financial-analyst

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:

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.