He Quantified 200 Years of Disruption | Kai Wu on Separating Software Survivors from Value Traps

Software stocks released 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 dye 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 being in the crossroad disruption, how did they do it? Two things. For many of these companies, say software stocks, I think it takes away is that the code is not the most. For many of these companies, code is one of them, anything they do. We as investors need to look beyond that to ask the question of what other intangible assets or just most in general do these possess? Hey, Kai, welcome back. It's good to be back, guys. Our audience is familiar with you. You've been in the podcast a few times. We always like having you back because in addition to running Sparkline Capital and managing the ETFs that you run and that you've built on 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 intangible value. Your recent piece that you put out in May titled AI Disruption, Motem Value Traps is looking at the recent sell-off in software and this possible opportunity that it has created. 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. I think as we work through this great piece that you did, we'll get into what the setup might be in some of these software names and you're using your unique aspects of natural language processing and research to uncover these possible opportunities. 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 in our audience so we can get really down to the nitty-gritty detail on the research that he's done. I just thought we'd start Kai with your exhibit too, which shows where the software premium or lack of premium is today and how unique that is in terms of software stocks after the sell-off. I guess the first thing just to set the context is that historically software stocks have commanded a premium valuation. Historically investors have liked software stocks more than say the average industrial in the S&B 500 because they're asset light because they have predictable SaaS style revenues and for variety of other reasons that fast growing and such. Over the past roughly 20 years since this data began, their Ford V ratio of software relative to BSNP has been at a 30% premium. That's been the historical average. There have been some fluctuations. It dipped a little bit in '09 and went on a secular bull run peaking in 2021. If you remember, that was the COVID bubble where people were working from home and interest at all time lows and stimulus was coming to the market. Software stocks were about their all time high valuations. Then 2022 things started to reverse. The valuations started to fall. They went through their historical average around 23 and then the past two years, they've been continually falling, reverting back to first parity with the market. More recently over the course of this year have actually fallen to a discount to the market. There's also a chart floating around from, I think it was an ultmark's letter. They're another value manager, but they cited empirical research partners. They take the same data back to about 1980 and what they show is the same that over the past five decades. First, we are at all time lows, which regards to the spread between PE ratios of software versus the market. By the way, that we're at a discount in absolute terms to say the median stock or the average stock in the S&P, which is something that we haven't really seen before. That's one of the core ideas in this paper is trying to determine now that this sell-off has happened. These are trading at these types of historically low valuations, whether or not this is a, these are possible value traps. Talk to, I think it would be helpful. 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, they look like they're no brain or buys, 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. Talk to that. All of value trap is a stock or company that is basically the audience way to oblivion, but that for a variety of reasons appear cheap on traditional or on face value and standard metrics. For example, stock that has a low P ratio, but only has a low E because, low P ratio because everyone knows that he's going to zero would be a classic example. What I show here in the paper is the example of four iconic companies, Blockbuster, Borders, Red Your Shack and McClashy, which owns a bunch of newspapers, each of which were disrupted by Amazon, Netflix, Google over the past couple of decades. These were at one time large multi-billion dollar companies, but over time they kind of became cautionary tales. 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. What you can see is that when the disruption first happened, investors quickly pedicked and started to sell down the stocks. As the internet became more and more pervasive, stocks like Red Your Shack and Blockbuster and Borders started to press start to fall. Importantly, the actual fundamentals only fell with a long lag. In the case of Blockbuster, it took many, many years for the sales per share to fall. In the case of Borders and Red Your Shack, they actually increased their sales per share for a period of time before it all fell off the wagon. Provinces were maybe deteriorating as well over this period. In case of McClashy, there's an acquisition and a lot of that taken on. The overall picture I think is pretty clear, which is that to the extent that prices in the stock market are for looking and they're going to price into 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. You're always going to end up with a window of time when, say, the price of sales ratio of these companies is looking really attractive to a traditional value investor. Yet, again, that's just kind of a trap. It's sucking you into bringing only more to ship as it's about to collapse and sink into the sea. That's what a value trap is. These examples pretty cleanly illustrate what investors as they approach the software, which should be concerned about. A lot of listeners or viewers might not realize, but I remember when Netflix first came out with, you could get the three DVDs in the mail. It was like, how is this ever going to disrupt a blockbuster? You couldn't see it. With all these examples, it's always hard to see early on when this disruption is possibly happening in front of your eyes. That was another thing that I thought was very interesting in the paper, your methodology for a way to measure disruption exposure and what that tells us about the current environment that we're in. Yeah. I think the key here was, obviously, those four examples are a share effect. They're helpful anecdotes. The question I really wanted to answer was that if you were more systematic about doing this, would it turn into the case that these four examples of the blockbusters and borders are actually representative of a systemic problem that traditional dinosaurs might face? To build on that and set that up, obviously, it ended up in a