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A portfolio optimization model based on information entropy and fuzzy time series

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Abstract

The traditional portfolio models assume securities’ returns are normally distributed and the future distribution of returns is the same as the historical distribution. For the two stringent assumptions, this paper develops two entropy-based portfolio optimization models which are flexible and effective in measuring risk. The second motivation of this paper is to combine the fuzzy time series technique to portfolio optimization. In fact, forecasting securities’ returns distribution is an important issue for portfolio. And among the many forecasting methods, the fuzzy time series technique is more suitable to deal with the fuzzy data in financial data. The empirical results on the historical data of the Stock Exchange in Chinese financial market show effectiveness of the proposed models. Both the entropy based models outperform the traditional ones and the fuzzy time series forecasting model also helps to further improve the actual performance.

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References

  • Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228.

    Article  MathSciNet  MATH  Google Scholar 

  • Cai, X. Q., Teo, K. L., Yang, X. Q., & Zhou, R. X. (2000). Portfolio optimization under a minimax rule. Management Science, 46(7), 957–972.

    Article  MATH  Google Scholar 

  • Cui, H. B., Zhao, X. N., & Zhang, L. B. (2004). On the application of a method of measuring security investment risks. Systems Engineering, 22(3), 123–129.

    Google Scholar 

  • Freitas, F. D., De Souza, A. F., & de Almeida, A. R. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10), 2155–2170.

    Article  Google Scholar 

  • Gaivoronski, A. A., & Stella, F. (2003). Online portfolio selection using stochastic program. Journal of Economic Dynamics and Control, 27(6), 1013–1043.

    Article  MathSciNet  MATH  Google Scholar 

  • Huang, X. X. (2008). Mean-entropy models for fuzzy portfolio selection. IEEE Transactions on Fuzzy Systems, 16(4), 1096–1101.

    Article  Google Scholar 

  • Huarng, K. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems, 123(3), 387–394.

    Article  MathSciNet  MATH  Google Scholar 

  • Konno, H., & Yamazaki, H. (1991). Mean-absolute deviation portfolio optimization model and its applications to tokyo stock market. Management Science, 37(5), 519–531.

    Article  Google Scholar 

  • Li, S. T., Cheng, Y. C., & Lin, S. Y. (2008). A fcm-based deterministic forecasting model for fuzzy time series. Computers & Mathematics with Applications, 56(12), 3052–3063.

    Article  MathSciNet  MATH  Google Scholar 

  • Li, P. K., & Liu, B. D. (2008). Entropy of credibility distributions for fuzzy variables. IEEE Transactions on Fuzzy Systems, 16(1), 123–129.

    Article  Google Scholar 

  • Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.

    Google Scholar 

  • Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2, 21–42.

    Google Scholar 

  • Rodder, W., Gartner, I. R., & Rudolph, S. (2010). An entropy-driven expert system shell applied to portfolio selection. Expert Systems with Applications, 37(12), 7509–7520.

    Article  Google Scholar 

  • Singh, P., & Borah, B. (2013). High-order fuzzy-neuro expert system for time series forecasting. Knowledge-Based Systems, 46, 12–21.

    Article  Google Scholar 

  • Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series: part I. Fuzzy sets and Systems, 54(1), 1–9.

    Article  MathSciNet  Google Scholar 

  • Usta, I., & Kantar, Y. M. (2011). Mean-variance-skewness-entropy measures: a multi-objective approach for portfolio selection. Entropy, 13(1), 117–133.

    Article  MATH  Google Scholar 

  • Wu, M., Kong, D. W., Xu, J. P., & Huang, N. J. (2013). On interval portfolio selection problem. Fuzzy Optimization and Decision Making, 12(3), 289–304.

    Article  MathSciNet  Google Scholar 

  • Yu, M., Takahashi, S., Inoue, H., & Wang, S. Y. (2010). Dynamic portfolio optimization with risk control for absolute deviation model. European Journal of Operational Research, 201(2), 349–364.

    Article  MathSciNet  MATH  Google Scholar 

  • Yu, M., Bian, Jz, Xie, H. b, Zhang, Q., & Ralescu, D. (2013). Study on the resampling technique for risk management in the international portfolio selection based on chinese investors. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(01), 35–49.

    Article  MathSciNet  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhou, R. X., Cai, R., & Tong, G. Q. (2013a). Applications of entropy in finance: A review. Entropy, 15(11), 4909–4931.

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China Grant No. 71171012 and No. 70901019, the training plan of science research of undergraduate of Ministry of Education of the People’s Republic of China Grant No. 201310010049, Chinese Universities Scientific Fund of Beijing University of Chemical Technology (No. ZZ1319, ZY1321), the National Science and Technology Support Program (No. 2013BAK04B02) and Taft Travel Grants. This work is also supported by Humanity and Social Science Foundation of Ministry of Education of China (No.14YJA790075), Program for Excellent Talents, UIBE.

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Authors and Affiliations

  1. School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China

    Rongxi Zhou & Zebin Yang

  2. School of Finance and Banking, University of International Business and Economics, Beijing, 100029, China

    Mei Yu

  3. Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, 45221, USA

    Dan A. Ralescu

Authors
  1. Rongxi Zhou
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  2. Zebin Yang
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  3. Mei Yu
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  4. Dan A. Ralescu
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Correspondence to Dan A. Ralescu.

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Zhou, R., Yang, Z., Yu, M. et al. A portfolio optimization model based on information entropy and fuzzy time series. Fuzzy Optim Decis Making 14, 381–397 (2015). https://doi.org/10.1007/s10700-015-9206-8

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