We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.
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But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.
Here are the articles for the week ending 11 August 2024:
1. Ted Weschler Case Study – DirtCheapStocks
To set the stage – Weschler’s Valassis purchases started in 2008 and ended in 2010.
Markets were in free fall in the back half of 2008. The S&P 500 traded down 12% in the first six months of the year. This was already a blow to investors. But things were about to get much worse. In the second half of the year, the S&P would trade down another 26%. 2008 was the worst year for the S&P since the 1930’s. Investors were scared. The country was frozen…
…There was blood in the streets, no doubt, but market participants were getting the investment opportunity of a lifetime. Weschler bought the bulk of his Valassis shares in the 4th quarter of 2008.
Valassis was a direct mail marketing company. It made the coupons that come in the daily paper along with the other marketing material sent directly to your mailbox. Junk mail, basically.
But this junk mail has a reasonably high conversion rate. There’s a reason it shows up in our mailbox daily.
In early 2007, Valassis had purchased ADVO, the direct mail business. The purchase of ADVO doubled the size of the company, taking revenues from $1 billion to $2 billion. ADVO was acquired for $1.2B, financed almost entirely by debt. Prior to the ADVO acquisition, Valassis operated with only ~$115MM of net debt. Debt grew 10x over night. The company levered up – big time…
…Valassis stock was destroyed in late 2008. Shares traded as high as $16.80 in the second quarter. At the lows of the fourth quarter, shares dipped to $1.05. A 94% drop…
…Weschler began buying in the fourth quarter of 2008. The stock price at that time ranged from $1.05 to $8.73. I don’t know exactly what he paid, but the stock fell hard on volume. Weshler was able to purchase 6.24% (or 3,000,000 shares) of the business in the quarter. We’ll assume he paid ~$3/share…
…Valassis was trading at a ridiculously cheap price. This underscores how afraid investors were in the moment. At some point in the fourth quarter, shares dropped as low as $1.05 – meaning someone paid less than one times free cash flow for this business.
Shares were cheap on a market cap basis, but when considering the heavy debt burden, they looked a lot more expensive…
…The 8.25% Senior Notes weren’t due until 2015. So at the time Weschler was buying, he would’ve known the company had ~7 years before that debt was to be repaid/refinanced. The 2015 notes required no scheduled principal repayment prior to maturity…
…Term loan B matured in 7 years, and required minimal principal payments…
…Long story short, the business had 7 years of cash flow generation before it would need to reconsider its debt situation. EBIT, even in the depths of the recession, was enough to cover interest expense. At the end of 2008, Valassis was in compliance with all of its covenants…
…Here’s the cash flow statement from 2009 – 2011:…
- …Operating cash flow is consistently positive.
- There is minor capex, leaving loads of excess cash.
- All free cash flow was used for debt repayment and stock repurchases…
…In February 2014, Harland Clarke Holdings acquired Valassis for $34.05/share.
Weschler’s 2008 purchases would’ve compounded at a rate of 52.5% for a little less than 6 years…
…We don’t know exactly what Weschler was thinking when he bought his shares. But I’d guess the combination of an extremely cheap price, favorable debt repayment schedule and consistent cash flow were the deciding factors.
2. What Bill Ackman Got Wrong With His Bungled IPO – Jason Zweig
This week, Bill Ackman, the hedge-fund billionaire who has 1.4 million followers on X, had to pull the plug on his new fund before it could launch its initial public offering.
That’s because he’d organized his proposed Pershing Square USA, or PSUS, as a closed-end fund…
…Ackman, who has styled himself as a crusader for the investing public, could have tried using his new vehicle to shatter the status quo on fees. Instead, it would have cemented the status quo.
The fund’s 2% annual management fee, which Ackman was going to waive for the first year, would have been competitive at a hedge fund—but far more costly than at market-tracking ETFs.
Then there was the load, or sales charge, of 1.5% for individual investors and somewhat lower for institutions—an irksome cost of admission that people no longer have to pay on most other assets…
…If demand is high, closed-end shares can trade at a premium, or more than the sum of their parts known as net asset value. Usually, they trade at a discount, or less than what the portfolio is worth. The lower a fund’s return and the higher its expenses, the deeper the discount will tend to go.
According to the Investment Company Institute, more than 80% of closed-end funds recently traded at discounts. Stock funds were trading at almost 10% less than their net asset value; bond funds, about 9% below their NAV.
