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- [00:01]
A
Oh, such a clutch off season pickup, Dave.
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B
I was worried we'd bring back the same team.
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A
I meant those blackout motorized shades.
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B
Lines.com made it crazy affordable to replace our old blinds.
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A
Hard to install?
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B
No, it's easy. I installed these and then got some from my mom. She talked to a design consultant for free and scheduled a professional measure and install.
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A
Hall of fame, son.
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B
They're the number one online retailer of custom window coverings in the world.
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A
Blinds.com is the goat shop. Up to 40% off site wide, plus an extra 10% off every order and a free professional measure happening right now@blinds.com.
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B
They'Ve done a lot of stupid things like rolling up veterinary clinics at 22 times EBITDA and building lawn care enterprises and, you know, just stuff that's just dumb. And I think finally the reckoning is arriving. Private credit, which has been funding most of this, has been the next darling because at least they're getting a yield. And now you're seeing, you know, bankruptcies on the fringes of private credit, especially the small end. Every tech innovation they've come up with has been a gold mine. And so, like, I'm willing to kind of give them a huge amount of rope to go out and do this, but I look at it and I think these companies are distinctly worse businesses than they used to be before they had to spend massive amounts of capex. Everyone is really fired up about investing in sort of the hyperscalers that are building the AI models. But like, what about the value companies that are most likely to benefit from it, that are sort of stealth adopters, that are actually, you know, using AI in a very productive way that you wouldn't sort of realize.
- [01:32]
A
Dan, welcome back to Excess returns.
- [01:34]
B
Thank you. It's nice to be back at Verdad.
- [01:38]
A
You and your team are focused on putting out research and building investment strategies that are rigorous, empirical and systematic. And you're also known, I think, for sort of challenging the consensus narratives out there in the market with, you know, deep research, a long term perspective and a willingness to go, I think, where the evidence leads, even if it's a little bit uncomfortable or contrarian in nature. And that's kind of what I really appreciate and like about, you know, your views on the market. I think it's Monday mornings, your, you know, daily piece of research comes out like clockwork and it's really great stuff. I encourage our audience to go to Verdad's website, which is verdad cap.com and sign up to receive their notes. And it's always a very diverse set of topics that you're talking about and we're going to use, I think, some of those topics today as sort of a springboard to work through some of these things with you. So thanks for being here. I'm happy to be joined with my special guest host today, Kai Wu from Sparkline Capital. Hey, Kai, thanks for doing this with me.
- [02:46]
C
Yeah, good afternoon. Good to see you again, Dan.
- [02:49]
B
Good to see you too, Kai.
- [02:51]
A
Yeah, last time it was the three of us kind of working through some topics. And so where we want to start with you, Dan, today is this was the, I think the core of what we talked about last time, but just maybe get some additional perspective or see if anything's changed with you is sort of your views on private equity. And you've been very vocal about the risks of private equity and we did a deep dive in July. People can go back to listen to that if they want to. But has any, has there been any new developments or has anything changed with your view that are important to sort of something you want to highlight or, or is it kind of the same thing?
- [03:26]
B
Yeah, I think a few things, Justin, I'd say first, you know, the performance continues to be bad and you know, unsurprisingly. Right. I mean, the most, you know, you had virtually every investment allocator in the world thinking they were going to generate 400bps of net of fee alpha on an asset class that charges 400 to 600 basis points. And so, you know, I don't know, they probably believe in unicorns too, but I don't think that exists. And I think, I think finally a reality is dawning on people that they're stuck in this stuff because the purchase prices that were paid were too high, which was pretty obvious that they end up owning a bunch of pretty crappy businesses that are low margin, you know, GDP grower subscale. And you know, they've done a lot of stupid things like rolling up veterinary clinics at 22 times EBITDA and building lawn care enterprises and you know, just stuff that just dumb. And I think finally the reckoning is arriving. And I think the other thing you'll see is private credit, which has been funding most of this, has been the next darling because at least they're getting a yield. And now you're seeing bankruptcies on the fringes of private credit, especially the small end, the sort of sub 25 million of EBITDA businesses. You're seeing a real spike in bankruptcy rates and that's sort of lower Mid market pe, which is the darling of every investor's eyes seemingly. But turns out the smaller businesses are riskier and more likely to go bankrupt. And now we're actually seeing that come true. And usually what you see is once the smaller companies are going bankrupt, the middle and big ones are next. There's just been excessive lending. So I think the industry is in a sort of come to Jesus moment. And I think one of the things that I've been looking at, which is kind of fun, is the sort of industry level statistics. The number of private equity firms has probably doubled in the last 10 years. And if you look at the number of hedge funds, for example, it's basically flat from 10 years ago. And I think when you see an asset class that's doubled the number of participants, what do you sort of make of that? And I think it's the Darwinian evolutionary terms. We've had speciation, we've had, you know, a wide divergence. And now the next stage of evolution is coming when the herd gets trimmed. And that happened to hedge funds after the great financial crisis where you know, talk to any allocator or high net worth individual and try to pitch them on hedge funds and you hear like the same reflexive thing like I'm going to pay high fees to underperform the market and who cares about volatility being lower because you can't eat sharp, you know, blah blah, blah, blah blah. And then you can walk into private equity and it's just like again, believing in rainbows and sunshine. And I think what that environment has caused is this really kind of competitive dog eat dog zero sum world in which like to survive in hedge funds you've got to have been like an apex predator who's able to sort of cut through all this stuff and develop some really cool technology or research or process. And then in private equity it's just been like anyone can open a firm and talk about operational improvements and a long term vision and partnership. And so what inevitably happens when you've had those sort of dynamics for a long period of time is people get fat and happy in one area and they get lean and mean in the other. And I think we're finally seeing a reversal where last year, you know, again, private equity performance lagged. But look at some of the headlines about hedge funds, right? Like, look at the excellence that's being achieved in some of these areas in the market. It's really quite impressive.
