Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the All Thoughts podcast.
Speaker 3: I'm Tracy Alloway and.
Speaker 4: I'm Joe Wisenthal.
Speaker 2: Joe, in your in your history of being a financial journalist,
Speaker 2: what are you like most proud of in terms of
Speaker 2: coining phrases? Oh, it gave you a hint just then,
Speaker 2: because I assume it's Mint the coin.
Speaker 1: Yeah, Oh, good one, Yes, sure, Mint the coin. That's right, Okay,
Speaker 1: thank you.
Speaker 2: Yes, So I have a few. I have China's Great
Speaker 2: Ball of Money.
Speaker 4: I like how you're like, what are you most proud of?
Speaker 1: And this is going to be my saying, so what
Speaker 1: I'm I keep going?
Speaker 3: Thank you?
Speaker 2: You called me out Europe's sovereign bank loop, although no
Speaker 2: one believes that I invented that one, and flows before pros,
Speaker 2: which has come up quite a bit on this podcast.
Speaker 2: So the idea that you know, when valuations are extremely
Speaker 2: high and everyone's buying everything no matter what the price,
Speaker 2: that it's kind of momentum that matters more than fundamentals. Today,
Speaker 2: I'm very happy to say we are going to be
Speaker 2: speaking to a pro who knows flows.
Speaker 1: As so extremely well done, Tracy, Thank you excellent setup.
Speaker 1: Of course I appreciate all of the tracy neologisms. Man,
Speaker 1: what a time in the market to we talk about flows?
Speaker 1: Do we talk about momentum, etc. I feel like I
Speaker 1: you know, it's always chaotic, it's always uncertain, you'll never
Speaker 1: get an answer. But man, things feel really noisy right now.
Speaker 2: They feel super noisy. So we are recording this on
Speaker 2: January thirtieth. It is a week that has seen a
Speaker 2: very sharp sell off in tech stocks thanks to anxiety
Speaker 2: over deep seak coming out of China. We're going to
Speaker 2: talk about that and more generally, we're just going to
Speaker 2: talk about what's going on in the market right now,
Speaker 2: how investors might be handling it, and how the market
Speaker 2: structure might have changed over the years. And as I said,
Speaker 2: I'm very excited because we do have the perfect guests,
Speaker 2: the pro who knows his flows. He has a lot
Speaker 2: of nicknames, actually Gandalf being one of them as well.
Speaker 2: Actually I didn't realize one of our colleagues at Bloomberg
Speaker 2: kind of coined that name for him. But of course
Speaker 2: we are speaking with Marko Kolonovich. He is JP Morgan's
Speaker 2: former chief Global market strategist and co head of Global Research,
Speaker 2: and now he's with us to talk about the market.
Speaker 2: And Marco, thank you so much for coming on.
Speaker 5: O thoughts, thank you so much for having me.
Speaker 2: I'm very excited. I know I've said that three times now,
Speaker 2: but I guess we should start with the recent selloff,
Speaker 2: like the deep Seek, deep stock selloff. They are all
Speaker 2: these superlatives that you can use to describe Monday's action,
Speaker 2: like the biggest single stock plunge in history in the
Speaker 2: form of Nvidia, and eight of the top ten biggest
Speaker 2: one day drops in the S and P five hundred,
Speaker 2: et cetera, et cetera. And actually, as we're recording this
Speaker 2: on January thirtieth, Microsoft is down six percent after earning,
Speaker 2: so maybe the tech selloff isn't over yet. But one
Speaker 2: of the interesting things about this week is there hasn't
Speaker 2: really been broad contagion. So most stocks in the S
Speaker 2: and P five hundred were kind of like, meh, we
Speaker 2: don't care. When would you expect some of the anxiety
Speaker 2: over Deep Seek to maybe start having a bigger impact
Speaker 2: on the broader market.
Speaker 6: So, you know, as you said, there was not much contagion,
Speaker 6: you know, and if you look at the different stocks
Speaker 6: in SMP. Many of them were actually up, you know,
Speaker 6: and many even in the tech sectors sector were up.
