High-Throughput Asset Pricing
Abstract
We apply empirical Bayes (EB) to mine data on 136,000 long-short strategies constructed from accounting ratios, past returns, and ticker symbols. This ``high-throughput asset pricing'' matches the out-of-sample performance of top journals while eliminating look-ahead bias. Naively mining for the largest Sharpe ratios leads to similar performance, consistent with our theoretical results, though EB uniquely provides unbiased predictions with transparent intuition. Predictability is concentrated in accounting strategies, small stocks, and pre-2004 periods, consistent with limited attention theories. Multiple testing methods popular in finance fail to identify most out-of-sample performers. High-throughput methods provide a rigorous, unbiased framework for understanding asset prices.
Keywords
Cite
@article{arxiv.2311.10685,
title = {High-Throughput Asset Pricing},
author = {Andrew Y. Chen and Chukwuma Dim},
journal= {arXiv preprint arXiv:2311.10685},
year = {2025}
}