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Stock price forecast with deep learning

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  • Firuz Kamalov
  • Linda Smail
  • Ikhlaas Gurrib

Abstract

In this paper, we compare various approaches to stock price prediction using neural networks. We analyze the performance fully connected, convolutional, and recurrent architectures in predicting the next day value of S&P 500 index based on its previous values. We further expand our analysis by including three different optimization techniques: Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation. The numerical experiments reveal that a single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test Mean Absolute Error of 0.0150 and 0.0148 respectively.

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  • Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021. "Stock price forecast with deep learning," Papers 2103.14081, arXiv.org.
    • Handle: RePEc:arx:papers:2103.14081
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    References listed on IDEAS

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    1. Jinho Lee & Jaewoo Kang, 2020. "Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-20, April.
    2. Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    3. Sujin Pyo & Jaewook Lee & Mincheol Cha & Huisu Jang, 2017. "Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
    4. Firuz Kamalov & Ho Hon Leung, 2020. "Outlier Detection in High Dimensional Data," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.
    5. Svetlana Borovkova & Ioannis Tsiamas, 2019. "An ensemble of LSTM neural networks for high‐frequency stock market classification," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 600-619, September.
    6. Ikhlaas Gurrib & Firuz Kamalov, 2019. "The implementation of an adjusted relative strength index model in foreign currency and energy markets of emerging and developed economies," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(2), pages 105-123, May.
    7. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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