← Excess Returns 
Excess Returns
He Quantified 200 Years of Disruption | Kai Wu on Separating Software Survivors from Value Traps
01:03:56
Loading summary
Transcript105 lines
- [00:00]
Justin
Study and play come together on a
- [00:03]
Kai
Windows 11 PC and for a limited
- [00:06]
Justin
time, college students get the best of both worlds. Get the unreal college deal Everything you need to study and play with select Windows 11 PCs. Eligible students get a year of Microsoft 365 Premium and a year of Xbox
- [00:19]
Jack
Game Pass ultimate with a custom color Xbox wireless controller.
- [00:23]
Justin
Learn more@windows.com studentoffer while supplies last ends June 30th terms at aka mscollegepc
- [00:31]
Sponsor/Ad Voice 1
when you need to build up your team to handle the growing chaos at work, use Indeed Sponsored Jobs. It gives your job posts the boost it needs to be seen and helps reach people with the right skills, certifications and more. Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a $75 sponsored job credit@ Indeed.com podcast that's Indeed.com podcast. Terms and conditions apply. Need a hiring hero? This is a job for Indeed Sponsored
- [00:59]
Kai
Jobs Software stocks, at least on this basis, are trading currently at a 10% discount to the market, which has never happened before. Over the course of this sample, what you find is that when you apply the value factor in the insulated sectors, actually the poor performance has been great, been just fine. You almost see no difference between 2010 on and the beginning period. These companies survived and then ultimately thrived despite, you know, being in the crosshair of disruption. How did they do it? Two things I think for many of these companies say software stocks. I think the takeaway is that look the, the code is not the moat, right? Like for many of these companies, code is one of the many things they do. But you know, we as investors need to look beyond that to ask the question of what other intangible assets, or just moats in general do these possess?
- [01:48]
Justin
Hey Kai, welcome back.
- [01:49]
Kai
It's good to be back, guys.
- [01:51]
Justin
Our audience is familiar with you. You've been on the podcast a few times. We always like having you back because in to running Sparkline Capital and and managing the ETFs that you run and that you've built on sort of this intangible value framework, you're also consistently putting out very interesting, deep pieces of research on where the markets may be misunderstanding disruption, innovation and the way that you look at sort of intangible value. And your recent piece that you put out in May titled AI Disruption Moes and Value Traps is looking at the recent sell off in software and this possible, you know, opportunity that it has created. And there's this idea right now in the market that AI is going to be this existential threat to these software names and not necessarily an opportunity. But I think as we work through this great piece that you did, you know, we'll kind of get into what the setup might be in some of these software names and, and sort of how, you know, you're using your unique aspects of natural language processing and research to sort of uncover these possible opportunities. And so this is one of the episodes where we're going to be pulling in a lot of charts. Kai is going to be working through these with us. He shared these charts with us and our audience so we can get like really down to the nitty gritty detail on the research that he's done. So I just thought we'd start Kai with, you know, your exhibit two, which kind of shows how the software, where the software premium or lack of premium is today and how unique that is in terms of, you know, software stocks after this sell off.
- [03:34]
Kai
So I guess the first thing just to set the context is that historically software stocks have commanded a premium valuation, right? Historically, investors have liked software stocks more than say the average industrial and the S&P 500 because they're asset light, because they have predictable SaaS style revenues and for a variety of other reasons, fast growing and such. So over the past roughly 20 years since this data began, their forward PE ratio of software relative to the S and P has been at a 32% premium. So that's been the historical average and there have been some fluctuations. So it kind of dipped a little bit in 09 and then on kind of a secular bull run peaking in 2021. If you remember, that was kind of the COVID bubble, right? People were working from home and interest rates were at all time lows and stimulus was coming to the market. So software stocks were kind of at their all time high valuations. And then 2022 things started to reverse. Valuation starts to fall. They went through their historical average around 23 and then the past two years they've been continually falling, kind of reverting back to first parity with the market and then more recently over the course of this year have actually fallen to a discount to the market. So software stocks, at least on this basis, are trading currently at a 10% discount to the market, which has never happened before. Over the course of this sample there's also a chart floating around from, I think it was an Oak Marks letter. They're another value manager, but they cited empirical research partners that takes the same data back to about 1980. And what they show is the same that over the past five decades, you know, first we are at, you know, all time lows with regards to the spread between PE ratios of software versus the market and by the way that we're at a discount in absolute terms to say the median stock or the average stock in the S and P, which is something that we haven't really seen before.
- [05:28]
Justin
And so that's the one of the core ideas in this paper is trying to determine now that this sell off has happened and these are trading at these types of, of you know, historically low valuations, whether or not this is a, these are possible value traps. And so talk to, I think it'd be helpful. I mean most people I think know what a value trap is, but just talk to what a value trap is and then what you kind of why. I guess value traps are problematic in the sense that sometimes when these securities get down to such low valuations, you know, they look like they're no brainer buys, but that, but, but they're actually a value trap and they don't actually add value or they don't ever appreciate from that point going forward because their model is effectively being disrupted. So talk to that.
- [06:19]
Kai
Yeah, so all a value trap is, is a, is a stock or a company that is, you know, basically on its way to oblivion, but that for a variety of reasons appear cheap on traditional or on face value and standard metrics. And so for example, a stock that has a low PE ratio, but only has a low PE ratio because everyone knows that the E is going to zero would be a classic example. What I show here in the paper is the example of four iconic companies. Blockbuster, Borders, Radio Shack and McClatchy which owns a bunch of newspapers, each of which were disrupted by Amazon, Netflix, Google over the past couple of decades. And these were at one time right large multi billion dollar companies. But over time they kind of became cautionary tales. And what I show here in this exhibit is actually interesting because I compare the stock price to the fundamentals, in this case the revenue per share in the red. And what you can see is that when this disruption first happened, investors quickly panicked and started to sell down the stocks. So as the Internet became more and more pervasive, stocks like, you know, Radio Shack and Blockbuster and Borders started to, the price started to fall. But importantly, the actual fundamentals only fell with a long lag. So in the case of Blockbuster, it took many, many years for the sales per share to fall. In the case of Borders and Radio Shack, they actually increased their sales per share for a period of time. Before it all kind of fell, the wheels fell off the wagon. Now, you know, profits also were maybe, you know, deteriorating as well over this period. And in case of McClatchy, there was an acquisition and a lot of debt taken on. But the overall picture I think is pretty clear, which is that to the extent that prices in the stock market are forward looking and they kind of price in to some extent disruption, you're going to almost always end up in a situation where prices fall faster than fundamentals which are lagged can keep up. And so you're always going to end up with a window of time when say the price to sales ratio of these companies is looking really attractive. And to a traditional value investor, yet again, that's just kind of a trap. It's sucking you into bringing on board a ship as it's about to collapse and sink into the sea. So that's what a value trap is. And I think these examples pretty cleanly illustrate what investors, as they approach the software boom, should be concerned about.
- [08:42]
Justin
Yeah, and a lot of listeners or viewers might not realize, but I remember when Netflix first sort of came out with its, you could get the three DVDs in the mail and it was like, how is this ever gonna, you know, disrupt like a blockbuster? You couldn't see it. But with all these examples, you know, it's always hard to see early on when this disruption is, you know, possibly happening in front of your eyes. And so, you know, that was another thing that I thought was very interesting in the paper. Your sort of methodology for a way to just measure, I guess, this disruption exposure and kind of what that tells us about maybe the current environment that we're in.
