Yet Another Value Podcast - Investing in Biotech with Verdad Capital

Episode Date: February 24, 2026

Dan Rasmussen and Greg Obenshain of Verdad Capital discuss their white paper on quantitative investing in biotech. Topics include why biotech’s complexity makes it attractive for systematic investors, how specialist fund ownership serves as a quality signal, and why insider buying and spending-based valuation metrics can outperform traditional financial analysis. The conversation also examines momentum within therapeutic categories, risk management on the short side, and how diversification and rebalancing help address biotech’s event-driven volatility.Verdad paper on investing in biotech: https://t.co/JZ1uDURDG2[00:00:00] Introduction to biotech quant paper[00:02:53] Why biotech attracts value investors[00:05:07] Specialist ownership as quality signal[00:08:24] Defining biotech sector specialists[00:11:29] Acquisition patterns and return drivers[00:19:37] Managing short risk in biotech[00:23:06] Short interest as negative signal[00:27:38] Insider buying predictive power[00:32:44] Spending-based valuation framework[00:40:21] Classifying biotech by clinical trials[00:45:34] Momentum within therapeutic categories[00:48:23] Events versus underlying return drivers[00:51:34] Verdad’s contrarian investing philosophyLinks:Yet Another Value Blog - https://www.yetanothervalueblog.com See our legal disclaimer here: https://www.yetanothervalueblog.com/p/legal-and-disclaimerProduction and editing by The Podcast Consultant - https://thepodcastconsultant.com/

You're about to listen to the yet another value podcast with your host, me, Andrew Walker. Today I have the team from Verdad Capital on, Dan and Greg. We are going to talk about their paper they have on kind of value and fundamental quant investing in biotech. Look, I read the paper and I instantly reached out to them. I told that I found it fascinating. You're going to hear how excited I am. I'm like a kid in the candy shop asking all these questions. Obviously, I do like more microlevel securities and they're talking macro quantitative, like putting everything in.

But I found the paper fascinating. I think you're going to find the conversation fascinating. I learned a lot. I'm going to include a link to the paper in the show notes, of course, so you should go read the paper, but you're going to really enjoy this conversation. We're going to get there in one second, but first, a word from our sponsors.

This podcast is sponsored by Trada. Look, I've been meant you've heard me talking about Trada for months on this podcast. There's a reason. It is a really, really good fit for you if you like this podcast. Trada is interviews between two bysiders who are talking about stocks they like. Sometimes you get a bear and a bear. Sometimes you get a bull and a bull.

Sometimes you get a bull and bear. Whatever it is, it is two bysiders who are interested enough in a stock that they've done research and they want to go on and talk to someone else about the stock. And you kind of get to be a fly on the wall and listen and learn. I'll tell you what. I am recording this in the middle of February. It has been the SaaS Poccolops and Trada has been so, so good. So many different companies are covered.

And, you know, in real time, you're seeing people talk about, hey, is the AI risk real here? Hey, I talked to a CIO of a company who, you know, they were looking into this and they don't need this anymore. Hey, I talked to a CEO who said there is no chance in hell that we will get off of this product. So I just think Trotta, if you have not tried it, you should try it. The most frequent feedback I get from people who try it through this podcast, they come to me and say, hey, I really like it. I wish there was more of it. I wish there were more coverage.

I love it. So look, if you haven't tried it, you should go to try trotta.com. That's try, T-R-Y, Trata, T-R-A-T-A dot com and go check it up. All right, hello, and welcome to the Yet Another Value Podcast. I'm your host, Andrew Walker. me today. I'm excited to have from Verdad Capital, Dan Rasmussen and Greg Obachain. Guys, how's it going? Good. Thanks for us on, Andrew. Thank you guys so much for coming on. As I told you before, we're here to talk about you released a biotech paper investing in biotech. I'll include a link to

the show notes. As soon as I read it, I was like, I've got to have these guys on. I was just so titillated by it. But we'll start talking there in one second. Just before we get started, same disclaimer. I started every podcast with. Nothing on this podcast is investing advice. That's always true, maybe particularly true today because we're just talking about the paper, no stocks in particular. There's a full disclaimer at the end of the podcast and in the show notes if you want to get there. So, guys, I'll just start here. You published the biotech paper investing in biotech. There's lots of stuff I want to dive in, but I'll toss it over to you or you.

What is the biotech paper? What does it say? What are the kind of conclusions and why did you start researching this? Yeah. So I think, you know, biotech is fascinating, I think especially as a value investor because it's a huge percentage of the small-cap universe. but totally illegible to us because in some sense, right, like they're all money losing. So you're like, but should I just write this entire sector off? But it's 25% of the Russell 2000.

So like, what the hell do I do? I think that was really our starting point. I'm sure you guys know Joel Greenblatt, the little book that beats the market. You know, he's got you rank things on EB to EBIT on one side and ROC on the other. And by the way, you just exclude the entire biotech sector from the entire screen. and he is not the only one, like tons of quants do it. So, yes, I just love that insight. Yeah, and I think, you know, as a investor, right,

you're always looking for interesting sources of return that are uncorrelated to things that you already own. And biotech is the least correlated sector. It's really weird. And so for us, I sort of said, you know, look, this is a huge percentage of the small cap universe. It's the least correlated sector. And no one else is really doing it in a systematic way.

And so we need to go figure it out. And so I said, Greg, you know, bonds are boring. You want to stop doing bonds for a year, not stop doing bonds because never, Greg, I can't stop doing a stay job. But, you know, do you want to do a one year long project and figure out biotech? Since biotech stocks are just like bonds, I guess. There's a lot of cash in both of them. Greg, did you want to add anything to that?

No, I mean, biotech is, it is strange because you can't really use the financial statements. And I would tell you that Dan asked me to do this. And at first, I kind of ignored it because I thought, there's no way you can build a quantitative strategy in biotech. But, you know, as we started to look at it and think about how biotech work and just sort of meet, we decided to meet biotech on its own terms, only use factors that actually made sense in the realm of biotech, we started to see results. And we started to see that it actually worked.

