The Korean Levered ETFs Shaking Markets All Around the World

23:17So there's a reasonable amount of out of sample data around this framework. It's got 19 inputs, none of those inputs. We have a mantra on my team. I tell everyone this in my team every week, which is if you can't quantify it, you don't have the right to talk about it. and using the word feels and seems are banned. If you use the word feels and seems, you're fired. I think we'd be in trouble, Joe. Yeah, but it's a good rule. But the idea is it's a very simple rule, which is we just want to try and really deep root the team in quantifiable data, right? And just to take a step back, when Scott and Ronnie, who run the equity business, when they sort of effectively were hired into Barclays to kind of really revamp as an ascendant equity business, one of their main priorities was like, we need content at the center of the mindshare in order to sort of like really partner and hold hands with our clients and so that's when they brought me in and because we wanted to focus on quantifiable content as opposed to it so that's the backstory as to why betty was created because we wanted to have a market timing model that removed feels and seems and and and so it's got 19 inputs it's got everything ranging from real yields to spot fall correlation to looking at discretionary flows like what a mutual fund beat what are hedge fund beaters what are ctas doing what are vol control doing what level etf doing what so everything from the discretionary and non-discretionary vertical from the vol vertical from the from positioning we just the only thing we didn't include was sentiment indicators we weren't interested in fields and seams so we there's no ai bull bear index or anything like that and so your question was it's been in this record sort of warning territory now yes it has been in record warning territory.

24:55And effectively, that's been driven by primarily momentum crowding. Now, as you know, at the time of recording, we had a big momentum pullback over the past couple of weeks. So that's actually sort of been relatively healthy and reselling that. Real yields, problematic. If you're competing for capital at the same time as between the equity market, obviously record year for issuance, possibly this year. Government, going to be record year of issuance. IG, record year of issuance. Yeah, this is the other big market story is like a blockbuster summer for IG bond issuance. Absolutely. So if you think about what real yields effectively represent, that dynamic of that competition of capital versus equities, if you just generally look at where real yields are today in, say, a post-GFC environment or a post-COVID environment and benchmark it against where equity multiples are, equity multiples shouldn't be this high, right?

25:44So we can get into a bigger debate about, oh, dot-com, real yields were very high and equity multiples skyrocketed. I'm like, yeah, but the government wasn't asking you cap in hand for tons of money. Also, that didn't end great. Yes, but hell of a ride it was in the build-up to that. So BETTI effectively is taking all of these inputs. And what is it telling you right now? It's just telling you that the forward return profile of the S &P from an asymmetry perspective on a tactical two-month time horizon is just not great right now. Now, we're looking at if you were to pick a random day in time and say, all right, I'm going to buy the S &P and hold it for two months, 42 trading days.

26:21The average return would be around about 190 bps. Not bad, right? And your hit rate of making money is about 73%. So betty, when it is in this kind of territory, so 6, 7, it was high as sort of 10 or 11. It was telling you that you only had around about a 35 % chance or even lower of making money in the S &P over that same time horizon. and your average return was basically negative. So it's not so much necessarily that it's telling you, oh my gosh, the market's going to crater. It's really just an illustration of a bunch of quantitative inputs that are telling you, actually, the asymmetry is not great.

26:57And that's exactly what we've seen. The model first flashed as a warning signal late May. S &P's kind of done nothing since then. And so maybe in anticipation of some big correction in the equity market isn't forthcoming. And that's okay. I still take a lot of validation in the model that is kind of telling you that to just sort of pump the brakes a bit, let some of this froth in the equity market come out and actually get a reset, which then actually sets us up better. I mean, ultimately earnings are still good, right? We're still driving AI in the economy and we can get into a debate about whether that's sustainable or not.

27:31And then we've also got to work off the assumption we're still running a massive fiscal deficit. And whilst that environment is happening, it's quite hard to create any kind of fundamental economic downturn.

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28:27Tracy Alloway:Setting aside market timing and what's going to happen in the next two or three months, et cetera. Just on the sort of like bigger question, the combination of market valuations, however you want to measure them, all the classical ratios that people like versus the increase in real yields, like how historically expensive is this market right now? All right. So if we take the post GFC environment, the challenge is always going to be that S &P margins were much lower for a long period after the GFC than where they are today. And we have to acknowledge that higher margins should map into higher multiples.