- [07:08]
A
How do you think it kind of works itself out on the backside of it? Do you see this Just as a. Because last time you were on, you talked about like this private equity being a money trap. But do you see it as more of like this long term slog or is there something more maybe like a systematic type crisis beneath the surface that could come out of it?
- [07:29]
B
I think it all depends on the bankruptcy environment, right? I mean, I think if we get a high bankruptcy environment, PE is toast because you're going to see large percentages of portfolio companies go bankrupt. But predicting bankruptcy wave in the United States has been a losers game since 2000. So who knows, maybe no companies will ever go bankrupt again. But that's, I think that's the question. You probably need some sort of macroeconomic shock. But the other thing to remember is that private equity is massively overweight tech. Massively overweight tech. And they're overweight subscale, you know, subscale tech. You know, sort of the, you know, buying a software provider that served auto dealerships or something, right? Like classic PE deal, constellation software type thing. And who knows if AI is just going to eat that stuff, and if AI eats that stuff, then the 40% of the private equity capital that's been deployed into essentially software is going to get annihilated. So, you know, I think things might even AI might actually be making things distinctly worse. I just don't know yet. But that's what I'm watching for.
- [08:38]
C
And Dan, on the other side of the equation, the institutions that have up until recently been clamoring to be in these funds, what's going on over there? Are they actually attempting to reduce their exposure? I saw some news out of Yale and such, but what's going on the buy side here?
- [08:55]
B
My general view is that they're only reducing their exposure when they're sort of forced to, that they have too many capital calls or that sort of thing and they maybe were overallocated. Not that they've lost any enthusiasm. I haven't seen that yet. And I sort of think like you think about at a private equity firm, you know, you raise fund two, some deals are good, some deals are bad. You know, you go out to raise fund three, you fire the partners that did the bad deals in fund two, and then you sort of pitch the LPs on, you know, here's the track record of the partners that are running the firm now. And you know, you add some new partners and you sort of repeat, right? And there's the sort of ability of sort of selection, you know, for you to sort of say, you know, in a high dispersion asset class. You know, if you funded 15 private equity managers 10 years ago, you know, five of them are probably looking awesome and five are disasters, but they're still marked at one and then the other five are sort of fine. And so you're going to basically go back and say, you know, it's not that I don't like private equity, I just, I've learned a lot and I've learned to like these types of managers, like these five that worked. And I've learned to. I've learned that the red flags to watch out for with like these five and so I haven't. My enthusiasm for private equity is undiminished. I'm just relying on sort of the learnings I've had. And so I think that's sort of the phase we're probably in where people are still positive on the asset class as a whole because there are individual managers they love who have made them a lot of money, which is sort of what you'd expect in a high dispersion asset class. And people haven't kind of pulled back and said hey, in aggregate this has kind of sucked. And I think they're going to. And I think the longer this underperformance of private equity continues and it's been a few years now, the more pressure there's going to be down from say the trustee level or the CIO level to say, hey, look, wait a second, why is our public equity group outperforming for the fifth year in a row and we have so much staff in the private equity group and you told me that our co invest was fee free and so we should outperform the benchmark and we're not. And the benchmark's terrible. I just think that sort of snowballs. But it takes a while and I think people have incentives and so many people have been staffed up. It's really a people intensive business to run a private equity program. And it's gotten more so because the focus has been on co invests and direct deals and fundless sponsors and continuation vehicles. There's a lot of diligence to get done. All those people, none of them are saying, oh, let's cut our private equity exposure because it means cutting their jobs.
- [11:28]
D
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- [11:58]
C
I understand, like the agency issues at stake here. So I guess the bright side for the private equity industry would be a potential escape hatch in the form of 401k investors. There's of course an executive order democratizing access to alternative assets for 401k investors. What are your thoughts on that? And maybe I'll just jump into the second part of the question, which is there are various narratives around what exactly putting private equity into a 401 or any kind of retail accessible vehicle might be. Right. On one hand you have the narrative around democratizing access to kind of elite strategies that previously were only the purview of the highest end institutions. And the other more skeptical narrative, let's say, is it's exit liquidity for a pool of capital that basically doesn't have the money trap argument. So let me ask you where you come down on this and your general thoughts around, you know, where we're headed with regards to kind of retail access to these sorts of strategies.
- [13:03]
B
Yeah, I think, I think the word democratizing, like the word reimagining, should set off alarm bells. You know, whenever you hear them, it's almost whatever's going to happen next is going to be bad. And I think that's probably the case here. So, you know, I think what's really made this possible is the rise of these interval fund structures which have become very, very popular and they propose to offer quarterly liquidity. Of course there are gates, you know, B Read, you know, Cliffwater, very successful integral fund providers. And those have been great for sort of the RIA community who doesn't have the staff to manage capital calls or do that type of intensive due diligence. And so the interval fund structure, you know, I Capital, right. This has been a very promising thing for to allow, you know, multifamily offices and RAs to access privates. The challenge that we've seen is that is this mark to market smoothing issue, which is at certain times B REIT was down zero and the public REITs were down 20. And everybody was saying, oh gee, I should sell B REIT and buy public REITs. And they all tried to sell B REIT, none of them could and it got gated and everyone was angry. And then more people tried to pull and it got worse. Those types of things happen with these interval fund structures. It seems like there's an exit door, but it's very tight and it's easy to get locked in. So I think that one of the things I've looked at is the London listed private equity funds where you have about a dozen or maybe two dozen private equity funds that actually listed LP fund to fund vehicles, not the management company but the actual LP vehicles in London. So they're even better than interval funds because they're publicly traded. And what you see there is that those funds trade at 30 to 40% discounts and they're wildly volatile, much more volatile than large cap equities, even more volatile than small cap equities. And so I think, okay, we're going to put that into retirement accounts and are people going to like what they get? Right, they're either going to get something that looks like the London listed vehicle that's wildly volatile and trades at a 40% discount like most closed end funds or they're going to be stuffed in these interval structures which have gates and queues to get out. And I just don't think people are going to like it and I think it's going to generate a lot of lawsuits. The FEES are huge, 400 to 600 basis points probably for one of these interval funds. When you add everything together, I just don't know how the 401k market which has become so geared towards low costs is going to justify that, particularly at a time when private equity performance has been miserable. It's like, oh yeah, all those rich guys are in it and it sucked. Isn't a great marketing pitch.
