Lead-Lag Live

The Brand Advantage with Kai Wu

• Michael A. Gayed, CFA

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Speaker 1:

Tariff man is still tariffing. The stock market just seemingly doesn't care, at least for now. At what point is it signal versus noise? Because I think you can argue that that initial reaction was such an aggressive overreaction that it was almost on random noise starting leverage being so high versus where we are now.

Speaker 2:

Yeah, it's surprising actually that the market is now kind of at all-time highs, despite the little tariff tantrum we had in the interim. I mean, I'm actually a little bit surprised that is the case because even if Trump does kind of back off on a lot of these things, the expectation should be that tariffs will be higher than they were entering this year, which I would have thought would be more of a drag on the economy and on corporate earnings. But so far the market seems to shrug that off and isn't super concerned.

Speaker 1:

So the cool thing, folks, for those that are starting to see this stream, is that we were scheduled for 14 minutes ago, but Kai and I were talking about so many fun things that we're like you know, maybe we'll go live a little bit later. For those that are watching us across X, instagram, linkedin, youtube certainly appreciate the support and talking about intangible value, talking about AI as an investment tool how to think about that for a lot of people that are paying attention to that side of the investment world. If any of you want to ask questions during this roughly 35, 40 minute conversation, I can see your comments, I can see your posts, which means don't hesitate to put it on whatever platform you're on. We'll bring it up, make this as interactive as possible and, as always, this will be an edited podcast. All of your favorite platforms Apple, youtube and Spotify.

Speaker 1:

With all that said, my name is Michael Guy, a publisher of the Lead Lag Report. Joining me here is Kai Wu of Sparkline. He's got a couple of funds we'll be touching on. I'm a big fan of Kai's deep way of thinking about things. He's got a lot of great research and actually, let's start off with what you and I were just talking about this kind of teaser of this new paper that you put out this new paper that you put out or are going to put out oh, hope to do it next week, so I'll leak some out for you right now.

Speaker 2:

But yeah, so you know, I do know, michael, a lot of the work I've done is, you know, quantitative analysis and you know, in particular, working with factors. So that's your value, momentum, quality and intangible value, of course, being kind of the most interesting, kind of unique factor that I've been focused on and also, you know, obviously implement via my ETFs. And so one of the big items in the news cycle this past quarter has been obviously Warren Buffett's announcement that he's planning on retiring by the end of the year, and you know he's obviously in his 90s. So this is a well-deserved, you know, time to end on a high note. This is a well-deserved time to end on a high note. But one of the things I wanted to do was to just kind of take this chance to kind of, as a retrospective, go through basically 60 years of his track record, looking at his positions. I spent days in the New York Public Library digging out old archives on Buffett's positions and the valuations of the valuations of these stocks, you know, and also even running a factor analysis on his returns. So some of you who might be more quantitatively oriented maybe familiar with a AQR paper called Buffett's Alpha and basically replicating some of that results and then kind of expanding it to a broader set of factors.

Speaker 2:

And you know, basically the takeaway here is that you know a lot of people associate Buffett with. You know Ben Graham, who was his mentor. When Buffett started his career in the 50s he was literally working for Graham and was a student of his at Columbia and obviously Ben Graham is known as kind of your cigar butt investor, finding these kind of mediocre businesses but selling at really attractive prices below liquidation value. You buy these things, they kind of get back to par, you sell them, you move on, and for many years Buffett did follow this strategy. In fact he bought Berkshire. Hathaway itself was a struggling textile mill that he bought for pennies and intended to turn around but actually did not work out as an investment. In some of his letters he talks about his experience working with Charlie Munger, who later became his business partner, who told him to abandon Ben Graham as a mentor and instead change his philosophy to be one that prioritizes one of her book businesses at fair prices and then kind of around the same time.

