The DOI matches its Crossref record; 135 of the 135 references it cites were found in a registry or an index.
Investor Sentiment Aligned: A Powerful Predictor of Stock Returns
Open accessAbstract
We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices both in- and out-of-sample, and the predictability becomes both statistically and economically significant. In addition, it outperforms well recognized macroeconomic variables and can also predict cross-sectional stock returns sorted by industry, size, value, and momentum. The driving force of the predictive power appears stemming from investors’ biased belief about future cash flows.
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Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
White, H. (1980) · Econometrica · doi:10.2307/1912934 · Cited by 25,861
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
A Theory of Cognitive Dissonance
(2017) · Macat Library eBooks · doi:10.4324/9781912282432 · Cited by 18,122
The General Theory of Employment, Interest and Money.
Schumpeter, J. A., & Keynes, J. M. (1936) · Journal of the American Statistical Association · doi:10.2307/2278703 · Cited by 14,485
An Intertemporal Capital Asset Pricing Model
Merton, R. C. (1973) · Econometrica · doi:10.2307/1913811 · Cited by 6,508
Noise Trader Risk in Financial Markets
De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990) · Journal of Political Economy · doi:10.1086/261703 · Cited by 6,234
The General Theory of Employment, Interest and Money, by John Maynard Keynes
Beckhart, B. H. (1936) · Political Science Quarterly · doi:10.2307/2143949 · Cited by 6,220
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
By Force of Habit: A Consumption‐Based Explanation of Aggregate Stock Market Behavior
Campbell, J. Y., & Cochrane, J. H. (1999) · Journal of Political Economy · doi:10.1086/250059 · Cited by 5,210
Cited by
Can economic policy uncertainty predict stock returns? Global evidence
Phan, D. H. B., Sharma, S. S., & Tran, V. T. (2018) · Journal of International Financial Markets, Institutions and Money · doi:10.1016/j.intfin.2018.04.004 · Cited by 282
Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability
Avramov, D., Cheng, S., & Metzker, L. (2023) · Management Science · doi:10.1287/mnsc.2022.4449 · Cited by 217
Machine Learning Versus Economic Restrictions: Evidence from Stock Return Predictability
Avramov, D., Cheng, S., & Metzker, L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3450322 · Cited by 74
Metadata
- Authors
- Huang, D., Jiang, F., Tu, J., & Zhou, G.
- Venue
- Review of Financial Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-10-31
- Volume
- 28
- Issue
- 3
- Pages
- 791-837
- ISSN
- 0893-9454 (ISSN-L)
- 1465-7368
- OpenAlex
- W2101620928
- Cited by
- 1,123
- References
- 135
- FWCI
- 58.67
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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