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LBO modelling course
Feb – 3 days
February 7 @ 9:30am - February 9 @ 5:30pm
This modelling course provides delegates with the ideal opportunity to practise and improve their ability to analyse LBOs using Excel.
Attendees imagine working with their own case business – one that is contemplating an LBO – starting from a totally blank Excel spreadsheet.
Delegates ‘build up’ an integrated financial statement model for a company that has taken on debt as part of an LBO, looking at the deal from the bank’s and equity-sponsor’s perspective.
Building the core of an LBO model – course day 1 of 3
Starting by opening an empty Excel workbook, step by step course delegates create an operating cash flow model for their own case business. Through their work, and with guidance provided by the course tutor, attendees find themselves resolving key modelling issues as they make progress with their model build:
- How do we get all the key financial statement linkages working?
- How do we deal with inputs?
- Where are the outputs going to fit?
- How can we set the model up to run scenarios instantly at the flick of a switch?
- What guidelines can we establish about good modelling practice?
- What are the features of Excel that are the most useful to us right now?
Course day 2 – completing the operating model
Delegates finalise their cash flow model and make sure their balance sheet is balancing and that their calculations have some internal integrity. Their attention swings to the new deal contemplated for the case business:
- How could valuation issues be addressed?
- Apart from buying the business, on the day the deal is done, what else do we need cash for?
- What are likely sources of funds for the deal?
- What initial constraints should we put on debt capacity?
Delegates build the new deal into their model and start adjusting the model for the changes envisaged by the transaction.
Course day 3
Delegates complete their buy out model, creating outputs and calculating returns to financial sponsors – before starting to iterate with and optimise the model. Across all the outputs and inputs we are experimenting with, who can craft the ‘best’ deal?