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Career Resources
It’s hilarious. We have a massive model with myriad tabs, schedules, build-ups for everything. My MD gave buyers guidance around $Xmm. Nobody bid. Cuts the valuation range the next day ??? Why even have a model
Just to keep us busy, it’s basically just fake work lol. Shit I’m not complaining, make a $100k+ salary at 22-24 years old
Great execution from your MD LOL
This is an example of bad modeling. Many banks that can tell a compelling growth story gain credibility. Prime examples would be when the company being sold has multi-year contracts with their customers, essentially locking them in. As those customers scale their business with the company being sold, there is a transparent growth story.
Oftentimes we will use some variation of management / the banks financial forecast in our IOI model.
it's the illusion of work so that they can charge what they charge. they're rather pointless otherwise
Literally. Such bullshit lol
They are a complete joke and I agree with the sentiments of everyone else, but I want to provide a bit of a counterpoint. As a buyer, I obviously don't trust or care about the assumptions/outcome, but it helps me understand the business. I can look at the model to get a sense of how the finances work: what drives revenue, what are the big cost items, are they variable or fixed, what are cash flow needs, etc. I know they're put together by analysts that don't know that much, but it's still helpful to level set how I should think about the business.
Also, I’m going to have to make one, and it’s much easier for me if I can rip a lot of it from the bank model.
For sure yeah that makes sense. Good insight.
yep. perfect illustration of the sell-side vs buy-side dynamic for me - the SS just helps the BS for illustration purposes
Second this. Also, providing a dynamic model as a buy-side advisor helps justify their fee versus advisors who don't. Recently worked on a deal at our fund where advisor fee was cut by like 75bps because no working model was provided
I'm in PE and do think that models are extremely useful. Not because they are super useful in predicting the actual value of a company, but because they help you understand the value drivers in a deal. For example, we're currently heading into a downturn which will result in negative impact on revenue and profitability. However, this might allow us to purchase add-ons at a lower multiple in relation to their "normalized" earnings, which will actually benefit the returns even if we take a big hit next year organically. It also helps you see what you need to believe in to achieve a desired return and then you can conclude whether that development is actually likely operationally. However, I do totally agree that sell-side models are usually pointless. Can't even count how many times we have received IMs for companies that have grown 5-10% past years that are suddenly supposed to grow 20%+ per year and increase margins by 5-10 percentage points. Gets even more ridiculous as the assumptions are rarely backed up by anything that is even remotely reliable.
Yea great insight totally agree. Yeah the out of nowhere revenue growth by 20% always sends me.
Yeah simply put - models are useful if you’re figuring out worst case scenarios for returns / earnings as you can get an idea of what those figures look like when things don’t go according to plan. Most sell-side banking models are indeed useless practically speaking given they are 1) created on assumptions used to back into a pre-agreed upon value and 2) are based on a myriad of assumptions which almost always generally promote some type of growth in the business
sus assumptions are 1 thing - then u have brittle valuations backed by some fragile formula on the top line that flows thru the entire calculation which swings drastically based on the validity of the assumptions in the top formula lol. Makes people have to work extra hours overnight checking if it were the assumptions that were too sketch or if it's just literally a matter of calculation and formatting errors lmfao
Sell side models start at T = 0, or sometimes T=1, and never show any historical periods. It is amazing that we accept this.
Think you work with the wrong sell-side shops... boutiques always do historical trend analysis, and variance analyses as each month actualized.
If material variances occur / operations change (headcount, key contracts, upsell, etc.) We will always include a detailed line by line MD&A report with a waterfall to new forecasts.
This is why I moved away from bulge / middle market. Yall spin your wheels on the wrong shit
Funny you're in PE and say sell side is useless. When I was reading the thread north of your post all I could think of is quite useful on the buy side and pointless on the sell side. I imagine it's quite useful on your side to help mange to certain outcomes based on those market drivers you mention. If X isn't producing Y%, get rid of it. If A is and can be better scaled, do more of it. Etc. And you can point to examples of having achieved that elsewhere, perhaps in a similar industry.
Thanks for your post!
Sorry for my lack of knowledge everyone.
Could anyone tell me what "IM" is shorthand for?
"We have received IMs..."
This is a pretty common criticism from juniors in finance actually...
Is it that the concept of modelling in general is useless, or is it just sell-side models that are useless? People seem to suggest that it's important to get modelling experience for the buyside, so does that mean the models are more actionable/insightful in PE?
It's also p interesting to see how investors like Buffett hardly use modelling in the traditional sense (DCFs, Comps, LBOs), and they rely more heavily on qualitative aspects of a company's business model
I second both your insights
Investors like Warren Buffett are still using quantitative models, they're just highly simplified. He does a ton of reading into financial statements (famously, the reason he didn't buy into Enron was because he couldn't make heads or tails of their financial statements), but the analysis he does isn't a complex DCF, it's more simplified ratio analysis.
In my views, Models are especially helpful when looking at companies with contract backlog or to understand the areas of growth of a business (e.g. opening of a warehouse in X or Y country)
If you're using a model purely to generate a final number of how much a company should be valued, then you're using it wrong. The value of the model isn't in predicting the future - everyone knows the assumptions are basically worthless and nobody, least of all a fresh university graduate has any idea how a company might grow with any amount of accuracy.