way in point in time, in real time, quantify when there is disruption and which companies are exposed to the disruption. Why I did this was in a two-step process. I built on a paper I wrote in 2022 called Investing in Innovation. What I did was I looked at this dataset of all the patents ever filed with the US Patent and Trade Mark Office. Really cool dataset. It goes back to 1790. The first patent was signed by George Washington. You can use it to see over the course of the past two centuries. The horizon falls on new technologies from the automobiles electricity to the internet. What I do, as I say, is that at each point in time, hundreds, thousands, tens of thousands of patents, what you care about is trying to cluster them into like-roppings of similar technologies. From there, to see whether it is an increase in technology. Oftentimes, you find that there might be full-starts where, say, a technology starts to gain prominence, but then eventually, it fades. Electrovegicles were famously, compared to the internal combustion engine, but then ultimately lost out 100 years ago or so. And so you want to-- want to find trending technologies, you also want to find technologies that aren't on 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's, you know, applies of course to software use cases, but in theory, be able to automate and, you know, do a lot of the human labor that is obviously a factor of production for most economic activity. Right? So I want to be 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 earnings called transcripts to patents themselves to, you know, company filings, analyst commentary to figure out which companies are exposed to each technology. So for example, like if e-commerce becomes, you know, a thing which companies are potentially exposed to that disruption. And then what we do is we actually roll it up 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, you know, even though some maybe it may not be exposed and some might be exposed on average they have this level of exposure. Right? So we're creating an industry level exposure. And then in this chart that you pulled out here, you know, I basically highlight over the past, you know, a few decades, I think seven different major disruptor waves ranging from the advent of Internet infrastructure to e-commerce, social media, M&AI, right? And as you roll through, I can look at what periods of these things, these disruptions most prevalent when the pressure was the highest on the strategies, which are some examples of patent clusters that, you 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, 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 presenters of that technology. 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 what automatically does it find it like through its, the natural language processing or does the system find it, these clusters automatically? Or do you have to instruct it as to what to look for? I mean, there are some hyper parameters like how many clusters, like how sensitive they do at due thresholds, but putting that aside, it's fully automated. So it'll kind of go through and say, hey, we 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 several leads instead of two, then I've identified companies and then industries that are exposed to each sector. So for example, like I have my code set, you know, we could show up in 20 years from now, you know, dust it off or whatever and it'll have a totally new set of technologies and companies. This is another aside, but does this at all allow you to like rank these disruptive waves against each other? So like everybody's talking about like the AI is the biggest disruptive wave we've ever seen in history. Like does this tell you anything about that at all? I mean, I think it's a fair point. If you measured by pervasiveness, right? As I mentioned, you know, 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 facts. Especially once robotics and such are in play of the economy. I think one thing I would add though is that, you know, they're all dependent on each other, right? This is the idea like me stacking the stacking of innovation over time, right? Like we wouldn't have AI if we didn't have, you know, electricity, right? We wouldn't have electricity if we didn't have, I don't know, fire. Right? So all of the technology over civilizations history has kind of compounded over time, I'm building on each other. You know, and so I think it's important to remember that like AI is obviously really cool technology. It requires really advanced computing, requires, you know, big data, which has obtained many cases through, you know, the internet being able to digitize information and put it into a format that we can all see. So these things all attended build on each other. And so, you know, maybe we are at the apex currently in terms of like where innovation and disruption is, but, you know, a lot of that just depends on previous innovations that had they not occurred. We will, you know, be able to be where we are now. The stack gets into this next chart we want to look at in poor retail is all I can think about. It's been like getting the crap beat out of it, but I think every innovation for decades here. But can you just talk about the idea of what we're seeing here? Yeah. So this is David Seven. Take the example of retail. I mean, could have picked on a different industry. I guess it was mean. But the idea was, you know, the show through time, it's the amount of disruption it's being absorbing from the seven or so different like themes that I mentioned through time. So kind of the tea inside here is that they stack. So in other words, first e-commerce comes around, the internet comes around, and that, you know, obviously Amazon's there and, you know, if you're like a 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 the deal, digital media, and then you have the deal social media and then you have the deal with AI, right? Like, agentic shopping and such, moving forward. Right? And these things stack, in other words, it's not that companies that they don't also have to deal with. So retailers that they don't also have to deal with e-commerce 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 the all the different themes over time, what do you find is that, you know, yes, they come in waves, right? The peak of e-commerce disruption, you know, happened and then it kind of like subsided before social media really kind of came into play. But the trend is kind of this secular increase over time as, you know, company as innovation is accelerating as technology compounds on itself on each other. We end up in a situation where, yeah, these companies are now being kind of exposed in all fronts. They're waging like a multi-front war, so to speak, you know, against innovations and technology coming from all these rangles now. It also explains a lot because it's a value investor. Like if you've watched your value screens over the