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Stock Market Prediction Performance of Neural Networks: A Literature Review

Author

Listed:
  • Ozgur Ican
  • Taha Bugra Celik

Abstract

In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures - namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) - have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

Suggested Citation

  • Ozgur Ican & Taha Bugra Celik, 2017. "Stock Market Prediction Performance of Neural Networks: A Literature Review," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(11), pages 100-108, November.
    • Handle: RePEc:ibn:ijefaa:v:9:y:2017:i:11:p:100-108
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    References listed on IDEAS

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    1. Jingtao Yao & Chew Lim Tan & Hean-Lee Poh, 1999. "Neural Networks For Technical Analysis: A Study On Klci," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 221-241.
    2. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    3. Teo Jasic & Douglas Wood, 2004. "The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965-1999," Applied Financial Economics, Taylor & Francis Journals, vol. 14(4), pages 285-297.
    4. Akhter Mohiuddin Rather & V. N. Sastry & Arun Agarwal, 2017. "Stock market prediction and Portfolio selection models: a survey," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 558-579, September.
    5. 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.
    6. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    2. Htet Htet Htun & Michael Biehl & Nicolai Petkov, 2023. "Survey of feature selection and extraction techniques for stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    3. Onur Enginar & Kazim Baris Atici, 2022. "Optimal forecast error as an unbiased estimator of abnormal return: A proposition," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 158-166, January.
    4. Hadi NekoeiQachkanloo & Benyamin Ghojogh & Ali Saheb Pasand & Mark Crowley, 2019. "Artificial Counselor System for Stock Investment," Papers 1903.00955, arXiv.org.
    5. Djoumbissie David Romain, 2020. "Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input," Papers 2011.13113, arXiv.org, revised Apr 2022.

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    More about this item

    Keywords

    ANN (Artificial Neural Networks); financial times series forecasting; stock markets prediction; review;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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