JOURNAL ARTICLE

Contrarians, Extrapolators, and Stock Market Momentum and Reversal.

  • Published In: Management Science (INFORMS), 2024, v. 70, n. 9. P. 5949 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Atmaz, Adem; Gulen, Huseyin; Cassella, Stefano; Ruan, Fangcheng 3 of 3

Abstract

This article investigates heterogeneity in investors’ expectations about aggregate stock market returns, focusing on two main types: extrapolators, who expect future returns to continue recent trends, and contrarians, who anticipate reversals. Using Gallup survey data, the authors document that extrapolators constitute about 60% of investors, while contrarians and unbiased investors each make up roughly 20%. Contrarians assign greater weight to the most recent stock performance, resulting in less persistent and more quickly corrected expectations compared to extrapolators. The authors develop a dynamic equilibrium asset pricing model incorporating these heterogeneous beliefs, showing that it generates the empirically observed pattern of short-term momentum and long-term reversal in stock prices. Empirical tests using survey data support three key model predictions linking the population shares and expectation formation differences of extrapolators and contrarians to stock market autocorrelation and return predictability. Robustness checks confirm these findings across alternative data classifications and benchmark return periods.

Additional Information

  • Source:Management Science (INFORMS). 2024/09, Vol. 70, Issue 9, p5949
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2023.4960
  • Accession Number:179339510
  • 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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