Equity Premium

Are there any seasonal, technical or fundamental strategies that reliably time the U.S. stock market as proxied by the S&P 500 Total Return Index? In the February 2018 version of his paper entitled “Investing In The S&P 500 Index: Can Anything Beat the Buy-And-Hold Strategy?”, Hubert Dichtl compares excess returns (relative to the U.S. Treasury bill [T-bill] yield) and Sharpe ratios for investment strategies that time the S&P 500 Index monthly based on each of:

• 4,096 seasonality strategies.
• 24 technical strategies (10 slow-fast moving average crossover rules; 8 intrinsic [time series or absolute] momentum rules; and, 6 on-balance volume rules).
• 18 fundamental variable strategies based on a rolling 180-month regression, with 1950-1965 used to generate initial predictions.

In all cases, when not in stocks, the strategies hold T-bills as a proxy for cash. His main out-of-sample test period is 1966-2014, with emphasis on a “crisis” subsample of 2000-2014. He includes extended tests on seasonality and some technical strategies using 1931-2014. He assumes constant stock index-cash switching frictions of 0.25%. He addresses data snooping bias from testing multiple strategies on the same sample by applying Hansen’s test for superior predictive ability. Using monthly S&P 500 Index levels/total returns and U.S. Treasury bill yields since 1931 and values of fundamental variables since January 1950, all through December 2014, he finds that:

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Is the conventional linear factor model comprised of a few presumably independent predictors the best, or even a good, way to model differences in returns across assets? In the December 2019 update of their paper entitled “The Cross-Section of Returns: A Non-Parametric Approach”, Enoch Cheng and Clemens Struck compare predictive powers of conventional linear models and less presumptive tree-based methods. The latter accommodate multivariate interactions and non-linearities across all predictors. They consider two linear and two tree-based methods with parameter settings commonly used in other studies:

1a. Logit – a linear regression model including all factors.

1b. LASSO – a linear regression model with a shrinkage term that sets betas to zero for (discards) predictors that do not add information, and thereby acts as a variable selection tool.

2a. Bagged regression trees – bootstrapping to create different samples from the original data, growing an individual tree on each and combining predictions of individual trees by a simple majority vote.

2b. Boosted regression trees – a modification to bagging whereby bagging and growing trees takes place sequentially with bootstrapping subsequently adjusted to improve prediction accuracy for the forest with each new tree.

Specifically, they measure relationships between 59 predictor variables and next-month (4-week) return for a universe of 28 liquid commodity futures series. This asset universe has low trading costs and avoids survivorship bias. They use nearest, second and third month contracts, the latter two only to construct signals and the first for trading. They generally roll contracts 10 days before the last trade date. The 59 predictors include time series (intrinsic or absolute) momentum variants, moving average variants, volatility variants, value metrics, miscellaneous variables, dummies for calendar months and dummies for each of the 28 commodity contract series. They consider long-short portfolios based on top half-bottom half, top five-bottom five and top three-bottom three assets in terms of expected returns. Their break point for in-sample and out-of-sample testing is the end of 2013. Using monthly data for the 28 commodity contract series and the 59 predictors during January 1987 through October 2019, they find that: Keep Reading

Does combining an economic growth variable trend with an asset price trend improve the power to predict stock market return? What is the best way to use such a combination signal? In his December 2019 paper entitled “Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA)”, Wouter Keller investigates variations in a basic Growth-Trend timing strategy (GT) that is bullish and holds the broad U.S. stock market unless both: (1) the U.S. unemployment rate is below its 12-month simple moving average (SMA12); and, (2) the S&P 500 Index is below its SMA10. When both SMAs trend downward, GT is bearish and holds cash. Specifically, he looks at:

• Basic GT versus a traditional 60-40 stocks-bonds portfolio, rebalanced monthly, with stocks proxied by actual/modeled SPY and bonds/cash proxied by actual/modeled IEF.
• Improving basic GT, especially maximum drawdown (MaxDD), by replacing assets with equal-weighted, monthly rebalanced portfolios with various component selections. His ultimate portfolio is the Lethargic Asset Allocation (LAA), optimized in-sample based on Ulcer Performance Index (UPI) during February 1949 through June 1981 (mostly rising interest rates) and tested out-of-sample during July 1981 through October 2019 (mostly falling interest rates).

He considers two additional benchmarks: GT applied to the Permanent portfolio (25% allocations to each of SPY, GLD, BIL and TLT) and GT applied to the Golden Butterfly portfolio (20% to each of SPY, IWN, GLD, SHY and TLT). He applies 0.1% one-way trading frictions in all tests. Using monthly unemployment rate since January 1948 and actual/modeled monthly returns for ETFs as specified since February 1949, all through October 2019, he finds that: Keep Reading

Which equity factors truly explain stock returns, and what group of them constitute the best model? In their November 2019 paper entitled “Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models”, Svetlana Bryzgalova, Jiantao Huang and Christian Julliard present a Bayesian estimation and model selection method for pricing of stock portfolios that allows simultaneous examination of the entire zoo of equity factors. They apply the method to 51 factors described in past papers, yielding a total of 2.25 quadrillion factor models of U.S. stock returns. They test abilities of these factors and models to price 25 portfolios of stocks sorted by market capitalization (size) and book-to-market ratio (value) and 30 industry portfolios. Using returns for factors available monthly during January 1970 through December 2017 and for factors available only quarterly during first quarter 1952 through third quarter 2017, and contemporaneous test portfolio returns, they find that: Keep Reading

