arXiv 2104.124842021-04-26q-fin.PM

用一种新的列式学习排序算法构造多空股票组合

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

Xin Zhang, Lan Wu, Zhixue Chen

摘要：

随着机器学习技术的快速发展，因子策略在工业界越来越受到欢迎。通常，多个因素被输入到一个算法中，用于一些横截面的收益预测，这些预测进一步用于构建一个多空组合。新兴的研究不是预测股票回报的价值，而是使用成熟的学习排名技术预测排名的股票列表。在这项研究中，我们提出了一个新的列表学习排名损失函数，其目的是强调排名列表的顶部和底部。我们的损失函数，由多空策略的动机，是内生的移位不变，可以被看作是一个直接推广的ListMLE。在不同的转换函数下，我们的损失可能导致与二元分类损失或排列水平0-1损失的一致性。我们的模型的概率解释也作为一个广义Plackett-Luce模型。基于2006 - 2019年中国A股市场68个因子的数据集，我们的实证研究证明了我们方法的优势，在夏普比率为2的情况下，样本外年收益率为38%。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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