Zihua (James) LIU
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I am passionate about combining frontier techniques like big data/AIGC(Artificial Intelligence Generative Content) in teaching finance and accounting class. I am currently primarily teaching Entrepreneur Finance(FIN403TC) and Digital Finance(FIN404TC) (as the module leader). My courses emphasize the integration of academic frontier knowledge with industry practices, which is highly practical and beneficial for students' career prospects.
Additionally, I have developed a comprehensive 1V1 teaching system in which I provide closely guidance to students on their Final Year Projects(FYP) and academic competitions(e.g., the SURF). Currently, I have mentored 9 students on their Final Year Projects, and my Quantitative Finance Research Group has closely guided 5 students on their quantitative finance projects.
Before pursuing the Ph.D. program, I have participated in several industry big data projects as an algorithmic engineer(machine learning, text mining , and complex network etc) in Microsoft Online, Singapore DBS Bank, Wind etc. Based on my extensive experience in computing, I am currently involved in the development of finance and accounting tools aimed at enhancing teaching. These tools include an robot advisor and wealth management, finance event study system, and an AIGC-based course Q&A tool.
- Teacher: Digital Finance(FIN404TC)(Master Course) (2023/24 Academic Year)
- Teacher: Entrepreneurial Finance(FIN403TC)(Master Course) (2023/24 Academic Year)
- Instructor: Corporate Social Responsibility (2022/23 Academic Year) Evaluation：4.2/5
- Instructor: Business Finance (2021/22 Academic Year) Evaluation：4.6/5
- TA: Python Summer Camp for Research (PhD Course) (2021)
- TA: Python Programming for Accounting and Finance (Master Course) (2020)
- TA: Investment(MBA) at CEIBS (2018)
- TA: M&A(EMBA) at CEIBS (2017)
Last Update: February 2022
The event research approach is one of the most widely utilized tools in academia and industry. The event study methodology can be used to elicit the effects of any event on the direction and magnitude of stock price changes, it is very versatile. However, Students often find the current Stata/SAS event study codes complicated to use and difficult to get started with. Accordingly, I developed a web-based version of the event research platform based on Python scripts. I have integrated stock prices, the Fama-French three-factor model, financial market events, etc., into the system. Students can only focus on the event study methodology logic and complete event study research via button click. Students can also zoom in event study result of a specific firm and conduct case study analysis.