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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the AULTS podcast.

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Speaker 2: I'm Tracy Allaway and I'm Joe.

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Speaker 3: Why isn't thal Joe?

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Speaker 2: When I think about the big market events this week?

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Speaker 2: This week ninth, July ninth, macro doesn't feel like it

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Speaker 2: matters that much anymore. So. We had renewed hostilities between

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Speaker 2: Iran and the US markets at least as of Thursday morning,

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Speaker 2: don't seem to care too much. You know, we've had

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Speaker 2: some fed minutes. They don't really change things that much.

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Speaker 2: The big stories this week, unusually for the US market,

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Speaker 2: have all stemmed from Korea.

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Speaker 3: Yeah, no, it's true. I mean, look, the Chips trade,

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Speaker 3: which then becomes the Korea trade and all of these

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Speaker 3: things is like the big thing in the entire world.

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Speaker 3: And I agree, I do not think people are going

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Speaker 3: to focus on much of this other stuff as long

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Speaker 3: as this thing is so crazy. And you know, the

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Speaker 3: Korean market, like we've just come to get used to

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Speaker 3: this major, you know, pretty big market in the grand

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Speaker 3: scheme of thing five percent a day, and we've suddenly

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Speaker 3: all become sort of normalized to it. Yeah, this is

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Speaker 3: the big thing.

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Speaker 2: Yeah, So the two big Korea events this week. We

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Speaker 2: had Samsung earnings already and then today, which means we're

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Speaker 2: recording this podcast either at the best or the worst time,

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Speaker 2: because we don't know how things are going to shake out.

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Speaker 2: But we have a US listing from sk Heinex. And

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Speaker 2: the reason Korean stocks are so interesting right now is

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Speaker 2: it's not just that the US market is kind of

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Speaker 2: like playing off with them as well, but it's because

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Speaker 2: you have all these new products in the Korean market,

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Speaker 2: all these things called single stock levered ETFs, which seem

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Speaker 2: to be having an impact on the overall movement of

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Speaker 2: the underlying shares.

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Speaker 3: It's so funny to me. You look at again the

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Speaker 3: performance of some of these chip companies, some of them up.

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Speaker 2: Like ten is not enough we need exactly you know what, I.

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Speaker 3: Look at this chart.

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Speaker 4: You know what.

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Speaker 3: The one thing I'm missing is some leverage here. This

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Speaker 3: is always also funny to me when people were like

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Speaker 3: trading crypto on leverage, just like what you're not getting

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Speaker 3: enough you need like twenty exit but people love it

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Speaker 3: and so yeah.

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Speaker 2: But even here's the thing, even if you personally don't

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Speaker 2: love leverage, leverage has an impact on you totally if

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Speaker 2: you're in that market.

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Speaker 3: You may not be interested in leverage, but leverage is

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Speaker 3: interested in you exactly. One of those guys said, all.

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Speaker 2: Right, so we should talk about all these products. You know,

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Speaker 2: I can't resist a product tail wagging underlying share price dogs.

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Speaker 2: So we really do have the perfect guest. We're going

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Speaker 2: to be speaking with Alex Altman aka alt. He is,

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Speaker 2: of course, the global head of Equities Tactical Strategies over

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Speaker 2: at Barkley's so Alty Alex, so much for coming on.

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Speaker 4: Thank you very much for having me.

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Speaker 2: So I believe in one note I read from you,

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Speaker 2: and the reason I wanted to get you on is

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Speaker 2: you specifically use the word terrifying to describe the amount

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Speaker 2: of notional exposure coming from levered ETFs. Can you give

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Speaker 2: us a little bit of context about how big this

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Speaker 2: market actually is now?

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Speaker 4: I think that the look. I'll give you the numbers

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Speaker 4: straight away, and as you highlighted, markets are moving really quickly,

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Speaker 4: so these numbers change a lot every day. And I

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Speaker 4: think at the time when we wrote about that, it

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Speaker 4: was the local highs of the AUM and it was

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Speaker 4: globally around about two hundred and fifty two hundred and

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Speaker 4: seventy billion dollars. The number itself is actually not enormously

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Speaker 4: terrifying at all. I would just say the the asymptotic

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Speaker 4: growth of the AUM over the past few months has

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Speaker 4: been somewhat sensational or just it's just meteoric. So just

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Speaker 4: to put some numbers behind that, Yeah, if we look

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Speaker 4: at say the Carean market, which you guys highlighted, at

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Speaker 4: the beginning of the year, apac AUM so Asia Pacific

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Speaker 4: AUM was somewhere around about twelve or thirteen billion, that's

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Speaker 4: now around about fifty.

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Speaker 3: Sorry, just sorry. And within these loverty.

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Speaker 4: Livery products in Asia there they are now around about

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Speaker 4: fifty to fifty five billion dollars, so talking about a

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Speaker 4: threefold increase or plus over a relatively short period of time.

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Speaker 4: And here in the US, if you take the beginning

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Speaker 4: of April, beginning of April, which was of course the

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Speaker 4: local lows for the market, we were talking around about

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Speaker 4: one hundred and twenty odd billion here in the US,

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Speaker 4: and that's now increased. I think at its peak it

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Speaker 4: was just north of two hundred billion. So these numbers

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Speaker 4: can move extremely quickly. And what's really interesting is that

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Speaker 4: if you look at the US market it's not like

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Speaker 4: you've actually seen significant inflows and share creation in that space.

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Speaker 4: Most of it, of that AUM growth has been because

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Speaker 4: of price performance. Oh interesting, whereas in Korea you've actually

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Speaker 4: seen the opposite, not the opposite, but you've seen huge

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Speaker 4: share creation on top of meteoric rises in the share prices.

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Speaker 3: Well that's super interesting.

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Speaker 2: But just to be clear, for the Korean products, as

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Speaker 2: the underlying share price goes up, the AUM also tends

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Speaker 2: to go up, right, So that and then when the

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Speaker 2: underlying goes down, it collapses. And so that's why you

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Speaker 2: get these big spikes in AUM.

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Speaker 4: Yes, so I think it's worth just like expanding all

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Speaker 4: the mechanics of it just for a second. So I

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Speaker 4: think that's really important to debunk and why they've become

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Speaker 4: such a relevant product in the market. So if you

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Speaker 4: just take we just actually put some numbers behind this.

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Speaker 4: Let's say you had one underlier whatever it was, and

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Speaker 4: it was an AUM of call it thirty billion dollars,

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Speaker 4: just to make the numbers simple, and let's say it

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Speaker 4: was triple levered. So now they're going to basically go

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Speaker 4: out and get a load of exposure for another sixty

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Speaker 4: billion dollars. That exposure is typically done through swap agreements, right,

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Speaker 4: so they can actually sort of have the synthetic exposure

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Speaker 4: through prime brokerages and so on and so forth. And

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Speaker 4: so now you've got ninety billion dollars exposure. Now, let's

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Speaker 4: say the stock goes down ten percent, so ninety billion

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Speaker 4: dollars ten percent drop, so that's going to be nine

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Speaker 4: billion dollar drop. So you're down to an eighty one

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Speaker 4: billion dollar of exposure now, but you've lost nine billion

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Speaker 4: dollars of your AUM, which was only member thirty billion,

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Speaker 4: So the thirty goes down to twenty one. And then

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Speaker 4: if you multiply that back by three to maintain your

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Speaker 4: triple level exposure, you're going to be at sixty three,

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Speaker 4: but you've got eighty one. So you need to reduce

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Speaker 4: down your exposure mechanically by over ten billion dollars in

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Speaker 4: order to sort of maintain what you've kind of put

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Speaker 4: in your prospectors as of you know, your your triple

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Speaker 4: leverage against that underlier. And that's what's creating these mechanical

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Speaker 4: adjustments on a daily basis, whether it's down obviously on

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Speaker 4: down days and then up on updates, so effectively you're

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Speaker 4: creating a new short gamma dimension in the market that

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Speaker 4: was relatively small only a couple of years ago. And

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Speaker 4: I think really importantly is that there's a lot of

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Speaker 4: dynamics that are moving non discretionary flows in the market.

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Speaker 4: So obviously in the ETF world, you've got a lot

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Speaker 4: of overwriting ETFs that are doing the opposite. They're effectively

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Speaker 4: selling volunt in the market, which is creating a long

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Speaker 4: gamma process and that would typically have netted off against

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Speaker 4: a lot of these levit ETFs. But because these levit

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Speaker 4: ETFs have become so large and they're rebalancing that now

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Speaker 4: net gamma profile is effectively becoming more.

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Speaker 2: Negative a bigger role in the market.

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Speaker 4: Yes, exactly.

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Speaker 3: Can you actually just maybe take a step backward? Can

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Speaker 3: you just walk us through the basic I don't know

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Speaker 3: if it's like the business or the simple mechanics of

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Speaker 3: setting up a LEVERTYTF. So I have a company, I

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Speaker 3: want to Okay, I'm an upstart ETF provider.

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Speaker 2: I want now do the odd lots three times, Yeah.

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Speaker 3: The odd oddlarge three times micron ETF whatever it is.