past, however many decades, like these retailers have like perpetually been in them. Like they don't ever leave them. Like these retailers and the ball and stuff like that. Like those are, oh, they're always sitting in these screens. And so they're long-grabbing. Innovation is stacked. Yeah. And they're probably in fitch over there. Yeah. Like you'd be surprised, like, Justin, you know, we've been running quad models for every, like you would have seen these, these various names like that you'd see if you walked to the ball right now, like William Sonoma, Abercrombing Fitch. You got Clairs used to be in there. They're all in there hot topic. And like I don't even know that if you exist anymore, they call 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, it's just good to maybe take a cuba to look at this and how the value factor is performed and why it hasn't been working for a long time. So can you talk to this chart? Yeah. So I'm sure your listeners are somewhat familiar at least with this idea. You know, value investing, you know, buying, you know, being down retailers. Like the general idea has, you know, has been long, 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. So the thing is that value investing, you know, however we define it has really had a tough time with it. And a lot of it has to do with the structure, which we'll get into more over the past couple of decades. You know, 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 like a long short factor. So in other words, you will long the cheap stocks, short the expense of stocks on a valuation metric. In this case, it's a blend of, I think four different things, price 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, you know, different metrics. And the point is this, which is this is the factor that, and if you extended the back test all the way back to the initial work 100 years ago, you would have seen consistent out performance or decades. And then around 2010, you would have started to see a drawdown. It starts to turn over and really has never recovered, you know, even as of today. Right. 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 principle of value on whining or value investors have lost supply, they're all to 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 nine, 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, most are the industries for which their technological exposure score, which we showed it in the case of retailers exceeds a fixed threshold. And then that would be one, and then the second would be insulated industries, so those 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. In other words, value is work just fine, as long as it's not in industry as they're exposed to the industrial 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. 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 industries such that the net return for the factor is negative. So I 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 companies, but sectors like retail that initially were not exposed, but now are heavily exposed, you're going to have 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. It's such an interesting 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, like basically at any period in the past, you have already decade, plus, you know, right. And I think it requires two things. One is a recognition that, hey, this disruption is coming. And second, a recognition, kind of like a lack of humor is 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, you know, for the longest time, he avoided tech stock. 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 like the entire market. And then what it is, you're made in cash. 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 values that work here, right? That's right. Yeah. So, one fun aside is that this paper is kind of the first one that I did where like 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 US. I want to test it in global stocks, international stocks, emerging market stocks. I want to look at the sectors, sub-industries, industry sector groups, so and so forth, right? And so kind of the workflow ended up becoming like, hey, you know, cloud, can you like take this and like apply it to the other things, like show me the results. Let me look at the table with you. I have a couple follow up questions, so and so forth. So it's kind of a fun way to scale like the analysis, you know, by kind of delegating a lot of the, you know, robustness exercises to its cloud. But yeah, so with this, that a lot of 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, like, this is a negative number because when you apply the traditional value factor and exposed industries, it does worse, right? Then insulated industries. And so the baseline shows a spread of negative seven percentage points per year. That's very bad. And then you can say, what if you look at just global stocks, not just US stocks, you know, kind of not so good as well? But if instead of looking at this blend of valuation metrics, if we'll just just on the canonical FOMA French price book ratio, okay, that doesn't work. What if you set your neutralize? I guess it's 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 impact here. What about if you do? So one of the other things these days is to do these double sorts where what investors will do is say, I recognize that price book or price earnings could have value trap risk. Therefore, I want to intersect it with say, ROE is some profitability metric in order to or momentum in order to say ideally weed out the value traps. So you want company that are cheap and profitable or cheap and I have not bad momentum. Well, it turns out that, you know, that actually helps by the way, an absolute, but on a spread basis, it's the same, right? So you're not, maybe the lines are not going from positive to negative, but they went from positive to positive, but less positive. But the point is being that the gap, you know, remains in this case, 6.3 percentage points. So like a meaningful, meaningful gap. So look, I mean, the point just being that this, this finding seems to survive, you know, the exact specification of the signal, the universe applied to and so on and so forth. So it's a pretty robust finding. We just did an episode of Cliff Asnes and this reminds me exactly what he's done in a lot of his papers. Like his international paper, a value investing is dead. He'll ask like every question is to what could possibly explain this and then once he's eliminated all those, he'll say, right, my conclusion is okay. Well, I mean, I guess it's impossible to prove anything, right? So we have to just kind of narrow down and try to throw it as many competing hypotheses as possible, right? 