- [09:25]
Kai
Yeah, so I think the key here was obviously those four examples are cherry picked, they're helpful anecdotes. But the question I really wanted to answer was that if you were more systematic about doing this, would it turn out to be the case that these four examples of the blockbusters and borders are actually representative of a systemic problem that traditional value investors might face. So in order to build on that and kind of set that up, obviously I didn't do the way to kind of in point in time, in real time, quantify when there is disruption and which companies are exposed to said disruption. So the way I did this was in a two step process. I built on a paper I wrote in 2022 called Investing in Innovation. And so what I did was I looked at this data set of all the patents ever filed with the US Patent and Trademark Office. Really cool dataset. It goes back to 1790. The first patent was signed by George Washington. And basically you can use it to see over the course of the past two centuries, the rise and falls of new technologies from the automobile to electricity to the Internet. And so what I do is I say there's obviously at each point in time, hundreds, thousands, tens of thousands of patents. What you care about is trying to cluster them into, like, groupings of similar technologies and then from there to see whether is an increase in the technology. Because oftentimes you find that there might be false starts where, say, a technology starts to gain prominence but then eventually fades. Electric vehicles were famously a competitor to the internal combustion engine, but then ultimately lost out 100 years ago or so. And so you want to find trending technologies. You also want to find technologies that aren't only just trending, but also are pervasive. And what I mean by that is that they're not just increasing a lot in one specific subdomain, say, like in healthcare, but they also are pervasive across industries. So AI is kind of the best example. They call it a general purpose technology, meaning that it applies, of course, to software use cases, but should in theory be able to automate and do a lot of the human labor that is obviously a factor of production for most economic activity. So I want pervasive things. I look specifically for increases in patent volume that are pervasive across industries. And so then that allows me to define the technologies themselves. And then the second step is to figure out what exposure firms and industries have to that disruption. And what we do is we look at a bunch of different documents, ranging from urgent call transcripts to patents themselves, to company filings, analyst commentary, to figure out which companies are exposed to each technology. So, for example, like, if E commerce becomes a thing, which companies are potentially exposed to that disruption? And then what we do is we actually roll it out to the industry level. Because any single company can be noisy, right? The data themselves can have noise. So you aggregate to the industry level so that you can say within retailers as a whole, even though some maybe may not be exposed and some might be exposed, on average, they have this level of exposure. So we're creating an industry level, you know, exposure. And then in this chart that you pulled out here, you know, I basically highlight over the past, you know, few decades, I think, seven different major disruptive waves, ranging from the advent of Internet infrastructure to E commerce, social media, and then AI, Right? And as you. And as you roll through, I kind of look at what periods were each of these Things these disruptions most prevalent when the pressure was the highest on disruptees, which are some examples of patent clusters that know collectively form this theme and then which sectors at each point in time were most exposed? I think one takeaway here, it's not always just, it's sometimes technology, it's not always right. Retailers and you know, newspapers, entertainment being good examples of sectors that were exposed to say digital media or E commerce, you know, even though they may not have been kind of the progenitors of that technology.
- [13:20]
Justin
Just a process question here and then I'll let Jack go is when you, when these clusters. This has nothing to do with the paper, it's more about the process, when these clusters are being built or formed. Are you telling the, are you telling it what to look for or will it automatically, does it find it like through its, the natural language processing or does the system find these clusters automatically? Or do you, do you have to instruct it as to what to look for?
- [13:50]
Kai
I mean there are some hyperparameters, like how many clusters, like how sensitive they do thresholds. But putting that aside, it's fully automated. So it'll kind of go through and say, hey, we'll look, look at all the patents and then figure out, you know, form the clusters and then figure out which clusters are trending and which are not. And then separately in step two, identify companies and then industries that are exposed to each sector. So for example, like I have my code set, you know, we can show up in 20 years from now, you know, dust it off or whatever, and it'll have a totally new set of, of technologies and companies.
- [14:23]
Jack
This is another aside, but does this at all allow you to rank these disruptive waves against each other? So everybody's talking about the AI is the biggest disruptive wave we've ever seen in history. Does this tell you anything about that at all?
- [14:35]
Kai
I think it's a fair point if you measure it by pervasiveness. As I mentioned, artificial intelligence is meant to be a general purpose technology. Not that these other things aren't necessarily, but it can in theory affect all facets, especially once robotics and such are in play of the economy. I think one thing I would add though is that they're all dependent on each other. This is the idea of the stacking of innovation over time. We wouldn't have AI if we didn't have electricity. We wouldn't have electricity if we didn't have fire. So all the technology over civilization's history has kind of compounded over time by building on each other. And so I think it's important to remember that AI is obviously a really cool technology. It requires really advanced computing, it requires big data, which is obtained in many cases through the advent of the Internet, being able to digitize information and put it into a format that we can all see. So these things all tend to build on each other. And so maybe we are at the apex currently in terms of where innovation and disruption is. But a lot of that just depends on previous innovations that had they not occurred, we wouldn't be able to be where we are now.
- [15:46]
Jack
The stacking gets to this next chart we want to look at and poor retail is all I can think about. It's been like getting the crap beat out of it by every innovation for decades here. But can you just talk about the idea of what we're seeing here?
- [15:55]
Kai
Yeah, so this is divit 7. Take the example of retail. I mean it could have picked on a divot industry. I guess it was mean. But the idea was to show through time the amount of disruption it's absorbing from the seven or so different themes that I mentioned through time. So kind of the key insight here is that they stack. So in other words, first E commerce comes around, the Internet comes around and that obviously Amazon's there and if you're I guess blockbuster that you don't even make it past that. But let's imagine that you do make it past that. Well then next you have to deal with digital media and then you have to deal with social media and then you have to deal with AI. Right, like agentic shopping and such moving forward. Right. And these things stack. In other words, it's not that companies today don't also have to deal with, so retailers today don't also have to deal with E commerce and as a threat they also just have to deal with that plus they have to deal with digital media plus AI. And so if you sum up the exposures to all the different themes over time, what you find is that yes, they come in waves. The peak of E commerce disruption happened and then it kind of subsided before social media really kind of came into play. But the trend is kind of this secular increase over time as innovation is accelerating, as technology compounds on itself on, on each other. We end up in a situation where yeah, the, the, these companies are now being kind of exposed on all fronts. They're waging like a multi front war, so to speak, you know, against, you know, innovations and technology coming from all different angles.
- [17:21]
Jack
Now it also explains a lot because as a value investor, like if you've watched your value screens over the past ever dec. However many decades. Like these retailers have like perpetually been in them. Like they don't ever leave them. Like these retailers in the mall and stuff like that. Like those are, they're always sitting in these screens and so innovation getting stacked.
- [17:37]
Kai
Yeah, yeah.
- [17:38]
Justin
Being Fitch over there. Yeah.
- [17:41]
Jack
Like, I mean you'd be surprised. Like Justin, you know, we've been running quant models forever. Like you and I have seen these, these various names like you'd see if you walk to the ball right now,
- [17:47]
Justin
like William Sonoma, Abercrombie Fitch. You got Claire's used to be in there.
- [17:52]
Jack
Oh yeah, they're all, they're all in there. Hot Topic and like I don't even know that even exists anymore. They got all these things. But anyway, back to the paper. This next chart gets this idea of the death of value investing. So before we get into kind of what's going on now, just good to maybe take a cumulative look at this and, and how the value factor has performed and why it hasn't been working for a long time. So can you talk to this chart?
