocks dominate commodity supply shocks. Commodity supply shocks play only a transient role in setting prices. Go underweight basic resources equities versus the market. Commodity currencies like the Canadian dollar and New Zealand dollar are likely to underperform versus the US dollar. Fractal trade: Short corn versus wheat. Feature Chart of the WeekDeclining Chinese Lending Is A Headwind For Metal Prices The recent collapse in China’s credit impulse has caught a lot of people’s attention, ours included. The collapse in the credit impulse quantifies the change in lending. Importantly, this means that even if the lending numbers themselves are large, the impulse will collapse if those lending numbers are declining – which is precisely what has happened in China. In the past year, China’s broad money supply has expanded by $17 trillion yuan, signifying a large amount of lending in the $100 trillion yuan economy. All well and good, except that the $17 trillion yuan has declined from an even larger $21 trillion yuan a year ago. To the extent that loans fund the demand for something, the $4 trillion yuan decline in those loans means that the demand for the something also declines. In the case of China, the something is the demand for industrial commodities, especially industrial metals (Chart of the Week). Using total social financing rather than the broad money supply reveals a similar downtrend in lending, and therefore a similarly collapsed impulse (Chart I-2 and Chart I-3) But as we explain in the next section, our preference is to focus on China’s broad money supply. Chart I-2Chinese Lending Is ##br##Declining... Chart I-3...So The 12-Month Credit Impulse Has Collapsed   Declining Chinese Lending Is A Headwind For Metal Demand When interpreting the lending numbers in any economy, there are four important things to keep in mind. First, we should focus on bank lending. This is because the magic of fractional reserve banking allows a bank to create money and new spending power out of thin air. When somebody borrows from a bank, his bank account and spending power goes up, but nobody’s spending power goes down. In contrast, when somebody borrows by issuing a bond, it just reallocates spending power from one person to another. The bond issuer sees his bank account and spending power go up, but the bond buyer sees his bank account and spending power go symmetrically down. Demand will rise to the extent that the borrower has a higher propensity to spend than the lender, but this may or may not be the case. Second, and as already mentioned, the impact on economic demand comes from the change in lending – which is to say the credit impulse. This is just to compare apples with apples. Remember that GDP, demand, and lending are all flow statistics. Meaning that the change in demand depends on the change in lending (and not from absolute lending itself).1  Third, the most important part of lending is bank lending to the non-financial sector.2  This is because not all loans generate economic activity. Bank-to-bank lending and reserves held at the central bank stay trapped in the financial system. The money supply – which is on the liabilities side of the banks’ balance sheet – might not pick up this distinction. It could be expanding rapidly due to a surge in bank-to-bank lending and/or in reserves, giving the false signal that demand should be growing. Hence, it is better to focus on bank lending – which is on the assets side of the banks’ balance sheet – and only count lending that is likely to generate economic activity. However, this logic only works if the official data on bank loans is accurate and complete. In China, this is unlikely to be the case, given its large shadow banking system. Total social financing includes most, but not all, shadow lending. Yet all shadow lending must eventually show up in the money supply. For this reason, in analysing Chinese lending, we prefer to focus on the broad money supply. Having said that, the messages coming from both the broad money supply and total social financing concur. Chinese lending is slowing. Chinese lending is slowing.  Fourth, we should choose the periodicity of the analysis to maximize its predictive power. This will depend on the specific lead times between the lending and the demand that it is funding, which will be discovered empirically. We find that the 1-year change in China’s broad money supply provides an excellent 1-year lead on industrial metal prices, because the lending leads commodity demand. The obvious rejoinder is, what about tight supply? The answer, from a recent academic paper – Drivers of commodity price booms and busts in the long run – is that for (non-oil) commodities, demand dominates supply. Specifically, “aggregate commodity and commodity-specific demand shocks appear to strongly dominate commodity supply shocks in driving variation in real commodity prices… commodity supply shocks play a rather secondary and transient role.”3 On this basis, we conclude that the sharp drop in Chinese lending over the past year is highly likely to weigh on (non-oil) commodity prices through the remainder of 2021 (Chart I-4 and Chart I-5). Chart I-4Declining Chinese Lending Is A Headwind For Industrial Metals... Chart I-5...And Iron Ore ##br##Prices   Chinese Lending Is An Investment ‘Super-Driver’ We are strong believers in investment reductionism. Our reductionist philosophy stems from two guiding principles: Occam’s Razor – which says that when there are competing explanations for the same effect, the simplest explanation is usually the best; and the Pareto Principle – which says that 80% of effects come from just 20% of causes.4 Investment banks hate investment reductionism. Given that they want to sell you as much product as possible, they make investment seem much more complicated than it is. Yet most of the moves in most financial markets result from a very small number of ‘super-drivers.’ Our objective is to un-complicate investment – to identify the super-drivers, and to call them right. Clearly, one super-driver right now is the evolution of the pandemic, and specifically the evolution of new variants of the virus, as we discussed in Viral Variants Strike Down The Reflation Trade. A second super-driver is the direction of the T-bond yield, because this drives the direction of many other market trends such as growth versus value, defensives versus cyclicals, and US versus Europe. As we discussed in Don’t Panic About US Inflation, the T-bond yield is likely to drift lower in the coming months. Today’s report identifies a third super-driver – the evolution of Chinese lending. To repeat, the sharp drop in Chinese lending over the past year is highly likely to weigh on (non-oil) commodity prices through the remainder of 2021.   