JOURNAL ARTICLE
Tech-Enabled Financial Data Access, Retail Investors, and Gambling-Like Behavior in the Stock Market.
Published In: Management Science (INFORMS), 2025, v. 71, n. 2. P. 1646 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Havakhor, Taha; Rahman, Mohammad Saifur; Zhang, Tianjian; Zhu, Chenqi 3 of 3
Abstract
This article investigates the impact of tech-enabled access to high-volume financial data, specifically through financial data application programming interfaces (APIs), on retail investors’ trading behavior. Leveraging the abrupt shutdown of the Yahoo! Finance API—the largest free API for retail investors—as a quasi-natural experiment, the study finds that retail trading volumes in stocks favored by active retail investors declined by approximately 8.6%–10.5% within one month after the shutdown, while the remaining trades became more predictive of future returns, indicating reduced gambling-like behavior. Complementing this, a randomized controlled experiment demonstrates that access to API-like data increases retail investors’ overconfidence—manifested as illusions of knowledge, precision, and control—leading to excessive trading and poorer investment performance. The findings highlight an unintended consequence of democratizing access to institutional-like financial data: while it reduces information acquisition costs, it may exacerbate behavioral biases among less sophisticated retail investors, underscoring the need for enhanced financial education and regulatory attention.
Additional Information
- Source:Management Science (INFORMS). 2025/02, Vol. 71, Issue 2, p1646
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2021.01379
- Accession Number:182990735
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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