Does Academic Research Destroy Stock Return Predictability?

McLean, R.D. and Pontiff, J., 2016. Does academic research destroy stock return predictability?. The Journal of Finance, 71(1), pp.5-32.

Research Question and General Results

This paper evaluates the in-sample return, post-sample return and post-publication returns of 97 characteristics shown to predict cross-sectional stock returns in top journals. The authors find that average predictor’s long-short return declines by 26% and 58% out-of-sample and post-publication, respectively. The decay is stronger for predictors with higher in-sample returns and t-statistics. Besides, predictors with less costly arbitrage accelerate a lower post-publication returns and have a higher trading volume after publication. Hence, the aforementioned results indicate that investors learn from academic publications to take advantage of mispricing of stock returns.

Research Methodology

The paper identifies 97 characteristics shown to predict cross-sectional returns in top economics, accounting, and finance journals. Then, authors construct a long-short portfolio based on each predictor in each month and compute equal-weighted returns across all portfolios. Meanwhile, authors identify specific in-sample dates, out-of-sample dates and publication dates anomaly and create corresponding dummy variables foor them. For instance, Post_sample_dummy equals to one if month t is after the end of original sample but before publication.

The general estimation is the following,

 The purpose of this approach is to evaluate how average predictor’s return is different from in-sample, post-sample and post-publication periods.

Empirical Results

Post-sample and post-publication returns decline relative to in-sample returns by 26% and 58%. This suggests that academic researchers cautiously choose their in-sample date and their results are susceptible to statistical biases. However, investors also learn information about mispricing variables from academic publications because average predictor’s returns decrease more in post-publication periods. The main result remains almost unchanged after controlling for time trends and persistence.

The authors also find stronger results for predictors with high in-sample means and t-statistics and less costly arbitrage. Simultaneously, investors trade more in post-sample and post-public time. These consequences reveal the existence of statistical biases and bolster the notion that investor obtain useful information about mispricing variables from academic publication and apply it to their trading activity.