Speaker 6: You know, if you look for instance, you know, Facebook
Speaker 6: yesterday or today, or a bunch of other names that
Speaker 6: sort of were perceived that they might be sort of
Speaker 6: benefiting from, you know, the sort of open architecture, you know,
Speaker 6: type of things that can come at the cheaper price,
Speaker 6: you know, and can be still you know, implemented in
Speaker 6: their business model when it comes to AI models.
Speaker 5: So it was early contained.
Speaker 7: You know.
Speaker 6: I'm a little bit surprised just because there were like
Speaker 6: three or four names that really got hammered, you know,
Speaker 6: and that can only be explained with not just with
Speaker 6: a panic, but some of the sort of forced selling,
Speaker 6: you know, maybe coming from options. You know, if you
Speaker 6: are you know, if you're selling in a video puts
Speaker 6: for the past few years, you could make a good
Speaker 6: living out of it. But then you know, you'll have
Speaker 6: a day like we saw this weekend, and basically you
Speaker 6: might get you know, forced out of these positions and
Speaker 6: maybe have a catastrophic loss.
Speaker 5: So it was fairly limited.
Speaker 6: I'm a little bit surprised, you know, just just because
Speaker 6: we didn't really have a meaningful sales since last summer,
Speaker 6: you know, sort of when at the back of the
Speaker 6: Bank of Japan, you know, so, so I do think
Speaker 6: we will we will see one. Perhaps it's a little
Speaker 6: bit too early in the year. There's still quite a
Speaker 6: bit of an optimism post election. There's a little bit
Speaker 6: of seasonality in January. People put money to work, they
Speaker 6: get paid, you know, they allocated capital. So maybe it's
Speaker 6: a little bit too early. I was somewhat inclined to
Speaker 6: see that we will see a bit more, you know.
Speaker 6: At the back of it, it's perhaps not over. We
Speaker 6: still have a few important earnings to come, so remains
Speaker 6: to be seen sort of what happens, you know, maybe
Speaker 6: another week of earnings, you know, whether there is any
Speaker 6: any sort of follow through, you know, but I do
Speaker 6: think that it's gonna be some investors will burn clearly,
Speaker 6: and a little bit of a tarnish on the sort
Speaker 6: of this thesis that some of these stocks like my video,
Speaker 6: just go up, you know every day you can't lose,
Speaker 6: you know, Like so I think people will think twice
Speaker 6: if something can drop like twenty percent in a day,
Speaker 6: you got to also think of it what it does
Speaker 6: to you to your risk and.
Speaker 1: Well so Tracy asked about contagion and the anxiety spreading
Speaker 1: across the market. But I guess I would flip the question,
Speaker 1: which is, as I've said before on the podcast, I'm
Speaker 1: a boring index investor. So like I look at a
Speaker 1: random American company that is like General Electric, it's doing fine.
Speaker 1: I'm not very exposed to General Electric because they're a
Speaker 1: small part of the index. I am very exposed to
Speaker 1: in Video and Microsoft and so forth. We did a
Speaker 1: recent episode about market concentration, but I'm sort of like
Speaker 1: curious your take on this fact that, like so many
Speaker 1: of these flows, they go into broad market indexes, but
Speaker 1: we're really all very exposed to a few concentrated market pads.
Speaker 6: Yeah, so the concentration is the highest, you know, sort
Speaker 6: sixties or seventies, so we're looking in fifty years half
Speaker 6: of the century history and concentration is sort of at
Speaker 6: the highest point. It's been a while for staying there
Speaker 6: at this level, like maybe past past year. So it's
Speaker 6: a weird market. You know, this concentration came for two reasons.
Speaker 6: You know, one is clearly thematic investing in technology. Then
Speaker 6: you also have investing in a large company. You have
Speaker 6: a theme of momentum sort of that is basically self fulfilling.