- [18:11]
Justin
Yeah.
- [18:11]
Kai
So I mean, you know, I'm sure your listeners are, are somewhat familiar at least with this, this idea, you know, value investing, you know, buying, you know, beating down retailers. Like the general idea has, you know, has been long, you know, a long held tradition amongst many investors ever since Ben Graham in the 1930s, Warren Buffett of course, being, you know, a famous proponent of the school. The thing is that value investing, however we define it, has really had a tough time of it and a lot of it has to do with disruption, which we'll get into more over the past couple of decades. Obviously there are many different ways of quantifying it. What I've done here for this exhibit is to do something pretty simple where I create a long short factor. In other words, you along the cheap stocks short the expensive stocks on a valuation metric. In this case, it's a blend of I think four different things. Price to earnings ratio, price to book ratio, price to sales ratio, and price to free cash flows. And basically the reason why you can diversify across different metrics. And the point is this, which is this is the factor that, and if you extended the backtest all the way back to the initial work 100 years ago, you would have seen consistent outperformance for decades. And then around 2010 you would have started to see a drawdown. It starts to turn over and really has never recovered, even as of today. And so this is what has led many people to declare this the death of the value investing that perhaps we should be doing. Meme stocks or the prison world of value don't buy anymore and value investors have lost supply. They're all too old school and they're just buying a bunch of Abercrombie stocks or whatever. Right? But to me it's always been that can't be right. I mean, value investing makes sense by definition. It's just maybe the way we measure it that could be problematic. And so this is kind of a really interesting study that we did here and kind of the conclusion is that value investing is not dead, it's maybe just being disrupted. So what I do here in Exhibit 9, and yeah, this is really the key exhibit of the paper, is I say let's not apply the value factor to the entire stock market, but let's instead apply it separately to two different parts of the market. First would be what we call exposed industries, which are the industries for which their technological exposure score, which we showed in the case of retailers exceeds a VIX threshold. And then that'd be one, and then the second would be insulated industries. So those are industries that are not exposed. Right. And those two things collectively by definition comprise the market. So divide the market into exposed versus insulated halves. What you find is that when you apply the value factor in the insulated sectors, actually the poor performance has been great, been just fine. You almost see no difference between 2010 on and the beginning period. In other words, values work just fine as long as it's not in industries that are exposed to module disruption. However, if you look at exposed industries, industries like retail starting in the mid-2010 or around then or a little earlier, you find that the performance has been quite bad. And so starting in 2010 you have this big drawdown. And by the way, the drawdown is so big it overwhelms the positive returns from applying the factor in the insulated industry, such that the net return for the factor is negative. So said differently, you can explain the demise of value investing through the lens of if you want to apply these traditional metrics to real estate companies or asset heavy businesses, fine, go ahead and do that. It's no different. But if you want to try to apply it into sectors that are now exposed to technological innovation, not just software, but sectors like retail that, you know, maybe initially were not exposed, but now are heavily exposed, you're going to have some, some issues and that's not going to work. And then to the extent that the market is more and more, as we'll see in exposed industries, that's going to overwhelm the positive returns you get from this kind of vanishingly small part of the market that is insulated experience.
- [22:03]
Sponsor/Ad Voice 1
A membership that backs what you're building with American Express Business Platinum. Enjoy complimentary access to the American Express Global Lounge Collection and apply to find out your welcome offer, which could be as high as 300,000 Membership Rewards points. American Express Business Platinum. There's nothing like it. Terms apply, welcome offers vary, and you may not be eligible for an offer. Learn more@americanexpress.com Business Platinum trading at Schwab
- [22:34]
Sponsor/Ad Voice 2
is now powered by Ameritrade, giving you even more specialized support than ever before, like access to the trademark desk. Our team of passionate traders ready to tackle anything from the most complex trading questions to a simple strategy. Gut check. Need assistance? No problem. Get 24. 7 professional answers and live help and access support by phone, email and in platform chat. That's how Schwab is here for you to help you trade brilliantly. Learn more@schwab.com trading it's such an interesting
- [23:04]
Jack
point because if you go back to my point about retailers or the mall, like, if I had known in advance that they were undergoing a disruption, I could have not applied value investing in that industry. And that's how it proved out to be the truth. Like, you did not want to use value investing in retail basically at any period in the past, however many decades plus.
- [23:21]
Kai
Right. And I think it requires two things. One is a recognition that, hey, this disruption is coming. And second, recognition, kind of like a lack of hubris to be like, yeah, you know, and by the way, I'm not going to try to apply my metrics in this thing because I just don't think it's going to work. Right. Warren Buffett talks about this circle of competency. For the longest time, he avoided tech stocks. He said, this is just not my cup of tea. I don't know how to. I just don't do this. Which works when tech isn't the entire market. And then when it is, you're basically in cash.
- [23:47]
Jack
So this next one, you did some robustness checks to check deeper to make sure there's nothing else going on that would explain why value's not working here.
- [23:53]
Kai
Right? That's right. Yeah. So one fun aside is that this paper is kind of the first one that I did where I relied heavily on, in this case, cloud code to do a lot of the experiments. And so the fun thing was that, you know, I basically did the Base. I created the baseline script and once I had it, I was like, hey, you know what? Like, I want to test like a bunch of different things, you know, not just the U.S. i want to test it in global stocks, international stocks, emerging market stocks. I want to look at like, you know, sectors, sub industries, industries, sector groups, so on and so forth. Right? And so kind of the workflow ended up becoming like, hey, you know, Claude, can you like take this and like apply it to the other things, like show me the results. Let me look on the table with you. I have a couple follow up questions, so on and so forth. So it's kind of a fun way to scale the analysis by kind of delegating a lot of the robustness exercises to Claude. But yeah, that allowed me to cover a lot of ground. I mean, what I'm showing here are just six or I guess five, five of the major robustness checks. What this shows, by the way, is the spread between exposed and insulated returns. So remember, this is a negative number because when you apply the traditional value factor in exposed industries, it does worse, right, than insulated industries. And so the baseline shows a spread of negative 7 percentage points per year. That's very bad. And then you can say, what if you look at just global stocks, not just U.S. stocks, you know, kind of, kind of not so good as well. What if instead of looking at this blend of valuation metrics, we focus just on the canonical FAMA French price to book ratio. Okay, that doesn't work. What if you set your neutralize, I guess is a little bit less bad because you're explaining some of the variation. But even within sector you're seeing that the company, you know, that there is kind of an effect here. What about if you do so one popular thing these days is to do these double sorts where what investors will do is they'll say, I recognize that price to book or price to earnings could have value trap risk. So therefore I want to intersect it with say ROE or some profitability metric in order to, or momentum in order to say ideally weed out value traps. So you want companies that are both cheap and profitable or cheap and have not bad momentum. Well, it turns out that actually helps by the way, in absolute. But on a spread basis it's the same, right? Maybe the lines are not going from positive to negative, but they go from positive to positive, but less positive. But the point just being that the gap remains, in this case 6.3 percentage points. So like a meaningful, meaningful gap. So look, I mean, the point just being that this Finding seems to survive the exact specification of the value signal the universe applied to and so on and so forth. So it's a pretty robust finding.