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 a very large number of companies that are exposed. Yeah, so what we see is a number that the percentage of market cap in say, I think this is in the US market exposed to innovation, however defined, increasing from about 40% to 70, mid 70s, 75% in school. Over the past 20 years, right? And that's the result of two things. So one is the fact that technology is affecting more and more industries, right? If you went back to the 1980s, tech was just like IBM, right? Now tech is like all companies have tech to be to give you one kind of simplified example. And second is just that the tech industry or whatever you would call these technologically exposed industries to be more precise or just a bigger part of market cap, right? But the point is that even if you look at things on an equal weight basis, at a name 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 US, the numbers are a little bit less extreme. So within like developed markets or emerging markets, the numbers aren't quite 78%. They are still up to 50%. And they still have the same feature of an increasing trend. Even with an emerging market, 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'm going to do the thing where I just like hide in non-expose sectors that kind of like has been, you know, increasingly challenging to do as those non-expose sectors kind of go away. Yeah, this is reminding. We talked to Andy Council 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 quarrelry we're talking about here. Right. And not only is tech as a, you know, say, Gix or MSI definition a bigger such bigger part, but tech cross cuts across all industries, even in industrial is more rely on technology today than it was 25 years ago. So yeah, but I would agree with Andy's point on that. 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 you're trying to address, right? Yeah. So I think this is where it's which gears. So I think the first piece part of the paper was more around, you know, what not to do. Right. And now it's like, okay, so like, can we actually study history and can it be actually illustrative onto what you should actually do? Right. And so here's where I bring the examples of a Walmart and New York Times into the discussion. You know, obviously Walmart is a retailer in New York Times as a newspaper. Two of the most beaten down industries from disruption. What do you find is that these companies survived and then ultimately thrived despite, you know, being in the crosshair of disruption. How did they do it? Two things. First, they, you know, maybe not initially, would eventually leaned into the technology that was disrupting them, right? Walmart, you know, has one of the biggest e-commerce businesses today. And second, they, you know, leaned into their unique, intangible assets that, you know, outside of technology, let's say, that allowed them to be who they were, right? Their brand or human capital and network effects. And so, you know, this actually, this is where I bring like a paper that I thought was really interesting by this guy in David Teese. So this paper was about like, who promised from technological innovation is written in like 1986. But the principles while, you know, dated our time, as I think. And, you know, the key insight was this, which is that, you know, the long-term winner of an innovation isn't always going to be the one, the initial winner or the innovator itself. Right? Oftentimes, the person, the firm who ultimately accrues the value, 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. So, I have here an exhibit, 14, that shows some examples of, you know, this is from a document 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 motor around that. Right? And he gets always really cool examples. He gives the example of a company called EMI, which is a UK-based company. And they actually invented the, the CAT scanner, which is a machine, you know, they sell the hospitals. But the problem was that selling stuff to hospitals was really hard. And it turns out that you need to, like, you know, do whole enterprise sales cycle and then you need to, like, do effectively four-to-point engineers, like train their, like, the people how to use the machine and then service it once it breaks down. Like, and that's a really hard thing for them to do. Um, what ended up happening was GD, 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 markets. 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 diet, the diet and canned Cola. So that was an innovation at the time. But they didn't have the shelf space or the distribution of their brand that cook with all in Pepsi had. and they obviously won that market. And then another example, what's kind of the opposite case is IBM. T-socks about 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 their, the strength of their technology, but rather through the ecosystem of software and peripheral that they kind of built around the IBM framework, right? What we call network effects today. And so you have these examples, which are quite illustrative, right? So 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, die code, they didn't invent the CAD scanner, but they had the necessary assets to win the market. Right? So I think that's a really important lesson to think about as we approach like the software self. Yeah, these software companies, they're, if their core mode is code, then yeah, maybe they are in trouble, but if it's the complementary assets around that, then, you know, purchase is framework, they actually might be fine, right? And conversely, yeah, in Throbbing and OpenA, are the early winners, they are the innovators in Google, I guess, of this new technology, the LM, 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 competition's unfold. 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 very, very co-capepsy. Really? Do you ever have Justin? Okay. Jack, no, but you are a soda expert, so I would imagine you're right. Yeah, you're right. You would be in poor here. I mean, you know, you don't run the 530 miles, Justin runs if you're drinking things like RC Cola. (laughing) It feels like either they're doing more of the consumption of RC Cola mix. 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 Wal-Mart's who had to lean into technology disruption. Now, this time, you've actually got tech firms that have to lean into technology disrupting their technology, which makes it a little bit unique we're going through right now. Yeah, but in a way they are, I guess, positioned a little better than some of the firms of yesterday 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. It's being a good traditional engineer, but it's definitely a lot closer than, you know, being somebody at, you know, at Blockbuster, let's say, when the streaming comes around. And we'll see the data on this too, that software companies are, you know, 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. So one of the things you talked about in the paper, which we've talked to you about in previous interviews, is this idea of intangible modes. 