At the Rényi Hour on November 27th, the following working paper was discussed. . . . AND THE CROSS-SECTION OF EXPECTED RETURNS by Campbell R. Harvey, Yan Liu, Heqing Zhu.


Hundreds of papers and hundreds of factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make any economic or statistical sense to use the usual significance criteria for a newly discovered factor, e.g., a t-ratio greater than 2.0. However, what hurdle should be used for current research? Our paper introduces a multiple testing framework and provides a time series of historical significance cutoffs from the first empirical tests in 1967 to today. Our new method allows for correlation among the tests as well as publication bias. We also project forward 20 years assuming the rate of factor production remains similar to the experience of the last few years. The estimation of our model suggests that today a newly discovered factor needs to clear a much higher hurdle, with a t-ratio greater than 3.0. Echoing a recent disturbing conclusion in the medical literature, we argue that most claimed research findings in financial economics are likely false.

The motivation behind the selection of this working paper was double: (i) the fuzz it had generated in the blogosphere related the possibility of rejecting famous and accepted financial factors when tested in a different framework (Multiple testing Framework); and (ii) to reinforce the learned material on multiple testing and Bayesian approach alternative.

The presentation was conducted by two students of the program: Gaston Besanson (presenting the ideas of the working paper) and Taras Krupskyy (discussing the working paper shortcomings).

If you are interested in the slides please contact each student.


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