The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Do Measures of Investor Sentiment Predict Returns?
Abstract
It has long been market folklore that the best time to buy stocks is when individual investors are bearish, and the best time to sell is when individual investors are bullish. We examine the forecast power of three popular measures of individual investor sentiment: the level of discounts on closed-end funds, the ratio of odd-lot sales to purchases, and net mutual fund redemptions. Using data from 1933 to 1993, we find that fund discounts and net redemptions predict the size premium, the difference between small and large firm returns, but little evidence that the odd-lot ratio predicts returns.
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Citations
Cites
FAMA, E. F. (1991) · The Journal of Finance · doi:10.1111/j.1540-6261.1991.tb04636.x · Cited by 5,350
Cited by
Investor Sentiment and the Cross‐Section of Stock Returns
BAKER, M., & WURGLER, J. (2006) · The Journal of Finance · doi:10.1111/j.1540-6261.2006.00885.x · Cited by 6,049
Measuring investor sentiment with mutual fund flows
Ben-Rephael, A., Kandel, S., & Wohl, A. (2012) · Journal of Financial Economics · doi:10.1016/j.jfineco.2010.08.018 · Cited by 358
Sawhney, R., Agarwal, S., Wadhwa, A., & Shah, R. R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.676 · Cited by 146
Investor sentiment in the stock market
Baker, M., & Wurgler, J. (2007) · Journal of Economic Perspectives · doi:10.1257/jep.21.2.129 · Cited by 94
Metadata
- Authors
- Neal, R., & Wheatley, S. M.
- Venue
- The Journal of Financial and Quantitative Analysis
- Publisher
- JSTOR
- Published
- 1998-12
- Volume
- 33
- Issue
- 4
- Pages
- 523
- ISSN
- 0022-1090 (ISSN-L)
- OpenAlex
- W2011293681
- Cited by
- 515
- References
- 1
- FWCI
- 7.39
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
