financial-analyst

DCF valuation automation takes structured JSON inputs to build enterprise and equity value with terminal value modeling and sensitivity analysis. It eliminates manual calculation errors by executing script-based modules that compute valuations directly from provided statements.

Ratio analysis computes profitability, liquidity, leverage, efficiency, and valuation metrics from structured financial statement inputs. It provides repeatable insights by automating these calculations, replacing time-consuming manual computations across multiple statement categories.

Rolling forecasts use driver-based inputs to generate projections including 13-week cash flow models with scenario modeling. The process takes structured data inputs and applies forecast construction scripts to produce forward-looking financial projections.

Budget variance analysis identifies material variances from structured inputs and classifies them as favorable or unfavorable. It surfaces department and category insights, automating the assessment process for corporate finance and budgeting workflows.

Financial data must be provided in JSON format to run automated ratio calculations, DCF valuation, variance analysis, and rolling forecasts. The skill uses Python's standard library to process these structured inputs across its analysis modules.

Yes, this approach is applicable in corporate finance, budgeting, valuation work, and financial planning scenarios where users analyze statements, construct valuations, assess variances, or develop forecasts from structured inputs.