Accuracy Driven Artificial Neural Networks in Stock Market Prediction

Selvan Simon

40 citations0/0 files storedInternational Journal on Soft Computing

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

Abstract

Globalization has made the stock market prediction (SMP) accuracy more challenging and rewarding for the researchers and other participants in the stock market.Local and global economic situations along with the company's financial strength and prospects have to be taken into account to improve the prediction accuracy.Artificial Neural Networks (ANN) has been identified to be one of the dominant data mining techniques in stock market prediction area.In this paper, we survey different ANN models that have been experimented in SMP with the special enhancement techniques used with them to improve the accuracy.Also, we explore the possible research strategies in this accuracy driven ANN models.

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References

1988Economic prediction using neural networks: the case of IBM daily stock returns 1998Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks 2002Stock market trend prediction using ARIMA-based neural networks 2010Stock market value prediction using neural networks 2010Applications of Artificial Neural Networks in Financial Economics: A Survey 2010APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR STOCK MARKET PREDICTIONS: A REVIEW OF LITERATURE 1996Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns 2007DJIA stock selection assisted by neural network

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2015Stock price prediction based on stock-specific and sub-industry-specific news articles 2015Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate prediction

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