Full Transcript: Dan Rasmussen On Private Equity, AI and a New Way to Look at Biotech

Private Equity Risks And Biotech Opportunities

Justin: Welcome back to Excess Returns.

Dan: Thank you. It’s nice to be back

Justin: At Verdad, 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 verdadcap.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 gonna 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, 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. Yeah, good afternoon. Good to see you again, Dan.

Dan: Good to see you too, Kai.

Justin: 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 wanna highlight or, or is it kind of the same thing?

Dan: 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, you had virtually every investment allocator in the world thinking they were gonna generate 400 bips of net of fee alpha, and 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. ‘cause 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, just stuff that’s just dumb. And I think finally the reckoning is arriving. And I think, you know, the other thing that you’ll see is, 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, and that’s sort of lower mid-market pe, which is every, 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, the, the middle and, and big ones are, are next. there’s just been excessive lending. so I think, I think the industry is in a, is in a, 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 a, a, a, an asset class that’s doubled the number of participants, you know, what do you sort of make of that? And, and I think it’s, it’s, it’s, I like the Darwinian evolutionary terms. You know, we’ve, we’ve had, we’ve had, we’ve had speciation, we’ve had, you know, a wide divergence.

and, and now, now, the, 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 gonna pay high fees to underperform the market, and who cares about volatility being lower?

‘cause you can’t eat, you know, Sharpe ratios. And then you can walk into private equity and it’s just like, again, believing in, in, in rainbows and sunshine. and I think what that environment has, caused is this, a really kind of competitive dog eat dog, zero sum world in which like to survive in hedge funds.

You’ve gotta operate like an apex predator who’s able to sort of, you know, cut. Through all this stuff and develop some really cool technology or research or process. And then in private equity it’s, you know, 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’re gonna lean and mean in the other. And I think we’re finally seeing a, a reversal where, where last year. Again, private equity, performance lag, 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.

Justin: 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 just long-term slog or is it, is there something more maybe sy like a systematic type crisis beneath the surface that.

Could come out of it.

Dan: I think it, it all depends on the bankruptcy environment, right? I mean, I think if we get a high bankruptcy environment, you know, PE is toast. ‘cause you’re gonna see large percentages of portfolio companies go bankrupt. but, you know, predicting, bankruptcy, a wave in the United States has been a losers, game for, since 2008.

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, and they’re overweight, subscale, you know, subscale tech, you know, sort of the, you know, buying a, a software provider that, served auto dealerships or something, right?

Like classic PE deal, constellation software type thing. Um. And who knows if AI is just gonna eat that stuff. and if AI eats that stuff, then the 40% of the private equity capital it’s been deployed into, essentially software is gonna 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.

Kai: And, and Dan on the other side of the equation, the institutions that have, you know, 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, I saw some news outta Yale and, and such, but.

You know what, what’s going on the, on the buy side here,

Dan: my general view is that they’re only reducing their exposure when they’re sort of forced to, that, you know, they have too many capital calls or, or that sort of thing. Right. And they maybe we’re over allocated. not, not that, not that they’ve lost any enthusiasm.

I haven’t seen that yet. and I, I sort of think like, you know, you think of it at a, 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, 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 gonna 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. These five. And so I haven’t, my enthusiasm for private equity is undiminished.

I, I’m just relying on sort of, the learnings I’ve had. And so I think that’s sort of the phase. you know, we’re, we’re probably in where, where people are still, you know, 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, the more pressure there’s going to be down from, you know, from the, you know, say the trustee level or the CIO level to say, Hey, look what, you know, wait a second.

You know, why is our public equity group, outperforming for the fifth year in a row? We have so much staff in the private equity group and you, you know, 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, you know, people drive, people have incentives and so many people have been staffed up. It’s really a, a people intensive business to run a private equity program, and it’s gotten more so because the focus has been on co-invest 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. ‘cause it means cutting their jobs.

Kai: No, I, I understand like the agency issues at stake here. so I guess the, 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, you know, maybe I’ll just jump into, you know, the second part of the question. Which is, you know, there are various narratives around what exactly putting private equity into a 401k 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, you know, only, the purview of the, the highest end institutions. And the other, you know, more skeptical narrative I say, let’s say, is that it’s exit liquidity for, a pool of capital that, you know, it basically doesn’t have.

The money trap, argument. So, you know, let me ask you where you come down on, 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.

Dan: Yeah, I think, I think the word democratizing, like the word re-imagining should set off alarm bells.

