# Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market

arXiv: https://arxiv.org/abs/2605.30363 · HTML: https://mlsanity.com/paper/2605.30363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 12 of 12)

- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023)
- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes](https://mlsanity.com/paper/1903.01672/) (2019)
- [Causal Parrots: Large Language Models May Talk Causality But Are Not Causal](https://mlsanity.com/paper/2308.13067/) (2023)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [Can large language models build causal graphs?](https://mlsanity.com/paper/2303.05279/) (2023)
- [Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference](https://mlsanity.com/paper/2310.15117/) (2023)
- [Reconstructing regime-dependent causal relationships from observational time series](https://mlsanity.com/paper/2007.00267/) (2020)
- [Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy](https://mlsanity.com/paper/2411.06211/) (2024)

## Abstract (arXiv)

Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration. They are nonetheless hard to identify: the data signal is noisy and heavily multicollinear, while the contemporaneous text that announces them is unstructured. Standard regime shift detection reads only the data panel and ignores this text, even though it typically signals the shift weeks before it materialises in observed prices. We address this with a text-enhanced pipeline that cross-validates the two signals. A large language model (LLM) proposes candidates from text, which a likelihood-ratio vector-autoregression (VAR) test validates on the panel. In parallel, any regime shift detector proposes data-side candidates that a second LLM call accepts via a permissive text check. Because the acceptance stage consumes a candidate set rather than an algorithm's internals, the data channel accepts any data-driven detector. We deploy the pipeline on the US Treasury market, pairing 2010-2024 FOMC minutes with a 14-variable Treasury / macro panel, with every method evaluated on this same panel. The pipeline reaches F1 = 0.82 and F2 = 0.86 against a verified anchor list of monetary-policy regime shifts (best with rolling PCMCI as the data channel), with same-day modal detection latency, and is detector-agnostic: any of four interchangeable data-driven detectors clears the strongest pure data-only baseline on both scores.