Kanchi Dividend SOP

Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Use when users ask for かんち式配当投資, dividend screening, dividend growth quality checks, PERxPBR adaptation for US sectors, pullback limit-order planning, or one-page stock memo creation. Covers screening, deep dive, entry planning, and post-purchase monitoring cadence.

No API

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Implement Kanchi’s 5-step method as a deterministic workflow for US dividend investing. Prioritize safety and repeatability over aggressive yield chasing.

2. When to Use

Use this skill when the user needs:

  • Kanchi-style dividend stock selection adapted for US equities.
  • A repeatable screening and pullback-entry process instead of ad-hoc picks.
  • One-page underwriting memos with explicit invalidation conditions.
  • A handoff package for monitoring and tax/account-location workflows.

3. Prerequisites

API Key Setup

The entry signal script requires FMP API access:

export FMP_API_KEY=your_api_key_here

Input Sources

Prepare one of the following inputs before running the workflow:

  1. Output from skills/value-dividend-screener/scripts/screen_dividend_stocks.py.
  2. Output from skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth_rsi.py.
  3. User-provided ticker list (broker export or manual list).

For deterministic artifact generation, provide tickers to:

python3 skills/kanchi-dividend-sop/scripts/build_sop_plan.py \
  --tickers "JNJ,PG,KO" \
  --output-dir reports/

For Step 5 entry timing artifacts:

python3 skills/kanchi-dividend-sop/scripts/build_entry_signals.py \
  --tickers "JNJ,PG,KO" \
  --alpha-pp 0.5 \
  --output-dir reports/

4. Quick Start

1) Define mandate before screening

Collect and lock the parameters first:

  • Objective: current cash income vs dividend growth.
  • Max positions and position-size cap.
  • Allowed instruments: stock only, or include REIT/BDC/ETF.
  • Preferred account type context: taxable vs IRA-like accounts.

5. Workflow

1) Define mandate before screening

Collect and lock the parameters first:

  • Objective: current cash income vs dividend growth.
  • Max positions and position-size cap.
  • Allowed instruments: stock only, or include REIT/BDC/ETF.
  • Preferred account type context: taxable vs IRA-like accounts.

Load references/default-thresholds.md and apply baseline settings unless the user overrides.

2) Build the investable universe

Start with a quality-biased universe:

  • Core bucket: long dividend growth names (for example, Dividend Aristocrats style quality set).
  • Satellite bucket: higher-yield sectors (utilities, telecom, REITs) in a separate risk bucket.

Use explicit source priority for ticker collection:

  1. skills/value-dividend-screener/scripts/screen_dividend_stocks.py output (FMP/FINVIZ).
  2. skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth_rsi.py output.
  3. User-provided broker export or manual ticker list when APIs are unavailable.

Return a ticker list grouped by bucket before moving forward.

3) Apply Kanchi Step 1 (yield filter with trap flag)

Primary rule:

  • forward_dividend_yield >= 3.5%

Trap controls:

  • Flag extreme yield (>= 8%) as deep-dive-required.
  • Flag sudden jump in payout as potential special dividend artifact.

Output:

  • PASS or FAIL per ticker.
  • deep-dive-required flag for potential yield traps.

4) Apply Kanchi Step 2 (growth and safety)

Require:

  • Revenue and EPS trend positive on multi-year horizon.
  • Dividend trend non-declining over the review period.

Add safety checks:

  • Payout ratio and FCF payout ratio in reasonable range.
  • Debt burden and interest coverage not deteriorating.

When trend is mixed but not broken, classify as HOLD-FOR-REVIEW instead of hard reject.

5) Apply Kanchi Step 3 (valuation) with US sector mapping

Use references/valuation-and-one-off-checks.md and apply sector-specific valuation logic:

  • Financials: PER x PBR can remain primary.
  • REITs: use P/FFO or P/AFFO instead of plain P/E.
  • Asset-light sectors: combine forward P/E, P/FCF, and historical range.

Always report which valuation method was used for each ticker.

6) Apply Kanchi Step 4 (one-off event filter)

Reject or downgrade names where recent profits rely on one-time effects:

  • Asset sale gains, litigation settlement, tax effect spikes.
  • Margin spike unsupported by sales trend.
  • Repeated “one-time/non-recurring” adjustments.

Record one-line evidence for each FAIL to keep auditability.

7) Apply Kanchi Step 5 (buy on weakness with rules)

Set entry triggers mechanically:

  • Yield trigger: current yield above 5y average yield + alpha (default +0.5pp).
  • Valuation trigger: target multiple reached (P/E, P/FFO, or P/FCF).

Execution pattern:

  • Split orders: 40% -> 30% -> 30%.
  • Require one-sentence sanity check before each add: “thesis intact vs structural break”.

8) Produce standardized outputs

Always produce three artifacts:

  1. Screening table (PASS, HOLD-FOR-REVIEW, FAIL with evidence).
  2. One-page stock memo (use references/stock-note-template.md).
  3. Limit-order plan with split sizing and invalidation condition.

6. Resources

References:

  • skills/kanchi-dividend-sop/references/default-thresholds.md
  • skills/kanchi-dividend-sop/references/stock-note-template.md
  • skills/kanchi-dividend-sop/references/valuation-and-one-off-checks.md

Scripts:

  • skills/kanchi-dividend-sop/scripts/build_entry_signals.py
  • skills/kanchi-dividend-sop/scripts/build_sop_plan.py