One of the world’s largest investment banks built a large language model to map cryptic government statements to future government actions.
What’s new: JPMorgan Chase trained a model based on ChatGPT to score statements by a United States financial regulator according to whether it plans to raise or lower interest rates, Bloomberg reported.
How it works: The U.S. Federal Reserve, a government agency that’s empowered to set certain influential interest rates, periodically comments on the national economy. Its words are deliberately vague to prevent markets from acting in advance of formal policy decisions.
- The JPMorgan Chase team trained the model on an unspecified volume of speeches and public statements.
- Given a new statement, it assigns a score. The higher the score, the more likely the agency will raise interest rates. For example, if the model assigns a score of 10, the firm’s economists predict a 10 percent probability that interest rates will rise.
- The team used the same technique to train similar models based on statements of the Bank of England and European Central Bank. It plans to train models for 30 more central banks in the coming months.
- In building its model, the team may have followed the Federal Reserve’s own work, in which the agency fine-tuned GPT-3 to classify its own statements and found that the model agreed with human experts 37 percent of the time.
Results: The team tested the model by scoring past 25 years of Federal Reserve statements and speeches. They didn’t describe the results in detail but said they found a general correlation between the predicted and actual interest rate fluctuations.
Behind the news: Prior to the advent of large language models, investors tried to predict the impact of central bank announcements via sentiment analysis, timing the interval between official meetings and publication of minutes, and watching the sizes of their briefcases.
Why it matters: Central banks use interest rates to steer their country’s economies. Lower rates spur economic growth and fight recessions by making money cheaper to borrow. Higher interest rates tamp down inflation by making borrowing more expensive. If you can predict such changes accurately, you stand to reap huge profits by using your predictions to guide investments.
We’re thinking: Custom models built by teams outside the tech sector are gaining steam. Bloomberg itself — which makes most of its money providing financial data — trained a BLOOM-style model on its corpus and found that it performed financial tasks significantly better than a general-purpose model.