Fundamental Valuation

Is the Buffett Indicator, the ratio of total stock market capitalization to Gross Domestic Product (GDP), a useful indicator of future stock market performance internationally? In their March 2022 paper entitled “The Buffett Indicator: International Evidence”, Laurens Swinkels and Thomas Umlauft extend Buffett Indicator research from the U.S. to 14 international equity markets. Because the value of the indicator varies so much across countries at a given time (for example, 1.48 for the U.S. and 0.55 for Germany at the end of 2019), they first look at time-series predictability of returns by the Buffett Indicator within each country. They then compare predictive power of the Buffett Indicator to those of Shiller’s cyclically-adjusted price-to-earnings ratio (CAPE or P/E10) and mean-reversion in stock returns. Finally, they test a trading strategy that invests in the stock markets of those countries having low values of the Buffett Indicator relative to their respective (expanding window) histories. Using stock market valuation and earnings data and GDP series for 14 countries as available during 1973 through 2019, they find that:

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Can investors improve the performance of stock momentum portfolios by isolating stocks that “hold” their momentum? In their April 2022 paper entitled “Enduring Momentum”, Hui Zeng, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti exploit firm characteristics to identify stocks that continue to be winners or losers after selection as momentum stocks (stocks with enduring momentum). They measure momentum by each month ranking stocks into equal-weighted tenths, or deciles, based on past 6-month returns, with the top (bottom) decile designated winners (losers). They then develop a model that uses information from 37 firm characteristics to estimate each month the probability that each winner or loser stock will continue as a winner or loser during each of the next six months. They verify that the model reasonably predicts momentum persistence and proceed to test the economic value of the predictions by each month reforming an enduring momentum hedge portfolio that is long (short) the 10 equal-weighted winner (loser) stocks with the highest probabilities of remaining winners (losers) and holding the portfolio for six months. They compare the performance of this portfolio to that of a conventional momentum portfolio that is each month long the entire winner decile and short the entire loser decile, also held for six months. Using returns for a broad sample of U.S. common stocks priced over $1.00 and 37 associated firm characteristics during January 1980 through December 2018, they find that: Keep Reading

The “Simple Asset Class ETF Value Strategy” (SACEVS) allocates across 3-month Treasury bills (Cash, or T-bill), iShares 20+ Year Treasury Bond (TLT), iShares iBoxx $ Investment Grade Corporate Bond (LQD) and SPDR S&P 500 (SPY) according to the relative valuations of term, credit and equity risk premiums. Does applying a simple moving average (SMA) filter to SACEVS allocations improve its performance? Since many technical traders use a 10-month SMA (SMA10), we apply SMA10 filters to dividend-adjusted prices of TLT, LQD and SPY allocations. If an allocated asset is above (below) its SMA10, we allocate as specified (to Cash). This rule does not apply to any Cash allocation. We focus on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios (using average monthly T-bill yield during a year as the risk-free rate for that year) of SACEVS Best Value and SACEVS Weighted portfolios. We compare to baseline SACEVS as currently tracked and to the SMA rule applied to a 60%-40% monthly rebalanced SPY-TLT benchmark portfolio (60-40). Finally, we test sensitivity of main findings to varying the SMA lookback interval. Using SACEVS historical data, monthly dividend-adjusted closing prices for the asset class proxies and yield for Cash during July 2002 (the earliest all funds are available) through March 2022, we find that:

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How does the U.S. stock market earnings yield (inverse of price-to-earnings ratio, or E/P) interact with the U.S. inflation rate over the long run? Is any such interaction exploitable? To investigate, we employ the long run dataset of Robert Shiller. Using monthly data for the S&P Composite Stock Index, estimated aggregate trailing 12-month earnings and dividends for the stocks in this index, and estimated U.S. Consumer Price Index (CPI) during January 1871 through February 2022 (over 151 years), and estimated monthly yield on 1-year U.S. Treasury bills (T-bills) since January 1951, we find that:

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Are firms offering products and services purported to mitigate climate change compelling investments? In the February 2022 revision of their paper entitled “Climate Solutions Investments”, Alexander Cheema-Fox, George Serafeim and Hui Wang analyze international reports, regional net zero frameworks, research papers and news to develop a list of 164 key words/phrases associated with climate change solution business areas. They apply these key words/phrases to firm descriptions to identify 632 actively traded pure plays in climate solutions. They then characterize geographies, accounting fundamentals and valuation ratios for this sample and construct monthly rebalanced value-weighted and equal-weighted climate solutions portfolios (CSP). Using monthly firm fundamentals and stock trading data for these 632 firms from the end of 2010 through October 2021, they find that: Keep Reading

“Consumer Credit and Stock Returns” finds that expansion (contraction) of consumer credit, available monthly from the Federal reserve with a delay of about five weeks, has little or no power to predict overall stock market returns. Might consumer credit be useful in predicting returns for just the consumer discretionary sector, as proxied by Consumer Discretionary Select Sector SPDR Fund (XLY)? Using monthly seasonally adjusted total U.S. consumer credit and monthly dividend-adjusted prices for XLY as available during December 1998 (inception of XLY) through January 2022, we find that: Keep Reading

