What is the Flagium AI CoinTree Momentum Model?

Overview

The CoinTree Momentum Model is a cross-sectional equity momentum framework designed to identify emerging market leaders within the Nifty 500 universe. The model ranks securities based on their relative price strength across multiple time horizons and combines these signals into a standardized momentum score.

The primary objective of the framework is to systematically identify stocks exhibiting persistent positive momentum while incorporating quality filters to improve portfolio construction and reduce exposure to low-quality businesses.

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Momentum Signal Construction

Return Horizons

Momentum is measured using Rate of Change (ROC) across six independent lookback periods:

| Signal | Lookback Period | | ------ | --------------- | | ZW1 | 1 Week | | ZW2 | 2 Weeks | | Z1M | 1 Month | | Z2M | 2 Months | | Z3M | 3 Months | | Z6M | 6 Months |

Standardization

To enable comparison across different time horizons, each ROC series is standardized using a cross-sectional Z-score calculation:

[ Z = \frac{ROC - \mu}{\sigma} ]

Where:

• ROC = Security return over the selected lookback period
• μ = Cross-sectional mean return of the universe
• σ = Cross-sectional standard deviation of returns

The Z-score represents the relative strength of a security compared with the broader investment universe.

Composite Momentum Score

A weighted composite score is constructed by combining the standardized momentum signals:

| Factor | Weight | | -------- | ------ | | 1 Week | 5% | | 2 Weeks | 5% | | 1 Month | 15% | | 2 Months | 15% | | 3 Months | 30% | | 6 Months | 30% |

The weighting scheme intentionally places greater emphasis on intermediate-term momentum persistence while retaining sensitivity to shorter-term trend acceleration.

The resulting Composite Z-Score serves as the primary ranking metric for stock selection.

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Security Selection Process

Stage 1: Momentum Screening

The investment universe consists of all constituents of the Nifty 500 Index.

Stocks are ranked in descending order based on their Composite Momentum Score.

The top-ranked securities are shortlisted as momentum candidates.

Stage 2: Quality Overlay

Momentum candidates are further evaluated using business quality and earnings growth criteria, including:

• Return on Capital Employed (ROCE) above 20%
• Consistent revenue growth trajectory
• Consistent profit growth trajectory
• Absence of significant balance-sheet deterioration

The objective of the quality overlay is to prioritize companies where price strength is supported by underlying business performance.

Stage 3: Portfolio Construction

Final portfolio constituents are selected from the highest-ranked momentum candidates that satisfy the quality criteria.

The model seeks concentration in demonstrated market leadership while maintaining exposure to businesses with improving fundamentals.

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Rebalancing Framework

Review Frequency

The model operates on a monthly review cycle.

Signal Updates

Momentum factors are recalculated periodically using the latest available market data.

Portfolio holdings are reviewed during each rebalance window and adjusted based on changes in relative strength rankings and quality metrics.

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Market Breadth & Regime Analysis

In addition to individual stock ranking, aggregate model outputs are used to evaluate broader market conditions.

Key indicators include:

Positive Breadth

Percentage of stocks with Composite Z-Scores above zero.

This metric measures the extent of participation in market advances.

Leadership Breadth

Percentage of stocks exhibiting exceptionally strong momentum characteristics (e.g., Z-Score > 1).

This serves as a proxy for the depth of market leadership.

Leadership Growth

Change in the number of momentum leaders over time.

Expanding leadership is generally associated with strengthening market conditions, while contraction may indicate deteriorating risk appetite.

Average Momentum Score

Average Composite Z-Score across the investment universe.

This provides a broad measure of market momentum and trend persistence.

Together, these indicators contribute to a systematic assessment of prevailing market regimes and internal market strength.

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Risk Considerations

• Momentum signals are relative and may remain negative during broad market drawdowns.
• Cross-sectional rankings continue to identify relative outperformers even in weak market environments.
• Momentum strategies are susceptible to factor reversals and regime shifts.
• Quality filters are incorporated to improve robustness but do not eliminate market risk.

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Future Enhancements

Potential areas for model development include:

• Volatility-adjusted momentum factors
• Sector and industry exposure controls
• Liquidity filters
• Trend-following overlays
• Position sizing based on risk contribution
• Automated portfolio rebalancing
• Multi-factor integration incorporating earnings revisions and quality metrics

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Conclusion

The CoinTree Momentum Model combines cross-sectional momentum analysis with fundamental quality filters to identify high-conviction equity opportunities within the Indian market. By emphasizing both relative price strength and business quality, the framework seeks to capture persistent market leadership while maintaining a disciplined and repeatable investment process.