%0 Journal Article
%T 基于LSTM的量化股票预测<br>LSTM Based Quantitative Stock Forecasting
%A 赵建群
%A 张岐
%A 王悦
%J Finance
%P 366-373
%@ 2161-0975
%D 2020
%I Hans Publishing
%R 10.12677/FIN.2020.104037
%X 股票特征通常夹杂较多噪声数据，而带噪数据会影响股票预测模型的预测精度。本文提出一种对股票数据特征进行量化编码的方法，并使用长短期记忆网络构建预测模型，对量化后的数据进行预测。数据集采用沪深300成分股，在对股票数据量化后进行3分类涨跌幅预测。实验结果表明，使用量化编码对股票特征处理后，预测效果优于使用原始数据预测。<br />
The features of stock are usually mixed with many noise data, and noisy data will affect the predic-tion accuracy of stock prediction model. In this paper, a quantitative coding method for stock data features is proposed, and a prediction model is constructed by using short and long term memory network to predict the quantified data. The data set uses the Shanghai and Shenzhen 300 compo-nent stocks, after the stock data quantification carries on the 3 classification rise and fall forecast. The experimental results show that the prediction effect is better than that of the original data after the stock feature is processed by quantitative coding.
%K 特征量化，LSTM，沪深300，涨跌幅预测<br>Characteristic Quantification
%K LSTM
%K Shanghai and Shenzhen 300
%K Forecast of Increase or Decrease
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=36533