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Predicting Next Trading Day Closing Price Of Qatar Exchange Index Using Technical Indicators And Artificial Neural Networks

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  • Adam Fadlalla
  • Farzaneh Amani

Abstract

Accurate prediction of stock market price is of great importance to many stakeholders. Artificial neural networks (ANNs) have shown robust capability in predicting stock price return, future stock price and the direction of stock market movement. The major aim of this study is to predict the next trading day closing price of the Qatar Exchange (QE) Index using historical data from 3 January 2010 to 31 December 2012. A multilayer perceptron ANN architecture was used as a prediction model with 10 market technical indicators as input variables. The experimental results indicate that ANNs are an effective modelling technique for predicting the QE Index with high accuracy, outperforming the well‐established autoregressive integrated moving average models. To the best of our knowledge, this is the first attempt to use ANNs to predict the QE Index, and its performance results are comparable to, and sometimes better than, many stock market predictions reported in the literature. The ANN model also revealed that the weighted and simple moving averages are the most important technical indicators in predicting the QE Index, and the accumulation/distribution oscillator is the least important such indicator. The analysis results also indicated that the ANNs are resilient to stock market volatility. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • Adam Fadlalla & Farzaneh Amani, 2014. "Predicting Next Trading Day Closing Price Of Qatar Exchange Index Using Technical Indicators And Artificial Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 209-223, October.
    • Handle: RePEc:wly:isacfm:v:21:y:2014:i:4:p:209-223
    DOI: 10.1002/isaf.1358
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    References listed on IDEAS

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    Cited by:

    1. Lohrmann, Christoph & Luukka, Pasi, 2019. "Classification of intraday S&P500 returns with a Random Forest," International Journal of Forecasting, Elsevier, vol. 35(1), pages 390-407.

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