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Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm.
- Source :
- Quantitative Finance; Feb2022, Vol. 22 Issue 2, p321-331, 11p
- Publication Year :
- 2022
-
Abstract
- Factor strategies have gained growing popularity in industry with the fast development of machine learning. Usually, multi-factors are fed to an algorithm for some cross-sectional return predictions, which are then further used to construct a long-short portfolio. Instead of predicting the value of the stock return, emerging studies predict a ranked stock list using the mature learn-to-rank technology. In this study, we propose a new listwise learn-to-rank loss function which aims to emphasize both the top and the bottom of a rank list. Our loss function, motivated by the long-short strategy, is endogenously shift-invariant and can be viewed as a direct generalization of ListMLE. Under different transformation functions, our loss can lead to consistency with binary classification loss or permutation level 0-1 loss. A probabilistic explanation for our model is also given as a generalized Plackett-Luce model. Based on a dataset of 68 factors in the China A-share market from 2006 to 2019, our empirical study has demonstrated the strength of our method which achieves an out-of-sample annual return of 38% with Sharpe ratio 2. [ABSTRACT FROM AUTHOR]
- Subjects :
- SHARPE ratio
ALGORITHMS
MACHINE learning
STOCKS (Finance)
VALUE investing (Finance)
Subjects
Details
- Language :
- English
- ISSN :
- 14697688
- Volume :
- 22
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Quantitative Finance
- Publication Type :
- Academic Journal
- Accession number :
- 155633547
- Full Text :
- https://doi.org/10.1080/14697688.2021.1939117