And by the end, we really got some terrific results. And I think we're firmly in the camp that a quantitative. approach works in biotech, and we'd be happy to dive into more detail on how we made it work. Let's do that. So I want to start with the first. I think there's lots of things in here. I was really just a lot of it, but the one that jumped out to me, and I'm sure it's going to jump out to people when they read it, and you basically lead with it is, hey, when you're investing in biotex, you want to invest in things that the sector specialists are heavily invested in, right? And if sector specialists own none of it, I think you guys say, like the returns from

the things that sector specialist don't own is basically zero over time. and if multiple sector specialists own it, then the returns are awesome, right? I'd love to just talk about how you guys started thinking about that because I will just say, having talked to several sector specialists and looked at it, like, you guys found a quantitative insight into something that I think sector specialists kind of knew in their gut. They'd look at something, and when I talked to them,

they'd be like, oh, no one owns that stock, and they would kind of know it was because the science was little for you, but how did you guys come up with that? Are there any other sectors that you can think of where the ownership itself serves as such a giant signal? Greg, you want to take that? Yeah, I mean, well, I think we learned about that from talking to sector specialists, right? So, you know, one of the nice things about Kwan is at its core, it's a fundamental exercise.

You go out and talk to people about how the industry works, how to think about it. And we talked to people and said, hey, we follow what other people own. And then we said, well, you know, we can do that. We can go out and get that data, download that data. We can test it. And like everything, it was just a thesis. We went out and tested it. we were surprised at how well it worked.

And, you know, one of the interesting things comes out of it is I think a lot of people say, well, I only follow the best specialists. And that helps a lot. And so there are very successful specialists out there. But what we found out is that if you treat specialists as a voting machine and just say, you know, consensus matters, right? We want to see more than one specialist in there. We want to see several specialists if we can.

that really helps. Then you need to do, you need to think about things like, well, if the company is small, and so it's unlikely it's owned by a lot of large funds anyway, what do you do? Well, then you say, well, let's not just look at the number of specialists that own it. Let's look at the number of specialists relative to all funds that own it. So what you actually care about is the vote of confidence from specialists relative to how many other people were invested in. And what you find, which is really fun, is if you actually look at companies that are owned by a lot of funds, but zero specialists, they do terribly. And so the specialists are really just a great guide to the industry.

And it really ends up being on quality metric. It ends up being an initial screen saying, hey, look here. This is going to be a good place to start. Are there any other sectors? And I want to dive into the specialists never. But are there any other sectors? And I know you guys do. I've liked your stuff on Japan.

I know you guys do stuff worldwide. Sectors, industries, markets, where kind of specialist or insiders have such a heavy weighting to Alpha. We're looking into that. I mean, I think it seems promising, actually. But biotech would be the biggest outlier. Makes sense. Let's stick on the specialist conversation for a second.

How do you guys define specialists? So you can define them in a lot of different ways. And we might change how we define specialists because we always we always go and improve our process. Just like biotech, always evolving. Yeah. But for right now it's pretty simple. We just say do you is more than 50% of your portfolio in biotech. Okay.

And what that allows us to do is to build a robust set of companies to look at going back in time. And as they they'll automatically drop in and out of the universe. Oh, please go ahead. Yeah. And what we've seen is that actually the number of specialist funds has actually increased over time pretty steadily. And right now in our data set, there's about 70 specialist funds that we're looking at. And you guys do not wait like, you know, this specialist fund has done a thousand percent over the past 10 years. These guys have done, you know, negative 10 percent alpha over the past 10 years.

It's just if they're a specialist fund, they are in there and they show up in the signal. Yep. Correct. One less question. There's some level of like, there's some level of quant, right, which is you're just sort of, you have to make some sort of simplifying assumptions, right?

So our simplifying assumption is that if they've been able to raise a certain amount of money to go deploy only in biotech, like they must have some, like the people working there have got to know something about biotech. And I'll come back to some other simplifying assumptions. But you're just sort of saying, hey, like, you know, for quant, that's good enough, right?

because we're going to take 70 of them. Like, the five of them are terrible, who cares? Right? But in aggregate, you know, there are specialists, and someone gave them money, and they must, you know, know something about the sector in order to be trading it. It's funny.

Every now and then I'll have a guest who someone didn't like on the podcast, and they'll be like, how could you let them on the podcast? I'll be like, dude, it might not be your taste. It might not even be my taste. But these are professionals. People are paying them to invest. Like, who are you and I'd say?

And it sounds like y'all are taking the same approach. One more. And I realized Quant is about, as you said, said, 70, the big data sense. But what about their, you know, specialized active ETFs? And I think the one that people might think of is arc genomics, which owns, you know, 10% of several biotechs. How would like a specialized ETF in general?

And I'm really thinking about ARC in particular. How would they get treated in your dataset? They'd be one data point. One vote. Completely okay. I wasn't trying to hate. I was just curious. All right.

So you've guys discovered that fun, that. that company biotechs that are highly owned by specialists, particularly when the specialists are concentrated, kind of outperform over time. I'd love to ask, is there any insight into what's driving the performance? Is it, hey, these firms are better at, you know, passing phase one, phase two, phase three trials? Is it when they're successful, the drugs are more at better results? I can't say effective issues, so I'm not going to say better results. So the stocks pop higher. Is it they're better at getting sold? Is it there's a lot? better cost discipline. I know we'll talk about cost versus value in a second, but is there anything

behind it or it can be all of the above? Yeah. So it turns out, I mean, the answer is I don't have, I don't have a direct answer for you, but I do know that those are the companies that tend to get acquired. Right. And that is the measure of success largely in this, in this industry, right, is do you get acquired? What's interesting for us is actually that we're not going to make, as quants, we're not going to make all our returns from events. We're not going to model events. If you actually look at the number of events that happen, if you think about just having a big sample size, there's not enough, right?

