quant-factor-screener

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:

1. Calculate raw metric for each stock
2. Rank within sector (if sector-neutral) or universe (if unconstrained)
3. Convert ranks to percentile scores (0–100)
4. 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:

1. Macro Regime Assessment — Current regime and factor timing view
2. Factor Crowding Dashboard — Which factors are crowded/uncrowded
3. Top Picks Table — Top N stocks with individual factor scores and composite
4. Sector Distribution — How the top picks distribute across sectors
5. Factor Exposure Summary — What the resulting list is tilted toward
6. Individual Stock Cards — Brief profile for each top pick
7. Risk Considerations — Factor drawdown history and current risks
8. 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.