基于LSTM的量化股票预测  

LSTM Based Quantitative Stock Forecasting

作　　者：赵建群[1] 张岐[1] 王悦[1] 

机构地区：[1]广东科贸职业学院经济管理学院,广东 广州

出　　处：《金融》2020年第4期366-373,共8页Finance

摘　　要：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。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.

关 键 词：特征量化 LSTM 沪深300 涨跌幅预测 

分 类 号：F83[经济管理—金融学]