Biotech Quant: Verdad Capital outlines a quantitative framework for biotech, with sector-specialist ownership as a core signal where consensus specialist ownership strongly correlates with better returns.
Insider Signals: Insider buying—especially from CFOs and non-CEO executives—shows durable predictive power over months, while CEO purchases are less informative in biotech due to routine selling behavior.
Shorting Approach: Biotech is fertile but dangerous for shorts; a diversified, risk-managed short book using signals like short interest, borrow cost, value, and momentum dampens volatility rather than chasing event outcomes.
Redefining Value: Traditional profit-based value fails in pre-revenue biotech, so they anchor value to cumulative spend (cash burn) relative to market cap, which outperforms other signals and helps drive rebalancing.
Momentum by Indication: They classify companies via clinical trial data to capture cohort momentum (e.g., obesity, mRNA), reflecting how themes and peer performance propagate across similar programs.
Market Structure: Biotech is a large slice of small caps and the least correlated sector, creating uncorrelated return potential when combined with disciplined quant factors and frequent rebalancing.
Ownership Nuances: Discussion includes specialist funds, pipes, warrants, and potential strategic stakes by big pharma as ongoing data enhancements to refine ownership-quality signals.
Examples Referenced: Illustrative mentions include Pfizer, Johnson & Johnson, AbbVie, ARK Genomic Revolution ETF, and XBI, mainly to explain strategic stakes, M&A dynamics, and sector exposure.
Transcript
You're about to listen to the yet another value podcast with your hosts me Andrew Walker. Today I have the team from Verdad Capital on uh 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 I found it fascinating. You're going to hear how excited I am. I'm like a kid in a 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 I found the paper fascinating. I think you're going to find the conversation fascinating. I learned a lot. Uh, 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 TRDA. Look, I've been me, you've heard me talking about TRDA for months on this podcast. There's a reason. It is a really, really good fit for you. If you like this podcast, TRDA is a 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 byiders 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 be 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 SAS apocalypse and TRDA 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 CIO who said there is no chance in hell that we will get off of this product. So I I just think TRDA 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 trtrada.com. That's tir trda t r a ta.com and go check it out. All right. Hello and welcome to the yet another value podcast. I'm your host Andrew Walker. And with me today, I'm excited to have from Verdad Capital, Dan Rasmmanson and Greg Obachen. Guys, how's it going? >> Good. Thanks for having us on, Andrew. >> Thank you guys so much for coming on. I, as I told you before, we're here to talk about you released a biotech paper, investing in biotech. Uh, I'll include a link to the show notes. I I as soon as I read it, I was like, I've got to have these guys on. I I was just so titilated by it. But, uh, we'll talk start talking there in one second. Just before we get started, same disclaimer I started every podcast with. Uh, 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 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 either 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 uh fascinating I think especially as a value investor because it's a huge percentage of the small cap universe um but totally illeible to us because uh 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. Um, >> I'm sure you guys know uh Joel Greenblat, the little book that beats the market. You know, he's got you rank things on EV 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 I I just love that insight. >> Yeah. Yeah, and I think you know as a 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. Uh it's really weird. Uh 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. Uh and uh no one else is really doing it in a systematic way. Uh and so we need to go figure it out. Uh, and so I said, "Greg, uh, you know, bonds are boring. Uh, you want to stop doing bonds for a year?" Not stop doing bonds because never Greg can't stop doing his day job, but, uh, you know, do you want to do a one-yearong project and figure out biotech since biotech stocks are just like bonds, I guess. >> Very, there's a lot of cash in both of them. Greg, did you want to add [laughter] anything to that? >> No, I mean, biotech is it is strange because you can't really use the financial statements. And I I will 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 in biotech. But uh 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 um in in the in the realm of biotech. We started to see results and we started to see that it actually worked. Um and by the end we really got some terrific results and does I think we're we're firmly in the camp that a quantitative approach uh works in in biotech and we'd be happy to dive into uh 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 I I was really interested in 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 biotech, 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 specialists 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 that 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 freaky. But how did you guys come up with that? And 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 what you know we learned about that from talking to sector specialists right so you know one of the what one of the nice things about Quandas 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 people said hey we follow what other people own and then we said 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 it was just a thesis we went out and tested um we were surprised at how well it worked Um, 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." Um, and that that helps a lot. And so there there are very sus 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 we we want to see more than one specialist in there. We want to see several specialists if we can. Um, that that really helps. Then you need to do you need to think about things like well if the company's 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 all funds that own it right so what you actually care about is um the uh the vote of confidence from specialists relative to how many other people are invested in it and what you find which is really fun is if you actually look companies that are owned by a lot of funds but zero specialists, they do terribly. Um, and so the specialists are really just a great a great guide uh uh to the industry and it really ends up being a quality metric. It ends up being an initial screen saying, "Hey, look here. Um, this is going to be a good place to start." >> Are there any other sectors and I want to dive into the specialists in general, but are there any other sectors and I know you guys do I've liked your stuff on Japan. And 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, >> yeah, >> I think it seems promising actually. Um, but biotech would be the biggest outlier. >> Makes sense. Uh, let's stick on the specialist conversation for a second. How do you guys define specialist? >> So, you can define them in a lot of different ways. Um and we might change how we define specialist because we always [clears throat] we always go and improve our process but biotech always evolving. [laughter] >> Yeah. Uh but but but for right now it's pretty simple. We just say do you is more than 50% of your portfolio in biotech. >> Okay. >> Um and uh what that allows us to do is to build a robust set of comp of funds to look at going back in time and as they they'll automatically drop in and out of the universe. Um >> yeah. >> Oh, please go ahead. >> Yeah. So, and what we've seen is that actually the number of specialist funds has actually increased um over time pretty steadily. Uh and right now in our data set, there's about 70 specialist funds um that we're looking at. >> And you guys do not wait like you know this specialist fund has done a,000% over the past 10 years. These guys have done you know negative 10% alpha over the past 10 years. It's just if they're a specialist fund, they are in there and they they show they show up in the signal. >> Yep. Correct. Um, >> one last question. There's some there's some level of like there's some level of quant, right, which is is you're just sort of um you have to make some sort of simplifying assumptions, right? Um, so our simplifying assumption is that if they've been able to raise a certain amount of money to go deploy in only in biotech like they must have some bio like the people working there have got to know something about biotech and and I'll come back to some other simplifying assumptions, but you're just sort of saying hey like you know for 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 uh, you know, know something about the sector in order to be uh 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 to say the sounds like y'all are taking the same approach?" One more. And I realize quant is about as you said 70 the big data cents but what about their you know uh 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 I'm really thinking about ARC in particular uh how would they get treated in your data set? >> They'd be one data point one vote >> that completely okay. I wasn't trying to hate I was just curious. All right. So you've guys discover that fund that company biotechs that are highly owned by specialists particularly when the specialists are concentrated uh kind of outperform over time. I'd love to ask is there any insight into what's driving that performance? Is it hey these 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 uh at better results? I can't say efficacious so I'm not going to say it. better results so the stocks pop higher. Is it there better at getting sold? Is it there's 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 it turns out I mean the answer is I don't have I don't have a direct answer for you, but I I do know that you know those are the companies that tend to get acquired. >> Yeah. >> Right. And and that is the measure of success largely in this in this industry, right? Is do you get acquired? Um what 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 there. 