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Economic Regime Model

The strategy employs a systematic, economic regime-based allocation model to dynamically shift between:
NIFTY MIDCAP 150 Momentum 50 Index
NIFTY 100 Low Volatility 30 Index
The shift is based on the whether the indicator points toward expansion, transition or a contraction phrase:
Delta: MoM change (absolute) ​Acceleration: 3-month EWMA of MoM change (absolute)
Expansion (100% Momentum, 0% LV): Delta > 0 and Acceleration > 0
Transition (60% Momentum, 40% LV):Delta > 0 and Acceleration < 0 Delta < 0 and Acceleration > 0
Contraction (0% Momentum, 100% LV): ​Delta < 0 and Acceleration < 0
This methodology is rooted in empirical research, leveraging the role of OECD’s Composite Leading Indicator (CLI) for identification of regimes. Research on the same by S&P using BSE factor indices is available here: .

Backtest Period: 1st April 2005 to 13th May 2025
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The CLI-Model has an average allocation of 52% over the backtest period, suggesting no unnecessary overweight on momentum all the time to generate alpha over a 60/40 Momentum/LV benchmark.
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The model generated an annualized alpha of 3 percentage points over its 60/40 counterpart and 10 percentage points over NIFTY 500 TRI. The performance is generated with consistency as is evident by the superior hit ratios, Sharpe & Sortino ratios and lower drawdowns.
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The CLI-model has an 86% hit ratio vs the base case model on a rolling 3-year CAGR basis with an average and median alpha of 2.8 and 2.2 percentage points.
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The model has been able to consistently outperform both NIFTY 500 and the base model i.e. a 60/40% split between NIFTY MIDCAP 150 Momentum 50 and NIFTY 100 Low Volatility 30 TRI.
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The model has generated alpha over the NIFTY 500 TRI benchmark in 16 out of 19 full calendar year with average positive annual alpha of 12.7% vs 13.1% for the 60/40% base model and average negative annual alpha of 3.9% vs 2.3% for the 60/40% base model.
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A trade-by-trade analysis shows that the model has an accuracy rate of ~71% i.e. when it shifts to momentum overweight (> 60% static) or low-volatility factor overweight (> 40% static), that factor outperforms the other 71% of the time. Secondly, the overweight factor outperforms its alternative by 15% on average while underperforming by only ~5% on average when the call goes wrong.
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The model is ultra-low frequency, with on average one signal a year.
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