When False Factors Are Exposed


The world of finance and asset pricing used to be fairly simple. At first, there was just the single-factor capital asset pricing model, with market risk (beta) as the sole factor to explain the differences in returns of diversified portfolios. Over time, the working model evolved into a still relatively simple four-factor model, adding value, size and momentum. Each of these four factors carried large premiums.

However, as John Cochrane put it, today we have a literal factor zoo, with more than 600 factors having been identified in the literature (roughly 300 of which have been identified in top journal articles and highly regarded working papers).

Subsequent research has found that in out-of-sample tests, about half the factors produced zero to negative premia, even prior to considering transaction costs. Thus, the findings involving these factors were likely the result of data mining, or they were just lucky outcomes. Either that or they were behavioral anomalies that, post-publication, would be easily arbitraged away.

The Effect Of Publication

In the study “Does Academic Research Destroy Stock Return Predictability?”, published in the January 2016 issue of the Journal of Finance, authors R. David McLean and Jeffrey Pontiff re-examined 82 factors published in tier-one academic journals and were only able to replicate the reported results for 72 of them. At least 10 out of 82 factors were artifacts of reporting mistakes in the databases, which have since been corrected.

They also found that, post-publication, the “average characteristic’s return decays by about 35%.” In addition, they found that “characteristic portfolios that consist more of stocks that are costly to arbitrage decline less post-publication. This is consistent with the idea that arbitrage costs limit arbitrage and protect mispricing.”

Paul Calluzzo, Fabio Moneta and Selim Topaloglu contribute to the literature and to our understanding of how markets work and become more efficient over time (the adaptive markets hypothesis) with their December 2015 study, “Anomalies are Publicized Broadly, Institutions Trade Accordingly, and Returns Decay Correspondingly.”

They hypothesized: “Institutions can act as arbitrageurs and correct anomaly mispricing, but they need to know about the anomaly and have the incentives to act on the information to fulfill this role.” To test their assumption, the authors considered whether “knowledge of the anomaly is in the public domain based on the year of academic publication” and if “the accounting data necessary to compute the anomaly rankings is publicly available.”

They then studied the trading behavior of institutional investors in 14 well-documented anomalies, building long-short portfolios to determine whether they exploited the anomalies and helped bring equity prices closer to efficient levels. The 14 anomalies they evaluated were: total accruals, net stock issues, composite equity issues, net operating assets, gross profitability, asset growth, capital investments, investment-to-assets, book-to-market, momentum, distress (failure probability), Ohlson O-score, return on assets and post-earnings announcement drift.

Anomaly Study Results

Their study covered the period January 1982 through June 2014. Following is a summary of their findings:

  • For both the annual and quarterly versions of the anomalies, trading with the anomaly was profitable in the original in-sample period. The alpha of the equally weighted portfolio was 1.54% per quarter.
  • Consistent with the findings of the aforementioned study by McLean and Pontiff, the decay in raw returns in the period after publication was an average of 1.05%, a 32% reduction. Using the Fama-French three-factor model, there is a reduction in nine of the 14 anomalies. Interestingly, the momentum premium (which is purely behavioral-based) actually showed a small increase in the post-publication period.
  • In the in-sample, prior-to-publication period, institutional investors don’t take advantage of stock return anomalies.
  • In the post-publication period, institutions trade to exploit the anomalies—the net change in aggregate holdings (the change in the long leg minus the change in the short leg) is positive.
  • Partitioning institutional investors into hedge funds, mutual funds and others, the results are strongest among hedge funds and then among actively managed funds with high turnover.
  • There is a significant negative relationship between institutional trading and future anomaly returns in the ex-post portfolio (the portfolio based on the aggregate ranking of all published anomalies). Institutional trading after anomaly publication is related to the post-publication decay in anomaly returns.
  • There is a significant increase in trading by hedge funds in the period just before publication, suggesting that hedge funds have knowledge about the anomalies prior to journal publication (likely through presentations at conferences or from postings on the Social Science Research Network).

The authors concluded: “Institutional trading and anomaly publication are integral to the arbitrage process which helps bring prices to a more efficient level.” Their findings demonstrate the important role that both academic research and hedge funds (in their role of arbitrageurs) play in making markets more efficient.

Increasing Challenges For Active Managers

In our book, “The Incredible Shrinking Alpha,” Andrew Berkin and I provide evidence showing there are four major themes behind the trend toward a persistently declining ability for active managers to generate true risk-adjusted alpha. One of these four explanations is that academic research continues to uncover anomalies that have generated alphas in the past.

The study by Calluzzo, Moneta and Topaloglu provides evidence demonstrating that by publishing the findings on factors that provide premiums, academic research converts what once was alpha into beta (or loading on a common factor that investors can easily access through low-cost and passively managed funds). This has a negative impact on the ability of active managers to generate future alpha. In addition, through the process of arbitrage, the premiums also tend to shrink, creating further hurdles for generating alpha.

This commentary originally appeared June 20 on ETF.com

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