Equity Premium

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2021, we find that:

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Does the U.S. employment quit rate, a measurement from the Job Openings and Labor Turnover Survey run monthly by the U.S. Bureau of Labor Statistics, have implications for future U.S. stock market or U.S. Treasury bond return? A high (low) quit rate may indicate a strong (weak) economy and/or may signal high (low) wage inflation. To investigate, we relate quit rate to future performance of SPDR S&P 500 (SPY) as a proxy for the stock market and of iShares 20+ Year Treasury Bond (TLT) as a proxy for government bonds. Using monthly quit rate (which has a release delay of about six weeks) during December 2000 through August 2021 and monthly dividend-adjusted returns for SPY and TLT as available during December 2000 through September 2021, we find that: Keep Reading

What is the best way to understand and anticipate variations in equity factor returns? Past research emphasizes factor return connections to business cycle variables or measures of investor sentiment (with little success). In his September 2021 paper entitled “The Quant Cycle”, David Blitz analyzes factor returns themselves to understand their variations, arguing that behavioral rather than economic forces drive them. He determines the quant cycle (bull and bear trends in factor returns) by qualitatively identifying peaks and troughs. He focuses on U.S. versions of four conventionally defined long-short factors frequently targeted by investors (value, quality, momentum and low-risk), emphasizing the most volatile (value and momentum). He also considers some alternative factors. Using monthly data for factors from the online data libraries of Kenneth French, Robeco and AQR spanning July 1963 through December 2020 (and for a reduced set of factors spanning January 1929 through June 1963), he finds that:

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Do equity premium predictors published in the past still work after extending their respective discovery samples through 2020? In their September 2021 paper entitled “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II”, Amit Goyal, Ivo Welch and Athanasse Zafirov reexamine the power of 29 variables found to predict the equity premium (stock market return minus U.S. Treasury bill yield) in 26 prominent published papers, with data samples ending between 2000 and 2017, by extending these samples through 2020. They test not only their predictive powers, but also their performances when applied to four simple market timing strategies with Treasury bills as the alternative asset:

1. Untilted, Unscaled – go long the equity premium when the predictor is above its historical median and short otherwise.
2. Tilted, Unscaled – go long the equity premium unless the predictor is below its historical 25th percentile.
3. Untilted, Z-scaled – first calculate a Z-score by subtracting the historical median from the current value and dividing by the historical standard deviation, and then scale the equity premium allocation by the Z-score.
4. Tilted, Z-scaled – first calculate a Z-score by subtracting the historical 25th percentile value from the current value and dividing by the historical standard deviation, and then scale the equity premium allocation by the Z-score.

The benchmark for these strategies is buying and holding the equity premium. Extending the original discovery sample for each of the 29 predictors through December 2020 (typically about 10 years additional data), they find that: Keep Reading

Do credit portfolio managers adjust their portfolios more expeditiously than equity managers, thereby offering a means to time the equity market? In his August 2021 paper entitled “Credit-Informed Tactical Asset Allocation – 10 Years On”, David Klein updates and enhances the strategy presented in his paper of June 2011 (see “Credit as a Tactical Asset Allocation Signal”). The strategy holds stocks (short-term Treasuries) when stocks appear undervalued (overvalued) relative to corporate bonds based on data from a rolling 6-month historical interval. His proxy for corporate bonds is the ICE BofA Single-B US High Yield Index Option-Adjusted Spread (converted to a default probability) and for stocks is the Russell 2000 Index (with dividends). He hypothesizes that stock prices tend to fall when credit spread widens, and small capitalization stocks are more sensitive to credit conditions than large capitalization stocks. Two enhancements to the original strategy are: (1) shorten the lookback from six to three months; and (2) increase the equity allocation by adding a premium to equity values. A third enhancement is taking a 120% long position in stocks when they are undervalued and a 20% short position in stocks when they are overvalued (with 2% estimated annual costs for implementation). Updating and enhancing this strategy with 10 years of new daily data through June 2021, he finds that:

