JOURNAL ARTICLE
Stock Market Ownership Transitions.
Published In: Management Science (INFORMS), 2025, v. 71, n. 6. P. 4977 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Bonaparte, Yosef; Korniotis, George M.; Kumar, Alok; Michaelides, Alexander; Zhang, Yuxin 3 of 3
Abstract
This article investigates the dynamics of U.S. household transitions into and out of stock market ownership using data from the Panel Study of Income Dynamics (PSID) spanning 1999 to 2019. It finds substantial entry and exit rates—about 11.8% and 7.5% respectively—across all age groups, challenging the notion that stockholders rarely exit before retirement. Empirical analysis shows that changes in income and wealth significantly influence these transitions, with wealth shocks playing a particularly strong role, especially for retired households. To interpret these findings, the authors develop a life cycle portfolio choice model incorporating participation costs and extensions for rare stock market disasters and elevated income risk; the model with rare disasters better matches ownership transitions among middle-aged and retired households, while higher income risk better explains young households’ behavior. Overall, the study highlights that ownership transitions respond to wealth and income shocks throughout the life cycle, and that participation costs alone cannot explain observed patterns without accounting for economic shocks.
Additional Information
- Source:Management Science (INFORMS). 2025/06, Vol. 71, Issue 6, p4977
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.00290
- Accession Number:187706352
- 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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