AI-Assisted Financial Analysis
Spring 2025
Instructor
Kerry Back
kerryback@gmail.com
J. Howard Creekmore Professor of Finance and Professor of Economics
Teaching Assistant
Anthony Zdrojewski
soon-to-be Assistant Professor of Finance, TCU
Meeting Schedule
McNair 214
TTh 12:30 – 2:00
3/18/2025 – 4/24/2025
Course Description
This is a project-based course. We will create workflows to estimate betas, do retirement planning, analyze financial statements, optimize portfolios, perform mutual fund performance evaluation, produce capital budgeting analyses, analyze options, and perform credit risk assessment. We will use generative AI and python, and we will use generative AI to write the python code. The course builds on the knowledge gained in the core finance and applied finance courses and provides additional practice in applying the concepts learned there. The course also provides an introduction to python and to using generative AI. No prior experience with AI or python is needed.
The platform that we will use is Julius.ai. It is a wrapper around several LLMs (large language models) including, at this time, GPT 4o, Claude Sonnet 3.5, and Gemini 2.0. It includes an integrated python environment that is superior to OpenAI’s Data Analysis tool, because it allows for the installation of additional python libraries and it can access the internet to scrape data. Instructions entered through the chat window are wrapped in Julius’ custom instructions and routed to one of the LLMs (you can choose which or use the default). If the instruction requires coding, then the LLM will return code, and Julius’ servers will run the code to produce what was requested. If the code has an error, which happens to everyone, including LLMs, then the Julius servers will automatically send the error message to the LLM to parse the message and improve the code. The end result is usually successful execution of the instruction. So, we can run python to do things without having python on our computers and without writing the code ourselves. What python can do is just about anything. We can get data from the web or upload our own, perform data analysis, create fine-tuned visualizations, and save everything as jpegs, Excel files, Word docs, or even as web pages.
What we will do in the course is to create structured prompts, saved as Julius workflows, that can be shared with others. This is somewhat similar to creating a custom GPT. For example, in analyzing financial statements, we can specify which financial ratios we want to compute and what type of trend analysis and visualizations we want to create, etc., and create a workflow that can be used repeatedly for different tickers. Time will be alloted to build workflows in class.
Assignments and Grading
Grades will be based on weekly assignments and on class participation. Assignments are due on Wednesdays by 11:59 p.m. Late assignments will not be accepted unless there is a significant and documented medical event. Except for Assignment 2, each assignment due date is at least 1 week after the topic is covered in class. All assignments other than Assignment 2 require submitting a link to a Julius workflow. Assignment 2 is a capital budgeting case that you should be able to do based on what you learned in the core finance and applied finance courses. The purpose of Assigment 2 in this course is to provide a foundation for studying AI-assisted capital budgeting the following week.
Course Schedule
| Date | Activity |
|---|---|
| Tue, Mar 18 | Estimating betas |
| Thur, Mar 20 | Fama-French model and performance evaluation |
| Tue, Mar 25 | Retirement planning simulation |
| Wed, Mar 26 | Assignment 1 due: performance evaluation (Julius workflow) |
| Thur, Mar 27 | Portfolio optimization |
| Tue, Apr 1 | Financial statement analysis |
| Wed, Apr 2 | Assignment 2 due: Sneaker 2013 (Excel upload) |
| Thur, Apr 3 | Sentiment analysis |
| Tue, Apr 8 | Capital budgeting |
| Wed, Apr 9 | Assignment 3 due: financial statement analysis (Julius workflow) |
| Thur, Apr 10 | Capital budgeting |
| Tue, Apr 15 | Web scraping |
| Wed, Apr 16 | Assignment 4 due: capital budgeting (Julius workflow) |
| Thur, Apr 17 | Option analysis |
| Tue, Apr 22 | Intro to machine learning |
| Wed, Apr 23 | Assignment 5 due: portfolio optimization (Julius workflow) |
| Thur, Apr 24 | Machine learning for credit risk analysis |
Signing up for Julius
Julius.ai provides a 50% academic discount. Sign up for a free account, then send an email using your Rice email account to team@julius.ai and ask for the academic discount. They will respond with a promo code to use. The Lite account ($8 per month after discount) allows 250 messages per month and may be ok. It is unlikely that will be enough, but you could try it. If the message limit becomes binding, you can switch to the Standard account ($18 per month after discount), which allows unlimited messages. Everything is run in the cloud from a web browser, so there is no software to download.
Honor Code
The Rice University honor code applies to all work in this course. Each student must do his or her own assignments, but students are allowed and in fact encouraged to seek advice from each other. Use of generative AI is of course permitted.
Disability Accommodations
Any student with a documented disability requiring accommodations in this course is encouraged to contact me outside of class. All discussions will remain confidential. Any adjustments or accommodations regarding assignments or the final exam must be made in advance. Students with disabilities should also contact Disability Support Services in the Allen Center.