一种融合舆情态势评分和图套索的股票收益系统预测研究  

A Stock Return System Prediction Model Utilizing Stock Evaluation Sentiment Trend Model and GLASSO

作　　者：王星[1,2] 彭谦 WANG Xing;PENG Qian(Center for Applied Statistics&School of Statistics,Renmin University of China,Beijing 100872;School of Statistics,Renmin University of China,Beijing 100872)

机构地区：[1]中国人民大学应用统计科学研究中心,北京100872 [2]中国人民大学统计学院,北京100872

出　　处：《系统科学与数学》2024年第2期285-303,共19页Journal of Systems Science and Mathematical Sciences

基　　金：社科基金项目(18ATJ004)资助课题。

摘　　要：为了解决证券投资组合收益预测模型在股票价格波动感知方面的语义细粒度量化不足和有效投资组合灵活性受限的挑战,文章提出了一种综合舆情态势评分模型(SESTM)和图套索(GLASSO)的股票收益系统预测模型.首先,采用分位回归方法对股价波动进行拟合建模,定义了波动幅度和均值序列两条曲线,用于发现与正收益波动相关的词汇.接着,运用SESTM模型从新闻公告语料中通过有监督的方式提取对股票价格波动灵敏感知的相关词汇,并形成与政策、估值和市场情绪密切相关的主题和匹配词典,进而生成舆情态势评分.最后,结合GLASSO方法构建股票价格之间的联动网络结构,并基于该网络构建个股投资组合策略.实证研究以疫情期间生物疫苗板块股票为对象,对网络联动和舆情态势评分模型开展了实验比较.实验结果显示:首先,以波动感知词汇为纽带构建的投资策略更适用于短期预测;其次,在融入反映联动网络的偏相关信息后,投资组合日均对数收益率达到1.6%,相较于未融入偏相关关系的1.4%的情况提高了14.3%,更是随机组合的日均对数收益率0.7%的2倍;而最高收益由随机组合的3.117提高到3.605,提升幅度达到15.6%.以上结果表明,通过SESTM+GLASSO组合模型的方式提供了一种高效且性能优越的系统预测模型,该模型能够分析股票价格之间的网络联动关系,更准确地预测股票收益以制定相应的投资策略.这对于推动动态价格感知和深化大语言模型中的生成式跨模态任务的统计应用研究具有积极意义.In this paper,to address the challenges of semantic granularity and limited flexibility in effective portfolio investment caused by the inadequate perception of stock price fluctuations in portfolio return prediction models,the authors propose a comprehensive system prediction model for stock returns by integrating the sentiment situation evaluation score model(SESTM)and graphical lasso(GLASSO).Firstly,the authors introduce quantile regression to model stock price volatility,defining volatility width sequence and volatility mean sequence to identify vocabulary related to positive return fluctuations.Next,the SESTM model is employed to extract perception vocabulary related to stock price volatility from news announcements and generate news sentiment scores based on closely related themes and matching dictionaries associated with policies,valuations,and market sentiments.Finally,by combining the GLASSO method,the authors construct a network structure of interdependence among stock prices and develop individual stock portfolio strategies based on this network.Empirical experiments are conducted using stocks from the biotechnology vaccine sector during the epidemic period to compare network interdependence and sentiment situation evaluation models.The results show that firstly,investment strategies constructed based on perception vocabulary are more suitable for short-term predictions;Secondly,incorporating information the reflected partial correlations in the interdependent network,the average daily logarithmic returns of the investment portfolio reached 1.6%,which is a 14.3%improvement by 14.3%compared to not considering partial correlations,and it is twice the average daily logarithmic returns of a random combination 0.7%.Moreover,the highest return increased from 3.117 for a random combination to 3.605,showing a significant improvement of 15.6%.These results indicate that the combination model of SESTM+GLASSO provides an efficient and superior system prediction model through a comprehensive approach that can anal

关 键 词：股票舆情态势评分模型 图套索 匹配词典 分位回归 联动网络 

分 类 号：O212.1[理学—概率论与数理统计] F832.51[理学—数学]