Speaker 2:

You know, in his evolution we saw what Buffett was doing was focusing less on these industrial businesses and instead more on asset like companies, and he talks at length in his I think, 83 letter about this. He calls it economic goodwill the ability for companies to earn rates of return far in excess of what you might expect based on tangible capital alone. How do they do that? It's because they have these intangible assets. He talks about brand economies of scale. He owned a lot of TV stations back then, so you know these are kind of government licenses that give them effective monopolies. These sorts of things allow companies to earn really high rates of return. These are intangible assets, of course, and you know, I actually went through all his historical holdings for again 50 years and what I found was really interesting was, you know, was people generally think of Buffett as being, as I mentioned, a Ben Graham investor buying stocks below book value.

Speaker 2:

In fact, he basically almost never bought stocks below book value. I think out of the 240 different observations in the sample, I think maybe 17 or 18 were below book value. So 7% Basically a vanishingly small percentage, and most of those, by the way, occurred in 1978, 1979, when the market was really really cheap. So if Buffett's not buying stocks below book value, what is he doing? He's buying Apple, he's buying Coca-Cola. These companies, I think when you enter in each of them had book values of price to book values of four. So their tangible value was only one quarter of the overall price he paid. What comprises the other three quarters? Well, it's intangible assets. In Coca-Cola's case it's brand, in Apple's case it's, you know, combination of brand, network effects and human capital.

Speaker 2:

So what I did was I said let's do a regression-based framework and try to parse out the contribution of the main factors to, you know, to Buffett's alpha. And it turns out that the value factor, the traditional price to book, doesn't really show up once you adjust for various controls. And really it comes down to two things First is the quality factor, so Buffett likes to look for companies with high returns on equity. And second is the intangible value factor. He likes to look for companies that have strong brands, human capital, ip, etc. And so that was kind of an interesting experiment and you know, you can also use that to trace the evolution of his thought, you know, from being kind of an old school value investor to one that's incorporating these more modern modes. And then you know, of course, the question becomes what's the takeaway? And you know, this is kind of what I'm still thinking through.

Speaker 2:

But you know, one of the things as a quant that I like about my craft is that if you're able to distill someone's style and someone's process into a kind of systematic formula, a rules-based approach, you can then take that and apply it elsewhere.

Speaker 2:

So Buffett and Berkshire are highly constrained with respect to the fact that they are now a trillion-dollar company and, as a result, can only invest in a small subset of stocks large and mega cap names, generally in the US. So obviously what would be interesting is can we take these factors and apply them elsewhere, say in smaller cap stocks or in international names that, for whatever reason, buffett has tended to eschew, or in the sectors that currently fall outside Buffett's circle of competence? But he has to be fair, made forays into owning Apple, as I mentioned, into technology, which historically he hasn't. But to the extent where we can apply these factors into the sectors that Buffett and Berkshire have generally ignored, that kind of expands the breadth of your universe and allows you to get around this issue that a lot of the large cap US stocks that represent his universe are currently kind of at record high valuations, record high prices, which is not necessarily conducive to a value investor. So yeah, so that's kind of what I'm thinking about now.

Speaker 1:

I'm sorry to ramble so much, but no, no, I love it and there's a lot of interesting things there. You mentioned brand and there's a lot of interesting things there. You mentioned brand. Let's talk about brand for a second here as kind of an intangible value sort of play. I mean, obviously that's much more you could say applies to European markets, but the tech sector. I never thought we would be at a position where brand matters for tech companies, but clearly it does right. I mean, you see that with NVIDIA, you can see that with, or you used to see that with, apple. Maybe we should touch on that too. But let's start with sort of the intangible value of a brand in today's world.

Speaker 2:

Yeah, I mean it's funny. So you know when Buffett bought Apple. Obviously, Apple has lots of different things going for it the network effects around iOS ecosystem, a lot of patents and IP but you know really the way I think that Buffett, when he talks about why he invested in Apple, he viewed it was as a consumer business. Right, like that kind of magic experience you got when you opened up, when you unboxed that kind of like white iPhone. And this again was 2012, when he made his first investment and he started. He bought through the next few years, built up his position. He's now selling out of Apple for what it's worth.

