JOURNAL ARTICLE
Make Hay While the Sun Shines: an Empirical Study of Maximum Price, Regret, and Trading Decisions.
Published In: Journal of the European Economic Association, 2025, v. 23, n. 2. P. 594 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Brettschneider, Julia; Burro, Giovanni; Henderson, Vicky 3 of 3
Abstract
This article investigates how the maximum price attained by a stock since purchase influences individual investors' propensity to sell, using a large dataset of U.S. household trades from 1991 to 1996. It finds that only about 31.6% of stocks sold for a gain are sold exactly on the day the stock reaches its maximum price since purchase, indicating that investors do not consistently follow optimal time-constant trading threshold strategies predicted by classical models like Expected Utility or Prospect Theory. The propensity to sell exhibits an inverse U-shaped relationship with the price's proximity to the past maximum and decreases as the time since the maximum price increases; notably, selling likelihood is highest when the stock price is somewhat below the maximum but the maximum occurred recently. These patterns are interpreted through psychological and behavioral mechanisms including regret, belief updating (particularly overreaction to recent negative information), and investor attention, suggesting that the past maximum acts as a salient reference point in both price and time dimensions. The study also highlights heterogeneity among investors, with more sophisticated and active traders more likely to follow threshold-consistent behavior, while older and affluent investors are less so.
Additional Information
- Source:Journal of the European Economic Association. 2025/04, Vol. 23, Issue 2, p594
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:1542-4766
- DOI:10.1093/jeea/jvae028
- Accession Number:184350995
- Copyright Statement:Copyright of Journal of the European Economic Association is the property of Oxford University Press / USA 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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