module specification

The module develops students’ understanding of financial and economic modelling using foundational econometrics and financial data analysis skills introduced at earlier levels. It provides an in-depth exploration of diverse financial and economic models, leveraging both traditional econometric software such as EViews and programming languages such as Python. Students gain comprehensive exposure to classical financial theories including the Capital Asset Pricing Model (CAPM), Arbitrage Pricing Theory (APT), and the Fama-French model, alongside main economic models such as consumption, production and investment functions, and growth models, and their practical empirical applications in finance, and economics.

• Classical Financial Models (CAPM, Arbitrage Pricing Theory, Fama-French Model) (L1; L2)
• Economic Models (Investment Functions, Aggregate Demand and Supply, Growth Models) (L1; L2)
• Simple and Multiple Regression Models (L3; L4)
• Time Series Modelling and Forecasting (L3; L4)
• Modelling Long-Run Relationships in Finance (Stationarity, Unit Root Testing, Cointegration) (L3; L4)
• Panel Data Models  (L3; L4)
• Introduction to Machine Learning for Economics and Finance (Decision Trees, Random Forests, LASSO) (L3; L5)

Scheduled teaching comprises weekly 2 hours lecture and 1 hour seminar, which provide students with theoretical grounding and applied examples in financial and economic modelling using econometric methods. Lectures establish the theoretical foundations of financial and economic modelling, introducing key concepts, models, and econometric principles that underpin applied analysis. Seminars offer a collaborative environment for problem-solving, econometric practice, and critical discussion of empirical results. Students are encouraged to extend their understanding and knowledge through group work assessment and guided independent study. These activities involve engagement with core readings, recommended case studies, and empirical data exercises. Independent learning plays a key role in preparing for the individual coursework assignment, enabling students to combine lecture content and apply modelling techniques to real-world problems.

The module adopts a blended learning approach supported by the university’s Virtual Learning Environment (Weblearn). Students have access to a range of materials including lecture slides, seminar exercises and handouts, Python notebooks, datasets, discussion forums, assessment briefs, grading criteria, and structured feedback. The module integrates reflective learning through structured feedback, group presentations, and project-based assessments. These activities promote self-evaluation, peer discussion, and help students connect theoretical models with real-world data analysis. Students are encouraged to reflect on model assumptions, interpretability, and policy relevance—key aspects of personal development planning (PDP) and professional growth. This integrated approach equips them with transferable skills directly applicable to further study and careers in finance and economics.

On successful completion of this module, students will be able to:

1. Demonstrate comprehensive knowledge of a wide range of financial and economic models, and apply appropriate econometric methods to estimate and evaluate these models using real-world data.
2. Critically evaluate the strengths and limitations of financial and economic models, with regard to their theoretical assumptions and empirical implementation.
3. Apply statistical and econometric techniques effectively, using software such as EViews and programming languages such as Python.
4. Interpret empirical results accurately and communicate the implications of financial and economic analyses in both professional and academic contexts.
5. Demonstrate practical proficiency in applying basic machine learning regression models such as Decision Trees and Random Forests.

Core:

Asteriou, D. and Hall, S. G. (2021). Applied econometrics, 4th edition, Bloomsbury. This is an E-BOOK. Hard copies are available at 330.015195 AST.

Brooks, C. (2019). Introductory econometrics for finance, 4th edition, Cambridge: Cambridge University Press. This is an E-BOOK and hard copies are available at 332.015195 BRO.

Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in finance (Vol. 1170). New York, NY, USA: Springer International Publishing. This is an E-BOOK. Access available for London Metropolitan University: https://link.springer.com/book/10.1007/978-3-030-41068-1

Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
Open access: https://pubs.aeaweb.org/doi/pdf/10.1257/jep.31.2.87

Additional Textbooks:

Wooldridge, J. (2025). Introductory Econometrics: A Modern Approach, 8th edition. Cengage international edition. Boston, Massachusetts : Cengage. Hard copies available at 330.015195 WOO.

Gujarati, D. N. (2014). Econometrics by example, 2nd ed., Bloomsbury Publishing. This is an E-BOOK. Hard copies available at 330.015195 GUJ.