Master Valuation Techniques That Actually Work
Learning valuation isn't about memorizing formulas. It's about understanding the story behind the numbers and building models that hold up under scrutiny. We focus on the techniques analysts actually use daily—not theoretical concepts that never leave the textbook.
Explore the ProgramDCF Modeling Without the Mystery
Discounted cash flow analysis sounds intimidating. But once you break it down into components, it becomes a logical framework for thinking about business value.
Revenue Projection
Start with understanding what drives revenue growth. We'll look at historical patterns, market dynamics, and competitive positioning to build defensible forecasts that analysts can explain confidently.
Cost Structure Analysis
Fixed versus variable costs matter more than most people think. You'll learn to model operating leverage and understand how margin expansion (or contraction) affects valuation outcomes significantly.
WACC Calculation
Getting the discount rate right is half the battle. We cover capital structure decisions, beta estimation, and risk premium debates—with practical approaches for handling uncertainty in these inputs.
Terminal Value
This single number often represents 70% of enterprise value. Learn both perpetuity growth and exit multiple methods, and understand when each approach makes sense for different business models.
Sensitivity Testing
No forecast survives first contact with reality. You'll build scenario analysis and understand which assumptions drive value most—crucial for presenting work to skeptical stakeholders.
Model Architecture
Clean models get used. Messy models get ignored. We emphasize building transparent, audit-ready spreadsheets with proper documentation and logical flow that others can follow months later.
Comparable Company Analysis That Makes Sense
Trading multiples provide a reality check for DCF valuations. But picking the right comparables and adjusting for differences requires judgment that develops through practice and exposure to multiple industries.
- Identifying truly comparable companies—looking beyond superficial industry classifications to operating model similarities
- Understanding when to use EV/EBITDA versus P/E ratios, and why certain multiples work better for specific business types
- Making adjustments for size, growth rates, profitability, and capital structure differences between target and comparables
- Recognizing when market multiples reflect irrational exuberance or unwarranted pessimism—and how to document that view
How the Learning Actually Works
Three terms running September through May, with most participants completing the program in nine months. Classes meet Tuesday and Thursday evenings, with additional weekend workshops for case study work.
Foundation Term
September – November 2025
We start with financial statement analysis because you can't value what you don't understand. This term covers how to read between the lines of annual reports and spot red flags before building models.
Modeling Term
December 2025 – February 2026
The core technical work happens here. You'll build your first complete DCF model and learn the comparable company methodology. Expect to spend significant time outside class refining your spreadsheet skills.
Application Term
March – May 2026
Real companies, real deadlines. Working in small teams, you'll complete comprehensive valuations for Australian listed companies and present findings to experienced analysts who'll ask the tough questions.
Ready to Start in September 2025?
Applications for the autumn intake open in May. Early applicants get first access to the limited spots available.
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Mirela Thornburg
Lead Valuation Instructor
I spent twelve years doing equity research and M&A advisory work before moving into education. That experience taught me something important: valuation is as much about communication and judgment as it is about spreadsheet mechanics.
Most training programs focus exclusively on technical skills. But I've seen plenty of technically proficient analysts struggle because they couldn't explain their assumptions or defend their conclusions under pressure. So we spend time on both—building robust models and articulating the reasoning behind them.