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. What the what the way we're making returns um from uh highly owned uh companies that are highly owned by specialists is that they behave better. their their their returns are higher relative to their volatility. They tend to have higher returns on average. Um and so they behave really well in a model when you're diversifying risk and trying to target those companies that are going to increase your return and take down your volatility. That's why they're good for us, you know, and and so we and we think about it on a shorter time horizon than four years, right? So we are constantly rebalancing our portfolio to take advantage of the relative relative valuations and risks among companies. So the the the specialist metric is just one metric that helps us to do that. Um so we don't really necessarily know exactly, you know, on a on a week-toeek basis why the specialists do better. Um but they they do. >> One more on this. And I 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, you know, 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 G's and 13 13Ds, 13 GS and stuff in quarter, but most of your data is probably coming from the 13F, right? So, is this strategy is it a lot different than everything else you've run or how do you think about it would seems to me like, you know, we're talking February, is it February 19th, 13F's just got released. Does this result in like a lot of turnover? because you know the 13F gets filed and you're like oh four specialists dropped out of this thing or three specialists came into this one. Is it a little bit different or is there ways to kind of smooth that over? >> Well, I think the thing about trading small cap, right, is that you know if you if you're a billion dollar hedge fund and you're putting $50 million into a biotech, you're completely illquid. I mean, you're not getting in or out of that thing. I mean, you're stuck, right? Uh and uh so uh our our view is that actually the 13Fs don't change all that much. Like it's not actually that fast moving 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. So, >> you know, you're definitely right. RA, RTW, 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 pot trial failed and they're just like washed my hands and done of it and then everybody's out and there's not just not a lot of salvage value. Anyway, there are some unique things about specialist funds and I'm particularly thinking about pipes. Uh, you know, specialist funds can raise pipes and they can do penny warrants. So, a lot of times I know from trolling through these uh beneficials, I'll be like, "Oh, there's no specialist fund in here." And then I'll look and be like, "Oh, no. What two of the specialist funds own 10% of this company through penny 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 $und00 million at $10 per share and all the funds that are participating are getting uh, 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 uh 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 structured, but what about the penny war penny stock ownership and kind of those complications when it comes to ownership? >> Yeah. So, um the answer is, you know, we're working on it. uh one one of the things we do uh we we publish the paper but we have a a research pipeline that we keep and this is sort of interesting about how we how we work in general is that that was a question that came up the minute we publish 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 um we're well aware of it and it's something that we're working on and the answer the answer is I don't have an answer yet but we will um so so yeah so right now we're we're working on >> judging from the and people know that I'm really interested Busted biotech, but I tweeted out I was having you guys on two hours ago and judging from the amount of inbounds I got, people were are very interested in this paper. Maybe it's just my circle, but one more question on sector specialists. Uh, a lot of the big pharma companies will invest in the in smaller companies and you know Fizer is one that I think about. Fiser actually has to file a 13F. There's like 15 different companies where they partner on the drugs. They'll buy five to 10% of the 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 we screen to make sure they can get screened out if they only own you know a few uh biotech. One of the things we do is screen out companies that are very that you know sector specialists that are small and sometimes those companies will show up as small even though they're not. Um so they they do show up and we and we do capture that. Um that is also one of the things that we're we're looking into whether whether having uh strategic um in the ownership is helpful but you can imagine that that data is a little messier and harder to get and harder to validate right so as a first pass way easier to get the specialist data um and as we go on that is something that we're digging into >> makes total sense and then you know another Fiser 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 the famous pharmaceutics example which you know J&J if I remember correctly was JVD on the key asset there. They did not own equity and then ABV buys them for a fortune and I bet J&J wish they had owned equity when Abby 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 I don't want to leave on the sector specialist in case there's any burning insights or any questions I should ask or anything on the sector specialist we should discuss. Yeah, I think the only thing that's that's sort of fun to note and uh you know when when we think about launching uh this strategy or or managing it, you know, one of the things people say, well, why wouldn't I go, you know, if the specialists are doing all the work, you know, 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 our thoughts in 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 the consensus is the signal. Not like you don't want us to have independent 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 you know famously every fund of funds or best funds ideas fund that launches fails and you guys might have found the one sector and the one strategy where it really starts to work. Uh 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 quant is kind of the nirvana the shorts underperform the longs that's how you really capture the alpha and everything but biotech is an interesting one to think about on the shorting side because you have these huge catalyst that can make for it can make it difficult to rebalance and I I want to just ask like I want to ask a lot of questions on the shorting 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 and the stock go up 10 20x. 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 uh it's the sector where the largest percentage of individual stocks end up losing money over time. Um 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. And you're like, "Yeah." So, a lot of them should fail, right? Cuz they're science projects. Uh, and very, you know, some few of them are going to work and be lottery ticket positives, but, uh, the majority of them, maybe 60 or 70% of them are going to be money losers for you. Uh, and so I think shorting has to be an important component of a biotech, uh, strategy. Uh, and then there's the question of like, okay, but right, this is a a sector where um, 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. Uh and so I think our 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. Uh and so you know why are they doing that? Well they've gotten burned on the shorts is the truth. um because um they apply the same approach to the shorting as they apply to the long where they do deep research and they say, "Well, I have high conviction that X is a fraud or something or B is never going to work." Um 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 6 months later, but it's just painful and they have so many scars from it, they just abandon it altogether. Uh 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 uh how big is the market cap, what's the liquidity, what's the current short interest, what's the borrow cost, right? 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 they 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 rebalancing frequently and being pretty diversified. Um, 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 you're on mute, but I think Greg's saying no, so I'm going to assume that's a no. Uh, so the first I so I jump straight to shorting, right? Using this on the long 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 uh percentage, the returns on them is hugely negative and basically every other quartile I 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 the science is so bad even journalists can figure it out because it kind of seems strange just that it could take up that high you know. >> Yeah. Yeah. Well, I think you got to remember that, you know, markets are are are very efficient and and this is this this this this short interest uh signal works across every sector. Um the stuff with really high borrow cost and really high short interest just does terribly. Um and I think you can think about that as just there's some stuff that sort of everyone knows is bad or dumb or fraudulent, right? Like it's like a you know they're going to cure cancer and they're out of run out of a strip mall in Miami and you're like I don't know like probably not. Um >> you you joke, but if you if [laughter] you trade cancer for Alzheimer's, I have seen that in the past year. >> There you go. You know, there there are these things that exist and it's sort of obvious to everybody, but the problem is the manager is very promotional and makes the stock pop every 3 months with some crazy news item that is totally fraudulent. um uh but um uh but by and large that that signal works across all sectors and it's because markets are efficient and it also creates the challenge where um the best things to short also have the highest borrow cost and so you're you know 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. Um, so it might say, "Oh, gee, it's better to be short 50 things we're kind of negative about about that have low borrow costs rather than 10 things that we're really negative about that have really high borrow costs um that might