Typically, a closed-end fund doesn’t issue new shares after its IPO; nor does it redeem, or buy your shares back. Instead, you have to buy from, or sell to, another investor. That means new buyers don’t increase the fund’s capital, and sellers don’t decrease it…
…That’s why the firms that run them call closed-end funds “evergreen assets,” or permanent capital.
Over the decades, a few great investors have used that structure to enrich their shareholders rather than to fill their own pockets…
…Those examples suggest to me that Ackman missed an opportunity to innovate.
It was institutions, not individual investors, that balked at the potential discount on his fund.
What if Ackman instead had bypassed the investment bankers and their 1.5% sales load, offering the fund directly to individuals only, commission-free? And what if he’d set a reasonable management fee of, say, 0.5%?
Such an innovative, self-underwritten deal is likely feasible, several securities lawyers say, but would have been more expensive for Ackman than a conventional IPO…
…In the past few weeks, the New York Stock Exchange and Cboe Global Markets’ BZX Exchange separately proposed rule changes that would eliminate the requirement for closed-end funds to hold annual meetings for shareholders.
Good luck trying to get a lousy fund to hire a new manager if you can’t even vote your disapproval without somehow convening a special meeting.
Boaz Weinstein, founder of Saba Capital Management, an activist hedge-fund manager that seeks to narrow the discounts on closed-end funds, calls the exchanges’ rule proposals “some of the most shocking disenfranchisement efforts against closed-end fund shareholders in over 100 years.”
3. How to Build the Ultimate Semiconductor for LLMs – Joe Weisenthal, Tracy Alloway, Reiner Pope, and Mike Gunter
Joe (17:30):
I know there’s always this sort of cliché when talking about tech, they’re like, oh, Google and Facebook, they can just build this and they’ll destroy your little startup. They have infinite amount of money, except that doesn’t actually seem to happen in the real world as much as people on Twitter expect it to happen.
But can you just sort of give a sense of maybe the business and organizational incentives for why a company like Google doesn’t say, “oh, this is a hundred billion market NVIDIA’s worth three and a half trillion or $3 trillion. Let’s build our own LLM specific chips.” Why doesn’t that happen at these large hyperscaler companies that presumably have all the talent and money to do it?
Mike (18:13):
So Google’s TPUs are primarily built to serve their internal customers, and Google’s revenue for the most part comes from Google search, that Google search, and in particular from Google search ads, Google search ads is a customer of the TPUs. It’s a relatively difficult thing to say that hundreds of billions of dollars of revenue that we’re making, we’re going to make a chip that doesn’t really support that particularly well and focuses on this at this point, unproven in terms of revenue market.
And it’s not just ads, but there are a variety of other customers. For instance, you may have noticed how Google is pretty good at identifying good photos and doing a whole variety of other things that are supported in many cases by the TPUs.
Reiner (19:06):
I think one of the other things too that we see in all chip companies in general or companies producing chips is because producing chips is so expensive, you end up in this place where you really want to put all your resources behind one chip effort. And so just because the thinking is that there’s a huge amount of return on investment in making this one thing better rather than fragmenting your efforts, really what you’d like to do in this situation where there’s a new emerging field that might be huge or might not, but it’s hard to say yet. What you’d like to do is maybe spin up a second effort on the side and have a skunk works, see how it works.
Joe (19:37):
Yeah that’s right. That would be amazing just to let Reiner, or just let the two of you go have your own little office somewhere else.
Reiner (19:44):
Yeah. Organizationally, it’s often challenging to do, and we see this across all companies. Every chip company really has essentially only one mainstream chip product that they’re iterating on and making better and better over time…
…Joe (21:49):
Let’s get to MatX. Tell us the product that you’re designing and how it fundamentally will differ from the offerings on the market, most notably from Nvidia.
Reiner (22:01):
So we make chips and in fact, racks and clusters for large language models. So when you look at NVIDIA’s, GPUs, you already talked about all of this, the original background in gaming, this brief movement in Ethereum, and then even within AI, they’re doing small models of large models. So what that translates to, and you can think of it as the rooms of the house or something. They have a different room for each of those different use cases, so different circuitry in the chip for all of these use cases. And the fundamental bet is that if you say, look, I don’t care about that. I’m going to do a lousy job if you try and run a game on me, or I’m going to do a lousy job if you want to run a convolutional network on me, but if you give me a large model with very large matrices, I’m going to crush it. That’s the bet that we’re making at MatX. So we spend as much of our silicon as we can on making this work. There’s a lot of detail in making all of this work out because you need not just the matrix multiplication, but all of the memory bandwidths and communication bandwidths and the actual engineering things to make it pan out. But that’s the core bet.
