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# 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 ```bash 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) ```bash 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 ```bash 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) ```bash 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: 1. **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). 2. **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.