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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 All Thoughts podcast.

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Speaker 3: I'm Tracy Alloway and.

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Speaker 4: I'm Joe Wisenthal.

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Speaker 2: Joe, in your in your history of being a financial journalist,

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Speaker 2: what are you like most proud of in terms of

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Speaker 2: coining phrases? Oh, it gave you a hint just then,

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Speaker 2: because I assume it's Mint the coin.

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Speaker 1: Yeah, Oh, good one, Yes, sure, Mint the coin. That's right, Okay,

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Speaker 1: thank you.

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Speaker 2: Yes, So I have a few. I have China's Great

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Speaker 2: Ball of Money.

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Speaker 4: I like how you're like, what are you most proud of?

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Speaker 1: And this is going to be my saying, so what

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Speaker 1: I'm I keep going?

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Speaker 3: Thank you?

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Speaker 2: You called me out Europe's sovereign bank loop, although no

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Speaker 2: one believes that I invented that one, and flows before pros,

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Speaker 2: which has come up quite a bit on this podcast.

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Speaker 2: So the idea that you know, when valuations are extremely

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Speaker 2: high and everyone's buying everything no matter what the price,

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Speaker 2: that it's kind of momentum that matters more than fundamentals. Today,

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Speaker 2: I'm very happy to say we are going to be

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Speaker 2: speaking to a pro who knows flows.

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Speaker 1: As so extremely well done, Tracy, Thank you excellent setup.

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Speaker 1: Of course I appreciate all of the tracy neologisms. Man,

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Speaker 1: what a time in the market to we talk about flows?

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Speaker 1: Do we talk about momentum, etc. I feel like I

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Speaker 1: you know, it's always chaotic, it's always uncertain, you'll never

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Speaker 1: get an answer. But man, things feel really noisy right now.

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Speaker 2: They feel super noisy. So we are recording this on

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Speaker 2: January thirtieth. It is a week that has seen a

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Speaker 2: very sharp sell off in tech stocks thanks to anxiety

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Speaker 2: over deep seak coming out of China. We're going to

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Speaker 2: talk about that and more generally, we're just going to

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Speaker 2: talk about what's going on in the market right now,

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Speaker 2: how investors might be handling it, and how the market

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Speaker 2: structure might have changed over the years. And as I said,

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Speaker 2: I'm very excited because we do have the perfect guests,

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Speaker 2: the pro who knows his flows. He has a lot

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Speaker 2: of nicknames, actually Gandalf being one of them as well.

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Speaker 2: Actually I didn't realize one of our colleagues at Bloomberg

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Speaker 2: kind of coined that name for him. But of course

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Speaker 2: we are speaking with Marko Kolonovich. He is JP Morgan's

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Speaker 2: former chief Global market strategist and co head of Global Research,

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Speaker 2: and now he's with us to talk about the market.

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Speaker 2: And Marco, thank you so much for coming on.

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Speaker 5: O thoughts, thank you so much for having me.

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Speaker 2: I'm very excited. I know I've said that three times now,

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Speaker 2: but I guess we should start with the recent selloff,

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Speaker 2: like the deep Seek, deep stock selloff. They are all

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Speaker 2: these superlatives that you can use to describe Monday's action,

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Speaker 2: like the biggest single stock plunge in history in the

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Speaker 2: form of Nvidia, and eight of the top ten biggest

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Speaker 2: one day drops in the S and P five hundred,

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Speaker 2: et cetera, et cetera. And actually, as we're recording this

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Speaker 2: on January thirtieth, Microsoft is down six percent after earning,

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Speaker 2: so maybe the tech selloff isn't over yet. But one

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Speaker 2: of the interesting things about this week is there hasn't

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Speaker 2: really been broad contagion. So most stocks in the S

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Speaker 2: and P five hundred were kind of like, meh, we

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Speaker 2: don't care. When would you expect some of the anxiety

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Speaker 2: over Deep Seek to maybe start having a bigger impact

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Speaker 2: on the broader market.

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Speaker 6: So, you know, as you said, there was not much contagion,

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Speaker 6: you know, and if you look at the different stocks

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Speaker 6: in SMP. Many of them were actually up, you know,

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Speaker 6: and many even in the tech sectors sector were up.

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Speaker 6: You know, if you look for instance, you know, Facebook

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Speaker 6: yesterday or today, or a bunch of other names that

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Speaker 6: sort of were perceived that they might be sort of

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Speaker 6: benefiting from, you know, the sort of open architecture, you know,

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Speaker 6: type of things that can come at the cheaper price,

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Speaker 6: you know, and can be still you know, implemented in

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Speaker 6: their business model when it comes to AI models.

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Speaker 5: So it was early contained.

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Speaker 7: You know.

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Speaker 6: I'm a little bit surprised just because there were like

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Speaker 6: three or four names that really got hammered, you know,

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Speaker 6: and that can only be explained with not just with

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Speaker 6: a panic, but some of the sort of forced selling,

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Speaker 6: you know, maybe coming from options. You know, if you

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Speaker 6: are you know, if you're selling in a video puts

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Speaker 6: for the past few years, you could make a good

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Speaker 6: living out of it. But then you know, you'll have

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Speaker 6: a day like we saw this weekend, and basically you

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Speaker 6: might get you know, forced out of these positions and

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Speaker 6: maybe have a catastrophic loss.

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Speaker 5: So it was fairly limited.

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Speaker 6: I'm a little bit surprised, you know, just just because

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Speaker 6: we didn't really have a meaningful sales since last summer,

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Speaker 6: you know, sort of when at the back of the

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Speaker 6: Bank of Japan, you know, so, so I do think

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Speaker 6: we will we will see one. Perhaps it's a little

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Speaker 6: bit too early in the year. There's still quite a

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Speaker 6: bit of an optimism post election. There's a little bit

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Speaker 6: of seasonality in January. People put money to work, they

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Speaker 6: get paid, you know, they allocated capital. So maybe it's

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Speaker 6: a little bit too early. I was somewhat inclined to

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Speaker 6: see that we will see a bit more, you know.

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Speaker 6: At the back of it, it's perhaps not over. We

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Speaker 6: still have a few important earnings to come, so remains

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Speaker 6: to be seen sort of what happens, you know, maybe

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Speaker 6: another week of earnings, you know, whether there is any

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Speaker 6: any sort of follow through, you know, but I do

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Speaker 6: think that it's gonna be some investors will burn clearly,

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Speaker 6: and a little bit of a tarnish on the sort

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Speaker 6: of this thesis that some of these stocks like my video,

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Speaker 6: just go up, you know every day you can't lose,

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Speaker 6: you know, Like so I think people will think twice

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Speaker 6: if something can drop like twenty percent in a day,

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Speaker 6: you got to also think of it what it does

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Speaker 6: to you to your risk and.

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Speaker 1: Well so Tracy asked about contagion and the anxiety spreading

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Speaker 1: across the market. But I guess I would flip the question,

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Speaker 1: which is, as I've said before on the podcast, I'm

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Speaker 1: a boring index investor. So like I look at a

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Speaker 1: random American company that is like General Electric, it's doing fine.

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Speaker 1: I'm not very exposed to General Electric because they're a

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Speaker 1: small part of the index. I am very exposed to

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Speaker 1: in Video and Microsoft and so forth. We did a

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Speaker 1: recent episode about market concentration, but I'm sort of like

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Speaker 1: curious your take on this fact that, like so many

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Speaker 1: of these flows, they go into broad market indexes, but

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Speaker 1: we're really all very exposed to a few concentrated market pads.

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Speaker 6: Yeah, so the concentration is the highest, you know, sort

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Speaker 6: sixties or seventies, so we're looking in fifty years half

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Speaker 6: of the century history and concentration is sort of at

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Speaker 6: the highest point. It's been a while for staying there

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Speaker 6: at this level, like maybe past past year. So it's

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Speaker 6: a weird market. You know, this concentration came for two reasons.

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Speaker 6: You know, one is clearly thematic investing in technology. Then

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Speaker 6: you also have investing in a large company. You have

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Speaker 6: a theme of momentum sort of that is basically self fulfilling.

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Speaker 6: You know, more something goes up, more money to tracts,

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Speaker 6: becomes bigger an index, you know, all the passive flows

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Speaker 6: into it. So there's a technical aspect, there's thematic aspect.

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Speaker 6: There's even geopolitical aspect. A lot of money went outside

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Speaker 6: of the other parts of the world. Europe is doing

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Speaker 6: worse when it comes to sort of economy. China has

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Speaker 6: been We've been a sort of with the brink of

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Speaker 6: this Cold War with China, so money has has left there.

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Speaker 6: You know, Latime has its own share of sort of issues,

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Speaker 6: so money has been also geopolitically moving area. So it's

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Speaker 6: a moving in the US, it's a moving in the indices,

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Speaker 6: it's a moving in the tech you know, and then

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Speaker 6: you end up with these give or take ten stocks

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Speaker 6: that that really sucked up all the all the capital

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Speaker 6: and evaluations got got very very high. Now, you know,

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Speaker 6: tech investors, they do have a sort of their rationalization,

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Speaker 6: So what's going to happen in the future. These things

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Speaker 6: just grow and grow and grow. And that's when we

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Speaker 6: when we saw with the deep Seek, we saw a

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Speaker 6: little bit of a dent in that in that thesis.

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Speaker 6: But these stocks didn't go up for the sake of

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Speaker 6: for the thesis. They wound up some of these these

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Speaker 6: these other flows, you know, so unprecedented concentration is not

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Speaker 6: going to stay there.

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Speaker 5: You know.

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Speaker 6: The big question is when will we see that rebalance.

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Speaker 6: Do we need to see you know, some cyclical downturn

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Speaker 6: first to purge and to normalize some of these valuations,

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Speaker 6: you know, because historically these pees were never this high.

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Speaker 6: I mean, in two thousand they were this high, and

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Speaker 6: we know how it then, you know, but a lot

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Speaker 6: of people got burn myself include that with some of

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Speaker 6: the we were more negative last year, you know. And basically,

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Speaker 6: you know, market has this tremendous momentum, so the timing

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Speaker 6: is is going to be challenge.

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Speaker 2: What does it take to I guess turn momentum at

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Speaker 2: this point, Like what what are the catalysts that actually

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Speaker 2: work here? Because it does feel looking at the market

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Speaker 2: so much of it is now technical or systematic in

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Speaker 2: some way. There's a lot of options selling, as you mentioned,

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Speaker 2: lots of multi strat funds that basically you know, just

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Speaker 2: have to sell or buy to rebalance their exposure. What

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Speaker 2: actually changes directions, like how do you get enough to

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Speaker 2: change a trend.

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Speaker 6: So, you know, so there's a technical aspect, sort of

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Speaker 6: the mechanical aspect of it, and there's a sort of

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Speaker 6: catalyst or more funda mental angle of things, you know.

