论文标题
通过学习排名来建立横截面系统策略
Building Cross-Sectional Systematic Strategies By Learning to Rank
论文作者
论文摘要
横截面系统策略的成功取决于在投资组合构建之前准确对资产进行排名。当代技术可以通过简单的启发式方法或通过从标准回归或分类模型中排序输出来执行这一排名步骤，这些模型已被证明是在其他领域中排名（例如，信息检索）的优势。为了解决这一缺陷，我们提出了一个框架，通过结合学习到秩算法来增强横截面投资组合，从而通过跨工具进行成对和列表的结构来提高排名准确性。使用横截面动量作为示范性案例研究，我们表明，现代机器学习排名算法的使用可以显着提高横截面策略的交易性能 - 与传统方法相比，Sharpe比率的提高约为三倍。
The success of a cross-sectional systematic strategy depends critically on accurately ranking assets prior to portfolio construction. Contemporary techniques perform this ranking step either with simple heuristics or by sorting outputs from standard regression or classification models, which have been demonstrated to be sub-optimal for ranking in other domains (e.g. information retrieval). To address this deficiency, we propose a framework to enhance cross-sectional portfolios by incorporating learning-to-rank algorithms, which lead to improvements of ranking accuracy by learning pairwise and listwise structures across instruments. Using cross-sectional momentum as a demonstrative case study, we show that the use of modern machine learning ranking algorithms can substantially improve the trading performance of cross-sectional strategies -- providing approximately threefold boosting of Sharpe Ratios compared to traditional approaches.
