Researchers from the Bank for International Settlements have used AI to produce daily forecasts of market dysfunction 60 business days before thy actually happen.<\/p>\n
Editorial<\/p>\n
This content has been selected, created and edited by the Finextra editorial team based upon its relevance and interest to our community.<\/p>\n
Predicting financial market stress has long proven to be a largely elusive goal. But, AI’s ability to handle large datasets and unearth hidden nonlinear patterns has provided some hope that this could change.<\/p>\n
For a working paper, BIS researchers built a two-step tool that forecasts market stress and explains the reasons behind its forecast. First, a recurrent neural network (RNN) learns from over one hundred daily market indicators. It predicts the average size of gaps between euro-yen traded directly and euro-dollar-yen traded via the US dollar. <\/p>\n
These “triangular arbitrage parity” gaps should vanish within seconds in normal times, and big or persistent gaps signal that market frictions are rising. <\/p>\n
Second, the model shows, day by day, which market indicators matter most for its signal. This information can then direct a large language model to search recent news about those high-importance indicators to add timely context.<\/p>\n
The system flags periods of likely dysfunction up to 60 business days ahead, say the researchers. In tests on data not used for training from 2021-24, it correctly highlights episodes later linked to real events, including the March 2023 banking strains. <\/p>\n
When the model raises an alert, its highest-weight indicators guide targeted news searches. In case studies, those searches pointed to discussions of the relevant drivers days before turbulence.<\/p>\n
The team concludes: “In short, the tool detects risk early and explains it in accessible terms, helping authorities focus their surveillance and prepare responses.”<\/p>\n
Read the paper:<\/p>\n