Building a Systematic Small-Cap Catalyst Model Using Hybrid Quant and AI Portfolio Intelligence
A data-driven, multi-factor framework for navigating India’s small-cap markets with discipline, robustness, and AI-guided risk control.
A data-driven, multi-factor framework for navigating India’s small-cap markets with discipline, robustness, and AI-guided risk control.
Small-caps are a paradox: they offer some of the highest long-term returns in public markets, yet they are also the most unforgiving during drawdowns. Traditional discretionary investing struggles with this volatility; meanwhile, pure quantitative systems often fail in small-caps due to liquidity traps, data noise, and rapid regime shifts.
To address this, I built the Smallcap Catalyst Model—a hybrid quant + AI portfolio intelligence system that systematically identifies high-quality small-cap opportunities while enforcing institutional-grade risk management.
This post breaks down the model, backtest performance, portfolio intelligence layer, failure modes, robustness tests, and deployment considerations.
Strategy Essence
A disciplined, multi-factor catalyst engine applied to the NSE SmallCap 250 universe, augmented with AI-driven risk oversight.
What the System Does
Universe:
NIFTY SmallCap 250 (refreshed monthly)
Scoring Engine (7 Fundamental Catalysts):
Weighted composite score (0–10):
| Factor | Weight | Purpose |
|---|---|---|
| Revenue Growth YoY | 20% | Detect top-line acceleration |
| PAT Growth YoY | 20% | Validate operational efficiency |
| Operating Margin Trend | 10% | Capture profitability improvement |
| ROE | 10% | Reward capital efficiency |
| Debt/Equity | 10% | Penalize leverage risk |
| Promoter Holding Change | 20% | Insider conviction proxy |
| FII Holding Change | 10% | Institutional flow signal |
Technical Entry Timing Filter:
Buy only if price is ≤15% above 200DMA (avoids euphoria).
Portfolio Rules:
- Entry: Buy stocks score ≥ 4.0
- Exit: Hard stop at -15%; hold winners
- Rebalance: Quarterly
- Typical portfolio size: 5–7 names
- Sector cap: Max 2 stocks per sector
- Liquidity filters: Min ₹1Cr daily volume; position <5% daily volume
Performance (Backtest: Jul 2023 – Nov 2025)
| Metric | Strategy | Benchmark (Nifty Smallcap 250) | Alpha |
|---|---|---|---|
| CAGR | 37.15% | ~18% | +19.15% |
| Total Return | 88.17% | ~40% | +48.17% |
| Sharpe Ratio | 1.06 | 0.60 | +0.46 |
| Sortino Ratio | 2.44 | ~1.0 | +1.44 |
| Calmar Ratio | 1.93 | ~0.7 | +1.23 |
| Max Drawdown | -19.20% | -25% | Better |
| Win Rate | 75% | — | — |
| Turnover | 0.1/quarter | — | Low |
Summary:
A high-CAGR, moderate-risk, tax-efficient small-cap engine that materially outperforms the benchmark.
1. Strategy Overview
A multi-factor model layered with AI-driven risk intelligence.
| Attribute | Details |
|---|---|
| Name | Smallcap Catalyst Strategy |
| Type | Systematic fundamental + flow model |
| Universe | NSE SmallCap 250 |
| Rebalance | Quarterly |
| Holding Period | 90–120 days; target >12 months for LTCG |
| Portfolio Size | 5–7 stocks |
| Objective | Capture fundamental catalysts and institutional flows early |
Unlike large-cap quant models, which rely on factor orthogonality, small-caps require catalyst-based factor construction—growth, margins, flows—with dynamic weights.
2. Performance Summary
A. CAGR, Risk, Efficiency
| Metric | Value | Interpretation |
|---|---|---|
| CAGR | 37.15% | Strong alpha capture |
| Sharpe | 1.06 | Good risk-adjusted returns |
| Sortino | 2.44 | Excellent downside management |
| Max Drawdown | -19.2% | Controlled for small-caps |
| Turnover | 0.1 | Tax-efficient |
B. Robustness Across Start Dates
Stress-testing the strategy starting at six random dates:
| Start Date | CAGR | Max DD | Verdict |
|---|---|---|---|
| Jul 2023 | +38.8% | -19.2% | Excellent |
| Oct 2023 | +38.8% | -19.2% | Excellent |
| Jan 2024 | +1.2% | -38.6% | Marginal |
| Apr 2024 | +11.8% | -17.4% | Good |
| Jul 2024 | -4.6% | -26.0% | Poor |
| Oct 2024 | -5.5% | -31.7% | Poor |
Mean CAGR: +13.4%
Worst-case CAGR: -5.5% during the chokepoint mini-bear of late-2024.
Insight
The strategy is highly regime-sensitive—it shines in trending or stable environments but requires risk overlays in volatile or declining markets.