The sharp drop in Chinese lending over the past year is highly likely to weigh on commodity prices through the remainder of 2021. This means that basic resources equities are likely to underperform both in absolute terms, and relative to the broader market (Chart I-6 and Chart I-7). On a 6-month horizon, go underweight basic resources versus the market. Chart I-6Declining Chinese Lending Is A Headwind For Basic Resources Equities, Both In Absolute Terms... Chart I-7...And Relative To The Broad ##br##Market It also means that commodity currencies like the Canadian dollar and New Zealand dollar are likely to underperform versus the US dollar (Chart I-8 and Chart I-9). Chart I-8The Canadian Dollar Just Tracks Inflation Expectations Chart I-9The New Zealand Dollar Just Tracks Inflation Expectations Commodities Are Fractally Fragile Reinforcing the super-driver of a Chinese lending slowdown, the 260-day fractal structure of the commodity complex is at the same extreme of fragility that heralded turning-points in 2009, 2010, 2018, and 2020 (Chart I-10). Chart I-10The Fractal Structure Of The Commodity Complex Is Extremely Fragile As a reminder, a fragile fractal structure is a warning that the time horizons of investors setting the investment’s price has become dangerously skewed to short-term horizons. At this point, as longer-term value investors are missing from the price setting process, the price becomes unmoored from the longer-term valuation anchor. Eventually though, when the longer-term investors re-enter the price setting process, the price snaps back towards the valuation anchor. A fragile fractal structure is a warning that the time horizons of investors setting the investment’s price has become dangerously skewed to short-term horizons. In early May, we highlighted this fragility in the commodity complex and, exactly as anticipated, most commodities then started to correct. We are trading the on-going correction in commodities through a short position in PKB as well as short CAD/USD, and both positions are now in healthy profit. Staying on the theme of commodities, the 60 percent outperformance of corn versus wheat over the past year is only starting to correct now (Chart I-11). Hence, a recommended trade is to short the corn future (number 2, yellow) versus the wheat future (number 2, soft red) setting the profit target and symmetrical stop-loss at 12 percent. Chart I-11The 60 Percent Outperformance Of Corn Versus Wheat Will Soon Correct Finally, relating to a non-commodity position, we have extended by 33 days the holding period of short France versus Japan. Thus far, the position has traded sideways so we are giving it more time to move into profit. Dhaval Joshi Chief Strategist dhaval@bcaresearch.com Footnotes 1 The change in lending is the definition of the credit impulse. 2 The non-financial sector includes households, (non-financial) firms and government. 3 Voxeu.org: Drivers of commodity price booms and busts in the long run, David Jacks and Martin Stuermer. 4 Often known as the 80-20 rule. In fact, it could be 90-10, 95-5, or even 100-5 as the numbers do not have to add up to 100. Fractal Trading System Fractal Trades 6-Month Recommendations Structural Recommendations Closed Fractal Trades Closed Trades Asset Performance Equity Market Performance   Indicators To Watch - Bond Yields Chart II-1Indicators To Watch - Bond Yields - Euro Area Chart II-2Indicators To Watch - Bond Yields - Europe Ex Euro Area Chart II-3Indicators To Watch - Bond Yields - Asia Chart II-4Indicators To Watch - Bond Yields - Other Developed   Indicators To Watch - Interest Rate Expectations Chart II-5Indicators To Watch - Interest Rate Expectations Chart II-6Indicators To Watch - Interest Rate Expectations Chart II-7Indicators To Watch - Interest Rate Expectations Chart II-8Indicators To Watch - Interest Rate Expectations  

Special Report

Highlights Now that the dust has settled on the hotly contested 2020 election, we introduce our revised and updated quantitative presidential election model. We will periodically update the model as a gauge of President Biden’s political capital as well as the Democratic Party’s evolving odds of keeping the White House in 2024. The model measures the probability of the ruling party’s winning the Electoral College vote for each of the 50 states. As of now, the Democrats only have a 53% chance. Granting that Republicans have a good chance of retaking at least one chamber of Congress in the 2022 midterm election, investors likely face a return to gridlock. Gridlock would mean neither too much nor too little spending and zero tax hikes. The Democratic Party’s success on its current legislative agenda in 2021-22 is highly significant as it will set US fiscal policy for the foreseeable future. Democrats are still highly likely to pass an infrastructure bill by year’s end that will hike corporate taxes and mark peak stimulus for this cycle. Stay long the BCA Infrastructure Basket. Feature The 2020 US Presidential Election has come and gone. Joe Biden defeated Donald Trump with a margin of 74 Electoral College votes to become the 46th president of the United States of America. 57 of these votes came from states where Biden’s margin of victory over Trump hovered around one percentage point or less, highlighting how close the race for the White House was. In this report – for your Independence Day reading pleasure – we introduce the US Political Strategy quantitative presidential election model. Sadly it is never too soon to gear up for the next US presidential election. Our election model is a state-by-state model that uses both economic and political variables to predict the probability of the incumbent party winning the Electoral College votes in each of the 50 states.1 We favor predicting the Electoral College vote over the popular vote since the winner of the presidential election is determined by the Electoral College. There have been five cases in history where the nationwide popular vote did not determine the outcome and two in recent history (George W. Bush in 2000 and Donald Trump in 2016). The college imposes a significant (and deliberate) constraint on majority opinion if it is not shared across America’s geographic regions. The model’s sample size includes ten presidential elections, from 1984-2020, across 50 states, netting 500 observations. The model incorporates the lessons of the narrow 2020 election which took place amid extreme political polarization and an economic recession. The Four Variables Our election model is based off a Probit regression that produces the probability that each state will remain under the control of the incumbent party. The dependent variable (classified as “elected”) is stated as follows: 1 = Incumbent party wins Electoral College votes in state; or, 0 = Incumbent party loses the Electoral College votes in state. This method allows us to measure the probability that a state with certain characteristics will fall into one of