Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Thimann, C.: Global roles of currencies. Int. Finance 11(3), 211–245 (2008)
Mańdziuk, J., Rajkiewicz, P.: Neuro-evolutionary system for FOREX trading. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE (2016)
Giles, C., Lawrence, S., Tsoi, S.: Noisy time series prediction using a recurrent ANN. J. Mach. Learn. 44, 161–183 (2001)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Voß, S., et al. (eds.): Meta-heuristics: Advances and Trends in Local Search Paradigms for Optimization. Springer Science & Business Media, New York (2012)
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)
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)
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)
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)
Galeshchuk, S.: Neural networks performance in exchange rate prediction. Neurocomputing 172, 446–452 (2016)
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)
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)
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)
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)
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)
Patel, J., et al.: Predicting stock market index using fusion of machine learning techniques. Expert Syst. Appl. 42(4), 2162–2172 (2015)
Ponsi, E.: The Ed Ponsi Forex Playbook: Strategies and Trade Set-Ups, vol. 453. Wiley (2010)
Černý, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45(1), 41–51 (1985)
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), 65–74 (2010)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Jamali, H., Bencharef, O., Nabaji, A., El Housni, K., Asebriy, Z. (2018). Improving Regression Models Using Simulated Annealing for Stock Market Speculation. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-77703-0_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-77703-0_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77702-3
Online ISBN: 978-3-319-77703-0
eBook Packages: EngineeringEngineering (R0)