Do stocks of firms focused on luxury goods outperform those of more prosaic companies? In his June 2019 paper entitled “Demand-Driven Risk and the Cross-Section of Expected Returns”, Alejandro Lopez-Lira examines aggregate performance of firms selling goods with high income elasticity (luxury goods), assuming that such firms are particularly exposed to demand-driven risk (consumption shocks). Hypothesizing that advertising, customer support and new feature development costs are relatively high for such firms, he proposes three accounting-based measures of demand-driven risk exposure:

1. Indirect cost ratio (selling, general and administrative expenses, divided by cost of goods sold plus selling, general and administrative expenses).
2. Indirect costs-to-net sales ratio.
3. One minus the direct costs-to-net sales ratio.

He excludes financial, utilities, mining, petroleum refining and pharmaceuticals firms from analysis due to their insulation from consumer demand. Each June, he ranks remaining firms into fifths (quintiles) based on their indirect cost ratios, with the highest quintile most exposed to demand-driven risk. He then tracks monthly returns of the value-weighted quintiles over the next year. He further investigates interactions of demand-driven risk with competitive pressure, measuring the latter via textual analysis of Form 10-K submittals to gauge competitor product similarities/sales. Using annual accounting data, monthly stock prices and annual Form 10-Ks for the specified firms and contemporaneous monthly factor model returns as available during January 1962 through December 2016, he finds that:

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Does past performance of Environmental, Social, and Corporate Governance (ESG) stocks derive mostly from shift in demand from other stocks to ESG stocks? In his September 2021 paper entitled “Flow-Driven ESG Returns”, Philippe van der Beck examines whether flow of investor dollars toward ESG mutual funds explains aggregate performance of ESG stocks, as follows:

• Construct an ESG portfolio that aggregates quarterly holdings of U.S. equity mutual funds that assert sustainability mandates.
• Measure perceived sustainability of each stock by calculating the deviation of its ESG portfolio weight from its market portfolio weight.
• Estimate the price pressure due to a flow of dollars into ESG mutual funds.
• Combine perceived stock sustainability and price pressure to explore sensitivity of past ESG portfolio returns to level of dollar flow into ESG mutual funds.

Using mutual fund descriptions (with respect to importance of sustainability in investment decisions) and quarterly Form 13F mutual fund holdings data during 2000 through 2020, and underlying stock prices through the first quarter of 2021, he finds that:

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Leading index providers have introduced thematic stock indexes to address transformative macroeconomic, geopolitical or technological trends (for example, cybersecurity, robotics, autonomous vehicles and clean power). How do these indexes relate to standard asset pricing models? In his August 2021 paper entitled “Betting Against Quant: Examining the Factor Exposures of Thematic Indices”, David Blitz examines the performance characteristics of these indexes based on widely used factor models of stock returns and discusses why investors may follow these indexes via exchange-traded funds (ETF) despite unfavorable factor exposures. He considers 36 S&P indexes (narrower, equal-weighted) and 12 MSCI indexes (broader, capitalization-weighted) with at least three years of history. Using monthly returns for these 48 indexes and for components of the Fama-French 5-factor (market, size, book-to-market, profitability and investment) model and the momentum factor as available during June 2013 through April 2021, he finds that:

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Can machine learning-generated stock market crash predictions be amenable to human interpretation? In their June 2021 paper entitled “Explainable AI (XAI) Models Applied to Planning in Financial Markets”, Eric Benhamou, Jean-Jacques Ohana, David Saltiel and Beatrice Guez apply a gradient boosting decision tree (GBDT) to 150 technical, fundamental and macroeconomic inputs to generate daily predictions of short-term S&P 500 Index crashes. They define a crash as a 15-day S&P 500 Index return below its historical fifth percentile within the training dataset. The 150 model inputs encompass:

1. Risk aversion metrics such as asset class implied volatilities and credit spreads.
2. Price indicators such as returns, major stock index Sharpe ratios, distance from a long-term moving average and and equity-bond correlations.
3. Financial metrics such as 12-month sales growth and price-to-earnings ratio forecasts.
4. Macroeconomic indicators such Citigroup regional and global economic surprise indexes.
5. Technical indicators such as market breath and index put-call ratio.
6. Interest rates such as 10-year and 2-year U.S. Treasury yields and break-even inflation level.

They first rank and filter the 150 inputs based on GBDT to discard about two thirds of the variables. They then apply the Shapley value solution concept to identify the most important of the remaining variables and thereby support interpretation of methodology outputs. Using daily values of the 150 model inputs and daily S&P 500 Index roll-adjusted futures prices from the beginning of January 2003 through mid-January 2021 (with data up to January 2019 used for training, the next year for validation and the rest for testing), they find that:

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