基于LSTM-文本分析的量化选股模型
Quantitative stock selection model based on LSTM-text analysis
陆芳玲 1赵家玮 1夏铁成1
作者信息
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.
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 predic-tion 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 Tech-nology with better forecasting effects.
关键词
量化选股/文本分析/长短期记忆(long-short/term/memory,LSTM)/预测Key words
quantitative stock selection/text analysis/long-short term memory(LSTM)/forecast引用本文复制引用
出版年
2024
NETL
NSTL
万方数据