Do both the long and short sides of portfolios used to quantify widely accepted equity factors benefit investors? In their November 2019 paper entitled “When Equity Factors Drop Their Shorts”, David Blitz, Guido Baltussen and Pim van Vliet decompose and analyze gross performances of long and short sides of U.S. value, momentum, profitability, investment and low-volatility equity factor portfolios. The employ 2×3 portfolios, segmenting first by market capitalization into halves and then by selected factor variables into thirds. The extreme third with the higher (lower) expected return constitutes the long (short) side of a factor portfolio. When looking at just the long (short) side of factor portfolios, they hedge market beta via a short (long) position in liquid derivatives on a broad market index. Using monthly returns for the specified 2×3 portfolios during July 1963 through December 2018, they find that:

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Do differences in expectations between institutional and individual investors in stocks and bonds, as quantified in weekly legacy Commitments of Traders (COT) reports, offer exploitable timing signals? In the February 2019 revision of his paper entitled “Want Smart Beta? Follow the Smart Money: Market and Factor Timing Using Relative Sentiment”, flagged by a subscriber, Raymond Micaletti tests a U.S. stock market-U.S. bond market timing strategy based on an indicator derived from aggregate equity and Treasuries positions of institutional investors (COT Commercials) relative to individual investors (COT Non-reportables). This Smart Money Indicator (SMI) has three relative sentiment components, each quantified weekly based on differences in z-scores between standalone institutional and individual net COT positions, with z-scores calculated over a specified lookback interval:

1. Maximum weekly relative sentiment for the S&P 500 Index over a second specified lookback interval.
2. Negative weekly minimum relative sentiment in the 30-Year U.S. Treasury bond over this second lookback interval.
3. Difference between weekly maximum relative sentiments in the 10-Year U.S. Treasury note and 30-year U.S. Treasury bond over this second lookback interval.

Final SMI is the sum of these components minus median SMI over the second specified lookback interval. He considers z-score calculation lookback intervals of 39, 52, 65, 78, 91 and 104 weeks and maximum/minimum relative sentiment lookback intervals of one to 13 weeks (78 lookback interval combinations). For baseline results, he splices futures-only COT data through March 14, 1995 with futures-and-options COT starting March 21, 1995. To account for changing COT reporting delays, he imposes a baseline one-week lag for using COT data in predictions. He focuses on the ability of SMI to predict the market factor, but also looks at its ability to enhance: (1) intrinsic (time series or absolute) market factor momentum; and, (2) returns for size, value, momentum, profitability, investment, long-term reversion, short-term reversal, low volatility and quality equity factors. Finally, he compares to several benchmarks the performance of an implementable strategy that invests in the broad U.S. stock market (U.S. Aggregate Bond Total Return Index) when a group of SMI substrategies “vote” positively (negatively). Using weekly legacy COT reports and daily returns for the specified factors/indexes during October 1992 through December 2017, he finds that: Keep Reading

Which equity factors from among those included in the most widely accepted factor models are really important? In their October 2019 paper entitled “Winners from Winners: A Tale of Risk Factors”, Siddhartha Chib, Lingxiao Zhao, Dashan Huang and Guofu Zhou examine what set of equity factors from among the 12 used in four models with wide acceptance best explain behaviors of U.S. stocks. Their starting point is therefore the following market, fundamental and behavioral factors:

They compare 4,095 subsets (models) of these 12 factors models based on: Bayesian posterior probability; out-of-sample return forecasting performance; gross Sharpe ratios of the optimal mean variance factor portfolio; and, ability to explain various stock return anomalies. Using monthly data for the selected factors during January 1974 through December 2018, with the first 10 (last 12) months reserved for Bayesian prior training (out-of-sample testing), they find that: Keep Reading

Who beats the stock market and why? In his October 2019 paper entitled “The Five Investor Camps That Try to Beat the Stock Market”, William Ziemba discusses how different categories of investors succeed. For investors pursuing active strategies, he addresses broadly the means of getting an edge and betting well. Based on his academic work and practical experience, he concludes that: Keep Reading

What are asset class return expectations and associated portfolio allocations of very sophisticated U.S. investors? In their February 2019 paper entitled “The Return Expectations of Institutional Investors”, Aleksandar Andonov and Joshua Rauh analyze disclosures of expected returns across asset classes among U.S. public pension funds, which hold assets of about $4 trillion (see the first chart below), including fixed income, cash, equities, real assets, hedge funds, private equity and other asset classes. Taking into account past fund performance, they investigate how fund managers estimate future returns. Disclosures also reveal target allocations to asset classes (see the second chart below). Together, expected asset class returns and target allocations allow calculation of expected portfolio returns. Using annual disclosures for 228 U.S. state and local government pension plans during 2014 through 2017, they find that:

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Is the conventional wisdom that exchange-traded funds (ETF) are efficient, low-cost alternatives to mutual funds correct? In their September 2019 paper entitled “The Performance of Exchange-Traded Funds”, David Blitz and Milan Vidojevic evaluate the performance of a comprehensive, survivorship bias-free sample of U.S. equity ETFs. They first divide the sample into three groups: (1) broad market index trackers; (2) inverse and leveraged funds; and, (3) others. They then subdivide group 3 into equity factor subgroups (small, value, dividend, momentum, quality or low-risk) based on either their names or their empirical exposures to widely accepted factor premiums. Finally, they compare performances of value-weighted ETF groups to those of the broad U.S. stock market and specified factors, focusing on data starting January 2004 when there are at least 100 ETFs of some variety. Using trading data and descriptions for 918 U.S. equity ETFs (642 live and 276 dead by the end of the sample period) and equity factor returns during January 1993 through December  2017, they find that: Keep Reading