Build It in a Day Seminar, Presented by the ERM Program

February 6 – February 27, 2026
Time for all workshops: 1:00 pm – 4:00 pm ET

These workshops offer the opportunity to experience risk management processes by building them. Examples include counterparty credit risk estimation, FAMA-French factor models, bank default modeling, risk management infrastructure at a lending company, the Fundamental Review of the Trading Book (FRTB), Comprehensive Capital Assessment and Review (CCAR). These major concepts are as much about their implementation as they are about their theoretical description; and organizations gain competitive advantage by effectively implementing these concepts. As a professional, a first step to adding value to this work at an organization is knowing how to build a simple version in a day.

How To Join a Session

Click here at 1:00 pm ET on the day of your desired workshop to join virtually. Each workshop ends at 4:00 pm ET.

Workshop Info 

Friday, February 6 | 1:00 pm to 4:00 pm ET
CCAR
Explore the entire process of CCAR and simulate one of the government defined scenarios for a given portfolio. The workshop also reviews different institutions and their official CCAR submissions.

Friday, February 13 | 1:00 pm to 4:00 pm ET
FRTB (Fundamental Review of the Trading Book) 1
Delve into the revised market risk framework. Build the standardized model to understand the impact on capital requirements and risk management practices for financial institutions.

Friday, February 20 | 1:00 pm to 4:00 pm ET
FRTB (Fundamental Review of the Trading Book) 2
Delve into the revised market risk framework. Build the Internal model to understand the impact on capital requirements and risk management practices for financial institutions.

Friday, February 27 | 1:00 pm to 4:00 pm ET
Building LLM-Driven Investment Strategies
The workshop guides students through the end-to-end development of an application that ingests news data, identifies investable companies, and generates investment signals using the latest GPT model. Participants begin by preparing and structuring news data to train the model and launching the training process. Using the resulting buy and sell signals, they then construct an investment portfolio and evaluate its performance by measuring investment returns and comparing them against the S&P 500 index as a benchmark. The workshop provides a detailed demonstration of how to effectively prompt and train a large language model to achieve superior investment results relative to the base GPT model. By the conclusion of the session, students will have built a complete, functioning application that transforms raw news data into an actionable investment strategy and rigorously measures the associated portfolio performance.