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Speaker 3: So it's like, who'sing the leverage? Just talk to us

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Speaker 3: about how this product, you know, it was originally conceived them,

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Speaker 3: setting aside market impact and all that stuff for now, sure,

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Speaker 3: how the product works.

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Speaker 4: The product, I mean, the product is very straightforward. It's

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Speaker 4: really no different to any other fund provider in the

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Speaker 4: ETF space. So effectively, if you're creating an ETF and

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Speaker 4: you typically go and get some kind of anchor investor

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Speaker 4: or some kind of co sponsor who's effectively going to

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Speaker 4: create the initial amount of AUM for you. And then

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Speaker 4: obviously because you need critical mass. Yeah, there are as

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Speaker 4: I'm sure you guys have talked about, there's more ETFs

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Speaker 4: in the world than there are single securities here in

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Speaker 4: the US. So creating the ETF and the fund is

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Speaker 4: not complicated. It all obviously has to go through SEC regulation,

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Speaker 4: and I think that's an important point to highlight when

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Speaker 4: it comes to all of these levit ETFs is whatever

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Speaker 4: your opinion on them may be, the fact is is

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Speaker 4: that the SEC, the regulator, has approved all of these.

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Speaker 4: So in the same way, these funds will then get approved.

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Speaker 4: And from there, of course, the question is where do

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Speaker 4: you get the leverage? So typically the leverage will come

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Speaker 4: from a banking partner, so through a prime channel that

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Speaker 4: they will effectively seek to get that exposure synthetically through

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Speaker 4: the bank, and that in turn obviously will effectively create

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Speaker 4: the balance of their notional exposure. Now from a bank's perspective,

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Speaker 4: that goes through the prime balance. And the interesting thing

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Speaker 4: about that is is that some of market participants have

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Speaker 4: been talking about an increase in financing rates that has

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Speaker 4: been driven by these levitytfs. I think we need to

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Speaker 4: debunk that for a hot second, because I know I've

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Speaker 4: a little bit off topic, but it is still very

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Speaker 4: much from me. Now.

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Speaker 3: I actually had a request recently someone wanted us to

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Speaker 3: talk about equity finance and cars, So I'm glad.

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Speaker 4: You brought that up. So it is perhaps a contributor.

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Speaker 4: You have to acknowledge the fact that if you've got

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Speaker 4: north of two hundred billion dollars of AUM that has

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Speaker 4: materialized in relatively short order or grown exponentially recently, and

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Speaker 4: you're obviously putting over two times leverage on that, you're

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Speaker 4: creating an additional four hundred billion plus of stuff that's

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Speaker 4: been brought and put on balance sheets in swap format.

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Speaker 4: It's going to create a degree of tightness within bank

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Speaker 4: balance sheets. That's not in isolation true. The fact of

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Speaker 4: the matter is is that bank balance sheets have become

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Speaker 4: tighter because my primary reason is markets have gone up,

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Speaker 4: and as market's gone up, the price of stuff has

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Speaker 4: gone up, and so that has been without a doubt,

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Speaker 4: the biggest reason for financing rates rocketing is the fact

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Speaker 4: that spot levels are higher. This has been a contributing factor.

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Speaker 4: But we shouldn't overlook the fact that if you just

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Speaker 4: look at the biggest driver of AUM growth within the

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Speaker 4: hedge fund community, it's the multi manager platform we're talking about.

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Speaker 4: Those guys are effectively are trillion dollars of AUM. Now

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Speaker 4: that's potentially tripled since COVID, and obviously those guys have

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Speaker 4: deployed a huge amount of risk on a long and

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Speaker 4: a short side. So again that's using balance sheet as well.

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Speaker 4: So it's not really so much just the levity ETFs

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Speaker 4: that are going out and asking the banks for balance

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Speaker 4: sheet that's causing financing rates to squeeze higher. It's a

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Speaker 4: it's one of many.

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Speaker 3: But balance capacity is scarce.

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Speaker 4: Balance sheet's capacity is scarce, and it will remain scarce

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Speaker 4: so long as SMP trading at seventy five seventy six

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Speaker 4: hundred year.

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Speaker 2: Well, just on a related note, okay, balance sheet is

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Speaker 2: scarce from the dealers, and it is true that like

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Speaker 2: if a lever in ETF comes to you and says

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Speaker 2: I have two times leverage and now I need like

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Speaker 2: forty billion dollars worth of exposure, You're not going to

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Speaker 2: want to extend like that much credit to a single

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Speaker 2: counterparty if you're a dealer. And so one of the

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Speaker 2: things I've heard in the market is that maybe some

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Speaker 2: of the levered ETF start going out and sourcing leverage

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Speaker 2: not just through swaps with dealers, but through the options market.

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Speaker 4: Have you heard this so that that was documented I

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Speaker 4: believe in the press as well, and I believe that

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Speaker 4: that was specific to one particular fund out in Asia

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Speaker 4: rather than specifically anything that's happened here in the US.

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Speaker 4: So I think that you can open a bit of

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Speaker 4: a can of worms when you start talking about the

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Speaker 4: availability of this leverage and to certain counterparties. And I

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Speaker 4: don't think that's specific necessarily even just to just to

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Speaker 4: the levity ETF market. I think you can really open

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Speaker 4: that up in any counterparty risk when you think about

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Speaker 4: does a bank really want to have so what you know,

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Speaker 4: bank would be very careful in Barclays, so I would say,

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Speaker 4: are pretty conservative and extremely diligent about how they choose

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Speaker 4: their counterparties, who they want to who they want to

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Speaker 4: partner with, and so as a general rule of thumb,

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Speaker 4: you have to always keep that in mind. Just we're

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Speaker 4: in the when the we're in the business of risk

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Speaker 4: management at the end of the day, and so yes,

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Speaker 4: this is sort of a I wouldn't even say a

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Speaker 4: new tool is not a new shiny part of the market,

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Speaker 4: but it's we would treat it exactly the same way

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Speaker 4: as we would any other counterparty.

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Speaker 3: As you mentioned in the beginning, that there's leverage single

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Speaker 3: stock ETFs in Korea. There's leverage single stock ETFs in

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Speaker 3: the US. The difference is that in Korea, so much

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Speaker 3: more of the AUM growth has come in through new

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Speaker 3: money rather than the underlying asset values going up. So

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Speaker 3: one thing I think about the Korean market. People talk

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Speaker 3: about incredible retail interest that probably dwarfs the US as

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Speaker 3: some sort of per capita basis. How does that matter?

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Speaker 3: Does that have implications in terms of the tail wagging

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Speaker 3: the dog, the spillover effect, This fact that there is

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Speaker 3: just a lot of interest in these products beyond just

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Speaker 3: the products going.

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Speaker 4: Up I think that the retail channel in general is

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Speaker 4: one of the most important dynamics for global equities in

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Speaker 4: the world today. Right, could we make the case that

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Speaker 4: Korea in particular is kind of ground zero for that

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Speaker 4: that retail cohort probably, you know, So for example, ninety

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Speaker 4: three percent of levet ETFs in Korea are owned by

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Speaker 4: the retail investor, whereas that number is lower in the US.

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Speaker 4: It's approximately seventy five percent. Still still high, but not

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Speaker 4: as high, right, And some may say, well, only seventy

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Speaker 4: five percent of why is it not high? And also

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Speaker 4: I think we should probably just be very clear, like

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Speaker 4: levet ETFs have made retail an awful lot of money, right,

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Speaker 4: So you know, there are plenty of guys driving around

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Speaker 4: McLaren's because of their returns that they've generated in LEVITYTF.

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Speaker 4: So you know, I think we sort of need to

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Speaker 4: balance out sort of.

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Speaker 3: No judgment here, We're just trying to We're just trying

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Speaker 3: to learn how it works. But I don't judge anyone

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Speaker 3: from making money on.

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Speaker 4: Balance a lot of money has actually got asked this morning,

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Speaker 4: you know, have you actually calculated how much money has

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Speaker 4: been made? And I said no, But on balance a

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Speaker 4: lot of it, Yeah, quite a bit. So going back

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Speaker 4: to the point on retail. Take the US for example,

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Speaker 4: the US, it's a phenomenon our lifetimes. Thirty four percent

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Speaker 4: of US household wealth is now in equities, right, It's

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Speaker 4: the highest on record. It's higher than dot Com. I

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Speaker 4: think what's even more amazing about that stat is if

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Speaker 4: you take the next largest component of household wealth in

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Speaker 4: the US is real estate, and that is around about

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Speaker 4: twenty six percent. So the eight percentage point difference between

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Speaker 4: those two sort of assets, so to speak, is also

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Speaker 4: the widest on record. We as a society have never

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Speaker 4: been this over index or overexposed to equities, and so

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Speaker 4: I think that what you've seen within, say the Levet

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Speaker 4: ETF space is really just another small part of that

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Speaker 4: broader ecosystem that has contributed to this enormous wealth creation.

