Robust multiple testing for alpha hunting

Music City Center 

The selection of skilled funds in the financial market is critical for both investors and researchers. While existing methods for identifying skilled funds under multiple testing frameworks are abundant, most overlook the challenges posed by heavy-tailed and serially dependent data. In this talk, we propose a general framework for multiple testing of alpha in a simple signal-noise model, leveraging adaptive regression techniques based on the data's tail properties. For heavy-tailed data, we introduce a quantile-adjusted method to correct alpha bias. Using a sample-splitting strategy, we derive symmetrically distributed test statistics under the null and compute a data-driven significance threshold. We also extend the model to account for time series dependence. This work is supported by the startup fund of City University of Hong Kong.

False discovery rate

Factor models

High-dimensional time series

Sample-splitting

Robust inference 

Business and Economic Statistics Section