Speaker 2:

But yeah, I mean, whether it's technology, whether it's automobiles, soft drinks I mean, basically, brands matter for any consumer business and even kind of B2B too.

Speaker 2:

As you mentioned in the video I'm being a good example People, just like they're drawn to brands in this kind of age where we have so much information, so much choice, right Like you can spin up a new product and have it drop ship from a factory in China and boom, there you go, but you're not going to want to buy that product. You're going to buy the one that you know has been associated with a brand. That's something that you know you, maybe your parents, it's something that you know you, maybe your parents, maybe your grandparents have a relationship with and, as a result, you'll both choose it but also be willing to generally pay a premium price to obtain it. So, yeah, I mean I'd say that brands are, you know, really, really powerful still, and perhaps even more so now, just given the kind of influx of choices we have, you know, when we as consumers, you know, go to go to the supermarket or go on Amazon.

Speaker 1:

So let's bring that conversation towards the brand of America versus the brand of everything outside of America, because that does obviously directly tie into tariffs.

Speaker 2:

Yeah, I mean obviously the the brand of America has has definitely over the past, I don't know since since Trump took office, you know been has changed in many ways.

Speaker 1:

Right, like you know, the very careful way of saying about.

Speaker 2:

Yeah, yeah, I'm trying to think how I can say that without pissing off half the half the world, half the people on the audience here. But yeah, I mean, look, look, that's. I think that's an important thing. But again, there's many, many different constituencies, not just geographically but also even amongst. Like what are we talking about? You know workers, potentially immigrants. We're talking about companies. You know who would be potentially doing business with American companies, other governments? I mean it's a pretty varied impact since then. But yeah, I mean, I guess it just speaks to the fact that the president is not only just a policymaker but also a figurehead.

Speaker 1:

So let's talk about tariffs a little bit more in depth, because we had that freak out and everyone seemingly doesn't care about it, although tariffs are still very much in play. We just got news, I think, around Brazil 50 percent tariffs, copper Right and tariff man is still tariffing. The stock market just seemingly doesn't care, at least for now. At what point is it? Is it signal versus noise? Because I think you can argue that that initial reaction was such an aggressive overreaction that it was almost on on random noise, starting leverage being so high versus where we are now.

Speaker 2:

Yeah, it's.

Speaker 2:

It's surprising actually that the market is now kind of at all time highs, despite, you know, the the little tariff tantrum we had in the interim Right.

Speaker 2:

I mean, I'm actually a little bit surprised that is the case because even if, you know, trump does kind of back off on a lot of these things, you know, the expectation should be that tariffs will be higher than they were, you know, entering this year, which I would have thought would be, you know, more, more of a drag on on the economy and on on corporate earnings. But you know so far that the market seems to have shrunk that off and isn't, you know, super concerned. I mean, you know, I think, with this most recent week of you know tariff news, I think the big thing is that Trump did extend, you know, give it another month to do negotiations, which I think is the most important thing. And obviously he's trying to bring people to the table through these letters. But you know, I don't think it is providing that much new information, you know, aside from the fact that you know he's not going to completely let this go.

Speaker 1:

All right, so we touched on brands intangible value tariffs. I want to touch on your funds for a little bit. Here. You got two ETFs. Let's talk about them and what makes them different.

Speaker 2:

Sure. So I have two funds. They both really focus on the same principle, which is intangible value, and this kind of ties into what I told you about the Warren Buffett story. That intangible value is this idea that you want to buy stocks that are cheap relative to intrinsic value. But intrinsic value should be defined more holistically to include brands, as we discussed, but also the value of intellectual property, human capital network effects of intellectual property, human capital network effects. And so we've built, basically built a quantitative factor that says you know, identify for any given stock, how cheap is it relative to this more holistic measure of intangible assets? And what we do is we say you know, for a given funds, we have two funds, one's in the US, one's non-US. So for the US fund, it's you know, take all the stocks in the Russian 1000 roughly, and you rank them on the score and then you buy the top names and there's some tilting towards larger companies and companies with higher scores. But the general gist is you're buying companies that have the most attractive intangible value and that tends to be companies, say, in technology, healthcare, consumer brands. But again, it's very much focused on value. So that you know I think we talked about this last, last month, which is, um, you know, currently we hold.