29:10But we'll do the full comparison. So post-GFC, because there's not that many periods where real yields are this high. Real yields today on a 10-year basis are running about the 95th percentile. And they're around about, I think, 230, give or take. So with that being said, if you take what the average S &P multiple when real yields were this high or higher, it's a scary low number. The S &P multiple would be around about 14 to 15 times, and we're currently trading on around about 20.2, 20.3. If you take the post-COVID environment, which I think is probably a better representation of what S &P operating margins look like today, we're still low.

29:48It's around about 18 and a half times. So you're still talking about basically a 10 % multiple contraction versus where the -

29:57Tracy Alloway:It's just a very simple question. Why are margins a more important factor here than expected growth or expected earnings growth? I mean, in my mind, I would pay higher multiples because I think that the earnings are going to have a faster growth rate than they used to have. That seems intuitive to me. Why is margins the lever here that we're looking at? It's a good question. well it's not exclusively just margins you can sort of measure it as roe as well you know there's there's different ways you can do it but effectively businesses are becoming more profitable if it's a more profitable business it's going to be working off the assumption to a degree it may not necessarily be accurate but that they're effectively that has a bigger moat it's more it's a more all right high quality business and therefore you'll put that on a higher so to speak can i ask a slightly personal question, which is I used to joke that I wanted to be reincarnated as an equity derivative strategist because then I could just find a number to like justify anything that's happening in markets.

30:56But does it feel like people taking more seriously now versus, say, 20 years ago when people were still talking about like fundamentals and stocks? Now you have this environment where flows are super important. You have all these mechanical things happening. you have the multi-strats as you pointed out vol targeting ctas all of this stuff is getting bigger does it feel like equity derivatives are more important now oh i thought you were going to say where it's my british accent that makes me sound more more well that too i will say more seriously because i'm because i'm i will say we get a higher listenership on episodes when our guest

31:30Tracy Alloway:has a british accent it's crazy because like everyone knows how much americans love a british accent but what's annoying is the brits know it too and exploit that oh you know just like you like know like you know what you're doing it's a reason why i stayed here for 10 years i married in america you know living living the american dream yeah sorry you know what you're doing i'll get i'll get back to the uh the actual question at hand so is there increased validation from having a more quantifiable world that we operate in yes but but on a person if you're asking a personal question i don't think that's a personal question if i was to give a personal answer my career wasn't always like that.

32:06I started my career as a fundamental single stock analyst. And I actually started my career actually as a generalist sales on at JP Morgan in 2004. But I then became a general generalist analyst, so to speak, on the buy side. And I did that within the adventure world for several years. And then when I went to the back to the sell side, I had to pivot, I had to change my investment approach, because of the fact that the industry was becoming increasingly specialized. And as a generalist, I was struggling to make an impact on clients that were becoming much more sophisticated in the weeds on their particular sectors and areas of granularity that I just couldn't compete with.

32:49And so at the time I was working at Citi and Citi had a very good quant business. And I was like, what's all this quantum factor stuff? What is all that. And I genuinely didn't know, and this is 2012 to be clear, 2013. And so I took it on a personal crusade to understand the postmortem of my portfolio management experience prior, understanding why was my P &L doing certain things, which I didn't understand at the time. And as it turned out, I was just a value investor. But the concept of being a value investor, other than sort of the Ben Graham or Warren Buffett model, was a bit as in value from a factor perspective.

33:28now it's ubiquitous sort of 14 years ago but back then it was really novel that you were going to customers discretionary customers generalists or just long short guys who weren't quant guys and explaining to them this quant phenomenon in layman parlance basically and that was the start of a pivot into a much more sort of quanti derivative-y knowledge experiment and it just ballooned from there yeah this is kind of what i'm getting at right like it has become more important in the market. I remember speaking of 2012. Do you remember, Joe, the headlines where like people would talk about CTA flows or something like that?

34:05Oh, yeah, of course. And it would be like a mysterious force in markets. Now everyone knows what CTAs are.

34:10Tracy Alloway:Yeah. And it's interesting to think, too, like if I just think in my mind about one of these platform shops, multi-strategy, whatever, the understanding of the companies that that pod invests in. On the buy side, it must be orders of magnitude greater than it was in the sort of heyday of the sell side analysts who were like, oh, we're going to issue a buy rating on GE Vernova or whatever like that. So those investors know that company insanely well. Oh, totally. And look, to put some anecdote around that, Barclays as a bank, as an equity business, as a research house, again, ascendant equity business, we've been working really hard over the past three years to hold our clients' hands and make them aware that we are a really prime and tier one equity franchise, a lot of that is about corporate access, right?

