Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for May 2024 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for May 2024 (Final)
1st ETF 2nd ETF 3rd ETF

Equity Premium

Governments are largely insulated from market forces. Companies are not. Investments in stocks therefore carry substantial risk in comparison with holdings of government bonds, notes or bills. The marketplace presumably rewards risk with extra return. How much of a return premium should investors in equities expect? These blog entries examine the equity risk premium as a return benchmark for equity investors.

Exploit VIX Percentile Threshold Rule Out-of-Sample?

Is the ability of the VIX percentile threshold rule described in “Using VIX and Investor Sentiment to Explain Stock Market Returns” to explain future stock market excess return in-sample readily exploitable out-of-sample? To investigate, we test a strategy (VIX Percentile Strategy) that each month holds SPDR S&P 500 ETF Trust (SPY) or 3-month U.S. Treasury bills (T-bills) according to whether a recent end-of-month level of the CBOE Volatility Index (VIX) is above or below a specified inception-to-date (not full sample) percentage threshold. To test sensitivities of the strategy to settings for its two main features, we consider:

  • Each of 70th, 75th, 80th, 85th or 90th percentiles as the VIX threshold for switching between T-bills and SPY.
  • Each of 0, 1, 2 or 3 skip months between VIX measurement and strategy response.

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as essential performance metrics and use buy-and-hold SPY as a benchmark. We do not quantify frictions due to switching between SPY and T-bills for the VIX Percentile Strategy. Using end-of-month VIX levels since January 1990 and dividend-adjusted SPY prices and T-bill yields since January 1993 (SPY inception), all through May 2023, we find that: Keep Reading

Tech Equity Premium?

A subscriber requested measurement of a “premium” associated with stocks of innovative technology firms by looking at the difference in returns between the following two exchange-traded funds (ETF):

Using monthly dividend-adjusted closing prices for these ETFs during March 1999 (limited by QQQ) through May 2023, we find that: Keep Reading

Are Low Volatility Stock ETFs Working?

Are low volatility stock strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight of the largest low volatility ETFs, all currently available, in order of longest to shortest available histories:

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the low volatility stock ETFs and their benchmark ETFs as available through May 2023, we find that: Keep Reading

Comparing Long-term Returns of U.S. Equity Factors

What characteristics of U.S. equity factor return series are most relevant to respective factor performance? In his May 2023 paper entitled “The Cross-Section of Factor Returns” David Blitz explores long-term average returns and market alphas, 60-month market betas and factor performance cyclicality for U.S. equity factors. He also assesses potentials of three factor rotation strategies: low-beta, seasonal and return momentum. Using monthly returns for 153 published U.S. equity market factors, classified statistically into 13 groups, during July 1963 through December 2021, he finds that:

Keep Reading

Increasing Concentration of Wealth Growth Among Stocks

Do the stocks that dominate shareholder wealth-building (accounting for share price changes, dividends, repurchases/new share issuances and investor money flows) increasing concentrate within a small pool? In his May 2023 paper entitled “Shareholder Wealth Enhancement”, Hendrik Bessembinder identifies the stocks with the largest increases and largest decreases in shareholder wealth since 1926. He examines the degree to which increases in shareholder wealth concentrate among stocks over time. Using monthly data (including delisting returns) for 28,114 publicly traded U.S. common stocks and contemporaneous 1-month U.S. Treasury bill yield as a benchmark during 1926 through 2022, he finds that:

Keep Reading

Shapes of U.S. Stock Market Bull and Bear States

What kind of return patterns are typical of beginnings and ends of equity bull and bear markets? In his April 2023 paper entitled “Investor Overreaction: Evidence From Bull and Bear Markets”, Valeriy Zakamulin examines return patterns of U.S. stock market bull and bear states as a way to decide when investors tend to overreact. He uses the S&P 500 Index as a proxy for the U.S. stock market. He applies a pattern recognition algorithm to: (1) identify index peaks and troughs; and, (2) ensure that a full bear-bull cycle lasts at least 16 months and bear or bull states last at least 5 months, unless the index rises or falls by more than 20%. He then standardizes the duration of each market state to 10 intervals and assumes that the bull or bear return evolves quadratically with state age. Because the available sample is relatively small, he applies bootstrapping to enhance reliability of findings. Using monthly S&P 500 Index returns (excluding dividends) during January 1926 through December 2022, he finds that: Keep Reading

Factor Zoo Shrinking?

