通过追随顶尖投资者选股，同时提升收益与推荐相关性。
Impact of Expert-Following Strategies in Financial Asset Recommendation
- 根据历史收益率识别顶尖投资者，推荐其持仓资产。
- 在四个阈值下，收益和相关性均显著优于市场基准。
- 适合金融推荐系统、量化投资研究者参考。
金融机构拥有丰富的交易记录，但如何同时最大化投资回报率（ROI）并保证偏好一致性仍是挑战。现有方法分别优化收益或偏好，导致两者存在根本权衡。本文提出专家追随策略：基于历史ROI识别表现最佳投资者，并推荐其买入的资产，按ROI加权购买频率排序。实验使用真实交易数据，在所有四个阈值下，该策略在ROI和nDCG上均显著优于市场平均基准。
原文摘要 · Abstract (English)
Financial institutions hold rich transaction histories, yet delivering recommendations that simultaneously maximize investment returns and ensure preference alignment remains a significant challenge. Existing approaches, namely return-based and preference-based strategies, each optimize a single objective, resulting in a fundamental trade-off between profitability (ROI) and relevance (nDCG). In this paper, we propose the Expert-Following Strategies: a framework that identifies top-performing investors based on their historical ROI and recommends the assets they purchased, scored by ROI-weighted purchase frequency. Our experiments using real-world transaction histories show that our strategy achieves statistically significant improvement over the market-average baseline in both ROI and nDCG simultaneously across all four thresholds.
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