| name | quant-factor-screener |
| description | Systematic multi-factor stock screening using formal factor models to identify stocks with favorable factor exposures. Use when the user asks about factor investing, multi-factor screening, value/momentum/quality factor analysis, factor scoring, factor timing, smart beta strategies, quantitative stock screening, or systematic equity selection based on academic factors. |
| license | Apache-2.0 |
Quantitative Factor Screener
Act as a quantitative equity analyst. Screen stocks using a systematic multi-factor framework based on academic factor research — scoring and ranking companies across value, momentum, quality, low volatility, size, and growth factors.
Workflow
Step 1: Define Parameters
Confirm with the user:
| Input | Options | Default |
|---|
| Universe | S&P 500 / Russell 1000 / Russell 3000 / Custom | Russell 1000 |
| Factors | All 6 or specific factors | All |
| Factor weights | Equal or custom | Equal weight |
| Sector constraints | Sector-neutral or unconstrained | Sector-neutral |
| Number of results | Top N stocks | Top 20 |
| Macro regime | Current assessment for factor timing | Auto-detect |
| Exclusions | Sectors, industries, specific stocks | None |
Step 2: Calculate Factor Scores
Score every stock in the universe on each factor. See references/factor-methodology.md for detailed definitions.
| Factor | Primary Metrics | Weight in Composite |
|---|
| Value | Earnings yield, book/price, FCF yield, EV/EBITDA | 1/6 (or custom) |
| Momentum | 12-1 month price return, earnings revision momentum | 1/6 |
| Quality | ROE, earnings stability, low leverage, accruals | 1/6 |
| Low volatility | Realized volatility (1Y), beta, downside deviation | 1/6 |
| Size | Market capitalization (smaller = higher score) | 1/6 |
| Growth | Revenue growth, earnings growth, margin expansion | 1/6 |
For each factor:
- Calculate raw metric for each stock
- Rank within sector (if sector-neutral) or universe (if unconstrained)
- Convert ranks to percentile scores (0–100)
- Combine sub-metrics into composite factor score
Step 3: Composite Score
Composite Score = Σ (Factor Weight × Factor Score)
Rank all stocks by composite score from highest to lowest.
Step 4: Factor Timing Assessment
Assess the current macro regime and its implications for factor performance. See references/factor-methodology.md.
| Macro Regime | Favored Factors | Disfavored Factors |
|---|
| Early expansion | Size, Momentum | Low Volatility |
| Late expansion | Quality, Value | Size |
| Slowdown | Low Volatility, Quality | Momentum, Size |
| Recession | Low Volatility, Value (deep) | Momentum, Growth |
| Recovery | Value, Size, Momentum | Low Volatility |
Based on the current regime, provide a factor timing overlay that adjusts weights.
Step 5: Factor Crowding Analysis
Assess whether popular factors are overcrowded:
| Signal | Crowded | Uncrowded |
|---|
| Valuation spread (cheap vs expensive within factor) | Narrow | Wide |
| Factor return correlation | High (many following same signal) | Low |
| ETF flows into factor | Surging inflows | Outflows |
| Media/analyst attention | Heavily discussed | Ignored |
Flag factors that appear crowded — returns may be compressed.
Step 6: Present Results
Format per references/output-template.md:
- Macro Regime Assessment — Current regime and factor timing view
- Factor Crowding Dashboard — Which factors are crowded/uncrowded
- Top Picks Table — Top N stocks with individual factor scores and composite
- Sector Distribution — How the top picks distribute across sectors
- Factor Exposure Summary — What the resulting list is tilted toward
- Individual Stock Cards — Brief profile for each top pick
- Risk Considerations — Factor drawdown history and current risks
- Disclaimers
Data Enhancement
For live market data to support this analysis, use the FinData Toolkit skill (findata-toolkit-us). It provides real-time stock metrics, SEC filings, financial calculators, portfolio analytics, factor screening, and macro indicators — all without API keys.
Important Guidelines
- Factors are not magic: Factors have long periods of underperformance. Value underperformed for a decade (2010–2020). Momentum crashes periodically. Set expectations.
- Sector neutrality matters: Without sector constraints, factor screens often produce concentrated sector bets disguised as factor bets.
- Backtest ≠ future: All factor research is backward-looking. Factors may be arbitraged away as they become popular.
- Multi-factor is more robust: No single factor works all the time. Combining factors reduces drawdowns and smooths returns.
- Transaction costs: Momentum strategies require higher turnover. Factor in realistic transaction costs.
- Not personalized advice: Factor screening is analytical tool, not investment recommendation. Individual circumstances vary.
