{"ID":2868394,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16616","arxiv_id":"2509.16616","title":"Learn to Rank Risky Investors: A Case Study of Predicting Retail Traders' Behaviour and Profitability","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\u0026L) 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\u0026L in financial scenarios. By prioritising P\u0026L, 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.","short_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 ch...","url_abs":"https://arxiv.org/abs/2509.16616","url_pdf":"https://arxiv.org/pdf/2509.16616v1","authors":"[\"Weixian Waylon Li\",\"Tiejun Ma\"]","published":"2025-09-20T10:41:13Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.IR\"]","methods":"[\"Transformer\"]","has_code":false}