Paper: arXiv 2311.10685

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.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced statistical methods like Empirical Bayes and discusses theoretical propositions, but the core mathematics (t-stat distributions, shrinkage formulas) is moderately complex rather than highly advanced. Empirical rigor is high due to the massive dataset (136,000 strategies), explicit out-of-sample testing, public code and data replication links, and detailed comparisons to standard multiple testing methods.

Research Flowchart

  flowchart TD
  A["Research Goal: Apply High-Throughput<br>Methods to Asset Pricing"] --> B{"Data: 136,000 Strategies<br>Accounting, Returns, Tickers"}
  B --> C["Methodology: Empirical Bayes &<br>Multiple Testing Correction"]
  C --> D["Computational Process:<br>Out-of-Sample Validation"]
  D --> E{"Key Findings"}
  E --> F["EB Matches Journal Performance<br>Bias-Free Predictions"]
  E --> G["Concentrated in Accounting,<br>Small Stocks, Pre-2004"]
  E --> H["Standard Methods Fail<br>to Identify Performers"]

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1 of 259 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.