In their latest episode of the VALUE: After Hours Podcast Jake Taylor, Tobias Carlisle, Jack Forehand, and Justin Carbonneau discuss:
- Data-Mined Factors With No Theoretical Explanation Perform Just As Well
- Negative Investor Returns: Despite Impressive Fund Performance
- Slime Molds: Problem-Solving Without a Brain
- Despite China’s Significant Economic Expansion The Stock Market Has Shown Minimal Growth
- The Complexity of Investing in Biotechs
- Institutional Interest Remains Surprisingly Low In Value Investing
- Wisdom of Crowds
- High-Octane Portfolios Are Hard For Investors To Stick With
- The All-Cap Portfolio Provides Greater Exposure to Factors
- Is It Time To Add International Exposure To Your Portfolio?
- Concentrated Portfolios And The Significant Impact From Individual Stocks
- P/B Ratio of the NASDAQ-100 Surpassed P/E Ratio of Chinese Stocks
- Uncorrelated Strategies For Diversification
You can find out more about the VALUE: After Hours Podcast here – VALUE: After Hours Podcast. You can also listen to the podcast on your favorite podcast platforms here:
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Transcript
Tobias: This meeting is being livestreamed. This is Value: After Hours. I am Tobias Carlisle, joined, as always, by my cohost, Jake Taylor. Our special guests today are Justin Carbonneau and Jack Forehand of Validea. How did I go, fellas? How are you?
Jake: [laughs]
Jack: Good [crosstalk] how are you?
Justin: Hey, guys. How are you?
Jake: Welcome, gents.
Jack: Good to be here.
Tobias: What’s Validea for the folks who haven’t encountered you before?
Justin: So, Validea is– There’s really two different businesses, but let’s just– We will talk about the research business, because that’s what most people might know us as is. Validea is an independent investment research company. The website is Validea, V-A-L-I-D-E-A. And on Validea, we run a series of investment strategies based off of the publicly disclosed methods from great investors, people like Peter Lynch, Benjamin Graham, Warren Buffett, and then a whole host of other strategies that have been written about in books and or academic papers.
What we look for are strategies that have proven the test at the time for the most– Some of the guru strategies don’t have back tests behind them, but all the other strategies beyond those famous investors come with significant back tests and are all based on fundamental–
Tobias: Do you guys recreate the back tests or do you use their–?
Justin: We codify the model and start running the model and start tracking the performance of it from the time it goes on the Validea site. Actually, there’s a little bit of– Let me restate. Some of our models start way back 2003, those are all live. And then there was a second set of models that we rolled out, and some of that performance is back tested, some of it is actual like live out as a sample.
Jack: Yeah. A lot of the strategies are tested in the research, a lot further back than we can go. They’ll go back decades. So, we don’t do that, but we will try to– When we put a new model on our site, people want to have some idea how it’s done, what they’re getting, because if you put it and start the performance on time zero, there’s really nothing there. So, we typically will put– We haven’t added a model in many years. But when we do, we run testing historically and it includes some performance with it as well.
Tobias: That’s one of the reasons I love chatting to you, guys, because you have a good idea what worked last year that was actually implemented. So, let’s start there. What happened last year? What worked? What didn’t work?
Justin: So, I’m going. I should have had this up pre-
Jake: And how did you create the Cathie model? [laughs]
Jack: Which one?
Jake: Cathie Wood.
Jack: Oh, yeah. That is one thing you cannot model, as far as I can tell, at least not with factors. So, it’s funny. We have like a separate tool, that’s like an ETF factor report where we try to get value and momentum and all these different exposures of ETFs. And if you look at that ETF, it is like zero across the board. There’s no quality, there’s no value, there’s no nothing, there’s no low volatility. Not to say that it’s a bad strategy, but it’s not a strategy that’s driven by any kind of the investment factors we look at.
Tobias: Is it negative? Is it like–
Jake: Inversely.
Tobias: Yeah, inverse to any factors?
Jack: Well, we percentile rank everything, so 1 to 99. So, the lowest you could possibly be is one. But there’s a lot of the scores. Like, last time I looked at these were 10 and below, which means, yeah, basically no exposure to any of these factors. It’ll have momentum sometimes when those types of stocks are doing well, but outside of that, I’ve never really seen it have much exposure to anything else.
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Concentrated Portfolios And The Significant Impact From Individual Stocks
Justin: So, last year was an interesting year. You guys have talked about this, and we know it. Obviously, the leadership was super narrow coming into whatever, October, November, then things exploded. So, when you get that type of explosive move, because we run such– On Validea, we run 10 and 20 stock models. So, it’s so super concentrated that just from a rebalancing and timing standpoint, if like a model– Like, the best performing model last year was a value model based on Ken Fisher Super Stocks book, which goes way, way back to whenever he wrote that in like the 1990s or something like that. Ken Fisher doesn’t even follow that. But it’s like the value, a lot of cyclicals get in there. But then also at the top of the pack was some growth models, which is what you might expect.
So, it was a weird year in the sense that it’s not like all the growth strategies or all the value strategies were on top or leading the way. It was a weird mix. And then it’s a lot of times important to look at what didn’t do well. And so, like the Greenblatt magic formula model didn’t keep up with the market. We run a strategy based on– And we’ve had him on the podcast, you know, Pim van Vliet at Robeco, he runs a low volatility conservative strategy that was actually like the worst performer. The surprising thing there is that’s high on quality, but something about low vol just didn’t work last year, probably because of what happened at the end of the year.
Tobias: Was it a high vol–?
Jack: It’s kind of weird, because we aren’t a great judge of whether value is working or whether quality is working because of their 10 and 20 stock models. There’s a lot of idiosyncratic things with individual companies. Like, the extreme version of that is back in 2021, we had a couple of models that had GameStop. And so, whatever happened to those models in that year doesn’t necessarily tell you a lot about the model itself, because if you have 10 stocks and one of them is GameStop, the whole thing is going to go crazy. So, we can see a little randomness there in terms of, yeah, if value is doing well, on average, our value models will do better, but we also have the outliers in both different directions because they’re so focused and they’re so dependent on the individual names in there.
Tobias: That’s one of the reasons that I like it though, because the idiosyncratic application of it is so much more valuable than the back tested theoretical application of it.
Jack: Yeah. We like the high-octane version of these things, which is why we have them there. If you’re going to have these strategies with these criteria, let’s find the stocks that meet them as closely as possible. Let’s not go to 200 names. Let’s really do it aggressively. But it goes hand in hand with the idea that you’re going to have these individual positions, they’re going to have a huge impact. And sometimes if one of them is a really bad position, it can have a big negative impact in a year. Or, if it’s GameStop, it can have a really positive impact. But I always try to keep that in mind when I’m looking at any of the performance in any given year knowing that it could be like one or two stocks that were a huge part of it.