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Speaker 4: And one of the things that we think about a

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Speaker 4: lot as a team. People ask us what keeps you

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Speaker 4: awake at night? It's not levity ETFs that keep me

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Speaker 4: awake at night. What keeps you awaken night is that

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Speaker 4: you have a structural impairment to equities that effectively no

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Speaker 4: economist on the planet has a cell in an econometric

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Speaker 4: model that says twenty percent inpairment to the SMP it

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Speaker 4: basically destroys let's just call it around about sixteen trillion

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Speaker 4: dollars of wealth. So let's just say that's half of

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Speaker 4: US GDP, right, and that is an instant impairment to

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Speaker 4: US consumption. It's your recession straight out the gate. I

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Speaker 4: don't want to get all sort of bearish or anything,

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Speaker 4: but I think it's just important to highlight within the

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Speaker 4: broader context that that retail investors have a huge amount

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Speaker 4: of exposure and therefore, in turn, the US government has

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Speaker 4: a huge amount of incentive to try and not do

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Speaker 4: anything too disorderly that could completely derail I mean, my

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Speaker 4: big stat is is that people used to say the

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Speaker 4: economy is not the stock market or stop market is

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Speaker 4: not the economy.

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Speaker 2: I just haven't said many times on the podcast.

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Speaker 4: Yeah, I just think the stock market is the economy now,

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Speaker 4: not in the traditional sense of like, oh, the stock

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Speaker 4: market is going up, therefore the economy is doing great.

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Speaker 4: But yeah, a twenty percent inpairment to the stock market

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Speaker 4: is kind of I think it will trigger a meaningful

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Speaker 4: downturn in US consumption.

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Speaker 2: The thing that worries me now is it's not just

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Speaker 2: the stock market is the economy. It's like AI is

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Speaker 2: the stock market, and therefore AI is the economy as well.

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Speaker 2: Like we know it's been driving not just macro growth,

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Speaker 2: but if it's also driving stocks and we're getting a

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Speaker 2: wealth effect and it's also driving consumption, then I don't know,

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Speaker 2: it's like an AI impact squared right.

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Speaker 4: And look again, just to talk about facts, the bulk

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Speaker 4: of levit etf AUM has grown within that cohort as well.

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Speaker 4: The largest AUMs are all within either nasdact rated products

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Speaker 4: or semis conductors or single names, most of which are

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Speaker 4: I mean the largest the largest single name levit TF

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Speaker 4: in the US is is Micron, right, so that kind

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Speaker 4: of tells you the largest largest single name levi DTF

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Speaker 4: in the world is Heinex. So these dynamics do play

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Speaker 4: an important role, and especially when you start thinking about

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Speaker 4: the tail wagging the dog, and price dictates narrative, right,

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Speaker 4: So people see prices going up, that gives them a

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Speaker 4: confirmation bias about oh, that means we maybe we can

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Speaker 4: spend more money on capex, or that means that the

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Speaker 4: AI story is alive and well. And that's not necessarily

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Speaker 4: not true, But how much of that is getting distorted

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Speaker 4: by these sort of these non discretionary flows that are

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Speaker 4: pushing prices up, or indeed they're.

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Speaker 2: Just general momentum, right, like momentum attracting more flows.

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Speaker 4: Correct. I mean, let's face it, momentum is the everything.

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Speaker 4: It's the most successful factor strategy in history, right. You know,

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Speaker 4: SMP goes up seventy eight percent of the time. Therefore,

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Speaker 4: by definition, investors generally want to be long momentum. They

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Speaker 4: want to be long stuff that goes up, and they

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Speaker 4: want to be short the stuff that goes down if

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Speaker 4: they do indeed short.

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Speaker 3: The MUU the Triple Averid I think it's triple Averid

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Speaker 3: whatever it is that was trading at twenty three a

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Speaker 3: year ago and it recently had a peak of over

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Speaker 3: twelve hundred. So that'll get you a lot of McLarens

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Speaker 3: on it.

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Speaker 4: Yeah. I mean again, this is the thing for as

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Speaker 4: a team. It's X, it's a it's a two xctif

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Speaker 4: that's okay, But.

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Speaker 3: Actually, can I ask a question about this? So you

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Speaker 3: mentioned that all of these products, like they go through

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Speaker 3: the regulatory process, and you know, we have the SEC.

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Speaker 3: I don't know what career's equivalent is but I assume

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Speaker 3: they have some sort of similar body. What is like

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Speaker 3: the SEC's bar?

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Speaker 1: Is it just yeah?

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Speaker 4: Like what is like?

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Speaker 2: What?

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Speaker 4: No?

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Speaker 3: Seriously or like you know ten x?

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Speaker 4: Like what is the point?

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Speaker 3: Is it just like Okay, this checks a certain set

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Speaker 3: of boxes? Because clearly it's not about like doubt it

378
00:19:18,800 --> 00:19:20,920
Speaker 3: should be you know, it's like is this product good

379
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Speaker 3: or bad?

380
00:19:21,520 --> 00:19:21,639
Speaker 4: Right?

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Speaker 3: That's not really the question. Does this product meet a criteria?

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00:19:24,440 --> 00:19:26,639
Speaker 3: Or does it? What is the criteria? Why don't we

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00:19:26,720 --> 00:19:28,080
Speaker 3: have ten XCTF?

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00:19:28,400 --> 00:19:30,879
Speaker 4: So there's actually an anecdote. There was a time, if

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Speaker 4: I'm not mistaken, relatively recently during the government shutdown, where

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00:19:35,000 --> 00:19:38,080
Speaker 4: there was an attempt to try and list and shelf

387
00:19:38,600 --> 00:19:40,479
Speaker 4: some I don't think it was ten x, but there

388
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Speaker 4: was I believe some five X levit ETFs, and effectively

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Speaker 4: the regulator I believe at that point just kind of

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Speaker 4: made it abundantly clear that that there are limitations to

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Speaker 4: to leverage just from a to your beginning point. You know,

392
00:19:57,520 --> 00:19:59,800
Speaker 4: you may not be interested in leverage. Yeah, leverage is

393
00:20:00,040 --> 00:20:03,080
Speaker 4: probably interested in you. And again I'm not privy to

394
00:20:03,200 --> 00:20:06,159
Speaker 4: those conversations in any way, shape or form, but I

395
00:20:06,280 --> 00:20:09,359
Speaker 4: do know and understand that there are limitations to what

396
00:20:09,560 --> 00:20:13,720
Speaker 4: a regulator would deem acceptable. Okay from a leverage perspective,

397
00:20:14,359 --> 00:20:15,840
Speaker 4: quite quite reasonably as sure.

398
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Speaker 2: Just going back to the momentum trade more broadly, so,

399
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Speaker 2: Barklay's you have this index product. I can't remember the

400
00:20:24,000 --> 00:20:26,640
Speaker 2: exact name, but it's like the market timing product.

401
00:20:26,280 --> 00:20:29,320
Speaker 4: Betty Betty Betty, Barclay's equity timing indicator.

402
00:20:29,560 --> 00:20:33,159
Speaker 2: Yeah, and that has been like in I don't know

403
00:20:33,240 --> 00:20:36,040
Speaker 2: if if you use the specific term overbought, but like

404
00:20:36,440 --> 00:20:39,879
Speaker 2: it's basically been in sort of bubblish speculative territory for

405
00:20:40,040 --> 00:20:42,040
Speaker 2: some time now, like a record amount of time.

406
00:20:42,160 --> 00:20:43,119
Speaker 4: Right, yep, that's right.

407
00:20:43,280 --> 00:20:47,400
Speaker 2: Yeah, Okay, what should investors do with this information? Sure,

408
00:20:47,520 --> 00:20:49,600
Speaker 2: because I feel like all the surveys right now show

409
00:20:49,640 --> 00:20:52,720
Speaker 2: everyone's super bolish. Everyone thinks stocks are overvalued, and it's like,

410
00:20:52,840 --> 00:20:55,040
Speaker 2: so what if the trade is momentum then you just

411
00:20:55,119 --> 00:20:56,160
Speaker 2: keep going Yeah.

412
00:20:56,119 --> 00:20:59,560
Speaker 4: So so Betty, Yeah, we love Betty. So this index

413
00:20:59,840 --> 00:21:03,480
Speaker 4: was created shortly after I joined Barclays, but it was

414
00:21:03,640 --> 00:21:06,760
Speaker 4: the first iteration was created back in twenty eighteen. So

415
00:21:06,840 --> 00:21:10,359
Speaker 4: there's a reasonable amount of out of sample data around

416
00:21:10,400 --> 00:21:13,160
Speaker 4: this framework. It's got nineteen inputs. None of those inputs.

417
00:21:13,359 --> 00:21:15,160
Speaker 4: We have a mant on my team. I tell everyone

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00:21:15,200 --> 00:21:17,200
Speaker 4: this in my team every week, which is, if you

419
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Speaker 4: can't quantify, you don't have the right to talk about it.

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Speaker 4: And using the word feels and seams a band. If

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00:21:22,200 --> 00:21:23,640
Speaker 4: you use the word fields and seems, you're fired.

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Speaker 2: I think we'd be in trouble.

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Speaker 3: Jill, Yeah, but it's a good rule.

