Research on stock index prediction based on ARIMA-CNN-LSTM model

Ziyan Zhang ()
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Ziyan Zhang: Zhuhai College of Jilin University, Dept. of Finance and Trade

A chapter in Proceedings of the 9th International Conference on Financial Innovation and Economic Development (ICFIED 2024), 2024, pp 558-565 from Springer

Abstract: Abstract As financial markets become ever more complicated and unpredictable, traditional stock index prediction models no longer meet the high frequency and big data market environment. To enhance forecast accuracy this study proposes a hybrid model comprised of autoregressive integral sliding average model (ARIMA), convolutional neural network (CNN), and long short term memory network (LSTM). According to the pre-data processing; Then the time-critical time series features are extracted. Finally, the sequence of capturing data dependence and output prediction results is carried out. ARIMA, CNN and LSTM models will be used. After experimental verification of multiple stock index data, compared with other traditional prediction models, ARIMA-CNN-LSTM model is better in prediction accuracy and robustness. The model provides a powerful tool for financial workers to better understand market dynamics and make informed investment decisions.

Keywords: stock index forecasting; ARIMA model; CNN-LSTM combination model; deep learning; financial markets (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-408-2_63

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DOI: 10.2991/978-94-6463-408-2_63

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