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个人信息Personal Information
副教授
博士生导师
硕士生导师
教师英文名称：Chia-Hung Wang
教师拼音名称：ChiaHung Wang
所在单位：计算机科学与数学学院
职务：副教授
学历：研究生(博士后)
办公地点：计算机科学与数学学院计算机科学与技术教研室
联系方式：校址：福建省福州市闽侯县上街镇学府南路69号福建理工大学 (邮编: 350118) 教师主页: http://faculty.fjut.edu.cn/wang_chiahung/zh_CN/index.htm ResearchGate Website: https://www.researchgate.net/profile/Chia-Hung_Wang2
学位：博士学位
在职信息：在职
主要任职：副教授
其他任职：研究生导师
毕业院校：(中国台湾)政治大学
学科：计算机科学与技术
电子信息工程
交通运输
信息与计算科学
数据科学与大数据技术
论文成果
当前位置： 王嘉宏博士（Chia-Hung Wang, Ph.D.）中文主页 >> 科学研究 >> 论文成果Stock conditional drawdown at risk portfolio optimization based on gated bidirectional temporal convolution and discrete cosine graph neural networks on hypervariable graphs
点击次数：
影响因子：7.6
DOI码：10.1016/j.knosys.2025.114456
发表刊物：Knowledge-Based Systems
关键字：Machine learning; Stock prediction; Portfolio optimization; Graph neural network; Conditional drawdown at risk; Spatiotemporal dependency; Decision support system
摘要：With the development of machine learning technology, the application of stock prediction in financial portfolio optimization has become increasingly important. This study proposes an intelligent portfolio optimization method that combines gated bidirectional temporal convolution-discrete cosine graph neural network (TDGNN) with the mean-conditional drawdown at risk (Mean-CDaR) model, aiming to improve the risk-return performance of the portfolio. The method consists of two main stages: first, the data is converted into a hypervariable graph through the TDGNN model, the gated bidirectional temporal convolution layer is used to capture the temporal dynamic characteristics, and the discrete cosine graph neural network is combined to effectively model the complex spatiotemporal relationship in the stock market; second, the Mean-CDaR model is used for portfolio optimization, and the maximum drawdown is used as a measurement indicator to achieve precise risk control. Experimental results show that on the CSI 300, S&P500, and Nikkei 225 data sets, TDGNN and Mean-CDaR models perform significantly better than traditional methods, with R2 of 0.9991, 0.9991, and 0.9983, respectively. Under the assumption of no transaction costs, the cumulative returns are 0.42, 0.62, and 0.93, respectively; considering 0.05 % transaction costs, the cumulative returns are 0.1, 0.25, and 0.49, respectively. The study shows that this method not only effectively captures the spatiotemporal dependency of stock data but also effectively controls risks while improving returns, providing investors with a robust and efficient decision support system.
论文类型：期刊论文
学科门类：交叉学科
文献类型：J
卷号：329
期号：B
页面范围：114456
ISSN号：0950-7051
是否译文：否
发表时间：2025-09-13
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0950705125014959
第一作者：王嘉宏
合写作者：林持旺