- [15:41]
A
Dan, what about this move to bring private assets into ETFs and trying to make those like more available? Is your line of thinking sort of along the same lines of this 401k thing?
- [15:51]
B
Yeah, I mean we've seen what happens with these London listed vehicles, right? Like it's just like yes, they're going to trade at a discount because these things, these private assets are almost by definition small and they're illiquid and they're niche and they're hard due diligence. There's not a lot of data and the end customers doesn't like that type of thing in the public markets, right? Like a bundle of opaque things. Unless you've got something super, super sexy like SpaceX stuffed into one of them or crypto into one of them, right? Like yeah, you can sell that stuff but like we've got like 3H vac roll ups and like a mid tier enterprise SaaS software company and like four other deals that we did seven years ago that we couldn't exit stuffed into this ETF, right? I just find it hard to think anyone's going to get all that excited about it.
- [16:37]
A
Let's move into Kai's wheelhouse. Kai, I'm going to let you roll with the AI section, so go for it.
- [16:44]
C
Yeah, sure. So, Dan, let's start with an article that your colleague Brian Shingono wrote on bubbles being kind of a necessary part of innovation. And it was a pretty interesting study, so maybe you could just spend a couple minutes on that. Yeah, but really what I'm interested in understanding is your perspective on how one would define a bubble. Are they going to exit at the identifiable and the role that that plays in kind of capital formation and innovation.
- [17:12]
B
Yeah. So Brian summarizes excellent academic paper which, which basically found that, you know, when there is this big innovation wave, these innovative companies need financing to fund what are essentially experiments. And if just one of those experiments pays off, like the railroad or the Internet or AI. Right. Like it pays for all of the, you know, capital that was wasted on the experiments that didn't go anywhere. And so the argument is that around the time the emergence of new technologies, you see what looks like a bubble where people are funding huge amounts of unprofitable science projects, essentially. And on the one hand, you're saying on a price to earnings basis or price to book basis, this looks crazy from a valuation perspective, but from a macroeconomic perspective, it's enormously healthy. Right. Like without all of that risk capital, the successful experiment would never happen. And out of all those failures typically emerges, you know, some very good positive outcomes for society. And this is sort of the essence of capitalism in some sense. And so the argument is essentially that bubbles are good, we want bubbles now, maybe we don't want them in tulips or things like that. Right. We want bubbles and productive assets. But if there are bubbles in these scientific experiment progress things, it's very, very good. Right. One could have argued that Tesla was a bubble for many, many years. But look at all the innovation that has come out of Tesla. Right. It's sort of amazing. And that's a very good thing for society. And then it comes to thinking about it from an investor perspective of should I buy into those science projects? And I think generally there are a few things going on. One, I think it's very hard to know ex ante. And I think my mental framework for thinking about efficient markets is from Mordecai Kurtz at Stanford, who has this idea of rational beliefs, which is that, you know, sitting where we are now, there are multiple possible futures that are all Equally plausible based on the data, the historical data that we have. And no one can know until the future actually happens which of those alternative futures is going to happen. Right. We can argue all this all the time about it, but no one knows. And they're all those beliefs are rational. Right? So when you think about efficient markets, markets are sort of pricing in something. They're pricing in sort of a spectrum of totally rational predictions of the future. And some of the futures that could happen are irrational. You know, nobody in February of 2020, if they'd come to you and said the world's going to be gripped by a mass pandemic and we're going to shut down every school and business so people can hide in their houses, you would have said, well, you're a nut job. That's an irrational prediction about the future. That's just not going to happen. And then it did. And so many sort of world historical events are sort of by definition, unanticipatable ex ante. So I think, you know, when we look at what's going on in the market today, you know, it certainly does seem like we're witnessing the emergence of a very powerful and very disruptive and very useful technology. It seems like almost certainly there's overcapitalization because if you look at so many prior technologies, usually there aren't 10 winners or five winners. Especially on the Internet, it often seems there's one winner or maybe two that emerge. And there's so much competition here that it's almost certain that costs are going to get driven down that it's hard to sort of think about how justifiable all that capex is for the investor. But, but you know, I think you've done a lot of work on this, Kai. Sometimes it's the beneficiaries of the, of the technological innovation that are the most interesting. Right. If you think sort of second order. Right. Okay, everyone, everyone was really fired up or everyone is really fired up about investing in sort of the, the hyperscalers that are building the AI AI models. But like, what about the value companies that are most likely to benefit from it, that are sort of stealth adopters, that are actually, you know, using AI in a very productive way that you wouldn't sort of realize. And I think that's sort of an exciting way to sort of think about playing this type of thing.