Justin: And the other thing that’s just worth mentioning is that when we initially developed this, we wanted to make it so these models were actually followable and implementable for your average investor. Most of the people that use Validea, not all, we have some professionals. But we get a lot of retail investors. So, if you’re trying to follow a systematic strategy or portfolio– Remember, this is way back in 2003, and we hadn’t expanded the portfolio size, but following 20 or more stocks can get– even if you’re doing it systematically, it can get tough to do. So, we tried to make it so these strategies are pretty easy and straightforward to follow.
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Uncorrelated Strategies For Diversification
Tobias: How often are you rebalancing or depends on the strategy?
Justin: Go ahead, Jack.
Jack: We do it four different ways for every strategy, because we try to give people as much information as possible and not to say, here’s the best thing or here’s the best. You know, this works better than this. So, we do all of them. We test them all monthly, quarterly, annually. And then we have one we call tax efficient, which is basically a monthly one where it’s trying to limit the turnover. Like, the regular monthly ones have pretty high turnover. So, the tax efficient is like a lower turnover monthly. We have the performance of all of them using each one of the rebalancing. So, anybody can look at any one of the strategies and say, “How did this one perform annually rebalance? How did it perform quarterly rebalance?”
What you find is what you would expect. The value stuff will tend to do better or at least the same with the less frequent rebalancing, and the momentum and growth stuff tends to need to be rebalanced more often for it to work, which is what you’d expect probably going in.
Tobias: Do you ever show the correlations between them, so you could find two that are uncorrelated?
Justin: We actually have like a correlation–
Jack: We have a little tool on the site for that.
Justin: So, yeah. So, I can choose the acquires multiple, which is a strategy we know.
Jake: We’ve never heard of it.
[crosstalk]Justin: Never heard of that guy. And look at very uncorrelated with the multifactor Pim van Vliet conservative stock strategy, interestingly enough. And the next one is the momentum model, which is what you expect, given [crosstalk] value.
Jack: What we’re looking at there is the correlation of the excess returns, because obviously, if we just look at the correlation of the pure returns, they end up being pretty correlated because they are looking at the data.
Tobias: Yeah.
Jack: So, yeah, we’re pulling out the excess returns and looking at the correlation of the excess returns with each other to try to get a better picture of like what’s actually not correlated with what.
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Tobias: Yeah, that’s very interesting. Let me just give a shoutout to the listeners. Petah Tikva, Israel. First in the house. Santo Domingo. Deano in Townsville. What’s happening? [unintelligible 00:08:45] Chapel Hill. Gothenburg, Sweden. Montenegro. Sherwood, Oregon. Toronto. Milwaukee. Valparaiso. Antigonish, Nova Scotia, Canada. Bangalore. Brandon, Mississippi. Durham. Savonlinna, Finland. Prishtina Kosovo. Clungeville, Scotland. Dead Cat Gully, New South Wales. Yeah, me too.
Jake: [laughs]
Tobias: I’ve jumped over. Braunschweig, Germany. Stockholm, Sweden. Nashville, Tennessee. Las Vegas. Toronto.
Jake: Oh, my God.
Tobias: Sweden. Hamburg, Germany. “Wilmette, Illinois with VPN through Nijmegen, Netherlands.” Bakken Oilfield. What’s up? Edinburgh. And Patrick Holland in Hong Kong. What’s up, Patrick? I went to primary school with Patrick. What’s happening, Patrick? [unintelligible 00:09:36] What’s happening, fellas? We’re back.
Jake: This is your life [unintelligible 00:09:42] [laughter]
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P/B Ratio of the NASDAQ-100 Surpassed P/E Ratio of Chinese Stocks
Tobias: Just before we came on, so Jake sent through this crazy chart that shows the price to book value of the China stocks.
Jake: Do you want me to explain it, Toby?
Tobias: Yeah, you explain it.
Jake: Yeah. So, it was NASDAQ-100 price to book, and has eclipsed China price to earnings ratios. And granted the caveat of NASDAQ being very tech heavy and gap not doing a great job of capitalizing on the balance sheet code. So, therefore, book values are– they’re suspect a little bit when it comes to tech companies. But with that caveat in mind, yes, the relative just absolute disconnect in valuations between China and US tech has gotten so extreme that the price to book of one is now higher than the price to earnings of the other.
Jack: Yeah, that’s crazy.
Tobias: So, what did you get book at–? Book for China was like– Sorry, book for the NASDAQ was about five times?
Jake: You don’t have book. Yeah, book for NASDAQ is, yeah, it’s in the five-plus range. It’s probably six or seven. And then the PE for China is down below that number.
Justin: Well, think about this. Think about your average US investor portfolio now, how much home country bias is just there because– US has just clobbered everything over the last 15 years. And it’s just crazy. And at some point, it might not be China, but international diversification is going to be important, and that’s something that’s probably being lost a little bit or a lot investors’ portfolios.
Jake: I saw profit margins in China right now are roughly 5%, and US is more like 13%.
Justin: Okay.
Jake: Obviously, the business quality is quite different too. But boy, almost three times the margin, how can that go on forever? I don’t know, that seems like a difficult bet to make.
Tobias: China has had pretty good consumer discretionary, which unlike the rest of the—So, US has got unusual in that. It’s got so many big consumer discretionary. I lump in in Google and Microsoft and Netflix and all those sort of the FAANMAG Seven, whatever we’re calling them these days, whereas a lot of the other countries in the world don’t. A lot of the other countries in the world are sort of heavily mining and resources or basic materials and financials, whereas China actually has some of that stuff. So, I always thought they had a higher quality stock market than many of the other countries in the world. Not reflected in the margins though.
Jake: No. Return on assets are fair amount lower than US return on equity is lower. It’s still a pretty relatively heavy industry. Of course, you have their national champions like the Tencent’s, Alibaba’s, and Pinduoduo. But yeah, it’s not quite.
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Despite China’s Significant Economic Expansion The Stock Market Has Shown Minimal Growth
Tobias: One of you guys was saying that there’s a big bifurcation between the performance of the economy and the performance of the stock market in China.
Jack: Yeah. Well, I think it was economic Jake, who shared that on Twitter. I think the economy is up several fold over whatever it is, 20 years or whatever it is, and the stock market is up zero. So, the whole idea of the economy is not the stock market. If you want an example to prove that’s the example. The economy has gone crazy and the stock market has literally returned zero. That’s a pretty amazing stat.
Tobias: There’s that famous Triumph of the Optimists that– It comes out once a year. They update all the data for the world, and that’s that Elroy Dimson, Marsh, Staunton, something like that.
Jake: Yeah, Marsh and Dimson.
Tobias: Dimson. That’s right. They made the comparison. I put this in one of my books, like a 2004– I think it was in Deep Value, where they looked at the performance of China as an economy and the performance of its stock market versus the performance of England as an economy and the performance of its stock market. And even though England has eclipsed in 1950 and losing its global dominance since 1950, the stock market had massively outperformed. Whereas China has been growing phenomenally quickly through that whole period, but the stock market performance has been nothing like even England’s stock market performance. The reason is that you’re just overpaying for China.
