Title: Fuzzy-neural model with hybrid market indicators for stock forecasting

Authors: A.A. Adebiyi, C.K. Ayo, S.O. Otokiti

Addresses: Department of Computer and Information Sciences, Covenant University, P.M.B. 1023, KM 10 Idiroko Road, Ota, Ogun State, Nigeria. ' Department of Computer and Information Sciences, Covenant University, P.M.B. 1023, KM 10 Idiroko Road, Ota, Ogun State, Nigeria. ' Department of Business Studies, Covenant University, P.M.B. 1023, KM 10 Idiroko Road, Ota, Ogun State, Nigeria

Abstract: A number of research had been carried out to forecast stock price based on technical indicators, which rely purely on historical stock price data. Nevertheless, their performance is not always satisfactory. In this paper, the effect of using hybrid market indicators of technical, fundamental indicators and experts opinion for stock price prediction is examined. Input variables extracted from these market hybrid indicators are fed into a fuzzy-neural network for improved accuracy of stock price prediction. The empirical results obtained with published stock data shows that the proposed model can be effective to improve accuracy of stock price prediction.

Keywords: artificial neural networks; ANNs; fuzzy logic; market indicators; stock prediction; electronic finance; stock index; e-finance; modelling; stock forecasting; stock prices.

DOI: 10.1504/IJEF.2011.041342

International Journal of Electronic Finance, 2011 Vol.5 No.3, pp.286 - 297

Available online: 16 Jul 2011 *

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