these two categories. We can then predict the probability of the incumbent party winning all the Electoral College votes in each of the 50 states. The model has four independent variables, or predictors: State economic health. Specifically, we use the Federal Reserve Bank of Philadelphia State Coincident Index for each of the 50 states. The coincident index combines four of a given state’s economic 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 (US city average). In other words, it captures job growth, manufacturing wages, joblessness, and real household income. Margin of victory in previous election. Specifically, we use the incumbent party’s margin of victory in the previous presidential election in each state. A “time for change” variable. This is a categorical variable indicating whether the incumbent party has occupied the White House for one or more terms. Since Biden is serving his first term as president this variable will have no impact on our model’s predictions for the 2024 election. If the Democratic Party were to win the 2024 election and hold the White House for a second term, this variable would then have a negative impact on the party’s odds of winning a third straight term in 2028. Presidential approval rating. Namely, we use the average approval level of the incumbent president in July of an election year. Biden Would Still Win The Election Today Our election model gives us an early look into the 2024 presidential race. We can also look back to see if Biden would win the 2020 presidential election if it were held again today. As it stands, Biden would still win with 308 Electoral College votes (Chart 1), two more than the official account of last year’s election. The two additional votes are a result of the model suggesting Florida (29 votes) would turn Democratic, while Arizona (11 votes) and Georgia (16 votes) would turn Republican, opposite to the 2020 election outcome. Chart 1Quant Model Gives Democrats Only 53% Chance Of Retaining The White House Biden’s overall probability of an election win lies at 53%, in line with early market predictions (Chart 2). These odds reinforce the fact that the 2020 election was closely fought, that the US public remains nearly evenly divided, and that national economic conditions contribute to this division. While it is still early days in the 2024 election cycle, there are some interesting takeaways from our model’s latest prediction. For starters, Florida remains a toss-up state but leans toward the Democrats. Philadelphia and Wisconsin, which were hotly contested in 2020, are only just favored to remain Democratic. Another interesting prediction concerns Arizona and Georgia. Both states were highly contested battlegrounds. For Arizona, it was the first time since the 1996 presidential election that the state turned Democratic; for Georgia it was the first time since 1992. Both states saw larger turnouts for Democrats than in recent elections. However, both states would flip back to Republican control if the election were held today, according to our model, by a more than 10 percentage point change in probability. This is an interesting prediction given that only seven months have passed since the 2020 election. Chart 2Market Has Democrats Ahead Of Republicans The stage was largely set for a Trump loss in 2020. Recessions are catastrophic for presidents running for reelection, especially if they take place during the election year. Coupled with a nationwide health pandemic, Trump was highly likely to lose. In fact his race with Biden proved a lot closer than many commentators expected – in large part due to his unwavering base of support, as reflected in the unprecedentedly small range of his approval rating. This is what prompted us to upgrade his odds from 35% to 45% in a BCA Geopolitical Strategy report on October 26, 2020 (for further discussion see Statistical Appendix). By contrast, Democrats are heavily favored to keep the White House in the 2024 cycle as they will ride the coattails of a recovering US economy, an increasingly vaccinated population, and a (likely) divided Republican opposition. US Still At Peak Polarization Our model produces a novel measure of US political polarization: it shows how many states will be won or lost with extreme certainty (less than 5% or greater than 95%). These are states that are not really competitive because of overwhelming partisan favoritism among their voting populations. Results of in-sample predictions from our model show a slight uptick in the degree of polarization in 2024, which is now above both 2012 and 2020 levels (Chart 3, Top Panel). This change is intuitive coming off the back of one of the most highly contested US elections in history. However, polarization should not rise much higher in the 2024 presidential election cycle. In better economic times, polarization tends to fall, as wider prosperity tends to blanket nationwide social grievances. If Trump wins the Republican nomination in 2024 then one would assume that polarization will remain near peak levels. But if the economy has improved substantially, as we expect, then Trump’s populist platform will have less appeal for voters and the Republican Party will remain divided. This would lead to a higher level of Republican approval of the Democratic candidate, i.e. falling polarization (Chart 3, bottom panel). Chart 3Still At Peak Polarization   Over the next five-to-ten years, we hold the contrarian view that polarization will fall. Generational change in the US will produce more domestic policy consensus, specifically on government spending and taxes, while geopolitical struggle with China will unify the nation against a common enemy for the first time since the Cold War. But our quantitative model pushes against this view at present. Accuracy In Back Tests Our model performs well during in-sample back-testing when comparing it to actual Electoral College vote outcomes for each election since 1984. The model correctly predicts all presidential election outcomes over our sample period (Chart 4), including last year’s narrow result. Chart 4Our Model Predicts All Election Outcomes In Our Sample … The model performs well in out-of-sample back testing too, with prediction accuracy of states at 92%. All election outcomes from 2000-2020 are correctly predicted (Chart 5).  