近日，我院副教授胡文彬以第一作者身份在SSCI期刊 Computational Economics上发表论文Building Technical Analysis Strategies Using Multivariate Longitudinal and Time-to-Event Data in Stock Markets，链接：https://doi.org/10.1007/s10614-024-10782-3，该期刊为SSCI和SCI双收录期刊。
论文题目
Building Technical Analysis Strategies Using Multivariate Longitudinal and Time-to-Event Data in Stock Markets
论文摘要
Stock market prediction by machine learning techniques has been attracting more and more attention. Technical analysis trading strategies with binary classifiers that can predict market moving direction are such typical applications. However, there exist two deficiencies in applying binary classifiers. First, they only predict whether instead of when the interested event will occur. Second, they usually only use the cross-sectional information, without taking account of the longitudinal evolution of the features. In this paper, we propose to utilize multivariate functional principal component analysis (MFPCA) to overcome the second deficiency and obtain better trading strategies. MFPCA is used as a data augmentation tool to systematically extract longitudinal informative features that can replace technical indicators. Technical analysis trading strategies enhanced by survival models with MFPCA are built, with backtesting on the daily trading data of S&P 500 stocks. The experimental results show that MFPCA can significantly improve both the performance of the survival models and the trading strategies. We further show the contribution and effectiveness of MFPCA by interpreting the deep learning survival model and comparing with the long short-term memory model that can process multi-timestep information.
作者简介
胡文彬，杭州电子科技大学经济学院副教授，硕士生导师。浙江大学运筹学与控制论（金融数学方向）博士、University of York访问学者、美国数学评论评论员、软件工程师、Expert Systems with Applications期刊匿名审稿人。主要研究方向为金融衍生品定价、风险管理、量化投资。在Quantitative Finance, Journal of the Operational Research Society，Journal of Computational and Applied Mathematics等期刊上发表SSCI/SCI论文10余篇。
个人主页：https://faculty.hdu.edu.cn/jjxy/hwb/main.htm
