主题：Applying Data Analytics in Finance and Business（数据分析在金融和商业中的应用）

主讲人：美国伊利诺伊大学香槟分校 Xing Gao助理教授

主持人：永利yl23411集团官网 罗瀚林

时间：2025年6月10日（周二）14:00-15:00

地点：柳林校区诚正楼650会议室

主办单位：数字经济重大基础理论与实践创新研究团队 新时代中国特色财务与会计理论创新与方法体系研究团队 永利yl23411集团官网 科研处

主讲人简介：

Xing Gao是美国美国伊利诺伊大学香槟分校的助理教授，并担任金融硕士项目的联合主任。Gao老师在2019年于伊利诺伊大学香槟分校取得了经济学博士的学位。Xing Gao is an Assistant Professor at University of Illinois Urbana-Champaign. She is also the Co-Director of MS Finance Program. She acquired her PhD degree of Economics in UIUC in 2019.

内容摘要：

这是一份面向对原始数据如何转化为完全处理过的数据集以及数据分析工具如何应用于现实场景感兴趣的本科生的调查性报告。

首先，本讲座会讨论数据管理，概述包括获取数据、探索和验证数据、准备数据、进行分析和报告以及最终导出结果的编程工作流程。这一部分还将介绍关键的金融数据库，并展示如何合并多个来源的数据。

在第二部分，讲座将探讨广泛应用于解决商业问题的统计和机器学习算法。我会区分用于预测分析和因果分析的工具集。我还会以汽车价格预测为例，解释变量选择技术。

最后一部分将专注于数据分析如何应用于投资决策。例子包括回测交易策略、分析市场对盈利公告的反应、比较成长型和价值型投资策略，以及通过自然语言处理和文本挖掘进行情绪分析。主讲人还将讨论解释性建模和预测性建模之间的区别。

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.

The talk 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, the talk 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, the talk 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. The talk will also discuss the distinction between explanatory modeling and predictive modeling.