35:06So actually having more research coverage and having more access to more companies, which in turn we can roadshow and actually give our customers access to those companies because it is that important to them. To your point, exactly, Joe, they have so much granularity and detail in their forensic modeling now that if you're a generalist, it's very, very hard to compete on such a granular level. So yes, I pivoted away completely and focused on the different mantra was, if you think about the verticals of equity investment, the way I see it is that you've got fundamentals, which is obviously what we talked about.

35:40You've got economics, you've got cross assets slash macro, you've got quant, which I would include factors, you've got derivatives, you've got positioning, right? And then you've probably got some other stuff as well but my view is the way that i i run the tactical strategies team is that we don't need to be a 10 out of 10 in all of these i'd love to be but it's just i just don't think it's possible in a world of ai we can obviously augment some of our game in certain verticals but the aim of of our game is if we can be like a seven or an eight in all of these and some of them we know will be less and some of them will be more some of them will be in nines or tens and others will be like fives or sixes, it basically means that what we can do is we can go into any customer, any client, any investor and say, hey, there's something about the market that I'm sure I can help you on that you're not as aware of.

36:31And that's where I think that the evolution of that kind of education, investor education has come to now rather than just focusing on just one vertical. Yeah. I don't mean to get all media navel-gazy here, but I think we are seeing a similar story media where like the niche subject matter experts are becoming much more important, much more popular versus the sort of generalized news platforms. I think that - You don't have to comment on that, by the way. No, but it does parlay back into the world of finance and the world of AI, because in the world of AI, everyone can have an army of quants in their pockets now of questionable accuracy.

37:09But the point is you can crunch orders of magnitude more data. So where's the real value? I'm a big believer that the value, I see it kind of, think about it as knowledge and wisdom, right? Everyone can now have access, infinite knowledge, but it doesn't necessarily mean that they've got wisdom. I mean, the old saying is, is that knowledge is knowing that tomato is a fruit, but wisdom is knowing that it doesn't go in a fruit salad. You know, this came up on another episode.

37:34Tracy Alloway:And by the way, Tracy, I just want to say, I meant to send you this. I saw someone with one of these viral fro-yo places that are like, they put little cherry tomatoes. Actually, that could be good. Yeah, that's what I'm saying. Tomato is a fruit. Not to get wildly off topic, but my tomatoes so far this year are freaking amazing. And come later this month, in August, you're going to have like boxes full. No, this is my take actually. I love the saying, knowledge, wisdom, tomatoes, et cetera. But I actually think some tomatoes actually do make the cut to make it into a fruit salad. This is my only point.

38:09Tracy Alloway:But I understand the point. Yeah. And so I think it's the same when it comes to sort of data and the world of finance. Yeah, you can be incredibly granular and specialized, but sometimes you're going to miss the forest for the trees. Can I just ask, not to turn this into just another AI conversation, but you must actually have some insight on the question. On some of these things, let's say like you want to construct an index or some new thing. like how good are the models right now at like really reliably being able to do quantitative work? I know it's like, you can get a lot of the way there and even someone like me can like hack together or something, but like to the standards that you have, like how much do they still like, no, this isn't nowhere near something that I would like then be ready to turn into a chart and send to a client.

38:58Yeah. I think that's a great question because we're doing that all the time. So, you know, we'll have a customer come in and let's say they've got a particular security, there's a single name item, they want to hedge that item and they want to hedge it with a basket, right? That's one of our most common kind of business problems that we face off with. And so we need to build a universe of securities that effectively replicates that particular instrument without using that instrument, of course. And as a consequence of that, we're effectively running a what is essentially a giant pairwise correlation model and then we've got to optimize the inputs depending on what individual correlations against that underlying instrument are it's relatively straightforward if you've got a really cool optimizer if you ask ai to do it what it's okay at is selecting what instruments you should so let's say you're like i need all right this is a household consumer product actually you know what let's do it in ai let's make it more new economy so we've got all right we've got a particular ai vertical let's say it's a neo cloud all right we need to hedge that neo cloud with something else all right so ai is quite good at selecting let's go and get all the other neo clouds that are listed and then let's find a whole bunch of other companies that most of which you'll be aware of maybe some you're not aware of that actually have a reasonably high correlation to that instrument that you're trying to hedge That's kind of where it ends.

