Big Ideas

How can researchers assess the true value and robustness of new stock return anomalies (predictors) in consideration for addition to the factor zoo? In their January 2023 paper entitled “Assaying Anomalies”, Robert Novy-Marx and Mihail Velikov present a protocol/tool set for dissecting and understanding newly proposed cross-sectional stock return predictors. The tools address the most important issues involved in testing asset pricing strategies, including some machine learning techniques. They pay particular attention to implementation costs that prevent exploitation of predictors with good gross returns (as with high turnover and/or overweighting small stocks). The tool set, including automated report generator, is available as a free web application and a public github repository. Key aspects of reports generated by this tool set are:

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Should investors presume that, in the absence of falsifiable theories, the body of factor investing research is largely spurious? In the January 2023 version of his paper entitled “Causal Factor Investing: Can Factor Investing Become Scientific?”, Marcos Lopez de Prado reviews the current state of confusion about causality in factor investing research and discusses ways to resolve that confusion. Specifically, he addresses:

• Differences between association and causation.
• Why the study of association alone does not create scientific knowledge.
• How observational studies, natural experiments and simulated interventions support investigation of causality.
• The current state of causal confusion in econometrics and factor investing studies.
• How to transform factor investing into a truly scientific discipline.

Based on many references and the logic of the scientific method, he concludes that:

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Given that finance is ultimately tied to human emotions, does the body of research support belief that abilities of machine learning to handle large amounts of data, non-linearities and variable interactions will revolutionize investing? In their January 2023 paper entitled “How Can Machine Learning Advance Quantitative Asset Management?”, David Blitz, Tobias Hoogteijling, Harald Lohre and Philip Messow study application of machine learning to investing from the perspective of a prudent practitioner. They define machine learning models as initially guided software programs that subsequently learn by themselves and then make predictions about unseen (out-of-sample) data. They describe benefits and pitfalls of machine learning versus classical (linear regression) econometrics. They discuss critical design choices for applying machine learning models to asset management. They focus on the ability of machine learning to beat the stock market, but also discuss asset risk forecasting, optimal portfolio construction and trading optimization. Based on the body of research and their collective experience in asset management, they conclude that: Keep Reading

If a published theory is correct, its empirical results should hold for years after original test samples end. Are peer-reviewed, theory-supported (risk-based) academic studies of stock return predictors thereby superior to other streams of predictor research, and thereby especially useful to investors? In their December 2022 paper entitled “Peer-Reviewed Theory Does Not Help Predict the Cross-section of Stock Returns”, Andrew Chen, Alejandro Lopez-Lira and Tom Zimmermann employ out-of-sample tests to address these questions in two ways:

1. Read the arguments used in papers from finance, accounting and economics journals reporting discoveries of 202 firm-level stock return predictors. Label each predictor as risk-based (resting on theory), mispricing-based (behavioral) or agnostic. Validate categories via software that counts ratios of risk-related words to mispricing-related words. Compare out-of-sample returns of the three categories, with expectations that: (a) risk-based returns will persist; and, (b) mispricing-based returns will decay as investors learn about them and alter their behaviors.
2. Compare out-of-sample returns of the 202 predictors from peer-reviewed studies to predictors matched on in-sample return statistics from a set of 18,240 trading strategies generated by brute-force sorting of firms on simple combinations of 240 accounting variables.

They calculate predictor returns monthly from periodically reformed equal-weighted or value-weighted portfolios that are long (short) the tenth, or decile, of stocks with the highest (lowest) expected returns. Using data from 45 years of cross-sectional asset pricing research for the 202 predictors from formal studies and returns for the 18,240 mined trading strategies during both in-sample and fixed out-of-sample intervals, they find that: Keep Reading

How should investors think about, and perhaps exploit, asset return volatility? In his December 2022 paper entitled “A Stylized History of Volatility”, Emanuel Derman reviews how generations of financial modelers have quantified volatility and ultimately created tradable volatility-based assets. He also discusses some general modeling considerations. Based on the body of research and his experience, he concludes that: Keep Reading

Why do portfolios formed from the principal components of many long-short stock return factors from two recent studies, one covering 207 factors and the other 153 factors (with overlap 97), have such different out-of-sample gross Sharpe ratios? In their November 2022 paper entitled “Factor Returns and Out-of-Sample Alphas: Factor Construction Matters”, Hendrik Bessembinder, Aaron Burt and Christopher Hrdlicka explore reasons for this divergence, such as differences in weighting methods used for individual factor portfolios, number of quantiles employed to construct these portfolios and differences in the total number of factors considered. Using descriptions/data from the prior studies spanning a total of 263 distinct factors with at least some factors in every month during 1926 through 2020, they find that:

