Since the introduction of ChatGPT, the world has been swept by the generative AI wave. While LLMs are still in the early stage of deployment, they have shown great promise to transform our future work with significant productivity gains. At the same time, it poses significant risks and may cause dislocations of many jobs. Like many technology shocks came before it, generative AI will create winners and losers, depending on how we individually adapt to the change.
The purpose of this course is to explore these potentials and risks for banking and finance careers. The key learning objective is to equip students with a good knowledge of the underlying technology beyond the “black box”, that is, Large Language Model (LLM), and how they can leverage its potentials in their future work in Finance.
Here is how the course is organized.
- Introduction to Large Language Models – We will first start with a basic introduction to LLM, which includes history of Natural Language Process (NLP), evolution to LLM, and key elements of the underlying algorithms. We will also touch upon training and fine-tuning of a LLM, as well as prompt engineering to get the most out of a LLM. We will not devolve into deep mathematical details of these models, rather we will focus on their applications in Finance.
- Introduction to various finance sectors and related jobs – We will introduce some of the basics of the banking business, and some select job categories that could potentially be impacted by Gen AI. The goal is to level the student’s understanding of basic finance knowledge that is relevant to this course.
- Understanding the potential and risks of Gen AI in Finance – We will explore case studies on a few select jobs such as investment banking analyst, equity researcher, sales and marketing as well as trading and asset management.
- Class Projects – One of the most important parts of the class is student projects, which account for more than 50% of the final grade. Students will be divided into teams, and work on assigned projects within given guidelines to achieve optimal results.
Prerequisites:
- Math/Statistics Knowledge: Math/Stat 230, Math 340/Stat 231, or Math 238L/Eng 238L, or ECON 104D
- Programing Skills: Good working understanding of Python programming: Preferably have taken class such as CS 101, or Math 281L or Math260L or willingness to self-learn.
- Finance Knowledge: While formal knowledge gained through a credited course is not required, some general knowledge about finance and financial markets is beneficial. Regardless, intellectual curiosity and independent study habits are strongly desired.