So we're not doing event studies. The way we're making returns from highly owned, companies that are highly owned by specialists, is that they behave better. Their returns are higher relative to their volatility. They tend to have higher returns on average. And so they behave really well in a model when you're doing. diversifying risk and try to target those companies that are going to increase your return and take down your volatility. That's why they're good for us. And so we think about it on a shorter time

horizon than four years, right? So we are constantly revalancing our portfolio to take advantage of the relative valuations and risks among companies. So the specialist metric is just one metric that helps us to do that. So we don't really necessarily know exactly, you know, on a week-to-week basis why the specialists do better, but they do. One more of this, and I definitely understand and we're going to talk about all the other metrics. I just thought the specialist was so unique from what I've seen. It's really the reason. It occurs to me that the specialist, you're getting most of your data, not all, because I'm sure you guys are updating for 13 Gs and 13Ds, 13Gs and stuff, intracorder. But most of your data is probably coming from the 13-X.

Right? So is this strategy, is it a lot different than everything else you've run? Or how do you think about it? It would seem to me like, you know, we're talking February, is it February 19th? Thirteen F's just got released. It does this result in like a lot of turnover? Because, you know, the 13F gets filed and you're like, oh, four specials dropped out of this thing or three specials came into this one. Is it a little bit different or is there ways to kind of smooth that over? Yeah. Well, I think that the thing about as trading small cap, right, is that, you know, if you if you're a billion hedge fund and you're putting $50 million into a biotech, you're completely illiquid.

I mean, you're not getting in or out of that thing. I mean, you're stuck, right? And so our view is that actually the 13Fs don't change all that much. Like, it's not actually that fast maybe a signal. Like, yeah, like, you'll get some new buys or whatever, but like they can't move in or out very quickly. And because we're looking at consensus, right, we're looking at the things that a lot of people own, you know, the chances that like 10 different biotech funds all sell simultaneously.

in a small cap, like, you know, it's going to tank the price. I mean, if that is happening. You know, you're definitely right. Like, R-A, R2W, all these guys, they own 10% of these small caps. They're there. It's got to be a success or failure. And as you're saying, if they're getting out, it's probably because the trial failed and they're just like, wash my hands and done of it.

And then everybody's out and there's just not a lot of salvage value. Anyway, there are some unique things about specialist funds. And I'm particularly thinking about pipes. you know, specialist funds can raise pipes and they can do penny warrants. So a lot of times I know from trolling through these beneficiaries, I'll be like, oh, there's no specialist fund in here. And then I'll look and be like, oh, no, two of the specialist funds own 10% of this company through penny warrants or more than 10% of the company. And the other thing is, speaking of orange, you know, you'll see a lot of times where a company will do a big raise. And they'll say, hey, we're raising $100 million at $10 per share.

and all the funds that are participating are getting, you know, warrants to buy stock at $15. Now, I could go on the market and buy the stock at $10 per share, but the specialist funds have definitely got a better deal than me because they've got the $15 warrant if things work out. So I'd love to just ask, how are you adjusting for kind of the warrant ownership, both, actually, I don't think the second one matters for the way you guys are strictions. But what about the penny stock ownership and kind of those complications when it comes to ownership? Yeah. So the answer is, you know, we're working on it. One of the things we do, we publish the paper, but we have a research pipeline that we keep.

And this is sort of interesting about how we work in general. That was a question that came up the minute we published the paper. So people will come back to us and say, hey, what are you doing about this issue? And we'll put it in our pipeline and research it. We're well aware of it. And it's something that we're working on it. And the answer is, I don't have an answer yet, but we will. So, yeah, so right now we're working on. Judging from the end, people know that I'm really interested in Busted Biotech, but I tweeted out I was having you guys on two hours ago.

And judging from the amount of inbound I got, people who are very interested in this paper, maybe it's just my circle. But one more question on sector specialists. A lot of the big pharma companies will invest in the, in smaller companies. And, you know, Pfizer is one that I think about. Pfizer actually has to file a 13 app. There's like 15 different companies where they partner on the drug. they'll buy five to 10% of equity. Do you guys consider them sector specialists or is that a question for another day?

No, actually, so they do show up on the sector specialist list, right? And one of the things we do is we screen to make sure they can get screened out if they only own, you know, a few biotecham. One of the things would do is screen out companies that are very, you know, sector specialists that are small. And sometimes those companies will show up as small even though they're not. So they do show up and we do capture that. That is also one of the things that we're looking into, whether having strategic in the ownership

is helpful. But you could imagine that that data is a little messier and harder to get and harder to validate. So as a first pass, way easier to get the specialist data. And as we go on, that is something that we're taking into. And then, you know, Pfizer likes to take some equity in them, whereas a lot of other ones might just want to do the straight partnership on the drugs. You guys lead with the famous pharmac athletics example, which, you know, J&J, if I remember correctly, it was JV'd on the key asset there. They did not own equity.

And then Advy buys them for a fortune. And I bet J and J and J wish they had owned equity when AvV came over the top and got them. But, you know, you have to think about all that. I completely hear you. I want to turn to some other questions on really interesting things in the paper. But I don't want to leave on the sector specialists in case there's any burning insights or any questions I should ask or anything on the sector specialists we should discuss. Yeah. I think the only thing that's sort of fun,

to note and, you know, when we think about launching this strategy or managing it, you know, one of the things, well, so why wouldn't I go, you know, if the specialists are doing all the work, you know, like, why would I, why would I go with this strategy rather than the specialists? And I thought one of one of the sort of our thoughts and response to that is to say, well, actually, when the specialists have unique ideas, they don't do as well, right? Like, you'd rather own the thing that everyone agrees on. Like, the consensus is the signal, not like, you don't want us to have independence opinions, like, you don't want your biotech manager to have independent opinions. Like, their alpha opinion where they're the only guy that thinks it is probably wrong.