- [21:12]
C
Yeah, I mean, I obviously agree completely with that view. And just, you know, going back to what you were saying about the historical episodes, right? The railroads, the Internet, and we have seen pretty much a Repeated and in fact invariable tendency for an overbuild which results in effectively an oversupply of capacity, which results in falling prices and effectively a subsidy from the builders to the customers. Right, which in this case you could broadly characterize as large cap tech versus small cap value. And so that I think fits nicely into a lot of the work that you've done on the quant side and around kind of the real economy companies. Right, folks that are not necessarily your kind of standard tech players. So yeah, let's talk more about that the Mag 7 because that's just been such a central part in most folks portfolio, obviously 33% of the S&P 500 and over the past few years 75% of the returns of the index. So you know, most investors, to the extent they are index investors and cap weighted indices, that's the bulk of their portfolio. And if you add on other infrastructure players like Broadcom is like the eighth biggest stock or something, you know, you're talking about 50% of the index in infrastructure firms, whether it's the hyperscalers or chip makers or any other firm kind of linked to this complex that depends on the continuation of capex spending in, in the build out of the data centers. So how do you think about that? Is that a risk? It sounds like based on your view it would be a risk. And how can investors position around this? What should they be doing if they do believe that this is a potentially tough situation to be in?
- [22:54]
B
Yeah, I think of I started talking earlier about bankruptcy risk. Bankruptcy risk is a really salient risk, right? Like you come and explain to someone, hey do you want to come and invest in some declining, you know, like a company that trying to think of a very good example saying has been disrupted by technology but you know, the company that makes fax machines or something, you're like, come on, like nobody uses fax machines. Like this thing is clearly going to zero. Like I have no interest. But statistically companies that are valued at very, very high multiples, you know, 50 times earnings or greater, you know, 10 times EV to sales or greater, exhibit almost the same return distribution as companies that are on the verge of bankruptcy. Right? Because you know, you could lose 80% of your value on multiple alone and still be valued at a pretty reasonable market multiple. And so I think though, you know, you can think of these as sort of growth bankruptcies. And so there is a risk in overvaluation and it's the risk of excessive optimism which is why we sort of miss it because these companies are so great and they're so wonderful and their track records are so great. But they can exhibit the same return distribution as bankruptcies. And so it's an, it's an enormous risk. Now we've become desensitized to that risk because the companies have been. Many of these big tech companies have been really overvalued for a really long time and kept putting up more amazing numbers, right? They've sort of exceeded the expectations that were baked into those optimistic multiples. And at this point everybody's sort of like, I can't think of other than the Metaverse, like, I can't think of a single error that these companies made. Like every tech innovation they've come up with has been a gold mine. And so like I'm willing to kind of give them a huge amount of rope to go out and do this, but I look at it and I think these companies are distinctly worse businesses than they used to be before they had to spend massive amounts of capex that accounted for like a certain somewhat large percentage of US GDP growth just purely based on their own spending and where you have to start thinking about return on assets, right? Like these were much better businesses and they were asset light and they didn't have to invest at all and they made this software that just printed money. And now, you know, AI is marginal costs and it is depreciation and who knows how long these Nvidia chips last. So I think that that's a really worrisome change. And so I tend to think, you know, however, right, there are multiple past possible futures, like maybe this will turn out to be a gold mine, just like all their previous technological innovations were. So it's hard to discount that future. But I think that I tend to look for sort of setups that feel easier and that feels really hard. Possible, but hard. And I look abroad and say, I think international markets, there's no valuation risk, right? Like you think European markets are overvalued? Like, nope. Like you think Asian markets are overvalued? Nope. Right. Like it's really hard to find an overvalued international market. I mean, I guess they exist, India maybe, but by and large international markets are cheap. And so I think the right answer first is, you know, if you're going to be balanced, let's be balanced, right? Like if you, if you're currently 80% US, let's take it down to 60. If you're at 60, let's take it down to 50. Try to diversify into things that feel like easier setups. From an investment perspective than this really one undiversified hard one. And I think that that's probably my initial idea of how to, how to play this or how to react to this, which is to diversify out of it.
- [26:23]
C
And when you say international markets, are there specific, like, pockets of. Because I agree with you that say Europe, no one's going to say Earth's overvalued. The risk there is not on the valuation side, it's on the growth side. Maybe it'll just be dead money for another 10 years. So how are you thinking about that risk that on one hand you can double down on the Mag 7 and hope that we go to Valhalla. On the other hand you could say, let's just not even play that game and go to European stocks which are, I think 50% of E5 is banks and industrials. And you know, but then the question is, you may miss out on the. If AI is is potentially this revolutionary technology, do you miss out? Or maybe not. Maybe there are early adopters in Europe that could benefit from AI that are just not being priced as such, kind of. How do you think about threading the needle, like, you know, between the two extremes of, you know, AI bulls, AI bears. There's got to be some more nuance to the conversation, I would assume.
- [27:16]
B
Yeah, I tend to think it's, it's, you know, you got to start from the efficient markets hypothesis and say, okay, look, let's say the US is 65 and international is 35. You know, you don't want to just say, oh, I'm 10% US and 90% international. It's too big of a bet, right? But starting from 65, 35, there are a lot of investors that are not there that are 80% US, 90% US and saying, hey, gee, like for you, this is obvious, right? Like, you can get all the benefits of diversification sort of for free and get closer to the efficient market line by taking your international exposure up to 35. Like, let's do. That's just easy. And then there are the investors that are sort of at market weight. Maybe they're 65, 35. And I think to those people, I would say, what do you feel more comfortable with? Right? I mean, because you got to stick with this for the long term. If it underperforms for a few years, are you more comfortable with overvaluation risk but owning really high growth, great companies, or are you more comfortable with really cheap opportunities at the risk that growth doesn't materialize? I'm a value Guy, I prefer that second story. But I wouldn't go, you know, I wouldn't go much past 50% international. Right. I'm not, I'm not too much of a cowboy. These calls are hard and so you don't want to diverge too much from the benchmark.
