Market Efficiency in the Age of Machine Learning

Leonidas G Barbopoulos, Rui Dai, Tālis Putniņš, Anthony Saunders

Research output: Working paper


As machines replace humans in financial markets, how is informational efficiency impacted? We shed light on this issue by exploiting unique data that allow us to identify when machines access company information (8-K filings) versus when humans access the same information. We find that increased access by machines, particularly from cloud computing services, significantly improves informational efficiency and reduces the price drift following information events. We address identification through instrumental variables, exogenous power and cloud outages, and a quasi-natural experiment. We show that machines are better able to handle linguistically complex filings and are less susceptible to bias from negative sentiment, whereas humans are better at combining incremental information.
Original languageEnglish
PublisherNYU Stern School of Business Research Paper Series
Number of pages57
Publication statusPublished - 2021


  • market efficiency
  • information acquisition
  • artificial intelligence
  • algorithmic trading

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