Tracy (23:05):
And why can’t Nvidia do this? So Nvidia has a lot of resources. It has that big moat as we were discussing in the intro, and it has the GPUs that are already in production and it’s working on new ones. But why couldn’t it start designing an LLM focused chip from scratch?
Mike (23:23):
Right? So you talked about NVIDIA’s moat, and that moat has two components. One component is that they build the very best hardware, and I think that is the result of having a very large team that executes extremely well and making good choices about how to serve their market. They also have a tremendous software moat, and both of these moats are important to different sets of customers. So they’re a tremendous software moat. They have a very broad, deep software ecosystem based on CUDA that allows it…
Tracy (23:59):
Oh yeah, I remember this came up in our discussion with Coreweave.
Mike (24:03):
Yeah. And so that allows customers who are not very sophisticated, who don’t have gigantic engineering budgets themselves to use those chips and use NVIDIA’s chips and be efficient at that. So the thing about a moat is not only does it in some sense keep other people out, it also keeps you in. So insofar as they want to keep their software moat, their CUDA moat, they have to remain compatible with CUDA and compatibility with that software. Compatibility with CUDA requires certain hardware structures. So Nvidia has lots and lots of threads. They have a very flexible memory system. These things are great for being able to flexibly address a whole bunch of different types of neural net problems, but they all cost in terms of hardware, and they’re not necessarily the choices to have those sorts of things. They’re not necessarily the choices, in fact, not the choices that you would want to make if you were aiming specifically at an LLM. So in order to be fully competitive with a chip that’s specialized for LLMs, they would have to give up all of that. And Jensen himself has said that the one non-negotiable rule in our company is that we have to be compatible with CUDA.
Joe (25:23):
This is interesting. So the challenge for them of spinning out something totally different is that it would be outside the family. So it’s outside the CUDA family, so to speak. And
Tracy (25:35):
Meanwhile, you already have high PyTorch and Triton waiting in the wings, I guess…
…Joe (39:00):
Tell us about what customers, because I’ve heard this, we’re all trying to find some alternative to Nvidia, whether it’s to reduce energy costs or just reduce costs in general or being able to even access chips at all since not everyone can get them. There are only so many chips getting made. But when you talk to theoretical customers, A, who do you imagine as your customers? Is it the OpenAIs of the world? Is it the Metas of the world? Is it labs that we haven’t heard of yet that could only get into this if there were sort of more focused lower cost options? And then B, what are they asking for? What do they say? You know what, we’re using NVIDIA right now, but we would really like X or Y in the ideal world.
Reiner (39:48):
So there’s a range of possible customers in the world. The way that we see or a way you divide them up and how we choose to do that is what is the ratio of engineering time they’re putting into their work versus the amount of compute spent that they’re putting in. So the ideal customer in general for a hardware vendor who’s trained to make the absolute best but not necessarily easiest to use hardware, is a company that is spending a lot more on their computing power than they are spending on the engineering time, because then that makes a really good trade off of, maybe I can spend a bit more engineering time to make your hardware work, but I get a big saving on my computing costs. So companies like OpenAI would be obviously a slam dunk.
There’s many more companies as well. So the companies that meet this criteria of spending many times more on compute than on engineering, there’s actually a set of maybe 10, 15 large language model labs that are not as well known as OpenAI, but you might think Character.AI, Cohere and many other companies like that and Mistral.
So the common thing that we hear from those companies, all of those are spending hundreds of millions of dollars on compute, is I just want better FLOPS per dollar. That’s actually the single deciding factor. And that’s primarily the reason they’re deciding on today, deciding on NVIDIA’s products rather than some of the other products in the market is because the FLOPS per dollar of those products is the best you can buy. But when you give them a spec sheet and the first thing they’re going to look at is just what’s the most floating point operations I can run on my chip? And then you can rule out 90% of products there on the basis of, okay, just doesn’t meet that bar. But then after that, you then go through the more detailed analysis of saying, okay, well I’ve got these floating point operations, but is the rest going to work out? Do I have the bandwidths and the interconnect? But for sure the number one criteria is that top line FLOPS.
Joe (41:38):
When we talk about delivering more flops per dollar, what are you aiming for? What is current benchmark flops per dollar? And then are we talking like, can it be done like 90% cheaper? What do you think is realistic in terms of coming to market with something meaningfully better on that metric?