- [28:30]
C
Got it? Yeah. I guess kind of what you're saying is that if someone's 9010 US and 50% of the US is linked to this AI capex trade, then you're basically 45% of your money is in this one trade like that, you know, potentially seems like an over diversified bet on a single thematic play.
- [28:47]
B
Exactly. And priced.
- [28:50]
C
And what about small caps? Because I know you kind of touched on it earlier on about how there's a risk that, you know, a lot of these private tech companies are disrupted, these SaaS companies disrupted by AI. Or is there anything in the small cap US space, let's say, that interests you with regards to kind of the.
- [29:05]
B
AI theme, I think sort of as a paradigm for what's working in the market today. Right. It's U.S. large growth and international small value. Right. And value markets. Smaller size works. Right. When value is working, the smaller companies work smaller, cheaper companies work better than the larger cheaper companies. Right. So you go internationally, international small value has just done far better than international value and international value has done far better than international growth. Right. Like don't invest in growth when growth doesn't happen. Simple rule like if you're in a cyclical or slow growth environment by value, if you're in a high growth environment, growth is what works. And it tends to be the largest growth companies that work the best. It's sort of the exact inversion of that. And so I think until we know that the paradigm has changed, US small cap money has been sort of dead money. It just hasn't been all that interesting. And it probably won't be until we see a reversal of market leadership. And I think for now, I think those reversals of market leadership tend to come in crises. So you have sort of a crisis and then market leadership flips. Coming out of the crisis has been the way it's worked historically. So I think there'll be a time to buy us small cap, but I wouldn't have a huge allocation to it today. Whereas international small cap I think is a really great, very compelling opportunity at the moment.
- [30:26]
A
Dan, I'm curious your your thoughts. You know, I think the numbers would show that there's less, you know, over time the number of companies going public has been declining and One of the, I think, differences between this possible bubble in AI and what we saw in the late 90s, early 2000s was, you know, it seems like the rush was to go public as quickly as possible back then. Now it seems to stay private for a much longer period of time. I think just today OpenAI, you know, announced a $50 billion raise from the Middle East. I think the valuation is 830 billion or something like that. So I'm just, you know, is that, is, is that good or bad for investors that these companies are staying private longer and raising. I'm talking about the individual investor now because on the one hand you would think if all these companies were rushing to go public, like there'd be a stampede of investors into them. There probably is still going to be, but they're going to be coming out at much higher valuation. So I'm just wondering how you kind of game that or think about that.
- [31:37]
B
Yeah, so I think, you know, first, you know, the, the number of US public companies has declined, but the number of international companies has increased. So, you know, this is a U.S. specific phenomenon. And then I think within the U.S. yes, the number of companies declined. The aggregate share of GDP that's public has increased. So you have fewer bigger companies. And a lot of what's happened is that bigger public companies have bought smaller public companies and sort of consolidated. And so that's sort of been the Trend in the U.S. and then you have this sort of wave of innovation, especially over the last sort of 15 years, that has really been in private markets. The capital formation has been in private markets and they've tended to IPO quite late in their lifespans. And so what do we sort of make of that? You know, I think, I think first of all, I think it's a, it's a, it's a bad thing for investors. Right? I mean, I think we would rather these companies were public. Why would we rather they were public? Because we would get much more disclosure. The investors in those companies would get more disclosure, the public would get more disclosure, people would be able to hedge better. The like, more liquid, more tradable, more transparent markets are a very, very good thing and create better price discovery. Right. There's all sorts of benefits of things being public. The fact they're public is a bad thing. And I think a lot of this is a regulatory problem. Like we need to just make it easier to raise money and for companies to be public and less of a reporting burden. And all these problems need to be solved. And I think for whatever reason, because of all this compliance stuff, you know, it's become much harder for these companies to be public and they're worried about all the lawsuits that are going to come if the stock goes down, et cetera. And I think instead we should be encouraging companies to go public. So I think that's a bad thing. I think at the end of the.
- [33:26]
C
Day.
- [33:29]
B
I think the challenge is that it's a little bit of selection bias. Like yes, we're missing out on these 10 companies, but then there are a hundred companies that we missed out on that ended up failing. And so we only hear about the winners that we wished had gone public and not about the losers that kept out of the market. So I think from the public market perspective, I don't know on a net net basis of like whether it's bad or good that these companies are staying private longer because there are probably a lot of dead unicorns out there that were thankful they never got, you know, floated to the market. So public market investors could lose money in them.
- [34:05]
A
Any before we move on to the biotech stuff, we want to talk to you about any thoughts on sort of the circular nature of sort of the deal structure that's happening. Like to me it seems like that's, you know, more of a risk than maybe investors are appreciating in the sense that a lot of these companies are all tied together, you know, around mostly maybe OpenAI but you know, just in general this, that circular, you know. And Kai, I think you might have even looked at this too. So any thoughts on that?
- [34:36]
B
Yeah, I don't know. I haven't followed that as closely. Kai has probably followed it closer than I have.
