Illegitimate Science: Why Most Empirical Discoveries in Finance Are Likely Wrong, and What Can Be Done About It (Presentation Slides)

Abstract

The proliferation of false discoveries is a pressing issue in Financial research. For a large enough number of trials on a given dataset, it is guaranteed that a model specification will be found to deliver sufficiently low p-values, even if the dataset is random.

Most academic papers and investment proposals do not report the number trials involved in a discovery. The implication is that most published empirical discoveries in Finance are likely to be false. This has severe implications, specially with regards to the peer-review process and the Backtesting of investment proposals.

We make several proposals on how to address these problems.

Keywords: Multiple testing, selection bias, backtest overfitting, p-values

JEL Classification: G0, G1, G2, G15, G24, E44