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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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    1. Stanley R Stansell & Stanley G Eakins, 2004. "Forecasting the direction of change in sector stock indexes: An application of neural networks," Journal of Asset Management, Palgrave Macmillan, vol. 5(1), pages 37-48, June.
    2. Adam Fadlalla & Chien-Hua Lin, 2001. "An Analysis of the Applications of Neural Networks in Finance," Interfaces, INFORMS, vol. 31(4), pages 112-122, August.
    3. Vijay Desai & Rakesh Bharati, 1998. "A comparison of linear regression and neural network methods for predicting excess returns on large stocks," Annals of Operations Research, Springer, vol. 78(0), pages 127-163, January.
    4. Philip Hans Franses & Paul van Homelen, 1998. "On forecasting exchange rates using neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(6), pages 589-596.
    5. Kyoung‐Jae Kim, 2004. "Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(3), pages 167-176, July.
    6. Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
    7. Yu, Hui-Kuang, 2005. "Weighted fuzzy time series models for TAIEX forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 609-624.
    8. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    9. Kanas, Angelos, 2001. "Neural Network Linear Forecasts for Stock Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 245-254, July.
    10. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    11. Franses Philip Hans & van Griensven Kasper, 1998. "Forecasting Exchange Rates Using Neural Networks for Technical Trading Rules," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(4), pages 1-8, January.
    12. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    13. Qing Cao & Mark Parry & Karyl Leggio, 2011. "The three-factor model and artificial neural networks: predicting stock price movement in China," Annals of Operations Research, Springer, vol. 185(1), pages 25-44, May.
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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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