An integrated DEA-MODM methodology for portfolio optimization

Portfolio optimization generally involves maximizing the expected return of a portfolio while minimizing its risk. However, when considering these 2 conflicting objectives in decision-making contexts, an efficient approach is required to evaluate the solution in the set of efficient frontiers/Pareto optimal solutions. Selecting and managing an investment portfolio are critical for investors. This study proposes an integrated method for portfolio optimization that involves decisions on stock screening, stock selection, and capital allocation. The initial step involves examining the financial data of investment targets. The portfolio is then selected according to the firms’ relative efficiency by using a data envelopment analysis (DEA) model. To determine the allocation of capital to each stock in the constructed portfolio, a multi-objective decision-making (MODM) model is developed. The integrated DEA-MODM approach is applied to Taiwan’s financial market and compared with several benchmarking mutual funds for empirical testing. This comparative study shows that the proposed approach outperforms the Taiwan Excel 50 ETF and 3 other benchmarking mutual funds in numerous quarters of the testing period regarding return rates and Sharpe ratios. Therefore, the proposed integrated DEA-MODM approach can be useful to both investors and researchers.

[1]  W. Sharpe OF FINANCIAL AND QUANTITATIVE ANALYSIS December 1971 A LINEAR PROGRAMMING APPROXIMATION FOR THE GENERAL PORTFOLIO ANALYSIS PROBLEM , 2009 .

[2]  Quey-Jen Yeh The Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation , 1996 .

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

[4]  H. Konno,et al.  Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market , 1991 .

[5]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[6]  Alberto Suárez,et al.  Hybrid Approaches and Dimensionality Reduction for Portfolio Selection with Cardinality Constraints , 2010, IEEE Computational Intelligence Magazine.

[7]  M. Farrell The Measurement of Productive Efficiency , 1957 .

[8]  Sergio Ortobelli Lozza,et al.  Reward and risk in the fixed income markets , 2013 .

[9]  Constantin Zopounidis,et al.  Multicriteria decision aid in financial management , 1999, Eur. J. Oper. Res..

[10]  Yazid M. Sharaiha,et al.  Heuristics for cardinality constrained portfolio optimisation , 2000, Comput. Oper. Res..

[11]  Fouad Ben Abdelaziz,et al.  Multi-objective stochastic programming for portfolio selection , 2007, Eur. J. Oper. Res..

[12]  Ralph E. Steuer,et al.  Multiple criteria decision making combined with finance: A categorized bibliographic study , 2003, Eur. J. Oper. Res..

[13]  Mark V. Cannice,et al.  The value-relevance of financial and non-financial information for Internet companies , 2002 .

[14]  Chih-Ming Hsu,et al.  An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming , 2014, Int. J. Syst. Sci..

[15]  Wlodzimierz Ogryczak,et al.  Multiple criteria linear programming model for portfolio selection , 2000, Ann. Oper. Res..

[16]  Andrea Schaerf,et al.  Local Search Techniques for Constrained Portfolio Selection Problems , 2001, ArXiv.

[17]  Kathrin Klamroth,et al.  An MCDM approach to portfolio optimization , 2004, Eur. J. Oper. Res..

[18]  Daniele Toninelli,et al.  Set-Portfolio Selection with the Use of Market Stochastic Bounds , 2011 .

[19]  B. Lev,et al.  Fundamental Information Analysis , 1993 .

[20]  V. Bawa OPTIMAL, RULES FOR ORDERING UNCERTAIN PROSPECTS+ , 1975 .

[21]  Yusif Simaan Estimation risk in portfolio selection: the mean variance model versus the mean absolute deviation model , 1997 .

[22]  Maria Grazia Speranza,et al.  Twenty years of linear programming based portfolio optimization , 2014, Eur. J. Oper. Res..

[23]  Joseph C. Paradi,et al.  Financial performance analysis of Ontario (Canada) Credit Unions: An application of DEA in the regulatory environment , 2002, Eur. J. Oper. Res..

[24]  A. Charnes,et al.  Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints , 1963 .

[25]  Po-Chang Ko,et al.  Resource allocation neural network in portfolio selection , 2008, Expert Syst. Appl..

[26]  P. Fishburn Mean-Risk Analysis with Risk Associated with Below-Target Returns , 1977 .

[27]  Vijay S. Bawa,et al.  Abstract: Capital Market Equilibrium in a Mean-Lower Partial Moment Framework , 1977, Journal of Financial and Quantitative Analysis.

[28]  Angelos Kanas,et al.  Neural network linear forecasts for stock returns , 2001 .

[29]  Sergio Gómez,et al.  Portfolio selection using neural networks , 2005, Comput. Oper. Res..

[30]  Antonella Basso,et al.  A Data Envelopment Analysis Approach to Measure the Mutual Fund Performance , 2001, Eur. J. Oper. Res..

[31]  W. Sharpe A Simplified Model for Portfolio Analysis , 1963 .

[32]  David N. Nawrocki A Brief History of Downside Risk Measures , 1999 .

[33]  Constantin Zopounidis,et al.  Stock Evaluation Using a Preference Disaggregation Methodology , 1999 .

[34]  Kalyanmoy Deb,et al.  Portfolio optimization with an envelope-based multi-objective evolutionary algorithm , 2009, Eur. J. Oper. Res..

[35]  Piero P. Bonissone,et al.  Multiobjective financial portfolio design: a hybrid evolutionary approach , 2005, 2005 IEEE Congress on Evolutionary Computation.