Are Stock Pricing Anomalies Driven by Risk or Mispricing?

/Are Stock Pricing Anomalies Driven by Risk or Mispricing?

Are Stock Pricing Anomalies Driven by Risk or Mispricing?

By | 2017-08-18T17:10:07+00:00 December 15th, 2015|Research Insights, Behavioral Finance|3 Comments

Anomalies and News


Using a sample of 97 stock return anomalies documented in published studies, we find that anomaly returns are 7 times higher on earnings announcement days and 2 times higher on corporate news days. The effects are similar on both the long and short sides, and they survive adjustments for risk exposure and data mining. We also find that anomaly signals predict analyst forecast errors of earnings announcements. Taken together, our results support the view that anomaly returns are the result of mispricing, which is at least partially corrected upon news arrival.

Alpha Highlight:

Over 300+ “anomalies” have been identified in the academic literature over the past few decades, although not all of them hold up under robustness checks. The source of these “anomalies” emerged as another major debate in the context of other strains of research. We have our own favorites in the form of value and momentum, but there are many others.

Three popular explanations for stock anomalies are:

  • Risk-based Explanations: Fama and French (1992, 1996) argue that the value premium represents compensation for additional risks born. But this explanation is hard to reconcile with many new anomalies. For example, momentum has become the “main embarrassment” of the three-factor model. That said, our own research suggests that risk certainly plays at least a partial role in anomalies.
  • Behavioral-based Explanations: Return-predictability reflects mispricing caused by human bias and because of market frictions, anomalies persist. This take is explained in our sustainable active investing framework.
  • Data mining: Correlation does not always equal causality; consider survivor bias and/or data selection bias.

This paper reviews the 97 different variables studied in McLean and Pontiff (2015) and compares the average anomaly returns associated with on versus off days with firm-specific news. The authors hypothesize that if anomaly returns are due to expectation errors, anomaly portfolios should perform better on days when new information is released, since new information lead investors to update their expectations.

The paper’s core results argue for the behavioral explanation and suggests that anomalies are at least partially driven by behavioral bias, which leads to systematic expectation errors.

Main Findings:

This paper investigate the performances of 97 anomalies from 1979 to 2013. Again, it compares the average anomaly returns on news versus non-news days.

Here are the main findings:

  • Anomaly returns are 7 times higher on earnings announcement days and 2 times higher on corporate news days!
  • When it comes to the long and short side of anomaly portfolios, anomaly returns are 5.5 times higher on earnings day for long-side stocks and 10 times lower for short-side stocks. (See figure 1)
  • This finding is robust across many anomalies.
anomaly returns on earnings announcement days

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

We’ll leave the final word to the authors:

Our results suggest that investors are surprised by news. When new information is released investors revise their biased beliefs, which in turn, causes prices to change, which in turn, causes the observed return predictability.

More Research Recaps about “Anomalies and News”:

  • The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).
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About the Author:

Wes Gray
After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes,, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.
  • Michael Milburn

    Hi Wes, Merry Christmas.

    I’ve been reading some posts various places making me think about the losers game (winning by not making mistakes), and wondered if you’ve research or posted about this. I’m wondering from a quantitative standpoint if value and momentum would be the best factors to use to identify an “avoid” list, or if there might be other particular factors that while they might not be good at picking winners, might be more appropriate at identifying losers? In your book I recall you identify methods of discarding companies w/ questionable accounting, but wonder if some factor based approach might also work disproportionately on the “loser” side of the spectrum. (debt, cashflow, profitability, etc)

    In particular I’m referencing this study showing that 39% of stocks have a negative lifetime return, and the general finding that indexes are carried by small % of winners.

    Anyhow, I enjoy learning from the posts here.

  • Phil Whittington

    Only partly relevant Michael, but I often read people saying something along the lines of “sure, picking the winners is hard, but we can just avoid the losers and beat the market that way”. But I don’t know of any reason why avoiding losers is easier than picking winners. I suspect it is equally difficult.

  • Michael Milburn

    Thanks Phil, I wonder the same thing also. I do wonder if maybe they’re not opposite sides of the same coin though – but perhaps different coins?

    In shorter term swing trading systems I’ve worked on (hobbyist stuff only), I tend to find that trying to avoid the losers typically hurts returns – by avoiding losers it seems like I always miss more winners. So it may just be too hard.

    At the heart of this thinking though, is observation that across value stocks, it seems like the lowest quintile of momentum significantly underperforms other value stocks – so I tend to think avoiding losers might have a place. I just don’t see much about it, but as a hobbyist I likely wouldn’t know about such studies if they exist for other factors.

    Avoiding poor momentum does seem to be a start toward avoiding losers though. I’m kindof hopeless in that regard, as I love to bottom fish and terrible momentum attracts me. (Yep, I’m overweight energy, commodities, and just opened small position in OUTR too after its collapse. I get my nose bloodied enough maybe I’ll stop buying into these type of value dogs).