Abstract
We separate downloads on the SEC EDGAR database into human and machine actions by the intensity of information retrieval (Ryans, 2017). The split shows that the extent of machine downloads has risen 35 times since 2004, accounting for over 96% of total downloads as of 2016. We formally investigate the relationship of machine automation in information processing and the cross-section of stock returns. We find that stocks in the lowest quintile of machine coverage outperform those in the highest quintile by 6 to 7% annually after adjusting for risk. Our results are consistent with recent theoretical work on big data (Begenau, Farboodi, and Veldkamp, 2018) and are supported by a natural experiment on the implementation of XBRL tags that enabled machine readable financial disclosure.
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