You know, whenever you hear them it’s al almost whatever’s gonna happen next is gonna 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, you know, proposed to offer quarterly liquidity.

Of course, they were gates, you know, b re, you know, Cliffwater. Very successful interval 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 RIAs to access privates. Um. The, the challenge that we’ve seen is that, is this mark to market smoothing issue, which is, you know, at certain times you remember B REIT was, you know, down zero, and the public REITs were down 20 and everybody was saying, oh, gee, you know, I should sell B REIT and buy public REITs.

And then they all tried to sell B reit, none of them could. And it got gated and everyone was angry. And the more people tried to pull and it got worse. And, you know, those types of things happen with these interval fund structures. the, the, it seems like there’s an exit door, but it’s very tight, and it’s easy to get locked.

Then, so I think that, one of the things I’ve looked at is the, the London listed private equity funds where, where you have a, you know, about a dozen, or maybe two dozen, private equity funds that actually listed lp, you know, fund to fund vehicles. Not, not the management company, but the actual LP vehicles in, in London.

so they’re even better than, interval funds ‘cause they’re publicly traded. And what you see there is that those funds trade at 30 to 40% discounts and they’re wildly volatile. you know, much more volatile than large cap equities. Even more volatile than small cap equities. and so I think, you know, okay, we’re gonna put that into, retirement accounts.

and we are people gonna like what they get, right? They’re either gonna 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 gonna be stuffed in these interval structures, which have gates and queues to get out.

And I just don’t think people are gonna like it. And I think it’s gonna generate a lot of lawsuits. The fees are huge, you know, 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 soaker towards low costs, is gonna justify that.

You know, particularly 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.

Justin: Okay. Dan, what about this move to bring private assets into ETFs and try trying to make those like more available? Is is your line of thinking sort of along the same lines of this 401k thing?

Dan: Yeah. I mean, we’ve seen what happens with these London listed vehicles. I, right? Like, it’s just like, yes, they’re gonna trade at a discount. ‘cause these things, these private assets are almost by definition small. And, and they’re illiquid. And they’re nichey and they’re hard to diligence. There’s not a lot of data.

And, and, and the end customers doesn’t like that type of thing. And the public markets right? Like a, a bundle of opaque things. Unless you’ve got something 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 three HVAC roll-ups and like a, 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, I just find it hard to think anyone’s gonna get all that excited about it.

Justin: Let’s move into Kai’s wheelhouse. Kai, I’m gonna let you roll with the, the AI section. So go for it.

Kai: Yeah, sure. so Dan, let’s start with an article that, your colleague Brian Shingo, wrote on, bubbles being kind of a necessary part of innovation, right?

And it was a pretty interesting study, so maybe you could just spend a couple minutes on that. yeah. But, but really what I’m interested in understanding is, you know, your perspective on how one would define a bubble. Are they, kind of x anti identifiable and, you know, the role that plays in kind of capital formation and innovation.

Dan: Yeah, so I, the, the, the, 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 you know, 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 in productive assets. but if there are bubbles in new 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, and that’s a very good thing for society. And then it comes to thinking about it from an investor perspective of, of should I buy into those science projects?

and I think, generally, you know, there are a few things going on. You know, one, I think it’s very hard to know ex andante. and I think my, my sort of mental framework for thinking about sort of efficient markets is, from Mordecai Kurz at Stanford who has this, idea of rational beliefs, which is that, um.

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 the, all the time about it, but no one knows.

and there they’re all those beliefs are rational, right? So when you think about efficient markets, markets are sort of pricing in something. their pricing in sort of 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 gonna be gripped by a mass pandemic and we’re gonna shut down, every school in business so people can hide in their houses.

you would’ve said, well, you’re a nut job. that’s an irrational prediction about the future. that’s just not gonna happen. and then it did. and so many sort of world historical events are, are sort of by definition unanticipated, exi. 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 over capitalization. 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. Um. that emerge and there’s, so much competition here, that it’s almost certain that costs are gonna 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. You know, 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 the, hyperscalers that are building the Ai models.

But what about the value companies that are most likely to benefit from it, that are stealth adopters that are actually, using AI in a very productive way that you wouldn’t realize. and I think that’s sort of an exciting way to sort of think about, playing this type of thing.

Kai: Yeah, I mean, I, I obviously agree completely with that, view. and, 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 in variable tendency for an overbuild, which results in effectively an over supply of capacity.

Which results in following 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, on the qu side and around kind of the, the real economy companies, right?