- [34:42]
C
Yeah, I mean look like there, there's two risks that, that stem from circular financing. One is kind of the dynamic that happened in the dot com boom where companies were effectively paying their customers to buy their stuff. The circular cross holdings circular financing issues that ultimately culminated in kind of fraudulent accounting which is obviously bad and illegal. And then there's the other component which is just kind of what Justin, I think is the more benign interpretation of what's going on. Just this kind of entanglement of different players in the ecosystem. So one thing you commonly hear from folks who are dismissive of AI capex risks is that oh well, the companies today are so different than in the dot com boom. In the dot com boom they're over levered telecoms today they're hyperscalers that have Google search and throw off tons of free cash flow. Right. And that is true but I think increasingly what's happening is that these Mag 7 companies with the impenetrable balance sheets are entangling themselves with, you know, Oracle, Core weave, you know, OpenAI, you know, through their kind of, through their proxies. And so to the extent there is an issue over there that maybe on the private markets doesn't affect, you know, in theory at least in a first order state, some of the investors in Google, well it will, it will affect these companies. And I think, you know Dan, you can speak to this as well with the kind of off balance sheet financing situations. You brought up private credit. Right. I mean there was a big, I think the biggest private credit deal was actually Hyperion, the metadata center, right. Where they got real Blue Owl in there and Pimco and kind of offloaded a lot of the debt financing off balance sheet to another set of investors. So there's lots of interesting stuff going on. I mean I think, and I think, yeah, I would love to hear Dan, what your thoughts are on.
- [36:26]
B
It's funny you were saying that Kai, I was thinking that there was some, some investment firm that posted a chart that showed, you know, what's different between the AI Capex bubble and the TMT bubble and they showed a chart of high yield bond issuance and they're like, look at all this high yield bond issuance that was done during the TMT and there's been no high yield bond issuance during the data center thing. So like therefore it's, it's riskless or something. Right? And you're like yeah, it's all in private credit, like and that's less risky than it being public and transparent. Right? Like it's clearly more, more risky but, but it's all been done in this sort of bespoke way. But I think that you're exactly right that the risk is there regardless, just.
- [37:04]
A
Kind of pivoting here. One of the things I'm always surprised about when I get certain pieces of your research, Dan, is how kind of coming out of left field you guys will look at something that I would never think you would look at just given my outside interpretation of your orientation of being, you know, more value investors, which you are, and you know, trying to buy things that aren't overvalued or expensive. And then you know, lo and behold you guys come out with this very interesting white paper on biotech investing. And so I thought maybe to start it be interesting to hear how sort of why you looked at this and what makes it such a strange and challenging sector for investors.
- [37:49]
B
Yeah, and I'M excited to talk about this with, with you and especially with Kai because we're going into Kai land, the world of intangible value. And honestly, Kai sort of inspired me on some of this thinking because there are certain sectors where you've really got to think about this intangible value, where it really matters and where the financial metrics are less helpful. And so this is my first foray into Kai's world. So it's going to be a fun conversation today. But the reason that we got into biotech or looking into biotech is when we were running multi factor equity models, risk models, return models, you know, we're always looking for places where there are mispricings, where the model isn't working. You know, where are we making mistakes, where are we finding idiosyncratic bad, bad outcomes? And we looked and we realized like 80% of our worst outcomes from a prediction standpoint were coming from biotech, right? And we're like, oh my gosh, like our standard quant factor model does not predict this at all. And we think it does and it's a disaster. And so like we could looked at like chart after chart of like, here's our expected return, here's actual return. There's just like no correlation. And you're like, oh my gosh, like, we gotta turn this off. So like a year and a half ago we just like turned off biotech. Like, we are not trading biotech until we figure this out. And then we started saying, okay, now we need to figure it out. And so the first, the first challenge was trying to make what we could of the financial metrics. So, you know, let's start with the easy stuff, like how do we just fix our financial thinking around biotech? And I think there are certain things that are true about biotech. Like biotech companies are small. Biotech companies are very high volatility. We sort of know that. And they're biotech, right? Like those risk factors, the size, the volatility and the fact they're biotech. Like, yeah, we agree on like that our model was getting right and then everything else it was getting wrong. And so the first thing is to say, okay, let's keep our understanding of them as small and volatile, but like, we need something that spreads returns from a financial perspective. And the first thing we found. Is that you got to fix your value metric. And so if you think about the denominator of enterprise value, which is market cap plus cash, a biotech company, the more cash they have on the balance sheet, the more Expensive. They looked to a traditional value metric. And so the first thing we do is, okay, we've got to exclude cash because cash is good, right? Like, biotechs need cash. That's their lifeblood. So you don't want to penalize them for having cash. So you just got to have only market cap as the denominator. And then let's think of the numerator. They're all unprofitable, and they're all basically spending money on science projects. And so what we then realized is that actually the level of spending, like, let's just take like a very crude assumption, which is that the level of spending correlates with the intangible value that's being created. Then you don't want to penalize a company that has no revenue for spending more in biotech, right? Like, actually, a company that spent $500 million last year on clinical trials is a lot more, is a lot more value than a company that spent 5 million on clinical trials. So if they're Both selling for 100 million of market cap, well, gee, you'd much prefer the one that spent $500 million last year because presumably someone gave them that money to do something promising. And, you know, presumably that $500 million of spending produced something promising. Right? Like there's got to, you know, you're sort of just taking the numbers on faith. But, like, if that's true, then like, yes, that should be a value stock and the one that's $5 million of spend last year and 100 million market cap is a very expensive stock and it should be penalized for that. There's just hopes and dreams. And so we actually found that that very simple framing of like, spend to market cap really nicely spread biotech returns. So that was sort of our first find. Like, okay, this is not totally impenetrable. You know, we haven't exited the land of any financial metrics working. There are some that work. And then the next challenge was thinking through the intangible value. What's the quality of these businesses? And Kai does a lot more sophisticated work around how to measure this, which is really cool. But we wanted a sort of cruder first pass that we could get through. And so what we again is, let's take biotech. One of the things we know about biotech is there are these biotech specialist hedge funds out there that have like 50 PhDs on staff that are going and researching, like, is