Geeksfino

quant-factor-screener

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.

Geeksfino 176 32 Updated 3mo ago

Resources

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GitHub

Install

npx skillscat add geeksfino/finskills/us-market-quant-factor-screener

Install via the SkillsCat registry.

SKILL.md

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.