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Do test portfolio construction decisions in published research on stock return predictors impound bias by fitting the noise (capturing luck) in historical returns? In his November 2022 paper entitled “Looking Under the Hood of Data-Mining”, Mathias Hasler re-evaluates research published in academic journals on 92 stock return predictors by testing alternatives for 12 portfolio construction decisions (for example, rebalance annually or monthly). He focuses on how original construction decisions correlate with noise in historical returns. Specifically, he compares returns of predictor test portfolios as specified in the original research to those constructed with a random combination of research decisions, both in-sample (original research sample period) and out of sample. He postulates that:

• An in-sample return difference may reflect the correlation of test portfolio construction decisions with either a predictable or an unpredictable (noise) part of historical returns.
• An out-of-sample return difference, however, reflects only the correlation of decisions with the predictable part of historical returns.
• Thus, the difference between in-sample and out-of-sample return differences estimates statistical biases in original portfolio construction decisions. This estimate is an upper bound of bias only, because investors exploiting published research may suppress out-of-sample return predictability.

He therefore conducts further tests to assess the roll of investor out-of-sample exploitation of return predictors. Using data to replicate the 92 stock return predictors both in-sample and out-of-sample during 1926 through 2021, he finds that: Keep Reading

How sensitive are findings about the magnitude and reliability of equity factor premiums to differences in the rules researchers use in sorting stocks to calculate them? In their July 2022 paper entitled “Non-Standard Errors in Portfolio Sorts”, Dominik Walter, Rüdiger Weber and Patrick Weiss examine variations in factor premiums (non-standard errors) due to differences in 14 portfolio sorting decisions applied to each of 40 factors found significant in previous studies. The 40 factors cover well-known sorting variables such as size, book-to-market ratio, asset growth, gross profits-to-assets, momentum and idiosyncratic volatility. The 14 sorting decisions consist of seven sample construction decisions (such as firm size restrictions and exclusion of financial firms) and seven portfolio construction decisions (such as reformation frequency and number of portfolios). For each set of decisions and each factor, they compute a monthly premium as the average return difference in monthly returns between the two portfolios with the highest and lowest expected returns. They also look at effects of portfolio sorting decisions on monthly 1-factor (market) and 3-factor (market, size, book-to-market) alphas.  Using U.S. stock/firm data used in the relevant asset pricing studies as available during 1968 through 2021, they find that: Keep Reading

Do the choices researchers make when constructing factor portfolios to explain stock returns materially affect their findings? In their June 2022 paper entitled “Mind Your Sorts”, Amar Soebhag, Bart van Vliet and Patrick Verwijmeren examine the extent to which such construction choices affect factor performance. They consider 11 distinct long-short factors as used in widely cited factor models of stock returns. They test 256 ways to assess each factor based on eight portfolio construction choices:

1. Use 70/30 or 80/20 percentile breakpoints to define high and low factor values.
2. Use NYSE or NYSE-AMEX-NASDAQ universes to calculate these breakpoints.
3. Include or exclude microcaps (smaller than the 20th percentile of NYSE market capitalizations).
4. Include or exclude financial firms.
5. Neutralize industry effects, or not.
6. Use value or equal weighting of stocks within portfolios.
7. Sort multiple variables independent or sequentially (nested).
8. Use most recent monthly market capitalizations or only annual June values.

They focus on the standard deviation of Sharpe ratios generated across portfolio construction choices as a measure of non-standard errors, for comparison with the standard error generated by factor return variability (randomness) within each choice. They estimate portfolio turnovers and trading frictions across choices to evaluate net Sharpe ratios. Using data required to calculate factor values and factor portfolio returns for a broad sample of U.S. common stocks spanning January 1972 through December 2021, they find that: Keep Reading

How do incentives distort research on financial markets, and how do incentivized researchers introduce the distortions? In his April 2022 paper entitled “The Pitfalls of Asset Management Research”, Campbell Harvey explores how economic incentives affect findings in both academic and practitioner research on financial markets, including evidence of distortion. Based on his 35 years as an academic, advisor to asset management companies and editor of a top academic finance journal, he concludes that: Keep Reading