home / skills / microck / ordinary-claude-skills / creating-financial-models
This skill provides a comprehensive financial modeling suite enabling DCF, Monte Carlo, and scenario planning to inform investment decisions.
npx playbooks add skill microck/ordinary-claude-skills --skill creating-financial-modelsReview the files below or copy the command above to add this skill to your agents.
---
name: creating-financial-models
description: This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
---
# Financial Modeling Suite
A comprehensive financial modeling toolkit for investment analysis, valuation, and risk assessment using industry-standard methodologies.
## Core Capabilities
### 1. Discounted Cash Flow (DCF) Analysis
- Build complete DCF models with multiple growth scenarios
- Calculate terminal values using perpetuity growth and exit multiple methods
- Determine weighted average cost of capital (WACC)
- Generate enterprise and equity valuations
### 2. Sensitivity Analysis
- Test key assumptions impact on valuation
- Create data tables for multiple variables
- Generate tornado charts for sensitivity ranking
- Identify critical value drivers
### 3. Monte Carlo Simulation
- Run thousands of scenarios with probability distributions
- Model uncertainty in key inputs
- Generate confidence intervals for valuations
- Calculate probability of achieving targets
### 4. Scenario Planning
- Build best/base/worst case scenarios
- Model different economic environments
- Test strategic alternatives
- Compare outcome probabilities
## Input Requirements
### For DCF Analysis
- Historical financial statements (3-5 years)
- Revenue growth assumptions
- Operating margin projections
- Capital expenditure forecasts
- Working capital requirements
- Terminal growth rate or exit multiple
- Discount rate components (risk-free rate, beta, market premium)
### For Sensitivity Analysis
- Base case model
- Variable ranges to test
- Key metrics to track
### For Monte Carlo Simulation
- Probability distributions for uncertain variables
- Correlation assumptions between variables
- Number of iterations (typically 1,000-10,000)
### For Scenario Planning
- Scenario definitions and assumptions
- Probability weights for scenarios
- Key performance indicators to track
## Output Formats
### DCF Model Output
- Complete financial projections
- Free cash flow calculations
- Terminal value computation
- Enterprise and equity value summary
- Valuation multiples implied
- Excel workbook with full model
### Sensitivity Analysis Output
- Sensitivity tables showing value ranges
- Tornado chart of key drivers
- Break-even analysis
- Charts showing relationships
### Monte Carlo Output
- Probability distribution of valuations
- Confidence intervals (e.g., 90%, 95%)
- Statistical summary (mean, median, std dev)
- Risk metrics (VaR, probability of loss)
### Scenario Planning Output
- Scenario comparison table
- Probability-weighted expected values
- Decision tree visualization
- Risk-return profiles
## Model Types Supported
1. **Corporate Valuation**
- Mature companies with stable cash flows
- Growth companies with J-curve projections
- Turnaround situations
2. **Project Finance**
- Infrastructure projects
- Real estate developments
- Energy projects
3. **M&A Analysis**
- Acquisition valuations
- Synergy modeling
- Accretion/dilution analysis
4. **LBO Models**
- Leveraged buyout analysis
- Returns analysis (IRR, MOIC)
- Debt capacity assessment
## Best Practices Applied
### Modeling Standards
- Consistent formatting and structure
- Clear assumption documentation
- Separation of inputs, calculations, outputs
- Error checking and validation
- Version control and change tracking
### Valuation Principles
- Use multiple valuation methods for triangulation
- Apply appropriate risk adjustments
- Consider market comparables
- Validate against trading multiples
- Document key assumptions clearly
### Risk Management
- Identify and quantify key risks
- Use probability-weighted scenarios
- Stress test extreme cases
- Consider correlation effects
- Provide confidence intervals
## Example Usage
"Build a DCF model for this technology company using the attached financials"
"Run a Monte Carlo simulation on this acquisition model with 5,000 iterations"
"Create sensitivity analysis showing impact of growth rate and WACC on valuation"
"Develop three scenarios for this expansion project with probability weights"
## Scripts Included
- `dcf_model.py`: Complete DCF valuation engine
- `sensitivity_analysis.py`: Sensitivity testing framework
## Limitations and Disclaimers
- Models are only as good as their assumptions
- Past performance doesn't guarantee future results
- Market conditions can change rapidly
- Regulatory and tax changes may impact results
- Professional judgment required for interpretation
- Not a substitute for professional financial advice
## Quality Checks
The model automatically performs:
1. Balance sheet balancing checks
2. Cash flow reconciliation
3. Circular reference resolution
4. Sensitivity bound checking
5. Statistical validation of Monte Carlo results
## Updates and Maintenance
- Models use latest financial theory and practices
- Regular updates for market parameter defaults
- Incorporation of regulatory changes
- Continuous improvement based on usage patterns
This skill provides an advanced financial modeling suite for valuation, risk assessment, and decision support. It combines DCF valuation, sensitivity testing, Monte Carlo simulation, and scenario planning into repeatable, auditable models. The outputs include full financial projections, probabilistic valuation ranges, and Excel workbooks for stakeholder review.
The toolkit builds a DCF from historical statements and user assumptions, computes WACC, projects free cash flows, and derives terminal value by perpetuity or exit multiple. Sensitivity modules generate data tables and tornado charts for key drivers, while the Monte Carlo engine runs thousands of iterations using specified distributions and correlations to produce confidence intervals. Scenario planning assembles best/base/worst cases with probability weights and produces probability-weighted outcomes and decision-support visuals.
What inputs are required to run a DCF?
You need 3–5 years of historical financials, revenue growth and margin assumptions, capex and working capital forecasts, terminal growth or exit multiple, and discount rate components.
How many Monte Carlo iterations are recommended?
Typically 1,000–10,000 iterations; use more for tighter confidence intervals and when modeling correlated variables.