Financial time series forecasting using independent component analysis and artificial neural network

Authors: Chi-Jie Lu, Tian-Shyng Lee, Jen-Lung Kao, Hsueh-Chun Chen
Journal: Chiao Da Management Review. Dec. 2008, 28(2): 187-216.
Keywords: Financial time series forecasting; Independent component analysis; Artificial neural network; Stock index
Abstract:
The characteristics of time series financial data are inherently high frequency, noisy, non-stationary and deterministically chaotic, which render the time series financial forecast extremely challenging. Owing to advantages in building non-parametric and non-linear models, artificial neural networks (ANN) have also been applied to time series predictions, especially for modeling financial time series forecasting. In ANN based financial time series modeling, one of the primary issues is the inherent high noise. It is an important but difficult task to identify and alleviate the noise in order to build a reliable ANN forecasting model. To minimize the influence of noise, we propose to conduct financial time-series forecasting by combining the approaches of both independent component analysis (ICA) and back-propagation neural network (BPN). The combined approach first applies ICA to deduce independent components (ICs) from the forecasting variables. After identifying and removing each IC contained noise, the filtered IC signals are then used to reconstruct forecasting variables and applied to the BPN forecasting model. In order to validate the performance advantage of the proposed approach, the Nikkei 225 opening cash price index has been used as an illustrative example. The experimental results show that the proposed model outperforms in forecasting accuracy the conventional BPN model with non-filtered forecasting variables, the random walk model, the simple moving average model, and the wavelet frame model.