Skip to main content
SpringerLink
Log in

Investor-friendly and robust portfolio selection model integrating forecasts for financial tendency and risk-averse

  • RAOTA-2016
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

This paper proposes an investor-friendly random fuzzy portfolio selection model considering both robustness and adjustment of future asset returns derived from investor’s forecasts for financial tendency using a fuzzy inference method. It is important to predict the price or the return of each asset appropriately considering current market trends in portfolio optimization. In this paper, a standard multi-factor model, namely Arbitrage Pricing Theory (APT), is introduced as an asset pricing model. In addition, in order to extend standard APT by integrating important rules of current markets trends derived from technical analysis and fundamental analysis, each factor of APT is assumed to be a random fuzzy variable whose mean is adjusted by the fuzzy reasoning method, particularly product–sum-gravity method. Furthermore, it is also important for the investor to reduce the worst case of the total loss in terms of risk-averse. Therefore, worst-case conditional Value-at-Risk, which is a robust programming approach without assuming some specific random distribution, is considered. Since the proposed model is formulated as a biobjective programming problem both minimizing the value of worst-case conditional Value-at-Risk and maximizing the total expected return, it is equivalently transformed into the deterministic nonlinear programming problem using the satisficing trade-off method, and the efficient algorithm to obtain the optimal portfolio is developed. By solving our proposed model, the investor can obtain the risk-averse optimal portfolio with the large total return complying with current market trends.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82.

    Article  Google Scholar 

  • Elton, E. J., & Gruber, M. J. (1995). Modern Portfolio Theory and Investment Analysis. New York: Wiley.

    Google Scholar 

  • Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.

    Article  Google Scholar 

  • Hasuike, T., & Katagiri, H. (2013). Robust-based interactive portfolio selection problems with an uncertainty set of returns. Fuzzy Optimization and Decision Making, 12(3), 263–288.

    Article  Google Scholar 

  • Hasuike, T., Katagiri, H., & Ishii, H. (2009). Multiobjective random fuzzy linear programming problems based on the possibility maximization model. Journal of Advanced Computational Intelligence and Intelligent Informatics, 13(4), 373–379.

    Article  Google Scholar 

  • Hasuike, T., Katagiri, H., & Ishii, H. (2009). Portfolio selection problems with random fuzzy variable returns. Fuzzy Sets and Systems, 160, 2579–2596.

    Article  Google Scholar 

  • Huang, X. (2007). Two new models for portfolio selection with stochastic returns taking fuzzy information. European Journal of Operational Research, 180, 396–405.

    Article  Google Scholar 

  • Inuiguchi, M., & Ramik, J. (2000). Possibilisitc linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111, 3–28.

    Article  Google Scholar 

  • Katagiri, H., Ishii, H., & Sakawa, M. (2004). On fuzzy random linear knapsack problems. Central European Journal of Operational Research, 12, 59–70.

    Google Scholar 

  • Katagiri, H., Sakawa, M., & Ishii, H. (2005). A study on fuzzy random portfolio selection problems using possibility and necessity measures. Scientiae Mathematicae Japonicae, 65, 361–369.

    Google Scholar 

  • Konno, H., Shirakawa, H., & Yamazaki, H. (1993). A mean-absolute deviation-skewness portfolio optimization model. Annals of Operations Research, 45, 205–220.

    Article  Google Scholar 

  • Konno, H., & Yamazaki, H. (1991). Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market. Management Science, 37, 519–531.

    Article  Google Scholar 

  • Leon, R. T., Liern, V., & Vercher, E. (2002). Validity of infeasible portfolio selection problems: A fuzzy approach. European Journal of Operational Research, 139(1), 178–189.

    Article  Google Scholar 

  • Lintner, B. J. (1965). Valuation of risky assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13–37.

    Article  Google Scholar 

  • Liu, B. (2002). Theory and Practice of Uncertain Programming. Heidelberg: Physica Verlag.