Reiner (41:56):
So NVIDIA’s Blackwell in their FP4 format offers 10 petaFLOPS in that chip, and that chip sells for ballpark 30 to 50,000, depends on many factors. That is about a factor of two to four better than the previous generation NVIDIA chip, which was the Hopper chip. So part of that factor is coming from going to lower precision, going from 8-bit precision to 4-bit precision. In general, precision has been one of the best ways to improve the FLOPS you can pack into a certain amount of silicon. And then some of it is also coming from other factors such as cost reductions that NVIDIA has been deploying. So that’s a benchmark for where NVIDIA is at now. You need to be at least integer multiples better than that in order to compete with the incumbent. So at least two or three times better on that metric we would say. But then of course, if you’re designing for the future, you have to compete against the next generation after that too. So you want to be many times better than the future chip, which isn’t out yet. So that’s the thing you aim for.
Joe (42:56):
Is there anything else that we should sort of understand about this business that we haven’t touched on that you think is important?
Mike (43:03):
One thing, given that this is Odd Lots that I think the reason that Sam Altman is going around the world talking about trillions of dollars of spend is that he wants to move the expectations of all of the suppliers up. So as we’ve observed in the semiconductor shortage, if the suppliers are preparing for a certain amount of demand and demand, in the case of famously of the auto manufacturers as a result of COVID canceled their orders and then they found that demand was much, much, much larger than they expected. It took a very long time to catch up. A similar thing happened with NVIDIA’s H100. So TSMC was actually perfectly capable of keeping up with demand for the chips themselves, but the chips for these AI products use a very special kind of packaging, which puts the compute chips very close to the memory chips and hence allows them to communicate very quickly called CoWoS.
And the capacity for CoWoS was limited because TSMC built with a particular expectation of demand, and when H100 became such a monster product, their CoWoS capacity wasn’t able to keep pace with demand. So supply chain tends to be really good if you predict accurately and if you predict badly on the low side, then you end up with these shortages. But on the other hand, these companies, because the manufacturing companies have very high CapEx, they’re fairly loath to predict badly on the high side because that leads them to having spend a bunch of money on capital CapEx that they’re unable to recover.
4. The Impact of Fed Rate Cuts on Stocks, Bonds & Cash – Ben Carlson
It can be helpful to understand what can happen to the financial markets when the Fed raises or lowers short-term rates.
The reason for the Fed rate cut probably matters more than the rate cut itself.
If the Fed is cutting rates in an emergency fashion, like they did during the Great Financial Crisis, that’s a different story than the Fed cutting because the economy and inflation are cooling off…
…Most of the time stocks were up. The only times the S&P 500 was down substantially a year later occurred during the 1973-74 bear market, the bursting of the dot-com bubble and the 2008 financial crisis.
It’s been rare for stocks to be down three years later and the market has never been down five years after the initial rate cut.
Sometimes the Fed cuts because we are in or fast approaching a recession, but that’s not always the case…
…Average returns have been better when no recession occurs but the disparity isn’t as large as you would assume.
Most of the time the stock market goes up but sometimes it goes down applies to Fed rate cuts just like it does to every other point in time.
Obviously, every rate cut cycle is different. This time it’s going to happen with stocks at or near all-time highs, big gains from the bottom of a bear market, a presidential election, and the sequel to Gladiator coming out this fall.
5. Enough! This Is How the Sahm Rule Predicts Recessions (Transcript Here) – Joshua Brown and Claudia Sahm
Brown (02:11): I’ve been around for a long time and I had not heard about the Sahm Rule but apparently it’s something that you created in 2019. The first person to mention it to me was Nick Koulos which he did on the show. And I guess it had a lot of relevance to start talking about now because we’re trying to figure out if the Fed is staying too tight and if the good economy we’ve had is going to start slipping away before the Fed can start easing and that’s why everyone’s talking about the Sahm Rule.
I want to try to explain it very succinctly and you tell me if I’m missing anything about how the Sahm Rule works. That’s important to the discussion. The Sahm Rule is a recession indicator you came up with about five years ago. Basically what you’re doing is calculating the three-month moving average of the national unemployment rate, so not just last month’s print, but you’ll take the last three, you’ll average those and you’re comparing them to the lowest three-month moving average for the unemployment rate that we’ve had over the last 12 months. Do I have that? Okay you’re nodding.
Sahm (03:28): That’s the formula. We’re there.
