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.