this a promising oncology drug drug or not, who are going to read the clinical trial data. And so we said, well, what if we just say, well, let's take these specialists and we can define them as people. Anyone that owns a certain amount of biotech and for whom biotech is a certain percentage of their holdings. Let's define as a specialist and then let's look at what percent of each company is owned by specialists versus non specialists, right? Like if we have a $3 billion biotech and not a single biotech specialist fund owns it, that's telling you something pretty bad about that company, right? Like that anybody that has a scientist on staff has passed. And the only people that own it are like people that are like thinking it's going to cure cancer for sort of magic reasons that are illiterate when it comes to science. And then conversely, you could have a company that's like 70% owned by like 10 of the top biotech specialist funds and you're like, whoa, like I, you know, I don't know, they all, they all seem to agree that this is a good thing. So like maybe I should be in it too. And so we basically said, well that's actually a quality metric, right? Like the amount of sort of scientific rigor and vetting that's been done and that's been expressed through ABET is a really healthy metric of quality. And so then we can sort of take quality and value, which are sort of the classic two quant factors, or their third momentum, which we'll get to and sort of spread returns in that way and say, okay, gee, what we're focused is on the cheap, high quality businesses. And then third was momentum. You know, traditional momentum as measured by stock price doesn't seem to work particularly well in biotech. I think probably because it's mostly event driven. The trials come out and boom, something works or not. And whether it's been going up or going down prior to that doesn't make much of a difference. So what we looked is at peer momentum, which is a kind of cool, interesting newer field in quant thinking. And it's become very popular in the past decade or so of thinking through, you know, peer momentum effects and network effects and how does a company's suppliers or competitors, et cetera, impact their stock price? And people find that they co move. And so what we looked at is saying, well, let's use the clinical trial, the sort of subjects of the clinical trials, what each biotech company is researching, look how similar that is to, you know, other companies and then define a peer set of like these are the set of companies that are doing the most similar Science. And if those companies are doing really well, you should expect your company to do really well. And so there's sort of a peer momentum effect within biotech, which sort of makes intuitive sense. Right. If everybody's getting pumped about oncology, your oncology drug should work as well. And that should be sort of a powerful, you know, network effect. So those are sort of the three core factors that we identified that work in biotech and a little bit about what motivated us to get into it.
- [45:03]
A
What and how does the clinical trial data come into this and play into it?
- [45:09]
B
Yeah, so we use it and we've got a lot more to mine. It took us, this was the most work intensive part of the project. But what we did is we basically took all the ClinicalDrials.gov data, married it to the corporate IDs. So you could say this biotech company is sponsoring these four clinical trials. And we had to make that point in time because you don't want to have any hindsight bias. So huge amount of work to kind of get that done. And then what we did is we each of these clinical trials is on this NIH mesh tree, which is like a, basically a science tree that's sort of saying, okay, like this is related, this is part of this subfield of science and then that's of this subfield of that. And, and so actually you can take that mesh tree and you know, you can categorize all the trials and then you can classify the companies essentially by where they're sort of positioned on that mesh tree. And that it gives you a sense of the distance or relationship between the different companies and how they relate to each other and what part of the world that they're in. And so we've been looking at peer momentum, basically how similar companies are performing. And we're also looking at peer value. If you are in the oncology space, but you're the cheapest oncology company, is that a good thing or a bad thing? And we're trying to piece some of those things together in quant. Often risk metrics are as important as return metrics. If you can sort of spread risk, it's really valuable. And we're working to use some of the clinical trial data to try to do that. Although I'd say our early findings is that biotech risk factors are sparse and a lot of it is very idiosyncratic to the company, more so than almost any other sector. It's the highest dispersion, highest idiosyncratic exposure sector. And so some of these things are Just inherently difficult.
- [46:57]
C
Are you not finding that several companies that are all competing, going after the same style of drug, they don't rise and fall together, that it's still going to increase in critical.
- [47:06]
B
They do rise and fall together. That pure momentum is strong.
- [47:10]
C
Got it. Okay. But you're also saying that there's still. Even once you strip that out, the residual IDO is still massive.
- [47:16]
B
Very, very high. Yes, exactly. So as like a risk model, it's hard to piece together even though the pure.
- [47:22]
C
But. But it does better than just using like a GICS standard industry classification.
- [47:26]
B
Exactly. Yes.
- [47:26]
C
Okay.
- [47:27]
B
Yeah, yeah.
- [47:28]
A
Kind of along the lines of that dispersion. I think one of the things that you point out in the paper is that, you know, the median company actually over the last few decades has lost money for shareholders, yet historically you're going back, the sector has actually outperformed. And you know, you also. So there's a, there's a return characteristic of these biotechs, but then also that the correlation within the sector is very low. So just in terms of building strategies and selecting biotech, Stan, like, you know those two things that high dispersion and low correlation, like how do you, how do you think that should sort of play into trying to build a portfolio of biotech stocks or at least selection of biotechs?
- [48:10]
B
I think one thing is that shorting is really important, right? Because. Because biotech beta is bad, right? Like, you know, the, the sort of median, you know, the median company loses money, the 70th percentile company loses money. Right. So you sort of, you want to have a strategy that can sort of mitigate some of that left tail, because there's a lot of left tail in biotech. And then at the same time, you want to be trying to capture the right tail as much as you can. And so I think it's a sector where a long short approach is very helpful, but where you've got to be very cautious about the shorts because they can triple or quadruple on some good clinical trial data that you didn't anticipate. And then I think you want to be fairly diversified as well. And I think our view is that there are things that fundamental investors are better at, like quant. We're never going to know better than a deep fundamental investor whether any individual biotech is going to produce a drug and what the TAM is. And that sort of stuff like that just has to be done by hand. But what quants are almost certainly better at is risk management and portfolio construction. And what you've seen is that many of the large biotech specialist funds have sort of given up shorting. You know, they'll be 105% long and 5% short. And it's like, why even bother saying that you're shorting? You know, it's like you're not really doing any of it. And I think part of it is they've gotten burned so badly, it's really hard. And so I think versus on the long side, where, you know, picking a concentrated set of very exciting, you know, prospects has been a winner for a lot of these big funds. So I think that's how I would. How I would approach it.