Folks that are not necessarily your, your kinda standard. tech, tech players. so yeah, let’s, let’s talk more about that. The Mag seven. you know, because that’s just been such a central part in, you know, most folks portfolio, obviously 33% of the s and b 500 and, you know. Over the past few years, 75%, of the returns of the index.

So, you know, mo most investors, to the extent they’re index investors and cap weighted indices, that’s the bulk of their portfolio. And if you add on other in infrastructure players like Broadcom’s, 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 kinda linked to this complex that depends on the continuation of CapEx spending.

In, in, in the, build out of the, AI data centers. So how do you think about that? Is it, is that a risk? It sounds like, you know, based on your view it would be a risk and, you know, how can investors, position around this? Like, what should they be doing, if, if they do believe that this is a, a potentially, kind of tough situation to be?

Dan: Yeah, I think, you know, I, I, I think of, you know, I, I started talking earlier about bankruptcy risk, right? Bankruptcy risk is a really salient risk, right? Like, you, you come and explain to someone, Hey, you know, do you want to come, and invest in some declining, you know, like a, a company that, um. Trying to think of a, a, of a, a very good example.

Something that’s been disrupted by technology, but you know, the, 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.

Uh. Return distribution as companies that are on the verge of bankruptcy, right? because the, 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 and overvaluation and it’s. The risk of excessive optimism, which is why we, 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 distributions as bankruptcies. and so it’s an, it’s an enormous risk.

Now we’re, 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 have made. Like every tech innovation they’ve come up with, that’s been a gold mine. And so 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 cer, 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’s printed money and now, you know, AI is marginal costs. Depreciation and who knows how long these Nvidia chips last. so I think that’s a, a really worrisome change.

and so I, 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. And 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.

a possible but hard. and I look abroad and say, you know, 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 gonna 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 set up from an investment perspective than this really one versified, hard one. and I think that’s probably, my, you know, initial idea of how to, how to play this or how to react to this, which is to diversify out of it

Kai: and when you say international markets, are there specific like pockets of, because I, I agree with you that say Europe, no one’s gonna say’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. Right. So, so how are you thinking about that risk that, you know, on one hand you can double down on the mag seven and, you know, hope that, we go to Valhalla. On the other hand, you could say, let’s just not even play that game and, and go to go to European stocks.

you know, which are. I think 50% of EFA is banks and industrials. Right. and you know, but then the question is, you may miss out on the, if AI is a, it is potentially this revolutionary technology, do you miss out? Or, 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, a AI bulls, AI bears, there’s gotta be some more nuance to the conversation, I would assume.

Dan: Yeah. I tend to think it’s, it’s, you know, you’ve gotta start from the efficient markets hypothesis and say, okay, look, let’s say the US is 65 and international’s 35.

you know, you don’t wanna just say, oh, I’m 10% US and 90% international’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. That’s just easy. and then there are, in the investors that are sort of at market weight, maybe they’re 65, 35. and I think to those people, I would say, you know, what do you feel more comfortable with? Right? I mean, ‘cause you gotta stick with this for the long term. if it underperforms for a few years, or 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, I prefer, 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, these calls are hard.

and so you don’t wanna diverge too much from the benchmark.

Kai: Got it. Yeah. I guess kind of what you’re saying is that if someone’s 90 10 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. Yeah. Like that, you know, potentially seems like a over diversified bet on a single thematic, play.

Dan: Exactly, that’s maybely priced.

Kai: And, 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. Are, is there anything in the small cap US space, let’s say that, that interests you?

with regards to kind of the AI theme?

Dan: I think sort of as a, a paradigm for what’s working in the market today, right? It’s, it’s us large growth and international small value, right? And, and value markets. Smaller size works, right? When, when, when, when, when, when value, when value’s working, the smaller companies work.

Smaller, cheaper companies work better than the larger, you know, 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.

Really a 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, and it tends to be the, the largest growth companies that work the best. it’s sort of the exact, inversion of that. and so I think, you know, 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 outta 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.

Justin: 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 nineties, early two thousands 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, I think just today OpenAI, you know, announced a $50 billion raise from the Middle East. I think the valuation’s 830 billion or something like that. So I’m just, you know, is that, is, 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.

‘cause 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 gonna be coming out at much higher valuation. So I’m just wondering how you kind of game, game that or, and think about that.

Dan: 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 US specific phenomenon. And then I think within the us yes, the number of companies declined with the aggregate. A 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, and so that’s sort of been the trend, in the us. and then you have this sort of wave of. innovation, especially over the last sort of 15 years, that has, has, has really been, in, in private markets.

