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Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

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  • Mahla Nikou
  • Gholamreza Mansourfar
  • Jamshid Bagherzadeh

Abstract

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.

Suggested Citation

  • Mahla Nikou & Gholamreza Mansourfar & Jamshid Bagherzadeh, 2019. "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 164-174, October.
    • Handle: RePEc:wly:isacfm:v:26:y:2019:i:4:p:164-174
    DOI: 10.1002/isaf.1459
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    References listed on IDEAS

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    Cited by:

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    5. Simona Hašková & Petr Šuleř & Róbert Kuchár, 2023. "A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study," Mathematics, MDPI, vol. 11(13), pages 1-17, July.

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