Chart 5… And During Out-Of-Sample Back Testing What Now? We are still a long way from the next presidential election, but the cycle has begun. This means we can begin to form an early view of what is to come over the next three and a half years. The model also gives us a look into what the election backdrop looks like just seven months after the 2020 election. Right now, the Democratic Party holds a decent margin over whoever the Republican competitor may be in 2024. Our model suggests the Democrats would win 308 Electoral College votes if a presidential election were run today, as mentioned. Overall, they have a 53% chance of victory. From a qualitative point of view, our model may be understating the Democrats’ odds in 2024, as things stand today. First, the surest rule of thumb in US politics is that voters will ask themselves whether they are better off than they were four years ago. It is unlikely that voters will be worse off in November 2024 than they were amid the pandemic, recession, and nationwide racial and social unrest of November 2020. Second, the split within the Republican Party over President Trump’s populism, symbolized by marginal Republican votes to convict him of incitement of insurrection over the January 6 riot on Capitol Hill, is likely to produce a closely fought Republican primary election or even a third party candidate, dividing the Republican vote. That’s not to say Republicans have zero chance. Republicans are likely to retake the House of Representatives in 2022, which will give them a base to mount a challenge over the succeeding two years. President Biden will be about to turn 82 years old when the 2024 vote is held – he may choose or be forced to hand the reins to Vice President Kamala Harris, who did not perform well in the 2020 Democratic primary election. Exogenous shocks could take the world by surprise and undermine the “return to normalcy” that the Democrats are trying to project. There are also some interesting toss-up states in 2024, but these will change as we continue to update our model with the latest data. If Biden has to step down, and the Republicans reunify, then the US could see another closely fought election. But Republican reunification is a stretch as things stand today. For now, Biden’s reelection bid will benefit from the recovery and Republican divisions. Investment Takeaways Our quantitative election model gauges the probability that the incumbent political party will retain the White House in the Electoral College vote. The model is based on state-level economic health, the president’s job approval rating, and the strength of his margin of victory in each state, plus an “incumbent advantage” for parties that have only held the White House for one term. The model currently shows that the Democratic Party would win if the 2024 election were held today, albeit with only a 53% probability – an indication of how nearly evenly divided the states remain after the hotly contested election of 2020. However, the model is likely underrating the Democrats as the economy will improve substantially between now and 2024. This will increase the odds of Democrats retaining critical swing states. It will also prolong Republican divisions by depriving them of an economic message around which to rally. But of course anything can happen over three and a half years. The Democrats are favored in 2024 notwithstanding the subjective 75% chance that Republicans retake the House of Representatives in the 2022 midterm elections. A new party in the White House almost always loses seats in Congress at its first midterm. While 2022 could be an exception, we still favor Republicans to regain the House. The takeaway from all of the above is that while 2022 will produce gridlock, nevertheless the 2024 election is unlikely to resolve it. Hence the US will see no drastic domestic legislative changes after 2021-22 period – fiscal policy will be frozen. This provides certainty for investors as it means neither excessive spending, nor austerity, nor tax hikes. Yet midterm elections that produce gridlock exhibit a “buy the rumor, sell the news” profile and are not more bullish for markets than those that produce single-party rule (Chart 6). Monetary policy will probably tighten in 2023 so everything will depend on where the market stands before the election. Incidentally, the model suggests that US political polarization, which hit extreme levels in 2020, will increase further in the 2024 cycle. But this result may not pan out. Over the long run as generational change and geopolitical conflict will force Americans to gather around a new consensus on key policies, namely government spending and foreign and trade policy. Still, we recognize that this reduction in polarization may not occur substantially by 2024 – and on a deeper level that US politics will always be very partisan, as they have been since the presidential election of 1800. Investors should stay constructive on the bull market in the second half of the year as President Biden’s infrastructure bill and/or American Jobs Plan is likely to pass Congress. However, passage in the Senate will mark the top of this cycle’s fiscal stimulus and investors should no longer underweight defensive sectors and growth stocks going forward. Chart 6Gridlock 2022 Will Give Investors Fiscal Certainty   Guy Russell Research Analyst guyr@bcaresearch.com   Statistical Appendix Some clients may be curious as to how our US Political Strategy election model differs from our Geopolitical Strategy model used in the 2020 elections, and where it has made improvements in its efficiency and predictive accuracy. We discuss these improvements herein. Changes To The Geopolitical Strategy Presidential Election Model The last update to the BCA Geopolitical Strategy presidential election model was published at the end of October 2020. We correctly forecast that Biden would win the election in March 2020 and maintained this view throughout the year. By October, however, our quantitative model gave President Trump a 51% chance of winning, predicting that he would gain 279 electoral college votes. We read the model as “too close to call” and stuck with our subjective judgement in favor of Biden for the final prediction, a testament to the need for both quantitative and qualitative analysis. The model missed four states: Arizona, Georgia, Michigan, and New Hampshire. The popular margin of victory in these states was 0.3%, 0.2%, 2.8%, and 8.4% respectively. We knew our model might be over-generous to Trump because we chose to use the range rather than the level of his popular approval rating as a key variable in the model. We did this to counteract the effect of “shy Trump voters,” which distorted traditional public opinion polling.2 Methodology And Variables For the most part, we retain the methodology and suite of economic and political variables used in previous versions of the model. For long-time clients and those who are new to the US Political Strategy and Geopolitical Strategy service, the original version of our model can be found here while the updated 2020 version can be found here.3 The one and only economic variable is now transformed by a six-month change to each state’s coincident index, capturing the improvement or deterioration of the state’s economy. The six-month change results in the best statistical fit for the overall model this time round. In the 2020 model, we transformed