Poster:Stock Price Prediction using Machine Learning

被引：0

作者：

Chen, Kuan-Yu ^{[1
]}

Lee, Pei-Ju ^{[1
]}

Liu, Shang-Chien ^{[1
]}

机构：

[1] Natl Chung Cheng Univ, Dept Informat Management, Chiayi, Taiwan

来源：

2023 IEEE 43RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS, ICDCS | 2023年

关键词：

stock price trend prediction; text mining; data mining; machine learning; deep learning; news-based analysis;

D O I：

10.1109/ICDCS57875.2023.00123

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Many research highlights the incorporation of various dimensions in stock price prediction, encompassing fundamental, technical, float, and news/community-based information analysis. However, differing opinions may hinder rapid investment decision-making. Therefore, this research aims to utilize stock price trend prediction models to assist investors in making more effective decisions upon receiving news/community-based information since it is more accessible and comprehensible to many investors. This research gathers stock and technical data from Yahoo Finance!, fundamental data from Macro Trends, text data for employee reviews from Glassdoor for various companies in the analysis. The study employs Random Forest, Extreme Gradient Boosting, Recurrent Neural Network, and Long Short-Term Memory to develop stock price trend prediction models, aiming to provide future guidance for investors' decision-making processes.

引用

页码：1067 / 1068

页数：2

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