Course Catalogue

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The course covers essential techniques to (i) formally translate real-world financial problems into empirically testable models, (ii) identify the informational contents and observational biases embedded in financial data, and (iii) incorporate conceptual problems with data to inform financial decision-making.

Main sessions include lectures in addition to workshops to illustrate data cleaning, modelling and visualisation methods during the classes and via asynchronous approaches. Tutorial Sessions are intended for the students to cover further quantitative and qualitative methodologies and exercises based on both analytical or programming problems.

Grade D3 or better in Financial Modelling or equivalent.

Please refer to the current undergraduate prospectus at : https://www.gla.ac.uk/undergraduate/

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Intended Learning Outcomes

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The aims of this course are 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.

■ Identify 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.

■ 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.

By the end of this course students will be able to:

1. Examine the modern empirical models in finance and assess their performance in informing financial decisions.

2. Translate conceptual or verbal theories into empirically testable financial models.

3. Develop software routines to implement statistical estimation and inference methods to quantify modelling outcomes.

4. Apply analytical and computational techniques to identify the observational and statistical biases within the context of empirical finance.

5. Apply the longitudinal data analysis to address the implications of observational and statistical biases within empirical finance.

6. Examine modern learning-based frameworks and inform model selection.

7. Communicate methodologies, empirical results and their verbal relevance to inform financial decisions via presentations.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.