A few days ago, the FT published an interesting article about the 2023 Carvana out-of-court restructuring where Apollo took a $1.3bn haircut on $5.6bn of unsecured junk bonds.
The article ends with a provoking sentence: "Apollo and others should rue that they did not demand equity warrants". If you are wondering how much money Apollo left on the table, you are in the right spot.
Based on my math -> $4.481Bn 1) Warrants in Restructuring 2) Assumptions of the Apollo Missed Warrants 3) Apollo Implied Value Equation 4) Current Value of Warrants 5) Sensitivity and Conclusion
A Thread 🧵
1) Warrants in Restructuring
In order to understand this thread, we need to understand the basics first.
In the beautiful world of restructuring and corporate reorganization, there are times when the value of the business is not enough to cover the claims of all debt holders. As a result, companies go to their creditors asking to renegotiate their claims in order to stay alive and maximize everyone's value in the long term.
But what if the company has nothing to offer? A possible solution is issuing warrants (security that gives the holder the right to buy or sell a specific number of shares of a company at a specific price, called the strike price before the warrant expires).
Let's say a company is trying to create a fair and equitable plan for all creditors, but the second lien creditors demand more. The issue is that there is no more debt capacity or equity to give. A possible solution is giving them (completely made-up numbers) " five-year new warrants issued to second lien lenders which would entitle them to purchase up to 12.5% of reorganized equity at a strike price equal to the value of reorganized equity".
We therefore now understand what the sentence "Apollo and others should rue that they did not demand equity warrants" is trying to tell us: Apollo wished they had negotiated some of these warrants, something that often happens in restructuring situations.
2) Assumptions of Missed Warrants
We now understand the dynamics, but why is missing out on the warrants so tragic here? Because the stock price has increased over 10x since the deal was signed so a lot of money could have been made.
How much money? Let's try to figure it out.
We need to make a few key assumptions here. Firstly, how much Carvana equity Apollo could have demanded? I believe 10% is a fair assumption (in the next tweet and the last one will show why I think this is a fair assumption).
Once we agree on how many warrants as % Total to issue, the next step is to calculate how many warrants are issued which we can calculate as Share Count * % Equity Issued = 21MM Warrants
The next key question to calculate profit would be to know the strike price of these contracts. This is the second key assumption we need to make (here I could be very wrong). It is hard to get a sense as most of the figures we know are from in-court situations while here we are out-of-court and therefore the equity is still fluctuating in value every day (differently from the example above "strike price equal to the value of reorganized equity").
I would doubt that Carvana would have ever given out warrants without premium (which would essentially mean diluting the equity by 10% right away).
For our calculation, let's use a 50% premium. Again, this might be wrong, but as the sensitivity analysis will show us, this assumption does not really matter. More on this later.
The last thing we need to understand is the price this premium will be applied to. This is usually not tricky but here we are in a unique scenario as the stock was seeing huge volatility.
The deal was announced on July 19, 2023 so for our calculation I took the last 90 days' average which comes out to $18.1 / share.
Putting these two things together, we can calculate the strike of the warrants = $27.2
Please note: the above assumptions are (obviously) solely my own.
3) Apollo Implied Value Equation
Before moving to calculate the value of the warrants today, I thought it would be useful to sense-check if we are on the right path (i.e. how much were these warrants worth at issuance compared to how much debt was written off).
First, we need to calculate the value of each warrant and then multiply it by how many were issued. I downloaded the beautiful Damodaran option price calculator and used the following assumptions (5 Year warrants duration, 100% implied vol for the stock, and 3% Risk Free Rate). With the stock price and strike price calculated above, I arrived at a value of $8.92 / warrant. This multiplied by the 21MM warrants issued gets $187MM of value issued.
Considering Apollo agreed to cut $1.3Bn of Debt, getting 14% of the value back through warrants seems reasonable.
Moving right along.
4) Current Value of Warrants
Let's get to the meat. How much did Apollo leave on the table?
Today, Carvana stock closed at $241. Let's assume Apollo owned these warrants and wanted to exit their position. They would have the right to buy 21MM shares at the strike price ($27.2) and then they would be able to sell them on the open market (let's assume no price impact despite a significant dilution for simplicity).
Overall, this would net $4.5Bn in Profits (remember there is no cost base here).
Yes, a lot of dollars.
5) Sensitivity and Conclusion
This is my favorite part of this analysis. We are sensitizing two assumptions, warrant premium (or strike price) and how many warrants we issuing. Three things stand out:
1) As expected, how many warrants we are issuing is really all that matters here. If you think Apollo would have never got 10% of the equity but much less, you can easily see how many dollars they left on the table (still in the billions range).
2) The warrant premium (or strike price) does not really move the needle. The stock ended up being so much higher than the strike price that the dollar impact is marginal. In general, creditors will fight for a low premium so the chances of these warrants having intrinsic value are higher.