Speaker 2:

When we first launched the fund, we held six out of the seven magnificent seven stocks, so just not tesla. And then, over time, as the prices went up, we started to sell gradually, take profits, and this is, you know, despite the fact that as a value investor you normally wouldn wouldn't buy these kind of tech companies. You know, we bought NVIDIA at a PE ratio of 100. I mean, you normally wouldn't do that. But you adjust for intangibles Like well, actually it's free, cheap, this is a good buy. But then the price goes up like 3x and you say, all right, well, never mind, ok, that's a little bit too much now.

Speaker 2:

But kind of in the more modern sectors that are less like your banking, insurance and energy materials that tend to comprise the bulk of more traditional value funds, and then in the other fund is the same exact concept but applied to non-US stocks. So our universe is different, but the algorithm is basically the same Selecting the cheapest stocks and its tangible value. The portfolio ends up looking a little bit different just by virtue of the types of good companies that are available, say in Europe and Japan. They tend to be more brand focused and a little bit less tech heavy, but even despite that they tend to be. These are high quality companies, good businesses, trading at pretty nice valuations, especially relative to the USS, and, to the extent you, you know, would hold both of these funds. You're getting some nice diversification because they are, you know, by definition different companies but also, you know, operating different sectors and, you know, have different characteristics, like that.

Speaker 1:

See a question from YouTube. Somebody's mentioned that he also invests in brands. How do you recognize brands are going to favor or catch the ride to the upside, which is, you know, somewhat related to the idea of investment process?

Speaker 2:

right, when it comes to screening, yeah, yeah, look so you know, our process is fully systematic and that means all data-driven, all empirical. It's not like, oh hey, you know, I happen to go to the store, it's not the Peter Lynch thing. My wife told me oh, this is a really cool product. I hadn't heard about it so I bought it, bought the stock. What we're doing here is we're kind of systematically looking at a variety of different data sets. So in the example of brand, that would include social media. So what sorts of brands are getting good engagement, good reach on social media? We map back all the accounts to the underlying stocks that are investable to the extent possible.

Speaker 2:

Also, trademarks are very useful. I did a whole paper on one of the examples where we looked at the Barbie product line, which is owned by Mattel, obviously, and this was around the time that the movie came out and you could kind of see through time the evolution of, through its trademark portfolio, Mattel's trademark portfolio, Barbie's brand extension. It was a doll. And it was a doll plus some accessories and then it was like all these new things. And then around the internet bubble they got over their skis, started doing trademarks for like credit cards and like email, and then that didn't work out. Obviously. It kind of went into a downfall and then you have this kind of renaissance the past several years under the new CEO, and the brand has been revived. So you can track the trademark activity, you can track on social media, and these sorts of things do give you a sense pretty early on as to which companies and which particular individual brands are gaining traction.

Speaker 1:

Use the term systematic. Let's talk about systematic strategies in an age of AI, because I think this is sort of the big question mark. Will we enter an environment where AI basically creates the processes and nobody needs to worry about trading because AI is going to do it for us? I want to get your thoughts on that.

Speaker 2:

Yeah, so you know, I mean deep learning and machine learning have been around for a long time, you know, I think when people speak about AI I been around for a long time, you know, I think when people speak about AI, I think they are more you know, they generally referring to large language models, which are a subset of machine learning models generally focused on with a particular architecture, on transformers, and generally focused on the processing of natural language, and so I've actually written extensively about this. I wrote a paper in 2019, actually called Deep Learning and Investing, and kind of the idea here is, this is when a lot of quants were very excited about the prospects of deep learning, and there's also maybe a year or two into the kind of like transformer large language model revolution. But I think these things are of experiments, but the upshot was I concluded that there's kind of two use cases for AI. For quants, one is to take a bunch of signals. So imagine you have a thousand or a million different signals, trading signals like price went up past five days, price went down past year, price to earnings ratio is X. These sorts of structured data um, you know, price of earnings ratio is x, these sorts of like structured data. What, then, you can do is you can say let's use a, an optimizer, to kind of build the optimal portfolio. You can, in theory, also use large language models to do that or deep learning models to do that. Let me to be more precise, and what I found is that that is helpful. It does add some value. Listen on medium frequency, but you know it. It's, you know, limited. You're getting maybe a 50% to 100% increase, which is nice, of course, but it's not like game changing right. You're not getting a 10 to 100x improvement.