What it's actually useful for is being able to adjust certain drivers and inputs and seeing how it might affect the valuation of the company. This way, you can easily calculate what happens to the business if revenues do increase by 10% next year, or if certain material costs end up decreasing by 5% next year, rather than just leaving it to estimation and having no idea how a certain scenario might actually affect the company. This is why all the best practices always point to ensuring the operating model is linked properly and you don't leave hardcodes everywhere - it's supposed to be more of a scenario analyzer rather than a crystal ball.
U sayin my 3 yrs of studying revenue-maximizing price & output, constrained optimization, auction theory, Cournot equilibria, Game theory, Langrangian multiples, econometrics, 2SLS regression models wont help me predict how a company will grow? Ha
After 3 yrs you still didn’t realize those are all kinds bs? Surprised lmao. Those models are rarely applicable in most situations. Models like those work until they don’t.
If you're sell-side, maybe yes. But on the buy-side things are different. For instance, just to give you an example, I'll take Howard Marks's thesis on risk (see below) which is one that I like. The graph, in a nutshell: The higher the potential return on an investment, the higher its risk deviation.
When you forecast 3 potential scenarios: Best, mediocre, and bad, you should take into account that all of those are possibilities and because two of them didn't end up happening it doesn't mean that they couldn't have happened (Fooled by Randomness is a great book about this topic). Ideally, a good financial model should allow you to put a number (%) on the possibility of different scenarios and calculate how much money you would be willing to invest to bear X% of risk for a Y% return.
Alternatively, if you forecast, let's say, 5 different scenarios (good, half-good, medium, half-bad, bad), now you're closer to predicting where your investment may go, and if you apply a second level of thinking, you can prepare preventively on how to react when the numbers take an undesired path. The more different scenarios you forecast, the greater the chance of nailing the scenario that ends up happening. Also, ideally, when you forecast the undesired scenarios, you should consider preventively what options may be available in that circumstances (e.g. if you forecast a "bad" scenario model with certain numbers, you could already know who would be interested to buy the asset at that price range). Some may say that all of those steps are too much, but I don't like uncertainty so always having a number prepared upfront and an exit strategy for A, B, C ... Z scenario allows me to sleep better.
Also, in private equity and real estate, forecasting may be even more relevant (compared to public markets) because certain aspects of your investments fall within your control (improving operations, cutting costs, acquisitions, etc.) or understanding (business cycles) so you can forecast some numbers based on the pre-planned decision on the business's financials or how you expect the economy to go.
Of course, I ain't saying that modeling is the ultimate decision-maker in investments, but it's just an instrument to support your investment decision. Not necessary, but could be helpful.
The whole idea of generating models with a significant range of output valuations is silly. The reason that models don't work is that growth rates aren't being created using a combination of historical growth + the type of business (low/med/high growth/cyclical/turnaround/asset play (see Lynch)) + qualitative factors (ala Phillip Fisher). If anything, you might have two or three models, one of which is the expected case, and the other two are based off the major risk factors to the business (TSMC = Geopolitical risk, Olaplex Holdings = Lack of demand, Clinical Stage Biopharma = Failure to execute). Each of the models should be provided a percentage of the valuation if the risk can be quantified, and if it can't, you'll just need to adopt a bit of a risk taker's perspective. You also shouldn't be DCFing unproven businesses with no track record. That way lies the road to ruin.
If you have those factors in your growth assumptions, you should be able to hit the nail on the head, from a valuation perspective.
Really, this isn't an impossible task from an (good, unbiased) ER or HF perspective, although M&A models are hopeless given the inherent conflicts of interest...
That's just my two cents.
.
I’m a newb. Why can’t you quantify the effects of these disruptors, but still provide different scenarios in a single model?
Also, I apologize if I strawmanned your argument, but I didn't want people to think of scenarios in the form of 100 models of various shades of good and bad, but rather models based on concrete potential outcomes focused on real risk.
The point is not the model itself but the sensitivities you run, that's the useful part imo
Sellside models are often inaccurate - just reverse engineering assumptions and inputs to goal seek to a predetermined output
Disagree here with you on this. I'd say I've seen my models come to fruition over the following 1-2 years from model creation with about 60-70% accuracy. I was in banking for quite some time as well.
Bankers are glorified salespeople. All of the work done during the deal process (making of the CIM, management presentations, valuation models) is to help justify the highest valuation for the company. Whether or not this actually comes true is an entirely different matter - both banks and the client want to sell the company at the highest price possible. So to answer "why do we even model?" - it's because modeling is a way of justifying the highest price so that the firm and its client can get paid the most.
Compare it to real estate - investment bankers are like investment sales teams working under brokers. Have I ever read a broker's OM forecasting 5% rent growth for the next 5 years on some dogshit "value-add" property and interpreted it to be an accurate prediction of an asset's future performance? Fuck no. Anyone who does deserves to lose money. But the underwriting serves as a sanity check and a justification of valuation - a starting point for the buyer and seller to find middle ground on price.