3) This said, the higher the warrant premium at issuance, the lower the value of the warrants which makes the warrant value as % of the debt haircut lower.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
THE SOFTWARE PRIVATE EQUITY FIRM OF THE FUTURE: BENDING SPOONS
This week, Bloomberg published a very interesting article about an Italian company which is building something unique: a roll-up of distressed apps
1) Origins of Bending Spoons 2) Cigar-butt investing approach to software 3) Off-shoring to Italy 4) Evernote Case Study 5) Perpetual holding period
A 🧵Thread about this unique private equity firm
1) Origins of Bending Spoons
According to Bloomberg, this begins as a bootstrapped story: "[The founder] last job was as a consultant with McKinsey & Co., a role he took to subsidize the startup dreams he shared with his co-founders while living in Copenhagen"
Venture capital markets are much more limited in Europe and Bending Spoons got started with funding brick by brick in 2013.
Rather than building a product itself, the company acquires digital tech products from other companies that have already proven a fit with their market, but that Bending Spoons thinks has “substantial untapped potential”.
They like to think of Bending Spoons as the product of “If Berkshire Hathaway and Google had a baby”"
2) Cigar-butt investing approach to software
Their way to create value goes something like this:
1) Find apps that are (i) serving an essential function to a small % of total users, (ii) are characterized by poor capital allocation that makes them (iii) mediocre / money losing businesses
2) Buy these apps for reasonable multiples are these are not considered "the best apps" and are not making a lot (if any) free cash flow
3) Roll-up operations within the existing Bending Spoon app workforce, aggressively cut existing workforce (see next tweet about off-shoring to Italy)
4) Stop investing in ancillary products within the app that are not what is driving the core offering
5) Aggressively raise prices for the core offerings, most customers will churn, but the percentage that stick around will make up more than enough profits that what lost
6) Generate cash into perpetuity (more on exit strategy on the 5th tweet)
Michael J. Mauboussin just published his latest paper
100+ pages on the topic of "Measuring the Moat"
Here are the 20 sections and exhibits that caught my eye
🧵
1/ Dis-Integration of the Computer Industry
2/ Regression Toward the Mean by Quintile for U.S. Companies, 2013-2023 of ROIC - WACC
Key Lesson: it is very hard to create value over time, but there is lots of evidence that some companies do deliver persistently attractive returns on investment
3/ Distribution of Morningstar’s Economic Moat Ratings, June 2002-June 2024
OpenAI Income Statement | Considerations from Large-Cap Private Equity Investor
Reviewing income statements is my job, and OpenAI is a fascinating case so I wanted to share a few thoughts I had when seeing the below chart --> 1) Service needed =/ great business model 2) Unit Economics are broken? 3) CapEx and Amortization Waterfall 4) Stock-based-comp is not a rounding error 5) Some other back-of-the-napkin math
A 🧵 Thread
1) Service needed =/ great business model
Just because a service is needed or new technology is created, there is no certainty that someone is going to be able to create a great business (defined as a business that is able to earn a return on its invested capital higher than its cost of capital) for an extended period of time.
I already know someone will disagree with this statement, so let’s look at an example to drive this concept home. Let’s take a technology that shapes our lives: flying. In 1903, Wilbur and Orville Wright became the first people to fly a heavier than air, power-controlled machine, known as the Wright Flye.
Over the next decades, the airline industry was born and today, the revenue of global airlines estimated to $908Bn. This said, collectively, airlines have generated no profits over the last century.
The goal of this post is not to analyze why airlines are bad businesses (hint: biggest cost is variable..), but to show that there is a chance (which might be remote), that the economics of language models are really hard to make work.
This said, it is entirely possible that this is just a matter of being in the early stages of an extremely capital-intensive business which brings us to the next point —→ scale / unit economics
2) Unit Economics are broken?
Let’s take a big step back and review a finance 101 concept.
Why is OpenAI valued at $150Bn if it loses a ton of money? Because investors believe (hope) that this situation is temporary.
Yes, OpenAI is losing a boatload of money every day, but it is becoming the leading player in an industry that could be worth trillions (but maybe zero for the previous tweet). Once it becomes the leading player and earns much more revenue, financial statements should look a lot better AS LONG AS revenue will increase much more than its costs.
Discovering hot water hear. Hold on, let me elaborate.
First of all, here we are looking at the income statement. Long-term investments (CapEx) only appear here under the form of depreciation, more on this later.
Is OpenAI is losing billions to actually run the models?
- They are not only losing money on a free cash flow basis as they are investing today in the infrastructure that will last decades
- They are not investing heavily in ads that will be able to be turned off once scale is achieved
- They are actually spending to provide the service
That is generally not good.
Ok, so why are people not that worried? Because people believe that these operating costs have actually a significant percentage of fixed costs vs being all a variable cost.
Let’s look at the biggest cost: compute to train model
The really big question is what happens to this cost when / if revenue increases 2x.
If this cost increases 2x, I think people are going to be a LOT more worried as that would mean that the cost is entirely variable and the unit economics simply do not work at the price (ie. the cost to service $1 of revenue is greater than $1).
