Trust anomalies’ judgment: Social ranking theories for portfolio construction
将社会选择理论中的多数判断法应用于股票排序，基于11个市场异常指标构建投资组合，实证表明该方法优于传统单因子策略，且对权重选择和市场状态稳健。
This paper presents a novel approach to anomaly-based portfolio construction, integrating multiple market anomalies into a unified selection framework. We adapt the Majority Judgment (MJ) method from social choice theory to financial decision-making, ranking stocks based on multiple anomalies, each treated as an independent evaluation criterion. Using 23 years of U.S. equity data and 11 stock anomalies, we construct equal- and value-weighted decile portfolios based on both single-factor breakpoints and MJ rankings. Our empirical results show that MJ-based portfolios consistently outperform single-factor strategies across most configurations and remain robust to changes in reallocation schedules, voter count, and voting system specifications. Comparative experiments highlight that MJ is more resilient to portfolio weighting choices and market regimesinclude than conventional anomaly aggregation methods and achieves the strongest performance under annual rebalancing. These findings underscore MJ’s robustness and scalability in high-dimensional financial decision-making, offering a transparent and interpretable alternative for multi-factor portfolio construction.