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The paper, titled 'Market Efficiency in the Age of Machine Learning', was co-authored by Rui Dai (Wharton Research Data Services, University of Pennsylvania), Talis Putnins (University of Technology Sydney) and Anthony Saunders (NYU Stern) and was awarded this year's Yuki Arai Faculty Award for the Best Research Paper in Finance.

The prize, established by Stern alumnus Yuki Arai, aims to recognise and promote excellence in research by Stern faculty, and is bestowed annually upon the best paper on the topic of Finance.

"In this paper, we sought to ask a fundamental question in the field of finance: as machines become more prevalent in financial markets, how is informational efficiency impacted?", said Leonidas.

"We found that increased information access by cloud computing services significantly improves informational efficiency and reduces the price drift following information events. Identification issues are addressed through exogenous power and cloud outages, a quasi-natural experiment, and instrumental variables."

The paper also highlighted that machines are better able to handle linguistically complex filings, less susceptible to bias from negative sentiment and less constrained in attention and processing capacity than humans.

The selection committee, including Professors Matt Richardson, David Yermack, and Richard Levich, commented that: "We were very impressed by the inventiveness and bold nature of the paper. We found it quite remarkable that data like that in this study could be uncovered and exploited in the manner accomplished. This study provides exciting evidence of how new technologies may reduce the cost of information acquisition and enhance market efficiency."

You can read the full paper online:

Leonidas Barbopoulos is Chair in Finance and Programme Director for the MSc Accounting and Finance at the University of Edinburgh Business School.