Speaker 2:

So then, the other use case at the time which I think has really developed into modern AI was, you know, kind of was working with this model called BERT, which is an early transformer by Google. It was an open source model. I by Google, it was an open source model. I was able to train it myself because back then they weren't so big. You had to build out your own GPU clusters, and what I found was there was a whole class of data called unstructured data. That's generally text, but it could be audio, video images that comprise 80 plus percent of information out there, and these data contain a lot of obviously valuable information about companies and corporate value.

Speaker 2:

But they're kind of locked up in a sense that you can't just take a large document of words and give it to a linear regression. So that's not going to make any sense. What you can do is you can put it through a large language model, or what now is a large language model, and it'll kind of convert it into a structured score. So, for example, I can say here's a million patents. So, for example, I can say here are, you know, here's, a million patents. Tell me which of these are related to AI or which of these are related to, you know, glp-1s, whatever theme you think is important, and it can give you that score. And it's not really going to hallucinate because it's a pretty like straightforward task. It's effectively like an SAT level question, right, and so that was kind of what I pointed out as being kind of an interesting path forward.

Speaker 2:

And you kind of fast forward several years and you know, obviously models just keep getting better and better and better. We had, you know, gpt-2, 3, now 4 and so on and so forth, and you know, basically what we're able to do is to take all this unstructured data and, you know, change the text into scores that can now go alongside other traditional factors in an investment process. So you can think about it as being kind of two steps. There's the processing of underlying raw data into factors, so we have text-based factors and traditional factors, and then the mixing of those factors into the final portfolio.

Speaker 2:

And where I found AI to be very useful is in the first step. In the second step a little less so, and also because you kind of run into issues around kind of transparency, especially around that kind of second stage. You really need transparency. People really want to know hey, so what are the pillars that matter? You have these four different intangible assets. What's the weights of each one? Those are all information that we know you can kind of see. It's very transparent. The AI is kind of obscure that because it's really difficult to audit inside the model. But if you just can kind of confine it in scope to just the first step, you end up with that being less of a concern.

Speaker 1:

Do you think AI will make markets more efficient or less efficient? And I know that sounds like a strange question, but where I'm going with that is you know it's like it's machine learning to the extreme, and that's really what AI is Right. Ok, so you've got all these algorithms that are trading off of each other. They're trying to gain each other. All right. I mean, most of the trading is happening is not driven by individuals, it's by code. How does, how does more advancement there maybe result in markets being either more or less volatile? I think.

Speaker 2:

There's different time horizons that matter. So, on a shorter time horizon, think high-frequency trading, stat R frequency, AIs and deep learning models have been in use now for a long time. So I don't know to the extent which large language models in particular as a subclass are, but the bots, the HFTs, have been using, you know, very advanced machine learning models for a long time. So, you know, I don't envision it being kind of a step change. It is an arm's race, as you point out, and these things kind of cancel out, I think, where I actually think that, to answer your question, I think that markets will become more efficient because of AI and here's the reason why, which is, at the end of the day, market efficiency, like what I care a lot more about, is the long term right. Like is stock? Is Apple priced efficiently relative to NVIDIA? Are these stocks priced relative well relative to European stocks, and so on and so forth.

