Start-up Financial Modeling Module
In our Start-up Module, trainees are presented with a sample start-up company. As part of the exercise, trainees are given a number of assumptions about the start-up costs and growth of the enterprise. Based on the assumptions presented, trainees build a comprehensive financial model including a detailed build-up of revenue and expenses. Once the entire financial model is built, trainees determine the amount of equity funding needed to sustain the start-up company until it gets to cash flow breakeven. Additionally, trainees determine possible investor returns based on an expected sale or exit of the start-up. Finally, several scenarios will be presented to pressure test the performance of the start-up and its equity returns.
The Start-up Financial Modeling Module is designed for trainees who have already participated in our Financial Modeling Course and Valuation Module. This module typically lasts approximately three to four hours. This module enables trainees to learn how to build a unit level start-up model from scratch, determine how to calculate funding needs, build an equity returns profile and perform sensitivity testing.
Trainees receive a case study in which assumptions are provided on what the operational and capital costs of the start-up will be and how the start-up will scale on a quarterly basis during the first four years of operations. Assumptions will be provided on customer / revenue growth, gross profit margin expansion, payroll expenses, other SG&A expenses and working capital. Trainees will receive a two-tab blank spreadsheet on which to create this model. During this module trainees:
- Build out a quarterly P&L based on assumptions provided
- Compile a fully integrated 3-statement model with a number of accompanying schedules based on assumptions provided
- Determine the amount of equity funding required and dilution that occurs
- Calculate the equity IRR and cash-on-cash return to investors
- Perform a sensitivity analysis of equity needed based on different performance assumptions