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.
Use when
- The user asks to "run financial ratios", "build a DCF", "analyze budget variance", or "build a forecast"
- A valuation range is needed for an acquisition, fundraise, or board presentation
- Actuals vs budget needs investigation (which variances are material, favorable/unfavorable, department breakdown)
- A rolling 13-week cash flow or driver-based revenue forecast needs construction
- Sensitivity analysis is required to stress-test valuation or forecast assumptions
- The user asks about profitability, liquidity, leverage, efficiency, or valuation metrics with industry context
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
- *Validate:* materiality threshold is explicit (absolute $ or %), accuracy target is a number, and the decision the analysis supports is named
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
- *Validate:* input JSON conforms to the expected schema (no missing statements, no mixed periods); WACC inputs sourced within the last quarter; terminal growth rate ≤ long-run GDP growth
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
- *Validate:* every material variance has a root-cause hypothesis; DCF sensitivity range is wider than ±15% on WACC and terminal growth
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
- *Validate:* executive summary leads with the decision-relevant conclusion, not the method; assumptions appendix lists source + last-reviewed date for each
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 profitability2. 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 73. 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 250004. 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,bearKnowledge 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.
Troubleshooting
| Problem | Cause | Solution |
|---|---|---|
| All ratios return 0.00 | Missing or zeroed financial statement fields in input JSON | Verify income_statement, balance_sheet, and cash_flow keys are populated with non-zero values; check field names match expected schema |
| DCF yields negative equity value | Net debt exceeds enterprise value, or WACC is set lower than terminal growth rate | Confirm net_debt is accurate; ensure terminal_growth_rate < WACC (typically 2-3% vs 8-12%); review capital structure assumptions |
| Sensitivity table shows "N/A" across entire row | WACC value in that row is less than or equal to every terminal growth rate in the range | Widen the gap between WACC and terminal growth; raise WACC inputs or lower the growth range in assumptions.terminal_growth_rate |
| Budget variance analyzer flags every line as material | Materiality thresholds set too low relative to the data scale | Increase --threshold-pct (e.g., from 5 to 10) and --threshold-amt (e.g., from 25000 to 100000) to match organizational materiality policy |
| Forecast builder produces flat projections | Historical data has fewer than 2 periods, or revenue_growth_rate is set to 0 | Provide at least 3-4 historical periods in historical_periods; set a non-zero revenue_growth_rate in assumptions |
| JSON parsing error on script execution | Malformed JSON input file (trailing commas, unquoted keys, encoding issues) | Validate input with python -m json.tool input_file.json; ensure UTF-8 encoding; remove trailing commas and comments |
| Valuation ratios all show "Insufficient data" | Missing market_data section in input JSON (share price, shares outstanding) | Add the market_data object with share_price, shares_outstanding, and earnings_growth_rate fields to the input file |
Success Criteria
- Forecast Accuracy: Revenue forecasts land within +/-5% of actuals; expense forecasts within +/-3% over rolling 12-month periods
- Variance Coverage: 100% of material variances (exceeding threshold) include documented root-cause explanations and corrective action plans
- Valuation Confidence: DCF-derived equity value falls within 15% of comparable-company and precedent-transaction benchmarks, validated through sensitivity analysis
- Report Timeliness: All financial analysis deliverables (ratio reports, variance analyses, forecast updates) published within agreed SLA -- target 100% on-time delivery
- Model Integrity: Every assumption in DCF and forecast models is documented with source, rationale, and last-reviewed date; WACC inputs refresh quarterly against market data
- Stakeholder Adoption: Financial models and dashboards referenced in at least 80% of executive budget reviews, board presentations, and investment committee decisions
- Analytical Efficiency: End-to-end analysis cycle time (data collection through report delivery) reduced by 40%+ compared to manual spreadsheet workflows, measured per reporting period
Scope & Limitations
This skill covers:
- Quantitative financial ratio analysis across profitability, liquidity, leverage, efficiency, and valuation categories with built-in industry benchmarking
- Discounted Cash Flow (DCF) enterprise and equity valuation using CAPM-based WACC, perpetuity growth and exit multiple terminal value methods, and two-way sensitivity analysis
- Budget variance analysis with materiality filtering, favorable/unfavorable classification, department and category breakdowns, and executive summary generation
- Driver-based revenue forecasting with 13-week rolling cash flow projection, base/bull/bear scenario modeling, and linear regression trend analysis
This skill does NOT cover:
- Real-time market data feeds, live stock price retrieval, or automated data ingestion from ERP/accounting systems (all input is via static JSON files)
- Qualitative analysis such as management quality assessment, competitive moat evaluation, ESG scoring, or regulatory risk judgment
- Tax optimization, transfer pricing, multi-entity consolidation, or jurisdiction-specific accounting treatments (IFRS vs GAAP reconciliation)
- Monte Carlo simulation, options pricing (Black-Scholes), credit risk modeling, or any analysis requiring external libraries beyond the Python standard library
Anti-patterns
| Anti-pattern | Failure mode | Fix |
|---|---|---|