- [49:46]
C
Yeah. I think, Dan, when I was reading your paper, one thing that did strike me was that the. There is alpha both on the long and short side. Right. Because what one could do is they could just say, I want to go long things I like and short the. What is it, XBI or whatever the ETF is. Right. But then you'd be giving up any kind of short alpha. But it turns out that based on the quintile runs that you looked at, actually the short side has as much, if not more alpha than the long side.
- [50:09]
B
Yes, there are a lot of bad biotechs, you know, the sort of proverbial, you know, company that's going to cure cancer, that's run out of a strip mall in Miami. It's like maybe, probably not shouldn't be 400 million a market cap.
- [50:21]
A
You know, I wonder, Dan, one of the things that you sort of talked about is the case for biotech coming off, you know, historic relative drawdowns or performance versus the S&P 500 in that, you know, maybe the setup might be for, you know, better times ahead for biotech. So I'm just two things on that. I'm kind of wondering what, you know, history would suggest about the returns after similar. Similar periods of biotech underperformance and then sort of going back to the AI conversation. You know, one of the things that I do hear a lot about is that, you know, these drug makers and companies that, you know, and biotechs included, that, you know, we'll be able to leverage, you know, AI in terms of, you know, more rapid development about curing all types of things. So I don't know, maybe those two things, that relative underperformance coupled with higher use of AI and research and development might be possibly a good sort of setup for. For biotech in general.
- [51:28]
B
Yeah. You know, I tend to be attracted to the sectors that have been starved of capital. Right. And Biotech, you know, it had its big boom during COVID and then a huge bust that lasted for three or four years and that we're seem to be maybe coming out of now. So in the last six months. So I think people are getting a little bit more excited and optimistic about biotech. But if you rewind a six to nine months ago, it was just a total, total disaster. And that was another thing that got me interested in because I like things that have been really, really beaten up that other people don't like. And this fit the bill, although it fits the bill a little bit less since there's been a big rally. I wish I could have published the paper six months earlier, but it just took time. Would have made me look smarter. But I think that's certainly true. And I think when you think about sort of the positives and negatives for the sector, one is that there's been a lot of capital starvation, two has been. There's been a sort of M and a drought. But all these big pharma companies, and they're really the exit, have lots of drugs coming off patent and they need to acquire small, exciting biotechs in order to revitalize their pipeline of patented drugs. I think that those are sort of the positives. I think on the negative side, China is a big competitive threat. China is basically trying to undercut the world in biotech research by making clinical trials really, really cheap to run and then saying basically like, come run in your clinical trials in China. And then they're basically going to build pharma companies around the ability to run efficient trials. And then they don't have a lot of respect for intellectual property. So they can just take, you know, patented drugs or, you know, FDA approval applications in the US Rip the drugs off, run the trials in China, be first to market or, or do their own research, and then sell the drugs they discover to Western firms to then license for those markets and do their own more expensive trials. And so I think it's going to be really interesting to see how the sort of Chinese competition threat plays out in biotech.
- [53:27]
A
And just one last one for me and kind of goes back to the beginning here, which is, you know, how often do you, like, maybe, I'm sure it happens a lot at quant firms, but I just find it like, appealing, like intellectually, I guess, that, you know, you guys kind of took a step back and said, okay, let's look at what our models are working on and what they're not working on. And if you figured out, you Know, okay, we've, here is a clear example and the ability to kind of step out of that and be like, okay, now we have to kind of start with a blank slate and relook at that. I mean, I think it's sometimes hard for quant firms to have that investigative like mentality of, listen, we need to start from, you know, over in terms of how we look at these, this type of area of the market. So I'm just. That's a, let's, that's a comment, I guess. But I'm asking you to comment on that sort of philosophy, if you will, of developing quant strategies over time.
- [54:27]
B
Yeah, I think part of it, you know, I run a relatively new firm. You know, we're very sort of entrepreneurial. So there are not a lot of, not a lot of, you know, fixed bureaucratic things or even, you know, we're very open to new thinking. And I like to think of like the research velocity as a measure, like how often are you doing new research and producing new ideas? And I want to keep that very high. And I think it's very important then to not, you know, to sort of be open minded and humble and say, hey, we really don't know. Like, let's be really humble about what we know and we don't know. Let's find things that we're doing wrong, let's fix them and let's just constantly churn out new ideas. Because I think that's how a firm survives and grows in this world. Like, you've gotta be fast, you've gotta be producing new ideas and you don't want to be getting stale. And I think whether that's in an equity portfolio, like one of the things you can observe about people that manage equity books is that the folks that have the higher name turnover do better. The people that are adding consistently new names to the book do better and the people just stick in things they've owned for years and years that a lot of new turnover. And I think the same is true in quant. You know, you look at how Kai's many, many excellent papers that seem to constantly come out, like that level of research velocity I think is really important.
- [55:38]
A
Good stuff. Thank you very much, Dan, really appreciate it. Kai, thanks for helping me out with this. Always a class act. So thank you.
- [55:45]
C
Thank you. Thanks, Dan.
- [55:47]
A
Thank you for tuning in to this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the Excess Returns network@excessreturnspod.com if you have any feedback or questions, you can contact us@xsreturnspodmail.com no information on this podcast.
- [56:07]
C
Should be construed as investment advice.
- [56:09]
D
Securities discussed in the podcast may be.
- [56:11]
C
Holdings of the firms of the hosts or their clients.