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Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks
[Comparativa de los models clásicos de series temporales con la red neuronal recurrente LSTM: Una aplicación a las acciones del S&P 500]

Author

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  • Javier Oliver Muncharaz

    (UPV - Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia)

Abstract

In the financial literature, there is great interest in the prediction of stock prices. Stock prediction is necessary for the creation of different investment strategies, both speculative and hedging ones. The application of neural networks has involved a change in the creation of predictive models. In this paper, we analyze the capacity of recurrent neural networks, in particular the long short-term recurrent neural network (LSTM) as opposed to classic time series models such as the Exponential Smooth Time Series (ETS) and the Arima model (ARIMA). These models have been estimated for 284 stocks from the S&P 500 stock market index, comparing the MAE obtained from their predictions. The results obtained confirm a significant reduction in prediction errors when LSTM is applied. These results are consistent with other similar studies applied to stocks included in other stock market indices, as well as other financial assets such as exchange rates.

Suggested Citation

  • Javier Oliver Muncharaz, 2020. "Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks [Comparativa de los models clásicos de series temporales con la red neuronal recurrente ," Post-Print hal-03149342, HAL.
    • Handle: RePEc:hal:journl:hal-03149342
    DOI: 10.46503/ZVBS2781
    Note: View the original document on HAL open archive server: https://hal.science/hal-03149342
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    References listed on IDEAS

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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Mingyue Qiu & Yu Song, 2016. "Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-11, May.
    3. Fat Codruta Maria & Dezsi Eva, 2011. "Exchange-Rates Forecasting: Exponential Smoothing Techniques And Arima Models," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 499-508, July.
    4. Hedayati , Amin & Hedayati , Moein & Esfandyari, Morteza, 2016. "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 89-93.
    5. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    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.
    7. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
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    Cited by:

    1. Jujie Wang & Shiyao Qiu, 2021. "Improved Multi-Scale Deep Integration Paradigm for Point and Interval Carbon Trading Price Forecasting," Mathematics, MDPI, vol. 9(20), pages 1-20, October.

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    More about this item

    Keywords

    Recurrent Neural Network; Long short-term neural network; S&P 500; Arima; Redes neuronales recurrentes;
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