近日，威尼斯官网胡文彬副教授以第一作者身份在SSCI期刊 Computational Economics上发表论文Trading Signal Survival Analysis: A Framework for Enhancing Technical Analysis Strategies in Stock Markets，链接：https://doi.org/10.1007/s10614-024-10567-8，该期刊为SSCI和SCI双收录期刊。 

论文题目

Trading Signal Survival Analysis: A Framework for Enhancing Technical Analysis Strategies in Stock Markets

论文摘要

Algorithmic trading is one important financial area of interest to both academic and industrial researchers. With the development of machine learning and deep learning, all kinds of models and techniques are utilized in algorithmic trading. This paper proposes a novel framework for enhancing stock technical analysis strategies by survival analysis. The main idea is to integrate an existing trading strategy with a survival model and make them complementary to each other. By means of survival analysis, the original trading strategy can be extended to introduce an investment target, which is treated as the event of interest. On the other hand, the original trading signal provides survival analysis with a simple and clear starting time point of observation. The trained survival models are used to filter out false trading signals to improve the strategy performance. Under the framework, we propose different filtering methods, utilize different deep survival models, and compare their performance from both trading and model perspectives. We perform extensive and strict backtesting on the daily trading data of 380 plus stocks. The experimental results show that the framework can well improve the performance of technical analysis strategies in different market situations.

作者简介

胡文彬，37000vip威尼斯37000vip威尼斯副教授，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