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Generative and Agentic AI for Finance - Home
  • Generative and Agentic AI for Finance
  • Course Syllabus: FINM 33200, Spring 2026
  • Course Overview

Discussions 📖

  • Discussion 1: The New Quant - LLMs in Finance
    • LLMs Transforming Finance: The New Quant
    • LLMs for Return Prediction: Prompts vs. Embeddings
    • ChartBook: Cataloging Research for Humans and AI
    • API Basics and Structured Outputs
    • Study Questions
  • Discussion 2: Text Representation and Tokenization
    • The Structure of Economic News
    • AI Copilots: History and Tools
    • From Words to Tokens
    • Optional extra: News, Financial Crises, and the Geometry of Meaning
    • Study Questions
  • Discussion 3: LLM Fundamentals
    • AI Roundup: Week 3
    • LLMs and Lookahead Bias
    • The Mathematics of microGPT
    • The OpenAI API Platform
    • OpenAI API Overview
    • Study Questions
  • Discussion 4: Benchmarking and LLM Tool Use
    • From Chain-Of-Thought To Tool Use
    • Benchmarking LLMs
    • Retrieval-Augmented Generation (RAG)
    • 01. Semantic Search of SEC Filings
    • 02. RAG Benchmark: Can Retrieved Context Fix a Weak Model?
    • Study Questions
  • Discussion 5: Agents
    • Publishing to GitHub Pages
    • Introduction to Agents
  • WARNING: Notes subject to change after this week

Homework 📝

  • Homework 0: Set Up Your Environment
    • Data Sources & Strategy
    • GDELT Filtering & Data Quality
    • Crosswalk Quality
  • Homework 1: Lopez-Lira, Tang (2023)
    • Data Sources Overview
    • NLP Pipeline & Portfolio Construction
    • Replication Results
  • Homework 2: Chen, Kelly, Xiu (2022)
    • Data Explorer
    • Text Embeddings: From Words to Vectors
    • Methodology: From Embeddings to Predictions
    • Replication Results: Chen, Kelly, and Xiu (2022)
  • Homework 3: Benchmarking RAG with SEC Filings
    • RAG Case Study: Data Overview
    • RAG Pipeline Walkthrough
    • Benchmarking RAG with SEC Filings
  • Homework 4: Agentic RAG Trace Audit
  • Final Project
    • Final Project Instructions
    • Potential Final Project Ideas

Exams 📋

  • Exam Preparation

Misc 📌

  • Acknowledgments
  • Appendix
    • The Mathematics of Karpathy’s nanochat
  • Repository
  • Open issue
  • .md

Appendix

Contents

  • Extra info for interested readers:
  • Setting up Python and Visual Studio Code

Appendix#

Extra info for interested readers:#

  • The Mathematics of Karpathy’s nanochat

Setting up Python and Visual Studio Code#

  • Please watch this video and pay attention to the Python Interactive Window at the end: https://www.youtube.com/watch?v=mpk4Q5feWaw

  • This additional video is also useful, but is less relevant to data scientists: https://www.youtube.com/watch?v=PwGKhvqJCQM

previous

Acknowledgments

next

The Mathematics of Karpathy’s nanochat

Contents
  • Extra info for interested readers:
  • Setting up Python and Visual Studio Code

By Jeremiah Bejarano

© Copyright 2025, Jeremiah Bejarano.