Tech-Enabled Financial Data Access, Retail Investors, and Gambling-Like Behavior in the Stock Market
Taha Havakhor (),
Mohammad Saifur Rahman (),
Tianjian Zhang () and
Chenqi Zhu ()
Additional contact information
Taha Havakhor: McGill University, Montreal, Quebec H3A 0G4, Canada
Mohammad Saifur Rahman: Purdue University, West Lafayette, Indiana 47907
Tianjian Zhang: California State University, Dominguez Hills, Carson, California 90747
Chenqi Zhu: University of California, Irvine, Irvine, California 92697
Management Science, 2025, vol. 71, issue 2, 1646-1670
Abstract:
Advancements in technology have reduced information acquisition costs, creating an improved information environment for retail investors. Specifically, new technologies, such as application programming interface (API), deliver high-volume, institutional-like raw data directly to Main Street investors. Although greater availability of information can be beneficial, it may also exacerbate retail investors’ existing trading deficiencies. Exploiting the sudden shutdown of Yahoo! Finance API, the largest free API for retail investors, this study examines how access to tech-enabled raw financial data affects retail investment. We find that retail trading volumes in stocks favored by active retail investors dropped by 8.6%–10.5% within one month of the API shutdown. The remaining retail trades collectively became more predictive of future returns, suggesting less gambling-like behavior after the API shutdown. Moreover, our randomized controlled experiment affirms the underlying mechanism: tech-enabled access to high-volume historical price data increases individuals’ overconfidence, which further leads them to engage in excessive trading. The study reveals an unintended consequence of technology-led, wider data access for retail investors.
Keywords: retail investors; financial technology; financial data; gambling; noise trading; stock market; application programming interface; quasi-natural experiment; randomized controlled experiment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2021.01379 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:2:p:1646-1670
Access Statistics for this article
More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().