It’s clear when you look at the chart that Jake was talking about earlier. In Chinese stock market in terms of a PE basis, peaked in 2008, and it’s been compressing since 2008, which is a long time. So, it’s like being a value investor. Multiples running against you for like 16 years, something like that.
Jake: That can happen. Buffett’s pointed out that 17-year cycle in the US even of– And that was like the 20th century [crosstalk]. Yeah. And before that, though, you had– whatever.
Tobias: 66 to 86.
Jake: Yeah, 65 to 82, where you went nowhere. And on a real basis, I think it was even worse. But yeah, there’s lots of doldrums to sail through in this ocean. It’s not always just up into the right.
Justin: I guess what I don’t know about the Chinese market is you know how– You think about the US market, US investors and how much exposure we have to the stock market as investors here and how that’s grown over the last 25 years in terms of– or even maybe since the 1960s or 1970s, it used to be pension plans. And then now most of the time people are investing in stocks to save for their retirement. I don’t know, in China, how their consumers [crosstalk] their–
Tobias: What the penetration is.
Justin: Yeah, the penetration. So, it’s like you could see if that’s– because you hear about the wages in China and how much– So, it’s interesting. You would think there’d be global demand for their equities, the shares in China, but I think to some extent what happened with Russia and Ukraine and the sanctions there that could put– It certainly puts some risk in terms of exposure to the Chinese market in the sense that something goes down with Taiwan or something like that. So, it could be that overhang as well, that is affecting things.
Tobias: Also, as we’ve discussed in the context of Alibaba that it’s not entirely clear what your ownership interest is. It’s through those VIE, those vehicles, which Jake knows a lot more about than I do. But you don’t have direct ownership. You’ve got this proxy ownership, and it’s not clear what your rights are. It’s hard to enforce them ultimately.
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Is It Time To Add International Exposure To Your Portfolio?
Jack: What do you guys think about international investing in general has just been such a challenge to talk to investors about, because it’s been so bad for such a long time. But theory is very strong. You’re more diversified. There’s probably no reason to believe that over a really, really long period of time. The US should just beat the rest of the world, how do you think about that? It’s really hard when you’re talking to investors, because the theory is really sound, but the practice has been horrible for so long. It’s a challenge to think about what do you do going forward? US only has been great. It’s worked really well. People say, “Why would I change? It’s been working.” But theory would tell you probably should have the international too.
Tobias: And in addition to that I would say all of the US companies have got this global exposure. But you don’t have to worry about that VIE problem. You don’t have to worry about foreign taxes. You can invest domestically and get the foreign exposure because all of their revenues are increasingly from overseas.
Jack: That’s right. And that’s Corey Hoffstein, when he was on our podcast made that point. You are getting a lot of international exposure by owning US companies. So, you may not need that additional international exposure.
Jake: I would say if you own Starbucks, you are making an implicit bet on the health of China or Nike-
Jack That is true.
Jake: -or apple or any of those, really.
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Data-Mined Factors With No Theoretical Explanation Perform Just As Well
Tobias: Jack, do you want to walk us through your data mining paper?
Jack: Oh, sure. Yeah. It was actually very interesting. We had a couple of researchers on our podcast that’s coming out this Thursday. And so, we have this idea, like an investing, like an in factor investing, whether it’s value or whether it’s momentum. If a factor works in testing, and I want it to continue working going forward, I need some explanation as to why it works. And so, typically, what researchers will come up with is these risk base and the behavioral explanation, which is, the risk base is pretty straightforward. Value stocks typically are riskier, you’d expect. They have problems with their businesses, they’re cheap, they’re riskier than the market, I would want an excess return for that risk.
On the other side, the behavioral side would be people overestimate the problems with value companies. They beat down their stock prices. For people who are willing to buy those stocks, if they’ve overestimated the problems, that’s an opportunity. So, typically, those are the two explanations for any factor that we’ve used going historically to say, “Here’s why they should persist in the future.”
Well, we had a couple of researchers, Andrew Chen from the Federal Reserve and Alejandro Lopez-Lira from the University of Florida on our podcast this week. And the idea they came up with is they said, “All right, let’s test this. So, let’s take all the factors that have a risk-based explanation, let’s take the factors that have a behavioral explanation, and then let’s do a third group, and let’s just data mine the crap out of the accounting database.”
So, basically, let’s just divide everything by everything, let’s come up with the ratios that do the best, and then let’s use those on a standalone basis. And then, let’s take these three groups. We’ll do it the same through Fama-French. So, the testing period ends in the early 1990s. And then let’s see out of sample from the early 1990s forward how they work. And the answer is there’s zero difference between the ones that have the risk-based [Tobias laughs] explanation, the ones that have the behavioral explanation, and the ones that were just purely mind, which will challenge a lot of theory that a lot of us that are factor investors based what we do on if that ends up being true.
So, for instance, I asked him on the podcast. So one of the examples, I think was something like property, plant and equipment divided by cost of goods sold, something like that. Something you would never divide in the real world. But that had a similar return in sample and a similar return out of sample to something like momentum. And so, what they were saying is, basically, there’s really no reason you could say momentum is better than property, plant and equipment divided by cost of goods sold. So, it’s a really interesting thing just to say– And we were talking before we came on, talking about Robert Mercer at Renaissance, They’ve said all along that some of their factors that work best are the ones that have zero explanation or the ones that make no sense.
It’s just an interesting thing to think about going forward. We all rely on these explanations as to why these factors work. And if we test them and we don’t have an explanation, then we shouldn’t use them. But what if the ones that have no explanation perform just as well as the ones that do? I don’t know the answer to it. Certainly, academics that are smarter than me are testing this stuff, but I thought it was an interesting paper, and it’s an interesting conclusion.
Jake: Yeah, they’re almost polytheistic, “Lets just worship all of the Gods.” [chuckles] Maybe Toby’s a little bit more monotheistic of worshiping the value God [laughs]
Tobias: That’s right.
Jack: Yeah, even you can even worship it. I have no idea why this is working, which is a whole different change from whether you’re a momentum guy or a growth guy or a value guy, you usually have some basis for it. This is like, I’m dividing numbers. I have no reason to believe that dividing these numbers, other than the fact that there’s a lot of evidence that accounting data does impact stock prices. They did something else in the paper where they tested just mining tickers as opposed to accounting data, and they found no results there. They couldn’t get any good results out of just mining tickers. So, there is something about accounting data where it is meaningful. In terms of stock prices, the ratios we’re used to thinking about, they weren’t thinking about like, here’s what I should test, because I think it works. It was more like, just throw it all together, and whatever works, that persists just as well as the ones we could explain.
Jake: Is it possible though in a monkeys typing hamlet way that there’s just simply not enough data there to actually make that kind of claim. I know they’re looking at pretty large data sets, but if you’re going to just throw random numbers together, you can find things that will match over some period of time. But I would imagine like, you need just a big ass data set to actually feel good about betting on that going forward, don’t you think?
Jack: Yeah. No, I would think so. I think there’s definitely some randomness to that. But you can also argue– [crosstalk]
Jake: Yeah. Hit the noise element. There’s so much noise to filter that much noise out, you just need a huge sample size.