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 modes that you measure. Right, so I touched on each of these. So the first of the four is intangible property. This is not just patents, but any kind of proprietary knowledge, data, you know, software technology. Second is brand equity. That's customer relationships, you know, brand loyalty, things like that. Third is human capital. That's not just the position of a talented workforce, but also when it's culturally aligned around a common goal, and the filing network effects, which is this ecosystem of external producers and consumers. You saw this with IBM, you know, examples today might be like over 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, and the economy is due to these four intangible pillars as opposed to traditional tangible capital, which I think is, you know, just having a lot of book value is that really a moat, right? Is that actually provide you giving the ability to kind of earn an extra street from ROIC? I would argue probably not. But yeah, to your question, though, how do we build the metric? We have like a bunch of different, like, underlying proxies for these pieces. So for example, like, you know, we looked at for the traditional value metric, you know, priced earnings combined with priced above Christian sales. In this case, for IP moites, let's look at, you know, the patents, the price, we might look at R&D expenditures, the price. So on cell fourth, smushed them together into a metric, do the same for brand, it was 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 the traditional value, these yield-based metrics looking at price value index, X being a measure of intangible capital for these four different pillars. And then we combine it into one final components of the score. So then 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 eight 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. And as we get into exhibit 17 here, this looks at that idea that traditional value investing is not working in these exposed industries, but when you just and use intangible value investing, we get a different story, right? - Yeah, that's exactly what we see here. Right, so if you remember the exhibits from before, we saw that, you know, the traditional value applied in the whole universe worked okay, and then it stopped working in around 2010, and it wasn't a drawdown, and that was, you know, it split into two pieces. It did totally well into insulated industries, which struggled in exposed industries. What we're seeing now for intangible values, and once you look at not just traditional metrics, but you also add in these intangible modes, what he's would call these complimentary assets, right? 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, right? Because exposed industries, if you remember, like put aside the jargon, these are just industries where you're facing disruption for technology, whether it's e-commerce, whether it's, you know, log computing or social media or AI. And, you know, what allows you to survive, what allows you to be the newer times or Walmart, are these competing assets, right? These complimentary assets, and in addition to, of course, being, you know, embracing the technology itself, right? All of which are in theory captured by this horror film framework. I think this actually an important point, right? That, you know, so even just step back, like the T's framework and the potential value framework are kind of like very actually related. So you think about it this way, which is like T says, there's like focal innovation, right? The focal innovation is a subset of the IP pillar, right? So a company, so forth, when we go out and you say, what is the intangible value looking for? Is 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 T's would call, he would call them, you know, complimentary intellectual property innovations like in robotics, right? Like in genomics. In the end, we don't even want that further. It's a, we were also looking for companies that have strong brand modes, human capital network effects, the true complimentary assets, right? So we kind of want all these different things. And so going back to the exhibit here, right? What you find is that once you kind of look more holistically aside just back, we're looking earnings and book value and look at what intangible mode companies have, now you're starting to be able to find, 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 wall marks from the block westers. - What's interesting too is an 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 would want to figure out if I'm in the disruptive period. I want to just work regardless. And I think that's what you're getting at here with intangible value versus traditional value that even in this disruptive period, non-destructive period, the performance has been pretty similar of intangible value. - Right, the key is consistency. I guess we could go all weather, right? It appears to work. So first of all, what this exhibit does is it cuts it into two dimensions. One is in exposed 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 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, um, health performance. Whereas if you look at the traditional, it's highly dependent. If you're looking at like 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, you can work, but like you have to be right, then you need to have a good modeling, a good understanding of when to apply and when not to, versus it being more normal weather. - This next one's really interesting 'cause 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 a tangible 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? - Yeah, so what does it give it shows? It's like a matrix, two-dimensional thing. So on the x-axis show is like the traditional value score from expensive to cheap, and on the y-axis it shows the same but for intangible value, right? So we have like the four quadrants, where like the diagonals are where they agree, and then the off diagonals are where they disagree. So the upper right is where, you know, companies, where both metrics agree, the lower right, whether they both disagree, the lower, sorry, lower left, the lower right is where, you know, a stop might look cheap, but in traditional, but not intangible, and then in upper left, it's the opposite. So the other thing I did here is a color code of each dot into three colors. So blue means it's a company that over the next 10 years was a winner, right? Apple, proger. Gray means it did okay, and then red means it was a loser. Like Las Vegas Sands GameStop did not do well for '07 to 2017, right? And 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, regardless of whether where scores are in traditional value, cheap intangible value stocks, I've done well the next 10 years. The other thing you see is that, if you focus on the off diagonals, is that traditional value has some, has some challenge challenges that like socks that look cheap on traditional value but expensive on tangible value like macy's or Wells Fargo, tend to be losers and socks that looked expensive on tangible value but cheap on tangible value like Amazon or Apple tend 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's because they sold the Amazon so they bought the macy's, whereas in tangible value because you're now taking into account the modes that the intangible modes that an Apple might have, the network effects, the brand, the human capital, the IP, suddenly Apple no longer seems expensive to use cheap, right? 