  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.