be subject to short squeeze if the sector pops the shorts. So 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% 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 biotechs 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 and that type of thing. Is it more slow bleed or just fast drops in the short side? Yeah, it's it's more the more the the slow bleed and and and to think about and to think about shorting um uh in general and what it does for a portfolio is it really dampens the volatility of the portfolio. It limits your losses when that when the market goes down so that you can reinvest profitably. I mean in quant speak right it's it's taking care of the volatility drag um and so it's it's 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 uh you can actually lose a little bit of money on your shorts over time the entire time and be far better off for having shorted you don't have to make money on your shorts for it to be a hugely value addition to the portfolio um when you're running a really disciplined uh discipline 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 were like, "Hey, I've lost 5% per year on tech stocks for the past 20 years," you'd be like, "You are the greatest short seller of all time, put you in all the portfolios. My god, could we lever you up against the QQQ?" Uh yeah, just last thought on I mean I thought it was so interesting because I've just got this one from my head and again I know as somebody who looks at the individual ones I I probably am focus too much on the individual ones but there was this company SB and I've seen several other ones are 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 them 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 X 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 on a short is oh my god, even if you started small, that is uh that is lifealtering. 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 you get hit and it's gone. So I I don't know anything on that or should we go to the next factor we're going to talk about? >> Highly diversified. [laughter] >> Uh 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 in a lot of them I look at is so poor just because the company insiders get lots of stock option and they're just kind of incentivized. Invest in the R&D. Invest in the R&D. Doesn't matter if it's a terrible EV because if it pays off you you get a fortune and if it doesn't pay off you you get zeroed anyway. You guys just mentioned, hey, the returns here are fantastic for company insider. So, I'd love to just talk about that finding and kind of what you see there. >> Yeah, this was this is really fun um to go and and look into this and try to it's hard it's really hard to look at company insider transactions and and um get that data clean and make it work. Um, very first thing I'd say about it is that, you know, um, actually in 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? If that that is a that is a standard thing. You're issued stock, you sell it. What's really interesting is the buys, right? And um when people generally when you do a non-rine buy um you only buy for one reason and you buy because you think your stock is going to do well. Now every now and it turned out that the CEO always buys whether or not the stock does well right so they're not particularly a good signal. Um but the the the uh the management team the other the rest of the management team uh especially the CFO I mean the CFOs are pretty bearish people. So when they start buying it's a it's a pretty decent signal. Um when combined with the other signals for um for who's who's the reason that you want your bond guy working on biotech by the way it's the [laughter] same logic. >> So 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 seauite. And what about boards of directors? >> Um, you know, they they actually are mildly uh predictive. Um, not as is the seauite. Um, and so yeah, so we we we count executives x the CEO. >> Okay. The other thing I think is interesting on the insider buys is these guys I I 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 two 300% 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 up 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 um looking at how rare the signal was relative to history and there's ways to do that. There's great papers on that. Um what I'd say about what we found in the in the insider buying signal is that much like um the in the specialist it was it's actually a relatively long signal. It had power for you know 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. If you think about it right if people were bullish on what they're doing they might they're going to know a long time before anybody else. And it might be for reasons that are independent of earnings. And and you go and read, and this is actually sort of an aside, 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. Right? This is their livelihood. 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. So >> I could think of a few management teams who might but in general I would agree with you. You know the other >> interesting but there's also a shark factor where you got to try to find those people but that's just too far you know that's too too rare to to play in 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 they're 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 2 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's 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, you know, if you're the CFO, you can say, "Hey, the science just didn't work. It wasn't on me like running bad things." But it I I do think it's kind of interesting versus a pool manufacturer, right? They buy and the economy takes down. 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. >> Uh, you do something interesting when you talk I I mean, you guys run a value shop, right? I I 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, IBIDA, 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 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 I can ask talk through some >> Yeah, it's uh 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 SGNA, whether it's called, I don't know, printer costs, doesn't matter. Um it's just how much money you're spending. And we don't try to bucket it in this metric. Um 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 um you know it's 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 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 a constant ROI for all biotech spending like you should value the ones that have spent more more um even though on a traditional value metric obviously the companies that lost more money are less [clears throat] valuable and worse it's just but it's you're sort of flipping that on its head and saying no these are science projects and what what what they spent on that matters and also the fact that somebody gave them the money to spend on that um because those people are going to want to get some return on their investment. Um, and so maybe even if that uh trial that they spent all that money on fails, you know, the people behind it are going to try to figure out a way to repurpose the research or something to try to make that uh uh make themselves whole. >> Yeah. And Andrew, sorry. 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. Uh, well, guess what? When you run the data and you figure out what's driving you return, the value metric works better than the specialist metric because as you need a way to measure how value is changing over 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 um 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&D that's getting spent is getting spent in a at at worst EV neutral basis and hopefully EV positive basis, but that that's kind of the way to think about it. >> That's the denominator, right? That's the um 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 company that spent two billion. >> Yeah. So Yep. So 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 and you need an anchor to compare to the market cap. >> No, it's just interesting because look, less today than I I know you guys started writing this and started doing this probably early last year when you know 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 they 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 push back 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 are they spending so much that they're getting neg worse than negative 50% ROI on that investment? because that's what it's calling for. But I I 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 on >> No, that's exactly right. Um and I think the um you know, part of that I think you you know, you you got interested in the sector around the same time we did. You know, 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. 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. Um, 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 like, well, oh, and then we'd be like, oh, you maybe it'll be market neutral. Like, oh, thank God, right? who would want long biotech beta and you're like the time the specialists it's like oil in 2015 or 16 right like the you know people just got annihilated and um and that's a you know always something that perks uh my interest in the sector >> on so you guys go to kind of you know the bigger the spend the better and I I definitely get you're adjusting for more Kevin thing but that does seem like it would push you more towards an oncology trial or an Alzheimer's trial is going to cost a lot less than a skin trial or an eczema trial or something. Did you I I guess another way of asking and 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, you know, that's that's the kind of thing that we know that in other other across I mean when you're investing across sectors um using sector level value can work. Um so it can add something but it just it's it's additive, right? It's it's not it's not a replacement. Um so the answer is yeah, we'll probably look into that at some point. Um we we're constantly constantly uh researching this stuff. I'm I 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 on college and Alzheimer's have been great but you know going forward it's a very tough uh area >> and rather 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 and it's it's way too high in Alzheimer's at when you actually go create a diversified portfolio, you'll even out, right? 