Higher Moments Portfolio Optimization with Entropy Based Polynomial Goal Programming

Document Type : Research Paper


1 Assistant Prof., Faculty of Management, Kharazmi University, Tehran, Iran.

2 Ph.D Candidate, Department of Financial Engineering, Faculty of Management, University of Tehran, Tehran, Iran


Objective: Portfolio selection is a critical factor in investment. Having considered a number of risky assets, fund
 managers must choose the optimum portfolio. Stock values can be affected by different types of events
 such as governmental crises, economic turmoil and industrial improvements. Due to the vague nature
 of these events, it is difficult to estimate the future value of stocks. However, Markowitz’s Modern
 portfolio theory, which is principally focused on portfolio risk, has introduced a novel model for stock
 diversification. When the normality assumption
 of return series of assets are not valid, higher moments can also be added to ensure the efficiency of
 the Markowitz model. On other hand, entropy can be used as diversification creteria in portfolio theory. In this paper, the affect of simulatnus usage higher moment and entropy is examined. Methods: In this paper, a polynomial goal programming based on the model of meanvariance-skidding-elongation-entropy and direct search algorithms is used. For estimation of entropy, Shannon and Ginny Simpson criteria have been used.  Results: Tehran Stock Exchange data was used to evaluate the models. The findings indicate portfolio performance measure is enhanced by using the proposed approach. Conclusion: Using a combination of higher moments and entropy, although it does not improve some of the target functions, but generally improves the performance of the portfolios.  


Main Subjects

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