Financial Analyst Skill
Overview
Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial analysts with 3-6 years experience performing 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)
- •Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
- •Build DCF models with WACC and terminal value calculations
- •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 sample_financial_data.json python scripts/ratio_calculator.py sample_financial_data.json --format json python scripts/ratio_calculator.py 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 valuation_data.json python scripts/dcf_valuation.py valuation_data.json --format json python scripts/dcf_valuation.py valuation_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 budget_data.json python scripts/budget_variance_analyzer.py budget_data.json --format json python scripts/budget_variance_analyzer.py budget_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 forecast_data.json python scripts/forecast_builder.py forecast_data.json --format json python scripts/forecast_builder.py forecast_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 |
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 |
Industry Adaptations
SaaS
- •Key metrics: MRR, ARR, CAC, LTV, Churn Rate, Net Revenue Retention
- •Revenue recognition: subscription-based, deferred revenue tracking
- •Unit economics: CAC payback period, LTV/CAC ratio
- •Cohort analysis for retention and expansion revenue
Retail
- •Key metrics: Same-store sales, Revenue per square foot, Inventory turnover
- •Seasonal adjustment factors in forecasting
- •Gross margin analysis by product category
- •Working capital cycle optimization
Manufacturing
- •Key metrics: Gross margin by product line, Capacity utilization, COGS breakdown
- •Bill of materials cost analysis
- •Absorption vs variable costing impact
- •Capital expenditure planning and ROI
Financial Services
- •Key metrics: Net Interest Margin, Efficiency Ratio, ROA, Tier 1 Capital
- •Regulatory capital requirements
- •Credit loss provisioning and reserves
- •Fee income analysis and diversification
Healthcare
- •Key metrics: Revenue per patient, Payer mix, Days in A/R, Operating margin
- •Reimbursement rate analysis by payer
- •Case mix index impact on revenue
- •Compliance cost allocation
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. See assets/sample_financial_data.json for the complete input schema covering all four tools.
Dependencies
None - All scripts use Python standard library only (math, statistics, json, argparse, datetime). No numpy, pandas, or scipy required.