Speaker 2:

On a longer time horizon, I think one of the big challenges is the incorporation of information into analysis, right? So you often have, you know, sell side researchers and buy side firms whose job it is to, you know, stabilize stock prices and you know, set valuations at a reasonable level, and they do so generally with limited information. If you're, you know, a traditional analyst, you can do your best job possible, covering the end, say, 10 to 20 stocks that you do, but you're never going to be able to have all the information. And so that's where AI comes in, because AI is really really good at ingesting massive quantities of news, regulatory filings, all those sorts of information staying on top of it.

Speaker 2:

One paper I wrote, I included an example where one thing you might be concerned about if you're an analyst is executive departures. To the extent the CFO of a company you know leaves under mysterious circumstances. That could be a red flag. Now, these you know AK filings contain that information. But there's so many of them Like there's millions of these things that come out all the time. This would be an obvious use case for an AI just kind of like sit there trolling the data feed and when everything's come out, alert you right. So those sorts of things should make markets more efficient, because you're now being able to bring more information into the fold and incorporate that into stock prices in a way that should be stabilizing for prices.

Speaker 1:

Which of course then brings into question you can argue the value of active management. Now to your point about timeframe. You know maybe active management matters more on a shorter timeframes versus longer timeframes. Markets more efficient longer term, independent of AI, than shorter term. How does that dynamic from a long-term perspective, maybe factor into the idea of screening properly for companies that have high intangible value assets? How does that impact? Maybe the funds or the thesis there?

Speaker 2:

So so can you restate that? So what you're saying? How will AI help incorporate more intangible information into stock markets?

Speaker 1:

Yeah, exactly Right, exactly yeah.

Speaker 2:

Yeah, I think that's, you know, that's actually something that I've been, you know, a drama. I've been meeting now for three or four years. You know, one of the first things I did when I set up my, you know, was doing the research for what ultimately became the funds, the ETFs is. You know I tried to say is there a way we can take existing accounting information and, you know, recast it, capitalize the intangible expenses, et cetera, in a way that will, you know, allow us to get around some of the limitations that we're seeing? You know, Of course you don't want to reinvent the wheel if you don't have to.

Speaker 2:

So, for example, some of the problems are that some of the inconsistencies in accounting, in GAAP accounting, are as follows If you're a company building a $100 million factory, that gets capitalized, not expensed, so you basically build up an asset on your balance sheet. After all, this investment is made that you didn't depreciate. In the case of R&D or marketing, that's not the case. If you spend $100 million on developing a new drug or new patent, that's a hit to your bottom line that year and you don't get an asset on your balance sheet. Hence why companies like Coca-Cola or Apple have ridiculous price to book ratios, because you're kind of missing, you're omitting all this value of their patent portfolio. Of all, the brand Coca-Cola has spent over $100 billion, if I'm not wrong, in marketing over the course of its entire lifetime, and that should be an actually honest balance sheet, but it's not.

Speaker 2:

And so one thing the first thing I did was I said no-transcript, and so that was kind of where, you know, the whole thesis became. Well, you know, lucky for us. I mean, now we're in 20, I guess it was 2020 at the time where we have access to big data, this exponential increase in information, especially unstructured data. Oh yeah, and, by the way, it just so happens that there's this new invention called AI, you know, large language models that give us now the tools to take this data and to incorporate into a systematic process. And so, you know, I think this is actually something that I believe, I believe pretty strongly that the, the use of these AI tools will, you know, be especially helpful for folks looking to value intangible assets, because of the, because of the limitations of the existing accounting framework.

Speaker 1:

Let's go back to the international side for a bit, because international has been quite strong this year and a lot of people say a large part of that's for the dollar is weak and yes, the dollar is a partial explanatory variable. But I am curious to hear your thoughts on the cycle broadly, maybe starting to favor international, and maybe touch on how the intangible side could be even more undervalued there.