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 on Alzheimer's, I don't know if there's a best of a publicly traded Alzheimer's, but uh speaking of sectors, you mentioned it. You guys also have something really interesting on momentum where you talk about I 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 uh companies that are running the same trials. I I I want to make sure I understand that piece uh 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. Um you are so we this is the classific we're just talking this the classification problem right you need to you need to classify these um the these biotechs you need to group them um so this value problem is exactly it right and we actually now that now you're talking I realize we actually did do the value problem that you're talking about and test it um we just didn't put it in the model um the um 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 And then you can actually even say I want to build a metric within a within a certain class of of of companies. Um 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 biotech can change over time. They go through phases. There are different phases at 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 and 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, on average they're doing these things. And those clinical trials are great because they have 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 metrics on 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 and Alzheimer's and then they'll have 10 pre-clinical or phase one 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 an al with uh a little bit of oncology, you know, call option sprinkled on top of it." How do you guys adjust for that? Do you give bigger waitings 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 as a hard problem for a fundamental analyst, >> um this is why no one This is why Quant 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 um there's a cool way that you can sort of classify companies. You can say who are they similar to, right? So um in and let's use this as a great example, right? You would say that this company is similar to I can't remember what your phase three drug was in but whatever in it 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 said 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, 1 through 376. Doesn't be don't care. It's a scale, right? And you say, 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 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 person. Right? Um, but actually what you're not saying is I'm most similar to that person. is saying I'm on average similar to these and my average the 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's my index act acting right what's my value relative to that index what's my momentum relative to that index and then that's that 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 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 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 uh a lot of things in parallel with each other. >> And just as an aside, I taught myself to code from books, and I'm just really upset that everybody knows how to code now. >> 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, right, in my looking at my notes, right? You guys find positive momentum inside of categories. You know, if I'm just just going to say blunt like if Alzheimer's drugs are doing well, all the Alzheimer's companies are start doing well is kind of my understanding. You can tell me if I'm understanding that why, but I'd love to say 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 our targets work. But, you know, if if your drug gets approved, I'm not going to have a monopoly. At best, it's going to be a duopoly. At worst, like it's just got all sorts of negative implications. So, I was 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 marketwide phenomenon where where sort of similarity momentum or pure momentum is a is works. Um and I think that um yes, in that sort of discrete case um of a clinical trial gets approved for a competitor that's bad for you. Um but the vast majority of the time like if your competitors are 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 it's these things are influenced by these big macro trends. Um, and the thing that you were trying to get at is is sort of the 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? Um where oh people are really uh 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. um and basically incorporate that sort of thematic judgment into your investing. Um 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 or peer firms. Um and often there's some external driver for why that's true. Um, you know, maybe mRNA uh 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 Fizer gets in a bidding word for Msera, every other obesity drug player goes up because they say, "Hey, whoever loses that bidding war, they've already shown they want to buy, so they'll probably come buy 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. >> Yeah. Andrew. Okay. So, I was gonna say one final thought on that. You know, I think when I when I talk to people, you've asked so many questions that we've been asked um before and really good question. >> I was hoping for some unique questions, Greg. >> No, no, no. But, you know, I talked to people in biotech, they're so event focused, right? You know, because biotech is events. It is event. It is events. It is event. Um 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 in the quant research, you find that 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. Um and so it's a very nonfunddamental way in once you uh you build the factors fundamentally but there's a very non-funddamental way to look at it. Um and it and it's extremely powerful. >> Yeah. I think you know there's a there's a a famous paper it's the title is something like what drives the value factor or surprising news about the value factor or something like that. And and the finding is just that um uh 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. Uh 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. Um, and so if you just say, hey, some of these events are just sort of random, like I don't know, 30% of trials fail or whatever. I mean, that's not the right number, but um, you know, I'm not going to be that good at knowing whether it's 25 or 35. Um, all I should know is that if people think it's our pricing that it's a 90% chance, um, that that's probably not so smart. And if it's 10% chance, well, you know, maybe we should be long it. Um, um, and I think that that's sort of the idea of quant. It's just sort of take take the sort of um, it 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 that study biotech know something about what they're doing. uh 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. But 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%. And you ask why 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. Um, I will leave my fa 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 finances until years after the value uh is known work? That was my favorite line in the paper. I think you guys have done a great job uh 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 I'm happy to wrap it up here. >> No, this is great. Well, thank you for having us on, Andrew. We're we're we're we're we're really excited about this. It's very cool. I mean, I think it's um biotech is such a weird sector. We did so much work like creating new metrics, figuring out a new way to trade it. And uh you know, we're excited to see how it develops and and to learn uh learn more about the sector and do more of this research. So, >> 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 uh I I just really enjoyed it. So, thank you guys. So, 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, you got What's the most bombed out sector as you and I speak? February 18th. You know, Andrew, it's it's a little bit frustrating for me right now because, you know, they uh they you know, Howard Mark says you want to be you want to be contrarian and right, right? Like have a non-conensus view and be right. Uh and uh and right now I think you know we've been sitting around and saying well what what if what is what if her dad's core arguments 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 >> this morning would uh 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 have 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 agrees with me now which bought 25 million of stock this morning though. So you guys might have to throw them into the insider purchase screen. >> Yeah. Well uh maybe it's good still good for KKR but um but I feel like that was one of our non-conensus 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. Uh we're, you know, we're we're having trouble kind [laughter] of refresh our basket of new ideas. It's sort of, uh, you know, for a value investor, uh, you know, you start to get worried when you have two or three good years in a row. Uh, you say, "Oh, gosh, maybe it's overvalued now." So, we're we're we're refreshing. Maybe we'll start becoming, uh, hu Greg Greg and I will just start becoming huge bulls on private credit BDC's or something. Yeah, they are trading for big discounts to [laughter] 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 bioche. Do you guys adjust for size of insider purchases and maybe size of insider purchase versus kind of like CO comp when y'all do the adjustments? I mean in general when we make metrics we'll do a count and a volume right so we and we'll average them or do you know some some look at both of those um in the in the insider um data we mostly just relied on counts right because we wanted the intention not and and who knows you know I I don't want to credit somebody more because they just happened to have more money right so we we just used counts in that 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 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 a hund00 million of stock over the past three years." Like, your stock's down 60%. Maybe you could like that. 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 canled, but Yeah. >> Yeah. And that's And was that the CEO? Because, you know, CEO was fine. >> It was the CEO. all the other execs canceled their 10v5s 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. Uh 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 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 adviser. Thanks.