- [26:34]
Jack
We just did an episode with Cliff, as in this. And this reminds me of exactly what he's done in a lot of his papers, like his international paper Value Investing is Dead. He'll ask every question as to what could possibly explain this. And then once he's eliminated all those, he'll say, all right, my conclusion is,
- [26:46]
Kai
okay, well, I mean, I guess it's impossible to prove anything. Right. So we had to just kind of narrow it down and try to throw out as many competing hypotheses as possible. Right.
- [26:56]
Jack
So, so Exhibit 12 is, it kind of gets back to what I asked at the beginning, but this idea of how big the disruption is. So what are we seeing here? I mean, obviously this is, this is a very large number of companies that are exposed.
- [27:07]
Kai
Yeah. So what we see is a number that the percentage of market cap, and say I think this is in the US market exposed to innovation, however defined, increasing from about 40% to mid 70s, 75% let's call it, over the past 20 years. And that's the result of two things. So one is the fact that technology is affecting more and more industries. If you went back to the 1980s, tech was just like IBM and now tech is like all companies have tech to be, to give you one simplified example. And second is just that the tech industry, or whatever you would call these technologically exposed industries to be more precise, are just a bigger part of market cap. Right. But the point is that even if you look at things on an equal wave basis, on a names basis, not just market cap, you get a similar result. I think it's 72 versus 78%. And by the way, if you look outside the U.S. the numbers are a little bit less extreme. So within developed markets or emerging markets, the numbers aren't quite 78%, but they are still above 50%. And they still have the same feature of an increasing trend even within emerging markets which have been of the, you know, been the kind of least technologically advanced of the major economies. You do find a trend where, you know, the idea that, hey, I'm a value investor, I want to do the thing where I just like hide in non exposed sectors. That kind of like has been, you know, increasingly challenging thing to do as those non exposed sectors kind of go away.
- [28:28]
Jack
Yeah, this, this reminds me, we talked to Andy Constant in the last episode. He was talking about this idea of what's different from 99 and now. And one of the differences was like tech's just a way bigger part of the economy and the market than it was then, which I think kind of is sort of a corollary to what you're talking about here.
- [28:41]
Kai
Right. And not only is tech as a, you know, say GICS or MSCI definition a bigger, such bigger part, but tech cross cuts across all industries, even in industrial is more reliant on technology today than it was 25 years ago. So yeah, but I would agree with Andy's point on that.
- [28:57]
Jack
So I guess the big question here is how do we differentiate the companies that are going to survive this, they're going to thrive in this, versus the companies that are going to be disrupted. And I think as we get further in the paper, that's what we were trying to address.
- [29:07]
Kai
Right?
- [29:08]
Jack
Yeah.
- [29:08]
Kai
So I think this is where we switch gears. So I think the first piece part of the paper was more around what not to do. And now it's like, okay, so can we actually study history and can it be actually illustrative onto what you should actually do? And so here's where I bring the examples of Walmart and New York Times into the discussion. You know, obviously Walmart is a retailer, New York Times is a newspaper. Two of the most beaten down industries from disruption. What you find is that these companies survived and then ultimately thrived despite being in the crosshairs. Disruption, how did they do it? Two things. First, they maybe not initially, but eventually leaned into the technology that was disrupting them.
- [29:47]
Sponsor/Ad Voice 1
Right.
- [29:47]
Kai
Walmart has one of the biggest e commerce businesses today. And second, they leaned into their unique intangible assets outside of technology, let's say, that allowed them to be who they were, their brand, their human capital and network effects. And so actually this is where I bring in a paper I thought was really interesting by this guy named David Thiess. This paper was about who profits from technological innovation. It was written in 1986. But the principles, while dated, are timeless, I think. And the key insight was this, which is that the long term winner of an innovation isn't always going to be the one. The initial winner or the innovator itself, oftentimes the person, the firm who ultimately accrues the value or captures the value of an innovative cycle, a disruptive cycle is not again the core innovator, but in fact the firm that possesses the complementary assets. This is his terminology, complementary assets that surround the innovation. I have Here an Exhibit 14 that shows some examples of. This is from Dirking from his paper of things that are considered complementary assets. That's manufacturing, distribution, customer service and then complementary technologies. So outside of the focal IP or other IP that surround and kind of cement a mode around that. And he gives all these really cool examples. He gives the example of a company called emi, which is a UK based company. They actually invented the CAT scanner, which is a machine they sell to hospitals. But the problem was that selling stuff to hospitals was really hard. And it turns out that you need to do a whole enterprise sales cycle and then you need to do effectively for deployed engineers, train the people how to use the machine and then service it once it breaks down. And that's a really hard thing for them to do. What ended up happening was ge, General Electric came in there and they had those other complementary assets in place. They didn't have the technology itself, but over time they figured it out and they developed it and then they won the market. They have another example he talks about called like RC Cola, which I guess was like a small cola company they sell. They actually invented the diet and canned cola. So that was an innovation at the time. But they didn't have like the shelf space or the distribution of their brand that Coca Cola and Pepsi had. And they obviously won that market. And then another example was kind of the opposite case is IBM. He talks about how IBM at the time was late to the PC market, but they managed to capture it in the 80s at least. And he says it's not through the strength of their technology, but rather through the ecosystem of software and peripheral that they kind of built around the IBM framework, what we would call network effects today. And so you have these examples which are quite illustrative. So who won? You know who wins? IBM, Coca Cola and ge. Right. They win on the back of these intangible assets, even though they weren't the one who actually innovated the underlying technology. They didn't invent Diet Coke, they didn't invent the CAT scanner, but they had the necessary assets to win the market.
- [32:46]
Jack
Right.
- [32:46]
Kai
So I think that's a really important lesson to think about as we approach the software sell off. Yeah, these software companies, if their core moat is code, then yeah, maybe they are in trouble. But if it's the complementary assets around that, then per Tisa's framework, they actually might be fine. And conversely, yeah, Anthropic and OpenAI are the early winners. They are the innovators in Google, I guess, of this new technology, the LLM. But that doesn't necessarily mean that they will capture all the profits because there's so many other things that matter when it comes to the way that these competitions unfold.
- [33:17]
Jack
Well, first of all, RC was really good. I don't know if you guys ever had it, but it was actually, I thought it was better than Coke and Pepsi.
- [33:22]
Kai
Really?
- [33:23]
Jack
Do you ever have Justin?
- [33:23]
Justin
Okay, Jack, No. But you are a soda expert, so I would imagine your ranking would be important here.
- [33:29]
Jack
I mean, you don't, you don't run the five, 30 miles Justin runs if you, if you're drinking things like RC Cola, it's people like me, they're, they're doing more of the consumption of RC Cola. But the other thing that was interesting, Kai, is we were talking about, like you were talking about the idea of leading into the disruption. And one of the things that crossed my mind is it's interesting because like in the past you've had like the Walmarts who had to lean into technology disruption. Now this time you actually got tech firms that have to lean into technology, disrupting their technology, which makes it a little bit unique, what we're going through right now.
- [33:57]
Kai
Yeah, in a way, they are, I guess, positioned a little better than some of the firms of yesteryear when the new technology comes around because they're already kind of tech facing. Is it a different type of technology? Yes. Right. Like being a good AI coder isn't necessarily the same thing as being a good traditional engineer, but it's definitely a lot closer than being somebody at Blockbuster. Let's say when the streaming comes around, and we'll see the data on this too, that software companies are amongst the most aggressive in terms of their adoption and investments in AI. So they see it coming, they know it's a threat and they know they're vulnerable. And they're doing in many cases what they can in order to offset that exposure.
