financial-analyst

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name: "financial-analyst" description: Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.

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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.