Regime-Shift Detection for FP&A: Hidden-State Models, Breakpoints, and Market Regime Filters in Excel & Python
Traditional FP&A models fail when the business environment shifts. Revenue patterns break. Cost curves bend. Forecast accuracy collapses. Yet most finance teams have no systematic way to detect when a regime shift has occurred, let alone model it.
Regime-Shift Detection for FP&A brings the mathematics of hidden-state modeling, structural break analysis, and advanced time-series filtering into the world of corporate finance. Graham Wexler translates high-level econometrics into practical, board-ready forecasting systems built directly in Excel and Python.
This book shows FP&A leaders, analysts, and strategic finance teams how to identify early signals of market change, build adaptive forecasting pipelines, and engineer models that survive volatility instead of collapsing under it.
Inside, you'll learn:
- How to detect structural breaks, business-cycle shifts, and KPI inflection points in real time
- Hidden-state model techniques such as Markov switching, Bayesian filters, and dynamic thresholds
- Excel and Python workflows for identifying breakpoints in revenue, demand, churn, and cost structures
- Regime-based forecasting systems that adjust scenario logic as underlying dynamics change
- How to build adaptive dashboards that monitor structural volatility and alert decision-makers
- Practical case studies for interpreting regime shifts across SaaS, retail, supply chain, and macro environments
In an era of uncertainty, finance teams need more than forecasting, they need detection systems. This book equips you with the tools to recognize when the game has changed and adjust your models before competitors even notice.
Forecasting is no longer enough. Adaptive finance begins with regime awareness.
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