It's funny. You know, famously, every fund of funds or best funds ideas, the fund that launches fails. And you guys might have found the one sector and the one strategy where it really starts to work. I want to go to shorting. So you guys at the end especially start talking about this. It's a long short strategy. And obviously, long short strategies for Quants is kind of the Nirvana, the shorts,

underperform the longs. That's how you really captured off and everything. But biotech is an interesting one to think about on the shorting side because you have these huge catalysts that can make for, it can make it difficult to rebalance. And I want to just ask, like, I want to ask a lot of questions on the short side, but how do you guys kind of think about the short side when you're facing the upside risk of, hey, if we're wrong, I mean, it's not unheard of for something with zero specialist to get acquired for a huge premium or announce out of nowhere great results in the stock go up 1020x. How do you guys think about just those dynamics on the short side? Yeah, so I think the first thing to note is that, you know, biotech is really a fertile place for shorting because it's the

sector where the largest percentage of individual stocks end up losing money over time. So biotech beta is bad, right? Which is like the value investor instinct. Like you're like, wait, these companies, these companies don't make profit. They don't even make revenue in many cases. You're like, yeah. So a lot of them should fail, right, because they're science projects. And, very, you know, some few of them are going to work and be lottery ticket positives, but the majority of them, maybe 60 or 70% of them are going to be money losers for you. And so I think shorting has to be an important component of a biotech strategy. And then there's the question about like, okay, but, right, this is a sector where it's highly,

it can be highly promotional, it can trade on news, right? Like, okay, we had a clinical trial positive outcome and the stock is up 40 or 50%, right? And even if the stock eventually fails, you know, that's a pretty painful day for you. And so I think our next observation is just around risk management on the short side. And I think what you've seen is a lot of biotech specialist funds have abandoned shorting. You know, they run like 105% long, 5% short and the 5% shorts in XBI. And so, you know, why are they doing that? Well, they've gotten burned on the shorts as the truth.

Because they apply the same approach to the shorting as they apply to the long where they do deep research. And they said, well, I have high conviction that X is a fraud or something or B is never going to work. And they put a 5% concentrated short position on it. And then they just get annihilated, right? Maybe they get annihilated for a month. Maybe they end up being right six months later. But it's just painful. And they have so many scars from it.

They just abandoned it altogether. And I think our view is that this is a place where Quant is just really, really good, right? Because you can look at things like how big is the market cap? What's the liquidity? What's the current short interest? What's the borrow cost? And you can say, hey, gee, you know what, I'd rather own, I'd rather be short like 70% of the biotech stocks individually than be by the short like seven of the highest, you know, highest, my highest conviction belief ones, right? So it's about position sizing. It's about diversification of risk. All the things that quants are really good at and fundamental managers tend to be really bad at because they get caught up in ideas and they double down on things and they work, don't go against them and things like that. Or if you're just really disciplined and rebound, balancing frequently and being pretty diversified.

By and large, if you're short things that specialists don't own that are pretty expensive on our value metric, and maybe have a little negative momentum, you're going to do fine on the short side. Greg, did you want to add anything there? I had some follow-ups, but I want to make sure I gave you a chance if you had anything. You're on mute, but I think Greg's saying no, so I'm going to assume that to know. So the first, so I jump straight to shorting, right, using this on Belongshore side. But another signal that you guys do use is you mentioned short interest is basically one of the signals. And I think you guys have, if this biotech stocks are in the most shorted percentage,

the returns on them is hugely negative. And basically every other quartile is kind of positive. If I remember the chart correctly, I'd love to ask just because you mentioned, I've talked to these guys, a lot of them are really hesitant to put shorts on. So if these companies are ticking up in terms of short interest, kind of like, who is the short interest and what signal do you think you're picking up on when these shorts are high? Is it, hey, there's a lot of fraud risk or or science is so bad even journalists can figure it out because it kind of seems strange just that it can take up that high, you know?

Yeah, well, I think you've got to remember the markets are very efficient. And this short interest signal works across every sector. The stuff with really high borrow costs and really high short interest just does terribly. And I think you can think about that there's some stuff that sort of everyone knows is bad or dumb or fraudulent, right?

Like it's like they're going to cure cancer and they're run out of a strip mall in Miami. And you're like, I don't know, like, probably not. You joke, but if you trade cancer for Alzheimer's, I have seen that in the past year. There you go. You know, there are these things that exist, and it's sort of obvious to everybody.

But the problem is that the manager is very promotional and makes the stock pop every three months with some crazy news item. It's totally fraudulent. But by and large, that signal works across all sectors, and it's because markets are efficient. And it also creates the challenge where the best things to short also have the highest borrow cost. And so it's basically neutralizing the borrow cost versus your expected return often are sort of neutralized.

So you actually need a really good model to layer in, you know, what's my expected return versus the borrow cost? And again, this is like a great problem for quants. So it might say, oh, gee, it's better to be short. 50 things were kind of negative about that have low borrow costs rather than 10 things that were really. negative about that of really high borrow costs that might be subject to short squeeze if the sector pops. The shorts, I'm just curious, is most of the return on the shorts generated from, hey, these guys come out and the drug fails and the stock goes down 90 percent and the shorts were kind of right

that the science was either very poor or fraudulent? Or is most of the returns from the shorts, again, as we've talked about, biotex burn cash. And it's just, hey, these guys are kind of the path to nowhere and they're just always burning their cash balance and that type of thing. it more slow bleed or fast drops in the short side? Yeah, it's more the slow bleed. And to think about shorting in general and what it does for portfolio is it really dampens the volatility of the portfolio that limits your losses when the market goes down

so that you can reinvest profitably. I mean, in quant speak, right, it's taking care of the volatility draft. And so it's limited. limiting losses to allow you to invest and get the upside, right? And so we don't really think about it as, you know, shorting for, you can actually lose a little bit of money on your shorts over time the entire time and be far better off for having shorting. You don't have to make money in your shorts for the do you a hugely valued addition to the portfolio when you're running a really disciplined, disciplined, disciplined, diversified, quantitative portfolio. And past month to six weeks aside, I mean, if you were running a large short book on tech stocks and you're like, hey, I've lost 5% per year on tech stocks for the past 20 years, you'd be like, you were the greatest short seller of all time, put you in all the portfolios. My God, could we lever you up against the QQQQ?

Yeah, just last thought. I mean, I thought it was so interesting because I just got this one in my head. And again, I know as somebody who looks at the individual ones, I probably am focused too much on the individual ones, but there was this company SBRB. and I've seen several other ones were like that, right, where they come out surprise announcement that the drug works and the stock went from eight to 180 overnight, right? So that's a 25x plus. And I just keep looking at that and be like, man, if you had asked me to guess, I would have said

99.9% this fails. And like, when you get a 25x on a success, I'm like, man, that is just a tough place, even if you've got all the quant signals because a 25X on a short is, oh, my God, even if you started small, that is life altering. And there's no, like, it went up 25X over 10 years so I could cover on the way. It's like, no, you get hit and it's gone. So I don't know. Anything on that or should we go to the next factor we're going to talk about?