the variable by a three-month change. A fast-changing economic environment coupled with a then-higher statistical impact in our model led us to this decision. We still weight the transformation of our economic variable in the same manner as we did in last year’s updated model. We take a weighted average of the six-month change of all the monthly state coincident indices in the presidential term preceding the election. Later months are weighted heavier than earlier months as the most recent context will have a greater impact on voter opinion in the election. In terms of our political variables, the margin of victory is simply measured as the incumbent party’s share of the popular vote minus the non-incumbent party’s vote share. This has not changed from previous versions of our model. For the 2024 model, we have switched back to including the average job approval level instead of range. We use the level as of July of the election year.4 July job approval data shows the highest correlation with the popular and Electoral College vote. October is marginally higher but not enough higher to justify losing three-months of data lead time in our estimation (Chart A1). Obviously whenever we update the model for predictive purposes ahead of November 2024, the latest month’s approval rating serves as a proxy for the final July 2024 reading. Chart A1July Job Approval Highly Correlated With Election Outcome Model Performance Predicted Error The 2024 model has made noteworthy improvements in predictive accuracy across recent elections when compared to the 2020 model. Most noticeable is the large difference in error (Chart A2). The 2020 model failed by a small margin to predict the election outcome. The 2024 model accurately predicts last year’s outcome, although it overpredicts the outcome by 27 Electoral College votes. Chart A2New Model Reduces Predicted Error Over Old Model … The 2024 model also performs well against a different version of the 2020 model, a “bare bones” version that relied exclusively on economic data. This version excluded Trump’s approval data, relying only on an economic explanatory variable to explain the variation in the model’s evolving prediction over time. Our last update to this bare bones model predicted a Trump loss, hence the low prediction error (Chart A3). We published this result alongside our official 2020 model (and other alternatives) for the sake of transparency and to enable clients to choose which of our models better suited their assumptions over ours. We still believe the incumbent president’s job approval data plays a significant role in the presidential election, which is why we included this variable in the GPS and USPS models. But the bare bones model was especially powerful given the economic backdrop in the US last year. Now that the US economy is showing increasing signs of making a full recovery, our 2024 model has learnt from past data and modeling, and still manages to predict 2020’s election outcome despite its inclusion of non-economic (i.e. political) variables. Chart A3… And Performs Well Against “Bare Bones” Economic Model If we create a new bare bones 2024 model and compare it to a comparable 2020 model we arrive at essentially the same outcome (Chart A4). These are two pure economic models, but the new version has a different (smoother) transformation applied to the coincident economic index. That is, changes in economic activity are less volatile. The older version under-predicted the 2020 election outcome by two crucial Electoral College votes, while the new one over-predicted the outcome by 16 votes. Chart A4New “Bare Bones” Economic Model However, for our official 2024 model we will not take this bare bones economic approach but rather will incorporate hard political data (presidential approval, state margin of victory, and a time for change variable). Minimizing predictive error while retaining an explanatory variable that we believe is causal provides us with the most robust model. Classification The 2024 model correctly classifies predicted outcomes at a rate of exactly 90%. That is, when the model makes a prediction of a certain state’s electoral outcome from 1984-2020, it is correct 90% of the time. This level of classification is the highest we have achieved across the several versions we have published since 2016 (Table 1). A close second is the bare bones 2020 model, at 89.11%. Table 1New Model Classifies Outcomes At The Highest Rate … Sensitivity And Specificity – Receiver Operating Characteristic Curve A Receiver Operating Characteristic (ROC) curve is a performance measurement for classification problems of binary modelled outcomes, among others. An ROC curve tells us how much the model is capable of distinguishing between classes. In our case, we have two classes: the dependent variable (classified as “elected”) is stated as 1 = incumbent party wins the Electoral College votes in each state; or 0 = incumbent party does not win the Electoral College votes in each state. The higher the area under the curve (AUC), the better our model is at predicting 0 classes as 0 and 1 classes as 1. An excellent model has AUC near to one. A poor model has an AUC near to zero, which means it has the worst measure of classifying classes correctly, labelling zeros as ones and vice versa. In fact, at a level of zero AUC, the model is reciprocating incorrect classes by predicting zeros as ones and ones as zeros. Statistically, more AUC means that the model is identifying more true positives while minimizing the number/percent of false positives. The ROC curve for our 2024 model has an AUC of 0.9668 (Chart A5), the highest AUC of all models we have developed and tested (Table 2). This means that the true positive rate for classifying outcomes is high and the false positive rate is low, further bolstering the model’s robustness. Chart A5Receiver Operating Characteristic Curve Of New Model Table 2… Has The Best Fit Compared To Older Models … F1 Scores A final grading of the 2024 model is by means of the F1 score. The F1 score is a measurement that considers both precision (specificity in the above ROC curve) and recall (sensitivity in the above ROC curve) to compute the score. The F1 score can be interpreted as a weighted average of the precision and recall values, where an F1 score reaches its best value at 1 and worst value at 0. The 2024 model produces the highest F1 score across our suite of historic models (Table 3). Table 3… And Is The Most Accurate Across All Models After discussing the above statistical metrics and elements of the 2024 model, we are happy to accept it as our new base case presidential election model, premised on its improvement in accuracy at predicting election outcomes in the past, as well as its ability to correctly classify outcomes as they were realized, relative to past published models of this nature. Appendix Tables Table A1USPS Trade Table Table A2Political Risk Matrix Table A3Political Capital Index Table A4APolitical Capital: White House And Congress Table A4BPolitical Capital: Household And Business Sentiment Table A4CPolitical Capital: The Economy And Markets Footnotes 1     We assume that the District of Columbia will vote for the Democratic candidate due to past voting outcomes overwhelmingly favoring Democrats. 