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A hybrid approach to forecast stock market index
by Gurbinder Kaur; Joydip Dhar; Rangan K. Guha
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 5, No. 2, 2015
Abstract: The forecasting of stock market problem from the available data is quite often of uncertain nature, hence the stock market prediction is a very challenging and difficult task. In this paper, we have investigated the predictability of stock market of Bombay Stock Exchange (BSE30), Hang Sang China Stock Index (HS), Japan Stock Index (NIKKEI) and Taiwan Weighted Index (TWI) with adaptive network-based fuzzy inference system (ANFIS) combined with subtractive clustering technique. In this process, we compared stock markets with variable numbers of data clusters. Optimised subtractive clustering is used to cluster the data and create fuzzy membership functions. Finally, a hybrid learning algorithm has been used to combine least square method and back propagation gradient-decent method for training the fuzzy inference system. This paper represents a state of the art for ANFIS application to forecast stock market index.
Online publication date: Wed, 15-Jul-2015
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