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High-Throughput Asset Pricing

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  • Andrew Y. Chen
  • Chukwuma Dim

Abstract

We use empirical Bayes (EB) to mine data on 140,000 long-short strategies constructed from accounting ratios, past returns, and ticker symbols. This "high-throughput asset pricing" produces out-of-sample performance comparable to strategies in top finance journals. But unlike the published strategies, the data-mined strategies are free of look-ahead bias. EB predicts that high returns are concentrated in accounting strategies, small stocks, and pre-2004 samples, consistent with limited attention theories. The intuition is seen in the cross-sectional distribution of t-stats, which is far from the null for equal-weighted accounting strategies. High-throughput methods provide a rigorous, unbiased method for documenting asset pricing facts.

Suggested Citation

  • Andrew Y. Chen & Chukwuma Dim, 2023. "High-Throughput Asset Pricing," Papers 2311.10685, arXiv.org, revised Mar 2024.
    • Handle: RePEc:arx:papers:2311.10685
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