Big Data Applications for Finance

Module code: ECOM151

Credits: 15.0
Semester: SEM2

Contact: Dr Daniele Bianchi

The past few years have witnessed enormous interest in the use of large datasets and new empirical techniques to uncover patterns in financial markets. In this course, we will examine how large datasets, empirical techniques for using large datasets such as (but not limited to) machine learning, and insights from decades of finance research come together in helping market participants take decisions, and affect financial markets. The use of such techniques forms the core of modern financial institutions, especially in retail markets that interact with financial consumers such as credit markets, and quantitative asset management strategies.
The primary purpose of this course is not to teach statistical methods, but to facilitate their use and the financial and economic interpretation of empirical estimates. We, therefore, will study tools and applications at the same time. At the end of the course, students will be able to use modern empirical techniques such as machine learning on large financial datasets, assess the informativeness of empirical estimates and their use in financial markets and visualize complex information sets. Students will be able to apply these tools to specific financial markets (for e.g. credit markets) and in asset management.

Connected course(s): UDF DATA
Assessment: 80.0% Examination, 20.0% Coursework
Level: 7