Jack: Yeah, exactly. And also, there’s a behavioral argument for– They’re not saying that the factors that have explanations don’t work out of sample. They’re just saying they work the same as the ones that don’t have explanations. So, it’s not really a challenge of factor investing doesn’t work. It’s really a challenge of, do we need these explanations for what we use? I don’t know the answer to it.
Jake: Let’s talk rationalization.
Jack: Exactly. But there’s arguments also like we asked them in the podcast like, do you– With the regular factors, did people mine the data to come up with book to market or come up with the explanation after the fact, or did they have the explanation first and then find book to market and the data? And they said they really didn’t know. Like, it depended on who did it, and so you could argue the other factors did it as well. So, it’s not something I really have a strong opinion on yet, but I just think it’s really interesting. The more I’ve been in the markets, the more I learned to challenge what I’ve learned and to say not to have these hard and fast beliefs and say, “No matter what I believe, you have to have an explanation for a factor.” I want to be open minded with this kind of stuff. So, I thought it was really interesting from that perspective.
Jake: I’ll have to ask Jim O’Shaughnessy next time I see him, what was he doing? Did he show up with the answers already and then try to back solve or was he following a more scientific approach?
Jack: I always thought it would be interesting to do like a– No one would ever buy it, but if you did like a factor ETF like X the ones that actually make sense.
Jake: Right.
Jack: So, you did an ETF of just the ones that don’t make any sense and used it as like a diversifying complement to your standard factor exposure, it would be interesting. No one would ever invest in it, but there’s so many– I mean, every ETF is taken these days, that’s at least one that would be– [crosstalk]
Tobias: I’m sure you can wrap narrative around. I’m sure you could say it’s like the– We’re just looking for signals where like– What’s that firm? Medallion? What’s the firm? Medallion? [unintelligible [00:24:31] Medallion?
Jake: Rentex.
Jack: Renaissance.
Tobias: Rentex. So, Renaissance, they’re just open about the fact that they don’t have any– There’s no explanation, we’re just going to test everything. But what you’d expect to find if you test tens of thousands of ratios through one data set, and then you find all of the ones that worked in that first data set, then you test them again through a second data set, there would be something that would survive just by pure chance, there are going to be things that survive through both data sets. But I think the more scary thing is what it says about momentum and probably value and other things too that you can’t even demonstrate that they are having survived two sets that even though there’s an explanation, like, they’re no better than the things that are cooked up by the computer.
Jack: What was interesting too is they all out of sample. It’s widely known that value out of samples had a lower premium than it did pre-1991. The ones that couldn’t be explained did as well. Not as much, but I can understand value going down. You could say, “All right, people became aware of it. They started following it. The premiums are less.” But cost of goods sold, divided by property, plant and equipment, those also deteriorated out of samples. So, we tried to understand that. I don’t really completely understand yet why that happened.
Tobias: It doesn’t make any sense at all.
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Justin: Well, I thought what the research showed back to the tickers was things with Z definitely underperformed. Tickers–
[laughter]Tobias: Yeah, don’t use Z. You got to go-
Jake: Go AA.
Tobias: AA [crosstalk]
Jake: Triple A. Get to the front of the phone book.
Justin: Triple Z.
Tobias: I’ve got a note here saying value, but I can’t remember why I wrote down value. Just probably just came to my head, often I write that down on stuff. Were we talking value before we came on?
Jack: I don’t remember. We might have been. We always talk value, so I’m sure we were.
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Institutional Interest Remains Surprisingly Low In Value Investing
Tobias: When’s value going to turn around? That’s the question I ask all of my guests.
Jake: [laughs] No motivated reasoning here. Just– [crosstalk]
Justin: I don’t know. Coming into this year, it just seemed the consensus was– And maybe I’m wrong about this, but it was both had a pretty good– A lot of things had a pretty good year last year, if you include the fourth quarter, which you got to include that. So, it ended up being decent for a lot of different strategies. Growth certainly was the leader, but then it seemed the consensus coming into this year was small cap, value, catch up. And anytime, I start hearing that anything too often. That seems like-
Tobias: Yeah, it’s got faded.
Justin: [crosstalk] trade. Yeah. So, I don’t know. With our experience in running these concentrated value strategies and we’ve talked about this, it’s very episodic. The best performance comes off the times when it’s the scariest and when value has been pounded, and then you get that massive return. It’s not this nice step up– At least in our types of value strategy, it’s not like this nice step up, churning out smooth, consistent return.
Jake: Yeah, it’s not that smooth 12 versus lumpy 15. It’s more like 0 and then 50.
Justin: Exactly. Yup, absolutely.
Tobias: I saw a few articles over the last few weeks that say, “When small and value have a bad start to the year like this, it tends to be not an indication that the year is going to be bad, but that small and value will catch up over the course of the year.” There’s not a lot of [unintelligible [00:27:57]. It was like eight or something like that. But they said seven out of eight, the year has ended up being very strong for small and value. That sounds like highly motivated reasoning to me.
Jake: Oh, my gosh.
Jack: We’ll take it if we can get it.
Tobias: My bearishness tends to suggest that when you have a weak start to the year for small and value, it’s going to be a weak year all around. But that’s not the case.
Jake: I’m picturing you like it’s taped up to the wall, there’s like strings [Tobias laughs] running all over the place. [laughs]
Tobias: Yeah, I’ve given up the macro. I’ve given up the macro for New Year’s. It’s my New Year’s resolution.
Jake: [unintelligible [00:28:29]
Justin: When rates were going higher, stocks that were more dependent on financing in the small cap space, you can make an argument that clearly, they’re going to be affected by higher rates, higher financing costs. And so, that flows down through to profitability. So, on the backside of it, lower rates should be favorable for those companies that are more dependent on debt. And that’s the thing with small caps. As a group, there’s just a lot of junky stuff in there.
Jake: And yet, the high yield spread never went anywhere hardly. Even when there was a lot of narrative about questioning financing, it was like, that didn’t blow out.
Justin: Right.
Tobias Carlisle: The option adjusted high yield spread. That data series that the Fed, that seems to be coincident with crashes. It really doesn’t get going until– I don’t know what information it provides. Like, if you find that you’re in a big drawdown, then you go and check it. It’s like, it’s always blowing off– [crosstalk]
Jake: Triggered. Yeah.
Tobias: it doesn’t really help you. It’s not predictive of anything.
Jack: Dan Rasmussen did some work around that. I thought it led things by a few months or something like that when it spiked. I forget what it was, but it was actually a good indicator for value when you would see a spike in those credit spreads.
Tobias: He uses it as a trigger when it gets over a set– I forget the number. But when it gets over 6% or 7%.
Jake: 6% spread.
Tobias: Yeah, which is rare. Like, you can look through the data. It doesn’t happen very often, but when it gets over that.
Jack: The last time was 2020, right?
