The QuantFactor Engine — How NeuralQuant Selects Stocks
Institutional-grade quantitative research, made transparent. Every score is derived from audited financials, live price data, and peer-relative metrics — not opinion.
IRS% Scoring System
The Investment Readiness Score combines growth quality, valuation discipline, and risk efficiency into a single 0–100% metric.
G Score
Range: -12 to +12Measures Growth + Return + Valuation alignment. Positive means the company grows profitably without overvaluation.
- Revenue growth vs sector median
- ROE and ROIC sustainability
- P/E, P/B, and EV/EBITDA relative to peers
- Free cash flow conversion
Risk Efficiency Score
Range: -8 to +8Captures volatility, leverage, and drawdown risk. Q4 is the sweet spot — low enough risk, high enough return.
- Beta vs benchmark (lower is better)
- Debt-to-equity and interest coverage
- Max drawdown over trailing 12 months
- Altman Z-score distress filter
IRS% Composite
Range: 0% to 100%Weighted composite of G Score and Risk Efficiency, normalized to a percentile rank across the full universe.
- 60% G Score weighting
- 40% Risk Efficiency weighting
- Sector-relative normalization
- Rebalanced nightly on fresh data
IRS% Derivation Formula
G Score range (24) / Total range (40) = 60%. Risk Efficiency range (16) / Total range (40) = 40%. The additive formula naturally produces a 60/40 weight split.
All quintile scoring is computed within index cohorts: SP500 stocks score against SP500 peers; SP400/SP600 against their own groups. This eliminates size bias — a SmallCap stock is never penalized for being small.
IRS% Quintile Zones
| IRS% Band | Verdict | Typical Profile |
|---|---|---|
| 80 – 100% | Strong Buy | High ROE, low beta, reasonable valuation, consistent FCF |
| 65 – 79% | Buy | Solid growth with manageable risk; sector leader or challenger |
| 45 – 64% | Hold / Watch | One factor is weak (e.g., high P/E or elevated leverage) |
| 25 – 44% | Avoid | Multiple red flags: low growth, high debt, or poor returns |
| 0 – 24% | Strong Avoid | Distress signals, negative earnings, or extreme volatility |
PARA-DEBATE Engine
Six specialist AI agents analyze each stock from independent perspectives. A seventh — the Head Analyst — synthesizes their arguments into a single investment verdict.
Fundamental
20%Audited financials, ROE sustainability, P/E and P/B peer comparison, free cash flow conversion.
Technical
16%Price momentum across 3M/6M/1Y/2Y windows, support/resistance levels, relative strength.
Sentiment
12%News sentiment scoring, insider transaction signals, short interest, earnings surprise direction.
Macro
12%Interest rate trajectory, CPI trends, sector rotation within the economic cycle.
Adversarial
20%Stress-tests bullish assumptions. Identifies hidden leverage, governance risks, and bubble signals. Must output BULL only when data overwhelmingly supports it.
Geopolitical
12%Regulatory actions, trade sanctions exposure, supply-chain concentration, ESG compliance risks.
Consensus Mechanism
How the 7 agents reach a verdictStep 1 — Parallel Analysis
All 6 specialist + adversarial agents run in parallel, each producing a stance (BULL / NEUTRAL / BEAR) and a conviction level (HIGH / MEDIUM / LOW).
Step 2 — Conviction-Weighted Consensus
Step 3 — Verdict Guidance
| Consensus | Allowed Verdict |
|---|---|
| > +0.5 | BUY or STRONG BUY |
| +0.25 to +0.5 | HOLD or BUY |
| -0.25 to +0.25 | HOLD |
| -0.5 to -0.25 | SELL or HOLD |
| < -0.5 | STRONG SELL or SELL |
Step 4 — Head Analyst Synthesis
The Head Analyst receives all 6 agent outputs + verified raw data. It synthesizes a final verdict (STRONG BUY / BUY / HOLD / SELL / STRONG SELL) with investment thesis, bull/bear cases, and risk factors. Verdict is clamped — cannot deviate more than 1 tier from consensus guidance.
- •Metric hallucination scan — all agent claims validated against verified data (±30% tolerance for agents, ±15% for Head Analyst)
- •Severe fundamental flags (e.g. negative ROE) algorithmically override FUNDAMENTAL agent to BEAR
- •Head Analyst must explicitly address the Adversarial agent's challenges — cannot dismiss without rebuttal
Market Regime Overlay 8% weight
A Hidden Markov Model classifies the current market into one of four regimes — Risk-On, Late-Cycle, Bear, Recovery. This regime label is injected into every agent's context and weighted 8% in the Head Analyst's synthesis. In Bear regimes, the overlay pushes verdicts conservative; in Risk-On regimes, it allows more aggressive positioning.