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Speaker 6: So to get things to start moving or to stall,

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Speaker 6: you usually do need to have some funda mental driver

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Speaker 6: of it. So perhaps there is concern about economy slowing down,

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Speaker 6: you know, perhaps there is a concern about something geopolitically,

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Speaker 6: maybe trade war with China or some sort of blockade

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Speaker 6: of Taiwan's trads or something like that. Right, so first

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Speaker 6: you need to have a little bit of a catalyst,

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Speaker 6: you know. But if the market is technically very strong,

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Speaker 6: the catalyst is not going to change the momentum. So

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Speaker 6: what that means is, you know, more specifically. You know,

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Speaker 6: so when you look at the trend investors, you know,

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Speaker 6: they have a range of signals, you know, like so

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Speaker 6: they can look at a one month price momentum, three

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Speaker 6: month price momentum, six month pricements, or twelve month price momentum,

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Speaker 6: maybe eighteen months, but that's about it, you know, And

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Speaker 6: there's some very short term momentum players that look intra

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Speaker 6: day or on a daily basis but most of these

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Speaker 6: signals are concentrated around twelve months and two hundred day

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Speaker 6: moving averages, right. So that's why when people look at

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Speaker 6: the two hundred mo average, they first when I, you know,

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Speaker 6: twenty years ago, when someone told me, I said, like,

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Speaker 6: what is this magic?

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Speaker 5: Why would this work? Right?

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Speaker 6: But it's reality is that many models, you know, systematic models,

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Speaker 6: primarily you know computer DRIM, but also psychological investors look

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Speaker 6: at these things and become self fulfilling. So you need

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Speaker 6: to actually come close enough to these levels to break them.

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Speaker 5: Right.

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Speaker 6: So for instance, earlier this week SMP got below around

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Speaker 6: six thousand or a bit below. We were close to

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Speaker 6: breaking twenty day moving average and fifty day moving average,

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Speaker 6: so you can think of it as about one month

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Speaker 6: and three month price momentum. So that's about the third

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Speaker 6: of a signal. The big signal is really twelve month

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Speaker 6: you know, or two hundred day moving average, right. But

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Speaker 6: when you start moving things you can actually unravel. It's

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Speaker 6: like a little bit like a snowball. So I thought like,

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Speaker 6: if the market is going to stay below twenty and

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Speaker 6: fifty days at the end of the day, you may

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Speaker 6: get enough selling from CTAs or de risking from cts

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Speaker 6: that they may get you to another leg lower right,

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Speaker 6: So it's basically you need to have set up that

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Speaker 6: you're close enough to these triggers on the downside to

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Speaker 6: move it. And again I think this week we got

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Speaker 6: very close. But there was also other flows like rotation.

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Speaker 6: You saw like selling on a video, but Apple and

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Speaker 6: Metal went up, you know, like so at the end

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Speaker 6: now it was like drop but did not drop a lot, Joe.

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Speaker 2: It kind of reminds me of like structured credit notes,

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Speaker 2: where there's that knockoup knockout level and then you get

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Speaker 2: this massive cliff risk and the whole thing kind of unwindes.

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Speaker 1: Right, you have all these rules based investors and something

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Speaker 1: happens like the model a system sell It is worth

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Speaker 1: noting that as we are talking ten nineteen am January thirtieth,

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Speaker 1: and Video is basically right at its two hundred day

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Speaker 1: moving average. I know this isn't the broader market, but

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Speaker 1: it's a big part of the broader market, so yeah,

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Speaker 1: it might as it might as well.

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Speaker 2: Be.

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Speaker 1: One thing that struck me on Monday, which I was

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Speaker 1: a bit surprised about in the selloff is that even

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Speaker 1: on Monday you mentioned that Meta actually closed Green and

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Speaker 1: they are a maker of a competitor to deep seek.

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Speaker 1: They have their open source Lama, but they're seen by

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Speaker 1: the market as a consumer of AI services because they

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Speaker 1: don't sell their product. They use it to do things

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Speaker 1: like better ad targeting, et cetera. Were you surprised that

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Speaker 1: even on this day in which there is this sort

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Speaker 1: of exogenous shock to the market, everyone wakes up to

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Speaker 1: some new thing that actually investors showed a fair amount

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Speaker 1: of discrimination in terms of what they don't.

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Speaker 6: Yeah, so I mean that was that was against you know,

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Speaker 6: January sentiment is still pretty positive. Economic data are strong,

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Speaker 6: so people are saying, okay, this is not the beginning

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Speaker 6: of economic downturn. This is isolated sort of event whereby

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Speaker 6: some companies will get hit, you know, their sort of

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Speaker 6: revenues will get hit yea, and some will be able

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Speaker 6: to do things for cheaper you know, like so we

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Speaker 6: had the salesforce I believe as well. And so it

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Speaker 6: ended up not a macro day but more of a

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Speaker 6: rotational day.

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Speaker 7: You know.

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Speaker 6: There is also a so called quant factors where you know,

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Speaker 6: even within technology, some stocks are higher multiples, some stocks

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Speaker 6: are lower multiple. So you know, some stocks are more

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Speaker 6: momentum less momentum, you know. Like, so there was a

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Speaker 6: bit of rotation. So Apple, which was a lagger, it

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Speaker 6: also kind of quite a bit, although I don't think

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Speaker 6: there was much fundamental stop going on for Apple. It

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Speaker 6: was probably just rotation. So so market kind of held up,

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Speaker 6: you know, and I was sort of at defense whether

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Speaker 6: whether these like technical levels twenty fifty will will get

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Speaker 6: broken and will go lower or not. We didn't, you know,

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Speaker 6: but I do think that sort of you know, evaluation positioning,

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Speaker 6: and some of these technicals are a bit stretched, you know,

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Speaker 6: like so I don't have I don't see like a

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Speaker 6: huge huge ob site from the market. So maybe I'm

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Speaker 6: switching the topic a little bit.

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Speaker 4: But no, No, that makes sense.

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Speaker 2: Can I ask you a sort of procedural question, which is,

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Speaker 2: you know, you were at JP Morgan for twenty years,

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Speaker 2: and you were in the industry even before that. How

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Speaker 2: did your sort of research and forecasting process change throughout

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Speaker 2: those years?

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Speaker 6: No, thank you, And it did change, and it's and

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Speaker 6: it's an interesting good question, you know. So, so I

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Speaker 6: got my PGE in physics, in theoretical physics, so there

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Speaker 6: was a lot of coding, There was a lot of modeling.

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Speaker 6: There was a lot of sort of trying to understand

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Speaker 6: why one thing leads to another. You know, what is

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Speaker 6: the cause, what is a concept? What's causality? You know,

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Speaker 6: and what's the noise? What's statistically important, what's statistically not important?

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Speaker 2: You know.

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Speaker 6: Like so, so in physics you build these type of models.

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Speaker 6: You try to understand what's significant, what's not, what you

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Speaker 6: can neglect, which factors you can you have to take

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Speaker 6: into account, and most important, how to simplify the complex.

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Speaker 6: You know, market is an extremely complex system, you know,

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Speaker 6: in many physical systems, so you need to sort of

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Speaker 6: move the noise on one side and drivers on the

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Speaker 6: other side, and try to recognize those patterns, right like So,

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Speaker 6: although I really never used any formula from the physics

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Speaker 6: in my almost never really in the finance, but the

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Speaker 6: way of thinking is similar. So I started Merrilynch in derivatives,

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Speaker 6: in derivatives research where I started looking, you know, interesting

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Speaker 6: that we are now in earning seasons. So my first

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Speaker 6: models were impact sort of earnings on a stock price

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Speaker 6: and what can you read from options market?

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Speaker 5: You know.

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Speaker 6: Like so I published some papers, you know, come up

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Speaker 6: with some formal as, and we were kind of backing out, okay,

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Speaker 6: what the option market is saying, and then we go

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Speaker 6: to analysts and say, hey, do you think it makes

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Speaker 6: sense or doesn't make sense?

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Speaker 5: Right?

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Speaker 6: And then if you if you think that options are

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Speaker 6: saying too much of a or too little of a movie,

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Speaker 6: you could trade these options and stuff like that. So

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Speaker 6: that was one example of okay, how do you sort

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Speaker 6: of you know, you have a catalyst, You look at

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Speaker 6: different markets, you see are these markets aligned? You put

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Speaker 6: some model together, and then you find the discrepancy with

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Speaker 6: the model. It's not always going to work, but if

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Speaker 6: you do it for one hundred stocks, maybe an average

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Speaker 6: and a portfolio level, you'll you'll be fine, you know.

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Speaker 6: Like so so so that the relative research and quant research.

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Speaker 6: I did a lot of quantity research. So you try

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Speaker 6: to process the data, you try to look at the measures.

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Speaker 6: And at that time, like you know, twenty twenty five

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Speaker 6: years ago, it was beginning really of trading of vicks

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Speaker 6: of volatility, swaps of correlation or dispersion. You know, like

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Speaker 6: so you kind of try to see, okay, you know,

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Speaker 6: you look at the vix you look at the market volatility.

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Speaker 6: What's driving market volatility? While people say, well, it's a

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Speaker 6: panic or it's not. But let's be more quantitative. You know,

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Speaker 6: you can how correlated stocks are, what individual volatility of

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Speaker 6: each stock is, you know, which part is due to

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Speaker 6: the macro factor the market, what's idiosyncratic, So you can

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Speaker 6: kind of break these down. Then you can look at

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Speaker 6: the sector correlation between sector, what's crushed within sector, So

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Speaker 6: you can kind of quantify these things and analyze and

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Speaker 6: get some insight, you know, like two thousand and eight,

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Speaker 6: for instance, we look at the tw thousand and seven

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Speaker 6: thousand and eight, I look at how the hedging of

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Speaker 6: options impacts the market, you know, like so you basically

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Speaker 6: need to look at how many options are out there

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Speaker 6: in index. Let's say you try to assess what's the

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Speaker 6: positioning from from the flows from the sort of no

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Speaker 6: knowing of industry hedging flows, and then you see, okay,

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Speaker 6: what are the hedging requirements at the end of the

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Speaker 6: day and two thousand and eight and then twenty eleven,

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Speaker 6: and like a low hanging fruit, you could see sometimes

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Speaker 6: these flows would be bigger than market can absorb, you know,

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Speaker 6: and they would all go stay away from level ETFs

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Speaker 6: from options, you know, so you see like, okay, there's

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Speaker 6: like twenty billion dollars to sell and market can't absorb it.

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Speaker 6: So you know, market towards last ten minutes will drop.

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Speaker 6: And that's you mentioned sort of Ganda. That's where where

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Speaker 6: some of these things because people, you know, if you

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Speaker 6: look from the outside, you say, oh, how can he

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Speaker 6: get that?

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Speaker 7: Right?

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Speaker 6: You know, and and but it is really understanding a

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Speaker 6: bit of technicalities, which is flows, option, convexities, liquidity and

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Speaker 6: how they how they sort of interfere. But you know,

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Speaker 6: after twenty fifteen sixteen, people figure it out, you know,

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Speaker 6: and then people put it in their models.

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Speaker 5: They create like.

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Speaker 3: A structured product of twenty eighteen.

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Speaker 2: Everyone sort of woke up to the vis especially right

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Speaker 2: Oh yeah, so that's.

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Speaker 6: A February em again and yeah, and you know stuff

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Speaker 6: like that. So you kind of analyze causes and consequences

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Speaker 6: in the market, something that is new that's not been

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Speaker 6: yet a look at, you know, and I have focused

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Speaker 6: on things that we're new in the market, like products, options, futures, CTAs,

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Speaker 6: those type of things.

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Speaker 1: You know, this it's not particularly like technical. But another

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Speaker 1: thing that seems to be true about the market right

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Speaker 1: now is, at least maybe up until week ago, and

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Speaker 1: there's just an incredible amount of optimism in any sort

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Speaker 1: of measure. So if you ask, you know, there's consumer measures,

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Speaker 1: do you think stocks are going to be higher in

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Speaker 1: a year from now?

