Tax Genius · India
Market Regime Research Framework
A Mission Saptanga methodology for studying market regimes using macro context, volatility, liquidity, cross-asset evidence and explicit transition tests.
Research framework published and architecture-reviewed:
Research framework
A regime label is useful only when the variables, transition rules and portfolio consequences behind it are inspectable.
Research question
Markets often behave differently across inflation, liquidity, volatility and growth environments. The research problem is not to invent a memorable label; it is to determine whether observable variables form sufficiently distinct states to improve analysis.
Regime construction
Mission Saptanga separates the construction of a regime model from the interpretation of that model. Candidate variables are defined first, normalization and lookback choices are recorded, and labels are applied only after the statistical structure is inspected.
- Macro: growth, inflation, policy and liquidity variables
- Market: trend, volatility, breadth and correlation variables
- Cross-asset: rates, currency, commodity and equity relationships
Validation discipline
A regime framework should be challenged for instability, look-ahead bias and overfitting. A useful research output documents where classification is uncertain rather than forcing every observation into a confident narrative.
Decision role
Regime research contributes context to portfolio and risk analysis. It does not replace security-level research, valuation, position sizing or investor-specific constraints.
Methodology
- Define observable regime variables before assigning labels.
- Separate descriptive clustering from predictive claims.
- Test regime stability across multiple windows and market conditions.
- Evaluate portfolio implications independently from the regime-classification step.
Evidence requirements
- Dated market and macro data with documented provenance.
- Explicit variable definitions and transformation rules.
- Sensitivity checks for threshold or clustering choices.
- Out-of-sample or later-period validation before any predictive use.
Limitations
- Regime boundaries are model-dependent and can change with the variables selected.
- Historical regime persistence does not guarantee future persistence.
- This framework is research methodology, not a personalized investment recommendation.