上海大学学报(自然科学版) ›› 2024, Vol. 30 ›› Issue (2): 352-361.doi: 10.12066/j.issn.1007-2861.2354

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基于 LSTM-文本分析的量化选股模型

陆芳玲, 赵家玮, 夏铁成   

  1. 上海大学 理学院, 上海 200444
  • 收稿日期:2021-07-16 出版日期:2024-04-30 发布日期:2024-05-15
  • 通讯作者: 夏铁成 (1960—), 男, 教授, 博士生导师, 博士, 研究方向为数据科学与云计算、孤立子理论. E-mail:xiatc@shu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目 (11975145)

Quantitative stock selection model based on LSTM-text analysis

LU Fangling, ZHAO Jiawei, XIA Tiecheng   

  1. College of Sciences, Shanghai University, Shanghai 200444, China
  • Received:2021-07-16 Online:2024-04-30 Published:2024-05-15

摘要: 随着国民生活水平的提高, 越来越多的人投身于股票市场. 为了科学有效地量化选股,通过将量化投资、深度学习及文本分析进行有机结合, 来建立量化选股模型. 首先, 通过文本分析筛选出基本面利好的股票; 然后, 通过长短期记忆 (long-short term memory, LSTM) 选出预测准确度良好的股票; 最后, 预测所选出的股票在未来几天的股价趋势. 在实证分析方面,通过本模型对部分股票进行运算, 选取预测效果较好的股票: 赢合科技.

关键词: 量化选股, 文本分析, 长短期记忆 (long-short term memory, LSTM), 预测

Abstract: With the improvement of people’s living standards, increasing numbers of people are involved in the stock market. To scientifically and effectively quantify stock selection, this study establishes a quantitative stock selection model through an organic combination of quantitative investment, deep learning, and text analysis. That is, stocks with good fundamentals are selected through text analysis. Then, those with good prediction accuracy are selected using long-short term memory (LSTM). Finally, the stock price trend of these stocks in the next few days is predicted. In terms of empirical analysis, the model is used to perform calculations on some stocks and the result shows Yinghe Technology with better forecasting effects.

Key words: quantitative stock selection, text analysis, long-short term memory (LSTM), forecast

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