基于文本生成语言模型的股指预测  

Stock Index Prediction with Text Generative Language Model

作　　者：温灿红 陈思 杨海生[2] WEN Can-Hong;CHEN Si;YANG Hai-Sheng(School of Management,University of Science and Technology of China,Hefei 230026,China;Lingnan College,Sun Yat-sen University,Guangzhou 510275,China)

机构地区：[1]中国科学技术大学管理学院,合肥230026 [2]中山大学岭南学院,广州510275

出　　处：《计算机系统应用》2023年第10期54-64,共11页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(72173141);广东省自然科学基金面上项目(2023A1515012434)。

摘　　要：股指预测是金融领域中一个重要课题.随着计算能力和技术的发展,从在线新闻中识别和量化有价值的信息为提高股指预测表现创造了机会.本文为将关于股票指数预测框架的计量经济学文献扩展到高维文本数据提出了一种基于生成语言模型的股票指数预测框架.该预测框架可以分为两个步骤.首先,使用有监督生成语言模型快速过滤噪声词语,并将剩余文本聚合成可以充分解释股指变动的新闻指数.其次,将该新闻指数和历史股指数据共同作为时变参数预测模型的自变量来预测股指未来价值.该框架不仅丰富了股票指数预测的影响因素并且揭示了这些因素与股票指数价值之间的时变动态关系.实证研究展示了该预测框架解释能力和样本外预测能力.在预测的6个行业股指中,本文提出的预测框架得到的均方误差普遍小于传统时间序列和机器学习方法.与没有考虑新闻信息的时变参数预测模型和长短期记忆网络相比该预测框架也表现了更好的预测性能.Stock index prediction is an important topic in the field of finance.With the development of computing power and technologies,there are opportunities to improve the performance of stock index prediction by identifying and quantifying valuable information from online news.In order to extend the econometric literature on stock index prediction frameworks to high-dimensional textual data,a stock index prediction framework based on generative language models is proposed.The prediction framework can be divided into two steps.First,a supervised generative language model is used to filter out noisy words quickly and aggregate the remaining text into a news index that can fully explain stock index changes.Second,the news index and historical stock index data are jointly used as independent variables of the timevarying parameter predictive model to predict future stock index values.The framework not only enriches the influencing factors of stock index prediction but also reveals the time-varying dynamic relationship between these factors and stock index values.Empirical research demonstrates the explanatory and out-of-sample predictive power of the proposed prediction framework.Among the six industrial stock indices predicted,the mean square error obtained by the proposed prediction framework is generally lower than that by traditional time series and machine learning methods.Compared with the time-varying parameter predictive model and long short-term memory model that do not consider news information,the proposed prediction framework also exhibits better predictive performance.

关 键 词：深度学习 分布式多项回归 负二项回归 股指预测 文本分析 时变参数模型 

分 类 号：F830.91[经济管理—金融学] TP391.1[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]