Tobias: That’s probably right. He uses that as his definition of a crisis. So, it’s time then to get more invested. It doesn’t get triggered very often though. But you would know. You could also say 6% on the OAS, it would be equivalent to whatever it is 10 or 20 just on the SPY being down.
Justin: What’s interesting, I’m on this– Actually, we’ve had him on the podcast. Who’s the guy the Schafer Cullen, Jack, the value investor guy that wrote the book? I’m blanking out.
Jack: Isn’t Jim Cullen? Is that it is?
Justin: Yeah, Jim Cullen. I think the firm is Schafer Cullen. Anyways, pretty decent sized value manager. And so, I’m on his distribution list, and they send out like a quarterly update. It was just interesting. And he writes like a letter. He talks about value investing sometimes, but he was talking to an institutional consultant who they actually work with because they have some institutional business. But that consultant was telling him that, I think in the last three to five years, they’ve had very, very little searches for value. So, even institutions are who you would think would be having this–
I was thinking like, could you get a big rebalance? I think BlackRock recently rebalanced into some that big ETF on the model portfolios. They saw a huge inflow into their value ETFs. And so, you would think institutions at the asset allocation level, they’d be looking at their exposures and saying, “Okay, now we got to tilt more towards US value.” And that starts with asking consultants to search the databases for value managers. Well, this consultant was telling this value manager that, no consultant’s doing that.
Jake: No one’s asking for you.
[laughter].Justin: No. No one’s knocking at your door, buddy. So, it’s pretty crazy.
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The All-Cap Portfolio Provides Greater Exposure to Factors
Tobias Carlisle: I definitely have seen something that there were major redemptions from value funds last year. There were a lot of money taken out. I’ve heard in other places that people were considering cutting their small and value exposure, which I think that they’re a little bit of both. I’ve actually got a question on the small. Do you guys have any theories on the continued beatings that the size factor is taking alongside value?
Jack: Yeah, not really. I’m not a big believer in the size factor, in general. That’s one place where Wes Gray has helped me a lot, like, come around in terms of how I think through those. I was never a believer in the size factor, but I would always be the guy that said, “Well, value works better in the small cap space.”
Tobias: Yeah.
Jack: What I had wrong about that is, that can actually be true. But what I had wrong about it is why. Value doesn’t work better in the small cap space because they’re small caps. Value works better in the small cap space because you can get more valueness in the small cap space. So, if I expand my portfolio to small caps, I’m going to be able to get cheaper stocks. So, I’m actually getting more value, more so than I’m just getting exposure to size and coupling it with value.
So, I’ve come around over time to say like, I don’t really believe in using size, really in any way. I try to use the other factors. I think using an all-cap database where you get small cap in there, it gives you an opportunity to get more exposure, typically to whatever factor you’re looking at, although that’s not necessarily true with quality.
Justin: Two things that might be impacting that. These are just theories. I have no idea. I think Verdad has done some stuff. The quality in the small cap space has deteriorated over time. And so, if that’s true, those would trade at a discount versus maybe a little bit higher quality. That’s one possible thing that could be influencing that. The other thing is, I wonder if– The number of companies– Correct me if I’m wrong here, Jack. I think the number of publicly traded stocks is very low relative to where– [crosstalk]
Jack: It’s come down a lot.
Tobias: Yeah. There’s not enough stocks to make the Wilshire 5,000. There aren’t 5,000 stocks.
Jack: Do you know how many there are? Is it close or is it way off?
Tobias: I doubt it was like 4,300, 4,600, something like that.
Jack: Okay.
Jake: So, Jack, are you saying that it’s not the size, it’s how you use it?
Jack: I think that is true. What’s the AQR paper? It’s like, size matters if you control your junk or whatever
[laughter]Jake: Oh, man, that’s even better. Damn it, Cliff.
Jack: That’s the best research paper title of all time, if it has to be. I’ve never seen one better than that.
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High-Octane Portfolios Are Hard For Investors To Stick With
Tobias: Yeah. Value Stock Geek’s got a good– “Sarbanes Oxley and private equity.” That’s what I think– I think there’s a big bifurcation from Sarbanes Oxley and when that came in because it increased the cost to be public by so much.
Jake: I felt that back down though. It was at one point over a million dollars of extra friction, which could matter for a small company. But now, it’s probably maybe a fifth of that. So, I don’t know.
Justin: Well, the other thing too are the good companies staying private longer, where normally maybe they would have come public before, early on in their life cycle. And you probably still would have gotten some– A lot of companies fail, but the ones that survive go on to become mid and large caps as they migrate up the chain. And now, they’re staying private longer and coming to the market, maybe more mature. I don’t know, that’s another–
Jack: Also, one of the things I’ve noticed is, when we look at our investable universe, a lot of the reduction in the number of public companies has come in companies you probably wouldn’t be investing in anyway, because they’re very small and illiquid. So, our investable universe, I think it’s like 2,700 now, that’s come down over time. But it hasn’t come down nearly as much as the overall universe has, because a lot of those companies were never in the investable universe anyway. They were kind of fringe public companies that were– We won’t invest in anything that’s not, let’s say, like below $150 million market cap and we need some liquidity as well. A lot of those companies that have gone away were outside of those parameters.
Tobias: One of the things I used to do when I was testing out of– O’Shaughnessy said– I forget what his cut off was, but he used an absolute number cut off, $25 million or something like that. When I tested it, in 2008 or 2009 at the very bottom, the smallest company in the Russell 2000 was a $29 million market cap. So, you’re chopping off a lot of the Russell 2000 as you went through there. So, I changed my definition to make it a percentile, so you always have the same number in there. But it was interesting, like, your universe grows and shrinks if you are using, which absolute makes sense, but if you have that fixed number. I don’t know, man. The smalls kill me a little bit, because I think that you can put together portfolios that are better quality and cheaper than in the bigger market at the moment at least. But they just seem to get they’re just punished regularly.
Jack: Yeah. That serious tracking area you introducing, yeah, you have to be willing to sit through it. But I agree with you. You can find what you’re looking for. You can find it a lot more in the small cap space a lot of the time, but that you’ve just got to be willing to look different. And for people like us who manage other people’s money, they’ve got to be willing to look different too. That’s always been a balance in our career is like, how focused do we want to be and how much do we want to worry about tracking error. And trying to get those two right, because you can’t run these things run these things.
My biggest lesson coming from just testing these things to running them in the real world is you can’t just run the most aggressive, high-octane portfolio you want to in the real world and expect people to stick with it. You have to have at least some standards in terms of how much tracking error you’re willing to take and how much tracking error people who follow it are willing to take. That’s been a lesson. I’ll probably never get that balance right, but it’s something I’ve gotten better as we’ve done it over time.
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Negative Investor Returns: Despite Impressive Fund Performance
Tobias: To some extent, Ken Heebner had the CGM focus fund, which is, famously one that– It returned something like 17% a year for a decade. He was Morningstar manager of the decade in 2011 or something like that. But if you look back a decade before, the average cash on cash return in his funds was like negative 11%, because people sold at the bottom and bought back at the top, and it was– [crosstalk]
Jack: Yeah. And the actual return was like significantly positive, right?
