基于多因子与多变量长短期记忆网络的股票价格预测  

Stock Price Prediction Based on Multiple-Factor and Multi-Variable Long Short Term Memory

作　　者：裴大卫 朱明[1] PEI Da-Wei;ZHU Ming(Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Heifei 230027, China)

机构地区：[1]中国科学技术大学信息科学技术学院自动化系

出　　处：《计算机系统应用》2019年第8期30-38,共9页Computer Systems & Applications

基　　金：国家重大科技专项(2017ZX03001019)~~

摘　　要：近年深度学习方法在金融领域受到广泛应用,尤其推动了股票价格预测的发展.本文针对一般股票价格预测中的单变量长短期记忆网络存在的准确率与鲁棒性不佳的问题,将经济学领域的量化选股策略中的多因子模型思想引入到股票价格预测中,计算股票的多因子并以其作为预测模型的输入特征.同时为了使模型适应多因子输入,因此在单变量长短期记忆网络的基础上建立了一个多变量长短期记忆网络股票价格预测模型.实验结果表明,随着多因子模型的引入,不仅提升了长短期记忆网络股票价格预测的准确率,同时在一定程度上也带来了更好的模型鲁棒性.In recent years, deep learning methods have been widely used in the financial field, especially promoting the development of stock price prediction. Aiming at the problem of poor accuracy and robustness of univariate Long Short Term Memory (LSTM) network in general stock price prediction, this paper introduces the idea of multiple-factor model in quantitative stock option strategy in the field of economics into stock price prediction, calculates the multiple-factor of stock and takes it as the input feature for the prediction model. At the same time, in order to make the model accept the multiple-factor input, a multi-variable LSTM model for stock price prediction is produced on the basis of the univariate LSTM model. The experiment results show that, with the introduction of multiple-factor model, it not only improves the accuracy of stock price prediction, but also brings better model robustness to some extent.

关 键 词：股票价格预测 量化选股 多因子 多变量 长短期记忆网络 

分 类 号：F832.51[经济管理—金融学]