Investing in Biotech with Verdad Capital
Summary
Transcript
You're about to listen to the yet another value podcast with your hosts me Andrew Walker. Today I have the team from Verdad Capital on uh 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 I found it fascinating. You're going to hear how excited I am. I'm like a kid in a 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 I found the paper fascinating. I think you're going to find the conversation fascinating. I learned a lot. Uh, 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 TRDA. Look, I've been me, you've heard me talking about TRDA for months on this podcast. There's a reason. It is a really, really good fit for you. If you like this podcast, TRDA is a 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 byiders 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 be 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 SAS apocalypse and TRDA 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 CIO who said there is no chance in hell that we will get off of this product. So I I just think TRDA 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 trtrada.com. That's tir trda t r a ta.com and go check it out. All right. Hello and welcome to the yet another value podcast. I'm your host Andrew Walker. And with me today, I'm excited to have from Verdad Capital, Dan Rasmmanson and Greg Obachen. Guys, how's it going? >> Good. Thanks for having us on, Andrew. >> Thank you guys so much for coming on. I, as I told you before, we're here to talk about you released a biotech paper, investing in biotech. Uh, I'll include a link to the show notes. I I as soon as I read it, I was like, I've got to have these guys on. I I was just so titilated by it. But, uh, we'll talk start talking there in one second. Just before we get started, same disclaimer I started every podcast with. Uh, 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 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 either 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 uh fascinating I think especially as a value investor because it's a huge percentage of the small cap universe um but totally illeible to us because uh 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. Um, >> I'm sure you guys know uh Joel Greenblat, the little book that beats the market. You know, he's got you rank things on EV 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 I I just love that insight. >> Yeah. Yeah, and I think you know as a 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. Uh it's really weird. Uh 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. Uh and uh no one else is really doing it in a systematic way. Uh and so we need to go figure it out. Uh, and so I said, "Greg, uh, you know, bonds are boring. Uh, you want to stop doing bonds for a year?" Not stop doing bonds because never Greg can't stop doing his day job, but, uh, you know, do you want to do a one-yearong project and figure out biotech since biotech stocks are just like bonds, I guess. >> Very, there's a lot of cash in both of them. Greg, did you want to add [laughter] anything to that? >> No, I mean, biotech is it is strange because you can't really use the financial statements. And I I will 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 in biotech. But uh 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 um in in the in the realm of biotech. We started to see results and we started to see that it actually worked. Um and by the end we really got some terrific results and does I think we're we're firmly in the camp that a quantitative approach uh works in in biotech and we'd be happy to dive into uh 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 I I was really interested in 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 biotech, 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 specialists 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 that 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 freaky. But how did you guys come up with that? And 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 what you know we learned about that from talking to sector specialists right so you know one of the what one of the nice things about Quandas 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 people said hey we follow what other people own and then we said 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 it was just a thesis we went out and tested um we were surprised at how well it worked Um, 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." Um, and that that helps a lot. And so there there are very sus 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 we we want to see more than one specialist in there. We want to see several specialists if we can. Um, that that really helps. Then you need to do you need to think about things like well if the company's 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 all funds that own it right so what you actually care about is um the uh the vote of confidence from specialists relative to how many other people are invested in it and what you find which is really fun is if you actually look companies that are owned by a lot of funds but zero specialists, they do terribly. Um, and so the specialists are really just a great a great guide uh uh to the industry and it really ends up being a quality metric. It ends up being an initial screen saying, "Hey, look here. Um, this is going to be a good place to start." >> Are there any other sectors and I want to dive into the specialists in general, but are there any other sectors and I know you guys do I've liked your stuff on Japan. And 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, >> yeah, >> I think it seems promising actually. Um, but biotech would be the biggest outlier. >> Makes sense. Uh, let's stick on the specialist conversation for a second. How do you guys define specialist? >> So, you can define them in a lot of different ways. Um and we might change how we define specialist because we always [clears throat] we always go and improve our process but biotech always evolving. [laughter] >> Yeah. Uh but but but for right now it's pretty simple. We just say do you is more than 50% of your portfolio in biotech. >> Okay. >> Um and uh what that allows us to do is to build a robust set of comp of funds to look at going back in time and as they they'll automatically drop in and out of the universe. Um >> yeah. >> Oh, please go ahead. >> Yeah. So, and what we've seen is that actually the number of specialist funds has actually increased um over time pretty steadily. Uh and right now in our data set, there's about 70 specialist funds um that we're looking at. >> And you guys do not wait like you know this specialist fund has done a,000% over the past 10 years. These guys have done you know negative 10% alpha over the past 10 years. It's just if they're a specialist fund, they are in there and they they show they show up in the signal. >> Yep. Correct. Um, >> one last question. There's some there's some level of like there's some level of quant, right, which is is you're just sort of um you have to make some sort of simplifying assumptions, right? Um, so our simplifying assumption is that if they've been able to raise a certain amount of money to go deploy in only in biotech like they must have some bio like the people working there have got to know something about biotech and and I'll come back to some other simplifying assumptions, but you're just sort of saying hey like you know for 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 uh, you know, know something about the sector in order to be uh 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 to say the sounds like y'all are taking the same approach?" One more. And I realize quant is about as you said 70 the big data cents but what about their you know uh 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 I'm really thinking about ARC in particular uh how would they get treated in your data set? >> They'd be one data point one vote >> that completely okay. I wasn't trying to hate I was just curious. All right. So you've guys discover that fund that company biotechs that are highly owned by specialists particularly when the specialists are concentrated uh kind of outperform over time. I'd love to ask is there any insight into what's driving that performance? Is it hey these 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 uh at better results? I can't say efficacious so I'm not going to say it. better results so the stocks pop higher. Is it there better at getting sold? Is it there's 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 it turns out I mean the answer is I don't have I don't have a direct answer for you, but I I do know that you know those are the companies that tend to get acquired. >> Yeah. >> Right. And and that is the measure of success largely in this in this industry, right? Is do you get acquired? Um what 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 there. 