37 Pages Posted: 10 Jan 2017 Last revised: 9 Feb 2017
Date Written: January 7, 2017
It is time that we reassess how we approach our empirical research in financial economics. Given the competition for top journal space, there is an incentive to produce “significant” results. With the combination of: unreported tests, lack of adjustment for multiple tests, direct and indirect p-hacking, many of the research results that we are publishing will fail to hold up in the future. In addition, there are some fundamental issues with the interpretation of statistical significance. Increasing thresholds, such as t > 3, may be necessary but such a rule is not sufficient. If the effect being studied is rare, even a rule like t > 3 will produce a large number of false positives. I take a step back and explore the meaning of a p-value and detail its limitations. I offer a simple alternative approach known as the minimum Bayes factor which delivers a Bayesian p-value. I present a list of guidelines that are designed to provide a foundation for a robust, transparent research culture in financial economics. Finally, I offer some thoughts on the importance of risk taking (both from the perspective of both authors and editors) to advance our field.
The transcript and presentation slides are available here: http://ssrn.com/abstract=2895842.
Keywords: P-hacking, Multiple testing, Selection, Data mining, Data dredging, Rare incidence, Type I error, Type II error, P-values, Minimum Bayes Factor, MBF, SD-MBF, Bayesian P-values
JEL Classification: G00, G10, G20, G30, C10, C11, C12, C58, M41, B41
Suggested Citation: Suggested Citation
Harvey, Campbell R., The Scientific Outlook in Financial Economics (January 7, 2017). Duke I&E Research Paper No. 2017-05. Available at SSRN: https://ssrn.com/abstract=2893930 or http://dx.doi.org/10.2139/ssrn.2893930