Financial Forecasting Training for Real Business Growth
We built this program after watching too many finance professionals struggle with outdated forecasting methods. It's focused, practical, and designed around real scenarios we've handled over the past decade.
Our autumn 2025 cohort starts in September. You'll work with actual financial data, not theoretical examples.
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What Makes This Different
Most training programs teach theory. We teach what actually works when you're sitting in front of a board expecting answers.
Reality-Based Scenarios
Every module uses real data from actual businesses we've worked with. You'll see the messy parts—incomplete datasets, unexpected market shifts, and the decisions that followed.
- Work with anonymized data from Taiwan tech sector companies
- Handle quarterly forecasts with variable input quality
- Practice explaining complex predictions to non-finance stakeholders
Small Group Format
We cap each cohort at 12 people. That's intentional. Everyone gets direct feedback, and we can adjust pacing based on where the group needs more time.
- Weekly live sessions with our finance team
- Individual forecast reviews for your specific challenges
- Access to recorded sessions for six months after completion
Tool Agnostic Approach
We don't care if you prefer Excel, Python, or specialized software. The principles matter more than the platform. Though we'll show you how each tool handles specific forecasting challenges.
- Compare approaches across different analytical platforms
- Learn when to use automation versus manual review
- Build templates you can adapt to your workflow
Post-Training Support
The learning doesn't stop after week twelve. You get quarterly check-ins and access to our forecasting resources library as market conditions change.
- Monthly office hours for graduate questions
- Updated templates as new best practices emerge
- Private forum for alumni collaboration
Who You'll Learn From
Our instructors come from different backgrounds—corporate finance, consulting, startup CFO roles. They've all dealt with the pressure of getting forecasts right when real money is on the line.
Dorian Falkenrath
Spent eight years at manufacturing firms doing budget forecasts. Now teaches the practical side of prediction modeling.
Lysander Thorne
Former strategy consultant who specializes in building multiple forecast paths for uncertain markets.
Bastian Wexford
Handles the technical side—clean data preparation and choosing the right analytical approach for different business contexts.
Thaddeus Greyson
Teaches how to present forecasts so they actually get used. Works on turning data into clear business recommendations.
Common Problems We Address
These are the issues participants bring to us most often. We spend real time on each one.
1 Handling Incomplete Historical Data
You need to forecast, but your company's records are inconsistent or you're working with a new product line. Traditional models fall apart without clean historical trends.
Our Approach:
- Identify which gaps actually matter versus cosmetic data issues
- Use proxy data from comparable business segments or market parallels
- Build confidence intervals that acknowledge uncertainty honestly
- Document assumptions so stakeholders understand forecast limitations
- Create review triggers to update forecasts as new data arrives
2 Communicating Uncertainty to Leadership
Executives want a single number, but responsible forecasting involves ranges. How do you present probability without seeming indecisive or uncommitted to targets?
Our Approach:
- Frame ranges around business decisions rather than statistical concepts
- Show what specific factors would push results toward either boundary
- Provide clear recommended planning numbers with explicit risk factors
- Use visual presentations that make probability distributions intuitive
- Practice delivering forecast presentations with constructive peer feedback
3 Adjusting for Market Disruptions
Your models work fine in stable conditions, then a major market shift happens. Suddenly historical patterns don't predict future performance accurately.
Our Approach:
- Identify early indicators that suggest your model needs recalibration
- Build alternative scenarios before disruption forces reactive changes
- Weight recent data appropriately without overreacting to noise
- Maintain baseline forecasts alongside disruption-adjusted versions
- Document model changes so future analysts understand your reasoning