Research on Stock Selection Method Based on LSTM Neural Network
- DOI
- 10.2991/978-94-6463-010-7_87How to use a DOI?
- Keywords
- LSTM; Neural Network; Stock Prediction
- Abstract
In the field of investment, the selection of good target stocks is one of the keys to the ultimate success of the investment activity. Stock prediction is a study that every investor is trying to do, ordinary investors confirm stock selection for trading by means of technical analysis, and researchers analyze stock data by building mathematical models. Stock data are represented as classical financial time series, and the use of neural networks for stock data prediction is a hot research topic in recent years. In this paper, we analyze the stock investment risk and investment analysis methods based on the actual process of stock investment selection, and analyze the applicability of LSTM in stock investment selection from the perspective of stock selection ability under the large number of stock market investments. The experimental results show that the proposed method has improved the accuracy of stock prediction compared with the single LSTM prediction model, and can predict the stock trend accurately and effectively to a certain extent.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yifan Gao PY - 2022 DA - 2022/12/02 TI - Research on Stock Selection Method Based on LSTM Neural Network BT - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) PB - Atlantis Press SP - 869 EP - 876 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-010-7_87 DO - 10.2991/978-94-6463-010-7_87 ID - Gao2022 ER -