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, see references/data-queries.md and run the shared scripts in ../findata-toolkit/scripts/.
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.
1---2name: quant-factor-screener3description: 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.4license: Apache-2.05---67# Quantitative Factor Screener89Act 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.1011## Workflow1213### Step 1: Define Parameters1415Confirm with the user:1617| Input | Options | Default |18|-------|---------|---------|19| Universe | S&P 500 / Russell 1000 / Russell 3000 / Custom | Russell 1000 |20| Factors | All 6 or specific factors | All |21| Factor weights | Equal or custom | Equal weight |22| Sector constraints | Sector-neutral or unconstrained | Sector-neutral |23| Number of results | Top N stocks | Top 20 |24| Macro regime | Current assessment for factor timing | Auto-detect |25| Exclusions | Sectors, industries, specific stocks | None |2627### Step 2: Calculate Factor Scores2829Score every stock in the universe on each factor. See [references/factor-methodology.md](references/factor-methodology.md) for detailed definitions.3031| Factor | Primary Metrics | Weight in Composite |32|--------|----------------|-------------------|33| Value | Earnings yield, book/price, FCF yield, EV/EBITDA | 1/6 (or custom) |34| Momentum | 12-1 month price return, earnings revision momentum | 1/6 |35| Quality | ROE, earnings stability, low leverage, accruals | 1/6 |36| Low volatility | Realized volatility (1Y), beta, downside deviation | 1/6 |37| Size | Market capitalization (smaller = higher score) | 1/6 |38| Growth | Revenue growth, earnings growth, margin expansion | 1/6 |3940For each factor:411. Calculate raw metric for each stock422. Rank within sector (if sector-neutral) or universe (if unconstrained)433. Convert ranks to percentile scores (0–100)444. Combine sub-metrics into composite factor score4546### Step 3: Composite Score4748```49Composite Score = Σ (Factor Weight × Factor Score)50```5152Rank all stocks by composite score from highest to lowest.5354### Step 4: Factor Timing Assessment5556Assess the current macro regime and its implications for factor performance. See [references/factor-methodology.md](references/factor-methodology.md).5758| Macro Regime | Favored Factors | Disfavored Factors |59|-------------|----------------|-------------------|60| Early expansion | Size, Momentum | Low Volatility |61| Late expansion | Quality, Value | Size |62| Slowdown | Low Volatility, Quality | Momentum, Size |63| Recession | Low Volatility, Value (deep) | Momentum, Growth |64| Recovery | Value, Size, Momentum | Low Volatility |6566Based on the current regime, provide a factor timing overlay that adjusts weights.6768### Step 5: Factor Crowding Analysis6970Assess whether popular factors are overcrowded:7172| Signal | Crowded | Uncrowded |73|--------|---------|-----------|74| Valuation spread (cheap vs expensive within factor) | Narrow | Wide |75| Factor return correlation | High (many following same signal) | Low |76| ETF flows into factor | Surging inflows | Outflows |77| Media/analyst attention | Heavily discussed | Ignored |7879Flag factors that appear crowded — returns may be compressed.8081### Step 6: Present Results8283Format per [references/output-template.md](references/output-template.md):84851. **Macro Regime Assessment** — Current regime and factor timing view862. **Factor Crowding Dashboard** — Which factors are crowded/uncrowded873. **Top Picks Table** — Top N stocks with individual factor scores and composite884. **Sector Distribution** — How the top picks distribute across sectors895. **Factor Exposure Summary** — What the resulting list is tilted toward906. **Individual Stock Cards** — Brief profile for each top pick917. **Risk Considerations** — Factor drawdown history and current risks928. **Disclaimers**9394## Data Enhancement9596For live market data, see `references/data-queries.md` and run the shared scripts in `../findata-toolkit/scripts/`.9798## Important Guidelines99100- **Factors are not magic**: Factors have long periods of underperformance. Value underperformed for a decade (2010–2020). Momentum crashes periodically. Set expectations.101- **Sector neutrality matters**: Without sector constraints, factor screens often produce concentrated sector bets disguised as factor bets.102- **Backtest ≠ future**: All factor research is backward-looking. Factors may be arbitraged away as they become popular.103- **Multi-factor is more robust**: No single factor works all the time. Combining factors reduces drawdowns and smooths returns.104- **Transaction costs**: Momentum strategies require higher turnover. Factor in realistic transaction costs.105- **Not personalized advice**: Factor screening is analytical tool, not investment recommendation. Individual circumstances vary.
yuping322