Spanning Analysis of Stock Market Anomalies Under Prospect Stochastic Dominance

• 成果类型: Article
• 署名作者: Arvanitis, Stelios; Scaillet, Olivier; Topaloglou, Nikolas
• 署名单位: Athens University of Economics & Business; University of Geneva; University of Geneva
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4953
• 发表日期: 2024
• 页码: 6002-6025
• 关键词: NONPARAMETRIC TEST; prospect stochastic dominance efficiency; prospect spanning; market anomaly; linear programming; absence of loss aversion

摘要

We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and linear programming. In an application, we use the prospect spanning framework to evaluate whether wellknown anomalies are spanned by standard factors. We find that of the strategies considered, a few of them expand the opportunity set of the prospect type investors and thus have real economic value for them and involve absence of loss aversion. Those are the net stock issue anomaly under the FF-5 model, the momentum and net stock issue anomalies under the M-4 model, and the momentum anomaly under the q model. In-sample and out-of-sample results prove remarkably consistent in identifying genuine anomalies for prospect investors.