FINN41615: Financial Modelling with Artificial Intelligence

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• Econometric Methods (FINN41715)

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• To build upon students' knowledge of financial econometrics and introduce advanced machine learning and AI-driven forecasting techniques.
• To equip students with the technical and analytical skills necessary to model and predict financial markets using modern data-driven approaches.
• To develop students ability to critically evaluate the strengths and limitations of AI and machine learning applications in financial modelling.

• This module introduces students to modern forecasting methods in financial econometrics, with an emphasis on AI and machine learning applications. Topics may include:
• Classical time series models, including ARMA and ARIMA
• Unit root testing, structural breaks, and non-stationary time series.
• Multivariate modelling approaches, including vector autoregressions.
• Volatility modelling and risk estimation (ARCH and GARCH).
• Machine learning for financial forecasting, including supervised learning techniques.
• Applications of deep learning and other AI-driven methods in predicting asset returns.
• Practical implementation of machine learning algorithms for asset return forecasting.
• Machine learning in cross-sectional asset pricing.

Subject-specific Knowledge:

• Have an advanced understanding of traditional and AI-driven forecasting techniques in financial modelling.
• Be able to critically evaluate the application of machine learning in financial time series analysis and cross-sectional prediction.
• Develop an appreciation of the theoretical foundations and practical challenges of AI methods in finance.

Subject-specific Skills:

• Be proficient in implementing advanced econometric and machine learning techniques in financial applications.
• Be able to select and apply appropriate AI-based modelling techniques for financial forecasting problems.
• Gain experience with programming and statistical computing tools commonly used in AI-driven financial modelling.

Key Skills:

• Written Communication;
• Planning, Organising and Time Management;
• Problem Solving and Analysis;
• Using Initiative;
• Numeracy;
• Computer Literacy.

• A combination of lectures, classes and guided reading will contribute to achieving the aims and learning outcomes of this module.
• The summative written project will test students' ability to apply advanced econometric methods and tools in order to conduct their own empirical investigation.

Work prepared by students for seminars; answers to questions either discussed during a seminar or posted on Blackboard; feedback on discussions with teaching staff during consultation hours, or via e-mail.

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