2     Large numbers of people polled in the 2016 and 2020 elections declined to say they were voting for President Trump, who was stigmatized in the mainstream media and society at large, or refused to participate in opinion polling. While some analysts rejected this idea after the 2016 election, the large polling misses in 2020 revived it. As many as one-fifth of Trump voters in 2020 might have kept their support secret. See Gregory Korte, “‘Shy Trump Voters’ Re-Emerge As Explanation For Pollsters’ Miss,” Bloomberg, November 19, 2020, bloomberg.com. See also Ed Kilgore, “What Did We Learn About Political Polling In 2020?” New York Magazine, March 26, 2021, nymag.com. 3    From here on out, the updated 2020 Geopolitical Strategy model will be referred to as the “2020 model”. 4    We we had originally introduced four measures covering this topic back in 2019, two require a longer period of job approval data to be put into estimation, these being the  “October momentum” and the “2-year change” job approval variables. We will revisit additional job approval measures and determine if they should be included in later estimations.  

Highlights The Indian rupee is about 7% cheaper than its fair value versus the US dollar. Expanding capital expenditures will boost India’s productivity and raise returns on capital. That will attract higher capital inflows, propelling the rupee. India also has a better inflation outlook compared to the US because of the government’s prudent fiscal policy and muted wage pressures. Foreign bond investors should stay overweight India in an EM local currency bond portfolio. Equity investors should upgrade India from neutral to overweight in view of receding pandemic-related disruptions. Feature The outlook for the Indian rupee over the medium term (six months to three years) is positive. In this report we will identify the two primary drivers of the rupee/US dollar exchange rate over this time horizon. The first is the relative purchasing power in the two economies. The second is return on capital; more specifically, relative return on capital in the two countries. Both indicate that the rupee will likely benefit from a tailwind over the next few years. The robust currency outlook also supports our bullish view on Indian local currency bonds versus their EM peers and US Treasuries. In this report, we will explain how this context, and the Indian market’s own idiosyncrasies, warrants favoring Indian bonds in a global fixed-income portfolio. Finally, we are upgrading Indian stocks back to overweight in an EM equity portfolio. Relative Purchasing Power Chart 1The Indian Rupee Is Below Its Fair Value The concept of “purchasing power parity (PPP)” theorizes that the currency of an economy with higher inflation will adjust lower (i.e., depreciate) relative to the currency of an economy that has lower inflation. The upshot is that the relative inflation dynamics of the two countries could provide insight into their exchange rate outlook.   The top panel of Chart 1 shows that the rupee is currently cheap when measured against what would be its “fair value”. The latter has been derived from a regression analysis between the manufacturers’ relative producer prices of the two countries and the exchange rate. Notably, a deviation from the fair value has also been a good predictor of where the nominal exchange rate will head in the years to come. Whenever the rupee appeared cheap relative to its fair value, it tended to appreciate over the next few years. The opposite has also been true. The current deviation from the fair value implies that the rupee could appreciate by 7% in the coming years (Chart 1, bottom panel). A deeper look into the inflation dynamics reveals that almost all significant directional moves in the rupee-dollar exchange rate over the past 25 years can be explained by movements in the relative inflation differential between the two economies. The rupee typically depreciates versus the dollar when Indian inflation is rising relative to that of the US; and appreciates when the relative inflation is falling. The only times they briefly diverged were during or in the immediate aftermath of a crisis, such as the global financial crisis or the COVID-19 pandemic. However, they were quick to return to their long-term correlations. Relative Inflation Outlook Going forward, the relative inflation outlook favors the rupee. This is because the fiscal and monetary policies in India will likely be tighter in India than in the US for the foreseeable future. Incidentally, India’s core inflation has fallen significantly relative to that of the US in the past decade (Chart 2). India’s inflation is driven mainly by two factors. The first is food prices; more specifically, the “minimum support price” that the Indian government pays to the farmers to procure food grains. Since the government is by far the single largest purchaser, the price it pays usually sets the floor in the market. The ebbs and flows of this procurement price have had a telling impact on the country’s inflation over the past few decades (Chart 3, top panel). Chart 2India's Inflation Has Fallen Significantly In The Past Decade Chart 3Notwithstanding The Temporary Pandemic-Era Surge In Fiscal Spending …   In recent years, however, the authorities have been careful and did not hike the procurement prices over much. That has helped to keep headline CPI in check. Further, the government legislated new farm laws last year, which will usher in private capital in the agriculture sector. This will help improve farm productivity and keep food prices under control1 in the future.  