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Big Ideas

These blog entries offer some big ideas of lasting value relevant for investing and trading.

Impact of Portfolio Formation Rule Variation on Factor Premiums

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

Factor Exploitability Uncertainty Due to Study Design Choices

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

Incentives for Distorted Market Research

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

Update on Demographics and the Stock Market

“Return-based Analysis of Demographics as Stock Market Predictor”, building on formal research summarized in “Demographic Headwind for U.S. Stock Market?” and “Classic Research: Demography and the Stock Market”, looks at interactions between U.S. age demographics and U.S. stock market behavior. What does adding a decade of data say? To investigate we look at interactions between:

We look at cohorts individually and at the ratio of the middle four cohorts (25-34, 35-44, 45-54, 55-64) to the youngest plus two oldest cohorts (<24, 65-74, >75), termed the productive ratio. We test how these metrics relate to stock index real total returns over the next 20 years. Using the specified annual data, measured in July, for 1900 through 2020, we find that: Keep Reading

Variability of U.S. Stock Market Returns

How should the variability of stock market returns shape the outlooks of short-term traders and long-term investors? How strong is the tailwind of the general drift upward in stock prices? How powerful is the turbulence of variability? Does the tailwind ever overcome the turbulence? To investigate we consider all holding periods for the S&P 500 Index ranging from one week to 208 weeks (about four years). Using weekly closes for the index during January 1928 through mid-March 2022 (4,915 weeks or about 94 years), we find that:

Keep Reading

How to Avoid Stupid Beta?

Why do the alphas generated by historical simulations/backtests disappear in live trading, with asset managers and brokers the only winners via fees and commissions. In their February 2022 paper entitled “Where’s the Beef?”, Robert Arnott, Amie Ko and Lillian Wu explore: (1) the ways that seasoned professionals fall prey to the simple blunders of data snooping and performance chasing; and, (2) how the industry could actually meet client expectations. Based on the body of research on investor behavior and fund performance and decades of investment management experience, they conclude that: Keep Reading

Variation in the Number of Significant Equity Factors

Does the number of factors significantly predicting next-month stock returns vary substantially over time? If so, what accounts for the variation? In their December 2021 paper entitled “Time Series Variation in the Factor Zoo”, Hendrik Bessembinder, Aaron Burt and Christopher Hrdlicka investigate time variation in the statistical significance of 205 previously identified equity factors before, during and after the sample periods used for their discoveries. Specifically, they track 1-factor (market) alphas of each factor over rolling 60-month intervals over a long sample period. Their criterion for significance for each factor in each interval is a t-statistic of at least 1.96 (95% confidence that alpha is positive). Using monthly returns for all common stocks listed on NYSE, AMEX and NASDAQ exchanges having at least 60 continuous months of data as available during July 1926 (with alpha series therefore starting June 1931) through December 2020, they find that: Keep Reading

Science as Done by Humans

Do the choices researchers make in modeling, sample grooming and programming to test hypotheses materially affect their findings? In their November 2021 paper entitled “Non-Standard Errors”, 164 research teams and 34 peer reviewers representative of the academic empirical finance community investigate this source of uncertainty (non-standard error, as contrasted to purely statistical standard error). Specifically, they explore the following aspects of non-standard errors in financial research:

  • How large are they compared to standard errors?
  • Does research team quality (prior publications), research design quality (reproducibility) or paper quality (peer evaluation score) explain them?
  • Does peer review feedback reduce them?
  • Do researchers understand their magnitude?

To conduct the investigation, they pose six hypotheses that involve devising a metric and computing an average annual percentage change to quantify trends in: (1) market efficiency; (2) realized bid-ask spread: (3) share of client volume relative to total volume; (4) realized spread on client orders; (5) share of client orders that are market orders; and, (6) gross client trading revenue. The common sample for testing these hypotheses is a set of 720 million EuroStoxx 50 index futures trade records spanning 17 years. Each of 164 research teams studies each hypothesis and writes a brief paper, and peer reviewers evaluate and provide feedback to research teams on these papers. They then quantify the dispersion of findings for each hypothesis and further relate deviation of individual study finding from the average finding to team quality, research design quality and paper quality. Using results for all 984 studies, they find that: Keep Reading

Financial Markets Flouters of Statistical Principles

Should practitioners and academics doing research on financial markets be especially careful (compared to researchers in other fields) when employing statistical inference. In the July 2021 version of their paper entitled “Finance is Not Excused: Why Finance Should Not Flout Basic Principles of Statistics”, David Bailey and Marcos Lopez de Prado argue that three aspects of financial research make it particularly prone to false discoveries:

  1. Due to intense competition, the probability of finding a truly profitable investment strategy is very low.
  2. True findings are often short-lived due to financial market evolution/adaptation.
  3. It is impossible to verify statistical findings through controlled experiments.

Based on statistical analysis principles and their experience in performing and reviewing financial markets research, they conclude that: Keep Reading

Post-discovery Effects on Anomaly Return Sequence

Does anomaly publication lead to its speedy exploitation? In his March 2021 paper entitled “The Race to Exploit Anomalies and the Cost of Slow Trading”, Guy Kaplanski studies a sample of widely accepted U.S. stock return anomalies to determine how discovery and publication of an anomaly affects the timing of future returns. He quantifies anomalies by each month sorting stocks into fifths, or quintiles, on each anomaly variable and reforming a portfolio that is long (short) the quintile with the highest (lowest) predicted returns. Using discovery (December of the last year in the discovery sample) and publication dates for 71 anomalies, along with associated anomaly data and daily prices for all reasonably liquid U.S. common stocks during January 1973 through December 2018, he finds that: Keep Reading

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