The capital formation has been in private markets and they’ve tended to IPO quite, quite late in their, in their lifespans. and so, you know, what do we, what do we sort of make of that? Um. 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’d 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 gonna 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 day, you know, I think. The, 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 like a hundred companies that we missed out on that ended up failing.

And so, you know, we only hear about the winners that we wished had gone public and not about the losers that kept out of the markets. So I think from the public market perspective, I don’t know, on a net. Basis of like whether it’s bad or good that these companies are staying private longer. ‘cause there are probably a lot of dead unicorns out there that, we’re, we’re thankful they never got, you know, floated to the market.

So public market investors could lose money on them.

Justin: Any, before we move on to the, the biotech stuff we wanted 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, a 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 open ai, 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?

Dan: Yeah, I don’t know. I, I, I haven’t followed that as closely. Kai has probably followed it closer than I have.

Kai: Yeah, I mean, look like there, there’s two risks that, that stem from circular financing. One is kind of the, the, the dynamic that happened in the.com boom. Right where, where companies were effectively, you know, paying their customers to buy their stuff.

The circular, cross holdings, circular financing issues that, you know, ultimately, culminated in kind of fraudulent accounting, which is obviously bad and illegal. and then there’s the other component which is just, you know, 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.com boom and the.com boom. They’re over delivered telecoms today, they’re hyperscalers that have, you know, Google search and throw all tons of free cash flow.

Right? And that is true. But I think increasingly what’s happening is that these Mag seven companies, with the independent rule balance sheets are entangling themselves with, you know, Oracle core Weave, you know, o open ai, you know, through their kind of, through their proxies. and so to the extent there is an issue over there that may be 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, the kinda 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, 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 on are on, on all this.

It’s funny

Dan: you, you were saying that Kai, I, I was thinking that there was some, some, investment from the posted 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 the, you’re exactly right that the risk is there regardless.

Justin: It’s kind of pivoting here. One of the things I’m always surprised about when I get certain pieces of your research, Dan, is how it kind of coming outta 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’d be interesting to hear how sort of why you looked at this and what makes it such a strange and challenging sector. For investors.

Dan: Yeah. And I’m excited to talk about this with, with you and, and especially with Kai because, we’re, we’re going into kailand the world of intangible value.

and, and honestly, Kai sort of inspired me on some of this thinking ‘cause it’s, it’s, it’s, you know, there are certain sectors where you’ve really gotta think about this intangible value or really matters and where the financial metrics are less helpful. and so, you know, this is sort of my first foray into Kai’s world, so it’s gonna be a fun, fun conversation today.

but the reason that we, we got into biotech, or looking into biotech is, when we were running, multifactor equity MO models, risk models, return models, you know, we’re always looking for places where there are mispricings, where the model isn’t working. What, you know, where are we making mistakes?

Where are we finding, idiosyncratic, bad, bad outcomes? and we, we looked, and, 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 you, we could, we 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, um. I 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.

like we sort of know that, and they’re biotech, right? Like those risk factors, the size, the volatility, and the vector, 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, is that, um. is that, you gotta fix your value metric. and so if you think about, the denominator of, of sort of enterprise value, which is market cap plus cash, your. By a, 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 did is, okay, we gotta exclude cash because cash is good, right? Like biotechs are need cash. That’s their lifeblood. So you don’t wanna penalize them for having cash. So you just gotta have only market cap as the denominator. And then let’s think of the numerator. Well, 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 in, is a lot more value than a company that spent 5 million on clinical trials. So if they’re both selling for a hundred million in market cap, well gee, you’d much prefer the one that spent $500 million last year because.

Pre, presumably someone gave them that money to do something promising and you know, presumably that 5 million spending produced something promising, right? Like there’s gotta, you, 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 a hundred million market cap is. It’s a very expensive stock and it should be penalized for that. there’s just hopes and dreams. and so that we, we actually found 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, of any financial metrics working. There are some that work. and then the next challenge was, was thinking through in, in the intangible value, you know, what’s the quality of these businesses?

and, and Kai does a lot more sophisticated work around, how to measure this, which is, which is really cool. But, we wanted to sort of, cruder first pass that we could get through. and so what we, again, is. Let’s take biotech. Like 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 or not?

