Learn to Rank Risky Investors: A Case Study of Predicting Retail Traders' Behaviour and Profitability

• URL: http://arxiv.org/abs/2509.16616v1
• Date: Sat, 20 Sep 2025 10:41:13 GMT
• Title: Learn to Rank Risky Investors: A Case Study of Predicting Retail Traders' Behaviour and Profitability
• Authors: Weixian Waylon Li, Tiejun Ma,
• Abstract summary: We propose a profit-aware risk ranker (PA-RiskRanker) that reframes the problem of identifying risky traders as a ranking task.<n>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.<n>Our research critically examines the limitations of existing deep learning-based LETOR algorithms in trading risk management.
• Score: 3.731289189298451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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