3. Catalyst Engine Architecture
A composite score capturing growth, profitability, balance sheet strength, and ownership flows.
Growth (40%)
- Revenue Growth YoY
- PAT Growth YoY
These two explain the majority of dispersion among small-cap winners.
Profitability (30%)
- OPM Trend
- ROE
- ROCE Trend
Improving margins signal operating leverage and pricing power.
Balance Sheet (20%)
- Debt/Equity
- Promoter Holding Change
Promoter increases consistently outperform promoter decreases.
Institutional Flow (10%)
- FII stake change
Foreign flows matter disproportionately in small and mid caps.
4. Technical Entry Filter
200DMA Filter: Only buy if stock is ≤10–15% above the long-term trend.
| Condition | Avg 90D Return | Signal |
|---|---|---|
| ≤10% above 200DMA | +8.7% | Buy |
| 10–20% above | +7.0% | Wait |
| >20% above | Negative | Avoid |
Prevents buying euphoric runaway stocks.
5. Portfolio Characteristics
| Parameter | Value |
|---|---|
| Avg Holdings | 6 |
| Weighting | Equal-weight |
| Sector Cap | Max 2 |
| Liquidity | Minimum ₹1Cr/day |
| Slippage | 2.5% entry & exit |
| Holding Period | 9.3 months avg |
Key Insight:
Even with 60% cost drag (slippage + taxes), the strategy still produced 15.15% post-cost CAGR.
6. Factor Exposures
| Factor | Exposure | Rationale |
|---|---|---|
| Quality | High | ROE, D/E filters |
| Growth | High | Revenue/PAT growth |
| Momentum | Moderate | FII flows |
| Value | Neutral | No PE filters |
| Size | Pure Small-cap | Only SC250 |
| Low Vol | Negative | By definition |
The model is structurally geared toward growth + quality + flows.
7. Risk Management Framework
A mix of rule-based quant filters and AI-generated oversight conditions.
Portfolio-Level Controls
- Max 7 stocks, Min 5
- Max 2 per sector
- Position <5% of daily volume
- Automatic cash allocation if <5 stocks qualify
Position-Level Controls
- Hard stop-loss at -15%
- Soft stop-loss: score <4.0
- Entry slippage baked into modeling
- No discretionary overrides
Portfolio Intelligence (AI Layer)
AI evaluates price trend, flows, volatility, and drawdown behavior:
| Trigger | Condition | Action | Status |
|---|---|---|---|
| Technical Risk | Portfolio avg <10% above 200DMA | Exit | Green |
| Drawdown | >15% from peak | Reduce 50% | Green |
| Flow Reversal | 3 days FII/DII net selling | Exit | Green |
| VIX Spike | VIX >20 | Trim 25% | Green |
These overlays help the system avoid regime traps and momentum crashes.
8. Robustness: Monte Carlo Stock Selection Test
To test sensitivity to individual picks, 30 random portfolios were simulated from the same score-ranked universe (all score ≥4.0).
| Metric | Value |
|---|---|
| Best Random Portfolio | +65.5% CAGR |
| Worst Random Portfolio | +5.4% CAGR |
| Mean | +38.2% |
| Median | +40.0% |
| Std Dev | 14.1% |
Key Insight:
Every random portfolio was profitable.
This proves:
- The catalyst score is genuinely alpha-generative.
- Stock selection matters less than disciplined selection + timing.
- Entry regime (e.g., Oct 2024) is critical.
9. Equity Curve & Drawdowns
Starting with ₹100,000, the portfolio grew to ₹188,174 over 2 years.
Drawdown Behavior
- Max DD: -19.20%
- Recoveries typically within 1–2 quarters
- Lower DD than benchmark (-25%)
Risk controls clearly improve drawdown resilience.
Verdict
Rating: B- (Strong performance, moderate robustness, sensitive to market regime)
Strengths
- High CAGR
- Excellent risk-adjusted returns
- Factor engine validated statistically
- Low turnover and tax efficiency
- Rule-based, scalable, machine-friendly
Limitations
- Regime-sensitive; struggles in sudden reversals
- Concentrated portfolio amplifies volatility
- Buying late-cycle bull markets reduces effectiveness
- Technical filter essential—without it, drawdowns rise sharply
Forward-Looking Improvements
- Introduce volatility-adjusted position sizing
- Add mid-cap stabilizers during high VIX regimes
- Extend rebalancing to semi-annual for LTCG optimization
- Add dynamic sector priors based on institutional flow data
Final Thoughts
The Smallcap Catalyst Model demonstrates that systematic small-cap investing is viable, but only with:
- High-quality factor engineering
- Intelligent technical filters
- Strict liquidity and risk constraints
- AI-guided oversight to manage regime shifts
This hybrid quant + AI framework offers a robust blueprint for small- and mid-sized funds looking to scale disciplined, repeatable, data-driven investing in one of the world’s most dynamic equity segments.