Programme Outlines and Overviews

This course introduces you to data-intensive and computational approaches to financial analysis using the Python programming language. You will focus on how modern data sources and machine learning methods can be used to model financial markets, measure risk, and support investment decisions. The course aims to showcase the new possibilities non-linear machine learning methods offer for financial analysis, as well as their caveats, hence supporting you in a career in finance. You will learn to apply machine learning methods to complex datasets, complementing traditional econometric approaches with non-linear data-driven tools. You will learn to master the full pipeline of machine learning-based analysis, including data handling, data preprocessing, feature engineering, supervised and unsupervised learning, model calibration and validation, as well as back-testing and cross-validation. The built models are interpreted using key tools from explainable AI. The course also introduces you to the analysis of unstructured data (for example, natural language processing), causal machine learning, and machine learning-based simulations. Applications are drawn from asset pricing, company performance prediction, portfolio optimisation, credit risk, and fraud detection.