COMM_V 486I - Applications of AI in Finance
This course explores the application of artificial intelligence and machine learning in modern finance, with an emphasis on investment strategies, market analysis, and financial decision-making. Students will examine AI-driven approaches across a range of analytical domains, including alternative data processing, factor investing, technical analysis, fundamental analysis, and economic forecasting. Drawing on current academic research and hands-on projects, the course equips students with the tools and frameworks to critically evaluate and deploy AI models in contexts such as return forecasting, trading strategy development, and portfolio management
Students will apply a range of AI models spanning traditional machine learning techniques, including decision trees and random forests, as well as deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Vision Transformers (ViT), alongside natural language processing (NLP) methods. Practical applications are defined collaboratively by students and faculty, tailored to the group's interests and skill sets. Examples of these applications include trading strategy development, AI-powered sentiment analysis, unstructured data-driven fundamental analysis, and transfer learning models for economic forecasting. All applications are built using real market data and are grounded in decision-making contexts drawn from leading financial institutions.
This is a highly applied course integrating programming, AI tools, and financial concepts. Students come from diverse backgrounds, including Commerce, Computer Science, and Engineering, among others, forming teams that blend strong quantitative and computational skills with financial knowledge and practical experience. This cross-disciplinary dynamic is one of the course's defining strengths, and has consistently yielded research projects of significant interest to the industry. Students who thrive here share a strong analytical mindset, a collaborative spirit, and a genuine appetite for tackling complex, data-driven problems in finance.
Learning objectives
By the end of this course, students will be able to:
- Determine the potential of artificial intelligence (AI) and machine learning (ML) in transforming financial services and markets.
- Design AI-driven solutions for key financial tasks, including portfolio optimization, investment analysis, and financial forecasting.
- Create end-to-end AI-driven financial applications, including trading strategies, sentiment analysis pipelines, and forecasting models, using real market data.
- Critically assess current academic research at the intersection of AI and finance, identifying methodological strengths, limitations, and opportunities for practical application.
- Synthesize insights from alternative data sources, quantitative models, and financial theory to construct and evaluate investment strategies across multiple analytical domains
- Collaborate across disciplinary boundaries to produce original, industry-relevant research that integrates computational, quantitative, and financial expertise.
Admissions
Admission to COMM 486I is by application only and is restricted to students entering Year 4 in any undergraduate program at UBC. To apply, please self-enroll in the course application site using this link and complete all sections of the application form provided.
For any questions about the course or the application process, please contact Jose Pizarro at jose.pizarro@sauder.ubc.ca.
We'll be accepting and evaluating applications on a rolling basis until December 11, 2026.
The first deadline to apply is Friday, May 15, 2026 at 4:00 PM. Successful applicants will be notified of acceptance by end-of-May. These students will be eligible to participate in the Applications of AI in Finance - Summer Camp (you can find information regarding this camp in the application site).
The course requires a combination of knowledge in three key areas: (1) finance fundamentals, (2) computer programming, and (3) quantitative skills in statistics or econometrics. This knowledge can be acquired through relevant coursework, for example:
- Finance: COMM 298, COMR 473, COMM/COEC 370, COMM/COEC 371, COMM 374, or equivalent
- Computer Programming: CPSC 103, CPSC 110, CPSC 310, COMM 337, COMM 475, or equivalent.
- Quantitative Skills: COMM 414, COMM 415, ECON 328, or equivalent.
As part of the admission process, applicants will be asked to provide information about their coursework or experience in these three areas.
Course credits: 3