Improving Regression Models Using Simulated Annealing for Stock Market Speculation

  • Hana Jamali
  • Omar Bencharef
  • Abdellah Nabaji
  • Khalid El Housni
  • Zahra Asebriy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)


The Forex aims at exchanging the so-called convertible currencies from one specific currency to another worldwide. The currency exchange rates can be increased or reduced according to time, between various participants (particular investors, central banks and enterprises). The main pillar of the Forex market is the temporal prediction of the currency exchange rate; it must be well-forecasted to invest in currencies and to maximize profits which will make the speculation more flexible. In the literature, many papers talk about the combination of two methods to improve the prediction of currency exchange. In this paper we propose a hybrid model which is combining both the regression algorithm and the simulated annealing algorithm in order to predict the daily exchange rates of the USD/EUR pair. Finally, the experiments validate that the Hybrid model of the regression algorithm and the simulated annealing algorithm can be beneficial for the prediction of exchange rates.


Forex Speculation Prediction Forecasting Regression algorithm Simulated annealing Optimization Hybrid model 


  1. 1.
    Thimann, C.: Global roles of currencies. Int. Finance 11(3), 211–245 (2008)CrossRefGoogle Scholar
  2. 2.
    Mańdziuk, J., Rajkiewicz, P.: Neuro-evolutionary system for FOREX trading. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE (2016)Google Scholar
  3. 3.
    Giles, C., Lawrence, S., Tsoi, S.: Noisy time series prediction using a recurrent ANN. J. Mach. Learn. 44, 161–183 (2001)CrossRefGoogle Scholar
  4. 4.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefGoogle Scholar
  5. 5.
  6. 6.
    Voß, S., et al. (eds.): Meta-heuristics: Advances and Trends in Local Search Paradigms for Optimization. Springer Science & Business Media, New York (2012)Google Scholar
  7. 7.
    Evans, C., Pappas, K., Xhafa, F.: Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation. Math. Comput. Model. 58(5), 1249–1266 (2013)CrossRefGoogle Scholar
  8. 8.
    Rehman, M., Khan, G.M., Mahmud, S.A.: Foreign currency exchange rates prediction using CGP and recurrent neural network. IERI Procedia 10, 239–244 (2014)CrossRefGoogle Scholar
  9. 9.
    Adhikari, R., Agrawal, R.K.: A combination of artificial neural network and random walk models for financial time series forecasting. Neural Comput. Appl. 24(6), 1441–1449 (2014)CrossRefGoogle Scholar
  10. 10.
    Deng, S., et al.: Hybrid method of multiple kernel learning and genetic algorithm for forecasting short-term foreign exchange rates. Comput. Econ. 45(1), 49–89 (2015)CrossRefGoogle Scholar
  11. 11.
    Galeshchuk, S.: Neural networks performance in exchange rate prediction. Neurocomputing 172, 446–452 (2016)CrossRefGoogle Scholar
  12. 12.
    Czekalski, P., Niezabitowski, M., Styblinski, R.: ANN for FOREX forecasting and trading. In: 2015 20th International Conference on Control Systems and Computer Science (CSCS). IEEE (2015)Google Scholar
  13. 13.
    Yong, Y.L., Ngo, D.C., Lee, Y.: Technical indicators for forex forecasting: a preliminary study. In: International Conference in Swarm Intelligence. Springer, Cham (2015)CrossRefGoogle Scholar
  14. 14.
    Özorhan, M.O., Toroslu, İ.H., Şehitoğlu, O.T.: A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms. Soft Comput., pp. 1–19 (2016)Google Scholar
  15. 15.
    Gonzalez, R.T., Padilha, C.A., Barone, D.A.C.: Ensemble system based on genetic algorithm for stock market forecasting. In: 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE (2015)Google Scholar
  16. 16.
    Ozturk, M., Toroslu, I.H., Fidan, G.: Heuristic based trading system on Forex data using technical indicator rules. Appl. Soft Comput. 43, 170–186 (2016)CrossRefGoogle Scholar
  17. 17.
    Patel, J., et al.: Predicting stock market index using fusion of machine learning techniques. Expert Syst. Appl. 42(4), 2162–2172 (2015)CrossRefGoogle Scholar
  18. 18.
  19. 19.
  20. 20.
    Ponsi, E.: The Ed Ponsi Forex Playbook: Strategies and Trade Set-Ups, vol. 453. Wiley (2010)Google Scholar
  21. 21.
    Černý, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45(1), 41–51 (1985)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), 65–74 (2010)CrossRefGoogle Scholar
  23. 23.
    Lino, A., Rocha, A., Sizo, A.: Virtual teaching and learning environments: automatic evaluation with symbolic regression. J. Intell. Fuzzy Syst. 31(4), 2061–2072 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hana Jamali
    • 1
  • Omar Bencharef
    • 1
  • Abdellah Nabaji
    • 1
  • Khalid El Housni
    • 1
  • Zahra Asebriy
    • 1
  1. 1.Superior School of TechnologyCadi Ayyad UniversityEssaouiraMorocco

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