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Speaker 4: But the idea is, it's a very simple rule, which

425
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Speaker 4: is we just want to try and really deep root

426
00:21:32,080 --> 00:21:35,359
Speaker 4: the team in quantifiable data, right, you know. And just

427
00:21:35,440 --> 00:21:37,280
Speaker 4: to take a step back, when Scott and Ronnie who

428
00:21:37,359 --> 00:21:39,959
Speaker 4: run the equity business, when they sort of effectively were

429
00:21:40,040 --> 00:21:43,199
Speaker 4: hired into Barclays as kind of really revamp as an

430
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Speaker 4: ascendant equity business, one of their main priorities was like,

431
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Speaker 4: we need content at the center of the mind shared

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Speaker 4: in order to sort of like really partner and hold

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Speaker 4: hands with our clients. And so that's when they brought

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Speaker 4: me in. And because we wanted to focus on quantifiable

435
00:21:57,680 --> 00:22:00,480
Speaker 4: content as opposed to it. So that's the backstory as

436
00:22:00,520 --> 00:22:03,440
Speaker 4: to why Betty was created. Because we wanted to have

437
00:22:04,040 --> 00:22:07,320
Speaker 4: a market timing model that removed fields and seems and

438
00:22:09,480 --> 00:22:12,120
Speaker 4: so it's got nineteen inputs. It's got everything ranging from

439
00:22:12,560 --> 00:22:17,160
Speaker 4: real yields to spot Volk correlation to looking at discretionary flows,

440
00:22:17,240 --> 00:22:18,840
Speaker 4: like what a mutual fund beat is, what a hedge

441
00:22:18,880 --> 00:22:20,840
Speaker 4: fund beat is, what are CTA is doing, what a

442
00:22:20,880 --> 00:22:23,760
Speaker 4: vult control doing, what elever TTF doing. So everything from

443
00:22:23,840 --> 00:22:26,760
Speaker 4: the discretion in, the non discretionary vertical, from the vole vertical,

444
00:22:27,280 --> 00:22:29,760
Speaker 4: from positioning. We just the only thing we didn't include

445
00:22:30,560 --> 00:22:33,760
Speaker 4: was sentiment indicators. We weren't interested in feels and seams,

446
00:22:33,800 --> 00:22:37,240
Speaker 4: so there's no AAI bullbear index or anything like that.

447
00:22:38,080 --> 00:22:41,000
Speaker 4: And so your question was it's been in this record

448
00:22:41,080 --> 00:22:43,399
Speaker 4: sort of warning territory. Now, yes, it has been in

449
00:22:43,520 --> 00:22:47,160
Speaker 4: record warning territory, and effectively that's been driven by primarily

450
00:22:47,240 --> 00:22:50,240
Speaker 4: momentum crowding. Now, as you know at the time of recording,

451
00:22:50,280 --> 00:22:52,520
Speaker 4: we had a big momentum pullback over the past couple

452
00:22:52,560 --> 00:22:54,960
Speaker 4: of weeks, so that's actually sort of been a relatively

453
00:22:55,000 --> 00:22:59,680
Speaker 4: healthy and reselling that real yields problematic if you're competing

454
00:22:59,720 --> 00:23:01,840
Speaker 4: for can capital at the same time as between the

455
00:23:01,880 --> 00:23:05,520
Speaker 4: equity market. Obviously, record year for issuance, possibly this year

456
00:23:05,880 --> 00:23:09,480
Speaker 4: government going to be record year of issuance IG record

457
00:23:09,600 --> 00:23:10,280
Speaker 4: year of issuance.

458
00:23:10,400 --> 00:23:12,600
Speaker 2: Yeah, this is the other big market story. Is like

459
00:23:12,720 --> 00:23:15,400
Speaker 2: a blockbuster summer for IG bond issuance.

460
00:23:15,600 --> 00:23:18,560
Speaker 4: Absolutely, So if you think about what real yiods effectively

461
00:23:18,640 --> 00:23:22,600
Speaker 4: represent that dynamic of that competition of capital versus equities,

462
00:23:22,800 --> 00:23:24,760
Speaker 4: if you just generally look at where real yields are

463
00:23:24,800 --> 00:23:27,720
Speaker 4: today in say a post GFC environment or a post

464
00:23:27,800 --> 00:23:32,080
Speaker 4: COVID environment and benchmarket against where equity multiples are, equity

465
00:23:32,119 --> 00:23:35,520
Speaker 4: multiples shouldn't be this high, right, so we can get

466
00:23:35,560 --> 00:23:37,680
Speaker 4: into a bigger debate about oh, dot Com real yolds

467
00:23:37,720 --> 00:23:40,840
Speaker 4: were very high and in equity multiple skyrocketing. I'm like, yeah,

468
00:23:40,840 --> 00:23:43,440
Speaker 4: but the government wasn't asking you cap in hand for

469
00:23:43,960 --> 00:23:47,680
Speaker 4: tons of money, so they didn't endgrate. Yes, but but

470
00:23:48,040 --> 00:23:49,640
Speaker 4: the hell of a ride it was in the build

471
00:23:49,680 --> 00:23:52,520
Speaker 4: up to that. But so better effectively is taking all

472
00:23:52,560 --> 00:23:54,600
Speaker 4: of these inputs and what is it telling you right now?

473
00:23:54,640 --> 00:23:57,240
Speaker 4: It's just telling you that the forward return profile of

474
00:23:57,280 --> 00:24:00,720
Speaker 4: the S and P from an asymmetry perspective a tactical

475
00:24:00,800 --> 00:24:02,919
Speaker 4: two month time horizon, it's just not great. Right now.

476
00:24:02,960 --> 00:24:05,520
Speaker 4: We're looking at if you were to pick a random

477
00:24:05,640 --> 00:24:07,760
Speaker 4: day in time and say all right, I'm gonna buy

478
00:24:07,800 --> 00:24:09,920
Speaker 4: the SMP, and I hold it for two months forty

479
00:24:09,960 --> 00:24:13,320
Speaker 4: two trading days, the average return would be around about

480
00:24:13,320 --> 00:24:16,000
Speaker 4: one hundred and ninety bibs. Not bad, right, and your

481
00:24:16,119 --> 00:24:18,280
Speaker 4: hit rate of making money is about seventy three percent,

482
00:24:19,040 --> 00:24:22,160
Speaker 4: so betty, when it is in this kind of territory,

483
00:24:22,359 --> 00:24:24,280
Speaker 4: So six seven it was high as sort of ten

484
00:24:24,400 --> 00:24:26,760
Speaker 4: or eleven. It was telling you that you only had

485
00:24:26,800 --> 00:24:29,120
Speaker 4: a round about a thirty five percent chance or even

486
00:24:29,200 --> 00:24:31,600
Speaker 4: lower of making money in the S ANDP over that

487
00:24:31,640 --> 00:24:34,720
Speaker 4: same time horizon, and your average return was basically negative.

488
00:24:35,200 --> 00:24:37,720
Speaker 4: So it's not so much necessarily that it's telling you,

489
00:24:37,920 --> 00:24:40,840
Speaker 4: oh my gosh, the market's going to crater. It's really

490
00:24:40,960 --> 00:24:43,840
Speaker 4: just an illustration of a bunch of quantitative inputs that

491
00:24:43,920 --> 00:24:46,639
Speaker 4: are telling you, actually, the asymmetry is not great. And

492
00:24:46,760 --> 00:24:48,879
Speaker 4: that's exactly what we've seen. The model first flashed as

493
00:24:48,920 --> 00:24:52,880
Speaker 4: a warning signal late May. SMP's kind of gone done

494
00:24:52,920 --> 00:24:57,399
Speaker 4: nothing since then, and so maybe in anticipation of some

495
00:24:57,840 --> 00:25:00,920
Speaker 4: big correction in the equity market isn't forthcoming, And that's okay.

496
00:25:01,160 --> 00:25:03,080
Speaker 4: I still take a lot of validation in the model

497
00:25:03,080 --> 00:25:05,360
Speaker 4: that is kind of telling you that to just sort

498
00:25:05,359 --> 00:25:07,680
Speaker 4: of pump the brakes a bit, let some of this

499
00:25:07,760 --> 00:25:10,240
Speaker 4: froth and the equity market come out and actually get

500
00:25:10,280 --> 00:25:13,040
Speaker 4: a reset, which then actually sets us up better. I mean, ultimately,

501
00:25:13,200 --> 00:25:17,159
Speaker 4: earnings are still good. Right, We're still driving AI in

502
00:25:17,280 --> 00:25:19,320
Speaker 4: the economy, and we can get into a debate about

503
00:25:19,320 --> 00:25:22,119
Speaker 4: whether that's sustainable or not. And then we've also got

504
00:25:22,200 --> 00:25:24,480
Speaker 4: to work off the assumption we're still running a massive

505
00:25:24,480 --> 00:25:28,399
Speaker 4: fiscal deficit. And whilst that environment is happening, it's quite

506
00:25:28,480 --> 00:25:31,640
Speaker 4: hard to create any kind of fundamental economic downtown too.

507
00:25:47,520 --> 00:25:51,280
Speaker 3: Setting aside market timing and what's going to happen in

508
00:25:51,359 --> 00:25:54,280
Speaker 3: the next two or three months, et cetera, just under

509
00:25:54,320 --> 00:25:59,720
Speaker 3: sort of like bigger question the combination of market valuations.