Speaker 2:

Yeah, I mean you have to remember that like obviously the US has dominated international stocks for the past say 15 years. But if you kind of go back over the past century, right, they have, like you know, studies and great research that shows like these things are fairly cyclical, like they tend to mean reverse. There are periods where, such as like the late 90s, when international stocks did really well and emerging markets had a big run up until 07 and then kind of like gave back and yeah, the US is on top now. But if you look at the long arc of history these things kind of ebb and flow and if you look at valuations in particular, they do seem stretched right. Us stocks Valuations in particular, they do seem stretched right. Us stocks traded a premium to international stocks. I think international stocks are 50% cheaper or US stocks are 100% more expensive on Shiller P or price to normalized 10-year normalized earnings. So you are definitely seeing these big discrepancies For value investors. We try not to predict catalysts, we try not to say, all right, well, it's going to turn around. Today, because of X, it's really difficult to know what ultimately will be the straw that breaks the camel's back. What we do know is that there is a pretty significant spread.

Speaker 2:

Now, one of the interesting so the way this ties into intangible value internationally is as follows which is one of the reasons why this big discount exists for international stocks is because the companies are less profitable. European stocks, for example, are less profitable than US stocks. They have grown much slower over the past 15 years than their US counterparts and hence their you know, their you know P ratios reflect the kind of expectation of lower future earnings growth as well. Now, that's true and it is what it is. And if you kind of take both things together lower valuations but lower quality, maybe they cancel out.

Speaker 2:

But one of the interesting things I've found is that there's, of course, a subset of international stocks that are a little bit different. They are much higher quality businesses because they have patents, they have trademarks, they have intangible assets. These are kind of the modern versions. These are the modern businesses within the international markets. So the top 100 companies out of the 3,000 or so that are investable things about these stocks is that while they have kind of US-like characteristics with respect to being kind of more cutting edge, more modern, they trade at the same discount on average as their peers in the international space. So you're almost getting like the best of both worlds. You're getting the international discount with US-like, say, innovation properties, or, conversely, us-like innovation properties at a European discount, and so that sounds pretty good. Us like innovation properties at a European discount, and so that sounds pretty good.

Speaker 1:

Yeah, I think that makes a lot of sense. Going back to the US, let's talk about on that side what sectors tend to have the highest intangible value across the sectors, broadly, like when you look at it from a sector by sector basis.

Speaker 2:

Yeah, I mean, it obviously varies through time, you know, if you go back to the late 90s or the mid-2000s, you have different answers, but today it's as you'd expect. I mean, the technology industry is the kind of crown jewel of American innovation and on the IP pillar in particular, it scores very high. Also in human capital. If you think about where top talent wants to go, they want to go to Silicon Valley, and that's because you can get paid 10 million bucks to be an AI engineer at a meta. Now, you know, but we also have other good sectors.

Speaker 2:

So financials, the financial sector, right, think of, you know, goldman Sachs, who was also in the news for a less good reason.

Speaker 2:

They, you know, they have, you know, really talented analysts or they, yeah, they have talented analysts to do it there, and so human capital has become a big element of the financial sector, as well as tangible capital in the consumer sectors, right, so that's consumer, discretionary, and you know, durables and such.

Speaker 2:

These sectors tend to be very high on brand, as you might expect, since if they are consumer facing that's kind of the whole way they differentiate their products, um, from a commodity and, um, you know, services, businesses that you're talking about, like accenture some sort of consulting business is of course human capital as well. And then communications companies, um, so that's including, you know, both your tech giants, um, as well as you know your traditional telecoms or your ubers, um, these sorts of communications businesses. They, um, you know, have very strong network effects. So it's interesting, if you go sector by sector, the answer is a little bit different. Each sector kind of has their intuitive thing that they tend to lean on. But then, of course, if you kind of peel one layer even deeper and look at the company level, there's obviously a ton of variance company to company.

Speaker 1:

Independent of the systematic approach of your funds. I'm curious, as you look at some of these individual stocks, do you have like a particular favor that to you it's like, yeah, this is right up my line of thinking, right, I mean anything that's a favor for you, not financial advice that you'd say, okay, that's a cool one.

