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quantitative-screening

Quantitative stock screening, forward-looking valuation outlier detection, backward-looking financial statement validation, PEG ratio analysis, earnings growth profile assessment, turnaround vs value trap discrimination, data mining bias prevention

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含まれるファイル(4)

  • SKILL.md7.5 KB
  • references/modes.md728 B
  • references/multi-factor-screening-methodology.md6.7 KB
  • references/quant-methodology.md30.5 KB

SKILL.md(原文)

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Methodology fused from professional trading and investment frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
screening_universeS&P 500 + Russell 1000 liquidBroad enough for diversity, liquid enough for execution
historical_years5Minimum years of financial data for trend analysis
peg_threshold1.0PEG < 1.0 suggests undervaluation relative to growth
fcf_conversion_min70%FCF/Net Income below 70% flags earnings quality issues
earnings_beat_threshold70%Beat frequency above 70% suggests conservative guidance

Preflight

Run canonical pre-flight per contracts/preflight.md. Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).

Data Source Priority

  1. Quantitative methodology — references/quant-methodology.md (bundled screening framework)
  2. Financial data — SEC XBRL facts via agentii MCP for historical financials
  3. Market data — ~~market_data placeholder for real-time valuation multiples
  4. Strategy frameworks — search_investment_strategies(domain=fundamental, kind=screening)

Methodology

Retrieval Scope

structured_only

Retrieval Strategy

Ownership & insider signals: search_institutional_holdings (top-10 holders + whale portfolios, direction=accumulating|reducing|new|exited) and search_insider_trades (Form-4 transactions with SEC URLs) are available as signal inputs.

Branch (a) Structured Data Query from contracts/retrieval.md: primary retrieval via XBRL facts for financial statement data. Supplement with search_investment_strategies for screening methodology validation. Detailed methodology in references/quant-methodology.md.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

This skill implements a two-directional screening process: forward-looking valuation discovery and backward-looking financial statement validation. Core principle: the market is mostly efficient. An outlier exists because either the market is wrong (your edge) or you are missing something. Non-participation is always an option.

Detailed methodology: peer selection protocol, turnaround financial scorecard, 7-step sector cleaning, and data mining bias catalog are in references/quant-methodology.md.

Foundational principle: P/E measures what the market is willing to pay for forward earnings — it is a market psychology metric, not intrinsic value. "Cheap" and "expensive" are not analytical conclusions. The question is: why has the market assigned this multiple? PEG < 1.0 is not a universal buy signal — calibrate sector-relatively, growth-rate-adjust, and cross-check with EV/EBITDA-to-Growth. This skill uses PEG as a screening filter only; for a standalone PEG-based valuation, defer to the peg-valuation skill.

Steps

  1. Universe and Macro Filter: Apply portfolio bias from orchestrator. Long → $3B-$10B mid-caps. Short → $20B+ large caps. Neutral → both, emphasize pairs. Weight sectors by macro regime preferences.

  2. Forward-Looking Valuation Scan: Screen using four-pillar framework (PE1, PE2; EG1, EG2; PEG1, PEG2; revenue multiples). Rank by deviation from sector median. Top/bottom decile advance. Calibrate PEG sector-relatively. Use EV/EBITDA-to-Growth as cross-check; prefer EBIT over EBITDA for capital-intensive sectors.

  3. Backward-Looking Financial Validation (execute in this order):

    • Revenue: growth trajectory, organic vs. acquisition quality, concentration risk
    • Earnings quality: GAAP vs. non-GAAP (> 20% gap = investigate), SBC > 10% revenue = red flag, "non-recurring" in 3+ of 4 quarters = recurring
    • Margin: gross margin trend, incremental margins (> 50% strong, < 20% weak)
    • Cash flow: FCF/Net Income conversion. > 80% excellent, 70-80% acceptable, 50-70% explain, < 50% hard stop for longs. DSO + inventory both rising = channel stuffing risk.
  4. Peer Selection (dual-path): Sector path (GICS → 10-K competition → sell-side → merger docs) + Fundamentals path (cluster by growth, margins, ROIC). Must converge on 4-6 names. Divergence = classification error. Use median. For a formal benchmarked peer set, hand off to peer-bench; for full multiple spreading and calendarization, hand off to comps — do not rebuild either here.

  5. Growth Profile and Trap Detection: EPS CAGR 3-5yr (consistency > magnitude). Estimate trajectory: rising + rising = aligned; falling + rising = danger. Beat/raise = strongest signal. Decompose growth source (revenue vs. cost-cutting vs. buybacks). Turnaround scorecard (0-10): 7-10 investigate long, 0-3 avoid/short. Exclude revenue-growth stories from turnaround classification. Scan for data mining biases.

  6. Sector Cleaning (when data errors suspected): Apply 7-step protocol from reference. Only clean < 20 candidates that pass initial screen.

  7. Output: Score each candidate (valuation × validation × growth). Flag GREEN/AMBER/RED. Handoff: ranked list, peer data, turnaround scores, data quality flags.

Output File

{ticker}/{YYYY-MM-DD_HHMM}_quantitative-screening_{affix}.md

Output Structure

  1. Executive Summary — Universe scanned, outliers found, top 5 candidates ranked
  2. Screening Parameters — Universe, macro filter, metrics used, thresholds
  3. Outlier Results — Ranked list with valuation metrics, sector comparisons
  4. Financial Validation — Revenue, earnings quality, margin, cash flow analysis per candidate
  5. Growth Assessment — EPS trajectory, estimates trend, earnings surprise history
  6. Trap Detection — Turnaround/value trap flags per candidate
  7. Data Quality Report — Bias checks, data freshness, caveats
  8. Handoff Summary — GREEN/AMBER/RED classification with recommended next steps
  9. Coverage Gaps — Data limitations, missing data points, degraded-mode flags

Error Handling

ErrorFallback
No XBRL data for candidateUse market data estimates; flag as lower confidence
Sector comparison data insufficientUse broad market medians; flag sector gap
search_investment_strategies unreachableProceed with manual methodology; flag

Memory Load

See contracts/memory-load.md.

Snapshot

See contracts/snapshot-synthesis.md.

Final Summary (TUI)

Include ### Key Citations block with 0-10 clickable /v/ URLs.

References

  • references/quant-methodology.md
  • contracts/citation-and-memory.md
  • contracts/output-frontmatter-schema.md
  • contracts/memory-load.md
  • contracts/snapshot-synthesis.md
  • contracts/preflight.md
  • contracts/retrieval.md

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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