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. Well struck me the most about this is no blue dots in the bottom so there were no like extremely expensive companies according to tangible value that ended up being the biggest winners. Right now I'm not in like the bottom like yeah yeah the bottom level of a chart which means that like there it was measuring value right is what 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. Right I mean to be clear this is just a top 100 stocks 10 under larger stocks at the beginning time so they could have been like some other names that would have been there it just would have been $20 so I didn't want to show you know the thousand dollars. Yes but it still is pretty interesting. So this next exhibit gets at the idea of look at the same four quadrants but now we're looking at return by quadric right. 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 want to look at the full sample but the the setups the same and so what you see is you know 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 um intangible value one so in other words the quarter and quarter expensive disruptors the apples and Amazon's did well 2.8 and then the value traps the stocks that the mace ease right that were that looked cheap on traditional but not unintangible 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 the intangible modes do appear to matter so that's good. The second thing we find though is that you know the when they when there's agreement it's actually more powerful than when just one when just intangible value thinks something right and so that that kind of goes back to this idea that I think we discussed on the podcast in the past that potentially there's a common a role for these two metrics to be complementary with each other right that you know the real red flag is not only when something's just expensive but intangible value when it's also expensive on intangible value when it's not on both metrics that's a pretty concerning right so I think that that's another you know interesting takeaway from this exhibit to me so there's 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 there a lot of people would recognize and looking at whether they're cheap or expensive on intangible value right so yeah this so what we find up to here we see most of the paper talking about like the historical you know disruptions like going through the past ways thinking about you know what metrics do and do not work what what like we went through teases framework of commentary asks us to understand what you know how to think about about modes 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 with software stocks having sold off significantly due to AI disruption fears what this chart shows 21 is stock software stocks that are down 30 percent 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 it I'm generally over the past over the past year and what I did was I showed the distribution and 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 probably look to be about like 0.3 or something suggesting that yeah these stocks which are in a large drawdown they sold out like 30 percent with the market up 30 percent over the past you know seven or eight months so 60 percentage points spread that these stocks may on average have been oversold you know shoot first ask questions later but the second thing you see is pre-decent dispersion and more importantly dispersion on the left side right so 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 you know have are down 80 percent or something but are still expensive on these metrics right that's you know generally unusual thing to see and again don't take don't reach too much into these logos that they're showing the relative purposes but like you know you you do see that like you know look at the hub spot versus Salesforce both of these are gonna see our company Salesforce is looking at least on these metrics you know on the cheaper side where's HubSpot looking more expensive so you do see some of this version even within comparable names which I think is is worth with is worth noting this interesting by the way too because he to your point like go daddy you know registry domain names building websites like I saw a wix I think is laying off a bunch of people like it makes sense like where these are based on what you think in terms of what their votes are like it would seem like a go daddy would not have a very strong vote 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 of the company might have right to like accumulated business logic like embedded in this in customer workflows customer relationships regulatory compliance burdens right and so one of the insights here is that this is pretty intuitive I think what people know this is that you know more enterprise facing comfort software companies that face like the largest enterprises will tend to actually have wider modes because switching costs are a lot higher these things are a lot more embedded there's just a record you know the compliance requirements are so much more onerous than say consumer facing things like you go daddy for example you know or I think I do a lingo here too where it's a little easier for a random person just switch off an app right and so I think that would be these things do correlate and you know you can look through each of the four intangible pillars and and you know I have this in an exhibit actually and look at you know kind of school you can sort you know a bullet point by bullet point to say hey which for a giving company acts where to score on on these you know for intangible pillars and on on each of the say 20 or several sub points within those pillars so there's basically two ways I think if I was software company there's there's two ways I could succeed here and I think you get it this in the paper one is I can have a vote which we've talked about the other is I can 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 yeah so if you remember the last thing we did together was on on it was called like AI adopters beneficiaries of the boom and the idea there was the fine companies that were positioned for AI adoption right because presumably they over time if AI becomes a thing would have a would separate from the laggards and that was like a one-dimensional thing what I'm saying now is let's take David Tease'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 other things going for them right that allowed them to be to survive