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Speaker 4: Very high levels?

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Speaker 1: If you look at things like the Bank of America,

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Speaker 1: cell side analyst sentiment very close to euphoric levels. If

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Speaker 1: you look at fund cash levels extremely low right now,

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Speaker 1: very over everyone is overweight long tech. How do you

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Speaker 1: ingest this information because on the one hand, you say, oh,

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Speaker 1: everyone's all in, this is negative. On the other hand,

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Speaker 1: people have been very optimistic for a while. How should

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Speaker 1: we as consumers of this information think about what it

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Speaker 1: says about the fragility of the ball market.

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Speaker 6: So you know, clearly in the markets things are mean reverting,

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Speaker 6: you know, Like so when think reached some very high levels,

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Speaker 6: you know eventually they will.

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Speaker 4: Gone a long time, Yeah, exactly.

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Speaker 6: So, so so there is this mean reversion, But there's

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Speaker 6: also trend, right you know, Like so you know, figuring

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Speaker 6: out the timing of that is hard, right, you know,

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Speaker 6: Like I mean if there is a sort of a

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Speaker 6: limited set of drivers, like in some of these technical markets,

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Speaker 6: you know, so for instance, ctias, you know, you know,

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Speaker 6: once when the all the levels are positive, you know,

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Speaker 6: all the singles are positive, and then volatility drops a

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Speaker 6: little bit. You know, their mac stout, you know, so

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Speaker 6: you know they're not going to buy more, right, you know,

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Speaker 6: like and so you can you that's a that's a

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Speaker 6: self contained, isolated system where you can say, okay, optimism

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Speaker 6: is too high. So there's the only downside, right with

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Speaker 6: the with the entirety of market, right you know, with

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Speaker 6: you know, you have a crypto, you have a fiscal measures,

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Speaker 6: you have like monetary stimulus, you have a sentiment shift.

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Speaker 6: It's hard to handicap all of those, you know. So

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Speaker 6: it's hard to say that for all of the investors

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Speaker 6: to be able to know exactly when this thing is

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Speaker 6: going to stall, right, sure, and and and there are

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Speaker 6: developments that is hard, like you know, this whole AI,

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Speaker 6: you know, And I see the I wrote a book

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Speaker 6: on twenty seventeen about AI with my colleague Rejaesh twenty eighteen.

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Speaker 6: So we were early on on AI. And you know,

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Speaker 6: six years ago, right, nobody was talking really about it

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Speaker 6: at time. So it's not that I don't understand it,

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Speaker 6: but I'm a little bit cynical about it now. I

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Speaker 6: think it's it's too hyped up, right, But it's hard

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Speaker 6: to assess how long people.

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Speaker 5: Will be excited about it, right, you know?

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Speaker 6: And yeah, so and then you have a change, So

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Speaker 6: you have political changes that can bring deregulation that can

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Speaker 6: be change in tax regimes, so you have like a

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Speaker 6: wild card.

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Speaker 5: So it's hard.

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Speaker 6: Your question I started like, there is a reversion always,

436
00:20:07,040 --> 00:20:09,000
Speaker 6: but where you're going to pick it, it can be very

437
00:20:09,040 --> 00:20:12,040
Speaker 6: frustrating and very sort of you can be wrong for

438
00:20:12,040 --> 00:20:12,600
Speaker 6: a long time.

439
00:20:12,920 --> 00:20:15,800
Speaker 2: Wait, can I just press you on that point about AI?

440
00:20:15,960 --> 00:20:18,600
Speaker 2: Because I think I think the difficulty that investors are

441
00:20:18,640 --> 00:20:23,000
Speaker 2: having is AI has a great story right now, and

442
00:20:23,359 --> 00:20:27,000
Speaker 2: there's this idea out there that it's this revolutionary technology

443
00:20:27,320 --> 00:20:30,080
Speaker 2: that's going to change the world. Joe keeps referring to

444
00:20:30,119 --> 00:20:33,359
Speaker 2: it as inventing God when it comes to AGI at least.

445
00:20:34,040 --> 00:20:36,960
Speaker 2: And at the same time, there is also a feeling

446
00:20:37,200 --> 00:20:41,360
Speaker 2: that people are maybe getting a little too optimistic about it.

447
00:20:41,680 --> 00:20:43,960
Speaker 2: There's too much hype in the market. You've started seeing

448
00:20:43,960 --> 00:20:46,800
Speaker 2: companies that you know, just put out a press release going, oh,

449
00:20:46,840 --> 00:20:50,080
Speaker 2: we're looking into AI, and their stock price goes up.

450
00:20:50,440 --> 00:20:55,080
Speaker 2: How should investors handle their exposure to AI? Like, how

451
00:20:55,119 --> 00:20:57,359
Speaker 2: do you actually play it at this point in time,

452
00:20:57,480 --> 00:20:59,480
Speaker 2: given that you were early to the topic.

453
00:20:59,280 --> 00:20:59,959
Speaker 4: And now cynical.

454
00:21:00,240 --> 00:21:01,919
Speaker 6: You know, I look at it from theoretical side. I

455
00:21:01,960 --> 00:21:03,879
Speaker 6: look at it more how to apply it sort of

456
00:21:03,880 --> 00:21:06,280
Speaker 6: in finance, in quantitative trading. You know, how to use

457
00:21:06,280 --> 00:21:08,919
Speaker 6: a large language model to assess the sentiment, changes in

458
00:21:09,000 --> 00:21:11,040
Speaker 6: sentiment and those type of things, right, so you know

459
00:21:11,080 --> 00:21:13,520
Speaker 6: how to read quickly things and summarize them and drive

460
00:21:13,600 --> 00:21:15,920
Speaker 6: some signals out of it. So there is obviously bigger

461
00:21:16,000 --> 00:21:17,639
Speaker 6: question of AI, you know, which you said, it's kind

462
00:21:17,640 --> 00:21:20,760
Speaker 6: of philosophical questions like are we going to be replaced?

463
00:21:20,800 --> 00:21:22,920
Speaker 6: You know, at which point what's going to be a

464
00:21:23,000 --> 00:21:25,439
Speaker 6: role of human once? When when we can you know,

465
00:21:25,800 --> 00:21:29,080
Speaker 6: kind of break down our way of thinking and effectively

466
00:21:29,119 --> 00:21:31,400
Speaker 6: training and replace it. You know, So their whole host

467
00:21:31,400 --> 00:21:33,600
Speaker 6: of other questions, you know, Like so, I'm not skeptical

468
00:21:33,640 --> 00:21:35,800
Speaker 6: that this is going to be hugely important, and it

469
00:21:35,880 --> 00:21:40,320
Speaker 6: is a hugely important you know, it's not very very

470
00:21:40,359 --> 00:21:43,520
Speaker 6: different of what people have been doing, you know, five

471
00:21:43,600 --> 00:21:46,040
Speaker 6: years ago or ten years ago or twenty years ago. Obviously,

472
00:21:46,080 --> 00:21:49,600
Speaker 6: big progress in computing power, big progress in the models

473
00:21:49,640 --> 00:21:51,920
Speaker 6: as well, you know, Like so, so it's it's but

474
00:21:52,240 --> 00:21:56,199
Speaker 6: I see it like more as an evolution, you know,

475
00:21:56,320 --> 00:21:58,720
Speaker 6: than something that changed with che GDP in twenty twenty three,

476
00:21:58,800 --> 00:22:00,639
Speaker 6: like two years ago, as a kind of like a

477
00:22:00,680 --> 00:22:03,199
Speaker 6: step function. I see it as an evolution. Always important,

478
00:22:03,280 --> 00:22:05,600
Speaker 6: right like ten years ago, when we use our smartphone

479
00:22:05,600 --> 00:22:08,400
Speaker 6: to take a pictures, like you know, camera would recognize

480
00:22:08,400 --> 00:22:10,680
Speaker 6: the face, it would zoom into face, it would kind

481
00:22:10,720 --> 00:22:12,919
Speaker 6: of do the proper focus and stuff like that. So

482
00:22:13,080 --> 00:22:16,000
Speaker 6: that's also you know, that's also AI and and things

483
00:22:16,040 --> 00:22:18,880
Speaker 6: are advancing, right, and we'll keep on advancing. Now, question

484
00:22:19,000 --> 00:22:22,679
Speaker 6: is going to be winners losers? How to monetize? You know,

485
00:22:23,280 --> 00:22:27,560
Speaker 6: does that suddenly re rates all of equity market multiple?

486
00:22:27,600 --> 00:22:29,480
Speaker 6: You know, like suddenly, okay, people are not gonna work.

487
00:22:29,640 --> 00:22:31,399
Speaker 6: These companies go to all the work, so we're just

488
00:22:31,400 --> 00:22:33,720
Speaker 6: gonna value them. Like who am I to say that?

489
00:22:33,800 --> 00:22:35,760
Speaker 6: And also who am I to say that that's wrong

490
00:22:35,800 --> 00:22:37,639
Speaker 6: as well? You know, but there's a lot of speculation

491
00:22:38,160 --> 00:22:40,560
Speaker 6: and a trust. You know, people often tell me, well,

492
00:22:40,840 --> 00:22:45,359
Speaker 6: imagine just how my way to search Internet has changed,

493
00:22:45,359 --> 00:22:47,959
Speaker 6: you know, like like okay, like we were searching Internet

494
00:22:48,000 --> 00:22:49,840
Speaker 6: for twenty five years the same way I used to

495
00:22:50,000 --> 00:22:52,440
Speaker 6: use like a Netscape like twenty five years ago, right,

496
00:22:52,480 --> 00:22:54,360
Speaker 6: and and the same thing you type in a bar

497
00:22:54,440 --> 00:22:56,480
Speaker 6: and you and you find something. So for christ sake,

498
00:22:56,520 --> 00:22:58,600
Speaker 6: of course it's going to change. Of course, at some

499
00:22:58,600 --> 00:23:01,200
Speaker 6: point it's gonna be we're gonna tell something to computer.

500
00:23:01,960 --> 00:23:04,480
Speaker 6: Computer will have its own ways of parsing and finding

501
00:23:04,480 --> 00:23:07,040
Speaker 6: what's relevant and giving us back information. So I'm not

502
00:23:07,200 --> 00:23:10,280
Speaker 6: as excited about that change. I think it's the way

503
00:23:10,320 --> 00:23:12,439
Speaker 6: over you change, you know, like, but there's a lot

504
00:23:12,480 --> 00:23:13,119
Speaker 6: of optimism.

505
00:23:13,160 --> 00:23:16,000
Speaker 1: Now there's Well, I was wondering because I was looking

506
00:23:16,040 --> 00:23:18,760
Speaker 1: at your LinkedIn and you mentioned you have a PhD

507
00:23:18,800 --> 00:23:22,160
Speaker 1: in physics graduating from NYU in two thousand and three,

508
00:23:22,960 --> 00:23:27,439
Speaker 1: theoretical high energy physics, cosmology, string theory, and finance. I

509
00:23:27,440 --> 00:23:31,160
Speaker 1: guess the two part question. A would do you think

510
00:23:31,200 --> 00:23:33,359
Speaker 1: there's a world in which if you graduated today you

511
00:23:33,359 --> 00:23:37,280
Speaker 1: would have gone into AI instead of going into finance,

512
00:23:37,320 --> 00:23:39,639
Speaker 1: because I imagine they would have hired you at those skills.