Highly diverse, but. Another factor that you guys mentioned is company insiders. And I thought this was interesting from a lot of angles. You know, one of them is the, again, I look at these individually, and the alignment of incentives and a lot of them I look at is so poor just because the company The Insiders get lots of stock option, and they're just kind of incentivized. Invest in the R&D. Invest in the R&D.

It doesn't matter if it's a terrible EV because if it pays off, you get a fortune. And if it doesn't pay off, you get zeroed anyway. You guys just mentioned, hey, the returns here are fantastic for company insiders. So I'd love to just talk about that finding and kind of what you see there. Yeah, this was really fun to go and look into this and try to. It's hard. It's really hard to look at company insider transactions and get that data clean and make it work. The first thing I'd say about it is that, you know, actually in biotech and across the industry,

sales don't tell you very much, right? They don't tell you who's bearish because everybody sort of sells their stock, right? That is a standard thing you're issued stock, you sell it. What's really interesting is the buys, right? And when people generally, when you do a non-routine buy, you only buy for one reason. and you buy because you think your stock is going to do well. Now, it turned out that the CEO always buys, whether or not the stock does well. Okay, so they're not particularly a good signal.

But the management team, the rest of the management team, especially the CFO, I mean, the CFOs are pretty bearish people. So when they start buying, it's a pretty decent signal when combined with the other signals for who's, you know, who's the reason that you want to be. You want your Bond guy working on biotech, by the way. It's the same logic. So you, the company Insider Signal, you see, you're actually highly discounting CEO buys. It's really focused on CFO and the rest of the C-suite.

And what about boards of directors? You know, they actually are mildly predictive, not as is the C-suite. And so, yeah, so we count executives X the CEO. Okay. The other thing I think is interesting on the insider buys is these guys, I haven't been on the inside of a biotech, but I would imagine they have much longer blackout periods than your normal company. So just what I have seen is a lot of times you see the inside suite buying after clinical data news. And often the stock is up to 300 percent. And I've seen CFOs buying.

And I, you know, you guys say it in the paper, insider signaling has a lot of signal here. I always think it's more, it's worth more signal when it's like, hey, It's very rare for us to be able to buy. And a lot of times, in my story, I told, they're buying when the stock is way off. And sometimes, you know, the negative data and they buy when the stock is down. But I just think it's interesting when they've got such limited windows and that it's actually sending a signal. So I don't know if you guys had anything else there, but I find it fascinating. Yeah, we, you know, we spent a lot of time looking at how rare the signal was relative to history.

And there's ways to do that. There's great papers on that. What I'd say about what we found in the insider buy. signal, there's that much like the specialist. It's actually a relatively long signal. It had power for months after you could observe it. Interesting. And so we weren't, this isn't a day trading signal at all. This is a signal in the power of the company. And you think about it, right? If people were bullish on what they're doing, they're going to know a long time before anybody else. And it might be be for reasons that are independent of earnings. And you go and read, this is actually sort of an

side, but you go and read the academic literature, and it's so focused around whether the insider bought right before earnings were good. And I don't think that's how people in a company think at all. This is their money. This is their money. If they think the company's good, they're buying it because they think the company's good, right? They're not buying it because they think the next earnings is going to beat street expectations of which they have no idea. I could think of a few management teams who might, but in general, I would agree with you. There's also a shark factor where you've got to try to find those people, but that's just too far, you know, that's too rare to play at biotech. The other interesting thing I think about insider buys and biotech is if you,

you know, the classic biotech is a one drug biotech, right? So there, if you're seeing an insider buy, a CFO, CO, whoever it is, who's coming and buying the company. And let's say it's on the heels of a successful phase two, whatever you want to call. I mean, they've already got most of their livelihood tied up to the success of this company. For them to go and buy, I mean, it is very much doubling, doubling, doubling down. Because if the phase three drug fails, I mean, the company is basically going to zero. And I'm sure these guys are highly skilled people. They can go find another.

It is tough to find after unsuccessful. But if you're the CFO, you say, hey, the science just didn't work. It wasn't on me like running bad things. But I do think it's kind of interesting versus a pool manufacturer, right? They buy and the economy takes down certain, oh, no, I'm down 20%. It's not like the business goes away. I just, I think it's interesting in terms of that signal as well. You do something interesting when you talk.

I mean, you guys run a value shop, right? I'm a value investor. You do something interesting where most of the time when you're talking about value stocks, you're talking about a company that trades cheaply to profits, you know, gross profits, EBITDA, whatever you want to do. You switch it here, right? As Dan said earlier, there are no profits here. There are often no revenues here.

So how do you find value? You guys switch it from profits to spend. I want to ask, like, how you guys came up with that and why you, you think spend is the right measure of value here? And could you define spend if you don't mind? Yeah, Greg, you want to define it? And then I can ask, talk through some of logic as well. Yeah, it's actually really simple. It's the gap between revenue and cash flow from operations. So it is all the cash out the door, whether it's called R&D, whether it's called SG&A, whether it's called, I don't know, printer costs. It doesn't matter. It's just how much money you're spending. And we don't try to bucket it in this metric.

