Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting

• Main Authors: Abdulkadir, Said Jadid, Yong, Suet-Peng, Marimuthu, Maran, Lai, Fong Woon
• Format: Citation Index Journal
• Published: 2014
• Subjects: HG Finance
• Online Access: http://eprints.utp.edu.my/12067/1/Hybridization%20on%20Ensemble%20Kalman%20Filter%20and%20Non-Linear%20Auto-Regressive%20Neural%20Network%20for%20Financial%20Forecasting.pdf; http://eprints.utp.edu.my/12067/

Financial data is characterized as non-linear, chaotic in nature and volatile thus making the process of forecasting cumbersome. Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. The proposed hybrid model is compared with commonly used Elman-NARX and static forecasting model employed by financial analysts. Experimental results on Bursa Malaysia KLCI data show that the proposed hybrid model outperforms the other two commonly used models.