513
00:23:39,680 --> 00:23:41,080
Speaker 4: But B, like when you.

514
00:23:41,080 --> 00:23:44,320
Speaker 1: Think about and Tracy mentioned, you know, like the true AGI,

515
00:23:45,040 --> 00:23:47,520
Speaker 1: do you think that the current AI research is on

516
00:23:47,560 --> 00:23:51,840
Speaker 1: a path to that sort of AGI inventing God that

517
00:23:51,920 --> 00:23:54,640
Speaker 1: a lot of the proponents believe, So you.

518
00:23:54,600 --> 00:23:57,560
Speaker 6: Know, I think eventually it will get there in a

519
00:23:57,640 --> 00:24:00,920
Speaker 6: sense that it will sort of address some very important,

520
00:24:01,440 --> 00:24:04,119
Speaker 6: you know questions which are kind of deeply what every

521
00:24:04,119 --> 00:24:08,480
Speaker 6: person fears or wonders or or sort of seeks, you know,

522
00:24:08,600 --> 00:24:11,040
Speaker 6: kind of meaning of our lives, like you know, you know,

523
00:24:11,160 --> 00:24:13,360
Speaker 6: future after we die and stuff like that. So they're

524
00:24:13,400 --> 00:24:16,720
Speaker 6: they're definitely interesting, interesting things there that can be done.

525
00:24:16,760 --> 00:24:18,760
Speaker 6: I mean, people are doing with these like assistants, right

526
00:24:18,800 --> 00:24:21,600
Speaker 6: like you train and I believe really this AI will

527
00:24:21,600 --> 00:24:23,240
Speaker 6: have to be a lot more personalized, you know, like

528
00:24:23,320 --> 00:24:25,520
Speaker 6: so you will train it really on your life experience,

529
00:24:25,560 --> 00:24:27,600
Speaker 6: you know, like so if AI can see every image

530
00:24:27,640 --> 00:24:29,720
Speaker 6: I saw, if it can read every email, you know,

531
00:24:29,760 --> 00:24:32,040
Speaker 6: I believe AI will be able to tell me when

532
00:24:32,080 --> 00:24:34,840
Speaker 6: did I make a mistake? When should I do something different?

533
00:24:34,880 --> 00:24:37,320
Speaker 6: You know, did you overreact in this live situation?

534
00:24:37,400 --> 00:24:39,320
Speaker 5: Did you not right? And going further?

535
00:24:39,440 --> 00:24:42,320
Speaker 6: Right like that will stay and and my my kids

536
00:24:42,400 --> 00:24:44,639
Speaker 6: can after I pass away, they can say, hey, what

537
00:24:44,640 --> 00:24:46,800
Speaker 6: would that say in this situation? Right, you know, like

538
00:24:46,840 --> 00:24:49,200
Speaker 6: what would maybe I'll be able to in some way

539
00:24:49,280 --> 00:24:51,760
Speaker 6: talk to them, right, so you'll blur all these things

540
00:24:51,800 --> 00:24:57,160
Speaker 6: which were which were sort of not blurred in the past, right,

541
00:24:57,240 --> 00:24:59,560
Speaker 6: you know. Also, you you'll start having these like very

542
00:24:59,600 --> 00:25:01,639
Speaker 6: very interest developments, you know, but I would kind of

543
00:25:01,920 --> 00:25:03,720
Speaker 6: not look at them from the sort of P and

544
00:25:03,840 --> 00:25:06,600
Speaker 6: L perspective, earnings perspective. There's also going to be a

545
00:25:06,640 --> 00:25:08,800
Speaker 6: lot of issues as we have already now. I mean,

546
00:25:09,160 --> 00:25:11,959
Speaker 6: sometimes I can give you wrong answer. Sometimes it can

547
00:25:12,000 --> 00:25:14,320
Speaker 6: be used to do bad things, to impersonate, to deceive,

548
00:25:14,400 --> 00:25:16,720
Speaker 6: to manipulate. So there's gonna be a lot of a

549
00:25:16,760 --> 00:25:19,119
Speaker 6: lot of interesting I would say, philosophical issues, you know,

550
00:25:19,240 --> 00:25:22,840
Speaker 6: technological issues and investing investing issues. But I just don't

551
00:25:22,840 --> 00:25:24,520
Speaker 6: think it's going to be as simple as like seven

552
00:25:24,560 --> 00:25:27,440
Speaker 6: companies are gonna have P of fifty and everyone else

553
00:25:27,440 --> 00:25:29,199
Speaker 6: will have P of ten and it's going to persist

554
00:25:29,200 --> 00:25:31,040
Speaker 6: that way. Yeah, I don't think it's going to be

555
00:25:31,119 --> 00:25:32,720
Speaker 6: like that in finance, at least him.

556
00:25:33,359 --> 00:25:35,760
Speaker 2: The other thing I wanted to ask you is, you

557
00:25:35,760 --> 00:25:39,000
Speaker 2: know you left JP Morgan in July and then pretty

558
00:25:39,040 --> 00:25:43,320
Speaker 2: much a month later we had a very sharp sell off.

559
00:25:43,800 --> 00:25:46,359
Speaker 2: When you look back at that particular sell off, you know,

560
00:25:46,400 --> 00:25:49,280
Speaker 2: we never got to hear from you your thoughts on that.

561
00:25:49,600 --> 00:25:53,840
Speaker 2: On that particular week in markets, what did you actually

562
00:25:53,920 --> 00:25:56,760
Speaker 2: see and observe there because there are still differing opinions

563
00:25:56,760 --> 00:25:59,720
Speaker 2: out there about what the proximate catalyst was for some

564
00:25:59,760 --> 00:26:01,919
Speaker 2: of the moves and what was exacerbating what.

565
00:26:02,359 --> 00:26:04,919
Speaker 6: No, So the catalyst was definitely moving rates related to

566
00:26:04,960 --> 00:26:07,960
Speaker 6: Japan in the currency, right, that was a sort of catalyst.

567
00:26:08,000 --> 00:26:10,439
Speaker 6: But you always have like a spark and a bucket

568
00:26:10,480 --> 00:26:13,280
Speaker 6: of fuel, right, and the bucket of fuel will stretched

569
00:26:13,560 --> 00:26:18,800
Speaker 6: CTAs stretched vault targeter systematic investors, too much optimism, you know,

570
00:26:18,880 --> 00:26:22,600
Speaker 6: and then you start basically hitting the stops across these strategies, right,

571
00:26:22,680 --> 00:26:26,040
Speaker 6: ctias hit their cell signals. Volt target is VIX goes up.

572
00:26:26,080 --> 00:26:28,600
Speaker 6: What goes up they need to sell, you know, if

573
00:26:28,600 --> 00:26:32,159
Speaker 6: you're selling puts on AI names, you suddenly to you know,

574
00:26:32,240 --> 00:26:34,040
Speaker 6: you need to kind of close. So so VIX was

575
00:26:34,160 --> 00:26:36,760
Speaker 6: very vixed. Behave most phenomenals. So it was a whole

576
00:26:37,400 --> 00:26:39,639
Speaker 6: lot of all short wall covering as well, you know.

577
00:26:40,240 --> 00:26:43,760
Speaker 6: But again I think it was what was missing for

578
00:26:43,800 --> 00:26:45,960
Speaker 6: this to be the turn in the cycle was I

579
00:26:45,960 --> 00:26:50,160
Speaker 6: guess you know, GDP employment still fine, right, still hope

580
00:26:50,160 --> 00:26:52,280
Speaker 6: that FED is going to cut, right, you.

581
00:26:52,240 --> 00:26:53,560
Speaker 5: Know, So it didn't. It didn't.

582
00:26:54,440 --> 00:26:56,520
Speaker 6: There was a little bit of conflagration but didn't kind

583
00:26:56,520 --> 00:26:58,840
Speaker 6: of burn everything down right, So it was a little

584
00:26:58,880 --> 00:27:01,720
Speaker 6: bit of satisfaction. But in too long it is.

585
00:27:01,720 --> 00:27:04,200
Speaker 1: Pretty markable because even we got a little sell off,

586
00:27:04,200 --> 00:27:06,679
Speaker 1: but it's a very minor sell off. Or basically, the

587
00:27:06,720 --> 00:27:08,879
Speaker 1: stock market is more or less at all time highs.

588
00:27:09,160 --> 00:27:12,159
Speaker 1: This is despite a pretty big repricing of the expectation

589
00:27:12,520 --> 00:27:14,679
Speaker 1: of the short end of the curve, where people were

590
00:27:14,680 --> 00:27:17,439
Speaker 1: expecting deep cuts to continue through last year and to

591
00:27:17,520 --> 00:27:20,000
Speaker 1: this year. We might not get any cut this year,

592
00:27:20,040 --> 00:27:22,440
Speaker 1: and yet still the market is close to.

593
00:27:22,680 --> 00:27:23,439
Speaker 4: All time highs.

594
00:27:23,800 --> 00:27:26,000
Speaker 1: It must be nice on some level to be out

595
00:27:26,040 --> 00:27:28,159
Speaker 1: of the game of having to come up with an

596
00:27:28,240 --> 00:27:30,240
Speaker 1: end of your price, because that sounds like a job

597
00:27:30,280 --> 00:27:33,440
Speaker 1: I would never want to take. But I also wonder,

598
00:27:33,600 --> 00:27:35,600
Speaker 1: you know, do you wake up in the morning, it's

599
00:27:35,640 --> 00:27:39,800
Speaker 1: still like your time to talk to us about you

600
00:27:39,840 --> 00:27:43,000
Speaker 1: know what you have a market outlook for right now?

601
00:27:43,080 --> 00:27:45,080
Speaker 1: Like give us some give us what's on your mind?

602
00:27:45,400 --> 00:27:47,480
Speaker 6: Sure like so, so look, it's nice once in a

603
00:27:47,520 --> 00:27:49,480
Speaker 6: while that you can be somewhere away and not look

604
00:27:49,480 --> 00:27:53,320
Speaker 6: at the fact parst every single world word. Although I

605
00:27:53,359 --> 00:27:55,440
Speaker 6: did it yesterday, you know, but a few months ago

606
00:27:55,440 --> 00:27:57,200
Speaker 6: I didn't you know, like, so it's nice to make

607
00:27:57,240 --> 00:28:00,719
Speaker 6: a break ord. Maybe it's necessary, you know, out markets

608
00:28:00,760 --> 00:28:03,320
Speaker 6: are a little bit of a sort of compulsion thing

609
00:28:03,359 --> 00:28:05,359
Speaker 6: of compulsion you when you feel like you need to

610
00:28:05,440 --> 00:28:08,399
Speaker 6: understand what's going on in the world. You know, So

611
00:28:08,480 --> 00:28:10,240
Speaker 6: I think it becomes part of your DNA if you

612
00:28:10,240 --> 00:28:12,720
Speaker 6: do it for a long time. So so I do

613
00:28:12,880 --> 00:28:15,640
Speaker 6: always think, and I do have an outcome, so give

614
00:28:15,720 --> 00:28:20,080
Speaker 6: us so you know, you know, on a sales side,

615
00:28:20,080 --> 00:28:21,639
Speaker 6: you kind of need to put a price target. And

616
00:28:21,680 --> 00:28:23,120
Speaker 6: I and I think it's a kind of poor way

617
00:28:23,119 --> 00:28:26,680
Speaker 6: to summarize everything into one number. It's basically almost you're

618
00:28:26,720 --> 00:28:31,760
Speaker 6: telling you're trying to focus probabilities in the world, you know,

619
00:28:31,800 --> 00:28:34,600
Speaker 6: because world real world actually works in terms of physics

620
00:28:34,640 --> 00:28:37,640
Speaker 6: deep deeply works in terms of probabilities, not just superficially,

621
00:28:37,680 --> 00:28:39,920
Speaker 6: you know, in a quantum physics. So you you need

622
00:28:39,960 --> 00:28:42,360
Speaker 6: to sort of have a sort of hype proabailistic view

623
00:28:42,400 --> 00:28:44,280
Speaker 6: and you're forced to have one view like one hundred

624
00:28:44,280 --> 00:28:47,360
Speaker 6: percent or nothing. Right, So so it gets over simplifies.

