Harnessing artificial intelligence for monitoring financial markets
Summary
Focus
We study how artificial intelligence can help monitor financial markets. We build a two-step tool that forecasts market stress and explains the reasons behind its forecast. First, a recurrent neural network 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. These "triangular arbitrage parity" gaps should vanish within seconds in normal times, and big or persistent gaps signal that market frictions are rising. 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.
Contribution
Forecasting stress is hard. Severe events are rare, links across markets are non-linear and standard early warning tools often miss new risks. Our approach joins statistical power with reasoning. The network's data-driven weights make its decisions transparent. Their movement over time is itself an early signal that market dynamics may be shifting. The language model then turns these signals into short narratives that point supervisors and analysts to the right topics at the right moment. This helps close the gap between "a number went up" and "here is why it may be rising".
Findings
Using more than one hundred daily indicators, the system flags periods of likely dysfunction up to 60 business days ahead. 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. 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. In short, the tool detects risk early and explains it in accessible terms, helping authorities focus their surveillance and prepare responses.
Abstract
Predicting financial market stress has long proven to be a largely elusive goal. Advances in artificial intelligence and machine learning offer new possibilities to tackle this problem, given their ability to handle large datasets and unearth hidden nonlinear patterns. In this paper, we develop a new approach based on a combination of a recurrent neural network (RNN) and a large language model. Focusing on deviations from triangular arbitrage parity (TAP) in the Euro-Yen currency pair, our RNN produces interpretable daily forecasts of market dysfunction 60 business days ahead. To address the "black box" limitations of RNNs, our model assigns data-driven, time-varying weights to the input variables, making its decision process transparent. These weights serve a dual purpose. First, their evolution in and of itself provides early signals of latent changes in market dynamics. Second, when the network forecasts a higher probability of market dysfunction, these variable-specific weights help identify relevant market variables that we use to prompt an LLM to search for relevant information about potential market stress drivers.
JEL classification: G14, G15, G17
Keywords: market dysfunction, liquidity, arbitrage, artificial intelligence, financial stability
