Introducing a Relational Network DEA Model with Stochastic Intermediate measures for Portfolio Optimization

Conflict intermediate measures in DEA models, especially in constraint and open the black box, is the main difference between traditional DEA and network DEA models. Furthermore, from the application's perspective, intermediate measures aren’t deterministic. So, for measuring the efficiency more precisely, they can be considered as imprecise data. The aim of this paper is introducing a stochastic relational model for measuring overall efficiency that deals with intermediate and outputs as stochastic data. The proposed model is applied for portfolio optimization. An actual data set of 27 Iranian stock industries is applied as numerical example. The result shows that SR-NDEA has better discriminant power than R-NDEA model.

[1]  Yongjun Li,et al.  Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures , 2016, Eur. J. Oper. Res..

[2]  Mohammadsaleh Saadatmand.  Stock Selection using Data Envelopment Analysis , 2018 .

[3]  Hsin-Hung Chen,et al.  Stock selection using data envelopment analysis , 2008, Ind. Manag. Data Syst..

[4]  Milos Kopa,et al.  Measuring of Second-Order Stochastic Dominance Portfolio Efficiency , 2010, Kybernetika.

[5]  William W. Cooper,et al.  Chapter 1 Introduction: Extensions and new developments in DEA , 1996, Ann. Oper. Res..

[6]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .

[7]  Léopold Simar,et al.  A robust nonparametric approach to evaluate and explain the performance of mutual funds , 2006, Eur. J. Oper. Res..

[8]  T ZiembaWilliam,et al.  The Stochastic Programming Approach to Asset, Liability, and Wealth Management , 2006 .

[9]  Zhimin Huang,et al.  Stochastic DEA Models With Different Types of Input-Output Disturbances , 2001 .

[10]  R. F. Rea,et al.  Network DEA , 1999 .

[11]  Leila Zamani,et al.  Portfolio Selection using Data Envelopment Analysis (DEA): A Case of Select Indian Investment Companies , 2014 .

[12]  Joe Zhu,et al.  Context-dependent performance standards in DEA , 2010, Ann. Oper. Res..

[13]  Joe Zhu,et al.  Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market , 2014, Eur. J. Oper. Res..

[14]  S. Uryasev,et al.  Drawdown Measure in Portfolio Optimization , 2003 .

[15]  N. C. P. Edirisinghe,et al.  Generalized DEA model of fundamental analysis and its application to portfolio optimization , 2007 .

[16]  Milos Kopa,et al.  A second-order stochastic dominance portfolio efficiency measure , 2008, Kybernetika.

[17]  William W. Cooper,et al.  Stochastics and Statistics , 2001 .

[18]  Joe Zhu,et al.  DEA model with shared resources and efficiency decomposition , 2010, Eur. J. Oper. Res..

[19]  William W. Cooper,et al.  Chance constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis , 2002, J. Oper. Res. Soc..

[20]  Timo Kuosmanen,et al.  Efficient Diversification According to Stochastic Dominance Criteria , 2004, Manag. Sci..

[21]  A. Stuart,et al.  Portfolio Selection: Efficient Diversification of Investments , 1959 .

[22]  H. Levy.  Stochastic Dominance: Investment Decision Making under Uncertainty , 2010 .

[23]  D. Galagedera,et al.  Australian mutual fund performance appraisal using data envelopment analysis , 2002 .