Citigroup partnering with Apollo in $25bn Private Credit Deal
On Thursday, 9/26, Citigroup announced it was partnering with mega-fund Apollo Global to expand its lending offerings to fund buyouts. Citi’s investment banking division is expected to source the loans and Apollo is expected to offer the financing. Such a deal is unprecedented in the industry and comes at no better time as Citi CEO Jane Fraser is looking to turn the company around
The overall partnership reflects Citi’s push to expand, the adaptation of bulge-banks from the rise of Private Credit, and the need of alternative asset managers to find new avenues to source investments
In today’s thread, we will be going over:
Private Credit & Its Impact on Banks
Implications of the Partnership
What Other Banks are Doing
Future Outlook
A thread with my takeaways →
1/ Private Credit & Its Implications on Banks
The biggest driver in private credit for the past few years can largely be attributed to the fall-back by banks to lend money. From regulatory scrutiny to hung debt (ex. Twitter), banks’ don’t have as much of an appetite to lend. According to LevFin Insights, loan volume in the broadly syndicated loans (BSL) market decreased from $800bn in 2021 to $200bn in 2022
As banks retracted in the face of liquidity constraints, private credit managers saw an opportunity to sweep in and offer capital solutions. AUM for PC firms has nearly doubled since 2020
However, as private credit firms continue to grow bigger, banks have been missing out on revenue from originating deals. However, given that banks typically have 10x leverage and take deposits, it’s more difficult for them to hold such illiquid loans on their balance sheet. At the same time, borrowers are opting to go the private credit route that provides more leeway in their credit agreements (cov-lite loans)
As a result, it has become more challenging for banks to directly compete with private credit firms. Consequently, banks are now exploring alternative strategies to capitalize on the ongoing boom in private credit
Enter partnerships
2/ Implications of Partnerships
The Apollo/Citi PC Partnership can be broken up into two key roles
Citi: sources deals
Apollo: deploys capital
While Apollo's revenue, like that of a typical private credit firm, will primarily come from interest and principal payments, Citi's revenue will largely be driven by fee generation
But this makes sense. As the US government is expected to increase capital requirements for banks, firms like Citi want to find new ways to maintain consistent, stable fee streams without having to tie up their own “regulated” balance-sheets
For managers like Apollo, this represents the opportunity of a lifetime: to be a bank without the regulatory scrutiny
To quote, Matt Levine “banks are now in the moving business, asset managers are now in the lending business”
KKR Launches a PE Middle Market Fund - My Thoughts as a Mega-Fund PE Associate
This past week, KKR announced closing its $4.6bn Ascendant Fund, the first KKR vehicle solely focused on opportunities in the middle market.
PE firms usually achieve greater success by progressively scaling their fund size and buying bigger and bigger companies, so why is one of the titans of the industry going in the opposite direction?
🧵 A thread covering —→ 1/ Less competition 2/ Lower multiples and less leverage 3/ Proprietary deals 4/ Easier to grow, more room for improvement 5/ The big issue with scaling 6/ Issues with buying smaller / less organized companies 7/ Adding yet again another strategy 8/ Bonus: middle market buyouts help playing the retail game?
1/ Less Competition
While the number of Mega-Funds is infinitely smaller than the number of middle market firms, the total amount of dollars chasing large deals is much higher than the amount of dollars chasing smaller deals.
This fact is compounded by the universe of companies by revenue size. In the US, there are ~8,000 companies with $250mm+ of revenue and ~110,000 companies with less than $250mm of revenue. This means there are more dollars chasing 7% of companies than dollars chasing the remaining 83% of companies.
For reference, my rough math is that a $250mm company with a 15% EBITDA margin results in ~$40mm of EBITDA. Assuming a 12x multiple, that results in a $500mm buyout. Assuming 60% leverage, the implied equity check is $200mm.
KKR’s $4.6Bn fund would therefore support ~20 portfolio companies which seems very reasonable to me.
2/ Lower multiples and less leverage
What is the natural consequence of this imbalance of supply and demand —> much less competition in the middle market which leads to more attractive valuations (lower multiples). There have been several studies that show that average multiples increase progressively with higher and higher TEV, and while my 12x EBITDA assumption might be valid in the middle market, that is definitely not the case with Mega-Funds.
Data showed how in 2023, deals in the sub $100mm category have median EV/EBITDA multiples of ~7x. The median multiple increased to ~11x in the next category up ($100mm-$250mm). Finally, for deals of $5bn+, see median multiples of ~17x for EV/EBITDA.
This is very aligned with my experience. For my group to bid something in the 10x-12x, the business is clearly below the average company we look at, and most deals get done closer to ~18x than ~10x EBITDA.
As a result of paying lower multiples, you can achieve the same LTV with less leverage, something that can get much more important in a world of structurally higher rates.
New York City paid millions of taxpayers' dollars to McKinsey, the leading consulting firm in the world, to study its trash problem and develop potential solutions
A consulting masterclass with some really cool (and dirty) analyses
🧵A Thread with the key slides of the final product, buckle up for a great read, some rat images, and to see where your tax dollars are going
1) Executive Summary
Setting the stage for the presentation, a classic of consulting firms
Here, they cover what is the problem (trash), what are the key obstacles, and summary of the solution
Time to dive in --->
2) Background & Purpose
NYC is really turning into a struggling corporation that does not know what to do and therefore thinks it is a good idea to pay 7 figures to McKinsey to solve its problems