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Is it possible to isolate environmental, social and governance characteristics (ESG) effects on stock returns from those of other stock characteristics? In their July 2021 paper entitled “Chasing The ESG Factor”, Abraham Lioui and Andrea Tarelli specify a cross-sectional long-short ESG factor that neutralizes exposures to other firm characteristics, such as size and book-to-market ratio. By creating a pure ESG factor, they are able to isolate ESG alpha and estimate its separate E, S and G contributions. Their approach also suppresses effects of arbitrary ESG rating scales. They further construct an ESG sentiment index based on media attention to ESG-related topics and employ it to understand variations in pure ESG alpha. Using monthly firm ESG ratings from three sources as available during 1991-2019 and associated stock characteristics and returns during December 1992 through December 2020, with tests spanning December 2002 through December 2020, they find that: Keep Reading

Financial media and expert commentators often cite the U.S. unemployment rate as an indicator of economic and stock market health, generally interpreting a jump (drop) in the unemployment rate as bad (good) for stocks. Conversely, investors may interpret a falling unemployment rate as a trigger for increases in the Federal Reserve target interest rate (and adverse stock market reactions). Is this variable in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted unemployment rate from the U.S. Bureau of Labor Statistics (BLS) and monthly S&P 500 Index levels during January 1948 (limited by unemployment rate data) through June 2021, we find that: Keep Reading

U.S. job gains or losses receive prominent coverage in the monthly financial news cycle, with media and expert commentators generally interpreting employment changes as an indicator of future economic and stock market health. One line of reasoning is that jobs generate personal income, which spurs personal consumption, which boosts corporate earnings and lifts the stock market. Are employment changes in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted non-farm employment data from the U.S. Bureau of Labor Statistics (BLS) and monthly S&P 500 Index levels during January 1939 (limited by employment data) through June 2021, we find that: Keep Reading

A reader commented and asked: “A wide credit spread (the difference in yields between Treasury notes or Treasury bonds and investment grade or junk corporate bonds) indicates fear of bankruptcies or other bad events. A narrow credit spread indicates high expectations for the economy and corporate world. Does the credit spread anticipate stock market behavior?” To investigate, we define the U.S. credit spread as the difference in yields between Moody’s seasoned Baa corporate bonds and 10-year Treasury notes (T-note), which are average daily yields for these instruments by calendar month (a smoothed measurement). We use the S&P 500 Index (SP500) as a proxy for the U.S. stock market. We extend the investigation to bond market behavior via:

• Vanguard Long-Term Treasury Investors Fund (VUSTX)
• Vanguard Long-Term Investment-Grade Investors Fund (VWESX)
• Vanguard High-Yield Corporate Investors Fund (VWEHX)

Using monthly Baa bond yields, T-note yields and SP500 closes starting April 1953 and monthly dividend-adjusted closes of VUSTX, VWESX and VWEHX starting May 1986, January 1980 and January 1980, respectively, all through June 2021, we find that: Keep Reading

Is outperformance of green (environmentally friendly) stocks relative to brown (not environmentally friendly) stocks due to firm performance or concern about the climate? In other words, do green stocks carry a climate concern premium? In their June 2021 paper entitled “Dissecting Green Returns”, Lubos Pastor, Robert Stambaugh and Lucian Taylor examine relative performance of green and brown stocks in the context of unexpectedly strong increases in environmental concerns (climate concern jumps). Specifically, they:

• Construct a green factor as the return on a portfolio that is each month long (short) green (brown) stocks weighted by greenness based on environment-focused elements of MSCI ESG Ratings.
• Devise and test a 2-factor (market and green) model of stock returns.
• Compute a monthly measure of public climate concern based on an associated media index, with focus on series jumps.

Using stock return and factor data during November 2012 (based on availability of ESG ratings) through December 2020 and climate concern data during November 2012 through June 2018, they find that: Keep Reading