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Portfolio analysis with DEA: Prior to choosing a model

Author

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  • Albane Christine Tarnaud
  • Herve Leleu

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper examines the definition of a technology and the choice of a model orientation prior to the analysis of portfolios of financial assets with Data Envelopment Analysis. We acknowledge the previous contributions in the field and provide answers to the questions raised in Cook, Tone & Zhu (2014). These answers allow to determine the purpose of the study and to define the underlying ‘financial’ technology through the identification of the decision-making units and the selection of input and output variables in a multi-moment framework. We also show their impact on the traditional set of axioms that further characterizes the technology and propose some adjustments to the traditional models. We provide illustrations to show the effects of such changes on the scores of technical efficiency and ranking of the portfolios.
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Suggested Citation

  • Albane Christine Tarnaud & Herve Leleu, 2018. "Portfolio analysis with DEA: Prior to choosing a model," Post-Print halshs-01720372, HAL.
    • Handle: RePEc:hal:journl:halshs-01720372
    DOI: 10.1016/j.omega.2017.02.003
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    2. Valadkhani, Abbas & Moradi-Motlagh, Amir, 2023. "An empirical analysis of exchange-traded funds in the US," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 995-1009.
    3. Catarina Alexandra Neves Proença & Maria Elisabete Duarte Neves & Maria Castelo Baptista Gouveia & Mara Teresa Silva Madaleno, 2023. "Technological, healthcare and consumer funds efficiency: influence of COVID-19," Operational Research, Springer, vol. 23(2), pages 1-42, June.
    4. Zeng, Ximei & Zhou, Zhongbao & Gong, Yeming & Liu, Wenbin, 2022. "A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    5. Xiao, Helu & Ren, Tiantian & Zhou, Zhongbao & Liu, Wenbin, 2021. "Parameter uncertainty in estimation of portfolio efficiency: Evidence from an interval diversification-consistent DEA approach," Omega, Elsevier, vol. 103(C).
    6. Ioannis E. Tsolas, 2020. "Precious Metal Mutual Fund Performance Evaluation: A Series Two-Stage DEA Modeling Approach," JRFM, MDPI, vol. 13(5), pages 1-13, April.
    7. Ioannis E. Tsolas, 2020. "The Determinants of the Performance of Precious Metal Mutual Funds," JRFM, MDPI, vol. 13(11), pages 1-10, November.
    8. Adam, Lukáš & Branda, Martin, 2021. "Risk-aversion in data envelopment analysis models with diversification," Omega, Elsevier, vol. 102(C).

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