Fuzzy Based Investment Portfolio Management

  • Mayank PandeyEmail author
  • Vikas Singh
  • Nishchal K. Verma
Part of the Fuzzy Management Methods book series (FMM)


In the present business environment, investment is an essential part carried out at both organizational as well as individual level. To obtain maximum return with minimum risk, investment is done across the diverse areas by maintaining a portfolio. For maximum possible gain, the portfolio needs to be optimally managed which involves selecting the best possible investment opportunities and avoiding the high volatile and high risk stocks. In the recent times, the use of soft computing based methods of fuzzy and neuro-fuzzy models has become increasingly popular for optimal selection and rejection of the portfolio elements to maximize the investor’s profit. A lot of investment and trading is still carried out based on an individual or a group of decision makers’ verbal instructions which are opaque in nature. Thus in this case, the role of fuzzy in transforming these vague sentences into the language and the machines can understand is phenomenal where the traditional mean variance models are lagging. Also, the self-learning feature of the given models are better than the statistical modeling based optimization models. In addition to that, generation of large amount of vague data in today’s digital world have given the fuzzy based methods a decisive edge over the statistical models. The problems related with volatility are dealt with probabilistic fuzzy c-means clustering and functional fuzzy rule based models. Overall, applying fuzzy based systems for portfolio optimization and managing is a novel approach with improved performance.


