Are Stock Pricing Anomalies Driven by Risk or Mispricing?

Are Stock Pricing Anomalies Driven by Risk or Mispricing?

December 15, 2015 Research Insights, Behavioral Finance
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(Last Updated On: January 18, 2017)

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”:

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About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray received a PhD, and was a finance professor at Drexel University. Dr. Gray’s interest in entrepreneurship and behavioral finance led him to found Alpha Architect. Dr. Gray has published three books: EMBEDDED: A Marine Corps Adviser Inside the Iraqi Army, QUANTITATIVE VALUE: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, and DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth. His numerous published works has been highlighted on CBNC, CNN, NPR, Motley Fool, WSJ Market Watch, CFA Institute, Institutional Investor, and CBS News. 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).