625
00:28:47,400 --> 00:28:49,840
Speaker 6: I think media, you know, and not referring to you,

626
00:28:49,880 --> 00:28:51,720
Speaker 6: but media does a bad job. They say, oh, what's

627
00:28:51,720 --> 00:28:52,040
Speaker 6: your price?

628
00:28:52,040 --> 00:28:52,240
Speaker 5: Starty?

629
00:28:52,280 --> 00:28:53,640
Speaker 6: They just want to talk about that, and they say, oh,

630
00:28:53,640 --> 00:28:57,400
Speaker 6: you're right, you're right. Yeah, so so no, so I

631
00:28:57,440 --> 00:28:59,440
Speaker 6: would say, like, you know, if if I can move

632
00:28:59,440 --> 00:29:01,680
Speaker 6: away from price, sorry, I do think we'll go back

633
00:29:01,720 --> 00:29:04,800
Speaker 6: down in five thousands this year. Sometimes I think at

634
00:29:04,800 --> 00:29:07,240
Speaker 6: that point in time, we will we will see whether

635
00:29:07,280 --> 00:29:10,080
Speaker 6: the cycle is still strong or it's not. You know,

636
00:29:10,160 --> 00:29:13,200
Speaker 6: I think we need to see the whole new political

637
00:29:13,320 --> 00:29:17,880
Speaker 6: climate whether it will lead to turmoil, and I believe

638
00:29:18,640 --> 00:29:20,800
Speaker 6: more likely than not it will, you know. Like so

639
00:29:20,880 --> 00:29:22,960
Speaker 6: those things I think will get us lower, right, you

640
00:29:23,000 --> 00:29:25,960
Speaker 6: know at that time, whether whether it becomes an end

641
00:29:26,000 --> 00:29:28,360
Speaker 6: of a cycle and we go much lower into four thousand,

642
00:29:28,440 --> 00:29:30,800
Speaker 6: that I don't know. I think there's some probability of that,

643
00:29:30,880 --> 00:29:33,120
Speaker 6: you know. And then conversely, on the upside, is everything

644
00:29:33,160 --> 00:29:35,800
Speaker 6: goes if really this is what they call it Golden age,

645
00:29:36,040 --> 00:29:38,800
Speaker 6: the Golden age of America, you know, then market will

646
00:29:38,840 --> 00:29:41,440
Speaker 6: stay in six thousands, it can go a bit higher.

647
00:29:41,680 --> 00:29:44,800
Speaker 6: I just see, I'm hard pressed to see it going

648
00:29:45,000 --> 00:29:48,240
Speaker 6: much much higher, right because evaluations are there, Positioning is

649
00:29:48,280 --> 00:29:51,400
Speaker 6: already there. As you said, FED is not cutting, right,

650
00:29:51,480 --> 00:29:53,000
Speaker 6: So it's a little bit of a chicken and egg.

651
00:29:53,000 --> 00:29:56,560
Speaker 6: I mean, I have been scratching my head, like at

652
00:29:56,600 --> 00:29:58,280
Speaker 6: these level of rates, which I do think is that

653
00:29:58,280 --> 00:30:02,040
Speaker 6: are restricted rates for now now more than two years

654
00:30:02,320 --> 00:30:04,960
Speaker 6: with the commercial real estate here and there, we saw

655
00:30:04,960 --> 00:30:07,600
Speaker 6: a few hiccups, you know, like, but you know, I

656
00:30:07,640 --> 00:30:10,840
Speaker 6: do think that is sort of under the hood of

657
00:30:10,880 --> 00:30:13,600
Speaker 6: economy some damage is being sort of built up and done.

658
00:30:13,640 --> 00:30:16,239
Speaker 6: So so I don't think like market really you know,

659
00:30:16,280 --> 00:30:18,480
Speaker 6: going to seven thousand or you know, sixty eight hundred

660
00:30:18,520 --> 00:30:19,800
Speaker 6: or something like that. So I would say, maybe it

661
00:30:19,800 --> 00:30:22,800
Speaker 6: can go sixty five, stay range bound, you know, Like,

662
00:30:22,840 --> 00:30:25,280
Speaker 6: so I would sort of formulate the view in terms of, okay,

663
00:30:25,320 --> 00:30:27,800
Speaker 6: you perhaps want to sell ups, give yourself a little

664
00:30:27,800 --> 00:30:29,800
Speaker 6: bit of a room for some more excitement first few

665
00:30:29,840 --> 00:30:32,360
Speaker 6: years of the of the first few months of the year,

666
00:30:32,480 --> 00:30:35,000
Speaker 6: but then also be ready to assess once when you

667
00:30:35,080 --> 00:30:37,160
Speaker 6: go in, you know, let's say fifty five hundred or

668
00:30:37,200 --> 00:30:41,440
Speaker 6: fifty seven hundred, to assess is the cycle potentially ending,

669
00:30:41,680 --> 00:30:44,080
Speaker 6: you know, or that's going to be buying opportunity. And

670
00:30:44,360 --> 00:30:46,560
Speaker 6: I wouldn't want to sort of say, hey, like it's

671
00:30:46,560 --> 00:30:48,360
Speaker 6: going to first stay at sixty one hundred, then it's

672
00:30:48,360 --> 00:30:49,880
Speaker 6: going to pull to fifty five, and then you buy

673
00:30:49,920 --> 00:30:52,680
Speaker 6: with both hands at fifty five and like that'll be

674
00:30:52,720 --> 00:30:55,840
Speaker 6: too predictive, you know, but I think some variation of

675
00:30:55,880 --> 00:30:58,480
Speaker 6: that path will be whereby sort of the depth of

676
00:30:58,520 --> 00:31:02,920
Speaker 6: a pullback will depends on trade war China, domestic political

677
00:31:02,960 --> 00:31:06,560
Speaker 6: situation rates, and like one off things.

678
00:31:06,320 --> 00:31:23,120
Speaker 7: Like we had a Monday.

679
00:31:23,400 --> 00:31:27,080
Speaker 2: I'm glad you mentioned domestic politics because one of the

680
00:31:27,120 --> 00:31:30,720
Speaker 2: other weird things about this week when we had the

681
00:31:30,760 --> 00:31:33,520
Speaker 2: deep seek cell off was everyone was focused on that,

682
00:31:33,800 --> 00:31:36,640
Speaker 2: and you know, tech stalks went down, as we mentioned,

683
00:31:36,640 --> 00:31:40,239
Speaker 2: But then on Tuesday everything started rebounding. Even though we

684
00:31:40,280 --> 00:31:43,960
Speaker 2: had headlines coming out of the White House about cutting

685
00:31:44,120 --> 00:31:48,400
Speaker 2: what amounted to a pretty big chunk of federal spending,

686
00:31:48,880 --> 00:31:51,680
Speaker 2: the entire market seemed to look through that, which is

687
00:31:51,760 --> 00:31:54,240
Speaker 2: kind of ironic because one of the things we've heard

688
00:31:54,280 --> 00:31:57,200
Speaker 2: for the past four years or so is this idea

689
00:31:57,240 --> 00:32:00,520
Speaker 2: that you know, deficit spending is driving the entireomy and

690
00:32:00,560 --> 00:32:03,600
Speaker 2: stuff like that. I feel like political risk is one

691
00:32:03,600 --> 00:32:07,640
Speaker 2: of those things that investors really struggle to price in

692
00:32:07,800 --> 00:32:10,280
Speaker 2: because there's so much uncertainty. A lot of it seems

693
00:32:10,480 --> 00:32:12,680
Speaker 2: very binary. How do you deal with that?

694
00:32:13,480 --> 00:32:16,200
Speaker 6: So you need to sort of, you know, put some scenarios,

695
00:32:16,240 --> 00:32:18,440
Speaker 6: you know, what can happen in terms of taxes, regulation,

696
00:32:18,680 --> 00:32:22,760
Speaker 6: you know, tariffs, trade wars, geopolitical conflict you know, and

697
00:32:22,760 --> 00:32:26,520
Speaker 6: then see what can how can they impact specific stocks

698
00:32:26,520 --> 00:32:30,200
Speaker 6: and industries, countries and maybe overall market sentiment, you know,

699
00:32:30,200 --> 00:32:32,840
Speaker 6: and maybe put some scenarios. That's the kind of a blueprint.

700
00:32:33,080 --> 00:32:35,240
Speaker 6: And market never goes by that blueprint, but at least

701
00:32:35,280 --> 00:32:37,840
Speaker 6: gives you some framework to try to understand if it

702
00:32:37,880 --> 00:32:40,440
Speaker 6: doesn't go by your sort of assessment, what have you

703
00:32:40,480 --> 00:32:42,479
Speaker 6: missed and what you need to what else you need

704
00:32:42,520 --> 00:32:44,480
Speaker 6: to take into count but you put some blueprints sort

705
00:32:44,520 --> 00:32:46,800
Speaker 6: of what can happen? So you know, I think you

706
00:32:47,040 --> 00:32:52,120
Speaker 6: pointed very well. He was talking about Navidia, Taiwan export sortiz.

707
00:32:52,360 --> 00:32:54,960
Speaker 6: Like so those type of things, right. So market that's

708
00:32:55,560 --> 00:32:57,400
Speaker 6: then market has its minds of its own, which is

709
00:32:57,400 --> 00:32:59,680
Speaker 6: tied to sentiment, you know, and it's tied to momentum.

710
00:32:59,720 --> 00:32:59,880
Speaker 7: You know.

711
00:33:00,080 --> 00:33:02,760
Speaker 6: Most people think momentum, you know, They don't calculate by

712
00:33:02,800 --> 00:33:05,240
Speaker 6: the thing. They just feel good about market. They see

713
00:33:05,280 --> 00:33:08,560
Speaker 6: good news about market. They their taxi driver or friend

714
00:33:08,680 --> 00:33:12,280
Speaker 6: or family feels good about investing, right and and they

715
00:33:12,360 --> 00:33:14,280
Speaker 6: choose them to ignore, you know, Like so on Monday,

716
00:33:14,320 --> 00:33:17,000
Speaker 6: I was watching CNBC and every single guest was saying, oh,

717
00:33:17,000 --> 00:33:19,040
Speaker 6: take your shopping list out, take your shopping list out,

718
00:33:19,080 --> 00:33:22,000
Speaker 6: take you buy this, buy this right. So you know,

719
00:33:22,040 --> 00:33:23,800
Speaker 6: it creates a little bit of a sentiment. You know,

720
00:33:23,880 --> 00:33:26,480
Speaker 6: it creates a sentiment, and people say, okay, you know,

721
00:33:26,800 --> 00:33:29,280
Speaker 6: I'll make a punt. I'll buy if it's twenty percent down,

722
00:33:29,360 --> 00:33:31,440
Speaker 6: maybe next day is going to that can bounce them,

723
00:33:31,520 --> 00:33:34,000
Speaker 6: So people buy right. Some people rotate it say okay,

724
00:33:34,040 --> 00:33:36,160
Speaker 6: like I'm getting rid of no video, but look Apple

725
00:33:36,200 --> 00:33:38,280
Speaker 6: has been you know, underperformance, so maybe I put my

726
00:33:38,320 --> 00:33:42,360
Speaker 6: money there. So the sentiment overall was still pretty strong.