| Building a DCF on a single-scenario forecast | False precision; one number presented as a target price | Always run base/bull/bear; present valuation as a range with sensitivity tables |
| Terminal growth rate ≥ long-run GDP growth | Valuation dominated by terminal value assuming perpetual above-economy growth | Cap terminal growth at 2-3% (long-run GDP proxy); if comps justify higher, flag explicitly |
| WACC inputs more than a quarter old | Rate environment moved; discount rate is wrong; valuation wrong | Refresh risk-free rate, ERP, and beta quarterly; document "last reviewed" date per input |
| Benchmarking ratios against a generic "industry average" | Peer set is wrong; conclusions are wrong | Use a specific comparable-company set (size, geography, business model) — see industry benchmarks in references/financial-ratios-guide.md |
| Reporting every variance instead of filtering by materiality | Stakeholders tune out; real issues buried | Apply a materiality threshold (absolute $ or % of budget); below threshold goes into an appendix, not the report |
| Favorable variance = "good"; unfavorable = "bad" | Misses revenue shortfalls masked by expense underspend; misses over-delivery hiding scope cuts | Always pair the classification with a root-cause note — direction alone is not insight |
| Mixing forecast periods (quarterly actuals against annual budget) | Variances that don't reconcile; trust collapses | Run the tools on matched periods only; if a period is partial, annotate and use period-adjusted comparisons |
| Treating model output as the answer | Model is a reasoning aid, not a decision | Lead the executive summary with the decision; put the model outputs in support |
Integration Points
| Related Skill | Domain | Integration Use Case |
|---|---|---|
c-level-advisor/ceo-advisor | C-Level Advisory | Feed DCF valuation outputs and scenario comparisons into CEO strategic investment decisions and board-ready presentations |
c-level-advisor/cto-advisor | C-Level Advisory | Provide technology investment ROI analysis and CapEx forecasts to support build-vs-buy and infrastructure scaling decisions |
business-growth/revenue-operations | Business & Growth | Connect revenue forecasts and unit-economics metrics (CAC, LTV, payback period) to pipeline and go-to-market planning |
product-team/product-manager | Product Team | Supply budget variance data and RICE-weighted financial projections for feature prioritization and resource allocation |
data-analytics/data-analyst | Data Analytics | Export ratio analysis and forecast outputs as structured JSON for BI dashboard integration and trend visualization |
project-management/project-financial-management | Project Management | Align budget variance analysis with project-level cost tracking, earned value management, and milestone-based funding releases |
Tool Reference
scripts/ratio_calculator.py
Calculate and interpret financial ratios across 5 categories with industry benchmarking.
usage: ratio_calculator.py [-h] [--format {text,json}]
[--category {profitability,liquidity,leverage,efficiency,valuation}]
input_file
positional arguments:
input_file Path to JSON file with financial statement data
(must contain income_statement, balance_sheet,
cash_flow, and optionally market_data objects)
options:
-h, --help Show help message and exit
--format {text,json} Output format (default: text)
--category {profitability,liquidity,leverage,efficiency,valuation}
Calculate only a specific ratio category;
omit to calculate all 5 categories (20 ratios)Ratios computed: ROE, ROA, Gross Margin, Operating Margin, Net Margin, Current Ratio, Quick Ratio, Cash Ratio, Debt-to-Equity, Interest Coverage, DSCR, Asset Turnover, Inventory Turnover, Receivables Turnover, DSO, P/E, P/B, P/S, EV/EBITDA, PEG Ratio.
scripts/dcf_valuation.py
Discounted Cash Flow enterprise and equity valuation with WACC calculation and sensitivity analysis.
usage: dcf_valuation.py [-h] [--format {text,json}]
[--projection-years PROJECTION_YEARS]
input_file
positional arguments:
input_file Path to JSON file with valuation data
(must contain historical and assumptions objects)
options:
-h, --help Show help message and exit
--format {text,json} Output format (default: text)
--projection-years PROJECTION_YEARS
Number of projection years; overrides the value
in the input file (default: 5)Outputs: WACC (CAPM), projected revenue and FCF, terminal value (perpetuity growth + exit multiple), enterprise value, equity value, value per share, and a two-way sensitivity table (WACC vs terminal growth rate).
scripts/budget_variance_analyzer.py
Analyze actual vs budget vs prior year performance with materiality filtering and executive summaries.
usage: budget_variance_analyzer.py [-h] [--format {text,json}]
[--threshold-pct THRESHOLD_PCT]
[--threshold-amt THRESHOLD_AMT]
input_file
positional arguments:
input_file Path to JSON file with budget data
(must contain line_items array with actual,
budget, and optionally prior_year values)
options:
-h, --help Show help message and exit
--format {text,json} Output format (default: text)
--threshold-pct THRESHOLD_PCT
Materiality threshold as percentage (default: 10.0)
--threshold-amt THRESHOLD_AMT
Materiality threshold as dollar amount (default: 50000.0)Outputs: Executive summary (revenue/expense/net impact), all variances with favorability classification, material variances filtered by threshold, department summary, and category summary.
scripts/forecast_builder.py
Driver-based revenue forecasting with rolling cash flow projection and multi-scenario modeling.
usage: forecast_builder.py [-h] [--format {text,json}]
[--scenarios SCENARIOS]
input_file
positional arguments:
input_file Path to JSON file with forecast data
(must contain historical_periods, drivers,
assumptions, cash_flow_inputs, and scenarios objects)
options:
-h, --help Show help message and exit
--format {text,json} Output format (default: text)
--scenarios SCENARIOS
Comma-separated list of scenarios to model
(default: base,bull,bear)Outputs: Trend analysis (linear regression, growth rates, seasonality index), scenario comparison table, per-period forecast detail (revenue, COGS, gross profit, OpEx, operating income), and 13-week rolling cash flow projection with runway calculation.