Open AccessThe Influence and Prediction of Industry Asset Price Fluctuation Based on The LSTM Model and Investor Sentiment
Author(s) -
Wang Hu,
Huan Liu,
Xiaoqiang Ma,
Xiong Bai
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/1113023
Subject(s) - asset (computer security) , financial economics , construct (python library) , sentiment analysis , capital asset pricing model , economics , business , computer science , artificial intelligence , computer security , programming language
In a real-world environment, not only can different levels of market expectations be triggered by factors such as macroeconomic policies, market operating trends, and current company developments have an impact on sector assets, but sector asset rises and falls are also influenced by a factor that cannot be ignored: market sentiment. Therefore, this paper uses LSTM to construct a forecasting model for industrial assets based on investor sentiment and public historical trading data of industry asset markets to determine future trends and obtains two conclusions: first, forecasting models incorporating investor sentiment have better forecasting effects than those without the incorporation of sentiment characteristics, indicating that the factor of investor sentiment should not be ignored when studying the problem of industry asset forecasting; secondly, investor sentiment quantified by different methods.