报告题目：Group penalized multinomial logit models and stock return direction prediction

报告时间：2024年3月4日 下午16:00

报告地点：教学科研楼409

主办单位：437必赢会员中心网页版

报告人：胡雪梅

报告人简介：重庆工商大学437必赢会员中心网页版教授，成渝地区双城经济圈建设研究院(原名长江上游经济研究中心)博士生导师，伦敦政治经济学院国家公派访问学者, 重庆经开区经济运行局、改革发展和科技局挂职副局长,中南大学理学博士，中科院数学与系统科学研究院控制论国家重点实验室系统科学博士后，“第五批重庆市高等学校优秀人才支持计划”人选，重庆市“统计学”研究生导师团队负责人，《随机过程》市级一流线下课程负责人。主要从事张量因子模型的稳健估计、高维统计模型的估计理论、高维数据模型的统计学习和随机微分方程的稳健推断等方面的研究，目前已在IEEE Transactions on Information Theory 、Journal of Multivariate Analysis、Statistical Papers、North American Journal of Economics and Finance、Journal of Forecasting、Journal of Nonparametric Statistic、Statistics & Probability Letters 、Expert Systems with Applications(中科院分区SCI一区)、Acta Mathematicae Applicatae Sinica、Chinese Annals of Mathematics、Soft Computing、《应用数学学报》和《系统科学与数学》等学术期刊上发表论文50多篇(SCI/SSCI收录30篇)，主持完成1项国家自然科学基金、1项教育部人文社项目、5项重庆市科委项目、2项重庆市教委项目，参与获得重庆市科学技术奖二等奖, 主持在研1项重庆市教委科学技术研究计划重大项目和1项重庆市社科规划项目,出版2部专著《高维统计模型的估计理论与模型识别》和《高维数据模型的统计学习方法与预测精度评估》。

摘要：Multinomial logit model(MLM) has been proposed as the most frequently regression model for multi-category response. To deal with correlated data, in this paper we propose G-LASSO/G-SCAD/G-MCP penalized MLM model to exert class discovery and class prediction for high-dimensional multi-category classification problems. Firstly, we develop a group coordinate descent(GCD) algorithm to simultaneously complete group selection and group estimation, and prove the convergence to the proposed GCD algorithm under mild conditions. Secondly, we apply the training set and group estimations to obtain class probability estimators, choose the Bayes classifier to identify class index information, and introduce the testing set and a few measures to assess multi-category prediction performance. Simulations show that the proposed methods outperform LASSO/SCAD/MCP penalized MLM, 3 deep learning methods and 3 machine learning methods in terms of Kappa, PDI(polytomous discrimination index), Optimal or Average Accuracy. Finally, we combine group penalized MLM with 58 technical indicators to predict up trends, sideways trends and down trends for stock returns, and show that the proposed methods outperform the other 9 methods in terms of Accuracy, PDI, Kappa and HUM. Therefore, the proposed method can not only accommodate the correlation information, but also improve multi-category prediction performance by shrinking group coefficients.