Screening Equity Opportunities

Screening Equity Opportunities

When To Use

• Building an initial watchlist from a broad equity universe (e.g., Russell 3000, S&P 500, MSCI EAFE)
• Narrowing candidates for a thematic or sector-specific investment thesis
• Identifying stocks meeting specific factor criteria (value, growth, quality, momentum)
• Refreshing an existing screen with updated financial data or revised parameters
• Generating a filtered candidate list for deeper fundamental analysis or portfolio construction

Inputs To Gather

• Investment universe: Index, sector, geography, or market-cap range to screen against
• Screening criteria: Quantitative thresholds (valuation multiples, growth rates, profitability metrics, leverage ratios) and qualitative filters (sector exclusions, ESG constraints, governance standards)
• Priority weighting: Which criteria are hard cutoffs vs. soft preferences for ranking
• Time horizon: Short-term tactical vs. long-term strategic — affects which metrics matter most
• Data source and vintage: Confirm whether using trailing twelve months (TTM), last fiscal year (LFY), or forward consensus estimates; note the as-of date
• Benchmark context: Reference index or peer group for relative comparisons

Workflow

1. Establish the universe and parameters

   • Confirm the starting universe size and any pre-filters (e.g., minimum market cap $500M, average daily volume > $5M, exclude ADRs)
   • Document hard exclusion criteria upfront (sector bans, sanctioned entities, regulatory restrictions)

2. Define quantitative screen layers

   • Valuation: P/E, EV/EBITDA, P/B, P/FCF — set thresholds relative to sector medians or absolute levels
   • Growth: Revenue CAGR, EPS growth rate, forward earnings revisions — specify lookback and forward periods
   • Profitability: ROE, ROIC, gross/operating margins — compare to cost of capital where relevant
   • Financial health: Net debt/EBITDA, interest coverage, current ratio, Altman Z-score for distress risk
   • Momentum/technicals (if applicable): Relative strength, 52-week price position, moving average crossovers

3. Apply qualitative overlays

   • Management quality flags: insider ownership trends, recent executive turnover, capital allocation track record
   • Industry positioning: competitive moat indicators, TAM trajectory, regulatory tailwinds/headwinds
   • ESG/exclusion filters: controversy scores, carbon intensity thresholds, board diversity metrics [VERIFY — ESG data provider methodology varies significantly]

4. Run the screen and rank results

   • Apply hard cutoffs first to reduce the universe, then score remaining names on soft criteria
   • Use composite scoring (e.g., weighted z-scores across factors) to produce a ranked list
   • Flag names near threshold boundaries — small data changes could move them in or out

5. Validate and stress-test output

   • Check for survivorship bias if using historical screens
   • Identify sector/geography concentration in results — excessive clustering may indicate an unintended factor tilt
   • Cross-reference top names against recent earnings surprises, analyst rating changes, or corporate actions (M&A, spinoffs) that may distort trailing data
   • Spot-check 3–5 names manually to confirm data accuracy and screen logic

6. Document and deliver

   • Produce the screening report with full methodology, parameter table, and ranked results
   • Note any data gaps, stale inputs, or names requiring [VERIFY] follow-up
   • Include pass/fail counts at each filter stage to show funnel attrition

Output

The screening report should contain:

• Methodology summary: Universe definition, each filter with threshold values, weighting scheme, data source and as-of date
• Funnel summary table: Number of names passing each successive filter layer
• Ranked results table: Top candidates with key metrics displayed (ticker, name, market cap, sector, and the screening metrics used)
• Concentration analysis: Sector, geography, and market-cap distribution of the output set
• Watchlist flags: Names near cutoff boundaries, names with stale or missing data, names with pending corporate events
• Exclusions log: Notable names eliminated and the specific filter that removed them (useful for audit trail and thesis refinement)

Quality Checks

• Confirm data vintage matches stated as-of date — mixing TTM and forward estimates without disclosure distorts comparisons
• Verify that sector classification system is consistent (GICS vs. ICB vs. custom) [VERIFY — classification differences can shift sector composition materially]
• Ensure screen logic handles missing data correctly (exclude vs. penalize vs. impute) — document the chosen approach
• Validate that no look-ahead bias exists if screen is meant to be backtested
• Check that the number of passing names is reasonable for the stated universe and criteria tightness — an unexpectedly high or low pass rate suggests a logic error
• Confirm compliance with any investment policy statement (IPS) constraints or client mandate restrictions before finalizing the watchlist