relative to any other legacy company that was trying to become an e-commerce company and that is the complimentary modes right so I'm adding to the AI adoption lens this additional lens which is you know really the remaining parts of the Intentionable Value 4 Billion Framework so that you know together you have these two things that sum up to the Intentionable Value Framework by a decouple interesting way where I have AI adoption and then everything else and you can kind of look at those things that almost distinct distinct lenses 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 you're thinking there 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 add the fence of the modes others that do not until there's going to be some winners there's going to be some losers but when I'll when the whole shake out happens and all of a sudden done the companies that do survive are actually in an interesting position because you think about like what is the biggest cost center for these companies right it is the production of code that's like the you know main factor of production for these companies at least from a cost standpoint and and bringing to this the the additional complexity around Stockway's Comp. So Stockway's Compensation has become this big big flashpoint amongst the most community because these companies have always software companies have been really kind of liberal users of SPC for a long time but now that their stocks are down investors are kind of like we say it was all this like why we doing this right because you know software engineering talents expensive and so to the extent that AI is the potential to you know reduce the labor intensivity of software code that's actually you know potentially going to alleviate the bottleneck allow these companies to you know do what they're currently doing but add a fraction of the cost or say differently to for fixed number of employees be a lot more productive right and so you know you you could conceive of an argument or actually you know contention on surviving which of course is a big hip you know AI is actually a boon to these companies talk about this next chart the you mentioned the dispersion but this the Sparkline AI adoption score and you know AI exposure and sort of this you know you're seeing to your point like software you know companies are way up to the right so there. obviously embracing AI, but how should we be thinking about this? Would you say? 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 and theory impact the day-to-day tasks of a company? So exposed sectors of course, software, banking, hardware, formal, non-exposed sectors are like, you know, I don't know, true and stable of retail, I don't know what I'm saying. And this is, again, this is on the production side. And then on the Y-axis, we see the adoption score. Right. And so what this here is showing the extent to which these companies are leaning into AI, whether they're hiring AI employees, getting AI pens, repositioning their businesses for AI. And then what I did is, he was I showed all the different industries in the scatter plot and I draw a breadline, which is basically like the line in this, 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 like, early adopters. And anyone below the line is actually kind of lagging. Like they realize that the how exposed they are, they're really not doing enough. And so yeah, software is actually, you know, the L-Layer here in terms of being, you know, they have the highest exposure, but they have by far the highest adoption. Right. So they are, as I said earlier, you know, truly recognizing the extent of the threat. And, you know, on average, at least not everyone's doing it, but doing, you know, the best they can to respond to it. Right. I have another chart in my paper showing like 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. 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 higher low based on AI adoption and higher low based on intangible value. Right. So all I'm doing here is putting these two dimensions together. So remember one dimension was how much AI adoption company has. And then the other adoption was the everything else section, which is your intangible value score minus AI adoption. So we're not double counting. And what I do here is a show in this case, this is for the software sale off. So all the software stocks that have fallen 30% or more, right, over the over the past year. So these are your software losers or perceived to be losers, you know, by, you know, based on the AI disruption. And what you can see is the upper right is where you want to be operates a sweet spot. These are companies in the upper right that in theory have a strongly defensible business due to the strong branch in capital network effects and a complimentary IP. Yet are also leading in the AI. So they don't have the full package. And then in the lower left or the opposite. So companies that have, you know, very limited intangible modes. You know, it's and they're not doing enough in AI. And then there's the kind of middle category too. And so you do see, you know, that there are a handful of companies in the red. And then you see some companies in the middle. And then the last more Georgia names are in in the kind of not so good section. Right. So the point just being that there's a ton of dispersion, right, that there are, you know, plenty of companies out there that are, you know, that have good preexisting businesses. Plenty of companies out there that have good AI, a few are number that have both. And many that have neither. 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 explaining to us what we're sort of looking at here. Right. So I 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 done what they're done about 30% peak the trough, but there are huge dispersion, right. Like, you know, go daddy sales wars 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 potentially value traps as well. So the third element of dispersion I wanted to bring into the mix was this idea of, you know, historically when you have these events happen, what happens over the next year to returns, right. Because this is 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, right. That would have been the kind of exposed sectors. So in order to build a metric of, you know, who are the kind of the folks and then cross our hairs of disruption, what I was able to say, I said this, I said, let's look for historically companies that were in both exposed sectors. So remember the definition before technologically exposed sectors that are also over a trough in 12 months in a 30% loss. Right. So this is, you know, guys in retail who are also the market things over 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 you actually disrupted or do you tend to bounce back? Right. And what's interesting is, first of all, the mediums. So what I show in this chart is the distribution of next one year returns for the risk of a stocks relative, as in the red relative to in the blue all stocks. And you can see that the mediums are based in the same six or seven percent. You know, the average is about the same to the thing is you're doing it with a grid geometric, whatever. The point being that like 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 she's all panic and say, oh, they must be zeros. It has very little information content with regards to the meeting the median expected return for 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 normal. It looks more normal. Right. So all stocks have to have a more normal distribution, whereas disruption scare stocks have a really fat distribution super wide. So in fact, it looks like 10% of these stocks go on to double over the next year versus 30% for the full market. 