Research on Shanghai Stock Exchange 50 Index Forecast Based on Deep Learning. (30th March 2022)
- Record Type:
- Journal Article
- Title:
- Research on Shanghai Stock Exchange 50 Index Forecast Based on Deep Learning. (30th March 2022)
- Main Title:
- Research on Shanghai Stock Exchange 50 Index Forecast Based on Deep Learning
- Authors:
- Ding, Yiling
Sun, Ning
Xu, Jiahao
Li, Pengyan
Wu, Jiaxin
Tang, Sai - Other Names:
- Jan Naeem Academic Editor.
- Abstract:
- 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.
- Is Part Of:
- Mathematical problems in engineering. Volume 2022(2022)
- Journal:
- Mathematical problems in engineering
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-03-30
- Subjects:
- Engineering mathematics -- Periodicals
510.2462 - Journal URLs:
- https://www.hindawi.com/journals/mpe/ ↗
http://www.gbhap-us.com/journals/238/238-top.htm ↗ - DOI:
- 10.1155/2022/1367920 ↗
- Languages:
- English
- ISSNs:
- 1024-123X
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 21326.xml