Speaker 6: You know, more something goes up, more money to tracts,
Speaker 6: becomes bigger an index, you know, all the passive flows
Speaker 6: into it. So there's a technical aspect, there's thematic aspect.
Speaker 6: There's even geopolitical aspect. A lot of money went outside
Speaker 6: of the other parts of the world. Europe is doing
Speaker 6: worse when it comes to sort of economy. China has
Speaker 6: been We've been a sort of with the brink of
Speaker 6: this Cold War with China, so money has has left there.
Speaker 6: You know, Latime has its own share of sort of issues,
Speaker 6: so money has been also geopolitically moving area. So it's
Speaker 6: a moving in the US, it's a moving in the indices,
Speaker 6: it's a moving in the tech you know, and then
Speaker 6: you end up with these give or take ten stocks
Speaker 6: that that really sucked up all the all the capital
Speaker 6: and evaluations got got very very high. Now, you know,
Speaker 6: tech investors, they do have a sort of their rationalization,
Speaker 6: So what's going to happen in the future. These things
Speaker 6: just grow and grow and grow. And that's when we
Speaker 6: when we saw with the deep Seek, we saw a
Speaker 6: little bit of a dent in that in that thesis.
Speaker 6: But these stocks didn't go up for the sake of
Speaker 6: for the thesis. They wound up some of these these
Speaker 6: these other flows, you know, so unprecedented concentration is not
Speaker 6: going to stay there.
Speaker 5: You know.
Speaker 6: The big question is when will we see that rebalance.
Speaker 6: Do we need to see you know, some cyclical downturn
Speaker 6: first to purge and to normalize some of these valuations,
Speaker 6: you know, because historically these pees were never this high.
Speaker 6: I mean, in two thousand they were this high, and
Speaker 6: we know how it then, you know, but a lot
Speaker 6: of people got burn myself include that with some of
Speaker 6: the we were more negative last year, you know. And basically,
Speaker 6: you know, market has this tremendous momentum, so the timing
Speaker 6: is is going to be challenge.
Speaker 2: What does it take to I guess turn momentum at
Speaker 2: this point, Like what what are the catalysts that actually
Speaker 2: work here? Because it does feel looking at the market
Speaker 2: so much of it is now technical or systematic in
Speaker 2: some way. There's a lot of options selling, as you mentioned,
Speaker 2: lots of multi strat funds that basically you know, just
Speaker 2: have to sell or buy to rebalance their exposure. What
Speaker 2: actually changes directions, like how do you get enough to
Speaker 2: change a trend.
Speaker 6: So, you know, so there's a technical aspect, sort of
Speaker 6: the mechanical aspect of it, and there's a sort of
Speaker 6: catalyst or more funda mental angle of things, you know.
Speaker 6: So to get things to start moving or to stall,
Speaker 6: you usually do need to have some funda mental driver
Speaker 6: of it. So perhaps there is concern about economy slowing down,
Speaker 6: you know, perhaps there is a concern about something geopolitically,
Speaker 6: maybe trade war with China or some sort of blockade
Speaker 6: of Taiwan's trads or something like that. Right, so first
Speaker 6: you need to have a little bit of a catalyst,
Speaker 6: you know. But if the market is technically very strong,
Speaker 6: the catalyst is not going to change the momentum. So
Speaker 6: what that means is, you know, more specifically. You know,
Speaker 6: so when you look at the trend investors, you know,
Speaker 6: they have a range of signals, you know, like so
Speaker 6: they can look at a one month price momentum, three
Speaker 6: month price momentum, six month pricements, or twelve month price momentum,
Speaker 6: maybe eighteen months, but that's about it, you know, And
Speaker 6: there's some very short term momentum players that look intra
Speaker 6: day or on a daily basis but most of these
Speaker 6: signals are concentrated around twelve months and two hundred day
Speaker 6: moving averages, right. So that's why when people look at
Speaker 6: the two hundred mo average, they first when I, you know,
Speaker 6: twenty years ago, when someone told me, I said, like,
Speaker 6: what is this magic?
