Creating LBO/Operating Models with Limited Info - How to Improve
Hi all, (M&A An2 here).
Something frequently discussed on this forum is the modelling transition when crossing to the buy side (PE specific). Sure, I've been practising the basics, and have run a variety of debt scenarios, toggles, OIDs, exit types...etc etc.
In some threads, I notice PE professionals say that one of the main expectations when you hit the desk is being able to model an LBO (full operating period and returns) with limited information.
From the searching I've done, this is a less covered topic and I'd love to hear exactly what sort of on-the-job scenarios people have been put in with regard. I'll start off with a few key questions I think would be helpful to cover, and then hearing some other stories, Asso expectations, and tips to practice (for incoming Assos and experienced Assos) would be super helpful.
1. What is a typical example of 'limited information' when building an LBO/operating model?
Ofc missing info from the company, you have an idea of what your lender relations will provide.
2. How do you use other information, industry comparables, and other resources to guess or assume reasonable values for the missing/limited information? (any common techniques?)
3. Which statements are you expected to build? i.e. are you ever given just a balance sheet and an income statement and told to build the CFS? Are there any scenarios in which you'll end up with just one statement and be told to work out or assume the other elements of the business?
4. I have a good understanding of how to build a three-statement model, but how do I approach building a three-statement model (especially with limited info) whilst thinking like an investor?
(not just, stress test commodities pricing, FX, capex, interest, and cases for sector growth...) - But I suppose more advanced techniques, perhaps what specific things are you looking for as an investor (sector dpd ofc).
Thanks for any input - look forward to hearing! - Cheers
To tackle your questions effectively, here's a breakdown based on the most helpful WSO content:
Typical Examples of 'Limited Information' in LBO/Operating Models:
In such cases, you'll often rely on industry benchmarks, management interviews, and public filings of comparable companies to fill gaps.
Using Industry Comparables and Other Resources:
Statements You’re Expected to Build:
Building a Three-Statement Model with an Investor Mindset:
Tips to Practice: - Use case studies or past deals to simulate scenarios with limited data. - Build models from scratch using only partial information, then validate your assumptions with actuals or industry data. - Leverage resources like WSO’s financial modeling templates and guides to refine your approach.
For further practice, consider exploring WSO’s financial modeling courses or templates, which provide structured frameworks for LBOs and operating models.
Sources: What're some examples of proprietary information you guys keep locked away at your firm? Competitive Intel etc., Sources and Uses statement - private LBO model, https://www.wallstreetoasis.com/forum/investment-banking/modelling-has-to-be-the-biggest-misnomer-in-this-industry?customgpt=1, How to analyse business models?, Creating own LBO assumptions
Any vets care to weigh in?
SaaSChimp SOFRsoGood m_1 terabyte [@PrivateTechquity 🚀GME🚀]
chihayafull saw this thread and you answered two of my recent private equity questions well any chance you could share your thoughts on this as is vry interesting?
I think you're over-thinking a little. In real life, assuming the deal is new, you'll get a CIM (and maybe a financial supplement) with basic detail on P&L and perhaps NWC/CapEx if you're lucky. You'll build the same type of model that you would for an interview - effectively light P&L forecast and some basic cash flow modeling (not a full 3-statements).
You don't need any outside info besides a grasp of comps/valuation techniques to ballpark the right entry/exit multiples to use based on the businesses profile (growth rate, profitability, retention metrics, customer size segment served, end market attractiveness, etc.). You'll get a better handle there with time.
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