Financial time series forecasting using Simulated Annealing and Support Vector Regression

  • Juan Javier González-Mancha Tecnológico Nacional de México: Instituto Tecnológico de Ciudad Madero, México.
  • Juan Frausto-Solís Tecnológico Nacional de México: Instituto Tecnológico de Ciudad Madero, México.
  • Guadalupe Castilla Valdez Tecnológico Nacional de México: Instituto Tecnológico de Ciudad Madero, México.
  • Jesús David Terán-Villanueva Tecnológico Nacional de México: Instituto Tecnológico de Ciudad Madero, México.
  • Juan Javier González Barbosa Tecnológico Nacional de México: Instituto Tecnológico de Ciudad Madero, México.
Keywords: Simulated Annealing, Financial Portfolios, Parallel Algorithm, Financial Forecast

Abstract

The behavior of stock exchanges around the world has an important role in the financial development of countries. The Mexican Stock Exchange (BMV, for its acronym in Spanish) is the financial entity in Mexico where investors build and manage investment portfolios to generate profit. Financial time series forecasting is an important problem for investment portfolios creation and optimization because it allows them to have a prediction of the value of investment assets over time, therefore reducing at some extent the uncertainty of these operations. Classical models, such as ARIMA, artificial neural networks and support vector machines, are usually used as forecasting tools. In this paper, we forecast financial time series of the BMV using a hybrid parallel algorithm that employs both simulated annealing (SA) and support vector machines for regression (SVR). Finally, the resulting Mean Absolute Percent Error (MAPE) of this hybrid algorithm is compared with that of an ARIMA model.

Published
2018-01-09
How to Cite
González-Mancha, J. J., Frausto-Solís, J., Castilla Valdez, G., Terán-Villanueva, J. D., & González Barbosa, J. J. (2018). Financial time series forecasting using Simulated Annealing and Support Vector Regression. International Journal of Combinatorial Optimization Problems and Informatics, 8(2), 10-18. Retrieved from https://ijcopi.org/ojs/article/view/9
Section
Articles