727
00:33:42,400 --> 00:33:45,840
Speaker 6: There's this aura of momentum, psychological momentum that it's harder

728
00:33:45,840 --> 00:33:47,360
Speaker 6: to break. You know, you don't need to have a

729
00:33:47,120 --> 00:33:49,280
Speaker 6: few punches for it to break, for people to sort

730
00:33:49,320 --> 00:33:49,760
Speaker 6: of give up.

731
00:33:50,240 --> 00:33:52,320
Speaker 2: So one of the other things I want to ask you,

732
00:33:52,320 --> 00:33:54,160
Speaker 2: you know, just again looking back at the sort of

733
00:33:54,240 --> 00:33:58,120
Speaker 2: long term changes in the market. And we touched on

734
00:33:58,160 --> 00:34:01,400
Speaker 2: this earlier, but we've just seen an hour absolute explosion

735
00:34:01,600 --> 00:34:05,600
Speaker 2: in different types of options trading and volatility trading, and

736
00:34:05,720 --> 00:34:09,280
Speaker 2: now you even have TikTok influencers who are like pitching

737
00:34:09,360 --> 00:34:12,680
Speaker 2: options investing as passive income, like, oh, don't buy a

738
00:34:12,760 --> 00:34:15,799
Speaker 2: US Treasury bond, do you an options? But which is

739
00:34:16,080 --> 00:34:20,600
Speaker 2: kind of crazy. How has that impacted the market and

740
00:34:20,600 --> 00:34:23,600
Speaker 2: how have you seen people, you know, trying to handle

741
00:34:23,880 --> 00:34:27,520
Speaker 2: some of that new I guess dynamic that's been introduced.

742
00:34:28,000 --> 00:34:30,560
Speaker 6: So so that's that's a very good question. It started

743
00:34:30,560 --> 00:34:32,960
Speaker 6: sort of with around the COVID time. People were locked in,

744
00:34:33,040 --> 00:34:36,640
Speaker 6: they got these stimulus checks, they started trading, right, proliferation

745
00:34:36,719 --> 00:34:41,360
Speaker 6: of these online brokers, no commission fees, options being traded

746
00:34:41,560 --> 00:34:45,319
Speaker 6: as a sort of very short, short and short maturities, right.

747
00:34:45,360 --> 00:34:48,520
Speaker 6: You know, options used to be sort of leaps and

748
00:34:48,560 --> 00:34:51,200
Speaker 6: then maybe like a monthly options, you know, second and first,

749
00:34:51,200 --> 00:34:55,719
Speaker 6: second and third month quarterly options moved to weeklies and

750
00:34:55,800 --> 00:34:57,799
Speaker 6: dailies you know, and then in the single names you know,

751
00:34:57,840 --> 00:35:00,640
Speaker 6: Like so you had sort of people locked they got money,

752
00:35:00,719 --> 00:35:03,920
Speaker 6: and they got these instruments, these extremely powerful instruments with

753
00:35:04,080 --> 00:35:06,600
Speaker 6: leverage about one hundred times leverage, you know, like so

754
00:35:06,640 --> 00:35:08,640
Speaker 6: you suddenly can make a bets of you know, millions

755
00:35:08,640 --> 00:35:10,400
Speaker 6: of dollars even if you have like ten thousand or

756
00:35:10,400 --> 00:35:13,000
Speaker 6: five thousand dollars to invest, you know, Like so that changed,

757
00:35:13,640 --> 00:35:16,280
Speaker 6: that changed a lot and for most of these people

758
00:35:16,400 --> 00:35:20,160
Speaker 6: actually it worked, right because since twenty twenty we had

759
00:35:20,200 --> 00:35:23,359
Speaker 6: that pullback when the FED started hiking, But for most

760
00:35:23,400 --> 00:35:26,840
Speaker 6: of it it worked, you know, Like so speculative trading activity,

761
00:35:26,920 --> 00:35:30,640
Speaker 6: especially on the alongside, it worked. Then you also had

762
00:35:31,040 --> 00:35:34,680
Speaker 6: in peril sort of crypto markets growing, right, you know,

763
00:35:34,719 --> 00:35:36,360
Speaker 6: Like so if you think of it, like, you know,

764
00:35:36,520 --> 00:35:39,120
Speaker 6: a few trillions of dollars of wealth was created there

765
00:35:39,920 --> 00:35:42,600
Speaker 6: with probably some of these similar type of investors and

766
00:35:42,640 --> 00:35:44,919
Speaker 6: similar type of people, you know, like so so it changed,

767
00:35:44,960 --> 00:35:47,719
Speaker 6: you know, so there is less a leverage in terms

768
00:35:47,760 --> 00:35:51,000
Speaker 6: of borrowing money with interesting but more a lot more

769
00:35:51,080 --> 00:35:53,080
Speaker 6: leverage in terms of option trading activity.

770
00:35:53,120 --> 00:35:53,279
Speaker 7: You know.

771
00:35:53,360 --> 00:35:56,080
Speaker 6: So, as you said, I'm always also surprised. You go

772
00:35:56,160 --> 00:35:58,240
Speaker 6: on some of these social media and then you see

773
00:35:58,320 --> 00:36:01,600
Speaker 6: all kinds of strategy is that can't lose money, that

774
00:36:02,040 --> 00:36:04,640
Speaker 6: making like tens of thousands every day. You just need

775
00:36:04,640 --> 00:36:07,080
Speaker 6: to follow him, and it becomes really kind of bizarre.

776
00:36:07,200 --> 00:36:09,000
Speaker 6: You have like these people who are at the same

777
00:36:09,000 --> 00:36:11,560
Speaker 6: time performer or like women who are like you know,

778
00:36:11,680 --> 00:36:15,600
Speaker 6: in the like underwear, suggesting how to trede options, like yeah,

779
00:36:15,800 --> 00:36:16,200
Speaker 6: it's all.

780
00:36:16,120 --> 00:36:17,000
Speaker 4: Big fans of mind.

781
00:36:17,040 --> 00:36:20,440
Speaker 1: They follow me on Twitter ADM very flattered.

782
00:36:20,600 --> 00:36:22,920
Speaker 6: So it's it's kind of crazy, you know, like and

783
00:36:22,960 --> 00:36:26,279
Speaker 6: we try to handicap it by looking at flows from Robinhood,

784
00:36:26,640 --> 00:36:29,239
Speaker 6: see which names are being sort of bold, which names

785
00:36:29,239 --> 00:36:31,879
Speaker 6: are being sold, try to see where the retail may

786
00:36:32,080 --> 00:36:34,160
Speaker 6: be forced out or something like that. So we did

787
00:36:34,200 --> 00:36:36,839
Speaker 6: some quantitative work. We did a lot of the sort

788
00:36:36,840 --> 00:36:40,320
Speaker 6: of language large language models sentiment wise, like from Twitter

789
00:36:40,400 --> 00:36:43,680
Speaker 6: and those type of other social medias which we could

790
00:36:43,920 --> 00:36:46,360
Speaker 6: we could get permission to do. So we're trying to

791
00:36:46,360 --> 00:36:49,759
Speaker 6: incorporate it. But I think overall it's hard to one

792
00:36:49,840 --> 00:36:52,920
Speaker 6: hundred percent handicap it. But for sure it added leverage

793
00:36:53,120 --> 00:36:56,799
Speaker 6: to the market, added speculative element to the market, and

794
00:36:56,880 --> 00:36:59,319
Speaker 6: at some point it's not going to probably end up

795
00:36:59,360 --> 00:37:01,600
Speaker 6: well right some point, you know, but it's hard to

796
00:37:01,640 --> 00:37:02,680
Speaker 6: say when exactly right.

797
00:37:02,719 --> 00:37:04,600
Speaker 2: It's again one of those things that can go on

798
00:37:04,760 --> 00:37:08,720
Speaker 2: for longer than you think. Since you mentioned getting data

799
00:37:08,920 --> 00:37:11,319
Speaker 2: from robin Hood just then, this is the other thing

800
00:37:11,440 --> 00:37:15,280
Speaker 2: I always wanted to ask an equity derivative strategist because

801
00:37:15,440 --> 00:37:18,400
Speaker 2: you alluded to this earlier. There's a lot of I guess,

802
00:37:19,160 --> 00:37:22,400
Speaker 2: misunderstanding or lack of understanding of what an equity derivative

803
00:37:22,440 --> 00:37:26,359
Speaker 2: strategist actually does, and exactly what data they're looking at

804
00:37:26,360 --> 00:37:29,640
Speaker 2: in order to make some of their conclusions. Can you

805
00:37:29,680 --> 00:37:32,680
Speaker 2: maybe give us like a quick one oh one in

806
00:37:32,920 --> 00:37:35,000
Speaker 2: where your data comes from? How much of it is

807
00:37:35,040 --> 00:37:38,799
Speaker 2: from official sources like I don't know an EPFR or

808
00:37:38,800 --> 00:37:43,319
Speaker 2: someone like that, versus like color that you're getting from

809
00:37:43,320 --> 00:37:44,360
Speaker 2: the market question.

810
00:37:44,640 --> 00:37:47,520
Speaker 6: Yeah, no, so sore. They're all kinds of data. So

811
00:37:47,560 --> 00:37:50,399
Speaker 6: there are price involume data or kind of technical data

812
00:37:50,400 --> 00:37:52,800
Speaker 6: that can be derived from from from those type of things,

813
00:37:53,120 --> 00:37:55,520
Speaker 6: which can also be a different time horizons, you know,

814
00:37:55,640 --> 00:37:58,200
Speaker 6: like they can be daily. Mostly they are daily, right,

815
00:37:58,239 --> 00:38:00,920
Speaker 6: you know, but increasingly you so want to look at

816
00:38:00,920 --> 00:38:04,480
Speaker 6: the intra day data, you know, intraday correlations, interramormentum volumes,

817
00:38:04,600 --> 00:38:07,720
Speaker 6: large blocks that are traded. So there's also high frequency

818
00:38:07,760 --> 00:38:09,600
Speaker 6: one day, but most of it is daily, I would say.