Tobias: The funds were something like 17% compound. The average investor was negative 11%. You probably find the same thing in Ark. The money flows in exponentially as the funds go up. And part of that creates some of the performance too. But equally, it means that the investors cash on cash returns are always negative, because when you take a header, you’ve got the bulk of your money has come in at the very top and it all gets– [crosstalk]
Jack: Fair holding CGM focused. A lot of those same idea, like, they all had the same thing. But it creates a conundrum as a manager, because have those funds added value for people or not? On one hand, the fund could argue, “Well, here’s my actual returns. This is what I put up. If you stayed the course, this is what you got.” On the other hand, do you acknowledge the fact that people are going to do what they do and I’m going to have a 30% investor return less than my actual return, I got to change the strategy, because this is not doing anyone a good. It’s hard. That’s the balance as a manager is, how do you think about those people–? [crosstalk]
Tobias: It’s outside of your control.
Jack: Yeah. No, it is.
Tobias: Got to be worried about it.
Jake: I think it depends on the structure too. If you’re managing an ETF where it’s somewhat easier come, easier to go, I think it’s harder to do. If you’re running SMAs and you have more of a relationship with the investor, I think you have a better chance of bringing everyone along with you to the finish line, maybe fund even more so. I don’t know.
Tobias: You can’t throw up gates. Nobody likes gates, and I don’t think you can justify them either.
Jake: I know.
Tobias: But it probably does lead to better performance.
Justin: Well, it is interesting. It’s funny you’re bringing this up, because for some reason, I heard somewhere else, I was like, “What is going on with those funds?” Actually, those funds liquidated at the end of 2022. So, CGM focus fund doesn’t even exist anymore.
Tobias: Wow.
Justin: I don’t know if the assets tra– But you go to the site and it’s like, CGM focus funds have closed, and it’s like there’s an unclaimed state property link to click on to claim your—
Jake: For everybody. [laughs]
Justin: Yeah. [chuckles] For people that didn’t redeem their shares, if you’re listening, you can still go and get your money, or at least some of it. [chuckles]
Jake: Yeah.
Tobias: Charles asks, “Is that a good argument for closed end funds, despite those funds immediately trading at a discount?” I think you need some ability to buyback your own stock as a closed end fund, which managers don’t want to do because it shrinks their assets, but equally, it gets rid of that discount. And I think that’s one of the reasons why– It’s one of the advantages that Buffett has that he’s got– When the market goes down, all anybody can do is sell Berkshire. They can’t pull money out of Berkshire. And so, Berkshire can trade at a discount, which creates an opportunity for him to buy back stock which as long as the stock is bought back at a good time that generates better performance in the future.
Jack: And to Jake’s point, I think it is a case that running these focused things in SMAs to some degree is good, because you can talk to the end investor on a regular basis. You can help them stay the course. And the other thing is, Eric Balchunas has talked about this a lot. I think you are seeing people use these things maybe a little bit better than they used to in the past. Like, they have the core and satellite thing. So, they’ll have their core portfolio and they’ll take something like Ark and they’ll size it smaller. And so, when it’s sized smaller relative to the rest of your portfolio, you’re going to do a better job of sticking with it.
One thing you can say for Ark is, they should have gotten a lot more redemptions than they did, given how bad the performance was coming off the peak. So, they have gotten buy in, I think, from their investors.
Tobias: Yeah, very much so.
Jack: Whether you like the strategy or not. A lot of these other funds in the past that have had those kind of performance numbers have had much bigger redemptions than Ark did. So, they have gotten buy in, at least.
Tobias: Yeah, I think that’s the most impressive thing about those funds is that even as they fell over, they were still getting positive flows for a long time. I don’t know if they’re still positive, but now they had a good year last year, had a great year last year.
Jake: Hell of a marketing machine.
Tobias: What do you think is the most survivable factor for outside investors?
Jack: [chuckles] [crosstalk]
Tobias: I think it’s got to be something that’s pro cyclical. It’s got to be momentum. When money’s flooding in and you keep on doing really well, and then you have a 2009 where everybody’s just running for cover and everything’s bombed out, so it doesn’t work for you then. But it didn’t work for anybody anyway. But then you go back into a booming bull market and you’re back into momentum land.
Jack: One of the cool things about momentum that a lot of people don’t think is a lot of people might think value is a more consistent factor than momentum, but that’s actually not true. If you look at the consistency of five-year periods producing a positive premium, momentum is actually better than value. It’s more consistent in terms of not having the long, long periods of struggle than value is. So, it’s good from that perspective.
But my big takeaway from value and momentum in my career has always been– [coughs] Excuse me. Sorry. They work really well together. It’s something where people tend to get in these camps and they tend to say, “Well, I’m a value guy, so that I shouldn’t use momentum in any way, or I’m a momentum guy.” When you do the look at the data on them, they work really, really well together. And that doesn’t mean you have to use them 50:50. Value people can use momentum for entry and exit. There’s other ways you can use it. I think they work really well together, and I think they’re great compliments. So, I try not to pick anymore between them.
Tobias: Is that long-short, or just the long only versions of them?
Jack: Just the long only. They work really well together.
Justin: There’s a really good chart by Larry Swedroe. You can google it to find it. You got to dig it up. I think it might be maybe on like– He writes for a lot of different places. So, you got to look around. But it’s like, Larry Swedroe factor persistence, maybe or something like that. He’ll show like value momentum, and then periods 1, 3, 5, 10, 20, the percentage of underperformance in any given year. When you combine the factors percentage, those periods of possible underperformance fall significantly. It’s a really powerful visual that’s out there that Larry’s done work on. So, that’s pretty cool to see, I think.
Tobias: I just realized, fellows, we haven’t done Jake’s veggies.
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Slime Molds: Problem-Solving Without a Brain
Jake: Better do some vegetables, huh? All right. So, yeah, we wouldn’t want to miss out on this one either. We’re talking about slime molds. [laughs] God forbid that we miss that. So, this passage that we’re going to go is inspired by some work that Robert Sapolsky has done in this new book that he just came out called Determined. So, what is a slime mold to begin with? It’s like billions and zillions of these single-cell amoebas that join forces and to grow and spread like a carpet over a surface. They ooze around mindlessly in search of food. Maybe they’re not so mindless. The individual cells are interconnected by these tubules that can stretch and contract depending on the direction. Somehow this collection amoebas, without any apparent centralization, has this problem-solving capabilities that you just wouldn’t believe.
Researchers have done some really amazing experiments around this. So, here’s the setup. Imagine that you spritz a dollop of slime mold into this little plastic well, and it leads down to two different corridors. One of the corridors has a single oat flake in it, and the second corridor has two oat flakes. And apparently, slime molds love oat flakes for some reason. But similar to the hive mind like insect strategy of sending out scouts that bees and ants use, the slime mold expands into both corridors, and it reaches both of the food sources. But within a few hours, the slime mold reacts, and it retracts from the single oat flake corridor, and it heads to the one with two. How does it know? Like, how do all these things that–? It’s not like they’re talking to each other. Well, and if also, you could stick the slime mold into two different corridor mazes of differing lengths, and it ends up finding the shorter route. You could stick it into a maze with a bunch of dead ends and this brainless slime mold finds an optimal solution to its beloved oak flake.
