This module aims to develop students’ ability to use real-world data and the appropriate econometric methodology to solve finance problems. Upon successful completion of this course, the students will be able to formulate and implement the appropriate econometric methodology to solve a finance problem. They should also be able to critically evaluate the empirical analysis and propose potential solutions to the identified issues.

(LO1) Students will be able to formulate a research hypothesis based on financial theory

(LO2) Students will be able to identify and explain the appropriate methodology to answer a research question

(LO3) Students will be able to retrieve and process the financial dataset most relevant to the research question

(LO4) Students will be able to critically evaluate the empirical analysis

(LO5) Students will be able to identify and address the limitations/biases inherent to an empirical analysis

(S1) Communication

(S2) Digital Fluency

(S3) Lifelong Learning

(S4) Analytical

(S5) Problem Solving

(S6) Teamworking

(S7) Organisation

(S8) Ethical Awareness

(S9) Leadership

Teaching Method - Lectures
Description: Lecture (12 Lectures of 2 hours each)
Scheduled Directed Student Hours: 24 hours
Attendance Recorded: Yes
Students will attend the weekly 2 hours lecture during which the theoretical concepts will be explained.

Teaching Method – Seminar (6 seminars of 2 hours each)
Description: Face to face sessions
Scheduled Directed Student Hours: 12 hours
Attendance Recorded: Yes
The 2-hour seminars will take place over 6 weeks in the computer lab. During these sessions, students will apply their knowledge to real-world datasets and develop their expertise in python, the programming language.

Self-Directed Learning Hours: 114 hours
Description: These independent learning hours are aimed at supporting the directed student learning. The module leader will provide guidance in the form of suggested readings, topics, or self-assessment quizzes to complete with the expectation that students are well prepared to contribute to the tutorial activities and to understand the content of lectures.

The key applications will be mostly done during the 6 seminars in the computer lab. Journal articles and financial press articles will be used to help illustrate the real-world applications of the key concepts. The teaching will also be supported by online activities. These include self-assessment quizzes that enable students to evaluate their own learning and receive formative feedback instantaneously. Furthermore, discussion boards will be used to further foster the exchange of ideas, thus deepening the knowledge of students and improving their communication skills. These activities will be moderated by the module instructor. Students will also be directed to key academic and practitioner readings to further develop their learning.

This module is a pre-requisite for the following modules:
ACFI331 Data Mining and Machine Learning

Skills/Other Attributes Mapping

Skills / att ributes: Communication
How this is developed: By contributing to in-class discussions (lectures and seminars), and by preparing the project reports and presentation.
Mode of assessment (if applicable): Group Project and Individual Report and Presentation

Skills / attributes: Digital Fluency
How this is developed: By using Python to undertake the empirical analysis during the seminars. The students will also develop their skills by using digital tools and specialist software to engage with the course material, to collaborate and communicate with others, e.g. Jupyter notebook.

Mode of assessment (if applicable): Group Project and Individual Report and Presentation

Skills / attributes: Lifelong Learning
How this is developed: In lectures and seminars by getting a good grounding in forecasting and how to rigorously assess the impact of an event on financial markets..
Mode of assessment (if applicable): Group Project and Individual Report and Presentat ion

Skills / attributes: Analytical
How this is developed: During the lectures & seminars, the students will be analysing new problems related to forecasting and event studies for instance. In doing so, they will write computer codes to analyse datasets.
Mode of assessment (if applicable): Group Project and Individual Report and Presentation

Skills / attributes: Problem solving
How this is developed: During the lectures & seminars, the students will gather and synthesise information and use their knowledge of programming and econometrics to answer finance problems.
Mode of assessment (if applicable): Group Project and Individual Report and Presentation

Skills / attributes: Teamworking
How this is developed: In lectures & seminars, the students will work in teams to complete the assigned tasks. They will also work together to complete the group project. In doing so, they will understand the importance of teamwork, manage the interaction and relationships with other group members, gain experience in negotiation, persuasion, influencing and managing conflict.
Mode of assessment (if applicable): Group Project

Skills / attributes: Organization
How this is developed: In lectures & seminars, the students will learn to manage their time carefully by prioritising and completing tasks within specific deadlines. They will also develop their organization skills by working on group and individual projects.
Mode of assessment (if applicable): Group Project and Individual Report and Presentation

Skills / attributes: Ethical
How this is developed: In seminars, by discussing ethical concerns associated with the analysis of financial datasets, e.g. p-hacking.
Mode of assessment (if applicable): Group Project and Individual Report and Presentation

Skills / attributes: Leadership
How this is developed: During the lectures & seminars, the students will have the opportunity to lead team activities. For instance, they will have the opportunity to plan the tasks, identify the resources needed to complete the task, monitor the progress of the group and review the plan if needed.
Mode of assessment (if applicable): Group Project

• Stationarity
• Predictability: Statistical evidence (in-sample and out-of-sample) and economic value
• Volatility modelling
• Event study
• Models for limited dependent variables
• Ethical issues in modelling: look-ahead bias, p-hacking, and others