What problem does it solve? Building a discount rate or finding an optimal debt ratio involves error-prone arithmetic across many steps: stripping sovereign spreads from riskfree rates, unlevering and relevering betas, deriving synthetic credit ratings from interest coverage, and solving the circular dependency between interest rates and coverage ratios. This Skill automates that arithmetic with tested, pure-standard-library Python engines so the analyst can focus on choosing and defending the inputs. ## Core Features & Use Cases - Discount rate construction: Compute synthetic ratings and cost of debt, bottom-up and total betas, market value of debt, implied equity risk premiums, currency conversion of rates, and full WACC assembly. - Optimal capital structure: Generate a cost-of-capital schedule across debt ratios, stress it against EBIT haircuts or rating floors, and cross-check the result with an adjusted present value (APV) calculation. - Bundled reference data: Look up 96 industry averages, 186 country risk premiums, synthetic rating tables, and default probabilities, each tagged with a vintage date and staleness warnings. - Use Case: An analyst valuing a Brazilian company with US revenue runs erp-for-operations to weight country risk by operations, builds a bottom-up beta from comparables, then runs debt-schedule and stress to find and defend an optimal debt ratio. ## Quick Start Ask the agent to compute a synthetic credit rating and after-tax cost of debt for a company with EBIT of 2000, interest expense of 250, a 4% riskfree rate, and a 25% marginal tax rate.
