A hybrid CRO-based FLANN for financial stock market forecasting Online publication date: Mon, 12-Sep-2016
by Soumya Ranjan Sahu; Himansu Sekhar Behera
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 8, No. 3, 2016
Abstract: Stock market prediction has been a challenging area of research due to the dynamically changing stock market values. Many research works have been carried out to predict the stock market using different kinds of neural network model and different learning and optimisation technique. In this work a trigonometric FLANN model has been proposed for the forecasting of four different stock indices; BSE-S&P100, BSE-S&P500, NASDAQ and S&P500. A hybridised learning algorithm of least mean square (LMS) and chemical reaction optimisation (CRO) is used to train the model. Root mean square error (RMSE) is considered as the cost function for training phase and during the testing phase means average percentage error (MAPE) is used to evaluate the models. The results obtained are compared with that of the FLANN-LMS model. The experimental result shows that FLANN-CRO-LMS model performs significantly better than FLANN-LMS model.
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