Who are going to read the clinical trial data? And so we said, well, what if we just say, well, what. Let’s take these specialists and we can define them as, you know, people, anyone that owns a certain amount of biotech and ver room biotech with 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. Like 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 gonna 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, the amount of, of sort of scientific rigor and vetting that’s been done. and that’s been expressed, you know, through a bet. Is a really healthy metric of quality. And so then we can sort of take quality and value, which is sort of the classic two quant factors, or the third momentwhich 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 is momentum. You know, traditional momentum is measured by stock price. Doesn’t seem to work particularly well in biotech. I think probably ‘cause it’s mostly event driven, right? Like the trials come out and boom, you know, something works or not. And you know, whether it’s been kind of 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, you know, new, newer field in, 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, suppliers or competitors, et cetera, impact their stock price.

And people find that they como and so what we looked at is saying, well. 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.

Justin: What and how does the clinical trial data come into this and play into it?

Dan: Yeah, so we use it, and we’ve got a lot more to mind. It took us, this is the most work intensive part of the project, but what we did is we, we basically, um.

I took all the clinical trials.gov data, married it to the corporate IDs. So you know, 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 cla 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. You know, part of the world that they’re in. and so we’ve been looking at you peer momentyou know, basically how similar companies are performing, and we’re also looking at, peer value.

You know, 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, 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, you know, biotech risk factors are sparse. and a lot of it’s a 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.

Kai: 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 got idio and credit?

They do rise and

Dan: fall together? That peer momentum is strong. Okay,

Kai: got it. Okay. But you’re also saying that there’s still, even once you strip that out, the residual idio is still massive.

Dan: Very, very high. Yes, exactly. Got it. So as like a risk model, it’s hard to. Piece together, even though the pure, but it pro,

Kai: but it does better than just using like a gix, a standard industry classification.

Yes, exactly. Yes. Okay. Yeah, yeah, yeah.

Justin: 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 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 biotechs and like, you know, those two things that high dispersion and low correlation, like how do you, how do you think that.

Sort of play into trying to build a portfolio of, of biotech stocks or at least selection of biotechs. I think,

Dan: I think one thing is that shorting is really important, right? Because, because biotech beta is bad, right? Like, you know, the, the, the sort of median, you know, the median company loses money. The 70th percentile company loses money, right?

So, you, you sort of, uh. You want to have a strategy that can sort of mitigate some of that, left tail. ‘cause there’s a lot of left tail in, in biotech. and then at the same time, you wanna be trying to capture the, 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 gotta be very cautious about the shorts because they can triple or quadruple and some good clinical trial data that you didn’t anticipate. and then I think you wanna be, you know, fairly, diversified as well. and I think our view is that there are things that fundamental investors are better at, like quant.

we’re, we’re never gonna know, you 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 had 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’s, 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.

Kai: 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?

‘cause what one could do is they could just say, I wanna 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, the quintile runs that you looked at, actually the short side has as much, if not more alpha.

Than the alongside.

Dan: Yes, there are a lot of bad biotechs. you know, the, the sort of proverbial, you know, company that’s gonna cure cancer, that’s run out of a strip mall in Miami. It’s like, eh, maybe, probably not. Shouldn’t be 400 million a market cap. You know?

Justin: 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 and p 500 and 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 under performance.

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, will 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 under performance coupled with higher use of AI and research and development might be possibly a good sort of setup for. For biotech in general. Yeah. It

Dan: tend tends to be attracted to the sectors that have been starved of capital. Right. And biotech wa you know, it had, its, its big boom during COVID and then a huge bust that lasted for three or four years and that we 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 it, you know, if you rewind us six to nine months ago, it was just a total, total disaster. and that was another thing that got me interested in ‘cause I like things that have been really, really beaten up, and 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’ve made me look smarter. but, um. But I think the, I, I think that’s certainly true, and I think when you, when you think about, sort of the, 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 running your clinical trials in China. And then they’re basically gonna 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. 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 gonna be really interesting to see, how the sort of Chinese competition threat plays out in biotech.

Justin: And just one last one from me, and it kind of goes back to the beginning here, which is, you know, how often do you, like, I maybe, I’m sure it happens a lot at quant firms, but I, I just find it like appealing, like in 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 re-look 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, I’m just, that’s a, that’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.

Dan: Yeah, I think part of it, you know, I’m run a relatively new firm, you know, we’re very sort of entrepreneurial, so there’re not a lot of, not a lot of, you know, fixed, bureaucratic, things or, or even, you know, we’re very open to new thinking and I like to think of like the research philosophy as a, 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, let’s be really humble, but we know and we don’t know. Let’s find things that we’re doing wrong.

Let’s fix ‘em. And let’s just constantly churn out, you know, 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, you know, whether that’s an QU 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 that 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 philosophy I think is really important.

Justin: 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. Thank you. Thanks, Dan.