819
00:38:09,880 --> 00:38:11,799
Speaker 6: And then there are longer term data, you know, when

820
00:38:11,800 --> 00:38:13,719
Speaker 6: you look at the sort of you know, some like

821
00:38:13,719 --> 00:38:18,160
Speaker 6: a monthly statistics on positioning or up to the sort

822
00:38:18,200 --> 00:38:21,880
Speaker 6: of filings you know, thirteen F filings like holdings and stuff

823
00:38:21,920 --> 00:38:24,120
Speaker 6: like that. You know, like so so different sort of

824
00:38:24,120 --> 00:38:30,120
Speaker 6: frequencies of positioning volume data price data. Then so directly

825
00:38:30,160 --> 00:38:33,840
Speaker 6: market observed data like open interest, you know, options volume

826
00:38:33,840 --> 00:38:36,239
Speaker 6: and options those type of things. Right, then you have

827
00:38:36,320 --> 00:38:38,880
Speaker 6: sort of fundamental data, you know, and fundamental data. You

828
00:38:38,920 --> 00:38:41,800
Speaker 6: have fundamental data for stocks, you know, which are related

829
00:38:41,840 --> 00:38:44,640
Speaker 6: to earnings, but increasingly you have a data which are

830
00:38:44,640 --> 00:38:47,359
Speaker 6: derived from non traditional sources, you know, so called big

831
00:38:47,480 --> 00:38:51,560
Speaker 6: data that can be sort of sentiment measures, but quantitatively

832
00:38:51,600 --> 00:38:54,640
Speaker 6: derived measures, objective not sort of a guesswork to some

833
00:38:54,800 --> 00:38:58,000
Speaker 6: very specific niche data like you know, satellite you know,

834
00:38:58,239 --> 00:39:00,640
Speaker 6: all kinds of like data that you know, we didn't have,

835
00:39:01,080 --> 00:39:01,480
Speaker 6: you know.

836
00:39:01,520 --> 00:39:03,880
Speaker 3: How many cars are parkeding parking lots.

837
00:39:03,640 --> 00:39:05,160
Speaker 5: And in front of Walmart and those type of things.

838
00:39:05,239 --> 00:39:08,960
Speaker 6: Right, So you have these stock specific data, earnings derived,

839
00:39:09,280 --> 00:39:12,720
Speaker 6: news derived, sentiment derived, and then also non traditional ones

840
00:39:12,760 --> 00:39:15,279
Speaker 6: you know, and then you have like micro data, you know,

841
00:39:15,560 --> 00:39:18,400
Speaker 6: typically lower frequencies, but increasingly also with some of these

842
00:39:18,400 --> 00:39:21,000
Speaker 6: alternative data sets, big data sets, you can try to

843
00:39:21,040 --> 00:39:24,080
Speaker 6: figure out like you know, shipping and again sort of

844
00:39:24,120 --> 00:39:27,880
Speaker 6: storage and oil tank tanks how full they are and

845
00:39:27,880 --> 00:39:28,520
Speaker 6: stuff like that.

846
00:39:28,719 --> 00:39:29,840
Speaker 5: So it's a whole.

847
00:39:29,640 --> 00:39:31,480
Speaker 6: Host of data, you know, like as a quant and

848
00:39:31,520 --> 00:39:33,480
Speaker 6: there at this person you probably focus most on the

849
00:39:33,520 --> 00:39:36,799
Speaker 6: market data, you know, so open enters, price volumes and

850
00:39:36,920 --> 00:39:39,080
Speaker 6: old stuff that is derived from that, but you also

851
00:39:39,120 --> 00:39:41,640
Speaker 6: want to want to supplement that with all these other

852
00:39:42,080 --> 00:39:43,799
Speaker 6: other data. And then and then some of the data

853
00:39:43,800 --> 00:39:45,360
Speaker 6: set you derive it on your own, you know. Like

854
00:39:45,440 --> 00:39:48,800
Speaker 6: so so, for instance, got my imbalance in SMP options

855
00:39:48,800 --> 00:39:51,160
Speaker 6: put minus call. So I was running that for fifteen

856
00:39:51,200 --> 00:39:53,080
Speaker 6: twenty years. And first people tell me to say, you know,

857
00:39:53,280 --> 00:39:55,520
Speaker 6: what's that. That's you cannot know what's you know. But

858
00:39:55,560 --> 00:39:58,000
Speaker 6: then now everybody has it, actually, you know, and the

859
00:39:58,440 --> 00:40:01,000
Speaker 6: same thing with like a CTA stuff, you know, I

860
00:40:01,160 --> 00:40:04,040
Speaker 6: and volt targeting exposure. I was getting so much sort

861
00:40:04,080 --> 00:40:07,080
Speaker 6: of critique in twenty eleven, twelve thirteen. You know, now

862
00:40:07,120 --> 00:40:09,960
Speaker 6: everybody has it, you know, kind of CTA positioning percenta.

863
00:40:10,239 --> 00:40:12,520
Speaker 6: So you can derive some on your own based on

864
00:40:12,680 --> 00:40:13,840
Speaker 6: understanding with markets.

865
00:40:14,040 --> 00:40:16,479
Speaker 1: All right, I just have one take and I bring

866
00:40:16,520 --> 00:40:18,400
Speaker 1: it up a lot, and I sort of feel like

867
00:40:18,400 --> 00:40:21,040
Speaker 1: the kool aid man. It's like every conversation I have

868
00:40:21,120 --> 00:40:23,799
Speaker 1: to jump it through the wall and interject this. But

869
00:40:24,400 --> 00:40:27,000
Speaker 1: you know, there's all sorts of like quant techniques, and

870
00:40:27,160 --> 00:40:31,239
Speaker 1: there's the definition of quant and changes over time, and

871
00:40:31,680 --> 00:40:34,040
Speaker 1: obviously there's an incredible amount of data that we can

872
00:40:34,160 --> 00:40:36,560
Speaker 1: use now. And then there's sort of like old fashioned

873
00:40:36,640 --> 00:40:38,799
Speaker 1: quant where you're just like, we're gonna buy you know

874
00:40:38,920 --> 00:40:41,719
Speaker 1: that I sort of associate with like AQR from years ago,

875
00:40:41,719 --> 00:40:43,759
Speaker 1: where like we're gonna buy the cheap stocks that are

876
00:40:43,760 --> 00:40:46,400
Speaker 1: exhibiting momentum, right, and we're going to short the expensive

877
00:40:46,400 --> 00:40:50,719
Speaker 1: stocks that are declining momentum. And why doesn't this work anymore?

878
00:40:50,800 --> 00:40:53,640
Speaker 1: And all these sort of hand ringing and the traditional

879
00:40:53,719 --> 00:40:55,719
Speaker 1: quant industry, why has it?

880
00:40:55,840 --> 00:40:57,240
Speaker 4: Why haven't things mean reverted?

881
00:40:57,600 --> 00:41:00,200
Speaker 1: How much is the fact that, like so many of

882
00:41:00,239 --> 00:41:04,200
Speaker 1: these sort of ideas about how the market should work

883
00:41:04,840 --> 00:41:08,600
Speaker 1: have been broken by the simple fact that a handful

884
00:41:08,640 --> 00:41:12,279
Speaker 1: of American companies that are very big exhibit year over

885
00:41:12,400 --> 00:41:15,120
Speaker 1: year earning his growth that are truly remarkable. And this

886
00:41:15,239 --> 00:41:17,720
Speaker 1: is a fact not about the market world, but about

887
00:41:17,719 --> 00:41:21,600
Speaker 1: the real world that, for whatever reason, these big tech

888
00:41:21,600 --> 00:41:24,160
Speaker 1: companies just keep getting bigger despite their size.

889
00:41:24,239 --> 00:41:26,360
Speaker 2: Wait, Joe, you have to end that by saying the

890
00:41:26,400 --> 00:41:28,200
Speaker 2: kool aid man catchphrase.

891
00:41:28,640 --> 00:41:29,359
Speaker 5: What did he say?

892
00:41:29,680 --> 00:41:31,240
Speaker 3: Oh yeah, oh yeah.

893
00:41:31,480 --> 00:41:33,520
Speaker 1: As long as the metas and the Googles and the

894
00:41:33,760 --> 00:41:35,880
Speaker 1: videos and maybe the Apples of the world just keep

895
00:41:35,920 --> 00:41:36,759
Speaker 1: growing earning is like.

896
00:41:36,760 --> 00:41:37,600
Speaker 4: Crazy every year.

897
00:41:38,480 --> 00:41:41,480
Speaker 1: How much does that bust any sort of notion of

898
00:41:41,800 --> 00:41:42,759
Speaker 1: mean reversion in market?

899
00:41:43,200 --> 00:41:45,080
Speaker 6: So I think it busts the notion of a value

900
00:41:45,080 --> 00:41:46,719
Speaker 6: as a factor. But value is the fact that we've

901
00:41:46,719 --> 00:41:49,000
Speaker 6: been straggling for a long time. Yeah, so sort of

902
00:41:49,360 --> 00:41:53,120
Speaker 6: probably since you know, decline in interest rates post two

903
00:41:53,120 --> 00:41:56,440
Speaker 6: thousand and eight, a lot of these, you know, and

904
00:41:56,640 --> 00:41:59,800
Speaker 6: growth of indexation, right, growth of dxation kind of spark

905
00:41:59,880 --> 00:42:02,239
Speaker 6: them momentum and change the structure of the market. So

906
00:42:02,280 --> 00:42:06,439
Speaker 6: some of these quants, quants models or quant factors work

907
00:42:06,680 --> 00:42:07,440
Speaker 6: less and less.

908
00:42:07,520 --> 00:42:07,719
Speaker 7: You know.

909
00:42:08,520 --> 00:42:11,399
Speaker 6: There is also another aspect, which is, you know, once

910
00:42:11,440 --> 00:42:14,360
Speaker 6: when you put money to work in these strategies, you

911
00:42:14,480 --> 00:42:16,719
Speaker 6: kind of squeeze out the alpha, you know, and and

912
00:42:16,719 --> 00:42:18,879
Speaker 6: and and these things are fully priced in so they

913
00:42:18,880 --> 00:42:21,400
Speaker 6: stop working. So sort of growth of quant funds, traditional

914
00:42:21,520 --> 00:42:24,640
Speaker 6: quant funds, you have quantity tfs, you have like broker

915
00:42:24,680 --> 00:42:28,319
Speaker 6: dealers doing quant strategies kind of squeezes out returns, right,

916
00:42:28,440 --> 00:42:30,919
Speaker 6: you know, on your question, sort of like these big

917
00:42:30,960 --> 00:42:34,080
Speaker 6: companies that keep on delivering what that's that's also a

918
00:42:34,160 --> 00:42:36,719
Speaker 6: very good point. You know, quant strategies are designed for

919
00:42:36,800 --> 00:42:39,400
Speaker 6: sort of steady state situation when kind of things are

920
00:42:39,440 --> 00:42:42,759
Speaker 6: fluctuating around something which is in a steady state. And

921
00:42:42,840 --> 00:42:45,640
Speaker 6: we had sort of you know, big sort of big

922
00:42:45,719 --> 00:42:48,640
Speaker 6: changes in the world right in technology, Yeah, and also

923
00:42:48,680 --> 00:42:51,440
Speaker 6: geopolitically sort of you know, capital moved to us and

924
00:42:51,440 --> 00:42:55,360
Speaker 6: then moved into these sectors of innovation, right you know,

925
00:42:55,560 --> 00:42:59,520
Speaker 6: And now so you may sort of you know, you

926
00:42:59,600 --> 00:43:01,920
Speaker 6: may con simply be out of equilibrium, right, you know,

927
00:43:01,960 --> 00:43:04,640
Speaker 6: where some of these mean reversion or quand strategies would work.

928
00:43:05,040 --> 00:43:07,480
Speaker 6: A certain type of con strategises have value based strategies.

929
00:43:07,520 --> 00:43:08,520
Speaker 5: Right, question is.