16% go on to lose more than half versus 7% of 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 of the upside but also the downside when technology comes around, it reshevels the deck and you know the entire, you know, the balls back, you know, the balls 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, you know, dispersion in terms of historical for terms, future returns and then current valuations on all these things. I've just kind of blown out due to the interest in discriminant selling and you know, just selling pressure and panic around around AI. Well, I think this kind of really ties back to like the value traps versus the modes 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. And discernment, the ability to discern when you're from losers matters more, right in a time like today, then it did historically or in insulated sector. So what about what happens when we apply the exhibit 29 when we apply the intangible value factor to those high scare dispersion stocks. Yeah. So just just to be clear, like I'm using intangible value as a, because I read built on it, like as a way of illustrating this point, but the points more general, the points more broad. And well, but I'll explain 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 exposed 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 bull's eye right a dark board. The full universe is the is the widest circle exposed stocks are subset of that and you know, insulated in the other part of the subset. And then within exposed stocks are disrupting scare stocks stocks that are both exposed and down 30% right now. So you know, investors are at least at the time perceiving them to be the the leaders. And what you find is that the return for this factor as applied to that final segment of disruption scare stocks is much higher than in the other than in the wider circles right. So in other words, when you apply the intangible factor to disruption scare stocks such as like software stocks today, but it could have been new people stocks in the past that the exposed returns have been higher right. And what is the saying right this kind of your general economy back to like you know your your finance textbooks is that you know your ultimately dispersion is something that allows you to amplify your edge. So for a given edge right. If you have a lot of dispersion in the market that means that your winners will be better and you lose your shorts will do better too right. And this is also people talk about this in the context of the venture capital or like private equity. Like one reason why people love to see private equity historically is because they've had high dispersion right. And so therefore a given edge can be can be amplified over you know higher absolute return right and again this this principle you know generalizes from intangible value to any any edge. So anyone has an edge in picking software stocks in disruptions right which is again a gift. 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 as such as today with software stocks, then this is actually a great time to be doing stockpicking because Hydrospergion is one of the things, we think that we'll be the same, but this version will almost certainly be higher. Well, let likely increase the returns to being able to separate videos from losers. - I love that idea of the dispersion and the edge kind of coming together. And the intersection that, you know, that if you have the high dispersion, if you have any edge, that's when it 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 that way, so that's pretty great. - 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 probably got their best opportunity set they'll ever see in their career right now. - Yeah, yeah, it was 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 sources will go to zero and many of the longs will be, you know, multi-baggers, right companies like that, you know, we're still 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 a stock picker in software these days. - Kai, your research is always super impressive and we're very honestly privileged and, you know, appreciative of you coming on with us in 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 is kind of a main takeaway, you know, from all this research, what would you say it is? - Look, I think for many of these companies, say software stocks, I think it take away is that, look, the code is not the mode, right? Like for many of these companies, code is one of them, anything they do, but, you know, we as investors need to look beyond that to ask the question of what other intangible assets or just most 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 complimentary 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, you know, I think that, you know, doing this research over the past month or so has given me kind of a deeper appreciation of the extent to which, you know, customer loyalty, brand equity, human capital network, these other modes for software in particular, you know, are more important than maybe we initially thought, when it comes to being able to survive a paradigm shift in the way, you know, technology works, right? And so, so simply saying, we're going to buy stocks because they're cheap, you know, I don't think that's sufficient. But Cheechee wants a price to earn, I don't think that's sufficient. Saying, I want to buy these stocks because they, you know, 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, it's insufficient. I think really what's come together in my mind more, having done this research and especially bringing in the work of David Thes, you know, has been the extent to which complimentary assets, you know, brand-driven capital IP are, are, you know, really quite, quite important, you know, as we kind of think about which companies will maybe winners and losers long-term, you know, from the current self. Good stop. Thank you, Guy. Thank you. Thank you for tuning into 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 at excessfreturnspod.com. If you have any feedback or questions, you can contact us at [email protected]. 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