Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for January 2021 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for January 2021 (Final)
1st ETF 2nd ETF 3rd ETF

Anomaly Evaluation

| | Posted in: Big Ideas

What is a financial market anomaly? How can investors determine whether an apparent anomaly is real (economically material)? In his March 2011 book chapter entitled “Perspectives on Capital Market Anomalies”, Mozaffar Khan provides a framework for interpreting academic research on anomalies and evaluating the exploitability of specific anomalies. His context is market efficiency: “Respect for the efficient markets theory, and an acknowledgment that it sometimes fails (i.e., that mispriced stocks can be identified), can coexist.” Key points are:

  • Anomalies are deviations from efficient markets theory, manifested as predictable non-zero risk-adjusted returns.
  • The typical approach for evaluating anomaly alpha is to: (1) rank stocks into (for example) deciles based on some historical firm or stock characteristic; (2) calculate the future return for a portfolio that is long the extreme outperforming decile and short the extreme underperforming decile; and, (3) debit this raw future by the expected return based on the risk of the portfolio (as specified by a risk adjustment model such as the Fama-French three-factor model).
  • While many academic studies do not, practitioners must debit information, search and trading costs from the alpha to assess anomaly exploitability.
  • The statistical reliability of an anomaly measures the level of assurance that its alpha differs from zero, with at least 95% assurance a typical criterion. Generally, if the variability of alpha over time is small compared to its average value, the associated anomaly is reliable. Uncertainties that confound determination of alpha reliability include:
    • The risk adjustment model may be defective, thereby either exposing false alphas or obscuring real ones.
    • The statistical tests used to assess alpha reliability may be defective (as evidenced by conflicts with alternative tests), perhaps because they have some built-in bias or they are unsuitable for the anomaly return distribution.
    • A discovered alpha may be a lucky result of intensive in-sample data snooping, either directly by the immediate researcher or indirectly by follow-up researchers using the same data. Luck is unlikely to persist out of sample. Tying the result conceptually to an otherwise testable economic rationale helps mitigate data snooping bias.
  • Major competing explanatory frameworks for anomalies (often difficult to distinguish empirically) are:
    • Investors lacking complete information about asset valuation parameters and/or being otherwise uncertain about them may rationally misprice associated assets.
    • Inherent investor behavioral and cognitive biases (such as emotional sentiment, overconfidence, self-attribution, conservatism and representativeness) may generate mispricings via underreactions and overreactions to information. Obstacles to market efficiency such as transaction costs, short sale constraints, absence of close substitutes and non-scalability may allow such mispricings to persist.

In summary, the typical process for evaluating the exploitability of financial market anomalies invokes a range of uncertainties that undermine investor confidence in associated implementation strategies.

The author does not address in this book chapter:

  • Potential asset return distribution wildness (susceptibility to Black Swans) and consequent statistical intractability.
  • The extreme difficulty of addressing trading frictions, which vary considerably over time, across assets and among investors.
  • Potential market adaptation to publicly known anomalies over time.

Compare and contrast this overview with that presented in “Investing Demons”.

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