Chart 4...Fiscal Policy Has Been Very Prudent Since The GFC The other driver of Indian inflation is fiscal expenditure. The rise and fall in government spending leads core inflation by about a year (Chart 3, bottom panel). Notably, even though fiscal spending has swelled over the past year to provide relief to a pandemic-stricken economy, this one-off surge is offset by collapse in output and demand. Besides, the odds are high that the government will revert to a tighter stance as soon as the pandemic is brought under control. Indeed, such a fiscal splurge represents a departure rather than a fixture in India’s fiscal policy. Ever since the global financial crisis, successive Indian governments adopted a rather prudent fiscal stance. Chart 4 shows that fiscal spending steadily declined from 17% of GDP in 2009 to 12% by 2019. The conservative stance was implemented by both the previous UPA government and the current NDA government which came to power in 2014. Such a stance not only helped to substantially reduce the country’s fiscal and primary deficits but was also instrumental to the steady decline in inflationary pressures. The wage pressures in the economy are also rather muted. In rural areas, both farm and non-farm wages have been growing at a slow pace and have often remained below consumer inflation for the past six years (Chart 5, top panel). A similar picture is seen in the central banks’ (RBI) industrial outlook surveys. The assessment for salary and remuneration shows a subdued outlook; in fact, the indicator is below zero (Chart 5, bottom panel). This implies that wage pressures in the industrial sector have also been very low since 2017. Going forward, as tens of millions of young people continue to join the work force every year, the broader picture is unlikely to change. Overall, subdued wage pressures will also keep a tab on general inflation in the economy. Relative Return On Capital The other important driver of the rupee versus the dollar over the medium term is the direction of Indian companies’ return on capital relative to those of the US. When the return on capital rises, especially relative to that of the US, foreign capital flows into India in search of higher profits. Those capital inflows help boost the rupee. Chart 6 shows that over the past 25 years the rupee strengthened versus the dollar during those periods when return on assets of Indian non-financial corporates rose. The rupee depreciated when this ratio dropped. Chart 5Inflation Outlook Remains Sanguine As Wage Pressures Are Muted Chart 6Rupee Strengthens When Relative Return On Capital In India Rises...   The same holds true when Indian firms’ return on assets are compared relative to those of the US. All major moves in rupee strength and weakness largely coincided with the relative rise and fall in return on assets (Chart 6, bottom panel). Chart 7...As Foreign Capital Inflows Into India Boosts The Rupee Thus, relative profitability clearly has a major influence on the exchange rate. And as alluded to earlier, the link is via capital inflows. The ebbs and flows of capital into India have a very explicit impact on the rupee (Chart 7). Going forward, a pertinent question is in which way will India’s return on capital be headed. Our bias is that, beyond the pandemic-related disruptions, it is heading higher over the medium term. We have the following observations: A sustainable rise in return on capital is highly contingent on productivity gains. And the latter depends on capital investment in new plants, machinery, technology, as well as on infrastructure. Thus, a meaningful and sustained rise in capital expenditures could be a harbinger of higher returns in the future. Firms, on their part, would engage in new capital expenditures once they are sanguine of future demand as well as profits. Notably, both gross and net profits of India’s non-financial sector have rebounded rather strongly. Capital expenditure has recovered in tandem (Chart 8). The latter indicates that companies do not consider profit recovery a fluke and are confident demand will remain upbeat. Corroborating the above, imports of capital goods have skyrocketed. This is also a precursor to higher capex down the road (Chart 9). Chart 8Rebounding Profits Have Encouraged Firms To Resume Capex... Chart 9...As Evidenced In Accelerating Capital Goods Imports Chart 10Capital Goods Imports Have Been Rising For The Past Several Years Markedly, India’s import profile has been encouraging in recent years. The share of capital goods in total imports and non-oil imports have been rising (Chart 10). This indicates that firms have not been averse to capital expenditure. This also shows that unlike in some other EM countries, imported consumer goods did not overwhelm India’s capital goods imports. The last time India saw a surge in capital goods imports was in the 2000s, a period when the country’s capex and profits also surged. That period coincided with a multi-year bull run in the rupee and stocks. The early 2010s, on the other hand, saw a deceleration in capex and capital goods imports – and was followed by a period of sub-par return on capital. Now, the tides are turning again. Finally, the quality of capital inflows has also improved over the past decade. India has been receiving ever higher amounts of FDI compared to portfolio inflows (Chart 11). The former is a much more efficient form of capital and are also more likely to boost capital expenditures enhancing productivity in the economy. Incidentally, India’s real gross fixed capital formation has hovered between 30% and 35% of GDP since 2008 – easily the highest rate globally, save China (Chart 12). Hence, if a new capex cycle ensues, which seems likely, it will happen over and above the base built over the past decades. That should help drive labor productivity and profits up by a notch. Chart 11...Along With Steady Growth In FDI Chart 12A New Capex Cycle On Top Of The Previous Base Will Boost Productivity   All in all, odds are that Indian productivity will improve going forward, which in turn will boost firms’ profitability metrics. That should help propel the rupee. Bond Bullish The combination of a stable currency, prudent fiscal policy, and a benign inflation outlook make Indian bonds highly desirable to foreign investors. Notably, thanks to some systemic factors, Indian bonds are not as sensitive to bouts of fiscal profligacy and/or inflation in India: Over the past 20 years or so, ten-year bond yields hovered in a rather narrow band of 6%- 9%. A crucial reason for that stability is very limited foreign holdings: only about 2% of Indian government bonds are held by foreign investors. This has reduced yield volatility substantially. In many EM countries, where foreign holdings are much higher, a negative growth shock usually leads to both rising bond yields and a depreciating currency – which perpetuate each other – as foreign investors head for the exit. In the case of India, a negative shock is tempered by falling bond yields, as domestic investors switch from riskier assets to government bonds. Not only are the foreign holdings in India too small to push up yields but the falling yields also encourage them to stay invested. That explains why bond yields in India fell during each of the crises: in 2008-09, 2014-15 and more recently in 2020. A second reason is the existence of captive domestic bond investors: commercial banks. As per the Reserve Bank of India mandate, all banks in India are obligated to hold a certain percentage (currently 18%) of their total deposits in government securities (called Statutory Liquidity Ratio, or SLR). These mandatory holdings have also helped reduce yield volatility. The impact of the above factors can often be seen at play. For one, a surge in India’s fiscal expenditure does not necessarily cause a spike in bond yields. This is because, devoid of any fear of dumping by foreign bond holders, India can and does ramp up government spending when growth is very weak. Those are the times when domestic investors shed riskier assets and move to the safety of government bonds. Hence, we see accelerating fiscal spending coinciding with low and falling bond yields, unlike in many other EM countries (Chart 13, top panel).   