Listwise Learning to Rank Models with Transformer for Financial Strategies.

Learning to Rank (LTR) is the application of machine learning techniques for ordering items based on relevance, with applications in domains such as search engines, recommendation systems, and natural language processing. Among its various approaches—pointwise, pairwise, and listwise—the listwise framework is particularly suited for capturing interdependencies among ranked items, making it compelling for complex scenarios like asset ranking in investment strategies. This research explores the application of the listwise LTR approach in the financial domain, focusing on ranking assets long-short portfolio construction. In particular, transformers are introduced as the listwise architecture.

Transformers have achieved a huge success in the state of the art across various fields due to their self-attention mechanism, which allows them to serve as a context-aware architecture. The implementation includes developing listwise loss functions specifically designed to leverage the model’s ability to incorporate contextual information during both training and inference phases. The study evaluates the performance of the listwise approach compared to traditional pointwise and pairwise methods previously explored by Axyon AI.

A key objective is to assess whether the listwise framework introduces heterogeneity in model predictions, potentially improving the robustness and accuracy of the ensemble rankings. These insights aim to enhance the understanding of inter-asset relationships, ultimately contributing to more effective investment strategies.