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.