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O. Castillo and P. Melin, “Simulation and Forecasting Complex Financial Time Series Using Neural Networks and Fuzzy Logic,” IEEE International Conference on Systems, Man, and Cybernetics, Tucson, 7-10 October 2001, pp. 2664-2669.

has been cited by the following article:

• TITLE: Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Network and Genetic Algorithm

  AUTHORS: Yusuf Perwej, Asif Perwej

  KEYWORDS: Stock Market; Genetic Algorithm; Bombay Stock Exchange (BSE); Artificial Neural Network (ANN);Prediction; Forecasting; Data; Autoregressive (AR)

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.2, May 23, 2012

  ABSTRACT: Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.