Stock market trend prediction with sentiment analysis based on LSTM neural network

Xu Jiawei, Tomohiro Murata

研究成果: Conference article

抄録

—This paper aims to analyze influencing factors of stock market trend prediction and propose an innovative neural network approach to achieve stock market trend prediction. With the breakthrough of deep learning recently, there occurred lots of useful techniques for stock trend prediction. This thesis aims to propose a method of feature selection for selecting useful stock indexes and proposes deep learning model to do sentiment analysis of financial news as another influencing factor influencing stock trend. Then it proposes accurate stock trend prediction method using LSTM (Long Short-term Memory).

元の言語English
ページ(範囲)475-479
ページ数5
ジャーナルLecture Notes in Engineering and Computer Science
2239
出版物ステータスPublished - 2019 1 1
イベント2019 International MultiConference of Engineers and Computer Scientists, IMECS 2019 - Kowloon, Hong Kong
継続期間: 2019 3 132019 3 15

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Neural networks
Feature extraction
Financial markets
Long short-term memory
Deep learning

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

これを引用

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title = "Stock market trend prediction with sentiment analysis based on LSTM neural network",
abstract = "—This paper aims to analyze influencing factors of stock market trend prediction and propose an innovative neural network approach to achieve stock market trend prediction. With the breakthrough of deep learning recently, there occurred lots of useful techniques for stock trend prediction. This thesis aims to propose a method of feature selection for selecting useful stock indexes and proposes deep learning model to do sentiment analysis of financial news as another influencing factor influencing stock trend. Then it proposes accurate stock trend prediction method using LSTM (Long Short-term Memory).",
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