Learn to Rank Risky Investors: A Case Study of Predicting Retail Traders' Behaviour and Profitability
Citation @article{10.1145/3768623, author = {Li, Weixian Waylon and Ma, Tiejun}, title = {Learn to Rank Risky Investors: A Case Study of Predicting Retail Traders’ Behaviour and Profitability}, year = {2025}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {1046-8188}, url = {https://doi.org/10.1145/3768623}, doi = {10.1145/3768623}, abstract = {Identifying risky traders with high profits in financial markets is crucial for market makers, such as trading exchanges, to ensure effective risk management through real-time decisions on regulation compliance and hedging. However, capturing the complex and dynamic behaviours of individual traders poses significant challenges. Traditional classification and anomaly detection methods often establish a fixed risk boundary, failing to account for this complexity and dynamism. To tackle this issue, we propose a profit-aware risk ranker (PA-RiskRanker) that reframes the problem of identifying risky traders as a ranking task using Learning-to-Rank (LETOR) algorithms. Our approach features a Profit-Aware binary cross entropy (PA-BCE) loss function and a transformer-based ranker enhanced with a self-cross-trader attention pipeline. These components effectively integrate profit and loss (P&L) considerations into the training process while capturing intra- and inter-trader relationships. Our research critically examines the limitations of existing deep learning-based LETOR algorithms in trading risk management, which often overlook the importance of P&L in financial scenarios. By prioritising P&L, our method improves risky trader identification, achieving an 8.4\% increase in F1 score compared to state-of-the-art (SOTA) ranking models like Rankformer. Additionally, it demonstrates a 10\%-17\% increase in average profit compared to all benchmark models.}, note = {Just Accepted}, journal = {ACM Trans. Inf. Syst.}, month = sep, keywords = {learning to rank, domain-specific application, individual behaviour modelling, risk assessment} }
Sep 3, 2025