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In his recent article, Michael Edesess argued that multiple empirical “anomaly” studies and the wide use of regression are ruining finance research. While some of his points are valid, his conclusion that the entire set of academic studies should be discarded goes too far.
I am a former academic researcher. But this is not an esoteric argument between researchers. It is important to understand the benefits and limitations of such research as it has strong bearing on the debate raging on whether or not the markets are informationally efficient, and, in turn, if it is possible to use anomalies for earning excess returns.
I am well aware of the limitations when conducting empirical studies in a field like finance with its extensive, historical primary and secondary databases, where highly sought after promotion, tenure, and academic prestige depend upon publishing referred articles.
On the one hand, these immense data sets provide an opportunity for testing theories both cross-sectionally and through time. In many respects, this is a researchers dream. But it also makes data-snooping possible and thus the publication of selective, mostly positive results. This is the problem Edesess highlighted in his article.
This problem clearly exists in the anomaly research studies on which he focuses. But depending upon the question being addressed, these studies still provide useful information.
Is the stock market informationally efficient?
Another way to pose this question is, “can public information, such as P/E ratios and momentum, be used to earn excess returns?” The anomalies empirical literature answers a resounding “yes!” The Campbell Harvey talk Edesess referenced is based on this paper in which over 300 statistically significant, academically reviewed anomalies studies are catalogued. On its face, this is conclusive evidence that markets are not informationally efficient, since numerous pieces of public information can be used to earn excess returns.
But Harvey argues that, as Edesess parrots in his article, in order to avoid a data-snooping bias, the minimum t-value should be raised from the traditional 2 to 3. But even when this is done, half of the anomalies remain statistically significant, still providing overwhelming evidence against market efficiency.
By C. Thomas Howard, read the full article here.