  1. 1.
    Angelov, P., Xydeas, C., & Filev, D. (2004). On-line identification of MIMO evolving Takagi-Sugeno fuzzy models. In 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542) (Vol. 1, pp. 55–60). Piscataway: IEEE.Google Scholar
  2. 2.
    Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259–1294.CrossRefGoogle Scholar
  3. 3.
    Barbosa, R. P., & Belo, O. (2010). Multi-agent forex trading system. In Agent and multi-agent technology for internet and enterprise systems (pp. 91–118). Berlin: Springer.CrossRefGoogle Scholar
  4. 4.
    Bharadhwaj, H., Singh, V., & Verma, N. K. (2019). Type-2 fuzzy TSK A type-2 fuzzy systems approach for clustering based identification of a T-S regression model. In 2017 International Conference on Computational Intelligence: Theories, Applications and Future Directions (ICCI-2017) (pp. 359–374). Singapore: Springer.Google Scholar
  5. 5.
    Cheng, S. H., Chen, S. M., & Jian, W. S. (2016). Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Information Sciences, 327, 272–287.CrossRefGoogle Scholar
  6. 6.
    Duan, J., & Zeng, J. (2015). Forecasting stock return using multiple information sources based on rules extraction. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 1183–1188). Piscataway: IEEE.CrossRefGoogle Scholar
  7. 7.
    Korczak, J., Hernes, M., & Bac, M. (2015). Fuzzy logic in the multi-agent financial decision support system. In 2015 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 1367–1376). Piscataway: IEEE.CrossRefGoogle Scholar
  8. 8.
    Krishnapuram, R., & Keller, J. M. (1993). A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1(2), 98–110.CrossRefGoogle Scholar
  9. 9.
    Le Caillec, J. M., Itani, A., Gueriot, D., & Rakotondratsimba, Y. (2017). Stock picking by probability–possibility approaches. IEEE Transactions on Fuzzy Systems, 25(2), 333–349.CrossRefGoogle Scholar
  10. 10.
    Li, X., Qin, Z., & Kar, S. (2010). Mean-variance-skewness model for portfolio selection with fuzzy returns. European Journal of Operational Research, 202(1), 239–247.CrossRefGoogle Scholar
  11. 11.
    Lin, C., & Hsieh, P. J. (2004). A fuzzy decision support system for strategic portfolio management. Decision Support Systems, 38(3), 383–398.CrossRefGoogle Scholar
  12. 12.
    Liu, S. Y. W. S., Wang, S. Y., & Qiu, W. (2003). Mean-variance-skewness model for portfolio selection with transaction costs. International Journal of Systems Science, 34(4), 255–262.CrossRefGoogle Scholar
  13. 13.
    Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 1(1), 41–66.CrossRefGoogle Scholar
  14. 14.
    Lumanpauw, E., Pasquier, M., & Quek, C. (2007). MNFS-FPM: A novel memetic neuro-fuzzy system based financial portfolio management. In 2007 IEEE Congress on Evolutionary Computation (pp. 2554–2561). Piscataway: IEEE.CrossRefGoogle Scholar
  15. 15.
    Maciel, L., Ballini, R., & Gomide, F. (2017). Evolving possibilistic fuzzy modeling for realized volatility forecasting with jumps. IEEE Transactions on Fuzzy Systems, 25(2), 302–314.CrossRefGoogle Scholar
  16. 16.
    Maciel, L., Gomide, F., & Ballini, R. (2012). MIMO evolving functional fuzzy models for interest rate forecasting. In IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (pp. 1–8). Piscataway: IEEE.Google Scholar
  17. 17.
    Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.Google Scholar
  18. 18.
    Pal, N. R., Pal, K., Keller, J. M., & Bezdek, J. C. (2005). A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 13(4), 517–530.CrossRefGoogle Scholar
  19. 19.
    Rajurkar, S., & Verma, N. K. (2017). Developing deep fuzzy network with Takagi Sugeno fuzzy inference system. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1–6). Piscataway: IEEE.Google Scholar
  20. 20.
    Refenes, A. P., Burgess, A. N., & Bentz, Y. (1997). Neural networks in financial engineering: A study in methodology. IEEE Transactions on Neural Networks, 8(6), 1222–1267.CrossRefGoogle Scholar
  21. 21.
    Sevakula, R. K., & Verma, N. K. (2015). Fuzzy rule reduction using sparse auto-encoders. In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1–7). Piscataway: IEEE.Google Scholar
  22. 22.
    Sevakula, R. K., & Verma, N. K. (2017). Compounding general purpose membership functions for fuzzy support vector machine under noisy environment. IEEE Transactions on Fuzzy Systems, 25(6), 1446–1459.CrossRefGoogle Scholar
  23. 23.
    Singh, D. J., Agrawal, P., Verma, N. K., Ghosh, A. K., & Malagaudanavar, A. (2018). Interval type-2 TS fuzzy model for angle of attack sensor of the aircraft. Journal of Intelligent and Fuzzy Systems, 34(6), 3891–3901.CrossRefGoogle Scholar
  24. 24.
    Singh, V., Dev, R., Dhar, N. K., Agrawal, P., & Verma, N. K. (2018). Adaptive type-2 fuzzy approach for filtering salt and pepper noise in grayscale images. In IEEE Transactions on Fuzzy Systems, 26(5), 3170–3176.CrossRefGoogle Scholar
  25. 25.
    Singh, V., Shambhav, A., Sevakula, R. K., Verma, N. K. (2016). A new approach for splitting datasets implemented using map reduce based fuzzy c-means clustering. In Fourth International Conference on Business Analytics and Intelligence (ICBAI). Bangalore: IISc.Google Scholar
  26. 26.
    Singh, V., & Verma, N. K. (2018). Deep learning architecture for high-level feature generation using stacked auto encoder for business intelligence. In Complex Systems: Solutions and Challenges in Economics, Management and Engineering (pp. 269–283). Cham: Springer.CrossRefGoogle Scholar
  27. 27.
    Song, Q., & Chissom, B. S. (1993). Fuzzy time series and its models. Fuzzy Sets and Systems, 54(3), 269–277.CrossRefGoogle Scholar
  28. 28.
    Tung, W. L., & Quek, C. (2002). GenSoFNN: A generic self-organizing fuzzy neural network. IEEE Transactions on Neural Networks, 13(5), 1075–1086.CrossRefGoogle Scholar
  29. 29.
    Vercher, E., Bermúdez, J. D., & Segura, J. V. (2007). Fuzzy portfolio optimization under downside risk measures. Fuzzy Sets and Systems, 158(7), 769–782.CrossRefGoogle Scholar
  30. 30.
    Verma, N. K. (2012). Estimation of fuzzy measures using covariance matrices in Gaussian mixtures. Applied Computational Intelligence and Soft Computing, 2012, 402420. CrossRefGoogle Scholar
  31. 31.
    Verma, N. K., & Abhishek R. (2014). Self-optimal clustering technique using optimized threshold function. IEEE Systems Journal, 8(4), 1213–1226.CrossRefGoogle Scholar
  32. 32.
    Verma, N. K., & Madasu H. (2010). Additive and non-additive fuzzy hidden Markov models. IEEE Transactions on Fuzzy Systems, 18(1), 40–56.CrossRefGoogle Scholar
  33. 33.
    Verma, N. K., & Madasu, H. (2010). Adaptive non-additive generalized fuzzy systems. Applied Soft Computing, 10(3), 820–831.CrossRefGoogle Scholar
  34. 34.
    Yoshida, Y. (2017). Portfolios optimization with coherent risk measures in fuzzy asset management. In 2017 5th International Symposium on Computational and Business Intelligence (ISCBI). Piscataway: IEEE.Google Scholar
  35. 35.
    Yu, J. R., Chiou, W. J. P., Chang, W. Y., & Lee, W. Y. (2013). Modeling transaction costs and skewness in portfolio: Application of fuzzy approach. In 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY) (pp. 397–401). Piscataway: IEEE.CrossRefGoogle Scholar
  36. 36.
    Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers (pp. 394–432). Singapore: World Scientific.CrossRefGoogle Scholar
  37. 37.
    Zhou, X. S., & Dong, M. (2004). Can fuzzy logic make technical analysis 20/20? Financial Analysts Journal, 60(4), 54–75.CrossRefGoogle Scholar
  38. 38.
    Zulkifli, M., Daud, M., & Omar, S. (2010). Maximizing portfolio diversification benefit via extended mean-variance model. In 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA) (pp. 675–680). Piscataway: IEEE.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mayank Pandey
    • 1
    Email author
  • Vikas Singh
    • 1
  • Nishchal K. Verma
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of Technology KanpurKanpurIndia

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