The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Cognitive Dissonance, Sentiment, and Momentum
Abstract
We consider whether sentiment affects the profitability of momentum strategies. We hypothesize that news that contradicts investors’ sentiment causes cognitive dissonance, slowing the diffusion of such news. Thus, losers (winners) become underpriced under optimism (pessimism). Short-selling constraints may impede arbitraging of losers and thus strengthen momentum during optimistic periods. Supporting this notion, we empirically show that momentum profits arise only under optimism. An analysis of net order flows from small and large trades indicates that small investors are slow to sell losers during optimistic periods. Momentum-based hedge portfolios formed during optimistic periods experience long-run reversals.
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Cites
Common risk factors in the returns on stocks and bonds
Fama, E. F., & French, K. R. (1993) · Journal of Financial Economics · doi:10.1016/0304-405x(93)90023-5 · Cited by 25,754
A Theory of Cognitive Dissonance
Festinger, L. (1957) · doi:10.1515/9781503620766 · Cited by 24,241
A Theory of Cognitive Dissonance
(2017) · Macat Library eBooks · doi:10.4324/9781912282432 · Cited by 18,122
Newey, W. K., & West, K. D. (1987) · Econometrica · doi:10.2307/1913610 · Cited by 16,936
A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix
Newey, W., & West, K. (1986) · National Bureau of Economic Research · doi:10.3386/t0055 · Cited by 12,278
Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency
JEGADEESH, N., & TITMAN, S. (1993) · The Journal of Finance · doi:10.1111/j.1540-6261.1993.tb04702.x · Cited by 11,584
Does the Stock Market Overreact?
De BONDT, W. F. M., & THALER, R. (1985) · The Journal of Finance · doi:10.1111/j.1540-6261.1985.tb05004.x · Cited by 7,578
Multifactor Explanations of Asset Pricing Anomalies
FAMA, E. F., & FRENCH, K. R. (1996) · The Journal of Finance · doi:10.1111/j.1540-6261.1996.tb05202.x · Cited by 6,700
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
Optimum consumption and portfolio rules in a continuous-time model
Merton, R. C. (1971) · Journal of Economic Theory · doi:10.1016/0022-0531(71)90038-x · Cited by 5,958
Cited by
Investor Sentiment Aligned: A Powerful Predictor of Stock Returns
Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015) · Review of Financial Studies · doi:10.1093/rfs/hhu080 · Cited by 1,123
Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle
STAMBAUGH, R. F., YU, J., & YUAN, Y. (2015) · The Journal of Finance · doi:10.1111/jofi.12286 · Cited by 940
Metadata
- Authors
- Antoniou, C., Doukas, J. A., & Subrahmanyam, A.
- Venue
- Journal of Financial and Quantitative Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2012-11-29
- Volume
- 48
- Issue
- 1
- Pages
- 245-275
- ISSN
- 0022-1090 (ISSN-L)
- 1756-6916
- OpenAlex
- W2056162038
- Cited by
- 405
- References
- 65
- FWCI
- 21.23
- Type
- journal article
- Language
- en
- Open access
- closed
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