This guide covers practical configuration of the Multi-Factor Screener — how to adjust metric weights, choose normalization methods, and interpret the output tabs.
Accessing the Screener
Navigate to Fundamental Analysis from the Dashboard or the Fundamental ribbon tab. Click on an index button (S&P 500, NASDAQ 100, etc.) to launch the screener with that universe.
The Factor Panels
Each of the four factors (Value, Quality, Momentum, Growth) has an expandable panel. Click a panel to reveal its individual metrics.
Adjusting Metric Importance
Every metric has a selector with four options:
- Off — Metric is not included in the factor score. Use this to remove metrics you do not trust or that are not relevant to your strategy
- Low — 0.5× weight. The metric contributes, but less than standard
- Med — 1.0× weight. The default for all metrics
- High — 2.0× weight. Doubles the influence of this metric on the factor score
If you believe FCF Yield is more reliable than P/E for identifying cheap stocks, set FCF Yield to High and P/E to Low within the Value panel. The factor score will emphasize cash-flow-based valuation over earnings-based valuation.
Adjusting Factor Weights
The four factor weight sliders control how much each factor contributes to the final composite score. They must sum to 100%. Common configurations:
- Balanced (default) — 25/25/25/25. No factor bias. Good starting point
- Deep Value — 40/30/10/20. Emphasizes cheapness and quality over price momentum
- Growth at Reasonable Price (GARP) — 20/25/15/40. Prioritizes growth backed by quality
- Quality Income — 15/45/10/30. Focuses on high-quality companies with strong financial health
- Momentum Tilt — 15/20/40/25. Follows stocks that are already moving up with fundamental support
Normalization and Outlier Settings
Z-Score vs Percentile Rank
Z-score is the default and is more informative — it preserves the magnitude of differences between stocks. Percentile rank is more robust when the data contains many extreme values (common in smaller indexes with less coverage).
Winsorization
On by default. Caps extreme values at the 1st and 99th percentiles. You can turn this off if you want the raw distribution, but this is rarely beneficial — outliers tend to distort scores unpredictably.
Reading the Results
Main Table
The primary results table shows:
- Rank — Position in the sorted results (1 = best)
- Ticker and Company Name
- Composite Score (0-100) — The final combined score
- Value / Quality / Momentum / Growth — Individual factor scores (0-100 each)
- Sector — Industry classification
Click column headers to re-sort. Click a row to expand and see the underlying metric values.
Sector Distribution Tab
Shows how the top results break down by sector. A screen heavily concentrated in one sector (e.g., 60% Technology) suggests sector-level factor exposure rather than stock-specific quality. Consider using sector exclusion to balance the results.
Excluded Stocks Tab
Lists every stock that was removed from scoring with the specific reason (missing earnings data, no price history, insufficient quarterly reports, etc.). If a stock you expected to see is missing, check here.
Metric Details Tab
Shows data quality statistics for each metric: how many stocks had valid data, how many were missing, and how many had extreme values that were winsorized. Low coverage on a specific metric suggests setting it to "Off" or "Low" importance.
Financial fundamentals update quarterly. Running the screener more often than monthly adds noise rather than signal. Price-based Momentum metrics update daily, but the fundamental factors (Value, Quality, Growth) only change meaningfully after earnings releases.
- Choose your index — Start with the index that matches your investment universe
- Set factor weights — Match your investment style (value, growth, balanced)
- Adjust key metrics — Turn off metrics with poor data coverage in your chosen index
- Keep winsorization on — Unless you have a specific reason to include outliers
- Review sector distribution — Exclude sectors if the results are overly concentrated
- Cross-reference with deep dives — Use Research Units to validate top candidates before acting