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
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. 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. 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. 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. 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. 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. The model is ultra-low frequency, with on average one signal a year.