# False (and Missed) Discoveries in Financial Economics

arXiv: https://arxiv.org/abs/2006.04269 · HTML: https://mlsanity.com/paper/2006.04269/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection](https://mlsanity.com/paper/2409.15844/) (2024)
- [Controlling False Discovery Rates under Cross-Sectional Correlations](https://mlsanity.com/paper/2102.07826/) (2021)
- [Leakage-Aware Benchmarking of LLM Forecasting: Real-Time Nowcasts as the Decision-Time Input for Macro Factor Ranking](https://mlsanity.com/paper/2606.22719/) (2026)

## Abstract (arXiv)

Multiple testing plagues many important questions in finance such as fund and factor selection. We propose a new way to calibrate both Type I and Type II errors. Next, using a double-bootstrap method, we establish a t-statistic hurdle that is associated with a specific false discovery rate (e.g., 5%). We also establish a hurdle that is associated with a certain acceptable ratio of misses to false discoveries (Type II error scaled by Type I error), which effectively allows for differential costs of the two types of mistakes. Evaluating current methods, we find that they lack power to detect outperforming managers.