Earning the Level 2 Chartered Financial Modeler (CFM) demonstrates an advanced and thorough understanding of Financial Modeling, Financial Analysis and Advanced Excel. This level builds on the topics covered in the Level 1 exam.
Level 2 (CFM) is for Professionals Who:
- Work closely with team leaders to provide financial modeling insights
- Ensure projects progress according to financial modeling best practices
- Use models to demonstrate the business case and “tell the story”
Body of Knowledge: Level 2 (CFM) Exam
During this 4-hour exam, candidates will complete a selection of questions from the following list of complex modeling topics:
- Revenue: Create a detailed revenue schedule that considers allocations by
division, geography, capacity constraints, currency issues
- Operating Costs: Create a detailed cost structure that includes tiered pricing on variable costs, multi-currency costing, semi-variable costs
- Depreciation: Incorporate accelerated depreciation, build functionality to prevent over-depreciation of assets
- Working Capital: Detailed inventory modeling, forecast Accounts Receivable and Accounts Payable in a monthly model
- Income Tax: Utilize cascading tax pools/tax losses, calculate earnings for a company in multiple jurisdictions including the impact of timing differences
- Debt: Build an advanced capital structure, incorporate multiple pieces of debt, model various debt types and covenants, sweeps, repayments and debt sculpting, incorporate refinancing switches
- Equity: Incorporate various concepts including convertibles, buybacks,
options/warrants, tiered dividends, and dilutive instruments
- Subsidiaries: Calculate non-controlling interests and long-term investments
- Model Automation: Create a detailed and automated summary page
- Timing Flexibility: Automate modeling starting/ending dates, multiple timing periods in a model
- Model Checking: Audit various calculation, linking, and other errors in a model
- Data Management: Perform data management with large data sets
- Sensitivity Analysis: Prepare analysis to automatically sensitize model outputs
