Aha! I found another risk premium. This has been the mantra of finance for well over the last decade, yet perhaps we should comment, "not so fast." While on the one hand, there has been an explosion of research finding new factors, what has been called the factor zoo, there has also been pushback by other researchers who have pointed out the issue of data mining. The argument of data mining is that conventional inferential frameworks are inappropriate when there are endless tests of different model specifications before the right one is reported. We will mine the data until we find the right result. Other researchers have pushed back against the data miner, with analyses showing that the posterior-expected alphas still exist even after the initial results were reported.
A new paper. "The False Discovery Rate in Finance: Identification failure and search-adjusted estimation" outlines how this problem can be solved. There should be a search-and-selection process for identifying factors. Knowing that process will help detect the false discovery rate, but if this search mechanism is unknown, there will be a greater likelihood of underestimating the true false discovery rate.
Using some lower bounds calculated from their work, the authors suggest that most reported discoveries in finance are likely false. Ouch, that is a strong indictment and has strong implications for academic research and the work of quants who use that research to develop profitable trading strategies.
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