- [34:40]
Jack
So one of the things you talk about in the paper, which we've talked to you about in previous interviews, is this idea of intangible moats, their ability to protect themselves using these things. So before we get into that, I thought maybe it would be good just to revisit that quickly. I'll put up this exhibit 16 here. Quick intangible value. If you can just talk about the different intangible moats that you measure.
- [34:56]
Kai
Right. So I touched on each of these. So the first of the four is intellectual property. Right. This is not just patents, but any kind of proprietary knowledge, data, software technology. Second is brand equity. That's customer relationships, brand loyalty, things like that. Third is human capital. That's not Just the position of a talented workforce, but also one that's culturally aligned around a common goal. And then finally, network effects, which is this ecosystem of external producers and consumers. We saw this with IBM. You know, examples today might include like Uber or like the New York Stock Exchange. Great ice. And so, you know, when you have these four types of intangibles, they can be, you know, really important. And in fact, I would argue that, you know, most value today, most value capture today in the economy is due to, to these four intangible pillars as opposed to traditional tangible capital, which I think is just having a lot of book value. Is that really a moat? Does that actually give you the ability to kind of earn excess roic? I would argue probably not. But yeah. To your question though, how do we build the metric? We have a bunch of different underlying proxies for these pieces. So for example, we looked at, for the traditional value metric, price to earnings combined with price to book, price to sales. In this case for ip, we might say let's look at, you know, the patents, the price, we might look at R and D expenditures to price, so on and so forth, smush them together into a metric. Do the same for brand with like trademarks and you know, social media, human capital with maybe job postings and you know, employee profiles. And you create like these scores, again similar to what we did with a traditional value, these yield based metrics looking at price relative to X, X being a measure of intangible capital for these four different pillars. And then we combine it into one final composite score so that every single company in your universe and you know, whatever 5,000 global companies, have a score that you can then kind of look, at least at any point in time, who's high, who's low, and you can kind of build factors around it the same way we did with traditional value.
- [36:53]
Jack
And as we get into exhibit 17 here, this, this looks at that idea that traditional value investing is not working in these exposed industries. But when you adjust and use intangible value investing, we get a different story, right?
- [37:03]
Kai
Yeah, that's exactly what we see here. If you remember the exhibit from before, we saw that the traditional value applied in the whole universe worked okay and then it stopped working around 2010 and was in a drawdown and he split it into two pieces. It did totally well in insulated industries, but struggled in exposed industries. What we're seeing now for intangible value, so once you look at not just traditional metrics, but you also add in these intangible moats, what he would Call these complementary assets. What you're finding is that the factor works in insulated industries, as it did before, but most importantly, it now goes from not working to actually working quite well in exposed industries. Because exposed industries, if you remember put aside the jargon, these are just industries where you're facing disruption from a technology, whether it's E commerce, whether it's cloud computing or social media or AI. And what allows you to survive, what allows you to be the New York Times or Walmart, are these competing assets, these complementary assets? And in addition to of course being embracing the technology itself, all of which are in theory captured by this horopillar framework. I think this is actually an important point. So even just step back. The TEAS framework and the intangible value framework are kind of very actually related. So you think about it this way, which is like t says there's a focal innovation, the focal innovation is a subset of the IP pillar, right? So a company, so a fourth, when we go out and we say, what is the intangible value looking for? It's looking for companies that are doing AI, of course, but we're not just looking for companies that are doing AI. We're looking for companies that also do other types of innovation. Other types of ip, as teacher would call, he would call them complementary intellectual property innovations, like in robotics, like in genomics. And we go even one step further and say we were also looking for companies that have strong brand modes, human capital network effects, the true complementary assets. So we kind of want all these different things. And so going back to the exhibit here, what you find is that once you kind of look more holistically outside of just backward looking earnings and book value and look at what intangible most companies have now you're starting to be able to put together a framework that now works not just in insulated, but also in exposed industries. Also when you're facing technological disruption, you're able to be able to separate the kind of Walmarts from the blockbusters.
- [39:20]
Sponsor/Ad Voice 2
When you finally find your thing, you want the whole world to know about that thing. So you use a thing called Canva to make it an even bigger and better thing. Whether you want to create flyers for that thing, make presentations for that thing, or design merch for that thing, you can do anything so people can see your thing, feel your thing, love your thing. The next thing you know, it's a thing. Canva, the thing that makes anything a thing.
- [39:50]
Sponsor/Ad Voice 1
Chronic migraine, 15 or more headache days a month, each lasting four hours or more, can make Me feel like a spectator in my own life. Botox Onobotulinum toxin a prevents headaches in adults with chronic migraine. It's not for those with 14 or fewer headache days a month. It's the number one prescribed branded chronic migraine preventive treatment prescription.
- [40:08]
Sponsor/Ad Voice 2
Botox is injected by your doctor. Effects of Botox may spread hours to weeks after injection causing serious symptoms. Alert your doctor right away as difficulty swallowing, speaking, breathing, eye problems or muscle weakness can be signs of a life threatening condition. Patients with these conditions before injection are at highest risk. Side effects may include allergic reactions, neck and injection site pain, fatigue and headache. Allergic reactions can include rash, welts, asthma symptoms and dizziness. Don't receive Botox if there's a skin infection. Tell your doctor your medical history, muscle or nerve conditions including including als, Lou Gehrig's disease, Myasthenia gravis or Lambert Eaton syndrome and medications including botulinum toxins as these may increase the risk of serious side effects.
- [40:44]
Sponsor/Ad Voice 1
Why wait?
- [40:45]
Kai
Ask your doctor.
- [40:46]
Sponsor/Ad Voice 1
Visit botoxchronicmigraine.com or call 1-844botox to learn more.
- [40:51]
Jack
What's interesting too is in Exhibit 18, like if I was trying to put together a more ideal value strategy, what I would want it to do is work regardless of the disruptive period. I wouldn't want to figure out if I'm in the disruptive period. I want to just work regardless. I think that's what you're getting at here with intangible value versus traditional value, that even in this disruptive period, non disruptive period, the performance has been pretty similar of intangible value.
- [41:11]
Kai
Right. The key is consistency. I guess what you call all weather, it appears to work. So first of all, what this exhibit does is it cuts it into two dimensions. One is inexposed versus insulated and the other dimension is by time. So we're looking at the first half of the sample when things are kind of better, and then the second half when things have been more challenging for traditional value. And so what you find is that intangible value, regardless of the time period or whether you're looking at exposed or non exposed industries, has tended to be pretty consistently around the same outperformance. Whereas if you look at the traditional, it's highly dependent. If you're looking at the first half of the sample and the insulated industries, you do great. But as soon as you start to go more recent or you start to go to more exposed industries, traditional value kind of falls down. Right? So that's the challenge which is like when it becomes so contextual then like, yeah, if you do factor timing, it can work, but like you have to be very. Then you need to have a good model and a good understanding of when to apply it and when not to versus it being more all weather.
- [42:08]
Jack
This next one's really interesting because you actually looked back to 2007 and you looked at the companies were out there and you looked at traditional value and you looked at intangible value. You looked at what they agree on, what they disagree on, and then what ended up performing well. So what is the lesson from this?