because, you know, spending can, there's a lot of valuable ways to spend and you spend if you think you have something. So I'll let Dan go on from here. Yeah, no, but I think I was going to say that, you know, it's, again, Andrew, you know, it's sort of the dumb insight, right? It's not saying, hey, we know which specialists are really good and we're going to copy their portfolios. It's not saying, hey, you know, we really know exactly the right type of spending or, you know, how to evaluate that. But we're saying, like, hey, if you spent $500 million doing some clinical trials, like you must have produced something,

like maybe it was a complete waste of money. But surely, like in the abstract, like on average, a company that spent $500 million on clinical trials is probably worth more than a company that spent $10 million on clinical trials. Like, and if they have the exact same market cap, presumably the one that spent more is worth more. So it's just sort of saying,

just take the spending as like, you know, who knows what the ROI on, but just assume that there's, there's a constant ROI for all biotech spending, like you should value the ones that have spent more and more. Even though on a traditional value metric, obviously, the companies that lost more money are less valuable and worse. It's just, but you're sort of flipping that on its head and saying, no, these are science

projects and what they spent on that matters. And also the fact that somebody gave them the money to spend on that, because those people are going to want to get some return on their investment. And so maybe even if that trial that they spent all that money on failed, the people behind it are going to try to figure out a way to repurpose the research or something to try to make that make themselves whole. And Andrew, I was going to say, you might be sitting here, and I think your listeners are probably thinking like, well, that specialist metric sounds pretty good, but I don't know about this value

metric. That sort of sounds like really simple. Well, guess what? When you run the data and you figure out what's driving you to return, the value metric works better than the specialist metric. Because you need a way to measure how values changing over time. time so you can you can react to it and the value metric does that so the value metric is actually one of the most powerful return metrics we have despite the fact that it's an incredibly simple

construction so if i'm thinking about it correctly it is just the value metric is cfo the more negative the cfo kind of the better right so it's basically assuming the r and d that's getting spent is getting spent in a at worst eve neutral basis and hopefully evy positive basis but that that's kind of the way to think about it? That's the denominator, right? That's the, or, you know, it's one side of it. It's one side of it. And then the numerator is the market cap. So obviously, you know, a billion dollar company that spent two billion is kind of weighted lower than a 500 million dollar company that spent two billion. Yeah. So, yeah, so when you think about that, think about that as an anchor of value. You need an anchor of value when you look at a company

and you need an anchor to compare to the market cap. No, it's just interesting because, look, less today than I know you guys started writing this and started doing this probably early last year when the pharma sector was just imploding and everything was trading below cash. That's when I got interested. It sounds like we kind of came at it from different angles, but the same way. You know, you would hear people and you say, hey, that company's trading for 10 and they've got $20 of cash on their balance sheet. And you'd hear people pushback and be like, yeah, but all that cash is going to R&D,

they're going to burn it all. And you say, yes, but like in the absence of more information, I kind of have to assume that it's getting spent hopefully somewhat rationally? Like, are they spending so much that they're getting worse than negative 50% ROI on that investment? Because that's what it's calling for. But I'll pause there. I do have questions on it, but I'll pause there if you want to add anything to my kind of story. No, that's exactly right.

And I think the, you know, part of that, I think you got interested in the sector around the same time we did. We just love things that have been really bombed out and destroyed because I think our general, like, meta view is like, you know, the more pessimistic people are about something, probably the more interesting of an opportunity, right? You're looking for places that are correlated beliefs. And, you know, you rewind, you know, when we first started doing this, you know, as Greg said, you know, one of the first things we did is find every specialist we knew

and ask them like a million dumb questions about how they invest, trying to sort of discern what some of these signals are. And the amount of pessimism you heard from these folks of like, you know, like, you know, why would you guys look at biotech? This is just like a terrible sector. And you're like, well, you've devoted your entire career. to it. Like, you know, they're just like, well, oh, and then we'd be like, wow, you know, maybe it'll be market neutral. Like, oh, thank God, right? Who would want long biotech beta? And you're like,

the time of the specialist, it's like oil in 2015 or 16, right? Like the, you know, people just got annihilated. And that's, you know, always something that perks my interest in the sector. So you guys go to kind of, you know, the bigger, the spend, the better. And I definitely get your adjusting for more Kevin, anything. But that does seem like it would push you more towards an oncology trier or an Alzheimer's trial is going to cost a lot less than a skin trial or an exoma trial or something. Did you, I guess another way of asking, I meant to ask this with the specialist as well, do you find that the signal works better in certain kind of subsectors? You know, again, oncology, much bigger spend, much bigger target market versus some other stuff,

or am I just imagining too much there? Like it kind of all comes out in a wash in the quant data. It kind of comes out in the wash, but, you know, when you talk about research, pipeline. That's the kind of thing that we know that in other, other across, I mean, when you're investing across sectors, using sector level value can work. So it can add something, but it's it's additive, right? It's not, it's not a replacement. So the answer is, yeah, we'll probably look into that at some point. We're constantly, constantly researching this So I'm interested because, again, when I first read the paper, my first thought was if they're going to spend, oh, boy, they're going to be in a lot of Alzheimer's and oncology drugs. And maybe what they found is the past 10 years oncology and Alzheimer's have been great. But, you know, going forward, it's a very tough area. And by the model, we also do diversify. You know, so remember that even let's say the model said just, oh, it thinks that Alzheimer's is the only thing. Well, at the one of one of your steps at the end is that and when you're building a risk model, it won't put. 100% of the portfolio in Alzheimer's.

So even if your value metric is wrong, right, because it's way too high in Alzheimer's, when you actually go create a diversified portfolio, you'll even that out. So you'll take the best of the Alzheimer's, right? And then you'll take the best of everything else. You know, just judging by the return than Alzheimer's, I don't know if there's a best of a publicly traded Alzheimer's.

Speaking of sectors, you mentioned it, you guys also have something really interesting on momentum, where you talk about, I think you guys go like trial, by trial and start classifying firms by what the trial is and then look about momentum, look at the momentum, kind of cross industry among companies that are running the same trials. I want to make sure I understand that piece correctly. So I do have some questions that, but I'd love to just ask you, you know, what was the methodology? What are you doing?

Am I kind of thinking about it correctly there? Yeah, you are. So we, this is the classification problem, right? You need to classify these biotechs. You need to group them. So this value problem is exactly it, right? And we actually, now you're talking, I realize we actually did do the value problem that you're talking about and test it. We just didn't put it in the model.

