workflow-er-thesis-tracker

name

workflow-er-thesis-tracker

description

WHAT: Investment thesis memo (3-5 pages) with bull/base/bear scenario definitions, quantified price targets per case, catalyst milestones with dates and expected impact, probability weights, and a quarterly drift-detection update cadence. Tracks whether incoming data confirms, challenges, or breaks the thesis. WHEN: Invoke when establishing or updating an investment thesis framework for a covered company — either at initiation (alongside workflow-er-initiating-coverage) or as a standalone quarterly review when new catalysts have resolved.

Equity Research: Investment Thesis Tracker

What this skill covers

Structures the bull/base/bear thesis framework with explicit scenario definitions, price targets, probability weights, and milestone catalysts. Defines the update protocol for quarterly thesis drift detection — flagging when actual results diverge from the base case and triggering a re-rating review when two or more catalysts resolve in the same direction.

Inputs

• Company ticker and current coverage rating
• Existing price target and valuation model output (from workflow-er-initiating-coverage or workflow-er-model-update)
• List of known upcoming catalysts with dates

Workflow

Step 1 — Bull case definition

Define the upside scenario with:

• 3-5 specific catalysts that drive the bull case (e.g., margin expansion, market share gain, product launch success)
• Bull case revenue, EBITDA, EPS assumptions (explicit; not narrative)
• Call dcf_model with bull case inputs to derive the bull price target
• Quantify upside: bull PT vs current price, implied return %
• Assign probability weight (typically 20-30%)

Step 2 — Base case definition

Define the central estimate:

• Core assumptions for revenue growth, margin, multiple — most likely outcome
• Call dcf_model with base case inputs to confirm the published price target
• Call comps_analysis for trading multiple cross-check
• This is the published price target and the current rating's basis
• Assign probability weight (typically 50-60%)

Step 3 — Bear case definition

Define the downside scenario with:

• 3-5 specific risks that drive the bear case (e.g., competitive entry, margin compression, end-market demand weakness)
• Bear case revenue, EBITDA, EPS assumptions (explicit)
• Call dcf_model with bear case inputs to derive the bear price target
• Quantify downside: bear PT vs current price, implied loss %
• Assign probability weight (typically 15-25%)

Validation: bull + base + bear probability weights must sum to 100%.

Step 4 — Probability-weighted price target

Compute: PT_weighted = (bull_PT × bull_weight) + (base_PT × base_weight) + (bear_PT × bear_weight)

The published price target is the base case PT; the probability-weighted PT is reported alongside for reference.

Step 5 — Catalyst milestone register

For each catalyst (from all three cases), create a register entry:

Catalyst	Expected date	Scenario relevance	Expected impact	Resolved?
Q2 margin guidance	Q2 2026 earnings	Base / Bull	±150bps on EBITDA	No
FDA approval, drug X	H1 2026	Bull only	+20% revenue in FY2027	No

Track resolution quarterly. A catalyst resolves as: confirming (aligns with base or bull), neutral, or challenging (aligns with bear).

Step 6 — Quarterly drift detection

At each quarterly update:

1. Mark resolved catalysts in the register
2. Tally: how many resolved as confirming vs challenging?
3. Trigger rule: if 2+ catalysts resolve in the same direction (both confirming or both challenging), initiate a scenario re-rating review
4. Call sensitivity_matrix to quantify the scenario weight shift
5. Document thesis status: on-track / drifting / broken

When thesis is broken (core assumption definitively refuted), initiate a rating change review and call target_price with updated case weights.

Output format

1. Thesis memo (3-5 pages) — structured as: bull case, base case, bear case, probability weights, probability-weighted PT
2. Catalyst milestone register — table as in Step 5, updated each quarter
3. Quarterly update log — per quarter: catalysts resolved, thesis status, weight changes

Quality gates

• All three scenario price targets derived from dcf_model — not narrative estimates
• Probability weights sum to 100%
• At least 3 catalysts per scenario defined with specific dates
• Drift detection rule documented and applied each quarter
• Rating change review triggered when 2+ catalysts break in the same direction

Related skills

• workflow-er-initiating-coverage — produces the initial model and valuation that seed the thesis cases
• workflow-er-earnings-update — quarterly earnings notes trigger catalyst resolution updates
• workflow-er-model-update — model revision invoked when thesis drifts materially
• workflow-er-idea-screening — provides quantitative backing for new thesis ideas