Research on Shanghai Stock Exchange 50 Index Forecast Based on Deep Learning

Yiling Ding, Ning Sun, Jiahao Xu, Pengyan Li, Jiaxin Wu, Sai Tang and Naeem Jan

Mathematical Problems in Engineering, 2022, vol. 2022, 1-9

Abstract: After decades of advance development, China's stock market has gradually arisen into one of the world's most important capital markets. The stock price index can well reflect the health status and macro change trend of a country's economic development, which can be said to be a barometer of the country's economic development. Studying the stock price index forecast is of great significance to the entire national economy and to each investor. Using 2 tools, Python and EViews8.0, and taking the Shanghai Stock Exchange 50 index as an example, the long short-term memory (LSTM) model in deep learning (DL) and the Autoregressive Integrated Moving Average (ARIMA) model are selected for fitting and prediction. The research results explain that the Root Mean Squared Error (RMSE) of LSTM model is lower, and the model based on DL method has stronger prediction ability on stock price index than traditional stock prediction model. This model is an effective stock prediction method.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1367920

DOI: 10.1155/2022/1367920

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