By analyzing central bank messaging for its level of dovishness or hawkishness and finding patterns in how it has historically affected markets, JPMorgan’s tool shows how AI models can give banks an edge in trading. 

JPMorgan debuted a ChatGPT-powered model for detecting the tenor of central bank messaging that can help interpret current signals and, ultimately, predict upcoming market shifts. 

It analyzed 25 years of Fed transcripts to score them based on how hawkish or dovish the statements were and then compared those scores to historical market moves. 

“Plotting the index against a range of asset performances, the economists found that the AI tool can be useful in potentially predicting changes in policy — and give off tradeable signals,” according to Bloomberg. “For instance, they discovered that when the model shows a rise in hawkishness among Fed speakers between meetings, the next policy statement has gotten more hawkish, and yields on one-year government bonds advanced.” 

The Fed is expected to raise its benchmark interest rate again this week, and JPMorgan economists said that preliminary applications of the model ahead of the Fed’s meetings are “encouraging.”  

The tool – which produces what JPMorgan refers to as its Hawk-Dove Score – will be applied to data from more than 30 central banks around the world in the coming months, the bank told Bloomberg. It also provides another example of how FinServs can experiment with OpenAI’s buzzy ChatGPT technology

Find more of Insights Distilled’s coverage on how generative AI is shaping financial services here.