Recent studies reveal that academic research impacts fund profitability due to a crowding effect when anomalies are revealed and investors learn about any mispricing. Fund managers and traders must try to stay ahead of academic research.
Below is an excerpt from the paper Quantifying Backtest Overfitting in Alternative Beta Strategies by Antti Suhonen, Matthias Lennkh, and Fabrice Perez, 2016:
McLean and Pontiff (2016) review the post-publication performance of 97 variables that academic research has shown to predict cross-sectional stock returns. The authors find that the returns are 26% lower out-of-sample, and 58% lower post-publication, indicating both a data mining effect (evidenced by the lower out-of-sample performance) and a crowding effect (investors learning about a mispricing from academic publications).
This is the abstract of the paper by McLean and Pontiff (2016)
We study the out-of-sample and post-publication return predictability of 97 variables shown to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%–26%) lower return from publication-informed trading. Post-publication declines are greater for predictors with higher in-sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post-publication increases in correlations with other published-predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.
The above should be obvious. I wrote in this blog about the challenges that academic research poses as far back as in 2012:
You have worked hard to come up with an edge and you have employed all sorts of measures to preserve it. You have built your fund business the hard way by delivering absolute return to clients. Next thing you know is that some professor and his graduate students publish your edge in a paper. You probably have to start all over again and your next challenge in addition is staying ahead of academic research.
and in this blog about Fundbusters:
The fundbusters team includes your familiar quant blogger, your local college professor and everyone else who can identify an edge and publish the results in a journal or in the blogosphere. As fundbusters get satisfaction from publishing edges rather than from using them, the rate of failure of funds of all types will increase and managers will be driven towards higher complexity edges.
The only way of surviving in the fund management and trading business is by staying ahead of academic research. This may be difficult to do in an environment where more and more professors get involved with investment research. But it is not impossible. For example, my algos for calculating short-term directional bias are unlikely to ever become the subject of academic research because they are too complicated to both conceive and use to generate results for publications.
Survival in investment business equates to being different from the crowd since markets rarely reward its behavior.
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