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

references/financial-ratios-guide.md

references/valuation-methodology.md

references/forecasting-best-practices.md

Templates

assets/variance_report_template.md

assets/dcf_analysis_template.md

assets/forecast_report_template.md

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

Input Data Format

All scripts accept JSON input files. See

assets/sample_financial_data.json

Dependencies

None - All scripts use Python standard library only (

math

statistics

json

argparse

datetime

Troubleshooting

income_statement

balance_sheet

cash_flow

net_debt

terminal_growth_rate

assumptions.terminal_growth_rate

--threshold-pct

--threshold-amt

revenue_growth_rate

historical_periods

revenue_growth_rate

assumptions

python -m json.tool input_file.json

market_data

market_data

share_price

shares_outstanding

earnings_growth_rate

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

Integration Points

c-level-advisor/ceo-advisor

c-level-advisor/cto-advisor

business-growth/revenue-operations

product-team/product-manager

data-analytics/data-analyst

project-management/project-financial-management

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.