Speaker 2:

Yeah, I mean, look like, if you look at the holdings, I tend again like not Magnetism 7, as I mentioned, we used to have six of them. We only currently have two, and those two are Google and Amazon. And Google's interesting, I mean it's trading at a pretty deep discount to the other Magnetism 7 stocks. It's by far the cheapest one. You know people have basically discounted it because of this idea that you know AI is going to be disruptive and it's going to take away their business, their cash cow, which is, of course, search. But you know, I'm not so sure and I actually think that you know you have to remember Google actually invented the transformer. They were the guys who kicked off the AI revolution and, to the extent that you know, ai is a sustaining rather than disrupting innovation. Right, it's one in which you take your existing business model and make it a little bit better. You incorporate AI into Gmail, you incorporate it into existing products. You have a huge installed base. You make it marginally better. You know that's like a.

Speaker 2:

I think there's a strong counter argument to be made for why Google is actually an interesting company at the time. Of course, they also have the call option on Waymo, which is you know from all everything I've heard pretty, pretty impressive technology and it has a TAM obviously. So you have a lot of interesting things going forward yet as the cheapest stock. And now again I'm just speaking from kind of like just my intuitive observations. Again, all the investments we make in the funds are based on kind of systematic models which happen to align with my intuitive view, which I guess is good. It kind of reinforces, just from a different angle you're kind of triangulating to the same point that this is a pretty interesting stock.

Speaker 1:

What have we not talked about that we should address, that the audience may be not aware of? I mean again, the concept of intangible value makes a lot of sense. You don't often hear about it in the way that you frame it. So a lot of credit to you for the research you've done, and then obviously putting the funds out. But what else do you think is not being talked about? That should be.

Speaker 2:

Well, I think we touched upon this a little bit. But, you know, when we do research, like we, you know, we tend to try to be kind of evergreen, like create. You know, these timeless principles that are intangible assets should be important, whether it's 2025 or 2045. These things will always matter and in fact, I believe they will matter more, right, as we kind of. You know, as the world brings more and more information focused and more advanced. You know, you look back a hundred years and intangible assets are basically nothing, right? So that trend's only going to continue in my mind. But, you know, I think it's also important to tie things to the current state of the world, right? Why now? Why are intangible assets more important now than they were, you know, 10 years ago and maybe more important than they will be in 20 years ago, on a relative basis? I think the answer is these tariffs. Right, I mean, over the past several months, this has been the number one thing in the news, which is all right. What is Trump going to do with tariffs, extraining a ton of volatility which is affecting the stock crisis, which is affecting the dollar?

Speaker 2:

One of the nice things about intangible assets and intangible companies is that you kind of don't need to worry about it. If you are a purely digital business, a purely intangible business, a consulting company, for example, it doesn't matter. You can't tariff goods at the border, you can't really tariff intangible assets. Now, of course, some companies like Apple is a good example they do have physical products that, of course, embody the IP, embody the brand, but they also have a physical component that can be tariffed, but it's going to be a percentage. So the point just being that the more intangible business, the more immune you are to a lot of the kind of noise and craziness going on in markets. If you are kind of pure importer, exporter or manufacturer, yeah, you're dead in the crosshairs of everything that's going on and that's not fun. But if, but, but to the extent you know, intangible businesses have this kind of inherent advantage that they're non tariffable. Yeah, I think that actually makes them, you know, pretty compelling today.

Speaker 1:

Yeah, I think that makes a lot of sense, Kai. For those who want to learn more about the funds and read some of your research, where would you point them to?

Speaker 2:

Yeah, just go to my website sparklinecapitalcom.

Speaker 1:

Nice and simple and Kai's got a good social media following, so make sure you're following him on that. So what's your handle? Again on that? It's ckaiwu. Simple enough on that end. Appreciate everybody that's watched this. Again, this is going to be an edited podcast under Lead Lag Live. Make sure you learn more about the funds and on teasing, Stay tuned. There's going to be a lot of interesting big changes on the LeadLag media, LeadLag live side, so you will see more than just my lovely face on these hosting. Thank you, Kai. I appreciate it. Thanks, Michael. Cheers everybody.

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Michael A. Gayed, CFA

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