Financial Analyst

Financial Analyst

Overview

A comprehensive financial analysis toolkit that enables systematic evaluation of financial health, investment opportunities, and business performance. This skill combines quantitative analysis with professional reporting to support informed decision-making.

Core Capabilities:

• Financial ratio analysis (Profitability, Safety/Liquidity, Efficiency)
• DCF/NPV/IRR investment evaluation
• Budget variance analysis with root cause decomposition
• Sensitivity and scenario analysis for risk assessment

Output Formats:

• Structured analysis reports (Markdown)
• Visualization charts (PNG via matplotlib/seaborn)
• Summary tables with interpretation

Prerequisites

• Python 3.8+ with the following packages:
  • pandas (for CSV data handling and variance analysis)
  • numpy (for numerical calculations)
  • matplotlib (optional, for visualization charts)
• Input data in JSON or CSV format (see Input Requirements in each workflow)
• Optional: Industry benchmarks for comparative analysis

Workflow

1. Gather Input Data - Collect financial statements, cash flow projections, or budget data
2. Select Analysis Type - Choose from ratio analysis, DCF evaluation, variance analysis, or sensitivity analysis
3. Execute Analysis - Run the appropriate workflow or script command
4. Review Results - Interpret metrics, identify areas of concern, compare to benchmarks
5. Generate Report - Produce structured output using templates

Output

• Financial Ratio Report (Markdown) - Profitability, safety, and efficiency metrics with interpretations
• DCF Analysis Report (Markdown) - NPV, IRR, payback period, and investment recommendation
• Budget Variance Report (Markdown) - Line-by-line variances with materiality flags
• Sensitivity Analysis - Tornado chart data and scenario comparison tables
• Visualization Charts (PNG) - Radar charts, tornado charts via matplotlib

When to Use This Skill

Use this skill when you need to:

1. Analyze Financial Statements

   • "Analyze these financial statements and calculate key ratios"
   • "What's the financial health of this company based on their balance sheet?"
   • "この会社の財務諸表から収益性を分析してください"

2. Evaluate Investment Opportunities

   • "Calculate the NPV and IRR for this investment project"
   • "Perform a DCF valuation for this acquisition target"
   • "この投資案件のDCF評価を行ってください"

3. Analyze Budget Performance

   • "Compare actual vs budget and identify variances"
   • "What's causing the budget overrun in Q3?"
   • "予算差異分析を行い、原因を特定してください"

4. Assess Risk and Scenarios

   • "Run a sensitivity analysis on revenue assumptions"
   • "What happens to NPV under different scenarios?"
   • "感度分析でリスク要因を評価してください"

Core Workflow 1: Financial Statement Analysis

Analyze financial ratios from structured data to assess company health.

Input Requirements

Accept data in JSON or CSV format:

{
  "company": "ABC Corp",
  "period": "2024-Q4",
  "balance_sheet": {
    "total_assets": 100000000,
    "current_assets": 40000000,
    "cash": 10000000,
    "inventory": 15000000,
    "receivables": 12000000,
    "total_liabilities": 60000000,
    "current_liabilities": 25000000,
    "long_term_debt": 30000000,
    "total_equity": 40000000
  },
  "income_statement": {
    "revenue": 80000000,
    "cost_of_goods_sold": 48000000,
    "gross_profit": 32000000,
    "operating_expenses": 16000000,
    "operating_income": 16000000,
    "interest_expense": 2000000,
    "net_income": 10000000
  }
}

Step 1: Load and Validate Data

Read the input file and validate required fields:

• Ensure all balance sheet items are present
• Verify income statement completeness
• Check for negative values that shouldn't be negative

Step 2: Calculate Financial Ratios

Profitability Ratios:

Safety/Liquidity Ratios:

Efficiency Ratios:

Step 3: Generate Interpretation

For each ratio:

1. Calculate the value
2. Classify as: Strong (green), Acceptable (yellow), or Weak (red)
3. Provide industry context if available
4. Note any significant deviations from norms

Step 4: Visualize Results

Generate a radar chart showing:

• All three ratio categories
• Comparison to benchmarks
• Strengths and weaknesses at a glance

Step 5: Output Report

Use assets/financial_report_template_ja.md or assets/financial_report_template_en.md as the template. Include:

• Executive summary with key findings
• Detailed ratio table with interpretations
• Visualization charts
• Recommendations based on analysis

Core Workflow 2: Investment Evaluation (DCF)

Perform Discounted Cash Flow analysis for investment decisions.

Input Requirements

{
  "project_name": "New Factory Investment",
  "initial_investment": 50000000,
  "projected_cash_flows": [10000000, 15000000, 20000000, 22000000, 25000000],
  "terminal_growth_rate": 0.02,
  "discount_rate": 0.10,
  "analysis_years": 5
}

Step 1: Calculate NPV

NPV = -Initial Investment + Σ(CFt / (1 + r)^t) + Terminal Value / (1 + r)^n

Where:
- CFt = Cash flow at time t
- r = Discount rate
- n = Number of years

Terminal Value Methods:

1. Gordon Growth Model: CF_n × (1 + g) / (r - g)
2. Exit Multiple: Final Year EBITDA × Industry Multiple

Step 2: Calculate IRR

Find the discount rate where NPV = 0 using Newton-Raphson iteration:

def calculate_irr(cash_flows, guess=0.10, tolerance=0.0001):
    rate = guess
    for _ in range(100):
        npv = sum(cf / (1 + rate)**i for i, cf in enumerate(cash_flows))
        if abs(npv) < tolerance:
            return rate
        derivative = sum(-i * cf / (1 + rate)**(i+1) for i, cf in enumerate(cash_flows))
        rate = rate - npv / derivative
    return rate

Step 3: Calculate WACC (if components provided)

WACC = (E/V × Re) + (D/V × Rd × (1 - Tc))

Where:
- E = Market value of equity
- D = Market value of debt
- V = E + D
- Re = Cost of equity
- Rd = Cost of debt
- Tc = Tax rate

Step 4: Calculate Payback Period

Simple Payback:

Payback = Years until cumulative cash flow >= initial investment

Discounted Payback:

Payback = Years until cumulative discounted cash flow >= initial investment

Step 5: Investment Decision Criteria

Refer to references/dcf_methodology.md for detailed methodology.