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. What the what the way we're making returns um from uh highly owned uh companies that are highly owned by specialists is that they behave better. their their their returns are higher relative to their volatility. They tend to have higher returns on average. Um and so they behave really well in a model when you're diversifying risk and trying to target those companies that are going to increase your return and take down your volatility. That's why they're good for us, you know, and and so we and we think about it on a shorter time horizon than four years, right? So we are constantly rebalancing our portfolio to take advantage of the relative relative valuations and risks among companies. So the the the specialist metric is just one metric that helps us to do that. Um so we don't really necessarily know exactly, you know, on a on a week-toeek basis why the specialists do better. Um but they they do. >> One more on this. And I 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, you know, 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 G's and 13 13Ds, 13 GS and stuff in quarter, but most of your data is probably coming from the 13F, right? So, is this strategy is it a lot different than everything else you've run or how do you think about it would seems to me like, you know, we're talking February, is it February 19th, 13F's just got released. Does this result in like a lot of turnover? because you know the 13F gets filed and you're like oh four specialists dropped out of this thing or three specialists came into this one. Is it a little bit different or is there ways to kind of smooth that over? >> Well, I think the thing about trading small cap, right, is that you know if you if you're a billion dollar hedge fund and you're putting $50 million into a biotech, you're completely illquid. I mean, you're not getting in or out of that thing. I mean, you're stuck, right? Uh and uh so uh our our view is that actually the 13Fs don't change all that much. Like it's not actually that fast moving 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. So, >> you know, you're definitely right. RA, RTW, 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 pot trial failed and they're just like washed my hands and done of it and then everybody's out and there's not just not a lot of salvage value. Anyway, there are some unique things about specialist funds and I'm particularly thinking about pipes. Uh, you know, specialist funds can raise pipes and they can do penny warrants. So, a lot of times I know from trolling through these uh beneficials, I'll be like, "Oh, there's no specialist fund in here." And then I'll look and be like, "Oh, no. What two of the specialist funds own 10% of this company through penny 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 $und00 million at $10 per share and all the funds that are participating are getting uh, 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 uh 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 structured, but what about the penny war penny stock ownership and kind of those complications when it comes to ownership? >> Yeah. So, um the answer is, you know, we're working on it. uh one one of the things we do uh we we publish the paper but we have a a research pipeline that we keep and this is sort of interesting about how we how we work in general is that that was a question that came up the minute we publish 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 um we're well aware of it and it's something that we're working on and the answer the answer is I don't have an answer yet but we will um so so yeah so right now we're we're working on >> judging from the and people know that I'm really interested Busted biotech, but I tweeted out I was having you guys on two hours ago and judging from the amount of inbounds I got, people were are very interested in this paper. Maybe it's just my circle, but one more question on sector specialists. Uh, a lot of the big pharma companies will invest in the in smaller companies and you know Fizer is one that I think about. Fiser actually has to file a 13F. There's like 15 different companies where they partner on the drugs. They'll buy five to 10% of the 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 we screen to make sure they can get screened out if they only own you know a few uh biotech. One of the things we do is screen out companies that are very that you know sector specialists that are small and sometimes those companies will show up as small even though they're not. Um so they they do show up and we and we do capture that. Um that is also one of the things that we're we're looking into whether whether having uh strategic um in the ownership is helpful but you can imagine that that data is a little messier and harder to get and harder to validate right so as a first pass way easier to get the specialist data um and as we go on that is something that we're digging into >> makes total sense and then you know another Fiser 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 the famous pharmaceutics example which you know J&J if I remember correctly was JVD on the key asset there. They did not own equity and then ABV buys them for a fortune and I bet J&J wish they had owned equity when Abby 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 I don't want to leave on the sector specialist in case there's any burning insights or any questions I should ask or anything on the sector specialist we should discuss. Yeah, I think the only thing that's that's sort of fun to note and uh you know when when we think about launching uh this strategy or or managing it, you know, one of the things people say, well, why wouldn't I go, you know, if the specialists are doing all the work, you know, 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 our thoughts in 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 the consensus is the signal. Not like you don't want us to have independent 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 you know famously every fund of funds or best funds ideas fund that launches fails and you guys might have found the one sector and the one strategy where it really starts to work. Uh 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 quant is kind of the nirvana the shorts underperform the longs that's how you really capture the alpha and everything but biotech is an interesting one to think about on the shorting side because you have these huge catalyst that can make for it can make it difficult to rebalance and I I want to just ask like I want to ask a lot of questions on the shorting 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 and the stock go up 10 20x. 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 uh it's the sector where the largest percentage of individual stocks end up losing money over time. Um 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. And you're like, "Yeah." So, a lot of them should fail, right? Cuz they're science projects. Uh, and very, you know, some few of them are going to work and be lottery ticket positives, but, uh, the majority of them, maybe 60 or 70% of them are going to be money losers for you. Uh, and so I think shorting has to be an important component of a biotech, uh, strategy. Uh, and then there's the question of like, okay, but right, this is a a sector where um, 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. Uh and so I think our 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. Uh and so you know why are they doing that? Well they've gotten burned on the shorts is the truth. um because um they apply the same approach to the shorting as they apply to the long where they do deep research and they say, "Well, I have high conviction that X is a fraud or something or B is never going to work." Um 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 6 months later, but it's just painful and they have so many scars from it, they just abandon it altogether. Uh 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 uh how big is the market cap, what's the liquidity, what's the current short interest, what's the borrow cost, right? 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 they 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 rebalancing frequently and being pretty diversified. Um, 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 you're on mute, but I think Greg's saying no, so I'm going to assume that's a no. Uh, so the first I so I jump straight to shorting, right? Using this on the long 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 uh percentage, the returns on them is hugely negative and basically every other quartile I 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 the science is so bad even journalists can figure it out because it kind of seems strange just that it could take up that high you know. >> Yeah. Yeah. Well, I think you got to remember that, you know, markets are are are very efficient and and this is this this this this short interest uh signal works across every sector. Um the stuff with really high borrow cost and really high short interest just does terribly. Um and I think you can think about that as just there's some stuff that sort of everyone knows is bad or dumb or fraudulent, right? Like it's like a you know they're going to cure cancer and they're out of run out of a strip mall in Miami and you're like I don't know like probably not. Um >> you you joke, but if you if [laughter] you trade cancer for Alzheimer's, I have seen that in the past year. >> There you go. You know, there there are these things that exist and it's sort of obvious to everybody, but the problem is the manager is very promotional and makes the stock pop every 3 months with some crazy news item that is totally fraudulent. um uh but um uh but by and large that that signal works across all sectors and it's because markets are efficient and it also creates the challenge where um the best things to short also have the highest borrow cost and so you're you know 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. Um, so it might say, "Oh, gee, it's better to be short 50 things we're kind of negative about about that have low borrow costs rather than 10 things that we're really negative about that have really high borrow costs um that might be subject to short squeeze if the sector pops the shorts. So 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% 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 biotechs 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 and that type of thing. Is it more slow bleed or just fast drops in the short side? Yeah, it's it's more the more the the slow bleed and and and to think about and to think about shorting um uh in general and what it does for a portfolio is it really dampens the volatility of the portfolio. It limits your losses when that when the market goes down so that you can reinvest profitably. I mean in quant speak right it's it's taking care of the volatility drag um and so it's it's 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 uh you can actually lose a little bit of money on your shorts over time the entire time and be far better off for having shorted you don't have to make money on your shorts for it to be a hugely value addition to the portfolio um when you're running a really disciplined uh discipline 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 were like, "Hey, I've lost 5% per year on tech stocks for the past 20 years," you'd be like, "You are the greatest short seller of all time, put you in all the portfolios. My god, could we lever you up against the QQQ?" Uh yeah, just last thought on I mean I thought it was so interesting because I've just got this one from my head and again I know as somebody who looks at the individual ones I I probably am focus too much on the individual ones but there was this company SB and I've seen several other ones are 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 them 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 X 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 on a short is oh my god, even if you started small, that is uh that is lifealtering. 