930
00:43:08,480 --> 00:43:10,959
Speaker 6: How long you know, how long can you know, going

931
00:43:11,000 --> 00:43:13,120
Speaker 6: back to the concentration, right, how long can it go?

932
00:43:13,880 --> 00:43:15,799
Speaker 6: My my question becomes like, let's say, if you have

933
00:43:15,840 --> 00:43:17,680
Speaker 6: like a social media company, like a Meta right, I

934
00:43:17,680 --> 00:43:20,439
Speaker 6: mean once when they have all the users in the world,

935
00:43:20,480 --> 00:43:22,759
Speaker 6: I mean, like you know, they can't go much further, right,

936
00:43:22,800 --> 00:43:23,600
Speaker 6: they can go to the.

937
00:43:24,400 --> 00:43:26,480
Speaker 3: Right, there's limits, there's limits.

938
00:43:26,120 --> 00:43:28,560
Speaker 4: You know, but numbers are creating fake ai FA.

939
00:43:29,200 --> 00:43:31,560
Speaker 6: Or go like to Marcel, there's no one there, so

940
00:43:31,719 --> 00:43:34,359
Speaker 6: there's some some limitations, right, like and then there's some

941
00:43:34,360 --> 00:43:36,799
Speaker 6: sort of also historical when you look at the weight

942
00:43:37,040 --> 00:43:39,120
Speaker 6: of stocks in an index, right, so so you're taking

943
00:43:39,160 --> 00:43:41,880
Speaker 6: a video percentage rat in SMP, and you know, you

944
00:43:42,000 --> 00:43:44,520
Speaker 6: run back history and you see that this basically never happen,

945
00:43:44,560 --> 00:43:47,319
Speaker 6: and even if it happens, never lasts forever. Right, But

946
00:43:47,400 --> 00:43:49,200
Speaker 6: to your point, it can last. You know, one or

947
00:43:49,200 --> 00:43:51,319
Speaker 6: two or three years is enough to ruin a lot

948
00:43:51,360 --> 00:43:52,560
Speaker 6: of investment strategies, you know.

949
00:43:52,920 --> 00:43:55,520
Speaker 2: All right, Marco Kolonovitch, thank you so much for coming

950
00:43:55,560 --> 00:43:57,640
Speaker 2: on odd lots of real treat for both of us.

951
00:43:57,800 --> 00:43:59,640
Speaker 4: Yeah, that was great, Thank you so much, Thank you.

952
00:43:59,600 --> 00:44:16,239
Speaker 2: So much, Joe, that was really fun. I'm so glad

953
00:44:16,280 --> 00:44:19,080
Speaker 2: I finally got to ask him a bunch of questions

954
00:44:19,080 --> 00:44:21,680
Speaker 2: about just being an equity derive strategist.

955
00:44:21,200 --> 00:44:22,919
Speaker 4: I've I love those.

956
00:44:23,040 --> 00:44:26,680
Speaker 1: I love those questions so much because they're this sort

957
00:44:26,719 --> 00:44:29,440
Speaker 1: of like dark fiber or the dark matter of how

958
00:44:29,440 --> 00:44:33,520
Speaker 1: this industry actually works. But everyone just abstracts over them.

959
00:44:33,560 --> 00:44:35,200
Speaker 1: It's like, oh, you look at the data and then

960
00:44:35,239 --> 00:44:35,600
Speaker 1: you do.

961
00:44:35,520 --> 00:44:37,400
Speaker 3: They but right, like what data?

962
00:44:37,440 --> 00:44:39,600
Speaker 4: Where where do you data actually come from?

963
00:44:39,680 --> 00:44:42,760
Speaker 1: Like I could listen forever to someone and we should

964
00:44:42,800 --> 00:44:45,400
Speaker 1: do more of that, just like talk about these aspects

965
00:44:45,440 --> 00:44:48,480
Speaker 1: like paying for data and data costs and all that stuff.

966
00:44:48,880 --> 00:44:51,160
Speaker 1: I really like Marco, It's a nice conversation.

967
00:44:51,360 --> 00:44:52,840
Speaker 2: The one thing I would say is, you know you

968
00:44:52,880 --> 00:44:55,360
Speaker 2: asked that question about like, well, the S and P

969
00:44:55,480 --> 00:44:58,880
Speaker 2: five hundred overall didn't do too bad on Monday, and like,

970
00:44:59,040 --> 00:45:01,240
Speaker 2: you know, if I'm an induct investor, I have exposure

971
00:45:01,280 --> 00:45:03,360
Speaker 2: to GE and so that's good because I don't have

972
00:45:03,400 --> 00:45:06,319
Speaker 2: to worry about AI. But I think the question really is,

973
00:45:06,400 --> 00:45:10,200
Speaker 2: like how much is GE exposed to AI? In very

974
00:45:10,440 --> 00:45:11,719
Speaker 2: indirect way?

975
00:45:11,840 --> 00:45:14,680
Speaker 1: Yeah, that's correct, right, and you do sort of I mean,

976
00:45:14,840 --> 00:45:17,279
Speaker 1: you know what, I think. I saw a tweet about this,

977
00:45:17,360 --> 00:45:20,319
Speaker 1: so I'm just gonna say it, and it may not

978
00:45:20,400 --> 00:45:23,799
Speaker 1: even be true, but if it's not true, it's truth y.

979
00:45:24,560 --> 00:45:27,480
Speaker 1: Someone tweeted that apparently there was like a Sherwin Williams

980
00:45:27,560 --> 00:45:30,880
Speaker 1: earnings call and someone asked about how much paint data

981
00:45:31,000 --> 00:45:33,640
Speaker 1: centers were going to need to paint their walls. I

982
00:45:33,640 --> 00:45:35,600
Speaker 1: don't know if it's true, but if it, because I

983
00:45:35,719 --> 00:45:37,759
Speaker 1: just saw the tweet. But if it's not true, it

984
00:45:37,760 --> 00:45:40,840
Speaker 1: doesn't really matter, because it is true, Like every company

985
00:45:40,920 --> 00:45:44,440
Speaker 1: is like are you doing something that could supply do

986
00:45:44,520 --> 00:45:47,400
Speaker 1: you sell some product that someone building an AI data

987
00:45:47,440 --> 00:45:51,000
Speaker 1: center is going to need at some point? But my

988
00:45:51,120 --> 00:45:53,320
Speaker 1: point about GE though, it was kind of the opposite,

989
00:45:53,360 --> 00:45:56,279
Speaker 1: which is like GE did fine on that day, but

990
00:45:56,360 --> 00:45:58,560
Speaker 1: I wish I had more exposure to GE. What I

991
00:45:58,600 --> 00:46:01,399
Speaker 1: really have is a bunch of video Microsoft exposure through

992
00:46:01,400 --> 00:46:02,160
Speaker 1: my index.

993
00:46:01,880 --> 00:46:05,200
Speaker 2: Fund, right, But you don't know how much exposure indirect

994
00:46:05,239 --> 00:46:08,200
Speaker 2: exposure you have to Microsoft through GE.

995
00:46:08,560 --> 00:46:10,480
Speaker 4: This is because this is true.

996
00:46:10,600 --> 00:46:13,600
Speaker 2: Sconda wrote that excellent column in the au Thought's newsletter

997
00:46:13,600 --> 00:46:15,959
Speaker 2: about how more and more of the economy is being

998
00:46:16,040 --> 00:46:19,560
Speaker 2: driven by AI. We actually saw on on Monday the

999
00:46:19,640 --> 00:46:23,040
Speaker 2: treasury market move a little bit, which you know, okay,

1000
00:46:23,400 --> 00:46:25,680
Speaker 2: treasuries will go up when there's a market sell off,

1001
00:46:25,680 --> 00:46:28,040
Speaker 2: but a lot of people were saying, well, this impacts

1002
00:46:28,440 --> 00:46:32,080
Speaker 2: growth expectations as well, and so that's why you're getting

1003
00:46:32,320 --> 00:46:33,000
Speaker 2: this reaction.

1004
00:46:33,560 --> 00:46:37,040
Speaker 1: No, totally, I think the degree. I mean, especially if

1005
00:46:37,160 --> 00:46:39,480
Speaker 1: you go back and like Trump announced the half a

1006
00:46:39,520 --> 00:46:42,200
Speaker 1: trillion dollar Stargate project, and I don't know what's really

1007
00:46:42,200 --> 00:46:45,719
Speaker 1: going to become of that, but like I believe the

1008
00:46:45,840 --> 00:46:51,160
Speaker 1: data centers and AI specifically through the data center channel,

1009
00:46:51,600 --> 00:46:54,960
Speaker 1: is a meaningful important, is a growing important part of

1010
00:46:55,320 --> 00:46:58,880
Speaker 1: the real economy right now. And if suddenly they're like,

1011
00:46:58,920 --> 00:47:01,560
Speaker 1: you know what this is a dead end or suddenly

1012
00:47:01,640 --> 00:47:04,080
Speaker 1: like we don't need this because we can get AGI

1013
00:47:04,520 --> 00:47:06,920
Speaker 1: so cheaply that it's just like on our laptop.

1014
00:47:07,360 --> 00:47:10,080
Speaker 4: That would raise some real econ concerns.

1015
00:47:10,360 --> 00:47:11,360
Speaker 3: Yeah, shall we leave it there?

1016
00:47:11,480 --> 00:47:12,239
Speaker 4: Let's leave it there.

1017
00:47:12,560 --> 00:47:14,120
Speaker 3: Oh yeah, this has.

1018
00:47:14,040 --> 00:47:17,400
Speaker 2: Been another episode of the Authoughts podcast. I'm Tracy Alloway.

1019
00:47:17,480 --> 00:47:19,320
Speaker 2: You can follow me at Tracy Alloway.

1020
00:47:19,400 --> 00:47:22,360
Speaker 1: And I'm Joe Wisenthal. You can follow me at the Stalwart.

1021
00:47:22,680 --> 00:47:26,480
Speaker 1: Follow our guest Marco Kolonovich, He's at Marco in and Why.

1022
00:47:26,640 --> 00:47:30,080
Speaker 1: Follow our producers Carbon Rodriguez at Carmen Arman, dash Ol

1023
00:47:30,080 --> 00:47:32,960
Speaker 1: Bennett at Dashbot, and kill Brooks at Kilbrooks. From our

1024
00:47:32,960 --> 00:47:35,680
Speaker 1: odd Lots content, go to Bloomberg dot com slash odd Lots,

1025
00:47:35,680 --> 00:47:37,880
Speaker 1: where we have transcripts of blog in the newsletter and

1026
00:47:37,920 --> 00:47:39,920
Speaker 1: you can chat about all of these topics twenty four

1027
00:47:39,960 --> 00:47:43,440
Speaker 1: seven in our discord Discord dot gg slash od Loots.

1028
00:47:43,719 --> 00:47:46,560
Speaker 2: And if you enjoy Oddlots, if you like it when

1029
00:47:46,600 --> 00:47:50,520
Speaker 2: we discuss the dark magic of equity derivatives, then please

1030
00:47:50,600 --> 00:47:54,120
Speaker 2: leave us a positive review on your favorite podcast platform.

1031
00:47:54,440 --> 00:47:57,239
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1032
00:47:57,280 --> 00:48:00,799
Speaker 2: listen to all of our episodes absolutely ad All you

1033
00:48:00,880 --> 00:48:03,360
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1034
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