510
00:26:00,119 --> 00:26:02,800
Speaker 3: You want to measure them all the classical ratios that

511
00:26:02,880 --> 00:26:07,040
Speaker 3: people like versus the increase in real yields, Like how

512
00:26:07,240 --> 00:26:10,479
Speaker 3: historically expensive is this market right now?

513
00:26:10,760 --> 00:26:14,920
Speaker 4: All right, So if we take the post GFC environment,

514
00:26:16,280 --> 00:26:20,320
Speaker 4: the challenge is always going to be that SMP margins

515
00:26:20,320 --> 00:26:23,679
Speaker 4: were much lower for a long period after the GFC

516
00:26:23,840 --> 00:26:26,200
Speaker 4: than where they are today, and we have to acknowledge

517
00:26:26,240 --> 00:26:30,040
Speaker 4: that higher margins should map into higher multiples. So but

518
00:26:30,200 --> 00:26:33,000
Speaker 4: we'll do the full comparison, so post GFC, because there's

519
00:26:33,040 --> 00:26:35,720
Speaker 4: not that many periods where real yilds of this high.

520
00:26:35,720 --> 00:26:37,960
Speaker 4: Real yilds today on a ten year basis are running

521
00:26:37,960 --> 00:26:40,920
Speaker 4: about the ninety fifth percentile, and they're around about I

522
00:26:40,960 --> 00:26:45,680
Speaker 4: think two thirty give or take. So with that being said,

523
00:26:45,720 --> 00:26:48,639
Speaker 4: if you take what the average SMP multiple when real

524
00:26:48,760 --> 00:26:52,359
Speaker 4: yields were this high or higher, it's a scary low number.

525
00:26:52,520 --> 00:26:54,879
Speaker 4: The S and P multiple would be around about fourteen

526
00:26:54,880 --> 00:26:57,560
Speaker 4: to fifteen times, and we're currently trading on around about

527
00:26:57,560 --> 00:27:00,480
Speaker 4: twenty point two to twenty point three. If you take

528
00:27:00,520 --> 00:27:03,399
Speaker 4: the post COVID environment, which I think is probably a

529
00:27:03,440 --> 00:27:06,400
Speaker 4: better representation of what S and P operating margins look

530
00:27:06,520 --> 00:27:08,920
Speaker 4: like today, we're still low. It's around about eighteen and

531
00:27:08,960 --> 00:27:12,280
Speaker 4: a half times, So you're still talking about basically a

532
00:27:12,440 --> 00:27:17,840
Speaker 4: ten percent multiple contraction versus where the Hey.

533
00:27:17,760 --> 00:27:20,440
Speaker 3: It's just a very simple question. Why are margins a

534
00:27:20,600 --> 00:27:25,359
Speaker 3: more important factor here than expected growth or expected earnings growth?

535
00:27:26,119 --> 00:27:28,440
Speaker 3: I mean, like, in my mind, I would pay higher

536
00:27:28,600 --> 00:27:31,720
Speaker 3: multiples because I think that the earnings are going to

537
00:27:31,800 --> 00:27:33,560
Speaker 3: have a faster growth rate than they used to have.

538
00:27:33,920 --> 00:27:36,719
Speaker 3: That seems intuitive to me. Why is margins the lever here?

539
00:27:36,800 --> 00:27:37,479
Speaker 3: That we're looking at.

540
00:27:37,880 --> 00:27:41,000
Speaker 4: It's a good question. Well, it's not exclusively just margins.

541
00:27:41,040 --> 00:27:43,200
Speaker 4: You can sort of measure it as row as well.

542
00:27:43,760 --> 00:27:46,199
Speaker 4: There's different ways you can do it. But effectively, businesses

543
00:27:46,240 --> 00:27:48,880
Speaker 4: are becoming more profitable. If it's a more profitable business,

544
00:27:49,560 --> 00:27:51,960
Speaker 4: it's going to be working off the assumption to a

545
00:27:52,040 --> 00:27:54,280
Speaker 4: degree it may not so be accurate, but that they're

546
00:27:54,280 --> 00:27:57,480
Speaker 4: effectively that has a bigger moat. It's more it's a

547
00:27:57,600 --> 00:28:01,200
Speaker 4: more high quality business, and therefore you'll put that on

548
00:28:01,280 --> 00:28:02,800
Speaker 4: a higher godes, so to speak.

549
00:28:03,720 --> 00:28:06,879
Speaker 2: Can I ask a slightly personal question, which is I

550
00:28:07,000 --> 00:28:09,679
Speaker 2: used to joke that I wanted to be reincarnated as

551
00:28:09,680 --> 00:28:12,080
Speaker 2: an equity derivative strategist because then I could just find

552
00:28:12,119 --> 00:28:16,359
Speaker 2: a number to justify anything that's happening in markets. But

553
00:28:16,480 --> 00:28:19,879
Speaker 2: does it feel like people taking more seriously now versus

554
00:28:20,000 --> 00:28:22,680
Speaker 2: say twenty years ago, when people were still talking about

555
00:28:22,760 --> 00:28:25,640
Speaker 2: like fundamentals and stocks. Now you have this environment where

556
00:28:25,800 --> 00:28:30,400
Speaker 2: flows are super important, You have all these mechanical things happening,

557
00:28:30,760 --> 00:28:34,400
Speaker 2: You have the multi strats, as you pointed out, Vaull targeting, CTAs,

558
00:28:34,560 --> 00:28:37,520
Speaker 2: all this stuff is getting bigger. Does it feel like

559
00:28:37,840 --> 00:28:39,440
Speaker 2: equity derivatives are more important.

560
00:28:39,480 --> 00:28:40,880
Speaker 4: Now, Oh, I thought you were going to say, but

561
00:28:40,960 --> 00:28:45,320
Speaker 4: it's my British accent that makes well that too serious

562
00:28:45,720 --> 00:28:46,200
Speaker 4: because I'm.

563
00:28:46,240 --> 00:28:49,280
Speaker 2: I will say, we get higher listenership on episodes when

564
00:28:49,320 --> 00:28:49,680
Speaker 2: our guests.

565
00:28:50,000 --> 00:28:54,560
Speaker 3: It's crazy because like everyone knows how much Americans love

566
00:28:54,600 --> 00:28:57,000
Speaker 3: a British accent. But what's annoying is the bridge to

567
00:28:57,040 --> 00:28:59,360
Speaker 3: know it too and exploit that. I just like you,

568
00:28:59,560 --> 00:29:01,000
Speaker 3: like know, like you know what you're doing.

569
00:29:01,080 --> 00:29:03,520
Speaker 4: That's a reason why I stayed in ten years married

570
00:29:03,560 --> 00:29:06,600
Speaker 4: an American, you know, living living in the American dream. Sorry,

571
00:29:06,640 --> 00:29:08,760
Speaker 4: do you know what you're doing? I'll get back to

572
00:29:08,880 --> 00:29:12,240
Speaker 4: the actual question at hand. So is there increased validation

573
00:29:12,480 --> 00:29:18,960
Speaker 4: from having a more quantifiable world that we operate in. Yes,

574
00:29:19,400 --> 00:29:21,800
Speaker 4: But on a person, if you're asking a person quert,

575
00:29:21,800 --> 00:29:23,000
Speaker 4: I don't thk ask a personal question if I was

576
00:29:23,040 --> 00:29:25,640
Speaker 4: to give a personal answer. My career wasn't always like that.

577
00:29:25,840 --> 00:29:28,320
Speaker 4: I started my career as a fundamental single stock analyst,

578
00:29:28,880 --> 00:29:31,200
Speaker 4: and I actually saw in my career actually as a

579
00:29:31,280 --> 00:29:34,320
Speaker 4: generalist sales at at JP Morgan in two thousand and four.

