Evaluating the Employment of Technical Indicators in Predicting Stock Price Index Variations Using Artificial Neural Networks (Case Study: Tehran Stock Exchange)

Mahmood Moein Aldin · Hasan Dehghan Dehnavi · Somayye Entezari

33 citations0/0 files storedInternational Journal of Business and Management

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

Stock price index is the initial significant factor influencing on investors' financial decision making. That's whypredicting the exact movements of stock price index is considerably regarded. This study aims at evaluating theeffectiveness of using technical indicators, such as Moving Average, RSI, CCI, MACD, etc in predictingmovements of Tehran Exchange Price Index (TEPIX). An artificial neural network is employed for stock priceindex forecasting. The existing data are achieved from Tehran Stock Exchange. To capture the relationshipbetween the technical indicators and the levels of the index in the market for the period under investigation, aback propagation neural network is used. The statistical and financial performance of this technique is evaluatedand empirical results revealed that artificial neural networks are dominant tools for financial market predicting.

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References

2003Financial time series forecasting using support vector machines 2004Forecasting stock market movement direction with support vector machine 2010Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange 2011Forecasting stock indices with back propagation neural network 2008Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network 2003A hybrid genetic-neural architecture for stock indexes forecasting 2006Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest 2006Forecasting the volatility of stock price index

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