Creating Financial Models

DCF analysis projects future free cash flows, discounts them to present value, and calculates enterprise value. This Skill automates DCF modeling with multiple growth scenarios and terminal value calculations, using historical financials and assumptions you provide in JSON format to generate valuation results in Excel workbooks.

Sensitivity analysis tests how key assumptions (revenue growth, discount rate) individually impact valuation; Monte Carlo simulation models uncertainty across thousands of random scenarios simultaneously. This Skill provides both methods—sensitivity analysis isolates assumption impacts, while Monte Carlo generates comprehensive risk distributions.

Yes. This Skill uses pandas and numpy to structure financial data, perform calculations, and generate projections. The DCF and sensitivity analysis scripts leverage these libraries to process historical financials and scenario inputs, then output results compatible with Excel for review and reporting.

Scenario planning models best, base, and worst-case outcomes using different assumption sets. This Skill automates scenario comparison by accepting multiple scenario definitions, running parallel analyses, and delivering side-by-side valuation and risk metrics in Excel to support strategic decision-making.

The Skill requires historical financials and assumptions structured in JSON format, including revenue, operating costs, capital expenditures, and growth rate scenarios. This standardized input format enables validation checks and ensures consistent data flow into DCF, sensitivity, and Monte Carlo analysis scripts.

Use Monte Carlo when uncertainty is significant and you need risk distribution insights beyond point estimates. This Skill applies Monte Carlo to model thousands of scenarios, generating confidence intervals and tail-risk metrics—ideal for volatile projects, emerging markets, or decisions requiring comprehensive risk assessment.