Speaker 5: Why would this work? Right?
Speaker 6: But it's reality is that many models, you know, systematic models,
Speaker 6: primarily you know computer DRIM, but also psychological investors look
Speaker 6: at these things and become self fulfilling. So you need
Speaker 6: to actually come close enough to these levels to break them.
Speaker 5: Right.
Speaker 6: So for instance, earlier this week SMP got below around
Speaker 6: six thousand or a bit below. We were close to
Speaker 6: breaking twenty day moving average and fifty day moving average,
Speaker 6: so you can think of it as about one month
Speaker 6: and three month price momentum. So that's about the third
Speaker 6: of a signal. The big signal is really twelve month
Speaker 6: you know, or two hundred day moving average, right. But
Speaker 6: when you start moving things you can actually unravel. It's
Speaker 6: like a little bit like a snowball. So I thought like,
Speaker 6: if the market is going to stay below twenty and
Speaker 6: fifty days at the end of the day, you may
Speaker 6: get enough selling from CTAs or de risking from cts
Speaker 6: that they may get you to another leg lower right,
Speaker 6: So it's basically you need to have set up that
Speaker 6: you're close enough to these triggers on the downside to
Speaker 6: move it. And again I think this week we got
Speaker 6: very close. But there was also other flows like rotation.
Speaker 6: You saw like selling on a video, but Apple and
Speaker 6: Metal went up, you know, like so at the end
Speaker 6: now it was like drop but did not drop a lot, Joe.
Speaker 2: It kind of reminds me of like structured credit notes,
Speaker 2: where there's that knockoup knockout level and then you get
Speaker 2: this massive cliff risk and the whole thing kind of unwindes.
Speaker 1: Right, you have all these rules based investors and something
Speaker 1: happens like the model a system sell It is worth
Speaker 1: noting that as we are talking ten nineteen am January thirtieth,
Speaker 1: and Video is basically right at its two hundred day
Speaker 1: moving average. I know this isn't the broader market, but
Speaker 1: it's a big part of the broader market, so yeah,
Speaker 1: it might as it might as well.
Speaker 2: Be.
Speaker 1: One thing that struck me on Monday, which I was
Speaker 1: a bit surprised about in the selloff is that even
Speaker 1: on Monday you mentioned that Meta actually closed Green and
Speaker 1: they are a maker of a competitor to deep seek.
Speaker 1: They have their open source Lama, but they're seen by
Speaker 1: the market as a consumer of AI services because they
Speaker 1: don't sell their product. They use it to do things
Speaker 1: like better ad targeting, et cetera. Were you surprised that
Speaker 1: even on this day in which there is this sort
Speaker 1: of exogenous shock to the market, everyone wakes up to
Speaker 1: some new thing that actually investors showed a fair amount
Speaker 1: of discrimination in terms of what they don't.
Speaker 6: Yeah, so I mean that was that was against you know,
Speaker 6: January sentiment is still pretty positive. Economic data are strong,
Speaker 6: so people are saying, okay, this is not the beginning
Speaker 6: of economic downturn. This is isolated sort of event whereby
Speaker 6: some companies will get hit, you know, their sort of
Speaker 6: revenues will get hit yea, and some will be able
Speaker 6: to do things for cheaper you know, like so we
Speaker 6: had the salesforce I believe as well. And so it
Speaker 6: ended up not a macro day but more of a
Speaker 6: rotational day.
Speaker 7: You know.
Speaker 6: There is also a so called quant factors where you know,
Speaker 6: even within technology, some stocks are higher multiples, some stocks
Speaker 6: are lower multiple. So you know, some stocks are more
Speaker 6: momentum less momentum, you know. Like, so there was a
Speaker 6: bit of rotation. So Apple, which was a lagger, it
Speaker 6: also kind of quite a bit, although I don't think
Speaker 6: there was much fundamental stop going on for Apple. It
Speaker 6: was probably just rotation. So so market kind of held up,
Speaker 6: you know, and I was sort of at defense whether
Speaker 6: whether these like technical levels twenty fifty will will get
Speaker 6: broken and will go lower or not. We didn't, you know,
Speaker 6: but I do think that sort of you know, evaluation positioning,
Speaker 6: and some of these technicals are a bit stretched, you know,
Speaker 6: like so I don't have I don't see like a
Speaker 6: huge huge ob site from the market. So maybe I'm
Speaker 6: switching the topic a little bit.
