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Modelling social responsibility in mutual fund performance appraisal: A two-stage data envelopment analysis model with non-discretionary first stage output

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  • Galagedera, Don U.A.

Abstract

The aim of this study is to assess mutual fund (MF) performance in a multi-dimensional framework with ethical level as one of its performance measures. MF overall management process is conceptualised as a serially linked two-stage process with operational management and portfolio management as the first and second stage processes. Recognising that corporate policy on ethical issues may reflect medium- to long-term commitment and may guide MF portfolio construction, ethical level is modelled as a non-discretionary output at the operational management stage. Under this performance appraisal framework, network data envelopment analysis (DEA) output-oriented models are formulated to compute overall and stage-level performance. A DEA model to determine frontier projection of inefficient MFs is also developed. Application of the proposed DEA models is demonstrated using a sample of large U.S. equity MFs as a case study.

Suggested Citation

  • Galagedera, Don U.A., 2019. "Modelling social responsibility in mutual fund performance appraisal: A two-stage data envelopment analysis model with non-discretionary first stage output," European Journal of Operational Research, Elsevier, vol. 273(1), pages 376-389.
    • Handle: RePEc:eee:ejores:v:273:y:2019:i:1:p:376-389
    DOI: 10.1016/j.ejor.2018.08.011
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    3. 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.
    4. Ioannis E. Tsolas, 2020. "The Determinants of the Performance of Precious Metal Mutual Funds," JRFM, MDPI, vol. 13(11), pages 1-10, November.
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    7. Xiong, Beibei & Chen, Haoxun & An, Qingxian & Wu, Jie, 2019. "A multi-objective distance friction minimization model for performance assessment through data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(1), pages 132-142.
    8. Petridis, Konstantinos & Tampakoudis, Ioannis & Drogalas, George & Kiosses, Nikolaos, 2022. "A Support Vector Machine model for classification of efficiency: An application to M&A," Research in International Business and Finance, Elsevier, vol. 61(C).
    9. Mohammad Nourani & Qian Long Kweh & Wen-Min Lu & Ikhlaas Gurrib, 2022. "Operational and investment efficiency of investment trust companies: Do foreign firms outperform domestic firms?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-26, December.
    10. Hsio-Yi Lin & Bin-Wei Hsu, 2023. "Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
    11. Fadillah Mansor & M. Ishaq Bhatti & Shafiqur Rahman & Hung Quang Do, 2020. "The Investment Performance of Ethical Equity Funds in Malaysia," JRFM, MDPI, vol. 13(9), pages 1-14, September.

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