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
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)
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
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)
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.
1---2name: financial-analyst3description: Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making4---56# Financial Analyst Skill78## Overview910Production-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.1112## 5-Phase Workflow1314### Phase 1: Scoping15- Define analysis objectives and stakeholder requirements16- Identify data sources and time periods17- Establish materiality thresholds and accuracy targets18- Select appropriate analytical frameworks1920### Phase 2: Data Analysis & Modeling21- Collect and validate financial data (income statement, balance sheet, cash flow)22- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)23- Build DCF models with WACC and terminal value calculations24- Construct budget variance analyses with favorable/unfavorable classification25- Develop driver-based forecasts with scenario modeling2627### Phase 3: Insight Generation28- Interpret ratio trends and benchmark against industry standards29- Identify material variances and root causes30- Assess valuation ranges through sensitivity analysis31- Evaluate forecast scenarios (base/bull/bear) for decision support3233### Phase 4: Reporting34- Generate executive summaries with key findings35- Produce detailed variance reports by department and category36- Deliver DCF valuation reports with sensitivity tables37- Present rolling forecasts with trend analysis3839### Phase 5: Follow-up40- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)41- Monitor report delivery timeliness (target: 100% on time)42- Update models with actuals as they become available43- Refine assumptions based on variance analysis4445## Tools4647### 1. Ratio Calculator (`scripts/ratio_calculator.py`)4849Calculate and interpret financial ratios from financial statement data.5051**Ratio Categories:**52- **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin53- **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio54- **Leverage:** Debt-to-Equity, Interest Coverage, DSCR55- **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO56- **Valuation:** P/E, P/B, P/S, EV/EBITDA, PEG Ratio5758```bash59python scripts/ratio_calculator.py sample_financial_data.json60python scripts/ratio_calculator.py sample_financial_data.json --format json61python scripts/ratio_calculator.py sample_financial_data.json --category profitability62```6364### 2. DCF Valuation (`scripts/dcf_valuation.py`)6566Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.6768**Features:**69- WACC calculation via CAPM70- Revenue and free cash flow projections (5-year default)71- Terminal value via perpetuity growth and exit multiple methods72- Enterprise value and equity value derivation73- Two-way sensitivity analysis (discount rate vs growth rate)7475```bash76python scripts/dcf_valuation.py valuation_data.json77python scripts/dcf_valuation.py valuation_data.json --format json78python scripts/dcf_valuation.py valuation_data.json --projection-years 779```8081### 3. Budget Variance Analyzer (`scripts/budget_variance_analyzer.py`)8283Analyze actual vs budget vs prior year performance with materiality filtering.8485**Features:**86- Dollar and percentage variance calculation87- Materiality threshold filtering (default: 10% or $50K)88- Favorable/unfavorable classification with revenue/expense logic89- Department and category breakdown90- Executive summary generation9192```bash93python scripts/budget_variance_analyzer.py budget_data.json94python scripts/budget_variance_analyzer.py budget_data.json --format json95python scripts/budget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 2500096```9798### 4. Forecast Builder (`scripts/forecast_builder.py`)99100Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.101102**Features:**103- Driver-based revenue forecast model104- 13-week rolling cash flow projection105- Scenario modeling (base/bull/bear cases)106- Trend analysis using simple linear regression (standard library)107108```bash109python scripts/forecast_builder.py forecast_data.json110python scripts/forecast_builder.py forecast_data.json --format json111python scripts/forecast_builder.py forecast_data.json --scenarios base,bull,bear112```113114## Knowledge Bases115116| Reference | Purpose |117|-----------|---------|118| `references/financial-ratios-guide.md` | Ratio formulas, interpretation, industry benchmarks |119| `references/valuation-methodology.md` | DCF methodology, WACC, terminal value, comps |120| `references/forecasting-best-practices.md` | Driver-based forecasting, rolling forecasts, accuracy |121122## Templates123124| Template | Purpose |125|----------|---------|126| `assets/variance_report_template.md` | Budget variance report template |127| `assets/dcf_analysis_template.md` | DCF valuation analysis template |128| `assets/forecast_report_template.md` | Revenue forecast report template |129130## Industry Adaptations131132### SaaS133- Key metrics: MRR, ARR, CAC, LTV, Churn Rate, Net Revenue Retention134- Revenue recognition: subscription-based, deferred revenue tracking135- Unit economics: CAC payback period, LTV/CAC ratio136- Cohort analysis for retention and expansion revenue137138### Retail139- Key metrics: Same-store sales, Revenue per square foot, Inventory turnover140- Seasonal adjustment factors in forecasting141- Gross margin analysis by product category142- Working capital cycle optimization143144### Manufacturing145- Key metrics: Gross margin by product line, Capacity utilization, COGS breakdown146- Bill of materials cost analysis147- Absorption vs variable costing impact148- Capital expenditure planning and ROI149150### Financial Services151- Key metrics: Net Interest Margin, Efficiency Ratio, ROA, Tier 1 Capital152- Regulatory capital requirements153- Credit loss provisioning and reserves154- Fee income analysis and diversification155156### Healthcare157- Key metrics: Revenue per patient, Payer mix, Days in A/R, Operating margin158- Reimbursement rate analysis by payer159- Case mix index impact on revenue160- Compliance cost allocation161162## Key Metrics & Targets163164| Metric | Target |165|--------|--------|166| Forecast accuracy (revenue) | +/-5% |167| Forecast accuracy (expenses) | +/-3% |168| Report delivery | 100% on time |169| Model documentation | Complete for all assumptions |170| Variance explanation | 100% of material variances |171172## Input Data Format173174All scripts accept JSON input files. See `assets/sample_financial_data.json` for the complete input schema covering all four tools.175176## Dependencies177178**None** - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required.
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