Speaker 4: But no, No, that makes sense.
Speaker 2: Can I ask you a sort of procedural question, which is,
Speaker 2: you know, you were at JP Morgan for twenty years,
Speaker 2: and you were in the industry even before that. How
Speaker 2: did your sort of research and forecasting process change throughout
Speaker 2: those years?
Speaker 6: No, thank you, And it did change, and it's and
Speaker 6: it's an interesting good question, you know. So, so I
Speaker 6: got my PGE in physics, in theoretical physics, so there
Speaker 6: was a lot of coding, There was a lot of modeling.
Speaker 6: There was a lot of sort of trying to understand
Speaker 6: why one thing leads to another. You know, what is
Speaker 6: the cause, what is a concept? What's causality? You know,
Speaker 6: and what's the noise? What's statistically important, what's statistically not important?
Speaker 2: You know.
Speaker 6: Like so, so in physics you build these type of models.
Speaker 6: You try to understand what's significant, what's not, what you
Speaker 6: can neglect, which factors you can you have to take
Speaker 6: into account, and most important, how to simplify the complex.
Speaker 6: You know, market is an extremely complex system, you know,
Speaker 6: in many physical systems, so you need to sort of
Speaker 6: move the noise on one side and drivers on the
Speaker 6: other side, and try to recognize those patterns, right like So,
Speaker 6: although I really never used any formula from the physics
Speaker 6: in my almost never really in the finance, but the
Speaker 6: way of thinking is similar. So I started Merrilynch in derivatives,
Speaker 6: in derivatives research where I started looking, you know, interesting
Speaker 6: that we are now in earning seasons. So my first
Speaker 6: models were impact sort of earnings on a stock price
Speaker 6: and what can you read from options market?
Speaker 5: You know.
Speaker 6: Like so I published some papers, you know, come up
Speaker 6: with some formal as, and we were kind of backing out, okay,
Speaker 6: what the option market is saying, and then we go
Speaker 6: to analysts and say, hey, do you think it makes
Speaker 6: sense or doesn't make sense?
Speaker 5: Right?
Speaker 6: And then if you if you think that options are
Speaker 6: saying too much of a or too little of a movie,
Speaker 6: you could trade these options and stuff like that. So
Speaker 6: that was one example of okay, how do you sort
Speaker 6: of you know, you have a catalyst, You look at
Speaker 6: different markets, you see are these markets aligned? You put
Speaker 6: some model together, and then you find the discrepancy with
Speaker 6: the model. It's not always going to work, but if
Speaker 6: you do it for one hundred stocks, maybe an average
Speaker 6: and a portfolio level, you'll you'll be fine, you know.
Speaker 6: Like so so so that the relative research and quant research.
Speaker 6: I did a lot of quantity research. So you try
Speaker 6: to process the data, you try to look at the measures.
Speaker 6: And at that time, like you know, twenty twenty five
Speaker 6: years ago, it was beginning really of trading of vicks
Speaker 6: of volatility, swaps of correlation or dispersion. You know, like
Speaker 6: so you kind of try to see, okay, you know,
Speaker 6: you look at the vix you look at the market volatility.
