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

Clarify First

Before the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Which analysis — ratios, DCF valuation, budget variance, or forecast (selects the tool and the required input schema; each needs different data)
• The decision it supports — acquisition, fundraise, board review, or budget reforecast (sets the executive-summary conclusion and which metrics lead)
• Materiality threshold — absolute $ or % of budget (drives which variances surface vs. go to the appendix; default 10% / $50K)
• Key assumptions + as-of date — WACC inputs, terminal growth, or forecast drivers and when each was last sourced (drives DCF/forecast outputs and the assumptions appendix)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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 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.

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.