Japanese researcher did an interesting study. He plopped a slime mold down into this strangely shaped like walled off area with oat flakes at very specific locations around it. And at first, the mold expanded and formed tubules connecting all the different food sources to each other in a bunch of multiple ways. It’s kind of a mess. Eventually though, the tubes retracted, and it ended up leaving close to the shortest path length connecting all the different food sources. Now, here’s where it gets interesting. The walls that this researcher put it in outlined the exact shape of the coastline around Tokyo. And the slime molds were deposited where Tokyo would be on the map. So, the oat flakes corresponded to the suburban train stations around Tokyo.
And out of this slime mold emerged a pattern of tubule linkages that were statistically similar to the actual train lines linking the stations that had been built. So, a slime mold, without a single neuron in it, had done the work of numerous urban planners.
Tobias: I’m just impressed that the humans got the same point that the slime molds did.
Jake: Well, I was going to make the joke that– I’m sure if we asked our friend, Moses Kagan, what he thought about LA city planners. He might say they also were operating without a single neuron.
[laughter]Tobias: Sorry, stepped on it.
Jake: I know. All right. Actually, even in computer science, there’s this famous– It’s called the traveling salesman problem, and it’s like an optimization thing. It follows like if you’re given a list of cities and the distances between each pair of the cities, what’s the shortest possible route to visit each city once and return back to the origin city? Actually, Carl Menger, who’s the son of the famous Austrian economist, Karl Menger, was one of the first mathematicians to make real progress on the traveling salesman problem in the 1930s.
Anyway, so, how does the slide mold actually do it? Let’s get into that a little bit. It’s actually a three-step process, which mimics the ants and bees strategies. There’s scouts that go out, and that’s the slime mold oozing all over. And then there’s quality dependent broadcasting. And then rich get richer recruitment. So, let’s go back to our first version of the two corridors where one oat flake or two oat flakes. The slime mold will initially ooze into both corridors, and this is like the scouting phase.
And then when the food is found, the tubules contract in the direction of the food, pulling the rest of the slime toward it. And the better the food source, the greater the contractile force generated in the tubules. This is that quality dependent broadcasting that’s effectively a form of communication. And the tubules that are a bit farther away dissipate the force by contracting in the same direction and increasing the force of contraction and recruiting more behind them, basically. And eventually, it pulls the whole slime mold towards the optimal pathway.
I’ll spare you from going into all the gory details, but it turns out that the way that our neocortex wires itself is a very similar strategy to the slime mold. Your neurons will send out scouts to connect with other neurons, and they’re climbing along these things called like radial glia, and there’s these reinforcing mechanisms to attract other neurons to hook up where there’s better connections found. So, basically, like bees, ants, slime molds, your brain wiring, it all happens without a master plan or constituent parts really knowing anything beyond their own immediate neighborhood.
And then there’s one more little branching mechanism that I’d like to share, just because it’s so freaking wild to me. All right. in your circulatory system, each cell in your body is at most only a few cells away from a capillary. That’s where the blood feeds the nutrients, expels the waste, moves things around, like it’s the transportation system. Well, the circulatory system accomplishes this by growing around 48,000 miles of capillaries in the average adult. So, 48,000 miles worth of capillaries inside of you. And yet, that 48,000 miles only takes up about 3% of the volume of your body.
Science is freaking amazing, isn’t it? Or, nature, I guess is. So, anyway, there’s more than you wanted to know about slime molds. I don’t have any real investment takeaways from that other than just the emergence of solving things perhaps using simple systems can lead into much more complex behavior than you would ever imagine.
Jack: Well, I’m wondering if I need to replace myself with slime molds to build our multifactor strategies. Maybe I could align oats in such a way that it can select the factors or something. [Tobias laughs] I don’t know, it’s something I got to look at. Maybe I was worried about AI. Maybe I should really be worried about slime molds.
Jake: [laughs] The original AI.
Justin: I got some investing thoughts from that. I was thinking like, it got me momentum investing, like the initial scouts are the early guys in these stocks, and then they send the signals of the market that there’s an opportunity here, and then you’ve got the reinforcement of more investors coming in, which drives momentum. And then, I don’t know, I was trying to weave in some things there.
Jake: Yeah, that’s farther than I made it. That’s great.
Jack: Its great stuff though, Jake. You should have been a teacher, dude. Well, you are a teacher, but you could have been a teacher, [chuckles] science teacher.
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The Complexity of Investing in Biotechs
Tobias: You guys have any strategies that look at biotechs or anything like that?
Jack: No. It’s hard to look at those with factors. Those are a lot of figuring out what’s happening with the latest drug or whatever. It’s not the kind of stuff that you can really do a good job with factors.
Jake: Has there been different points of like, I know that it’s happened where basically you could buy them for the cash on the balance sheet, and therefore, the pipeline was effectively free?
Tobias: Yeah, that’s where I was going to go.
Jake: Yeah. I don’t know if anybody’s ever really done a full quant treatment of that before. I’ve only just heard anecdotal like, “Gosh, the pharma industry is bombed out, and you can get all these pipelines for free.”
Jack: [crosstalk] Yeah, I don’t know. I haven’t seen that either. The problem is they’re typically bleeding cash. And so, it’s hard to look at the cash in the balance sheet because you can’t get access to it and they’re bleeding it. And so, by the time if it doesn’t work out, there is no cash. And so, those ones that trade like at a discount. Yeah, it’s outside of my purview. I’m like, “I’m not great at that.”
Jake: Toby… about the studies on net-nets that are somewhat comparable with losing versus making money and the return differences? What do you know about that?
Tobias: Yeah. I’ve never seen anything looking specifically at biotech. But if you believe that the cohort will justify its existence, which it seems to in the sense that they’ll earn enough returns to justify the investment in it over time, even if it’s not in any single one, because you’re going to have some giant winners and you’re going to have many losers. It seems that if you could get them at a discount to cash, discount to what everybody else has invested in, when that happens on occasion when you get a whole cohort, that’s what generated the question that we’re in a point of time now where there’s all of them trading at a big discount to cash. The whole industry or the sector or whatever you call it is trading at a discount to cash.
Jake: It’s like, free lotto tickets.
Tobias: Yeah. This is the time to– [crosstalk]
Jake: Or, mispriced lotto tickets, perhaps.
Tobias: Mispriced. Yeah.
Justin: The way people can figure that out on Validea, they can go– We have a screener. So, you can go like– It’s not all of it’s free, but you can noodle around with it. It’s under stock research and guru stock screener. And then you can add in healthcare and biotech and then see what companies are trading based on price to cash flow and a bunch of other different things if people want to play around with that.