Brown (03:29): Okay. If the current three-month average is 0.5 percentage points or more above the lowest three-month average from the last 12 months, that would signal the early stages of a recession – and we could talk about how early – but that would be the “trigger”. And I’m so excited to have you on today because as of the last employment report we got, the three-month average is now more than, just barely, 0.5% above the lowest three-month average that we’ve had, therefore the Sahm Rule is in effect…
..Brown (06:30): So according to your work the Sahm Rule, I guess on a back test, would have accurately signalled every actual recession we’ve had since the 1970s, without the false positives that can occur outside of recessions. This is in some ways similar to my friend Professor Cam Harvey who was trying to figure out why the inverted yield curve has been so accurate in predicting recessions and so far has not had a false positive either. Some would say recent history has been the false positive but he would argue “I’m still on the clock.” But it’s interesting that you created this for fiscal policy while working at the Fed.
Sahm (07:20): So as one of the analysts who covered consumer spending in 2008, understanding what consumers were doing with their, say, rebate checks or later tax credits, the Fed works around the edges. In the staff’s forecast, there are estimates of what fiscal policy does to the economy and the Fed can take that into consideration when they do their monetary policy. It may seem a little counterintuitive but that’s a very important piece of the health of the economy, understanding consumers. But I will say having watched that episode made me want to help improve the policy for next time. The Sahm Rule was part of a policy volume in early 2019 on how to – all kinds of automatic stabilizers, it was just a piece of it. It comes from the back test, I’m looking at history. Before that, it did pass the 2020, calling that recession with flying colours, but anyone could have done that. Yet there are some very unusual circumstances in this cycle that the Sahm Rule – in my opinion, I do not think the US economy is in a recession despite what the Sahm Rule is stating right now…
…Sahm (13:23): There are two basic reasons the unemployment rate goes up. One, there’s a weakening demand for workers, unemployment rate goes up. That’s very consistent with recessionary dynamics. That’s bad and it builds, there’s momentum. That’s where the Sahm Rule gets its accuracy from historically. The other reason that you can have the unemployment rate increase is if you have an increase in the supply of workers. In general, the unemployment rate can get pushed around. It’s even worse right now for the Sahm Rule because early in the pandemic we had millions of workers drop out of the labour force, just walk away. Then we ended up, because they didn’t all come back as quickly as, say, customers did, so we had labour shortages. The unemployment rate got pushed down, probably unsustainably, because we just didn’t have enough workers. Then in recent years, we’ve had a surge in Immigration, as well as we had a good labour market, so people were coming in from the sidelines. So we’ve had two rather notable changes in the labour supply.
I think as we’ve learned – and this is a broad lesson from this – is anytime we have really abrupt, dramatic changes, the adjustments can take a long time. So now as we have these immigrants coming in, this is solving the labour shortage. That is a very good thing, having a larger labour force particularly as we have many people ageing out. That helps keep us growing. That’s a good thing. But in the interim where they’re still searching for jobs, things have slowed down some in terms of adding jobs. That causes the unemployment rate to drift up. Now if it’s just about that supply adjustment, it’s temporary. And at the end of it it’s a good thing, because we’ve got more workers. And we’ve had recessions when there were expansions in the labour force like in the 1970s, so I don’t want to act like just because we have more workers now, everything is okay. It’s just the Sahm Rule – and again as you point out, it’s right at the cusp of its historical trigger. It’s got a lot going on under the hood…
…Sahm (19:52): The Sahm Rule itself, even the real time, has false positives. And then just this bigger conversation of history might not repeat. The one thing on Barry’s is there are cases, you have to go further back in history, there are times where we go into a recession with a low or lower unemployment rate than now. It is not recent. And we have a mix – I talked a lot about the labour supply that’s definitely in the mix. I spent some time looking at that 0.5. When we get across that threshold, what do the contributions from different types of unemployed – you can be because you were laid off, which Barry mentioned, you could be because you’re a new entrant to the workforce, you left a job. We see quite a bit of variation, the contributions. It is true right now we’re much more, there’s more of the entrants, the new job seekers, the coming back to the labour force. They’re a bigger contributor to getting across that 0.5 threshold than most recessions. But you go back to the ‘70s when the labour force is not that different. So it’s hard to pull it out. I’m not in the ironclad, recession is not a given, nor I think what I read – the history – that tightly. And yet I think there are real risks and as with Barry, I was, say in 2022, “A recession is coming,” or “We need a recession.” I was adamantly, I’ve never had a recession call in this whole time. I was kind of close when we got to Silicon Valley Bank but I have not had a recession call in and part of what I could say in 2022 was look at the labour market, look at consumers. We are still in a position of strength, but much less. And the momentum is not good.
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