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 you get hit and it's gone. So I I don't know anything on that or should we go to the next factor we're going to talk about? >> Highly diversified. [laughter] >> Uh 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 in a lot of them I look at is so poor just because the company insiders get lots of stock option and they're just kind of incentivized. Invest in the R&D. Invest in the R&D. Doesn't matter if it's a terrible EV because if it pays off you you get a fortune and if it doesn't pay off you you get zeroed anyway. You guys just mentioned, hey, the returns here are fantastic for company insider. So, I'd love to just talk about that finding and kind of what you see there. >> Yeah, this was this is really fun um to go and and look into this and try to it's hard it's really hard to look at company insider transactions and and um get that data clean and make it work. Um, very first thing I'd say about it is that, you know, um, actually in 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? If that that is a that is a standard thing. You're issued stock, you sell it. What's really interesting is the buys, right? And um when people generally when you do a non-rine buy um you only buy for one reason and you buy because you think your stock is going to do well. Now every now and it turned out that the CEO always buys whether or not the stock does well right so they're not particularly a good signal. Um but the the the uh the management team the other the rest of the management team uh especially the CFO I mean the CFOs are pretty bearish people. So when they start buying it's a it's a pretty decent signal. Um when combined with the other signals for um for who's who's the reason that you want your bond guy working on biotech by the way it's the [laughter] same logic. >> So 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 seauite. And what about boards of directors? >> Um, you know, they they actually are mildly uh predictive. Um, not as is the seauite. Um, and so yeah, so we we we count executives x the CEO. >> Okay. The other thing I think is interesting on the insider buys is these guys I I 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 two 300% 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 up 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 um looking at how rare the signal was relative to history and there's ways to do that. There's great papers on that. Um what I'd say about what we found in the in the insider buying signal is that much like um the in the specialist it was it's actually a relatively long signal. It had power for you know 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. If you think about it right if people were bullish on what they're doing they might they're going to know a long time before anybody else. And it might be for reasons that are independent of earnings. And and you go and read, and this is actually sort of an aside, 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. Right? This is their livelihood. 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. So >> I could think of a few management teams who might but in general I would agree with you. You know the other >> interesting but there's also a shark factor where you got to try to find those people but that's just too far you know that's too too rare to to play in 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 they're 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 2 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's 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, you know, if you're the CFO, you can say, "Hey, the science just didn't work. It wasn't on me like running bad things." But it I I do think it's kind of interesting versus a pool manufacturer, right? They buy and the economy takes down. 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. >> Uh, you do something interesting when you talk I I mean, you guys run a value shop, right? I I 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, IBIDA, 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 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 I can ask talk through some >> Yeah, it's uh 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 SGNA, whether it's called, I don't know, printer costs, doesn't matter. Um it's just how much money you're spending. And we don't try to bucket it in this metric. Um 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 um you know it's 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 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 a constant ROI for all biotech spending like you should value the ones that have spent more more um even though on a traditional value metric obviously the companies that lost more money are less [clears throat] valuable and worse it's just but it's you're sort of flipping that on its head and saying no these are science projects and what what what they spent on that matters and also the fact that somebody gave them the money to spend on that um because those people are going to want to get some return on their investment. Um, and so maybe even if that uh trial that they spent all that money on fails, you know, the people behind it are going to try to figure out a way to repurpose the research or something to try to make that uh uh make themselves whole. >> Yeah. And Andrew, sorry. 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. Uh, well, guess what? When you run the data and you figure out what's driving you return, the value metric works better than the specialist metric because as you need a way to measure how value is changing over 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 um 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&D that's getting spent is getting spent in a at at worst EV neutral basis and hopefully EV positive basis, but that that's kind of the way to think about it. >> That's the denominator, right? That's the um 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 company that spent two billion. >> Yeah. So Yep. So 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 and you need an anchor to compare to the market cap. >> No, it's just interesting because look, less today than I I know you guys started writing this and started doing this probably early last year when you know 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 they 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 push back 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 are they spending so much that they're getting neg worse than negative 50% ROI on that investment? because that's what it's calling for. But I I 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 on >> No, that's exactly right. Um and I think the um you know, part of that I think you you know, you you got interested in the sector around the same time we did. You know, 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. 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. Um, 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 like, well, oh, and then we'd be like, oh, you maybe it'll be market neutral. Like, oh, thank God, right? who would want long biotech beta and you're like the time the specialists it's like oil in 2015 or 16 right like the you know people just got annihilated and um and that's a you know always something that perks uh my interest in the sector >> on so you guys go to kind of you know the bigger the spend the better and I I definitely get you're adjusting for more Kevin thing but that does seem like it would push you more towards an oncology trial or an Alzheimer's trial is going to cost a lot less than a skin trial or an eczema trial or something. Did you I I guess another way of asking and 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, you know, that's that's the kind of thing that we know that in other other across I mean when you're investing across sectors um using sector level value can work. Um so it can add something but it just it's it's additive, right? It's it's not it's not a replacement. Um so the answer is yeah, we'll probably look into that at some point. Um we we're constantly constantly uh researching this stuff. I'm I 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 on college and Alzheimer's have been great but you know going forward it's a very tough uh area >> and rather 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 and it's it's way too high in Alzheimer's at when you actually go create a diversified portfolio, you'll even out, right? 