Open Access
RAIRO-Oper. Res.
Volume 56, Number 4, July-August 2022
Page(s) 2367 - 2387
Published online 29 July 2022
  • A. Basso and S. Funari, A data envelopment analysis approach to measure the mutual fund performance. Eur. J. Oper. Res. 135 (2001) 477–492. [Google Scholar]
  • A. Basso and S. Funari, Constant and variable returns to scale DEA models for socially responsible investment funds. Eur. J. Oper. Res. 235 (2014) 775–783. [Google Scholar]
  • A. Basso and S. Funari, The role of fund size in the performance of mutual funds assessed with DEA models. Eur. J. Finan. 23 (2017) 457–473. [CrossRef] [Google Scholar]
  • E. Basso, A. Allevi, F. Bonenti, G. Oggioni and R. Riccardi, Measuring the environmental performance of green SRI funds: a DEA approach. Energy Econ. 79 (2019) 32–44. [CrossRef] [Google Scholar]
  • P. Bonami and M.A. Lejeune, An exact solution approach for portfolio optimization problems under stochastic and integer constraints. Oper. Res. 57 (2009) 650–670. [CrossRef] [MathSciNet] [Google Scholar]
  • S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, Cambridge (2004). [Google Scholar]
  • M. Branda, Diversification-consistent data envelopment analysis based on directional-distance measures. Omega 52 (2015) 65–76. [Google Scholar]
  • W. Briec, K. Kerstens and J.B. Lesourd, Single-period Markowitz portfolio selection, performance gauging, and duality: a variation on the Luenberger shortage function. J. Optim. Theory App. 120 (2004) 1–27. [Google Scholar]
  • W. Briec, K. Kerstens and O. Jokung, Mean-variance-skewness portfolio performance gauging: a general shortage function and dual approach. Manage. Sci. 53 (2007) 135–149. [Google Scholar]
  • Z. Chen and R. Lin, Mutual fund performance evaluation using data envelopment analysis with new risk measures. OR Spect. 28 (2006) 375–398. [Google Scholar]
  • W. Chen, Y. Gai and P. Gupta, Efficiency evaluation of fuzzy portfolio in different risk measures via DEA. Ann. Oper. Res. 269 (2018) 103–127. [Google Scholar]
  • W.W. Cooper, Z. Huang, V. Lelas, S.X. Li and O.B. Olesen, Chance constrained programming formulations for stochastic characterizations of efficiency and dominance in DEA. J. Prod. Anal. 9 (1998) 53–79. [CrossRef] [Google Scholar]
  • W.W. Cooper, H. Deng, Z. Huang and S.X. Li, Chance constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis. J. Oper. Res. Soc. 53 (2002) 1347–1356. [CrossRef] [Google Scholar]
  • H. Ding, Z. Zhou, H. Xiao, C. Ma and W. Liu, Performance evaluation of portfolios with margin requirements. Math. Prob. Eng. 2014 (2014) 1–8. [Google Scholar]
  • Z. Huang and S.X. Li, Stochastic DEA models with different types of input-output disturbances. J. Prod. Anal. 15 (2001) 95–113. [CrossRef] [Google Scholar]
  • T. Joro and P. Na, Portfolio performance evaluation in a mean-variance-skewness framework. Eur. J. Oper. Res. 175 (2006) 446–461. [Google Scholar]
  • J.D. Lamb and K.H. Tee, Data envelopment analysis models of investment funds. Eur. J. Oper. Res. 216 (2012) 687–696. [Google Scholar]
  • R. Lin and Z. Li, Directional distance based diversification super-efficiency DEA models for mutual funds, Omega 97 (2019) 102096. [Google Scholar]
  • W.B. Liu, D.Q. Zhang, W. Meng, X.X. Li and F. Xu, A study of DEA models without explicit inputs. Omega 39 (2011) 472–480. [CrossRef] [Google Scholar]
  • W. Liu, Z. Zhou, D. Liu and H. Xiao, Estimation of portfolio efficiency via DEA. Omega 52 (2015) 107–118. [Google Scholar]
  • M.S. Lobo, L. Vandenberghe, S. Boyd and H. Lebret, Applications of second-order cone programming. Linear Algebra App. 284 (1998) 193–228. [CrossRef] [Google Scholar]
  • H. Markowitz, Portfolio selection. J. Finan. 7 (1952) 77–91. [Google Scholar]
  • B.P.S. Murthi, Y.K. Choi and P. Desai, Efficiency of mutual funds and portfolio performance measurement: a non-parametric approach. Eur. J. Oper. Res. 98 (1997) 408–418. [Google Scholar]
  • O.B. Olesen and N.C. Petersen, Stochastic data envelopment analysis – a review. Eur. J. Oper. Res. 251 (2016) 2–21. [Google Scholar]
  • T. Ren, Z. Zhou and H. Xiao, Estimation of portfolio efficiency considering social responsibility: evidence from the multi-horizon diversification DEA. RAIRO: Oper. Res. 55 (2021) 611–637. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • A.C. Tarnaud and H. Leleu, Portfolio analysis with DEA: prior to choosing a model. Omega 75 (2018) 57–76. [Google Scholar]
  • E.G. Tsionas and E.N. Papadakis, A Bayesian approach to statistical inference in stochastic DEA. Omega 38 (2010) 309–314. [CrossRef] [Google Scholar]
  • G. Yang, W. Shen, D. Zhang and W. Liu, Extended utility and DEA models without explicit input. J. Oper. Res. Soc. 65 (2014) 1212–1220. [CrossRef] [Google Scholar]
  • Z. Zhou, L. Lin, H. Xiao, C. Ma and S. Wu, Stochastic network DEA models for two-stage systems under the centralized control organization mechanism. Comput. Ind. Eng. 110 (2017) 404–412. [CrossRef] [Google Scholar]
  • Z. Zhou, H. Xiao, Q. Jin and W. Liu, DEA frontier improvement and portfolio rebalancing: an application of China mutual funds on considering sustainability information disclosure. Eur. J. Oper. Res. 269 (2018) 111–131. [Google Scholar]
  • Z. Zhou, E. Chen, H. Xiao, T. Ren and Q. Jin, Performance evaluation of portfolios with fuzzy returns. RAIRO: Oper. Res. 53 (2019) 1581–1600. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]

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