For a similar reason, a surge in India’s fiscal deficit does not necessarily cause a spike in bond yields either. If anything, widening budget deficits usually coincide with falling bond yields; and shrinking deficits with rising bond yields (Chart 13, bottom panel).  The explanation for this apparent anomaly is as follows: periods of stronger growth bring in more fiscal revenues and thus reduce the deficit. But strong growth and rising inflationary pressures also lead to higher interest rate expectations reflected in higher bond yields. The opposite happens when growth slows. Even though fiscal deficit goes up as revenues drop, decelerating inflationary pressures pave the way for lower bond yields. A pertinent question here is, given the idiosyncrasies of Indian bond markets, what then drives Indian bond yields? The simple answer is the business cycle. This is why rising bond yields coincide with stronger bank credit growth and falling yields with weaker credit growth (Chart 14). Chart 13A Surge In Fiscal Spending Or Deficits Doesn't Mean A Spike In Bond Yields Chart 14The Business Cycle Is The Ultimate Driver Of Indian Bond Yields   What is also notable is that the impact of any spike in consumer and/or producer price inflation on bond yields is not very pronounced (Chart 14, bottom panel). A crucial reason for that is again the SLR. Because of it, regardless of commercial banks’ own inflation expectations, they cannot dump government bonds. That puts a cap on bond yields even when inflation is rising. Besides, a rise in inflation usually coincides with accelerating bank credit and bank deposits. The latter causes higher demand for government bonds from banks (to maintain SLR). That in turn helps keep the bond yield lower than it otherwise would be. Chart 15The Spike In Public Debt Is Temporary, And Bond Investors Are Not Worried Bottom Line: The absence of foreign investors, the presence of large captive domestic investors and a long-held orthodox fiscal stance have turned the Indian bond market into a different ball game than many other EM local currency bond markets. One takeaway from this idiosyncrasy is that the current steep, but temporary, fiscal deficit should not be a matter of concern for bond investors. For a similar reason, the recent rise in the public debt-to-GDP ratio should have little impact on bond yields (Chart 15). Finally, a moderate rise in inflation is also unlikely to cause Indian bond yields to soar. Investment Conclusions The medium-term outlook for the Indian rupee is positive. It is also quite competitive, especially when compared to the currencies of India’s major competitors vying for multinationals to establish their manufacturing capacity (Chart 16). This means the rupee has some room for nominal appreciation without hurting its competitiveness. Chart 16The Indian Rupee is Quite Competitive This emphasizes our view that investors should continue to overweight India in an EM fixed-income portfolio. While strong growth and higher US bond yields can drive up Indian government bond yields, the former will also push up the rupee – as detailed in a previous section. The currency returns will offset any possible capital loss owing to rising yields, while a positive carry will boost total returns. Notably, because of the latter, a similar rise in yields (say, 100 basis points) in India and US bonds will have a much less negative impact on total return terms for Indian bonds than in the case of US Treasurys.  The long end of the Indian yield curve offers value: the 10-year bond yield is 200 basis points above the policy rate. The spread of India’s 5-year bond over that of the US is an impressive 550 basis points (Chart 17, top panel). Given the sanguine rupee outlook, odds are that Indian government bonds will continue to outpace US treasuries in total return terms – even when Indian growth accelerates and inflation rises modestly (Chart 18). Chart 17Indian Bonds Offer Value Relative To US And EM Counterparts Chart 18Higher Carry And A Stronger Currency Will Lead To Total Return OutperformanceWhen compared to the same-duration JP Morgan GBI-EM bond index, India offers a spread of 100 basis points. India has steadily outperformed that index in US dollar total return terms over the past several years (Chart 17, bottom panel). That is unlikely to change in future, thanks to the high carry and a relatively more stable currency. As such, investors should stay on with our recommendation of overweighting India in an EM local currency bond portfolio (Chart 18). Chart 19Go Overweight Indian Stocks In An EM Equity Portfolio Several factors that make the outlook for the rupee positive also argue for a positive outlook for Indian stocks. Like most other EM currencies, the rupee is pro-cyclical, and it tends to move with Indian share prices. Notably, Indian stocks have broken out of their previous highs (Chart 19). On a separate note, as the number of daily COVID-19 cases in the country have subsided, so have the chances of debilitating lockdowns. As such, economic activity is slated to gather steam. We had tactically downgraded India from overweight to neutral in an EM equity portfolio on April 22 in view of skyrocketing COVID-19 cases and deaths back then. Even though the pandemic situation had deteriorated considerably after our downgrade, share prices have staged a nice rebound to our surprise. It’s time to upgrade this bourse back to overweight (Chart 19, bottom panel). Investors should also stick with our sectoral recommendation of long Indian Banks and short EM banks. As we elaborated in our report on Indian banks, a recovery in the business and capex cycles would be very positive for Indian private sector banks (that make up 90% of the MSCI India Banks index) – given that they have aggressively cleansed their balance sheets of NPLs and have thereby already taken the hit in their earnings. Fixed-income investors should close the trade of receiving 10-year swap rates in India. We had recommended it along with other EM local rates back in April 2020 as a play on lower interest rates in EM. India’s 10-year swap rates have risen by 166 basis points since then. Rajeeb Pramanik Senior EM Strategist rajeeb.pramanik@bcaresearch.com   Footnotes 1 For more details see our report India’s Reform Drive: How Momentous (Part 1) dated 19 November 2020.