    Book  Google Scholar 

  • Luenberger, D. G. (1997). Investment Science. Oxford: Oxford Univ Press.

    Google Scholar 

  • Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 121(12), 1585–1588.

    Article  Google Scholar 

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

    Google Scholar 

  • Mizumoto, M. (1996) “Product-sum-gravity method=fuzzy singleton-type reasoning method=simplified fuzzy reasoning method”, In: Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, pp. 2098–2102.

  • Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783.

    Article  Google Scholar 

  • Nakayama, H., & Sawaragi, Y. (1984). Satisficing trade-off method for multiobjective programming. Lecture Notes in Economics and Mathematical Systems, 229, 113–122.

    Article  Google Scholar 

  • Nakayama, H., Yun, Y., & Yoon, M. (2009). Interactive programming methods for multiobjective optimization. In Sequential approximate multiobjective optimization using computational intelligence (pp. 17–43). Berlin: Springer.

  • Naved, M., & Srivastava, P. (2015). The profitability of five popular variations of moving averages on Indian market Index S&P CNX Nifty 50 during January 2004-December 2014. Advances in Economics and Business Management, 2(1), 27–32.

    Google Scholar 

  • Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3), 1–21.

    Article  Google Scholar 

  • Ross, S. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360.

    Article  Google Scholar 

  • Sharpe, W. F. (1964). Capital asset prices: A theory of market equivalent under conditions of risk. The Journal of Finance, 19(3), 425–442.

    Google Scholar 

  • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transaction on Systems, Man, and Cybernetics, 15(1), 116–132.

    Article  Google Scholar 

  • Tanaka, H., & Guo, P. (1999). Portfolio selection based on upper and lower exponential possibility distributions. European Journal of Operational Research, 114, 115–126.

    Article  Google Scholar 

  • Tanaka, H., Guo, P., & Turksen, I. B. (2000). Portfolio selection based on fuzzy probabilities and possibility distributions. Fuzzy Sets and Systems, 111, 387–397.

    Article  Google Scholar 

  • Tang, L., & Ling, A. (2014). A closed-form solution for robust portfolio selection with worst-case cVaR risk measure. Mathematical Problems in Engineering, 2014. Article ID, 494575.

  • Vercher, E., Bermúdez, J. D., & Segura, J. V. (2007). Fuzzy portfolio optimization under downside risk measures. Fuzzy Sets and Systems, 158, 769–782.

    Article  Google Scholar 

  • Watada, J. (1997). Fuzzy portfolio selection and its applications to decision making. Tatra Mountains Mathematical Publications, 13, 219–248.

    Google Scholar 

  • Yu, H., Nartea, G. V., Gan, C., & Yao, L. J. (2013). Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets. International Review of Economics & Finance, 25, 356–371.

    Article  Google Scholar 

  • Zhu, S., & Fukushima, M. (2009). Worst-case conditional value-at-risk with application to robust portfolio management. Operations Research, 57(5), 1155–1168.

    Article  Google Scholar 

Download references

Acknowledgements

Authors would like to thank the Editor and the anonymous reviewers for their valuable comments and detailed suggestions that have improved the presentation of this paper. The second author also acknowledges the support through UGC-BSR Research Start-Up-Grant F.30- 83/2014 (BSR) from the University Grants Commission, New Delhi, India.

Author information

Authors and Affiliations

  1. Faculty of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan

    Takashi Hasuike

  2. Department of Operational Research, University of Delhi, Delhi, India

    Mukesh Kumar Mehlawat

Authors
  1. Takashi Hasuike
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukesh Kumar Mehlawat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takashi Hasuike.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hasuike, T., Mehlawat, M.K. Investor-friendly and robust portfolio selection model integrating forecasts for financial tendency and risk-averse. Ann Oper Res 269, 205–221 (2018). https://doi.org/10.1007/s10479-017-2458-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-017-2458-7

Keywords

Search

Navigation