- [42:21]
Kai
Yeah, so what this exhibit shows, it's a matrix, a two dimensional thing. So on the X axis it shows the traditional value score from expense to the cheap. On the Y axis it shows the same but for intangible value. So we have the four quadrants where the diagonals are where they agree and then the off diagonals are where they disagree. So the upper right is where companies where both metrics agree. The lower right where they both disagree. The lower left. The lower right is where a stock might look cheap when traditional but not intangible. And then in upper left it's the, it's the opposite. So the other thing I did here is I color coded each each dots into three colors. So blue means it's a company that over the next 10 years was a winner. Right. Apple Kroger, gray means it did okay, and then red means it was a loser. Like Las Vegas Sands Gamestop did not do well from 07 to 2017. Right. And what's, what's immediately visible once you look at the colors is first that intangible value worked pretty well because most of the blue dots, the winners were in the top half of the exhibit. In other words, intangible value regard regardless of whether where it scores on traditional value. Cheap intangible value stocks had done well the next 10, 10 years. The other thing you see is that in if you focus on the off diagonals is that traditional value had some, had some challenges that like stocks that look cheap on traditional value but expensive on intangible value, like Macy's or Wells Fargo tended to be losers. And stocks that looked expensive on tangible value but cheap on intangible value, like Amazon or Apple tended to be winners. Right. So this kind of explains I think more intuitively what we just saw. Like why was it that traditional value struggled in exposed industries? Well, it was because they sold the Amazons and they bought the Macy's. Whereas intangible value, because you're now taking into account, say the moats that, the intangible moats that an Apple might have, the network effects, the brand, the human capital, the ip. Suddenly Apple no longer seems expensive, it seems cheap. So it helps you kind of more discriminate between companies that might seem expensive optically but are actually truly disruptive, and also companies that might seem cheap optically but are actually truly being disrupted.
- [44:25]
Jack
What struck me the most about this is no blue dots on the bottom. So there were no extremely expensive companies according to intangible value, that ended up being the biggest winners.
- [44:33]
Kai
Right? Not in the bottom.
- [44:36]
Jack
Yeah, yeah, the bottom of the whole chart. Which, which means that like there it was measuring value. Right. Is I think what it means because there were no. There was maybe there were some that were slightly expensive according to a tangible value, but there were another like extremely expensive according to tangible value that then ended up being like an Amazon type company.
- [44:51]
Kai
Right. I mean, to be clear, this is just the top 100 stocks, 10 under larger stocks at the period in time. So there could have been like some other names that would have been there. It just would have been too many dots. So I didn't want to show, you know, a thousand dollars. Yes.
- [45:02]
Jack
But it still is pretty interesting. So this next exhibit gets at the idea of looking at the same four quadrants, but now we're looking at return by quadrant, right?
- [45:09]
Kai
Yeah. So all I wanted to do here was just make sure that the results generalized. The previous chart showed just a 10 year period from 07 to 17. Now I wanted to look at the full sample, but the setup's the same. And so what you see is the stocks that both metrics agreed were cheap did the best 4.2% annualized returns. Stocks that they both thought were expensive to the worst negative 5.1. When there was disagreement, intangible value won. So in other words, the quote unquote expensive disruptors, the apples and amazons did well, 2.8. And then the value traps, the stocks like the Macy's. Right. That were. That look cheap on traditional but not on intangible value did negative 1.6%. Now a couple interesting findings. So first of all, first of all is the fact that, yeah, you know, the intangible moats do appear to matter. So that's good. The second thing we find though is that when there's agreement, it's actually more powerful than when just intangible value thinks something. And so that kind of goes back to this idea that I think we've discussed on the podcast in the past that potentially there's a role for these two metrics to be complementary with each other, that the real red flag is not only when something's just expensive on intangible value, when it's also expensive on tangible value, when it's expensive on both metrics. That's pretty concerning. Right? So I think that that's another interesting takeaway from this exhibit to me.
- [46:29]
Jack
So this next exhibit, we're actually taking this now, we're applying it to software. So we're looking at a tangible value score. And you're putting some of the names here that a lot of people would recognize and looking at whether they're cheap or expensive on a tangible value.
- [46:41]
Kai
Right. So what we found up to here, we spent most of the paper talking about like the historical disruptions, like going through the past waves, thinking about what metrics do and do not work. We went through Thesis framework of complementary assets to understand how to think about moats. So now what we do is we kind of say, let's bring it all to the present. Let's all put it together in a way that would be applicable to today. We're going through the current disruption with software stocks having sold off significantly due to AI disruption fears. What this chart shows, 21, is software stocks that are down 30% or more in the past one year. So these are not like your software stocks that are like. This is not all software stocks, but it's the ones that are considered losers because of AI generally over the past year. And what I did was I showed the distribution histogram of the intangible value scores for these names at this point in time. And so the first thing you see is that the average is positive.
- [47:42]
Sponsor/Ad Voice 1
Right?
- [47:42]
Kai
It looked to be about like.03 or something, suggesting that, yeah, these stocks, which are in a large drawdown, they sold off like 30% with the market up 30% over the past, you know, seven or eight months. So 60 percentage point spread that these stocks may on average have been oversold. You know, shoot first, ask questions later. But the second thing you see is pretty decent dispersion and more importantly, dispersion on the left side. Right. So you look at the left tail on the red. This is actually really important because this is not usual. Right. You don't usually see this much dispersion on the left side of companies that are basically value traps. Companies that are down 80% or something, but are still expensive on these metrics. That's generally unusual thing to see. And again, don't read too much into these logos. They're shown for illustrative purposes. But you do see that look at HubSpot versus Salesforce. Both of these are CRM type companies. Salesforce is looking at least on these metrics, you know, on the cheaper side where shops are looking more expensive. So you do see some dispersion even within comparable names which I think is, is worth with, is worth noting.
- [48:49]
Jack
It is interesting by the way too because to your point, like GoDaddy, you know, registering domain names, building websites, like I saw wix I think is laying off a bunch of people like it makes sense like where these are based on what you would think in terms of what their votes are. Like it would seem like a GoDaddy
- [49:02]
Kai
would not have a very strong boat. Right. And so there's another exhibit I have in this paper where I actually use this framework of the four intangible pillars and say like what are the, you know, what are the most that a company might have? Right. So like accumulated business logic, like embeddedness in customer workflows, customer relationships, regulatory compliance burdens. Right. And so one of the insights here is that, and this is pretty intuitive, I think most people know this is that you know, more enterprise facing software companies that face like the largest enterprises will tend to actually have wider moats because switching costs are a lot higher. These things are a lot more embedded. There's systems of record. The compliance requirements are so much more onerous than say consumer facing things like GoDaddy for example or Duolingo here too where it's a little easier for a random person just switch off an app. And so I think that these things do correlate and you can look through each of the four intangible pillars and have this in an exhibit actually and look at kind of scoring. You can sort a bullet point by bullet point to say hey, which for a given company X, where's your score on these four intangible pillars? And then on each of the say 20 or so sub points within those pillars.
- [50:19]
Jack
So there's basically two ways I think if I'm a software company there's two ways I can succeed here. And I think you get it this in the paper. One is I can have a moat which we've talked about. The other is I could really embrace AI. So as we get into the rest of the paper, those are kind of the two things you're looking at, right. In terms of the way to differentiate these ones that might succeed from the ones that won't.
