The study, carried out by researchers in Sheffield Business School at Sheffield Hallam University, analysed speeches given by Bank of England Monetary Policy Committee members prior to interest rate voting decisions. Using ChatGPT, the researchers classified each speech as dovish, neutral or hawkish based on the tone and content. This classification was then used in an econometric model which could successfully predict how each member would vote at the next one or two policy meetings.The results showed ChatGPT sentiment analysis of the speeches was a statistically significant determinant of future voting behaviour. Committee members who gave more neutral speeches were more likely to vote for interest rate hikes at subsequent meetings.Dr Drew Woodhouse, senior lecturer in economics in Sheffield Business School and lead author of the research, says: "Our findings highlight the predictive potential of tools like ChatGPT for processing human beliefs and expectations. This has major implications for forecasting policy decisions and modelling economic expectations.By leveraging natural language processing and textual analysis, ChatGPT demonstrated proficiency in classifying the nuanced language of central bankers, he says. The technology was able to grasp the intricacies of ’Fed speak’ and relate speech content to eventual policy actions.The researchers suggest this approach could be extended to study other aspects of central bank communications, like forward guidance. It also illustrates how publicly available AI like ChatGPT can empower economic analysis and financial decision-making.In April, it emerged that JPMorgan has already built a ChatGPT-based language model to analyse Federal Reserve statements and speeches in an effort to sniff out potential trading signals. In a note, the bank says "preliminary applications are encouraging," and the model has already been expanded to cover the European Central Bank and the Bank of England, with more central banks to follow.