Tech-Enabled Financial Data Access, Retail Investors, and Gambling-Like Behavior in the Stock Market

• 成果类型: Article
• 署名作者: Havakhor, Taha; Rahman, Mohammad Saifur; Zhang, Tianjian; Zhu, Chenqi
• 署名单位: McGill University; Purdue University System; Purdue University; University of California System; University of California Irvine
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.01379
• 发表日期: 2025
• 关键词: Retail investors; financial technology; financial data; gambling; Noise trading; Stock market; application programming interface; quasi-natural experiment; randomized controlled experiment

摘要

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, institutionallike 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.