- [50:36]
Kai
Yeah. So if you remember the last paper we did together was on, it was called like AI adopters Beneficiaries of the boom. And the idea there was to find companies that are positioned for AI adoption, right? Because presumably they, over time, if AI becomes a thing, would separate from the laggards. And that was like a one dimensional thing. What I'm saying now is let's take David Tiese's framework and say, hey, look, that's obviously important, but it's not the only thing that matters, right? The fact that Walmart figured out E Commerce was important, but they also had a lot of other things going for them that allowed them to survive relative to any other legacy company that was trying to become an E commerce company. And that is the complementary modes. So I'm adding to the AI adoption lens, this additional lens which is really the remaining parts of the intangible value four pillar framework. So that, you know, together you have these two things that sum up to the intangible value framework. But I decompose an interesting way where I have AI adoption and then everything else and you can kind of look at those things almost distinct, you know, lenses.
- [51:38]
Justin
One of the points that you brought up in the paper was, you know, some of the firms that actually survived this disruption, AI might actually help improve the margins and the profitability of those companies. Can you just explain the logic in your thinking there?
- [51:54]
Kai
Yeah, look, I mean, the idea is that obviously there's a ton of dispersion, right? So in the software sector, there's some companies that are aggressively adopting AI, others that are doing not much, some that have defensible modes, others that do not. And so there's going to be some winners, there's going to be some losers. But when the whole shakeout happens and all is said and done, the companies that do survive are actually in an interesting position because you think about what is the biggest cost center for these companies. It is the production of code that's the main factor of production for these companies, at least from a cost standpoint. And bringing to this, the additional complexity around stock based comp. Stock based compensation has become this big flashpoint amongst the investor community because these companies have always. Software companies have been really liberal users of SBC for a long time, but now that their stocks are down, investors are like, wait a second, what's all this? Why are we doing this? Because software engineering talent is expensive. And so to the extent that AI has the potential to reduce the labor intensivity of software code, that's actually potentially going to alleviate this bottleneck, allow these companies to do what they're currently doing, but at a fraction of the cost or said differently to, for a fixed number of employees, be a lot more productive. And so you could conceive of an argument or actually, you know, contingent on surviving, which of course is a big if, you know, AI is actually a boon to these companies.
- [53:17]
Justin
Talk about this next chart. You mentioned the dispersion, but the Sparkline AI adoption score and AI exposure and sort of this, you know, you're seeing to your point, like software companies are way up to the right, so they're obviously embracing AI. But yeah, how should we be kind of thinking about this, would you say?
- [53:40]
Kai
Yeah, so this chart here, this is exhibit 26, is comparing two different analyses I did over different points in time. So on the X axis it shows exposure of a given sector to the technology of AI. So in other words, to what extent can large language models in theory impact the day to day tasks of a company? So exposed sectors, of course, software, banking, hardware, pharma, non exposed sectors are like, you know, I don't know, food and staples, retailing or whatever. Right. You know, and this again, this is on a factor, this is on the production side. And then on the Y axis we see the adoption score. Right. And so what this here is, is showing the extent to which these companies are leaning into AI, whether it's they're hiring, hiring AI employees, getting AI patents, repositioning their businesses for AI. And then what I did here was I showed all the different industries in a scatter plot and I draw a red line which is basically the line of best fit the average. And so any company, any sector that's above the red line in theory is adopting AI more aggressively than they are exposed. So they're kind of your early adopters and anyone below the line is actually kind of lagging relative to how exposed they are. They're really not doing enough. And so yeah, software is actually the outlier here in terms of being they have the highest, have the highest exposure, but they have by far the highest adoption. So they are as I said earlier, truly recognizing the extent of the threat. And on average, at least not everyone's doing it, but doing the best they can to respond to it. I have another chart in my paper showing AI job postings and you know, software and software services and IT services are like by far the highest sector when it comes to, you know, the, the hiring of AI talent.
- [55:29]
Justin
So this next one is sort of like the sweet spot where we're coming back into software and now we're looking at the software companies that are high or low based on AI adoption and higher Low based on intangible value.
- [55:45]
Kai
Right. So all I'm doing here is putting these two dimensions together. So remember, one dimension was how much AI adoption a company has and then the other adoption was the everything else section, which is your intangible value score minus AI adoptions. We're not double counting. And what I do here is I show in this case, this is for the software sell off. So all the software stocks that have fallen 30% or more, right, over the past year. So these are kind of like your software losers or perceived to be losers by, you know, based on the AI disruption. And what you can see is the upper right is where you want to be. Upper right is a sweet spot. These are companies in the upper right that in theory have a strongly defensible business due to the strong brand, human capital network effects and complementary ip, yet are also leaning into AI. So they kind of have the full package. And then in the lower left are the opposite. So companies that have very limited intangible moats and are not doing enough in AI. And then there's the kind of middle category too. And so you do see that there are a handful of companies in the red and then you see some companies in the middle and then the vast majority of names are in the kind of not so good section. So the point just being that there's a ton of dispersion, that there are plenty of companies out there that have good pre existing businesses, plenty of companies out there that have good AI, a fewer number that have both and many that have neither.
- [57:12]
Justin
And then you have the next chart is the high dispersion of disruption scare stocks. So there's a lot going on with this in this one. But explain to us what we're sort of looking at here, right?
- [57:24]
Kai
So I'd already observed earlier that, you know, software stocks have huge dispersion, right? So if you go back to the very beginning of what I mentioned, like software stocks are down 30% as an index, right? The IGV down down about 30% peak the trough. But there is huge dispersion, right? Like you know, GoDaddy, Salesforce down 50 to 80%. Adobe, you know, some of these other names are down big. And so, you know, you see that and then you also see the thing I showed a few slides ago, which was that the intangible value scores, right, are, you know, have this, have a wide dispersion as well with like this, this big left tail of companies that are potentially value traps as well. So the third element of dispersion I wanted to bring into the mix was this idea of historically when you have these events happen, what happens over the next year to returns? Because there's another way of measuring dispersion. Now obviously today it's software stocks. But if you go back through time, it would have been newspapers, it would have been retailers, that would have been the exposed sectors. So in order to build a metric of who are the kind of the folks in the crosshairs of disruption, what I did was I said, I said this, I said let's look for historically companies that were in both exposed sectors. So remember the definition from before, technologically exposed sectors that are also over a trillion 12 months in a 30% loss. Right? So this is, you know, guys in retail who are also the market thinks are going to be losers because they punish them. So the question becomes, all right, so when the market thinks you're going to be disrupted, do are you actually disrupted or do you tend to bounce back? Right. And what's interesting is first of all the medians. So what I show on this chart is the distribution of next one year returns for the, for this group of stocks relative, that's in the red relative to in the blue, all stocks. And you can see that the medians are basically the same 6 or 7%. You go to average, it's about the same depending if you're doing arithmetic or geometric, whatever. The point being that like the, the fact that you're down on price alone says very little with regards to where you'll be the next year. Right? So just because software stocks are down today doesn't mean we should all panic and say, oh, they must be zeros. It has very little informational content with regards to the median expected return over the next year. But if you look at the distribution, this is where things get interesting. They're very different, obviously. So the blue line looks more, it's not normal. It looks more normal. Right. So all stocks tend to have a more normal distribution, whereas disruption scare stocks have a really fat tail distribution super wide. So in fact it looks like 10% of these stocks go on to double over the next year versus 3% for the full market. 16% go on to lose more than half versus 7% for the full market. So in other words, the dispersion of winners and losers is so much wider for these guys. These beaten down disrupted stocks both to the upside, but also to the downside. When technology comes around, it reshuffles the deck and, and the ball's in the air. And what we're kind of everything's in Play. Right. And so I think that's a really important point to add to this idea of dispersion. Right. So there's dispersion in terms of historical returns, future returns, and then current valuations and all these things I've just kind of blown out due to the indiscriminate selling and just selling pressure and panic around AI.