What you want to do is you want to be able to classify these things and group them together, right, so that you can look at how they move together from a risk perspective. And then you can actually even say, I want to build a metric within a certain class of, of companies. To be able to do that in biotech is you can do it very simply. You can just categorize them and call them something and say, this looks like that, this looks like this, this looks like that, right? But you get a time series problem, right? Is that biotex can change over time. They go through phases. There are different phases of different times. They might even change what they're targeting. Their lead trial might change. The one that is actually furthest along might change. And so what you need to do, to do that really well, is to go back

can create a time series of descriptors of the company, right? And the best way in biotech to create a descriptor of the company is to aggregate the clinical trials in which they're involved, right, and aggregate them up to the company level. So you can sort of say, oh, an average, they're doing these things. And those clinical trials are great because there's so much data, so much detail, so much classification data on what the companies are doing. So the whole purpose of that is to build time series classifications of companies so that we can then categorize them and run that and so. You know, one quick, so you mentioned the number.

Like, I do know of companies that will have, I'll just go back to Alzheimer's oncology. They'll have one phase three drug, late stage in Alzheimer's, and then they'll have 10 preclinical or phase one on oncology drug. That would, the way you described it, it seems like that company would come out as an oncology company, but anyone who knew this, who knew it would say, oh, no, that's an Alzheimer's company with a little bit of oncology,

you know, call options sprinkled on top of it. How do you guys adjust for that? Do you give bigger weightings to phase three trials versus phase one? Is there any, can you adjust for the amount of spend on the trials? How do you think about that? That's a really hard problem. And just this is a hard problem for a fundamental analyst. This is why Quantz didn't want to attack biotech, right? Because all of these are really hard problems, but they're really interesting. So we discussed this in the paper. And what we did is there's a cool way that you can sort of classify. companies, you can say, who are they similar to? Right?

So, and let's use this as a great example, right? You would say that this company is similar to some, I can't remember what your phase three drug was in, but whatever, in its phase three, and it's similar to a lot in its phase one drug. And then you have to sort of come up with some sort of algorithm and say, how similar is it to each one of the companies in the universe, right? And it doesn't matter what that number is. It could be, you know, one through 376.

It doesn't be, okay, it's a scale, right? I'm sort of like one similar to that one. I'm like 54 similar to that one. I'm like 98 similar to the other one based on all the phase, the indication, where my headquarters are. I don't care. Right. So, and then you can take an average across that whole thing and say, well, I'm most similar

to that post. Right. But actually, what you're not saying is I'm most similar to that, but you're saying, I'm on average similar to these. And my average, the average company that looks like me is this, right? And then I can go out and say, how are those companies acting? and how is my average acting? How is my index acting, right?

What's my value relative to that index? What's my momentum relative to that index? And then that's the way you can actually classify a company without really knowing, okay, that basically, you know, which one is more important? So we don't know. We don't have that analyst sort of in, but we have a really sophisticated way to say, hey, we can look at who's similar. You know, as you're saying that, that is a hard problem and a hard thing to put together.

It strikes me as AI has to be both the most terrifying and the best thing for you guys because AI, you know, feels like it's going to replace humans that are doing quant, but at the same time, what you described, you're never doing that without AI running a lot of a lot of things in parallel with each other. Just as an aside, I taught myself to code from books, and I'm just really upset that everybody has had a code now. And if I'm coding. I've been thinking about that a lot.

Let me stick on momentum. We can have the discussion on AI and vibe coding if you want to, but let me stick on momentum. If I'm remembering the paper writing, looking at my notes, right, you guys find out. positive momentum inside of categories. You know, if I'm just going to say blunt, like if Alzheimer's drugs are doing well, all the Alzheimer's companies start doing well as kind of my understanding. You can tell me if I'm understanding that why, but I'd love to say that. Why do you think momentum works?

Because to me, like when I look at these companies, what I often see is, hey, I'm going after bladder cancer, you're going after bladder cancer. You announce good bladder cancer results. My stock goes down because yes, maybe there's good signal that are, targets work, but if your drug gets approved, I'm not going to have monopoly. At best, it's going to be a duopoly at worst. It's just got all sorts of negative implications. So I'm surprised that all of them kind of work together. So I'd love to just ask like kind of why and what you're seeing in that data. Yeah, I think this is, it's interesting because this is actually a sort of market-wide phenomenon

where we're sort of similarity momentum or pure momentum is works. And I think that yes, and that sort of discrete case of a clinical trial gets approved. for a competitor that's bad for you. But the vast majority of the time, like, if you're competitors or like, if you're, think of like a car company, right? Like if Toyota's going up and you're Ford, you're probably going up, right? Like, it's, these things are influenced by these big macro trends. And the thing that you're trying to get at is sort of the as precise as possible. Like, what is really the exposure that's driving this?

Like, ideally you want to capture almost like the thematic thinking, right? where, oh, people are really jazzed about obesity drugs right now, or they're really off obesity drugs. And so I really want to capture that in my investing. It's like, as long as people like obesity drugs, I want to own obesity. And as long as they don't like it, I don't want to own it. And basically incorporate that sort of thematic judgment into your investing. And I think that's actually quite true of the way people trade, right?

People don't just, people get excited about certain topics or themes and the market trades things that way, whether it's for regions or sectors or industries. or peer firms, and often there's some external driver for why that's true. You know, maybe MRNA is having a terrible time and not getting anything approved, and so all our MRNA drugs sell off or whatever it may be. No, I think you're definitely right. I was probably too narrow of an example because you mentioned obesity drugs. Like I know when Pfizer gets in a bidding word for MSERA, every other obesity drug player goes

up because they say, hey, whoever loses that bittanymore, they've already shown they want to buy, so they'll probably come by it. And I know, like, you know, I use bladder cancer. Bladder cancer drug works. A lot of times all the bladder cancer drugs work because they say, hey, at least the target has been proved. At least the method of acting has been proven. So a lot of them work.

Okay. So I was going to say one final thought of that. You know, I think when I talk to people, you've asked so many questions that we've been asked before and really good questions. I was hoping for some unique questions. But, you know, I talk to people in biotech, they're so event folks, right? You know, because biotech is events. It is event.

It is events. It is event. And there's a lot of movement in the stocks in biotech that are not event-specific. In fact, most of the movements in biotech are not event-specific, right? And so when you actually do the research in the quantum research, you find that it's not the, you know, you're not trying to target the events or predict the events. You're trying to understand the core underlying drivers, right? And how they drive the stock prices over time and how they move together.