580
00:29:34,640 --> 00:29:39,040
Speaker 4: But I then became a generalist analyst, so to speak,

581
00:29:39,240 --> 00:29:41,720
Speaker 4: on the by side, and I did that within the

582
00:29:41,760 --> 00:29:44,320
Speaker 4: avenger of the world for several years, and then when

583
00:29:44,360 --> 00:29:47,080
Speaker 4: I went to the back to the cell side, I

584
00:29:47,560 --> 00:29:50,920
Speaker 4: had to pivot. I had to change my investment approach

585
00:29:51,320 --> 00:29:56,120
Speaker 4: because of the fact that the industry was becoming increasingly specialized,

586
00:29:56,640 --> 00:29:59,560
Speaker 4: and as a generalist, I was struggling to make an

587
00:29:59,600 --> 00:30:04,160
Speaker 4: impact on clients that were becoming much more sophisticated in

588
00:30:04,280 --> 00:30:07,720
Speaker 4: the weeds on their particular sectors and areas of granularity

589
00:30:07,880 --> 00:30:10,640
Speaker 4: that I just couldn't compete with. And so at the

590
00:30:10,720 --> 00:30:14,240
Speaker 4: time I was working at City and City has a

591
00:30:14,440 --> 00:30:16,720
Speaker 4: very good had a very good quant business, and I

592
00:30:16,840 --> 00:30:19,680
Speaker 4: was like, what's all this quantum factor stuff? What is

593
00:30:19,760 --> 00:30:22,360
Speaker 4: all that? And I genuinely didn't know, And this is

594
00:30:22,440 --> 00:30:24,640
Speaker 4: twenty twelve, to be clear, twenty thirteen, and so I

595
00:30:24,680 --> 00:30:28,080
Speaker 4: took it on a personal crusade to understand the post

596
00:30:28,160 --> 00:30:33,680
Speaker 4: mortem of my portfolio management experience prior understanding why was

597
00:30:33,800 --> 00:30:37,160
Speaker 4: my P and L doing certain things which I didn't

598
00:30:37,240 --> 00:30:39,040
Speaker 4: understand at the time. And as it turned out, I

599
00:30:39,160 --> 00:30:41,840
Speaker 4: was just a value investor. But the concept of being

600
00:30:41,880 --> 00:30:43,960
Speaker 4: a value investor other than sort of the Ben Graham

601
00:30:44,000 --> 00:30:46,480
Speaker 4: or Warren Buffett model was a bit as in value

602
00:30:46,520 --> 00:30:50,440
Speaker 4: from a factor perspective. Now it's ubiquitous sort of fourteen

603
00:30:50,480 --> 00:30:53,120
Speaker 4: years ago, But back then it was really novel that

604
00:30:53,320 --> 00:30:57,920
Speaker 4: you were going to customers, discretionary customers, generalists or just

605
00:30:58,160 --> 00:31:01,200
Speaker 4: long short guys who weren't quant guys and explain to

606
00:31:01,240 --> 00:31:05,600
Speaker 4: them is quant phenomenon in layman parlance basically, And that

607
00:31:05,840 --> 00:31:08,000
Speaker 4: was the start of a pivot into a much more

608
00:31:08,040 --> 00:31:12,400
Speaker 4: sort of quny derivative y knowledge experiment and it just

609
00:31:12,520 --> 00:31:13,360
Speaker 4: ballooned from there.

610
00:31:13,520 --> 00:31:15,400
Speaker 2: Yeah, this is kind of what I'm getting at right,

611
00:31:15,480 --> 00:31:17,600
Speaker 2: Like it has become more important in the market. I

612
00:31:17,720 --> 00:31:20,800
Speaker 2: remember speaking of twenty twelve, do you remember show the

613
00:31:20,920 --> 00:31:24,760
Speaker 2: headlines where like people would talk about CTA flows or something. Yeah,

614
00:31:24,840 --> 00:31:27,080
Speaker 2: of course, and it would be like a mysterious force

615
00:31:27,600 --> 00:31:29,040
Speaker 2: in markets. Now everyone knows.

616
00:31:29,080 --> 00:31:31,080
Speaker 3: Now that's what it has become. Yeah, and it's interesting

617
00:31:31,160 --> 00:31:34,560
Speaker 3: to think too, Like if if I just think in

618
00:31:34,680 --> 00:31:39,000
Speaker 3: my mind about one of these platform shops, multi strategy whatever,

619
00:31:39,760 --> 00:31:43,680
Speaker 3: the understanding of the comp of you know, the company,

620
00:31:43,760 --> 00:31:48,160
Speaker 3: is that that pod invests in like theyst on the buyside,

621
00:31:48,240 --> 00:31:51,520
Speaker 3: must be orders of magnitude greater than it was in

622
00:31:51,640 --> 00:31:54,360
Speaker 3: the sort of heyday of like the cell side analysts

623
00:31:54,360 --> 00:31:56,600
Speaker 3: who would like, Oh, we're going to issue a by

624
00:31:56,720 --> 00:32:00,480
Speaker 3: rating on GeV or Nova or whatever like that, those

625
00:32:00,560 --> 00:32:03,160
Speaker 3: investors no g company insanely.

626
00:32:02,840 --> 00:32:06,160
Speaker 4: Well totally and look to put some anecdote around that.

627
00:32:06,720 --> 00:32:09,240
Speaker 4: Barclays as a bank, as an equity business, as a

628
00:32:09,280 --> 00:32:13,480
Speaker 4: research house again ascend an equity business. We've been working

629
00:32:13,680 --> 00:32:15,360
Speaker 4: really hard over the past three years to sort of

630
00:32:15,440 --> 00:32:18,680
Speaker 4: hold our client's hands and make them aware that we

631
00:32:18,840 --> 00:32:22,640
Speaker 4: are a really prime and tier one equity franchise. A

632
00:32:22,680 --> 00:32:26,160
Speaker 4: lot of that is about corporate access, right, so actually

633
00:32:26,320 --> 00:32:30,120
Speaker 4: having more research coverage and having more access to more companies,

634
00:32:30,160 --> 00:32:32,840
Speaker 4: which in turn we can road show and actually give

635
00:32:32,920 --> 00:32:36,000
Speaker 4: our customers access to those companies because it is that

636
00:32:36,120 --> 00:32:38,640
Speaker 4: important to them. To your point exactly, Joe, like, they

637
00:32:38,720 --> 00:32:42,040
Speaker 4: have so much granularity and detail in their forensic modeling

638
00:32:42,160 --> 00:32:45,120
Speaker 4: now that if you're a generalist, it's very, very hard

639
00:32:45,160 --> 00:32:48,200
Speaker 4: to compete on such a granular level. So yes, I

640
00:32:48,400 --> 00:32:53,200
Speaker 4: pivoted away completely and focused on different mantra. Was if

641
00:32:53,240 --> 00:32:56,240
Speaker 4: you think about the verticals of equity investment, the way

642
00:32:56,280 --> 00:32:58,400
Speaker 4: I see it is that you've got fundamentals, which is

643
00:32:58,440 --> 00:33:01,720
Speaker 4: obviously what we talked about. You've got economics, you've got

644
00:33:01,840 --> 00:33:06,280
Speaker 4: cross assets, slash macro. You've got quant which I would

645
00:33:06,320 --> 00:33:10,640
Speaker 4: include factors, You've got derivatives, you've got positioning right, and

646
00:33:10,760 --> 00:33:12,680
Speaker 4: then you've probably got some other stuff as well. But

647
00:33:13,520 --> 00:33:16,120
Speaker 4: my view is the way that I run the tactical

648
00:33:16,160 --> 00:33:18,880
Speaker 4: Strategies teams is that we don't need to be a

649
00:33:18,960 --> 00:33:20,800
Speaker 4: ten out of ten in all of these. I'd love

650
00:33:20,880 --> 00:33:22,400
Speaker 4: to be, but it's just I just don't think it's

651
00:33:22,400 --> 00:33:24,560
Speaker 4: possible in a world of AI. We can obviously augment

652
00:33:24,680 --> 00:33:27,160
Speaker 4: some of our game in certain verticals, but the aim

653
00:33:27,280 --> 00:33:29,640
Speaker 4: of our game is if we can be like a

654
00:33:29,840 --> 00:33:32,840
Speaker 4: seven or an eight in all of these, and some

655
00:33:32,960 --> 00:33:35,000
Speaker 4: of them will be less and some of them will

656
00:33:35,000 --> 00:33:36,360
Speaker 4: be more, some of them will be in nines or

657
00:33:36,400 --> 00:33:38,480
Speaker 4: tens and others will be like fives or six is

658
00:33:39,000 --> 00:33:41,160
Speaker 4: it basically means that what we can do is we

659
00:33:41,240 --> 00:33:45,080
Speaker 4: can go into any customer, any client, any investor, and say, hey,

660
00:33:45,440 --> 00:33:48,080
Speaker 4: there's something about the market that I'm sure I can

661
00:33:48,360 --> 00:33:51,080
Speaker 4: help you on that you're not as aware of. And

662
00:33:51,320 --> 00:33:54,600
Speaker 4: that's where I think that the evolution of that kind

663
00:33:54,680 --> 00:33:58,120
Speaker 4: of education, investor education has come to now, rather than

664
00:33:58,200 --> 00:33:59,240
Speaker 4: just focusing.

665
00:33:58,920 --> 00:34:02,200
Speaker 2: On just one Yeah, I don't mean to get all

666
00:34:02,320 --> 00:34:05,240
Speaker 2: media naval gazy here, but I think we are seeing

667
00:34:05,280 --> 00:34:08,880
Speaker 2: a similar story in media where like the niche subject

668
00:34:08,960 --> 00:34:12,760
Speaker 2: matter experts are becoming much more important, much more popular,

669
00:34:12,920 --> 00:34:16,719
Speaker 2: versus the sort of generalized news platforms. I think they

670
00:34:16,800 --> 00:34:17,759
Speaker 2: don't have to comment on that.

671
00:34:17,880 --> 00:34:20,040
Speaker 4: By the way, No, but it does polly back into

672
00:34:20,080 --> 00:34:22,239
Speaker 4: the world of finance and the world of AI, because

673
00:34:22,280 --> 00:34:25,680
Speaker 4: in the world of AI, everyone can have an army

674
00:34:25,719 --> 00:34:29,200
Speaker 4: of quants in their pockets now of questionable accuracy. But

675
00:34:29,640 --> 00:34:32,479
Speaker 4: the point is you can crunch orders of magnitude more data.