Speaker 6: What's driving market volatility? While people say, well, it's a
Speaker 6: panic or it's not. But let's be more quantitative. You know,
Speaker 6: you can how correlated stocks are, what individual volatility of
Speaker 6: each stock is, you know, which part is due to
Speaker 6: the macro factor the market, what's idiosyncratic, So you can
Speaker 6: kind of break these down. Then you can look at
Speaker 6: the sector correlation between sector, what's crushed within sector, So
Speaker 6: you can kind of quantify these things and analyze and
Speaker 6: get some insight, you know, like two thousand and eight,
Speaker 6: for instance, we look at the tw thousand and seven
Speaker 6: thousand and eight, I look at how the hedging of
Speaker 6: options impacts the market, you know, like so you basically
Speaker 6: need to look at how many options are out there
Speaker 6: in index. Let's say you try to assess what's the
Speaker 6: positioning from from the flows from the sort of no
Speaker 6: knowing of industry hedging flows, and then you see, okay,
Speaker 6: what are the hedging requirements at the end of the
Speaker 6: day and two thousand and eight and then twenty eleven,
Speaker 6: and like a low hanging fruit, you could see sometimes
Speaker 6: these flows would be bigger than market can absorb, you know,
Speaker 6: and they would all go stay away from level ETFs
Speaker 6: from options, you know, so you see like, okay, there's
Speaker 6: like twenty billion dollars to sell and market can't absorb it.
Speaker 6: So you know, market towards last ten minutes will drop.
Speaker 6: And that's you mentioned sort of Ganda. That's where where
Speaker 6: some of these things because people, you know, if you
Speaker 6: look from the outside, you say, oh, how can he
Speaker 6: get that?
Speaker 7: Right?
Speaker 6: You know, and and but it is really understanding a
Speaker 6: bit of technicalities, which is flows, option, convexities, liquidity and
Speaker 6: how they how they sort of interfere. But you know,
Speaker 6: after twenty fifteen sixteen, people figure it out, you know,
Speaker 6: and then people put it in their models.
Speaker 5: They create like.
Speaker 3: A structured product of twenty eighteen.
Speaker 2: Everyone sort of woke up to the vis especially right
Speaker 2: Oh yeah, so that's.
Speaker 6: A February em again and yeah, and you know stuff
Speaker 6: like that. So you kind of analyze causes and consequences
Speaker 6: in the market, something that is new that's not been
Speaker 6: yet a look at, you know, and I have focused
Speaker 6: on things that we're new in the market, like products, options, futures, CTAs,
Speaker 6: those type of things.
Speaker 1: You know, this it's not particularly like technical. But another
Speaker 1: thing that seems to be true about the market right
Speaker 1: now is, at least maybe up until week ago, and
Speaker 1: there's just an incredible amount of optimism in any sort
Speaker 1: of measure. So if you ask, you know, there's consumer measures,
Speaker 1: do you think stocks are going to be higher in
Speaker 1: a year from now?
Speaker 4: Very high levels?
Speaker 1: If you look at things like the Bank of America,
Speaker 1: cell side analyst sentiment very close to euphoric levels. If
Speaker 1: you look at fund cash levels extremely low right now,
Speaker 1: very over everyone is overweight long tech. How do you
Speaker 1: ingest this information because on the one hand, you say, oh,
Speaker 1: everyone's all in, this is negative. On the other hand,
Speaker 1: people have been very optimistic for a while. How should
Speaker 1: we as consumers of this information think about what it
Speaker 1: says about the fragility of the ball market.
Speaker 6: So you know, clearly in the markets things are mean reverting,
Speaker 6: you know, Like so when think reached some very high levels,
Speaker 6: you know eventually they will.
Speaker 4: Gone a long time, Yeah, exactly.
Speaker 6: So, so so there is this mean reversion, But there's
Speaker 6: also trend, right you know, Like so you know, figuring
Speaker 6: out the timing of that is hard, right, you know,
Speaker 6: Like I mean if there is a sort of a
Speaker 6: limited set of drivers, like in some of these technical markets,
Speaker 6: you know, so for instance, ctias, you know, you know,
Speaker 6: once when the all the levels are positive, you know,
Speaker 6: all the singles are positive, and then volatility drops a
Speaker 6: little bit. You know, their mac stout, you know, so
Speaker 6: you know they're not going to buy more, right, you know,
Speaker 6: like and so you can you that's a that's a
Speaker 6: self contained, isolated system where you can say, okay, optimism
Speaker 6: is too high. So there's the only downside, right with
Speaker 6: the with the entirety of market, right you know, with
Speaker 6: you know, you have a crypto, you have a fiscal measures,
Speaker 6: you have like monetary stimulus, you have a sentiment shift.