40:21It's not very good at telling you, all right, what about the liquidity considerations?

40:26Tracy Alloway:But it can roughly sort of determine the first, the ingredients, right? Like you're making, Emmanuel Derman in his book talked about like the job of the trade is to make a, we were going back to the fruit salad question, but you have a bunch of wholesale fruit and then you slice it up in a certain way. It can identify the ingredients and then it's your job to figure out the optimal, like it can't do the proper slicing and allocation. Exactly. So it's, it's, yeah, it's pretty good at, it's pretty good at identifying what my universe is meant to look like. Cause I get the, sometimes I see these notes and I have never tried this, but like from, I don't know, I somehow I get, I got on like JP Morgan's like trading desk thing and they're like, oh, buy that.

41:05Tracy Alloway:We think it's time to go long this basket of, you know, energy related companies and short this basket of like, whatever it is. And I've been curious. I've never tried it. It's like, Could I get a model to like back out what these ingredients are? If I gave the model a line and a theme. Yeah. So again, I would, I mean, get this real credit to Barclays, just in terms of we've been pretty leading edge in experimenting and adopting as much AI as we can. And so we have Copilot, we've got Claude licenses. We are, we're working with other partners to sort of build in-house stuff, either using external models or own internal models.

41:44But the point being is that we've had plenty of time to operate in the sandbox now. And I'd say where AI is without a doubt most powerful is when you're giving it very identifiable data parameters. Right. So whether it's structured or unstructured data and say, hey, I need to try and figure out whatever my parameters are. That's the data. Where it's still less good is if you're basically giving it unstructured parameters to say, just can you think about an ethereal topic and sort of come back to me with an answer? And it will come back with a pretty comprehensive answer. But most of the time it needs to be fact checked and or annotated or just tested and scrutinized.

42:28Joe, you've successfully turned this into another AI conversation.

42:32Tracy Alloway:Well, how can you talk about quantitative identity? How can you talk about this stuff? We were doing pretty good for 40 minutes. So, all right, Alex, thank you so much for coming on Oddbots. Really appreciate it. Truly the perfect guest. Thank you so much for having me. Thank you.

42:58So, Joe, that was a fantastic conversation. A couple things stand out. So, first of all, it does seem like with the growth of all these products, the explosion, especially in Asia, that we have products who are sort of like more important marginal buyers and sellers in the market at a minimum. The other thing I was thinking about is like, okay, a regulator isn't necessarily going to approve a five times levered product or a hundred times levered product. But is there a point at which like the aggregate number of all these products actually becomes more of a concern?

43:33Tracy Alloway:Totally. I mean, there's two things. One, you have to take seriously where we are in terms of balance sheet capacity and how much balance sheet is being allocated to this leverage and what happens to bank balance sheets in the event of some major downturn. And then I thought just the idea of like, I had not realized how big that gap has grown between household equity exposure and household real estate exposure. I still feel like that's probably like a pretty, at least to me, like a pretty eye-opening stand because I know there's a lot of equity exposure and it's become important and no one needs to argue with me.

44:13Tracy Alloway:It's like the stock market is the economy. But in my mind, I'm like, oh, it's still like real estate is the core of like people's holdings. And not only is it not the core, at least in the aggregate, I'm sure the median household is still way more exposed to their house than the stock market. But in overall, it's pretty staggering how much we're on. And then you start wondering. The whole thing is geared to the stock market. Geared to the stock market. How much of the stock market is now geared towards leverage? And then how much of it is also geared towards AI? It's, yeah, it's a lot. No, it's a lot.

44:42Tracy Alloway:And he really spelled it out well. Yeah, we have to have him back on. Definitely. Shall we leave it there for now? Yeah, let's leave it there. This has been another episode of the All Thoughts Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our producers, Carmen Rodriguez at Carmen Armin, Dashiell Bennett at Dashbot, Kale Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to Bloomberg.com slash OddLots. We have a daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash OddLots.

45:16And if you enjoy Odd Lots, if you like it when we talk about single stock levered ETFs, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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