Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices

S.C. Nayak, B.B. Misra, H.S. Behera
2017 Ain Shams Engineering Journal  
The underlying system models of time series prediction are complex and not known a priori, hence, accurate and unbiased estimation cannot be always achieved using well known linear techniques. The estimation process requires more advanced prediction algorithms, such as multilayer perceptrons (MLPs). This paper presents an artificial chemical reaction neural network (ACRNN), which uses artificial chemical reaction optimization (ACRO) to train the MLP models for forecasting the stock market
more » ... s. The underlying motivation for using ACRO is the ability to overcome the issues of convergence, parameter setting and overfitting and to accurately forecast financial time series data even when the underlying system processes are typically nonlinear. Historical data of seven different stock indices have been collected for 15 years to test the performance of the ACRNN approach. After extensive experimentation, it is observed that the ACRNN technique demonstrates significant improvements in prediction accuracy over the MLP approach. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Nayak SC et al., Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices, Ain Shams Eng J (2015), http://dx.
doi:10.1016/j.asej.2015.07.015 fatcat:6vfz4cj6xbg2lj2dwqjqt37cba