Advanced Financial Modelling
The course covers essential techniques to formulate real-world financial problems into empirically testable models. This aim provides a foundation for the students to critically examine and re-express verbal problems into measurable quantities with a logical connection that proxies their real world relationships. The content focus on identifying information and observational biases embedded in data for the purpose of addressing financial problems. This aim provides a deeper insight into the empirical methods to distinguish between uncovering the underlying financial or economic behaviour embedded in the data versus spurious findings. Lastly the course shows how to develop financial modelling skills to inform decision-making based on financial market data, financial reports and stylised facts in empirical finance. This aim provides a basis to apply the empirical methods to several contexts and evaluate how data-driven approaches enhance predictions and performance.
The course is organised according to theories and empirical facts related to financial markets and institutions. Both aspects are essential in terms of understanding the course material and examinations but also in terms of their importance towards developing a foundation for future careers in finance within or outside academia. Financial markets and financial institutions is delivered across the following main units:
- Examine the modern empirical models in finance and assess their performance in informing financial decisions.
- Translate conceptual or verbal theories into empirically testable financial models.
- Develop software routines to implement statistical estimation and inference methods to quantify modelling outcomes.
- Apply analytical and computational techniques to identify the observational and statistical biases within the context of empirical finance.
- Apply the longitudinal data analysis to address the implications of observational and statistical biases within empirical finance.
- Examine modern learning-based frameworks and inform model selection.
The course is delivered via weekly sessions and six tutorial workshops. There are practice problem sets with solutions to further illustrate theories and implementations. There are three assessment assignments through the semester timetable below. The timetable below is subject to change, please review this timetable on weekly basis:
Friday 10-11 am, Gilbert Scott Building
Course Tutorials and GTA Support
You are expected to have covered the material ahead of the tutorials. Tongtong Wang holds weekly office hours, starting in week 1. The schedule will be posted on MyGlasgow.
Financial Datasets and Empirical Exercises
The course contents, practice problem sets and assessment components are based on real-world financial data. Therefore, it is a requirement that all class participants set up their accounts with the data platforms described below:
- Register your accounts on Financial Analysis Made Easy (FAME) via the university library and additionally Wharton Research Data Services directly on their platform using the university email address.
- This registration is then activated by the business database administration within one week. Please initiate the registration in the first week of the course before we progress towards further course contents and assignments.
- Key statistics and learning outcomes arising from the activities related to the data will be part of the exam. Treat the empirical exercises as an essential part of the learning experience
- As a financial analyst or a research financial economist, you will work with the very same data providers repeatedly. Developing an understanding of the empirical counterparts of theories will be an important takeaway for future careers in finance.
- Individual Project Presentation (25%): The project is proposed based on one of the course methodologies to examine a context of interest. The written project is expected to outline the significance of the topic and demonstrate how the main findings are quantified based on the statistical evidence and methods. The projects are presented during a 15 minutes slot followed by Q&A.
- Degree exam in April/May (75%): The final assessment will be based on the written report presented earlier and fully developed to examine the research question, methodologies and a comprehensive interpretation of the results.
- Grading is based on meeting the course intended learning outcomes examined in each assignment and following the University's Schedule A. Grades are rewarded based on both the input and output presented in each part thus demonstrating intermediate steps building up towards an overall answer are required and graded.
- Problem set and assignments require accessing real-world financial data from the professional platforms, thus class participants are required to register and activate their accounts with data providers by following the information provided.
Answers to the assignments will be provided in the subsequent week after the deadline and after everyone's submissions are received. Aside from the assessed assignments indicated above, the course includes two practice problem sets with solutions. These are distributed to practice theories and implementations during the semester. Students are expected to attend the office hours and tutorial workshops for reviewing specific queries.
Past exam papers are available via the university portal. These can serve as a basis for preparation, however, note that the exam and course contents are subject to changes on an annual basis.
Textbook and Reading List
- Financial Decisions and Markets, John Campbell, 2017 Edition [MT-1]
- Introduction to Banking, Casu, Girardone and Molyneux, 2nd or 3rd Edition [MT-2]
- The Economics of Money, Banking and Financial Markets (Frederic Mishkin) [OT-1]
Further to the textbooks, there will be journal article readings cited throughout the course. Journal articles indicated as 'required reading' should also be studied in conjunction with textbook reading and form part of the assessments: Reading List. There are additional articles from the Financial Times to complement and relate to the ongoing economic and financial outcomes.