Economics of Financial Markets and Big Data
Module Code: ECP77513
- ECTS Credit: 5
- Mandatory/Optional: Optional
- Semester/Term Taught: Hilary Term
- Module Coordinator: Professor Niamh Wylie
Module Learning Aims
The module is designed to introduce students to working with large and complex datasets in the context of financial and cryptocurrency markets. Students will apply traditional asset pricing theory and models, such as the EMH and CAPM, in addition to algorithmic models based on Machine Learning for market insights and analytics. The module will be both applied and theoretical in nature. Students will be expected to complete a weekly exercise for continuous assessment and a final individual assignment.
Module Content
| Content | Indicative Number of Hours |
|---|---|
Lecturing hours |
10 |
Tutorial hours |
5 |
Preparation for lectures |
5 |
Individual continuous assessment |
20 |
Reading of assigned materials and active reflection on lecture and course content |
30 |
Individual assignment |
20 |
Total |
90 |
This module is structured around a series of lectures and tutorials. Lecture slides will be provided in advance of lectures. Lectures will be delivered over 5 weekly 2-hour blocks and a weekly 1-hour tutorial to practically apply the learnings. Reference will be made to Journal articles to strengthen the links from theory to practice. Useful resources such as lecture notes, data sets and readings will be available on Blackboard.
Module Pre-Requisites
- ECP 77413 Introduction to Big Data for Economics
- ECP 77453 Machine Learning for Economists
Assessment Details
Assessment for this course will be as follows:
- Continuous Assessment - 50%
- Individual Assignment - 50%
Course Communication
Typically via email and during office hours, and I will be available for feedback on continuous assessment and questions on the assignment. This will be communicated to students in due course. Any changes to the timetable or lectures will be announced on Blackboard.
Please note that all course related email communication must be sent from your official TCD email address. Emails sent from other addresses will not be attended to.