Building Cross-Sectional Systematic Strategies By Learning to Rank
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalLearning-To-RankRetrievalSimilar Papers 제목 키워드 기반
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies
We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-series and cross-sectional momentum strategies by trading assets based on their cross-sectional momentum features over time. While …
Time SeriesTime Series AnalysisFairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness
Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidanc…
Data-Driven Analysis of Intersectional Bias in Image Classification: A Framework with Bias-Weighted Augmentation
Machine learning models trained on imbalanced datasets often exhibit intersectional biases-systematic errors arising from the interaction of multiple attributes such as object class and environmental conditions. This pap…
Image ClassificationData AugmentationEnhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention
The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction. While this ranking step is traditionally performed using heuristics, or by sorti…
Information RetrievalLearning-To-RankRe-RankingRetrievalOn Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model
Quantitative trading strategies rely on accurately ranking stocks to identify profitable investments. Effective portfolio management requires models that can reliably order future stock returns. Transformer models are pr…
Information Retrieval