Alma Mater:National Chengchi University, Taiwan
Education Level:Postgraduate (Postdoctoral)
[MORE]1. Awardee of Excellent Young Role Models from Tainan County, Taiwan 2. Leading Scientists of the World 2009 3. Who's Who in the World 2013-2021 4. Who's Who in Science and Engineering 2016-2017 5. Who's Who in Asia 2017-2018 6. 福建省福州市台湾人才库成员 7. 中国海外杰青汇中华交流团(CSP)成员 8. 福建工程学院「本科优秀毕业设计(论文)指导教师奖」
Impact Factor:7.6
DOI number:10.1016/j.knosys.2025.114456
Journal:Knowledge-Based Systems
Key Words:Machine learning; Stock prediction; Portfolio optimization; Graph neural network; Conditional drawdown at risk; Spatiotemporal dependency; Decision support system
Abstract: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.
Indexed by:Journal paper
Discipline:interdisciplinary subject
Document Type:J
Volume:329
Issue:B
Page Number:114456
ISSN No.:0950-7051
Translation or Not:no
Date of Publication:2025-09-13
Included Journals:SCI
Links to published journals:https://www.sciencedirect.com/science/article/pii/S0950705125014959
First Author:Chia-Hung Wang
Co-author:Chiwang Lin
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