676
00:34:32,840 --> 00:34:35,040
Speaker 4: So where's the real value. I'm a big believer that

677
00:34:35,200 --> 00:34:38,560
Speaker 4: the value I see it kind of think about it

678
00:34:38,840 --> 00:34:42,120
Speaker 4: as knowledge and wisdom. Right, everyone can now have access

679
00:34:42,480 --> 00:34:46,840
Speaker 4: infinite knowledge, but it doesn't necessarily mean that they've got wisdom.

680
00:34:46,880 --> 00:34:48,759
Speaker 4: And the old saying is is that knowledge is knowing

681
00:34:48,840 --> 00:34:51,880
Speaker 4: that tomato is a fruit, But wisdom is knowing that

682
00:34:52,120 --> 00:34:52,279
Speaker 4: you know.

683
00:34:53,239 --> 00:34:55,359
Speaker 3: Another episode And by the way, Tracy, I just want

684
00:34:55,400 --> 00:34:58,279
Speaker 3: to say I meant to send you this. I saw

685
00:34:58,640 --> 00:35:01,880
Speaker 3: someone with one of these viral froyo places that are

686
00:35:01,960 --> 00:35:04,600
Speaker 3: like they put little cherry tomatoes.

687
00:35:04,880 --> 00:35:05,839
Speaker 2: Actually that could be good.

688
00:35:05,960 --> 00:35:09,640
Speaker 3: Yeah, that's I'm saying tomato is a fruit in the not.

689
00:35:09,760 --> 00:35:12,600
Speaker 2: To get wildly off topic, but my tomatoes so far

690
00:35:12,719 --> 00:35:16,080
Speaker 2: this year are freaking amazing, and come later this month

691
00:35:16,239 --> 00:35:17,960
Speaker 2: in August, you're gonna have.

692
00:35:18,160 --> 00:35:19,200
Speaker 4: Like, No, this is my take.

693
00:35:19,280 --> 00:35:23,560
Speaker 3: Ex I love the same knowledge, wisdom tomatoes, et cetera,

694
00:35:23,800 --> 00:35:27,120
Speaker 3: but I actually think some tomatoes actually to make a

695
00:35:27,200 --> 00:35:29,760
Speaker 3: different sale. This is my only point. But I understand,

696
00:35:30,080 --> 00:35:30,960
Speaker 3: I understand the point.

697
00:35:31,120 --> 00:35:32,640
Speaker 4: Yeah, and so I think it's the same when it

698
00:35:32,719 --> 00:35:35,120
Speaker 4: comes to sort of data and the world to find.

699
00:35:35,560 --> 00:35:38,839
Speaker 4: You can be incredibly grindar and specialized, but sometimes you're

700
00:35:38,880 --> 00:35:40,120
Speaker 4: going to miss the forest for the trees.

701
00:35:40,320 --> 00:35:42,480
Speaker 3: Can I just ask not to turn this into just

702
00:35:42,520 --> 00:35:45,280
Speaker 3: another AI conversation, but you must actually have some insight

703
00:35:45,400 --> 00:35:48,560
Speaker 3: on the question on some of these things. Let's say,

704
00:35:48,719 --> 00:35:52,200
Speaker 3: like you want to construct an index or some new thing,

705
00:35:52,520 --> 00:35:56,520
Speaker 3: like how good are the models right now at like

706
00:35:56,719 --> 00:36:02,680
Speaker 3: really reliably being able to do quantitative work. I know

707
00:36:02,800 --> 00:36:04,279
Speaker 3: it's like you can get a lot of the way there,

708
00:36:04,400 --> 00:36:07,680
Speaker 3: and even someone like me can like hag together something,

709
00:36:07,760 --> 00:36:11,400
Speaker 3: but like to the standards that you have, like how

710
00:36:11,480 --> 00:36:13,600
Speaker 3: much do they still like? No, this isn't that nowhere

711
00:36:13,680 --> 00:36:15,719
Speaker 3: near something that I would like then be ready to

712
00:36:16,080 --> 00:36:17,720
Speaker 3: turn into a chart and send to a client.

713
00:36:17,880 --> 00:36:20,759
Speaker 4: Yeah, I think that's a great question, because we're doing

714
00:36:20,800 --> 00:36:22,360
Speaker 4: that all the time. So, you know, we'll have a

715
00:36:22,400 --> 00:36:26,240
Speaker 4: customer come in and let's say they've got a particular

716
00:36:26,320 --> 00:36:29,279
Speaker 4: security there's a single name item. They want to hedge

717
00:36:29,360 --> 00:36:31,600
Speaker 4: that item, and they want to hedge in with a basket. Right,

718
00:36:31,680 --> 00:36:34,720
Speaker 4: That's that's one of our most common kind of business

719
00:36:35,160 --> 00:36:38,319
Speaker 4: problems that we face off with, and so we need

720
00:36:38,400 --> 00:36:41,880
Speaker 4: to build a universe of securities that effectively replicates that

721
00:36:42,080 --> 00:36:46,160
Speaker 4: particular instrument without using that instrument, of course, And as

722
00:36:46,200 --> 00:36:49,600
Speaker 4: a consequence of that, we're effectively running a what is

723
00:36:49,680 --> 00:36:52,480
Speaker 4: essentially a giant pairwise correlation model, and then we've got

724
00:36:52,520 --> 00:36:56,760
Speaker 4: to optimize the inputs depending on what individual correlations against

725
00:36:56,840 --> 00:37:00,359
Speaker 4: that underlying instrument are. It's relatively straightforward if you've got

726
00:37:00,360 --> 00:37:02,800
Speaker 4: a really cool optimizer if you ask AI to do it.

727
00:37:03,640 --> 00:37:09,240
Speaker 4: What it's okay at is selecting what instruments you should.

728
00:37:09,280 --> 00:37:11,040
Speaker 4: So let's say you're like, I need, all right, this

729
00:37:11,239 --> 00:37:13,600
Speaker 4: is a household consumer product. Actually know what, Let's do

730
00:37:13,640 --> 00:37:15,800
Speaker 4: it in AI. Let's make it more new economy. So

731
00:37:15,920 --> 00:37:18,480
Speaker 4: we've got all right, we've got a particular AI vertical.

732
00:37:19,040 --> 00:37:21,120
Speaker 4: Let's say it's a neocloud, all right, we need to

733
00:37:21,200 --> 00:37:24,160
Speaker 4: hedge that neo cloud with something else, all right. So

734
00:37:24,560 --> 00:37:27,640
Speaker 4: AI is quite good at selecting. Let's go and get

735
00:37:27,680 --> 00:37:30,879
Speaker 4: all the other neoclouds that are listed, and then let's

736
00:37:30,920 --> 00:37:33,080
Speaker 4: find a whole bunch of other companies that most of

737
00:37:33,080 --> 00:37:34,560
Speaker 4: which you'll be aware of, maybe some of you're not

738
00:37:34,640 --> 00:37:37,560
Speaker 4: aware of, that actually have a reasonably high correlation to

739
00:37:37,920 --> 00:37:40,320
Speaker 4: that instrument that you're trying to hedge. That's kind of

740
00:37:40,360 --> 00:37:43,040
Speaker 4: where it ends. It's not very good at telling you,

741
00:37:43,160 --> 00:37:45,799
Speaker 4: all right, what about the liquidity consideration, but it.

742
00:37:45,880 --> 00:37:50,080
Speaker 3: Can roughly sort of determine the first step the ingredients,

743
00:37:50,440 --> 00:37:53,719
Speaker 3: right like you're making Emmanuel Derman in his book talked

744
00:37:53,719 --> 00:37:56,759
Speaker 3: about like the job of the trailers to make if

745
00:37:56,800 --> 00:37:58,560
Speaker 3: we were going back to the fruit saling question. But

746
00:37:58,680 --> 00:38:00,880
Speaker 3: you have a bunch of wholesale and then you're sliced

747
00:38:00,880 --> 00:38:02,600
Speaker 3: it up in a certain way. It could it can

748
00:38:02,719 --> 00:38:05,440
Speaker 3: identify the ingredients, and then it's your job to figure

749
00:38:05,440 --> 00:38:08,480
Speaker 3: out the optimal Like you can't do the proper slicing

750
00:38:08,560 --> 00:38:09,840
Speaker 3: and allocation exactly.

751
00:38:09,960 --> 00:38:12,399
Speaker 4: So it's it's, yeah, it's pretty good at it's pretty

752
00:38:12,440 --> 00:38:14,920
Speaker 4: good at identifying what my universe is meant to look

753
00:38:15,040 --> 00:38:16,040
Speaker 4: like because I get the.