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 on Alzheimer's, I don't know if there's a best of a publicly traded Alzheimer's, but uh speaking of sectors, you mentioned it. You guys also have something really interesting on momentum where you talk about I 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 uh companies that are running the same trials. I I I want to make sure I understand that piece uh 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. Um you are so we this is the classific we're just talking this the classification problem right you need to you need to classify these um the these biotechs you need to group them um so this value problem is exactly it right and we actually now that now you're talking I realize we actually did do the value problem that you're talking about and test it um we just didn't put it in the model um the um 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 And then you can actually even say I want to build a metric within a within a certain class of of of companies. Um 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 biotech can change over time. They go through phases. There are different phases at 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 and 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, on average they're doing these things. And those clinical trials are great because they have 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 metrics on 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 and Alzheimer's and then they'll have 10 pre-clinical or phase one 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 an al with uh a little bit of oncology, you know, call option sprinkled on top of it." How do you guys adjust for that? Do you give bigger waitings 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 as a hard problem for a fundamental analyst, >> um this is why no one This is why Quant 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 um there's a cool way that you can sort of classify companies. You can say who are they similar to, right? So um in and let's use this as a great example, right? You would say that this company is similar to I can't remember what your phase three drug was in but whatever in it 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 said 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, 1 through 376. Doesn't be don't care. It's a scale, right? And you say, 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 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 person. Right? Um, but actually what you're not saying is I'm most similar to that person. is saying I'm on average similar to these and my average the 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's my index act acting right what's my value relative to that index what's my momentum relative to that index and then that's that 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 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 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 uh a lot of things in parallel with each other. >> And just as an aside, I taught myself to code from books, and I'm just really upset that everybody knows how to code now. >> 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, right, in my looking at my notes, right? You guys find positive momentum inside of categories. You know, if I'm just just going to say blunt like if Alzheimer's drugs are doing well, all the Alzheimer's companies are start doing well is kind of my understanding. You can tell me if I'm understanding that why, but I'd love to say 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 our targets work. But, you know, if if your drug gets approved, I'm not going to have a monopoly. At best, it's going to be a duopoly. At worst, like it's just got all sorts of negative implications. So, I was 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 marketwide phenomenon where where sort of similarity momentum or pure momentum is a is works. Um and I think that um yes, in that sort of discrete case um of a clinical trial gets approved for a competitor that's bad for you. Um but the vast majority of the time like if your competitors are 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 it's these things are influenced by these big macro trends. Um, and the thing that you were trying to get at is is sort of the 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? Um where oh people are really uh 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. um and basically incorporate that sort of thematic judgment into your investing. Um 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 or peer firms. Um and often there's some external driver for why that's true. Um, you know, maybe mRNA uh 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 Fizer gets in a bidding word for Msera, every other obesity drug player goes up because they say, "Hey, whoever loses that bidding war, they've already shown they want to buy, so they'll probably come buy 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. >> Yeah. Andrew. Okay. So, I was gonna say one final thought on that. You know, I think when I when I talk to people, you've asked so many questions that we've been asked um before and really good question. >> I was hoping for some unique questions, Greg. >> No, no, no. But, you know, I talked to people in biotech, they're so event focused, right? You know, because biotech is events. It is event. It is events. It is event. Um 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 in the quant research, you find that 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. Um and so it's a very nonfunddamental way in once you uh you build the factors fundamentally but there's a very non-funddamental way to look at it. Um and it and it's extremely powerful. >> Yeah. I think you know there's a there's a a famous paper it's the title is something like what drives the value factor or surprising news about the value factor or something like that. And and the finding is just that um uh 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. Uh 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. Um, and so if you just say, hey, some of these events are just sort of random, like I don't know, 30% of trials fail or whatever. I mean, that's not the right number, but um, you know, I'm not going to be that good at knowing whether it's 25 or 35. Um, all I should know is that if people think it's our pricing that it's a 90% chance, um, that that's probably not so smart. And if it's 10% chance, well, you know, maybe we should be long it. Um, um, and I think that that's sort of the idea of quant. It's just sort of take take the sort of um, it 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 that study biotech know something about what they're doing. uh 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. But 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%. And you ask why 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. Um, I will leave my fa 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 finances until years after the value uh is known work? That was my favorite line in the paper. I think you guys have done a great job uh 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 I'm happy to wrap it up here. >> No, this is great. Well, thank you for having us on, Andrew. We're we're we're we're we're really excited about this. It's very cool. I mean, I think it's um biotech is such a weird sector. We did so much work like creating new metrics, figuring out a new way to trade it. And uh you know, we're excited to see how it develops and and to learn uh learn more about the sector and do more of this research. So, >> 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 uh I I just really enjoyed it. So, thank you guys. So, 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, you got What's the most bombed out sector as you and I speak? February 18th. You know, Andrew, it's it's a little bit frustrating for me right now because, you know, they uh they you know, Howard Mark says you want to be you want to be contrarian and right, right? Like have a non-conensus view and be right. Uh and uh and right now I think you know we've been sitting around and saying well what what if what is what if her dad's core arguments 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 >> this morning would uh 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 have 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 agrees with me now which bought 25 million of stock this morning though. So you guys might have to throw them into the insider purchase screen. >> Yeah. Well uh maybe it's good still good for KKR but um but I feel like that was one of our non-conensus 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. Uh we're, you know, we're we're having trouble kind [laughter] of refresh our basket of new ideas. It's sort of, uh, you know, for a value investor, uh, you know, you start to get worried when you have two or three good years in a row. Uh, you say, "Oh, gosh, maybe it's overvalued now." So, we're we're we're refreshing. Maybe we'll start becoming, uh, hu Greg Greg and I will just start becoming huge bulls on private credit BDC's or something. Yeah, they are trading for big discounts to [laughter] 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 bioche. Do you guys adjust for size of insider purchases and maybe size of insider purchase versus kind of like CO comp when y'all do the adjustments? I mean in general when we make metrics we'll do a count and a volume right so we and we'll average them or do you know some some look at both of those um in the in the insider um data we mostly just relied on counts right because we wanted the intention not and and who knows you know I I don't want to credit somebody more because they just happened to have more money right so we we just used counts in that 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 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 a hund00 million of stock over the past three years." Like, your stock's down 60%. Maybe you could like that. 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 canled, but Yeah. >> Yeah. And that's And was that the CEO? Because, you know, CEO was fine. >> It was the CEO. all the other execs canceled their 10v5s 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. Uh 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 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 adviser. Thanks.