And so it's a very non-fundamental way. And once you build the factors fundamentally, but there's a very non-fundamental way to look at it. And it's extremely powerful. Yeah, I think, you know, there's a famous paper. The title is something like, what drives the value factor, or surprising news about the value factor or something like that.

And the finding is just that when an event happens, the event's impact on a stock is variable, dependent on the valuation coming in. Like, if you're a really expensive stock, news tends to have a negative impact on your share price. And when you're a really cheap stock, news tends to have a positive impact because essentially what happens is the people think that the world is much more predictable than it is. They price in, they get very optimistic or very pessimistic at the extremes.

And then the news cycle just sort of, you know, brings things back to the mean in some sense, right? Like it's just random dispersal. And so if you just say, hey, some of these events are just sort of random. I don't know, 30% of trials fail or whatever. I mean, that's not the right number. But I'm not going to be that good at knowing whether it's 25 or 35. All I should know is that if people think it's our pricing, that it's a 90% chance,

that's probably not so smart. And if it's 10% chance, well, you know, maybe we should be long. And I think that that's sort of the idea of quant. It's just sort of take the sort of, it pushes you to this base rate driven, you know, let's assume the market sort of is normal. Let's assume that spend has a return. Let's assume that all these smart people at study biotech know something about what they're doing. And let's assume that events are going to unfold unpredictably for everybody. And so what really matters is are you positioned in a way so that the events end up playing in your favor? Yeah. There's no space like proving that

like biotech. Like I know companies that have announced a drug fails and the stock goes up 100 percent and you asked why and say, well, because people knew this drug was shitty and they were worried that they were going to get like just enough to continue spending and burn all their cash on it. Or, hey, this company announced that the drug works. Why is the stock down 80%? Well, it was priced like it was going to be the best in class, literal cure for cancer. And it came out and it looks like a Me Too cancer drug. So there's no place that like that.

I will leave my faith, I told Greg before, my favorite line in the paper was, can a quantitative approach in a sector with idiosyncratic successes that are not reflecting the finance until years after the value is known work. That was my favorite line of the paper. I think you guys have done a great job explaining all the paper and why it probably does work on this podcast. So unless you guys want to say anything else or have any last thoughts, I'm happy to wrap it up here.

No, this is great. Well, thank you for having us on, Andrew. We're really excited about this. It's very cool. I mean, I think it's biotech is such a weird sector. We did so much work, like creating new metrics, figuring out a new way to trade it. And we know, we're excited to see how it develops and to learn more about the sector and do more of this research.

I was just blown away by the paper. I thought it was so awesome, so creative to find so many unique signals. And again, some of the signals were things that you talk to sector specialists and they'd be like, this is what my gut says. But so many unique things. And I just really enjoyed it. So thank you guys. Oh, last question. Dan, I'll put you on the spot.

You mentioned I love bombed out sectors. That's kind of what Verdad's focus on. You know, you had Japan. What's the most bombed out sector? as you and I speak, February 18th. You know, Anna, it's a little bit frustrating for me right now because, you know, they, you know, Howard Mark says you want to be, you want to be contrarian and right, right? Like have a non-consensus view and be right.

And right now, I think, you know, we've been sitting around and saying, well, what, what if her dad's core argument's been the last few years? Well, one, we've said private equity is in a bubble. Stay the F out of it, like first and foremost. Blue out this morning would have something to say about that. Yeah, look at the front page of the newspapers. Now people agree with us. Like, I don't know.

I'm going to start, like, now, like, I don't know, like me saying, like, private equity sucks. And, like, I'm not, and nobody, like, yells at me like, you're crazy anymore because everybody's agrees with me now, which is sort of nice. KKR has bought $25 million of stock this morning, though. So you guys might have to throw them into the insider purchase screen. Yeah.

Well, maybe it's still good for KKR. But I feel like that was one of our non-consensus bets that's looking pretty smart. And then we've been saying, hey, Japan's a great place to invest. And all of a sudden, you know, the last few years, Japan's been probably the best performing market out there. We're having trouble kind of refresh our basket of new ideas. It's sort of, you know, for a value investor, you know, you start to get worried when you have two or three good years in a row.

You say, oh, gosh, maybe it's overvalued now. So we're refreshing. Maybe we'll start becoming a huge, Greg and I will just start becoming huge bulls on private credit BDCs or something. Yeah, they are traded for big discounts to books, so maybe. that is the best. Hey, can I ask one last question? I'm sorry to prolong it. On insider purchases, the KKR one brought it to mind, right? 25 million is a lot of money. I don't know if it's that much money to them. So you can put it to everything or buy a set. Do you guys adjust for

size of insider purchases and maybe size of insider purchase versus kind of like CEO COCOF when y'all do the adjustments? I mean, in general, when we make metrics, we'll do a count to end of volume. Right. And we'll average them or do some, look at both of those. In the insider data, we mostly just relied on accounts, right? Because we wanted the intention, not, and who knows, I don't want to credit somebody more because it just happened to have more money, right? So we just used counts in that. Again, this might be quant versus storage, but like service now.

The CEO says, hey, I'm canceling my 10B5 and I'm going to buy $3 million of stock over the next year through a 10B5. You're like, hey, that's a nice signal. But you get paid $40 million per year and you've sold $100 million of stock over the past three years, like, your stock's down 60%. Maybe you could, like, that feels like very much like, hey, guys, I'm here with you, you know? So, I don't know.

Well, the question is, is anybody else following? Right. All the execs canceled, but yeah. Yeah. And it was that the CEO? Because, you know, CEO was fine. It was the CEO. All the other execs canceled their 10B5s, which I think is also interesting.

But that's just the one that came to mind where I was like, maybe we could like add a zero to that or something. Guys, it was a great paper. There's going to be a link in the show notes. I really appreciate you coming on. And hopefully have you guys again for the next interesting one. Thanks, Andrew. This was a blast. A quick disclaimer.

Nothing on this podcast should be considered an investment advice. Guests or the hosts may have positions in any of the stocks mentioned during this podcast. Please do your own work and consult a financial advisor. Thanks.