Portfolio optimization with asset preselection using data envelopment analysis

Mohammad Mehdi Hosseinzadeh (), Sergio Ortobelli Lozza (), Farhad Hosseinzadeh Lotfi () and Vittorio Moriggia ()
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Mohammad Mehdi Hosseinzadeh: University of Bergamo
Sergio Ortobelli Lozza: University of Bergamo
Farhad Hosseinzadeh Lotfi: Islamic Azad University
Vittorio Moriggia: University of Bergamo

Central European Journal of Operations Research, 2023, vol. 31, issue 1, No 11, 287-310

Abstract: Abstract This paper uses data envelopment analysis (DEA) approach as a nonparametric efficiency analysis tool to preselect efficient assets in large-scale portfolio problems. Thus, we reduce the dimensionality of portfolio problems, considering multiple asset performance criteria in a linear DEA model. We first introduce several reward/risk criteria that are typically used in portfolio literature to identify features of financial returns. Secondly, we suggest some DEA input/output sets for preselecting efficient assets in a large-scale portfolio framework. Then, we evaluate the impact of the preselected assets in different portfolio optimization strategies. In particular, we propose an ex-post empirical analysis based on two alternative datasets: the components of S &P500 and the Fama and French 100 portfolio formed on size and book to market. According to this empirical analysis we observe better performances of the DEA preselection than the classic PCA factor models for large scale portfolio selection problems. Moreover, the proposed model outperform the S &P500 index and the strategy based on the fully diversified portfolio.

Keywords: Portfolio theory; Data envelopment analysis; Efficiency; Asset performance criteria (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10100-022-00808-2

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