This module builds on Econometrics for Finance I (ACFI225) to develop the students’ ability to design and implement the right empirical methodology to test finance theories. The emphasis on this module is on the effective use of programming, quantitative techniques, and the relevant data to answer real-world finance questions. The lectures introduce students to finance topics related to predictability, volatility modelling, event studies, and ethical issues in quantitative modelling. The seminar sessions, which take place in the computer lab, are mostly practical. They will provide students with the opportunity to apply their knowledge by using python, a programming language, to extract, process, and analyse data from industry databases. The course will involve the replication of academic studies to illustrate the applications of the concepts. The assessment is conducted via a group project (40%), an individual report (50%), and a presentation (10%) at the end of the course. Overall, the students will develop a range of skills, including communication, digital fluency, analytical, and problem-solving skills.