What question did this study set out to answer?

To present a new framework for early detection of financial recovery phases following crises.

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April 15, 2026Open Access

The Maat Cycle: A Symmetric Framework for Detecting Recovery From Financial Stress

Key Points

To present a new framework for early detection of financial recovery phases following crises.
Developed the SEWS-R framework for recovery detection.
Utilized 80 quarters of U.S. banking sector data from 2005 to 2024.
Applied the Stability-Balance Theorem in the analysis.
Detected recovery from the Global Financial Crisis approximately 12 quarters early.
Identified recovery from the COVID-19 shock around 4 quarters prior to conventional indicators.

Abstract

This paper presents SEWS-R (Spectral Recovery Detection), the symmetric completion of the SEWS framework, and introduces the Maat Cycle: a four-phase model of complete system stress and recovery (STABLE → DRIFT → BROKEN → RETURN). Using 80 quarters of U.S. banking sector data (2005–2024), SEWS-R successfully detected the early onset of recovery from the Global Financial Crisis (approx. 12 quarters before conventional confirmation) and the COVID-19 shock (approx. 4 quarters early). The framework is built on the Stability-Balance Theorem (DOI: 10.5281/zenodo.19539578) and extends the SEWS Bank preprint (DOI: 10.5281/zenodo.19544383).

The Maat Cycle: A Symmetric Framework for Detecting Recovery From Financial Stress

Key Points

Abstract

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