A mean-maverick game cross-efficiency approach to portfolio selection: An application to Paris stock exchange

Abstract

We propose a novel framework to portfolio selection based on a combination between the maverick index and Data Envelopment Analysis (DEA) game cross-efficiency approach. While game cross-efficiency is developed as a remedy for weight multiplicity in the original cross-efficiency and peer-evaluation, we improve its use as a tool for portfolio selection. In our analysis, each financial asset is viewed as a player competing for investment funds through boosting its ranking compared to its opponents. Thus, a set of unique Nash equilibrium DEA scores to shares are provided. In addition to unique rank among financial assets, we suggest the deviation from the equilibrium rating score, the maverick index, as a consistent risk measure, that can be used as a good indicator for sensitivity to environment volatility in portfolio management. The empirical part employs a large database of European firms listed in Paris Stock Exchange to demonstrate that our approach can constitute a promising tool for stock portfolio selection. We show that the resulting portfolio is well diversified and yields higher risk-adjusted returns than other benchmark portfolios for a 6-year sample period from 2010 to 2015.