Prediction of Stock Price Based on LSTM Model

DOI

10.2991/aebmr.k.210319.037How to use a DOI?

Keywords

LSTM, stock, stock index, doe theory, AI, listed company

Abstract

With the development of the stock market, listing has become one of the best ways for companies to develop. These companies are able to raise money in the equity market, while different types of investors come with an expectation of benefiting from potential stocks. The evidence suggests that this is the case stock investment is risky as well as large-duty, so investing wisely remains to be obviously crucial. As progress of artificial intelligence technology moves far ahead, it enables to introduce study about artificial intelligence into stock detecting. In this paper, combined with the actual stock market, a wealth of experiments are carried out on the relevant data sets. First of all, this paper collects the daily market data of China’s A-share market. The data are preprocessed and the correlation coefficient characteristics of technical indicators are extracted. After the feature engineering processing of the stock data set, the relevant algorithm model is built, and a detailed comparative experiment is done. The prediction process of stock is a relatively complex process. The prediction based on LSTM model can be used as a reference for the factors of stock index and market analysis, and cannot accurately predict the trend.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yangtian Yan
PY  - 2021
DA  - 2021/03/22
TI  - Prediction of Stock Price Based on LSTM Model
BT  - Proceedings of the 6th International Conference on Financial Innovation and Economic Development (ICFIED 2021)
PB  - Atlantis Press
SP  - 199
EP  - 206
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.210319.037
DO  - 10.2991/aebmr.k.210319.037
ID  - Yan2021
ER  -