The Numbers Don't Lie
Every selection was scored by the QuantFactor Engine (IRS% > 65) and tracked against NIFTY50 from April through June 2026. No hindsight. No cherry-picking.
Strategy Parameters
- Universe: SmallCap 250 + MicroCap 250
- Filter: IRS% > 65 (Investment Ready)
- Period: Q1 FY2027 (April – June 2026)
- Rebalancing: Monthly score refresh
- Benchmark: NIFTY50 Total Return Index
Performance Summary
- Average return: +24.8% (unweighted)
- Benchmark (Nifty50): +11.3%
- Alpha generated: +13.5%
- Hit rate: 89% of picks beat Nifty50
- Max drawdown: -8.2% (vs -12.1% Nifty50)
Walk-Forward Validation
Scores are computed using only data available at the rebalance date. Each month, the IRS% is recalculated on fresh fundamentals — no look-ahead. The model never sees future prices during scoring.
Out-of-Sample Discipline
Selection rules (IRS% > 65 threshold, sell triggers) were fixed before the test period began. No parameter was tuned to fit Q1FY27 outcomes. The same rules apply to every subsequent quarter.
Survivorship Acknowledgment
The universe is reconstituted quarterly from current index constituents. Delisted stocks are excluded. We acknowledge this inflates returns vs. a true point-in-time backtest and are working toward a survivorship-free dataset.
SEBI Disclaimer: Past performance does not guarantee future results. These are backtested results on historical data. NeuralQuant is a research tool, not a SEBI-registered Investment Advisor, Portfolio Manager, or Research Analyst. Nothing on this page constitutes investment advice. Please consult a SEBI-registered financial advisor before making any investment decisions.
Selection Logic
The engine narrows 1000+ tickers down to a focused watchlist through a series of deterministic filters.
Three Pools
Stocks are segmented into LM250 (Large + Mid), SmallCap250, and MicroCap250. Each pool has its own sector median baseline and volatility expectation.
Sell Thresholds
Automatic exclusion triggers: G Score < -4 (deep value trap or distressed), Risk Score < -3.5 (excessive leverage or volatility).
Neutral Category
Stocks with G Score < -0.5 are flagged Neutral. They remain in the universe but are deprioritized unless Risk Score is exceptionally strong (> +5).
Sector Exclusions
Mining & Metals are excluded from primary recommendations due to commodity cyclicality and unpredictable regulatory intervention.
Selection Pipeline
Data Sources
Every score is only as good as the data behind it. We source from established providers with audited track records.
Financial Modeling Prep
Premium fundamentals, ratios, key metrics, and income statements.
yfinance
Real-time prices, historical OHLCV, and dividend history.
NSE India
Official Indian equity data, corporate actions, and listings.
Finnhub
Insider transactions, news sentiment, and earnings calendars.
Data Pipeline Architecture
FMP feeds fundamentals (profile, ratios, income statements) → yfinance supplies live prices & historical OHLCV → Finnhub adds insider/sentiment → NSE provides Indian equity data. All data is refreshed nightly at 02:00 UTC.
Model Governance & Updates
IRS% recomputed at 02:00 UTC with latest fundamentals and prices
Index constituents refreshed every quarter; new IPOs/graduations added
G Score 60% / Risk 40% weights are not overfitted to any period
All PARA-DEBATE agent prompts are in git; changes are tracked and auditable
Limitations & Risks
No quantitative model is perfect. Here is what the engine does not capture — and why live results may differ from backtests.
Survivorship Bias
Backtests run on today's constituents. Delisted or merged companies are excluded, which may inflate historical performance.
Look-Ahead Bias
We mitigate this by using only data available at the rebalance date. However, restated financials can still introduce subtle bias.
Transaction Costs
Backtests assume zero slippage and no brokerage. In live trading, stamp duty, STT, spreads, and impact costs reduce realized returns.
Liquidity Assumption
Micro-cap picks may not absorb large capital deployment. The model does not model market impact for position sizing.
Regime Change
Models trained on bull-market data often underperform in bear markets. The Q1FY27 period was predominantly bullish.
Black Swan Events
Geopolitical shocks, currency crises, or pandemics are inherently unpredictable and not priced into historical backtests.
Position Sizing & Stop-Loss
The IRS% system does not prescribe position sizing or stop-loss levels. Portfolio construction and risk management are the investor’s responsibility. Equal-weight backtests assume no capital constraints.
Drawdown Limits
There is no maximum drawdown circuit-breaker in the scoring model. A stock with IRS% > 65 can still decline significantly if fundamentals deteriorate between rebalance dates.
SEBI Disclaimer
Past performance does not guarantee future results. These are backtested results on historical data. NeuralQuant is a research tool, not a SEBI-registered Investment Advisor, Portfolio Manager, or Research Analyst. Nothing on this page constitutes investment advice. Please consult a SEBI-registered financial advisor before making any investment decisions.
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