Core Workflow 3: Budget Variance Analysis

Compare actual results to budget and identify causes.

Input Requirements

Two data files: actual.csv and budget.csv with matching structure:

category,item,amount
Revenue,Product Sales,85000000
Revenue,Service Revenue,15000000
Cost,Material Costs,35000000
Cost,Labor Costs,25000000
Expenses,Marketing,8000000
Expenses,Administration,5000000

Step 1: Calculate Variances

For each line item:

Variance = Actual - Budget
Variance % = (Actual - Budget) / Budget × 100

Classify variances:

• Favorable: Revenue > Budget or Cost < Budget
• Unfavorable: Revenue < Budget or Cost > Budget

Step 2: Decompose Variances

For volume-driven items, decompose into:

• Price Variance: (Actual Price - Budget Price) × Actual Quantity
• Volume Variance: (Actual Quantity - Budget Quantity) × Budget Price
• Mix Variance: For multi-product analysis

Step 3: Identify Material Variances

Flag items where:

• Variance % exceeds threshold (typically 5-10%)
• Absolute variance is significant
• Trend indicates persistent deviation

Step 4: Root Cause Analysis

For each material variance, document:

1. Category and item
2. Variance amount and percentage
3. Potential causes (internal/external)
4. Recommended corrective actions

Step 5: Generate Variance Report

Use assets/budget_variance_template_ja.md or assets/budget_variance_template_en.md:

• Summary dashboard with key variances
• Waterfall chart showing variance buildup
• Detailed variance table by category
• Action items and recommendations

Core Workflow 4: Sensitivity and Scenario Analysis

Assess risk by varying key assumptions.

Step 1: Identify Key Variables

Common sensitivity variables:

• Revenue growth rate
• Cost inflation
• Discount rate
• Market size
• Price elasticity

Step 2: Single-Variable Sensitivity

For each key variable:

1. Define range: Base ± 10%, ± 20%
2. Calculate NPV at each point
3. Record results

| Variable     | -20%    | -10%    | Base    | +10%    | +20%    |
|-------------|---------|---------|---------|---------|---------|
| Revenue     | $2.1M   | $3.5M   | $4.8M   | $6.2M   | $7.5M   |
| Costs       | $6.5M   | $5.7M   | $4.8M   | $4.0M   | $3.1M   |

Step 3: Generate Tornado Chart

Rank variables by impact on NPV:

• Calculate NPV swing (high case - low case)
• Sort by swing magnitude
• Visualize as horizontal bar chart

Step 4: Multi-Variable Scenario Analysis

Define scenarios:

• Best Case: Favorable assumptions
• Base Case: Expected assumptions
• Worst Case: Conservative assumptions

For each scenario:

1. Set all variable values
2. Calculate NPV, IRR, Payback
3. Assess probability

Step 5: Break-Even Analysis

Calculate break-even points:

• Revenue required for NPV = 0
• Cost level where IRR = WACC
• Volume needed to recover investment

Refer to references/sensitivity_analysis_guide.md for methodology details.

Python Script Usage

The scripts/financial_analyzer.py provides CLI automation:

Financial Ratio Analysis

python scripts/financial_analyzer.py ratios <input.json> --output report.md --visualize

DCF Valuation

python scripts/financial_analyzer.py dcf <project.json> --discount-rate 0.10 --terminal-method gordon

Budget Variance

python scripts/financial_analyzer.py variance <actual.csv> <budget.csv> --threshold 5

Sensitivity Analysis

python scripts/financial_analyzer.py sensitivity <model.json> --variables revenue,costs --range 20

Best Practices

Data Quality

1. Verify source data before analysis
2. Standardize formats (currency, units, dates)
3. Document assumptions clearly
4. Cross-check calculations with known values

Analysis Rigor

1. Use multiple metrics - No single metric tells the whole story
2. Consider industry context - Benchmarks vary by sector
3. Account for timing - NPV is time-sensitive
4. Stress test assumptions - Challenge optimistic projections

Reporting

1. Lead with key findings - Executive summary first
2. Visualize data - Charts communicate faster than tables
3. Provide recommendations - Analysis should drive action
4. Document limitations - Be transparent about uncertainties

Resources

Scripts

• scripts/financial_analyzer.py - Main analysis automation script

References

• references/financial_ratios_guide.md - Detailed ratio definitions and benchmarks
• references/dcf_methodology.md - DCF calculation methodology
• references/sensitivity_analysis_guide.md - Sensitivity/scenario analysis guide

Templates

• assets/financial_report_template_ja.md - Japanese financial report template
• assets/financial_report_template_en.md - English financial report template
• assets/budget_variance_template_ja.md - Japanese variance report template
• assets/budget_variance_template_en.md - English variance report template

Quick Reference

Financial Ratio Formulas

Investment Metrics

Variance Classification