Course Catalogue

Advanced Financial Modelling ACCFIN4085

  • Academic Session: 2026-27
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

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.

Timetable

10 weeks of 2-hour lectures/workshops plus a revision session

6 weeks of 1-hour tutorials

Requirements of Entry

Grade D3 or better in Financial Modelling or equivalent.

Excluded Courses

None

Co-requisites

None

Assessment

1. Written assignment, including essay; Individual; 2000 words; 75%; ILOs 1-6.

2. Oral assessment & presentation; Individual; 15 minutes plus Q&A; 25%; ILOs 1-4, 7.

Course Aims

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.

Intended Learning Outcomes of Course

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.

Minimum Requirement for Award of Credits

No exceptions