Speaker 6: It's hard to handicap all of those, you know. So
Speaker 6: it's hard to say that for all of the investors
Speaker 6: to be able to know exactly when this thing is
Speaker 6: going to stall, right, sure, and and and there are
Speaker 6: developments that is hard, like you know, this whole AI,
Speaker 6: you know, And I see the I wrote a book
Speaker 6: on twenty seventeen about AI with my colleague Rejaesh twenty eighteen.
Speaker 6: So we were early on on AI. And you know,
Speaker 6: six years ago, right, nobody was talking really about it
Speaker 6: at time. So it's not that I don't understand it,
Speaker 6: but I'm a little bit cynical about it now. I
Speaker 6: think it's it's too hyped up, right, But it's hard
Speaker 6: to assess how long people.
Speaker 5: Will be excited about it, right, you know?
Speaker 6: And yeah, so and then you have a change, So
Speaker 6: you have political changes that can bring deregulation that can
Speaker 6: be change in tax regimes, so you have like a
Speaker 6: wild card.
Speaker 5: So it's hard.
Speaker 6: Your question I started like, there is a reversion always,
Speaker 6: but where you're going to pick it, it can be very
Speaker 6: frustrating and very sort of you can be wrong for
Speaker 6: a long time.
Speaker 2: Wait, can I just press you on that point about AI?
Speaker 2: Because I think I think the difficulty that investors are
Speaker 2: having is AI has a great story right now, and
Speaker 2: there's this idea out there that it's this revolutionary technology
Speaker 2: that's going to change the world. Joe keeps referring to
Speaker 2: it as inventing God when it comes to AGI at least.
Speaker 2: And at the same time, there is also a feeling
Speaker 2: that people are maybe getting a little too optimistic about it.
Speaker 2: There's too much hype in the market. You've started seeing
Speaker 2: companies that you know, just put out a press release going, oh,
Speaker 2: we're looking into AI, and their stock price goes up.
Speaker 2: How should investors handle their exposure to AI? Like, how
Speaker 2: do you actually play it at this point in time,
Speaker 2: given that you were early to the topic.
Speaker 4: And now cynical.
Speaker 6: You know, I look at it from theoretical side. I
Speaker 6: look at it more how to apply it sort of
Speaker 6: in finance, in quantitative trading. You know, how to use
Speaker 6: a large language model to assess the sentiment, changes in
Speaker 6: sentiment and those type of things, right, so you know
Speaker 6: how to read quickly things and summarize them and drive
Speaker 6: some signals out of it. So there is obviously bigger
Speaker 6: question of AI, you know, which you said, it's kind
Speaker 6: of philosophical questions like are we going to be replaced?
Speaker 6: You know, at which point what's going to be a
Speaker 6: role of human once? When when we can you know,
Speaker 6: kind of break down our way of thinking and effectively
Speaker 6: training and replace it. You know, So their whole host
Speaker 6: of other questions, you know, Like so, I'm not skeptical
Speaker 6: that this is going to be hugely important, and it
Speaker 6: is a hugely important you know, it's not very very
Speaker 6: different of what people have been doing, you know, five
Speaker 6: years ago or ten years ago or twenty years ago. Obviously,
Speaker 6: big progress in computing power, big progress in the models
Speaker 6: as well, you know, Like so, so it's it's but
Speaker 6: I see it like more as an evolution, you know,
Speaker 6: than something that changed with che GDP in twenty twenty three,
Speaker 6: like two years ago, as a kind of like a
Speaker 6: step function. I see it as an evolution. Always important,
Speaker 6: right like ten years ago, when we use our smartphone
Speaker 6: to take a pictures, like you know, camera would recognize
Speaker 6: the face, it would zoom into face, it would kind
Speaker 6: of do the proper focus and stuff like that. So
Speaker 6: that's also you know, that's also AI and and things
Speaker 6: are advancing, right, and we'll keep on advancing. Now, question
Speaker 6: is going to be winners losers? How to monetize? You know,
Speaker 6: does that suddenly re rates all of equity market multiple?
