Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market
This research offers an incremental improvement in regime shift detection for financial market analysts and policymakers by incorporating previously underutilized unstructured textual data.
This paper addresses the challenge of reliably detecting regime shifts in financial markets by integrating unstructured central-bank communications with structured financial time-series data. The proposed text-enhanced pipeline, which combines LLM reasoning with statistical validation, achieved an F1 score of 0.82 against a verified list of monetary-policy regime shifts, demonstrating consistently stronger performance than pure data-driven baselines.
Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration. They are nonetheless difficult to detect reliably because the data signal is noisy and heavily multicollinear, while the contemporaneous text that announces them is unstructured. Standard regime shift detection methods rely solely on structured time-series data and ignore policy communications, even though these texts often signal shifts before they materialise in observed prices. We propose a text-enhanced regime shift detection pipeline that combines large language model (LLM) reasoning over central-bank communications with statistical validation on multivariate financial time series. The framework is detector-agnostic: text-proposed candidates are validated using a bootstrap likelihood-ratio test on a vector autoregression (VAR), while data-driven candidates from arbitrary regime detectors are ratified through a lenient LLM text check. We evaluate the framework on 2010-2024 FOMC minutes paired with a 14-variable U.S. Treasury and macroeconomic panel, using four interchangeable data-driven detectors. The proposed pipeline achieves F1 = 0.82 against a verified anchor list of monetary-policy regime shifts, with same-day modal detection latency and consistently stronger performance than pure data-driven baselines. The results demonstrate that combining unstructured policy text with statistical structural-break detection improves the robustness and interpretability of regime shift identification in financial markets.