How does the U.S. stock return factor zoo, corrected for data snooping bias, change over time? In their March 2023 draft paper entitled “Useful Factors Are Fewer Than You Think”, Bin Chen, Qiyang Yu and Guofu Zhou tackle this question by asking:

  • How many of 207 published factors remain significant after controlling for false discovery rate? In general, returns for each factor are for a portfolio that is each month long (short) subsamples stocks sorted on the factor with the highest (lowest) expected returns. 
  • How does the number of significant factors in rolling 20-year subsamples change over time? 
  • Taking into account factor redundancies, how many clusters of similar factors based on high return correlations (risk sources) are there?

In a supporting test, they compare pre-publication and post-publication factor performance. Using monthly returns for 207 published long-short factors applied to U.S. stocks during 1967 through 2021, they find that:

Keep Reading

Benefit of Complexity in Machine Learning Models

Is model complexity (large number of parameters) more an analytical benefit in predicting asset returns, or more an avenue to discover in-sample luck? In their March 2023 paper entitled “Complexity in Factor Pricing Models”, Antoine Didisheim, Shikun Ke, Bryan Kelly and Semyon Malamud examine the theoretical relationship between input complexity and output accuracy for machine learning asset pricing models. They focus on a complexity wedge, the combination of overfitting (data snooping) and limits to learning that causes in-sample performance of a trained model to exceed out-of-sample performance. They apply ridge shrinkage (controlled by a regularization parameter that sets the strength of an overfitting penalty) to suppress data snooping bias and improve the limits to learning. They assess model performance by out-of-sample Sharpe ratio and out-of-sample pricing errors of optimal portfolios. They test theoretical conclusions on a broad sample of publicly traded U.S. stocks and a set of 110 monthly stock return factors, the latter augmented by a random feature generator that expands the 110 raw factors to any desired number of derivative factors. Using monthly data for the 110 stock return predictors and monthly U.S. stock returns during February 1963 through December 2019, they find that: Keep Reading

Suppressing Long-side Factor Premium Frictions

Are their practical ways to suppress the sometimes large reduction in academic (gross) equity factor premiums due to trading frictions and other implementation obstacles? In their March 2023 paper entitled “Smart Rebalancing”, Robert Arnott, Feifei Li and Juhani Linnainmaa first examine the performance and related turnover of seven long-only factor premiums: annually reformed (end of June) value, profitability, investment, and a composite of the three; and, monthly reformed value and momentum, and a composite of the two. Their long-only factor portfolios hold market-weighted stocks in the top fourth of factor signals. They reinvest any dividends in all stocks in the portfolios, such that dividends do not affect portfolio weights. They test three ways to suppress periodic turnover via a turnover limit:

  1. Proportional Rebalancing – trade all stocks proportionally to meet the turnover limit.
  2. Priority Best – buy stocks with the strongest factor signals and sell stocks with the weakest, until reaching the turnover limit.
  3. Priority Worst – buy stocks that only marginally qualify for the factor portfolio and sell those that just barely fall out (with the strongest buy and sell signals last), until reaching the turnover limit.

They also apply these three turnover suppression tactics to non-calendar reformation, triggered when the difference between the current and target portfolios exceeds a specified threshold. They ignore the 100% initial formation turnover common to all portfolios. Using  accounting data and common stock returns for all U.S. publicly listed firms during July 1963 through December 2020, with portfolio tests commencing July 1964, they find that: Keep Reading

Constructing and Deconstructing ESG Performance

Do good firm environmental, social and governance (ESG) ratings signal attractive stock returns? If so, what is the best way to exploit the signals? In their February 2023 paper entitled “Quantifying the Returns of ESG Investing: An Empirical Analysis with Six ESG Metrics”, Florian Berg, Andrew Lo, Roberto Rigobon, Manish Singh and Ruixun Zhang test performance of long-short ESG portfolios of U.S., European and Japanese stocks based on proprietary ESG scores from six major rating sources. They consider ESG scores from individual sources and apply several statistical and voting-based methods to aggregate ESG ratings across sources, including: simple average, Mahalanobis distanceprincipal component analysis, average voting and singular transferable voting. They consider equal-weighted and ESG score-weighted portfolios. They consider different percentile thresholds for long and short holdings. They assess ESG portfolio alpha with respect to widely used 1-factor (market), 3-factor (plus size and value) and 5-factor (plus investment and profitability) models of stock returns. They further test long-short portfolios from aggregations of E, S and G scores separately across sources. Using proprietary ESG ratings, monthly returns of associated stocks and monthly factor model returns during 2014 through 2020, they find that:

Keep Reading

Daily Email Updates
Filter Research
  • Research Categories (select one or more)