Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm

TL;DR

This paper introduces a novel listwise learn-to-rank loss function tailored for constructing long-short stock portfolios, emphasizing both top and bottom ranks, and demonstrates its effectiveness with significant empirical results in the Chinese stock market.

Contribution

The paper proposes a new shift-invariant listwise learn-to-rank loss function, extending ListMLE, with a probabilistic interpretation, for improved stock ranking in portfolio construction.

Findings

Achieves 38% annual return in empirical tests

Outperforms existing methods in ranking accuracy

Demonstrates robustness across different transformation functions

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 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 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 the Sharpe ratio being 2.