How complex are actual models?

There are seemingly huge differences in the complexities of the models provided with Rosenbaum and Pearl and from Macabacus.com. For example the lbo model from Rosenbaum is a few hundred rows spread over several worksheets but the LBO from Macabacus is 1623 rows.

Which model is more inline with what is used in IBD?
(If it is the Macabacus, do analysts understand all of the intricacies found in 1600 rows?)

 

It's all different. Each company requires different assumptions, has different equity and debt structures and capital allocations. Different companies have different adjustments, line items, covenants, credit ratings etc. Literally, it depends.

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NYBC02:

There are seemingly huge differences in the complexities of the models provided with Rosenbaum and Pearl and from Macabacus.com. For example the LBO model from Rosenbaum is a few hundred rows spread over several worksheets but the LBO from Macabacus is 1623 rows.

Which model is more inline with what is used in IBD?
(If it is the Macabacus, do analysts understand all of the intricacies found in 1600 rows?)

Usually somewhere in between. 1600 rows on one sheet is ridiculous. I have built models for more complex than the Macabus model, but I would never have a single sheet that long. Much easier to follow on multiple worksheets. Typically when models get complex, it's because the pro forma analysis is complex or you are layering in lots of different numbers from various sources, not because you are building in very nuanced valuation elements, as in the Macabus model.

"For all the tribulations in our lives, for all the troubles that remain in the world, the decline of violence is an accomplishment we can savor, and an impetus to cherish the forces of civilization and enlightenment that made it possible."
 

Never had a model with 1600 on a single page, however depending on the type of analysis you're looking for and the data you have to work with, the total number of lines can pass that. For example, I've made long term operating models for energy companies where I recreated type curves for a number of different layers and the result was over 3,000 total rows. It could easily pass this if you needed to incorporate years of historical data.

With that said, number of rows is a poor way to judge the complexity of the model. I would work on trying to understand what the model is trying to do and whether or not thats the most efficient/correct way to do it.

 
Kassad:

It isn't as complex as it is simply vast in terms of how much data you input. For bigger models, you have to input a lot of information to get the results you want.

That's why interns are so clutch.

Sorry, I wasn't clear. When I said complexity I meant more along the lines of vastness.

 
TheBlueCheese:

Just as an FYI- Macabacus intentionally models out everything on one tab/page

I realize that but it is still a much larger model than the Rosenbaum, for example, when all of the rows of multiple sheets are counted.

 
NYBC02:
TheBlueCheese:

Just as an FYI- Macabacus intentionally models out everything on one tab/page

I realize that but it is still a much larger model than the Rosenbaum, for example, when all of the rows of multiple sheets are counted.

In terms of raw size, it's fairly similar, if not a bit small compared to the models used on most of my live deals. In terms of the number of minute financial concepts flowing through, it's definitely overboard. Most deals don't involve as many potential financing structures, nuanced balance sheet projections, etc. as the Macabacus model does.

"For all the tribulations in our lives, for all the troubles that remain in the world, the decline of violence is an accomplishment we can savor, and an impetus to cherish the forces of civilization and enlightenment that made it possible."
 

Pretty sure Macabacus model is mostly for learning purposes. In this case it is much better to incorporate additional/even rarely used stuff rather than exclude something. Also, complexity depends on the particular model you are building, your firm etc, so you can never say that there should be an X number of rows.

 

Very good point. I know models will have unique elements, but how often would you need to build something like this. You want to be able to project out high-level concepts and then explain to seniors/clients. There will be cases where going in the weeds makes sense on a project, but something like this is definitely overboard. Agreed w above, done so out of learning purposes, an attempt to throw just about anything you could see at you (although there are other elements not included f course)

 

Best part is being asked to explain what happens in year 2045, while other groups model 5 years out. 

 

They do get pretty complex man - some of them have some serious daddy issues too. 
 

hang on, replied right after reading your headline, guess that wasn’t exactly your question.

 
Most Helpful

There’s different types of modeling done in IB. I’ll speak to MM IB modeling for M&A as we don’t do as much as EB or BB groups (merger models etc).

There’s two main types of modeling that we do. 1) Valuation and 2) Operating Models. The former is usually conducted during the pitch stage / initial discussion period before being hired by the client, but may also apply to board presentations or future process discussions after being hired. With valuation modeling, we focus primarily on spreading trading and M&A comps, as well as constructing a basic P&L, often with pro forma assumptions for FY+1 / FY+2. These assumptions may include normalization of margins, expected revenue growth at higher rates / prices, or post-transaction adjustments. We then apply our pro forma financials to a valuation framework that applies to the firm. If it’s a capital raise or minority recap, we’ll highlight the possible pre-money and post-money valuation ranges, as well as a hypothetical cap table post-raise. If it’s a majority sale, we will apply a DCF, LBO, SOTP but we often don’t go into extremely granular detail for these. Overall, valuation models are primarily illustrative, with the goal being to provide clients with an understanding of what they may expect the market to offer and how that can be structured. The second form of modeling is to build operating models. These are constructed once a company has engaged the investment bank and are designed to answer buyer questions through detailed schedules that flow into the financial statements. In many cases, we won’t build a full 3-statement model as many of our clients are not capital-intensive, so most of the focus is on revenue and costs. As mentioned, operating models include detailed schedules, the most common being a revenue schedule, direct cost schedule, and operating expense schedule. This is where unit economics come into play in creating projections as you will generally project units sold x price per unit. Often, these schedules vary dramatically between companies. Some revenue schedules may include a detailed list of future contracts and revenue per contract, others may be tied to labor, showing # of employees x revenue per employee. With cost schedules, direct costs tend to mirror the revenue schedule while opex schedules are often tied to headcount and salaries x # of employees. In general, operating models tend to very company specific and ideally management will provide you with their own internal model that they use to create projections that you would then use to build your own model. Overall, I would say that operating models are much more complicated than valuation models because of the level of granularity required to create them, but each individual piece is quite easy to follow.

 

Depends honestly. As Ive transitioned to PE the goal has been to create simple models with as little rows as possible vs creating the model of all models

Generally as one poster said, the question you should ask is:

1.) What are you trying to solve for?

2.) What is the most efficient way to get there and what don’t I need?

The less assumptions the better, and in reality there are only so many drivers of value in any one business

 

Many teams have well-built templates that can factor in most inputs and assumptions you'd like to incorporate in your model.

 

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