event-driven-analyzer

To calculate expected return for merger arbitrage, you build a probability-weighted scenario tree separating deal close, break, and timing outcomes. This approach automates deterministic event math for cash spreads, hedge ratios, and expected value while validating probability sums to eliminate manual spreadsheet errors.

Underwriting special situations like spin-offs and activism requires deterministic event math and strict separation of facts, assumptions, and judgments. This process applies tailored playbooks for over twelve event types, enforces primary-source discipline from regulatory filings, and flags missing inputs to prevent overprecision.

The best way to structure event-driven investing analysis for a portfolio manager is to deliver PM-calibrated output formatting. This includes generating standalone HTML reports or dashboard payloads containing source-validated fact packs, probability trees, trade expression, monitoring plans, and red-team critiques.

Yes, probability modeling can evaluate contingent value rights (CVR) present values in public equity. The analysis applies deterministic event math to calculate CVR present values, build scenario expected value frameworks, and compute market-implied close probabilities while enforcing primary-source discipline.

Yes, event-driven analysis requires separating facts, assumptions, and judgments from press commentary to avoid overprecision. This strict source hierarchy prioritizes primary regulatory filings over press commentary, ensuring that scenario math and probability trees rely on validated facts rather than speculative narratives.

You need trade expression and monitoring plans for litigation and regulatory catalysts when underwriting dated public-equity special situations. This analysis automates scenario math for event-driven positions, generating gating items, red-team critiques, and PM-calibrated recommendations to track regulatory approvals and litigation outcomes.