Sell side advisors are much more value-add than CRE brokers though. Listen to Jim Donovan when it comes to the job of an IB team.
I remember we were going to tour a 280 units multifamily asset in Texas and the broker sent us the wrong address and then didn’t even show up lmao. Also their OMs are so unbelievably trash and lacking information. At least CIMs ATTEMPT to provide valuable info. I definitely see the comparison but business transactions are substantially more complicated and sophisticated than CRE trades.
They always fail to realize that they’re just selling buildings
It is an insane amount of false precision. I truly believe that looking at historical income statements, hearing Management's growth projections and taking a non-analysis driven guess of what next year's Net Income will be, is more accurate than a 20 tab model the majority of the time.
The other thing that drives me crazy about models is sometimes the intangibles really are more important than historical trends. If your latest product/software is game-changing in terms of product value-prop and/or if you add one or two massive key accounts to your client roster, hockey-stick level growth really is possible. And it isn't going to follow some mathematical formula like "Okay for every additional $1M of marketing spend we're going to increase our page views by A%, increase our page-view-to-customer-purchase conversion rate by B% and increase our average order size per customer by C%. It's just not how things shake out in real life. And from my personal experience, the bottom-line estimates that management gives you are often times far closer to correct than are the ultra-formulaic growth projections, with dozens of ultra-specific assumptions embedded, that bankers put together. Just my $0.02.
I would say that supporting schedules within an operating model are very useful from an investor perspective.
I want to know that if I can X number of visits per day, which means X number month, then I can expand FCF yield by 200 bps and increase my ROIC.
Simultaneously, I definitely agree on false precision. There are some very thick-headed accounting-types that obsess over making sure that every single GL account is tied to the perfect, exact, audited amount and that headcount in a 5,000 person company isn’t off by even 0.5 people. Most of the time you have people that aren’t competent enough to determine what is accurate so they instead obsess over being precise to create the appearance of accuracy.
Really great point and agree on above poster about false accuracy.
CYA
Correct answer
IMO it is useful for scenario analysis / understanding a range of outcomes. What do you need to hit an IRR if X? What level of debt should we be able to service for this add-on? However really detailed models are stupid and false precision (both sell and buy-side).
There's more to investment banking than sellside M&A.
Models matter - a lot - in buyside advisory and lev fin. And even in sellside M&A they matter (again, a lot) in carveouts/divestitures, and LBOs.
Buyside here (corporate):
We actually look at banks’ models. They are basically a way to outsource work we don’t have time to do ourselves). We go through the 100 pages pitchbooks, etc. Not everything resonates ofc
Solid & creative ideas generate follow up & investigation (we are open minded as a firm).
Ok, fine. Let's accept your premise that they're bullshit. Then what do you replace it with? Give us a better mousetrap. Until you come up with a better way to put a value on something, keep modeling.
Come model an E&P company, pretty grounded model comparably to DCFs / etc. with a myriad of far fetched assumptions
Honestly, you work in a casino and they keep changing the rules so wealthy people never have to pay up on a bad bet.
More like we live in a casino and chose to be the dealers rather than players because we aren't imbeciles.
Don't hate the player hate the game
Analyst at a MMPE shop and sort of agree - I do think it can speak to the reputation/quality of a bank/bankers if, for example, they show something bad in their model. For example, saw a CIM the other day with a projected decline in revenue. Of course, EBITDA was trending up, but pretty ballsy (or maybe the MD was just stupid idk) to put declining revenue in the CIM. If we were to pursue the deal with this bank, and the MD didn't present as totally dumb on the phone, then this level of honesty in their model speaks to their character, which could be helpful with process dynamics. If they told us they had another buyer at $50m and we needed to get to $52m to sign it up, I would be more convinced that the banker was telling the truth and not just lying to squeeze extra $ out of us.
But also agree with OP - have seen plenty of CIMs with historical revenue/EBITDA CAGR of 10-20%, projecting 60% for the next 5 years (often with not much backing up these claims). Screenshots of these instances flow through our teams messages with plenty of eyeroll and laughing emojis.
On a separate note, one of our VPs was telling us over lunch that some IB MD he knew went and sold some company to some PE group. And then the PE group hired the MD as CEO because he knew the ins and outs of the business better than anybody else. Could you imagine building that model, and then having to go execute on it?
buy side credit here for different view - simple models that focus on key drivers are helpful. complex models with a million tabs are not. and we are focused almost entirely on downside scenarios, unless we have mezzanine and/or equity coinvests.
when you are lending, you basically want to know “what needs to happen for this company to break, and when do I need to be at the table to make sure I don’t lose money here” - so it is super, super helpful to know that you are setting your covenants correctly and protecting your loan for your investors. the deeper in the capital stack you go, the more important it is.
For the laughs. As a lender and previous buy-side analyst, it cracked me up seeing the sellside broker or investor CIMs with growth rates double above any historical references. Not that that isn't doable, but that without any context, it wasn't all that useful. The value comes from understanding what drives the models, as others have commented here. Is it a gross margin increase, is it wages, are there other levers that can be pulled that help bottom line cash flows, etc.... The models facilitate the understanding of these movements.
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