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Comparison of LSTM and ARIMA in Price Forecasting: Evidence from Five Indexes

In: Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023)

Author

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  • Zizhe Zhang

    (Hebei University of Technology, School of Economics and Management)

Abstract

The financial industry has been increasingly researching and applying artificial intelligence in both academia and industry. The classical deep learning model, I.E., long-short term memory (LSTM) neural network model, has great advantages in predicting financial time series. This study uses data such as daily opening, closing, high and low prices of five representative global stock indices from 2015 to 2022 to predict stock prices using the LSTM neural network model and the linear autoregressive moving average model (ARIMA). The predicted results are compared with the actual stock prices, and the study findings demonstrate that the LSTM model outperforms the ARIMA in predicting stock index prices. Thus, incorporating deep learning models in a reasonable way can not only improve the accuracy of investment decision-making, but also enrich the methods for processing and analyzing financial time series data, so as to enhance the ability to monitor and warn of financial market risks.

Suggested Citation

  • Zizhe Zhang, 2024. "Comparison of LSTM and ARIMA in Price Forecasting: Evidence from Five Indexes," Advances in Economics, Business and Management Research, in: Faruk Balli & Hui Nee Au Yong & Sikandar Ali Qalati & Ziqiang Zeng (ed.), Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023), pages 40-46, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-268-2_6
    DOI: 10.2991/978-94-6463-268-2_6
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