Cao, Ruanmin and Horváth, Lajos and Liu, Zhenya and Zhao, Yuqian (2019) A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis. Review of Quantitative Finance and Accounting, 54 (1). pp. 335-358. DOI https://doi.org/10.1007/s11156-019-00791-x
Cao, Ruanmin and Horváth, Lajos and Liu, Zhenya and Zhao, Yuqian (2019) A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis. Review of Quantitative Finance and Accounting, 54 (1). pp. 335-358. DOI https://doi.org/10.1007/s11156-019-00791-x
Cao, Ruanmin and Horváth, Lajos and Liu, Zhenya and Zhao, Yuqian (2019) A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis. Review of Quantitative Finance and Accounting, 54 (1). pp. 335-358. DOI https://doi.org/10.1007/s11156-019-00791-x
Abstract
We apply a functional data analysis approach to decompose the cross-sectional Fama–French three-factor model residuals in the Chinese stock market. Our results indicate that other than Fama–French three factors, there are two orthonormal asset pricing factors describing the behavioral biases in their historical performances: between winner and loser stocks, and extreme and mediocre-performing stocks, respectively. We explain these two factors through investors’ overreaction, overconfidence and the lead-lag effect. These findings empirically show the existence of momentum and disposition effects in the Chinese stock market. A buy-and-hold mean-variance optimized portfolio incorporating these two market anomalies boosts the Sharpe ratio to 1.27 .
Item Type: | Article |
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Uncontrolled Keywords: | Momentum effect; Disposition effect; Functional principal component analysis; Portfolio selection; Chinese stock market |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 06 Nov 2019 10:43 |
Last Modified: | 23 Sep 2022 19:35 |
URI: | http://repository.essex.ac.uk/id/eprint/25820 |
Available files
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