Event-Driven Catalyst Analysis Framework
Decompose corporate events into outcome scenarios and quantify event-driven alpha
What You Can Do
You can decompose complex corporate catalysts—mergers, restructurings, regulatory decisions, bankruptcies—into probabilistic outcome trees with timeline-based inflection points. Claude models post-event price discovery across multiple scenarios, quantifies risk-adjusted returns, and documents investment theses with transparent reasoning chains that identify alpha opportunities while stress-testing against consensus views and tail risks.
Features
breaks events into discrete scenarios with probability weighting and binary/continuous distributions
identifies key decision inflection points, approval gates, and liquidity event dates
calculates conviction-weighted returns across scenarios with downside protection quantification
builds merger arb, risk arb, and regulatory arb theses with hedge leg positioning
contextualizes event catalysts within broader market positioning and correlation drivers
generates peer-reviewable decision logic with consensus gap identification and tail scenario analysis
weights scenarios by probability and return magnitude to size positions appropriately
Example Output
M&A Deal Analysis Example:
- Merger probability: 85% (regulatory approval 3Q, financing confirmed) | 15% (deal breaks, price correction -12%)
- Price discovery: Spread narrows from current 6.2% to <2% on approval signal by Q2
- Risk-adjusted return: +4.8% annualized across 18-month hold with 2.3:1 return/downside ratio
- Key inflection point: FTC decision (June 15) triggers ±8% move
Bankruptcy Emergence Scenario:
- Equity recovery: 35% (senior lender deal) | 60% (Chapter 11 plan confirmation) | 5% (liquidation)
- Timeline: Plan vote Q3, emergence Q4, 6-month volatility post-emergence
- Conviction thesis: Creditor committee positioning suggests 60% confirmation path; 15% equity upside vs. consensus 8%
- Risk factors: Litigation delay, covenant breach, asset impairment
FDA Approval Catalyst:
- Approval probability: 72% | Conditional approval: 18% | Rejection: 10%
- Post-approval trajectory: 120-day label expansion, peak sales modeling, peak timing risk
- Current premium: Stock pricing 55% approval probability; thesis supports 72%
- Downside protection: Establish stop-loss 8% below pre-data close
What's Included
- SKILL.md instruction file with framework methodology and usage guidelines:
- Event decomposition template: probability tree structure, outcome scenario definition, and distribution modeling
- Catalyst timeline worksheet: key dates, approval gates, decision points, and volatility triggers
- Risk-adjusted return calculator: scenario weighting, return modeling, and conviction scoring methodology
- Investment thesis documentation checklist: consensus gap analysis, tail risk scenarios, and peer review prompts
Who It's For
- Event-driven hedge fund analysts — modeling discrete catalysts for alpha generation and position sizing
- Special situations specialists — evaluating mergers, restructurings, bankruptcies, and going-private transactions
- Regulatory/catalyst traders — timing FDA decisions, patent litigation, antitrust approvals, and policy outcomes
- Risk arbitrage desks — building multi-leg theses with hedge structures and correlation mapping
- Long/short equity portfolio managers — identifying conviction gaps between consensus pricing and catalyst outcomes
Best For
- Merger arbitrage analysis with regulatory uncertainty quantification
- Special situations modeling (bankruptcies, spin-offs, asset sales, going-private)
- Binary event catalysts (FDA approvals, patent litigation, regulatory decisions, shareholder votes)
- Timing liquidity events (secondary offerings, lock-up expirations, index inclusions, activist campaigns)
- Multi-scenario stress testing and tail risk identification across event timelines
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