- [60:44]
Justin
Well, I think this kind of really ties back to like the value traps versus the moats. Like clearly in this case, you want to avoid the 16% or so that lose more than half and try to be on the, you know, the, I guess the right side of the chart with the ones that survive. Right, Right.
- [61:01]
Kai
And discernment, the ability to discern winners from losers matters more. Right. In a time like today than it did historically or in an insulated sector.
- [61:11]
Justin
So what about what happens when we apply the exhibit 29, when we apply the intangible value factor to those high scare dispersion stocks?
- [61:24]
Kai
Yeah. So just just to be clear, like I'm using intangible value as a, because I already built on it like as a way of illustrating this point. But the point's more general, the point's more broad and I'll explain the exhibit first and then we'll get to the point. So what we see here is the returns which we already saw of the intangible value factor applied to the full universe in the blue and then the explosive sectors in the red. And then what we do in, in addition is to do disruption scare stocks. And so remember, the full universe think of like a bullseye, right? A dartboard. The, the full universe is the, is the widest circle. Exposed stocks are a subset of that and you know, insulated in the other part of the subset. And then within exposed stocks are disruptive and scarce stocks, stocks that are both exposed and down 30%. Right. And also, you know, investors are at least at the time perceiving them to be the, the losers. And what you find is that the return for this factor as app in applied to that final segment of disruption secure stocks is much higher than in the other than in the kind of wider circles. Right. So in other words, when you apply the intangible value factor to disruption scare stocks such as like software stocks today, but it could have been newspaper stocks in the past that the ex post returns have been higher. And what is the saying, this is your grind old and con, if you go back to your finance textbooks, is that ultimately dispersion is something that allows you to amplify your edge. So for a given edge if you have a lot of dispersion in the market, that means that your winners will do better and your losers, your shorts, will do better too. And so, and this is also. People talk about this in the context of venture capital or private equity. One reason why people love BC and private equity historically is because they've had high dispersion. And so therefore a given edge can be amplified over higher absolute return. And again, this principle generalizes from intangible value to any edge. So anyone who has an edge in picking software stocks in disruptions, which is again, a big if, but if you think you have a framework for picking that that works not all the time, but more importantly also specifically in times of disruption, such as today with software stocks, then this is actually a great time to be doing stock picking. Because high dispersion is one of the things we don't know. We think the mean will be the same, but the dispersion will almost certainly be higher, will likely increase the returns to being able to separate winners from losers.
- [63:50]
Sponsor/Ad Voice 1
Have no fear. Chosen Foods is here to defend your favorite foods from the forces of seedy oils and sketchy ingredients. With cooking oils, salad dressings and mayo, all powered by the good fats from 100% pure avocado oil and simple delicious ingredients. Chosen Foods this episode is brought to you by Palmolive. Family time isn't just the big moments. It's weeknight dinners, sitting around the table, everyone talking all at once. So when the plates are empty and the sink is full, use Palmolive Ultra. Palmolive's most powerful formula removes up to 99.9 kg percent of grease, leaving your dishes sparkling clean. And the new convenient pump makes cleaning even easier so you can spend less time tackling dishes and more time together. Shop now@palmolive.com
- [64:37]
Justin
I love that idea of the dispersion and the edge kind of coming together and that the intersection that you know that if you, the high dispersion, if you have any edge, that's when it comes, can become, you know, possibly amplified. I think that's a very great way to think about it. And just conceptually, I've never heard anybody explain it that way. So that's pretty.
- [64:59]
Jack
It's interesting too, by the way, just on the human side of things, like thinking about, like the great software stock picker, like they've probably got their best opportunity set they'll ever see in their career right now.
- [65:07]
Kai
Yeah, yeah. Because not only do they have an edge, presumably in software, but there's just a crazy amount of dispersion and you know, there will be many companies, many shorts will go to zero and many of the longs will be, you know, multi baggers, right? Companies like that, you know, were sold down 60% that, you know, may go on to be the next Walmart of their sector. Right. So, you know, very interesting time, you know, to be, to be a stock picker in software these days.
- [65:33]
Justin
Kai, your research is always super impressive and we're very honestly privileged and you know, appreciative of you coming on with us and our audience and kind of working through, you know, all this stuff. If you were to, and maybe we've already hit on the main takeaway, I don't know. But if there, if there is kind of a main takeaway, you know, from, from, from, from all this research, what would you say it is?
- [65:58]
Kai
Look, I think for many of these companies, say software stocks, I think the takeaway is that, look, the, the code is not the moat, right? Like for many of these companies, code is one of the many things they do. But you know, we as investors need to look beyond that to ask the question of what other intangible assets or just moats in general do these possess? Because if you go look, historically, you know, based on all the works through prior disruptions, you know, it turns out that these other complementary assets are, you know, potentially the most important, you know, indicator of which companies will survive and ultimately thrive through disruption. Right. Now, of course, AI adoption is important too, but I think that doing this research over the past month or so has given me kind of a deeper appreciation of the extent to which customer loyalty, brand equity, human capital network, these other moats for software in particular are more important than maybe we initially thought when it comes to being able to survive a paradigm shift in the way technology works. And so simply saying we're going to buy stocks because they're cheap, I don't think that's sufficient. A cheat cheap. Once they price the earnings, I don't think that's sufficient saying I'm going to buy these stocks because they have the most AI adoption. Now obviously I've talked about that in the past and I do think that's important, but I think that's just insufficient. I think really what's come together in my mind more, having done this research and especially bringing in the work of David Thies, has been the extent to which complementary assets, Branchman Capital IP are really quite important as we kind of think about which companies will ultimately be winners and losers long term from the occurring sell off.
- [67:42]
Justin
Good stuff. Thank you, guy thank you thank you for tuning in to this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the Excess Returns network@excessreturnspod.com. if you have any feedback or questions, you can contact us@excess returnspodmail.com no information
- [68:04]
Kai
on this podcast should be construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the host.
- [68:12]
Sponsor/Ad Voice 2
You can't reason with the sun. Trust us, we've tried. This summer, it's time to put that angry ball of fire on mute. Columbia's Omnishade technology is engineered to protect you from the sun's harsh, harsh rays that can burn and damage your skin. The sun is relentless, but so is our gear. Level up your summer@columbia.com to spend more time outside and less time slathering on aloe lotion. You're welcome Columbia. Engineered for whatever@blyze.com it's not just about window treatments. It's about you. Your style, your space, your way. Whether you DIY or want the pros to handle it all, you'll have the confidence of knowing it's done right. From free expert design help to our our 100% satisfaction guarantee, everything we do is made to fit your life and your windows. Shop up to 40% off with minimum purchase and a free professional measure right now@blinds.com plus pay at your own pace starting at 0% APR with affirm terms
- [69:11]
Kai
apply for their clients.