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Wisdom of Crowds
Tobias: You guys want to make any predictions for what you think this year is going to hold in terms of–? Is it a momentum year, is it a growth year, is it a value year? What do you think?
Jake: No, I don’t want to do that.
Tobias: [laughs]
Jack: If I did, it would be the exact opposite of what I said. So, I’ve stopped calling value bottoms a long time ago because I think I’m fighting against myself by doing it. So, I guess from that perspective, I should call it a huge growth year and hope I’m wrong like I always am.
Tobias: Justin?
Justin: What was my S&P 500 prediction, Jack? Was it like 5,800 on the S&P?
Jack: Oh, yeah. We did like a joke episode of our podcast. We’re very much against these S&P 500 targets. So, we’re like, “The best way to prove that that is garbage is to actually do it ourselves.” So, the three of us on our podcast, we all three of us came up with targets for the year and predictions as to what would work and what wouldn’t work. So, now we could just make fun of ourselves at the end of the year and show that we’ve proven, just like all the other guys, that we have no idea how to do this.
Tobias: [crosstalk] If you take the average, if that’s more accurate.
Justin: Probably.
Jack: If you take the average– [crosstalk]
Tobias: There’s a wisdom of crowds thing that does seem to work in that stuff.
Jake: Yeah, canceling of errors in either direction.
Justin: Although, if you take that approach and go back to early 2023, the average strategist had the S&P flat for the year. So, you would have been very wrong that way.
Jack: I guess we were on the optimistic side, Justin, relative to the strategies for this year. Like, all three of us were actually pretty bullish in our targets.
Justin: I took like $250 on the S&P. I tried to do a methodology to it, at least. I took like $250 in earnings on the S&P. I assigned, I don’t know, a 24 multiple, which is a little bit high. But if rates are declining and we’re soft landing and growing, maybe that’s not unreasonable to think the multiple could be a little bit higher. So, I try to back into it that way, but then the counter to that is like, I look at the performance of these large cap growth names and really what drove the market. I’m thinking to myself like, “Okay, Microsoft and Apple are both $3 trillion companies. They’re going to be the ones to have to drive this thing higher.” What are we looking at, like, $4 trillion by the end of 20–? I don’t know. It’s just like, I just have a hard time extrapolating the past, like, two years of performance on those very large companies and bringing it forward, because you just start talking numbers that are just like ridiculous.
Jake: It’s AI. Don’t worry about it.
Justin: [laughs] Yeah, exactly. I’m still trying to figure out how to put Copilot in my Microsoft Office suite. I can’t figure it out. [chuckles]
Tobias: I think AI would have come up with that paper that you were discussing earlier, Jack, where COGS and PP&E or whatever the ratio was. It feels like that’s a very AI kind of outcome.
Jack: Yeah, I’m sure that was partially used in the creation of it. I think it’s really interesting because I just think challenging– Whether it proves itself as it gets tested further or not, the idea of challenging these core beliefs is something I think is really good to do occasionally. Even if the end result is you still have the core belief that you always had, I think that, to me, has always been helpful for me, when I can ever, I can take a step back and challenge something that I really believe strongly.
Tobias: It seems hard to see where the bigger companies go from this point. But then when they hit a trillion, I probably would have said the same thing. So, why not $10 trillion?
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AI Could Increase Company Profitability Through Improved Productivity
Jack: Do you guys have any thoughts? It’s probably too much to the end to talk about this, but what you think AI means for the overall stock market, it’s something I think about a lot right now, but I don’t really have– We had Adam Butler on our podcast, and he was talking about how a lot of the impact really could be in small companies, 2%, 3%, 4%, 5%, 10%, 20% companies could really be the big beneficiary.
Obviously, Nvidia or whatever is going to sell a bunch of chips. Microsoft has OpenAI, but it’s unclear how this will actually impact the stock market, because there’s some people on Twitter calling for like a bubble that’s greater than the dotcom bubble here, because they see AI as a bigger technology and they see the bubble it could create being bigger. So, I think about that a lot. I don’t really have any conclusions, but I think about that a lot these days.
Jake: There was an interesting paper that came out of– There’s a good account to follow. I think he’s a professor, Ethan Mollick, and he does a lot of AI, ChatGPT research and posts findings on it. One of the things he posted was about– I think it came out of Bain, I believe. I might be wrong. One of the big consulting firms. What they did was they took consultants and got like a baseline measurement of productivity, output, quality of the work, and then they gave half of the population ChatGPT and the other half not, and then had them do their work and then looked at the results like, what was the output, what was the quality?
It turned out that the high-end consultant, like the people who were on the highest on the baseline, didn’t really move their needle very much upward. However, the people on the bottom were actually lifted quite a bit higher from off of the low. So, it might actually be providing– it’s more of a floor than really like moving the ceiling up, which shoot from an income inequality average productivity per worker type of lens of the world. That actually could be really encouraging.
We have a lot of disparity right now in the US in wealth and wages, and the US worker hasn’t really participated as well as the US corporations have in growing the pie over the last our lifetimes, really. But yeah, if AI was somehow to boost their productivity in a way that made them closer to the higher end, that could be really encouraging. I don’t know.
Justin: Well, and I think also to that point, if you can get a boost in productivity and it helps add to the profitability of companies that are really engaging in this, whether they’re able to do more with less, they’re able to get more from their existing workforce. And then you look at all the future cash flows and then bring them back to now, how much is that worth? That’s worth probably trillions of dollars, if it comes to fruition.
Jake: I think it’s more likely to be everybody’s going to be standing on their tippy toes trying to see– [crosstalk]
Tobias: Yeah, I think it becomes table stakes too. At one point, if you had dotcom in your name, that was enough to list on the stock market. And then after a while, it just became a website and dotcom or just table stakes.
Justin: But I’m talking about, I guess, the underlying profitability of firms and how it could. But listen, margins are high already, historically. So, there’s a lot of people that think that they have to revert down. It’s just, there is an upside case here, which is AI is a technology that level sets or sets the bar higher, I should say, or makes companies more profitable potentially. And then what is the true value of that in today’s dollar terms? I don’t know, it’s just an interesting thing to think about.
Jack: Whenever I try to predict this, I always think back to myself in the late 1990s and the internet, and what I would have thought would have happened with the internet and what actually happened with the internet, and they’re so different than each other that I pretty much realize like, I have no clue what this is going to mean. I’ve used this enough to think it’s going to be a huge impact on our society in a lot of different ways. But what that like, I’m just not smart enough to figure it out.
Tobias: And on that note, it’s time. If folks want to follow along with what you’re doing or get in contact, what’s the best way of doing that?
Justin: Yeah, check us out at validea.com. We also have the podcast, Excess Returns. You can hit Jack and I up on Twitter. I’m @jjcarbonneau and Jack is at @practicalquant.
Tobias: Thanks, gents.
Jake: Good seeing you boys.
Jack: Thanks for having us. It’s good to be back.
Tobias: Thanks, JT. We’ll be back next week.
Justin: See ya. Bye.
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