Mathis LINGER

This study presents a novel approach that integrates asset ranking and portfolio weighting within a single framework. Unlike traditional methods, which separate asset ranking from portfolio weighting, this research employs a multi-task neural network to concurrently learn asset rankings and optimize the number of assets for long and short positions. This innovation aims to better align the investment strategy with investor preferences right from the model prediction phase. To assess its effectiveness, the authors conduct experiments using historical weekly market data from China A-shares. The findings indicate that incorporating portfolio weighting into a multi-task learning framework significantly improves out-of-sample financial performance in contrast to benchmark methods that rely on heuristics or historical estimations.

This article delves into the challenges posed by ranking biases inherent to traditional learn-to-rank loss functions, particularly focusing on their impact on the construction of long–short portfolios. Through the analysis of synthetic data, the authors uncover inherent biases in these methods, particularly detrimental for long–short portfolios where equal importance lies with top- and bottom-ranked assets. To address these challenges, the authors propose enhanced versions of learn-to-rank loss functions—ListNet-Fold, ListMLE-weighted, and ListFold-weighted. These adaptations, tailored for long–short strategies, draw inspiration from pairwise approaches and adjust weighting mechanisms. Empirical results using a real-world dataset sourced from the China A-share market consistently reveal enhancements in ranking metrics, notably improving accuracy in ranking extreme assets, which are more traded in long–short portfolios. Furthermore, financial performance metrics validate the efficacy of these methods, demonstrating enhanced risk-adjusted returns, profitability, and robustness across varying numbers of assets included in the long–short strategy. This research offers valuable insights and practical remedies for mitigating biases in learn-to-rank algorithms, presenting promising tools for constructing long–short portfolios.