754
00:38:16,160 --> 00:38:19,000
Speaker 3: Sometimes I see these notes and I have never tried this,

755
00:38:19,200 --> 00:38:21,000
Speaker 3: but like from I don't know, somehow I get I

756
00:38:21,080 --> 00:38:23,520
Speaker 3: got on like JP Morgans like trading desk thing, and

757
00:38:23,520 --> 00:38:25,480
Speaker 3: they're like, oh, by that, we figured it's time to

758
00:38:25,560 --> 00:38:29,719
Speaker 3: go along this basket of you know, energy related companies

759
00:38:29,840 --> 00:38:33,480
Speaker 3: in short, this basket of like whatever it is. And

760
00:38:33,560 --> 00:38:35,839
Speaker 3: I've been curious. I've never tried it. It's like, could

761
00:38:35,880 --> 00:38:39,359
Speaker 3: I get a model to like back out what these ingredients? Sorry?

762
00:38:39,480 --> 00:38:41,040
Speaker 3: If I gave the model line.

763
00:38:40,840 --> 00:38:43,120
Speaker 4: And the theme, yeah, so again I would I mean

764
00:38:43,200 --> 00:38:46,200
Speaker 4: get this real credit to the to Barclays just in

765
00:38:46,360 --> 00:38:50,800
Speaker 4: terms of we've been pretty leading edge in experimenting and

766
00:38:50,920 --> 00:38:54,200
Speaker 4: adopting as much AI as we can, and so we

767
00:38:54,400 --> 00:38:58,000
Speaker 4: have co pilot, we've got CLAWD licenses, we are we're

768
00:38:58,080 --> 00:39:01,000
Speaker 4: working with other other partners sort of build in house

769
00:39:01,040 --> 00:39:03,640
Speaker 4: staff either using external models or own internal models. But

770
00:39:03,719 --> 00:39:06,080
Speaker 4: the point being is is that we've had plenty of

771
00:39:06,160 --> 00:39:09,480
Speaker 4: time to operate in the sandbox now and I'd say

772
00:39:09,560 --> 00:39:13,359
Speaker 4: where AI's without a doubt most powerful is when you're

773
00:39:13,680 --> 00:39:19,040
Speaker 4: giving it very identifiable data parameters. Okay, right, So whether

774
00:39:19,040 --> 00:39:22,279
Speaker 4: it's structured or unstructured data and say, hey, I need

775
00:39:22,400 --> 00:39:26,160
Speaker 4: to try and figure out whatever my parameters are. That's

776
00:39:26,239 --> 00:39:29,040
Speaker 4: the data. Where it's still less good is if you're

777
00:39:29,080 --> 00:39:33,600
Speaker 4: basically giving it unstructured parameters to say just can you

778
00:39:34,080 --> 00:39:37,160
Speaker 4: think about an ethereal topic and sort of come back

779
00:39:37,200 --> 00:39:38,680
Speaker 4: to me with an answer, And it will come back

780
00:39:38,719 --> 00:39:42,080
Speaker 4: with a pretty comprehensive answer, but most of the time

781
00:39:42,400 --> 00:39:45,680
Speaker 4: it needs to be fact checked and or annotated or

782
00:39:45,880 --> 00:39:47,520
Speaker 4: just tested to scrutinize it.

783
00:39:48,120 --> 00:39:51,480
Speaker 2: Joe, you've successfully turned this into another AI conversation.

784
00:39:51,880 --> 00:39:54,960
Speaker 3: Well, how can you talk about quantitative identify? You know,

785
00:39:55,080 --> 00:39:56,640
Speaker 3: how can you talk about those Yeah.

786
00:39:56,440 --> 00:39:59,800
Speaker 2: We were doing pretty good for forty minutes, So all right, Alex,

787
00:40:00,120 --> 00:40:01,960
Speaker 2: thank you so much for coming on all lots really

788
00:40:02,000 --> 00:40:03,600
Speaker 2: appreciate it. Truly the perfect guest.

789
00:40:03,840 --> 00:40:04,480
Speaker 4: Thank you so much.

790
00:40:04,520 --> 00:40:21,439
Speaker 2: Asking So, Joe, that was a fantastic conversation. A couple

791
00:40:21,520 --> 00:40:23,719
Speaker 2: things stand out. So, first of all, it does seem

792
00:40:23,800 --> 00:40:26,920
Speaker 2: like with the growth of all these products, the explosion,

793
00:40:27,160 --> 00:40:30,399
Speaker 2: especially in Asia, that we have products who are sort

794
00:40:30,400 --> 00:40:34,480
Speaker 2: of like more important marginal buyers and sellers in the

795
00:40:34,600 --> 00:40:37,200
Speaker 2: market at a minimum. The other thing I was thinking

796
00:40:37,239 --> 00:40:40,320
Speaker 2: about is like Okay, a regulator isn't necessarily going to

797
00:40:40,360 --> 00:40:43,960
Speaker 2: approve a five times levered product or one hundred times

798
00:40:44,040 --> 00:40:47,759
Speaker 2: levered product. But is there a point at which, like

799
00:40:47,840 --> 00:40:52,160
Speaker 2: the aggregate number of all these products actually becomes more

800
00:40:52,200 --> 00:40:53,880
Speaker 2: of a concern totally.

801
00:40:54,000 --> 00:40:57,000
Speaker 3: I mean there's two things. One you have to take seriously,

802
00:40:57,280 --> 00:41:00,719
Speaker 3: like where we are in terms of balance sheet and

803
00:41:01,160 --> 00:41:04,520
Speaker 3: how much balance sheet is being allocated to this leverage

804
00:41:04,560 --> 00:41:06,960
Speaker 3: and what happens to bank balance sheets in the event

805
00:41:07,160 --> 00:41:11,320
Speaker 3: like some major downturn. And then I thought, like the

806
00:41:11,880 --> 00:41:14,680
Speaker 3: just the idea of like I had not realized how

807
00:41:14,719 --> 00:41:18,480
Speaker 3: big that gap has grown between household equity exposure and

808
00:41:18,600 --> 00:41:22,840
Speaker 3: household real estate exposure. I still feel like that's probably

809
00:41:22,960 --> 00:41:25,480
Speaker 3: like a pretty at least to me, like a pretty

810
00:41:25,520 --> 00:41:28,000
Speaker 3: eye opening stand because they're just I know, there's a

811
00:41:28,040 --> 00:41:31,040
Speaker 3: lot of equity exposure. It's become important and no one

812
00:41:31,120 --> 00:41:33,279
Speaker 3: needs to like argue with me. It's like the stock

813
00:41:33,320 --> 00:41:35,600
Speaker 3: market is the economy. But in my mind, I'm like, oh,

814
00:41:35,640 --> 00:41:37,719
Speaker 3: it's still like real estate is the core of like

815
00:41:37,800 --> 00:41:40,479
Speaker 3: people's holdings, And not only is it not the core,

816
00:41:40,880 --> 00:41:43,720
Speaker 3: at least the aggregate I'm sure the media and household

817
00:41:43,840 --> 00:41:46,080
Speaker 3: is still way more exposed to their house than the

818
00:41:46,160 --> 00:41:50,200
Speaker 3: stock market, but in overall it's pretty staggering. And then

819
00:41:50,200 --> 00:41:53,279
Speaker 3: you start lot being geared to the stock market, geared to.

820
00:41:53,320 --> 00:41:55,080
Speaker 2: The stock market. How much of the stock market is

821
00:41:55,080 --> 00:41:57,279
Speaker 2: now geared towards leverage and then how much of it

822
00:41:57,440 --> 00:42:00,520
Speaker 2: is also geared towards AI. It's yeah, it's a lot.

823
00:42:00,920 --> 00:42:03,319
Speaker 3: No, it's a lot. And he really spelled it out.

824
00:42:03,320 --> 00:42:05,000
Speaker 2: Well, yeah, we have to have them back on definitely.

825
00:42:05,040 --> 00:42:06,000
Speaker 2: Shall we leave it there for now?

826
00:42:06,080 --> 00:42:06,920
Speaker 3: Yeah, let's leave it there.

827
00:42:07,280 --> 00:42:09,680
Speaker 2: This has been another episode of the aud Thoughts podcast.

828
00:42:09,840 --> 00:42:12,960
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.

829
00:42:12,840 --> 00:42:15,640
Speaker 3: And I'm Joe Wisenthal. You can follow me at the Stalwart.

830
00:42:15,800 --> 00:42:19,640
Speaker 3: Follow our producers Carmen Rodriguez at Carmen Arman, Dashel Bennett

831
00:42:19,640 --> 00:42:23,359
Speaker 3: at Dashbot, Kilbrooks at Kilbrooks, and Kevin Lozano at Kevin

832
00:42:23,440 --> 00:42:25,960
Speaker 3: Lloyd Lozano and from our Odd Lots content. Go to

833
00:42:26,000 --> 00:42:28,840
Speaker 3: Bloomberg dot com slash odd Lots for a daily newsletter

834
00:42:28,880 --> 00:42:31,080
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835
00:42:31,120 --> 00:42:33,080
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836
00:42:33,239 --> 00:42:36,640
Speaker 3: discord Discord dot gg slash onlines and.

837
00:42:36,719 --> 00:42:39,000
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838
00:42:39,040 --> 00:42:41,960
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839
00:42:42,000 --> 00:42:45,480
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840
00:42:45,560 --> 00:42:48,000
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841
00:42:48,160 --> 00:42:51,040
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842
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843
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844
00:43:01,719 --> 00:43:01,759
Speaker 4: In