A Combination of Regression Techniques and Cuckoo Search Algorithm for FOREX Speculation

  • Said Achchab
  • Omar BencharefEmail author
  • Aziz Ouaarab
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 569)


This paper describes a hybrid model formed by a mixture of regression techniques and Cuckoo Search algorithm to speculate USD/EUR variations. Inspired by ARMA model we propose a dataset composed of historical data of USD/EUR and (JYN, EUR and BRP) variations. The dataset is used to train four regression algorithms: Multiple linear regression, Support vector regression, Partial Least Squares regression and CRT regression tree; the generated regression weights of these algorithms will be used as inputs to Cuckoo Search algorithm. The effectiveness of the proposed system against classical regression algorithms is confirmed by experiments on exchange rate prediction within the period from January 2014 to January 2016.


FOREX speculation Cuckoo Search Regression 


  1. 1.
    Alkhasawneh, M.S., Ngah, U.K., Tay, L.T., Mat Isa, N.A., Al-Batah, M.S.: Modeling and testing landslide hazard using decision tree. J. Appl. Math. 2014 (2014)Google Scholar
  2. 2.
    Berutich, J.M., López, F., Luna, F., Quintana, D.: Robust technical trading strategies using gp for algorithmic portfolio selection. Expert Syst. Appl. 46, 307–315 (2016)CrossRefGoogle Scholar
  3. 3.
    de Brito, R.F., Oliveira, A.L.: Comparative study of Forex trading systems built with svr+ ghsom and genetic algorithms optimization of technical indicators. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, vol. 1, pp. 351–358. IEEE (2012)Google Scholar
  4. 4.
    Brown, C.T., Liebovitch, L.S., Glendon, R.: Lévy flights in dobe ju/hoansi foraging patterns. Hum. Ecol. 35(1), 129–138 (2007)CrossRefGoogle Scholar
  5. 5.
    De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)CrossRefGoogle Scholar
  6. 6.
    Deng, S., Yoshiyama, K., Mitsubuchi, T., Sakurai, A.: 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
  7. 7.
    BfiSMe Department: Foreign exchange turnover in April 2013. Preliminary global results (2013)Google Scholar
  8. 8.
    Erdemlioglu, D., Laurent, S., Neely, C.J.: Econometric modeling of exchange rate volatility and jumps. Federal Reserve Bank of St. Louis Working Paper No (2012)Google Scholar
  9. 9.
    Hochba, D.S.: Approximation algorithms for np-hard problems. ACM SIGACT News 28(2), 40–52 (1997)CrossRefGoogle Scholar
  10. 10.
    Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E., Liu, M.: Application of evolutionary computation for rule discovery in stock algorithmic trading: a literature review. Appl. Soft Comput. 36, 534–551 (2015)CrossRefGoogle Scholar
  11. 11.
    Huang, S.J., Shih, K.R.: Short-term load forecasting via arma model identification including non-gaussian process considerations. IEEE Trans. Power Syst. 18(2), 673–679 (2003)CrossRefGoogle Scholar
  12. 12.
    Kumar, A., Agrawal, D.P., Joshi, S.D.: Study of Canada/US dollar exchange rate movements using recurrent neural network model of FX-market. In: R. Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 409–417. Springer, Heidelberg (2003). doi: 10.1007/978-3-540-45231-7_38 CrossRefGoogle Scholar
  13. 13.
    Lam, K., Lam, K.C.: Forecasting for the generation of trading signals in financial markets. J. Forecast. 19(1), 39–52 (2000)CrossRefGoogle Scholar
  14. 14.
    Lavanya, V., Parveentaj, M.: Foreign currency exchange rate (forex) using neural network. Int. J. Sci. Res. 2(10), 174–177 (2013)Google Scholar
  15. 15.
    Li, X., Yin, M.: A hybrid cuckoo search via lévy flights for the permutation flow shop scheduling problem. Int. J. Prod. Res. 51(16), 4732–4754 (2013)CrossRefGoogle Scholar
  16. 16.
    Miozzo, M., Pulvermüller, F., Hauk, O.: Early parallel activation of semantics and phonology in picture naming: evidence from a multiple linear regression meg study. Cereb. Cortex 25(10), 3343–3355 (2015)CrossRefGoogle Scholar
  17. 17.
    Ni, H., Yin, H.: Exchange rate prediction using hybrid neural networks and trading indicators. Neurocomputing 72(13), 2815–2823 (2009)CrossRefGoogle Scholar
  18. 18.
    Ouaarab, A., Ahiod, B., Yang, X.-S.: Improved and discrete Cuckoo Search for solving the travelling salesman problem. In: Yang, X.-S. (ed.) Cuckoo Search and Firefly Algorithm. SCI, vol. 516, pp. 63–84. Springer, Cham (2014). doi: 10.1007/978-3-319-02141-6_4 CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Pang, S., Song, L., Kasabov, N.: Correlation-aided support vector regression for forex time series prediction. Neural Comput. Appl. 20(8), 1193–1203 (2011)CrossRefGoogle Scholar
  21. 21.
    Paz, J.: The Forex Trading Manual: The Rules-Based Approach to Making Money Trading Currencies. McGraw Hill Professional, New York (2012)Google Scholar
  22. 22.
    Quinlan, J.R.: Simplifying decision trees. Int. J. Man Mach. Stud. 27(3), 221–234 (1987)CrossRefGoogle Scholar
  23. 23.
    Rao, T., Srivastava, S.: Modeling movements in oil, gold, forex and market indices using search volume index and twitter sentiments. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 336–345. ACM (2013)Google Scholar
  24. 24.
    Rosenstreich, P.: Forex Revolution: An Insider’s Guide to the Real World of Foreign Exchange Trading. FT Press, Upper Saddle River (2005)Google Scholar
  25. 25.
    Shi, Y., Shu, Z., Sun, W., Yang, Q., Yu, Y., Yang, G., Wu, W., Chen, S., Huang, W., Wang, T., et al.: Risk stratification of de-compensated cirrhosis patients by the clif consortium scores: a classification and regression tree analysis. Hepatol. Res. (2016)Google Scholar
  26. 26.
    Tiong, L.C., Ngo, D.C., Lee, Y.: Forex trading prediction using linear regression line, artificial neural network and dynamic time warping algorithms. In: Proceedings of Fourth International Conference Computing Informatics, pp. 71–77 (2013)Google Scholar
  27. 27.
    Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O’donnell, J., Rowe, C.M.: Statistics for the evaluation and comparison of models (1985)Google Scholar
  28. 28.
    Yang, X.S., Deb, S.: Cuckoo search via lévy flights. In: World Congress on Nature & Biologically Inspired Computing 2009, NaBIC 2009, pp. 210–214. IEEE (2009)Google Scholar
  29. 29.
    Zhang, Q., Zhang, Q., Sornette, D.: Early warning signals of financial crises with multi-scale quantile regressions of log-periodic power law singularities. Swiss Finance Institute Research Paper No. 15–43 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ENSIASMohammed V University of RabatRabatMorocco
  2. 2.Higher School of Technology, EssaouiraCadi Ayyad UniversitMarrakeshMorocco
  3. 3.LRIT, CNRST (URAC 29)Mohammed V University of RabatRabatMorocco

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