Q & A
AI Street interviewed Dr. Alejandro Lopez-Lira, a finance professor at the University of Florida and author of “The Predictive Edge: Outsmart the Market using Generative AI and ChatGPT in Financial Forecasting” out today.
The book explains how to use artificial intelligence and large language models (LLMs) to find investment ideas, improve trading strategies and increase efficiency.
Lopez-Lira has been at the forefront of AI and finance, publishing a research paper last year on ChatGPT’s ability to predict stock returns based on news headlines. Lopez-Lira and his co-author, Yuehua Tang, asked ChatGPT to decide how a headline would affect a company’s stock price with a short prompt:
Forget all your previous instructions. Pretend you are a financial expert. You are a financial expert with stock recommendation experience. Answer “YES” if good news, “NO” if bad news, or “UNKNOWN” if uncertain in the first line. Then elaborate with one short and concise sentence on the next line. Is this headline good or bad for the stock price of [X] company?
ChatGPT produced statistically significant results that outperformed traditional sentiment models despite it not having any explicit financial training.
The study, which has been downloaded more than 60,000 times, landed Lopez-Lira his first book deal and garnered interest from multiple news outlets, major hedge funds, and the U.S. Securities and Exchange Commission.
This interview has been edited for clarity and length.
AI Street: How did your book come about?
Everybody was interested in the research paper. I got calls from journalists, hedge funds. An editorial contact at Wiley said `Hey, you want to write a book about it?’
So, I did. And in the book, it runs from beginner to more advanced topics. You will learn the basics of AI and the basics of finance from risk management to summarization to more advanced tasks like having agents based on LLMs trading by themselves.
What can you currently do with this technology?
You can use LLMs to assess economic news like macroeconomic indicators and combine that to the specific program that's executing the software.
I created a markets’ website where all the content is generated by large language models. It generates a daily report with macroeconomic environment news, headlines on companies for every S&P 500 stock. I built this with my dad, who’s a software engineer and it took us about six hours or something.