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Neural networks for stock price prediction

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  • Yue-Gang Song
  • Yu-Long Zhou
  • Ren-Jie Han

Abstract

Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models.

Suggested Citation

  • Yue-Gang Song & Yu-Long Zhou & Ren-Jie Han, 2018. "Neural networks for stock price prediction," Papers 1805.11317, arXiv.org.
    • Handle: RePEc:arx:papers:1805.11317
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    References listed on IDEAS

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    Cited by:

    1. Adrian Millea, 2021. "Deep Reinforcement Learning for Trading—A Critical Survey," Data, MDPI, vol. 6(11), pages 1-25, November.
    2. Shayan Halder, 2022. "FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis," Papers 2211.07392, arXiv.org.
    3. Federico Mecchia & Marcellino Gaudenzi, 2022. "The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network," Papers 2206.09899, arXiv.org.

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