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Spanning Analysis of Stock Market Anomalies Under Prospect Stochastic Dominance

Stelios Arvanitis (), Olivier Scaillet and Nikolas Topaloglou ()
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Stelios Arvanitis: Department of Economics, Athens University of Economics and Business, 10434 Athens, Greece
Nikolas Topaloglou: Department of Economics, Athens University of Economics and Business, 10434 Athens, Greece; Institut de Préparation à l’Administration et à la Gestion (IPAG), Business School, 15006 Paris, France

Management Science, 2024, vol. 70, issue 9, 6002-6025

Abstract: 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 well-known 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.

Keywords: nonparametric test; prospect stochastic dominance efficiency; prospect spanning; market anomaly; linear programming; absence of loss aversion (search for similar items in EconPapers)
Date: 2024
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http://dx.doi.org/10.1287/mnsc.2023.4953 (application/pdf)

Related works:
Working Paper: Spanning analysis of stock market anomalies under Prospect Stochastic Dominance (2020) Downloads
Working Paper: Spanning analysis of stock market anomalies under Prospect Stochastic Dominance (2020) Downloads
Working Paper: Spanning analysis of stock market anomalies under prospect stochastic dominance (2020) Downloads
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