Impact of Expert-Following Strategies in Financial Asset Recommendation
This work addresses the trade-off between profitability and preference alignment in financial recommendation for institutions, but the approach is incremental as it combines existing ideas of expert following and weighted scoring.
The paper proposes Expert-Following Strategies for financial asset recommendation that identify top-performing investors based on historical ROI and recommend their purchased assets scored by ROI-weighted purchase frequency, achieving statistically significant improvements in both ROI and nDCG over market-average baselines across all thresholds.
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.