2025年第36期(总第1077期)
演讲主题：Applying Data Analytics in Finance and Business
主讲人：Xing Gao 伊利诺伊香槟分校（UIUC）助理教授
主持人：李安泰 计算金融系副教授
活动时间：2025年6月6日（周五）10:00-11:30
活动地点：管院大楼107室
主讲人简介：
Dr. Xing Gao is a Teaching Assistant Professor of Finance at the University of Illinois Urbana-Champaign (UIUC) and currently serves as the Co-Director of the Master of Science in Finance (MSF) program. Her research interests lie in empirical corporate finance and investment, and her work was recognized with the Best Paper Award on Derivatives at the Northern Finance Association in 2018.
Dr. Gao teaches a range of data analytics courses, including Big Data Analytics in Finance, Financial Data Management and Analysis, and Advanced Data Science and Python for Finance. She was honored with the Best Professor Award by the MSF Class of 2024 and received campus nominations for Excellence in Graduate and Professional Teaching in both 2024 and 2025.
活动简介：
This is a survey-level presentation designed for undergraduate students who are curious about how raw data is transformed into fully processed datasets and how data analysis tools can be applied in real-world scenarios.
I will begin by discussing data management, outlining the programming workflow that includes accessing data, exploring and validating it, preparing it, conducting analysis and reporting, and finally exporting the results. This section will also introduce key financial databases and demonstrate how to merge data from multiple sources.
In the second part, I will explore widely used statistical and machine learning algorithms that are applied to solve business problems. I will distinguish between toolsets for predictive analysis and causal analysis. I will also use car price prediction as an example to explain variable selection techniques.
In the final part, I will focus on how data analytics can be applied to investment decision-making. Examples will include back testing trading strategies, analyzing market responses to earnings announcements, comparing growth and value investing strategies, and conducting sentiment analysis through natural language processing and text mining. I will also discuss the distinction between explanatory modeling and predictive modeling.
