Financial Analyst Skill

Overview

Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for 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)
Validate input data completeness before running ratio calculations (check for missing fields, nulls, or implausible values)
Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
Build DCF models with WACC and terminal value calculations; cross-check DCF outputs against sanity bounds (e.g., implied multiples vs. comparables)
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
`references/industry-adaptations.md`	Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare)

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

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.