Enhancing Stock Prices Forecasting System Outputs Through Genetic Algorithms Refinement of Rules-Lists

  • Abraham Ayegba Alfa
  • Ibraheem Olatunji Yusuf
  • Sanjay MisraEmail author
  • Ravin Ahuja
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 121)


The intent of stock market was to amass capital in an economy and distribute of same to high-yielding return ventures. Recently, stock markets are considered the foremost meeting point of information such as macroeconomic, national and investor. There are significant mechanisms for measuring future progress in the economy and the markets. Studies have revealed that fuzzy logic control (FLC)-based forecasting models rely on the composition of rules-lists, which are often redundant due to poor mapping of their antecedents and conditions to the consequents. This paper introduced a process of refining the rules-lists with the use of genetic algorithm. A refined rules-list was constructed for FLC rules base after the removal of inherent redundancy. To evaluate the proposed enhanced FLC model, the inputs and output variables were opening, highest and closing prices of Dangote Cement Company Shares, respectively. The outcomes showed that rules-lists of the enhanced FLC were shortened to five (5) rules as against the nine (9) rules in the human expert system. Also, the forecasts of enhanced FLC constructed with refined rules-lists were better than those FLC built with human expert system on the basis of mean square error (MSE) and mean absolute percentage error (MAPE) calculated. In the case of MSE, forecasts improved from 24.898% to 75.102%. Similarly, MAPE forecasts accuracy improved from 32.424% to 67.576% for the enhanced FLC against FLC.


FLC Rules-Lists Rule base Enhanced FLC Genetic algorithm Refine Forecast 



We would like to acknowledge the sponsorship and support provided by Covenant University through the Centre for Research, Innovation and Discovery (CUCRID).


  1. 1.
    Sun, C.: Stock market returns predictability: does volatility matter? Unpublished Ph.D. Thesis, University of Nottingham, Nottingham, United Kingdom, pp. 1–180 (2008)Google Scholar
  2. 2.
    Feng, H.M., Chou, H.C.: Evolutionary fuzzy stock prediction system design and its application to the Taiwan stock index. Int. J. Innov. Comput. Inf. Control. 8(9), 6173–6190 (2012)Google Scholar
  3. 3.
    Karlsson, M., Orselius, H.: Economic and business cycle indicator: accuracy, reliability and consistency of swedish Indicators. Unpublished Masters’ Thesis, Department of Business Administration, Jonkoping University, Sweden, pp. 1–64 (2014)Google Scholar
  4. 4.
    Taylor, S.J.: Modeling Financial Time Series. No. 2nd, New York: World Scientific Publishing (2008)Google Scholar
  5. 5.
    Jabbari, E., Falhi, Z.: Prediction of stock returns using financial ratios based on historical cost, compared with adjusted prices (accounting for inflation) with neural network approach. Indian J. Fundam. Appl. Life Sci. 4(4), 2231–6345 (2014)Google Scholar
  6. 6.
    Naik, R.L., Manjula, B., Ramesh, D., Murthy, B.S., Sarma, S.S.V.N.: Prediction of stock market index using neural networks: an empirical study of BSE. Eur. J. Bus. Manag. 4(12), 60–71 (2012)Google Scholar
  7. 7.
    MATLab: Fuzzy Logic Toolbox; User’s Guide. R2013a: The MathWorks, Inc. (2013)Google Scholar
  8. 8.
    Kayacan, E.: Interval type-2 fuzzy logic systems: theory and design. Unpublished Ph.D. Thesis, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey, pp. 1–149 (2011)Google Scholar
  9. 9.
    Chen, C., Li, M., Sui, J., Wei, K., Pei, Q.: A genetic algorithm-optimized fuzzy logic controller to avoid rear-end collisions. J. Adv Transp. vol. 50, no. 8, pp. 1735-1753 (2016)Google Scholar
  10. 10.
    Tahmasebi, P., Hezarkhani, A.: A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Elsevier J. Comput. Geosci. 42, 18–27 (2012)CrossRefGoogle Scholar
  11. 11.
    Delnavaz, B.: Forecasting of the stock price index by using fuzzy-neural network and genetic algorithms. J. Appl. Sci. Agric. 9(9), 109–117 (2014)Google Scholar
  12. 12.
    Yefimochkin, O.: Fundamental: using macroeconomic indicators and genetic algorithms in stock market forecasting. Unpublished Master’s Thesis, Department of Computer Engineering, The Technical University of Lisbon, Portugal, pp. 1–120 (2011)Google Scholar
  13. 13.
    Hadavandi, E., Shavandi, H., Ghanbari, A.A.: Genetic fuzzy expert system for stock price forecasting. In: Proceedings of 7th IEEE International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, China, pp. 41–44 (2010)Google Scholar
  14. 14.
    Acheme, D.J., Vincent, O.R., Folorunso, O., Olusola, O.I.: A predictive stock market technical analysis using fuzzy logic. Comput. Inf. Sci. 7(3), 1–17 (2014)Google Scholar
  15. 15.
    Akinwale, A.T., Arogundade, O.T., Adekoya, A.F.: Translated Nigeria stock market prices using artificial neural network for effective prediction. J. Theor. Appl. Inf. Technol. 1, 36–43 (2009)Google Scholar
  16. 16.
    Floreano, D., Mattiussi, C.: Bio-inspired artificial intelligence. The MIT Press, Cambridge, Massachusetts (2008)Google Scholar
  17. 17.
    Larsen, J.I.: Predicting stock prices using technical analysis and machine learning. Unpublished M.Sc. Thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway, pp. 1–82 (2010)Google Scholar
  18. 18.
    Wiecek, P.: Intelligent approach to inventory control in logistics under uncertainty conditions. In: Elsevier 12th Conference on Transport Engineering, vol. 18, pp. 164–171 (2016)Google Scholar
  19. 19.
    Alhassan, J., Misra, S.: Using a weightless neural network to forecast stock prices: a case study of Nigerian stock exchange. Sci. Res. Essay 6(14), 2934–2940 (2011)Google Scholar
  20. 20.
    Alhassan, J.K., Misra, S., Ogwueleka, F., Inyiama, H.C.: Forecasting Nigeria foreign exchange using artificial neural network. J. Sci. Technol. Math. Educ. 9(1), 47–56 (2012)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Abraham Ayegba Alfa
    • 1
  • Ibraheem Olatunji Yusuf
    • 2
  • Sanjay Misra
    • 3
    Email author
  • Ravin Ahuja
    • 4
  1. 1.Kogi State College of EducationAnkpaNigeria
  2. 2.Federal University of TechnologyMinnaNigeria
  3. 3.Covenant UniversityOttaNigeria
  4. 4.Vishvakarma Skill UniversityGurgaonIndia

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