financial-analyst

name financial-analyst description Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis. Financial Analyst Skill Overview Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis. 5-Phase Workflow Phase 1: Scoping Define analysis objectives and stakeholder requirements Identify data sources and time periods Establish materiality thresholds and accuracy targets Select appropriate analytical frameworks Phase 2: Data Analysis & Modeling Collect and validate financial data (income statement, balance sheet, cash flow) Validate input data completeness before running ratio calculations (check for missing fields, nulls, or implausible values) Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation) Build DCF models with WACC and terminal value calculations; cross-check DCF outputs against sanity bounds (e.g., implied multiples vs. comparables) Construct budget variance analyses with favorable/unfavorable classification Develop driver-based forecasts with scenario modeling Phase 3: Insight Generation Interpret ratio trends and benchmark against industry standards Identify material variances and root causes Assess valuation ranges through sensitivity analysis Evaluate forecast scenarios (base/bull/bear) for decision support Phase 4: Reporting Generate executive summaries with key findings Produce detailed variance reports by department and category Deliver DCF valuation reports with sensitivity tables Present rolling forecasts with trend analysis Phase 5: Follow-up Track forecast accuracy (target: +/-5% revenue, +/-3% expenses) Monitor report delivery timeliness (target: 100% on time) Update models with actuals as they become available Refine assumptions based on variance analysis Tools 1. Ratio Calculator ( scripts/ratio_calculator.py ) Calculate and interpret financial ratios from financial statement data. Ratio Categories: Profitability: ROE, ROA, Gross Margin, Operating Margin, Net Margin Liquidity: Current Ratio, Quick Ratio, Cash Ratio Leverage: Debt-to-Equity, Interest Coverage, DSCR Efficiency: Asset Turnover, Inventory Turnover, Receivables Turnover, DSO Valuation: P/E, P/B, P/S, EV/EBITDA, PEG Ratio python scripts/ratio_calculator.py assets/sample_financial_data.json python scripts/ratio_calculator.py assets/sample_financial_data.json --format json python scripts/ratio_calculator.py assets/sample_financial_data.json --category profitability 2. DCF Valuation ( scripts/dcf_valuation.py ) Discounted Cash Flow enterprise and equity valuation with sensitivity analysis. Features: WACC calculation via CAPM Revenue and free cash flow projections (5-year default) Terminal value via perpetuity growth and exit multiple methods Enterprise value and equity value derivation Two-way sensitivity analysis (discount rate vs growth rate) python scripts/dcf_valuation.py assets/sample_financial_data.json python scripts/dcf_valuation.py assets/sample_financial_data.json --format json python scripts/dcf_valuation.py assets/sample_financial_data.json --projection-years 7 3. Budget Variance Analyzer ( scripts/budget_variance_analyzer.py ) Analyze actual vs budget vs prior year performance with materiality filtering. Features: Dollar and percentage variance calculation Materiality threshold filtering (default: 10% or $50K) Favorable/unfavorable classification with revenue/expense logic Department and category breakdown Executive summary generation python scripts/budget_variance_analyzer.py assets/sample_financial_data.json python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --format json python scripts/budget_variance_analyzer.py assets/sample_financial_data.json --threshold-pct 5 --threshold-amt 25000 4. Forecast Builder ( scripts/forecast_builder.py ) Driver-based revenue forecasting with rolling cash flow projection and scenario modeling. Features: Driver-based revenue forecast model 13-week rolling cash flow projection Scenario modeling (base/bull/bear cases) Trend analysis using simple linear regression (standard library) python scripts/forecast_builder.py assets/sample_financial_data.json python scripts/forecast_builder.py assets/sample_financial_data.json --format json python scripts/forecast_builder.py assets/sample_financial_data.json --scenarios base,bull,bear Knowledge Bases Reference Purpose references/financial-ratios-guide.md Ratio formulas, interpretation, industry benchmarks references/valuation-methodology.md DCF methodology, WACC, terminal value, comps references/forecasting-best-practices.md Driver-based forecasting, rolling forecasts, accuracy references/industry-adaptations.md Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) Templates Template Purpose assets/variance_report_template.md Budget variance report template assets/dcf_analysis_template.md DCF valuation analysis template assets/forecast_report_template.md Revenue forecast report template Key Metrics & Targets Metric Target Forecast accuracy (revenue) +/-5% Forecast accuracy (expenses) +/-3% Report delivery 100% on time Model documentation Complete for all assumptions Variance explanation 100% of material variances Input Data Format All scripts accept JSON input files in either of two shapes: Flat — the tool's expected keys at the top level (e.g., income_statement / balance_sheet for the ratio calculator, historical / assumptions for DCF, line_items for variance, historical_periods / drivers / assumptions / cash_flow_inputs for forecasting). Nested (bundled) — inputs for all four tools in one file, nested under per-tool keys: ratio_analysis , dcf_valuation , budget_variance , forecast . See assets/sample_financial_data.json for the complete bundled schema; every quick-start command above runs directly against it. Each script auto-detects the shape (flat keys win if present) and exits non-zero with a clear error if neither shape yields usable data. Dependencies None - All scripts use Python standard library only ( math , statistics , json , argparse , datetime ). No numpy, pandas, or scipy required.