Speaker 6: You know, like suddenly, okay, people are not gonna work.
Speaker 6: These companies go to all the work, so we're just
Speaker 6: gonna value them. Like who am I to say that?
Speaker 6: And also who am I to say that that's wrong
Speaker 6: as well? You know, but there's a lot of speculation
Speaker 6: and a trust. You know, people often tell me, well,
Speaker 6: imagine just how my way to search Internet has changed,
Speaker 6: you know, like like okay, like we were searching Internet
Speaker 6: for twenty five years the same way I used to
Speaker 6: use like a Netscape like twenty five years ago, right,
Speaker 6: and and the same thing you type in a bar
Speaker 6: and you and you find something. So for christ sake,
Speaker 6: of course it's going to change. Of course, at some
Speaker 6: point it's gonna be we're gonna tell something to computer.
Speaker 6: Computer will have its own ways of parsing and finding
Speaker 6: what's relevant and giving us back information. So I'm not
Speaker 6: as excited about that change. I think it's the way
Speaker 6: over you change, you know, like, but there's a lot
Speaker 6: of optimism.
Speaker 1: Now there's Well, I was wondering because I was looking
Speaker 1: at your LinkedIn and you mentioned you have a PhD
Speaker 1: in physics graduating from NYU in two thousand and three,
Speaker 1: theoretical high energy physics, cosmology, string theory, and finance. I
Speaker 1: guess the two part question. A would do you think
Speaker 1: there's a world in which if you graduated today you
Speaker 1: would have gone into AI instead of going into finance,
Speaker 1: because I imagine they would have hired you at those skills.
Speaker 4: But B, like when you.
Speaker 1: Think about and Tracy mentioned, you know, like the true AGI,
Speaker 1: do you think that the current AI research is on
Speaker 1: a path to that sort of AGI inventing God that
Speaker 1: a lot of the proponents believe, So you.
Speaker 6: Know, I think eventually it will get there in a
Speaker 6: sense that it will sort of address some very important,
Speaker 6: you know questions which are kind of deeply what every
Speaker 6: person fears or wonders or or sort of seeks, you know,
Speaker 6: kind of meaning of our lives, like you know, you know,
Speaker 6: future after we die and stuff like that. So they're
Speaker 6: they're definitely interesting, interesting things there that can be done.
Speaker 6: I mean, people are doing with these like assistants, right
Speaker 6: like you train and I believe really this AI will
Speaker 6: have to be a lot more personalized, you know, like
Speaker 6: so you will train it really on your life experience,
Speaker 6: you know, like so if AI can see every image
Speaker 6: I saw, if it can read every email, you know,
Speaker 6: I believe AI will be able to tell me when
Speaker 6: did I make a mistake? When should I do something different?
Speaker 6: You know, did you overreact in this live situation?
Speaker 5: Did you not right? And going further?
Speaker 6: Right like that will stay and and my my kids
Speaker 6: can after I pass away, they can say, hey, what
Speaker 6: would that say in this situation? Right, you know, like
Speaker 6: what would maybe I'll be able to in some way
Speaker 6: talk to them, right, so you'll blur all these things
Speaker 6: which were which were sort of not blurred in the past, right,
Speaker 6: you know. Also, you you'll start having these like very
Speaker 6: very interest developments, you know, but I would kind of
Speaker 6: not look at them from the sort of P and
Speaker 6: L perspective, earnings perspective. There's also going to be a
Speaker 6: lot of issues as we have already now. I mean,
Speaker 6: sometimes I can give you wrong answer. Sometimes it can
Speaker 6: be used to do bad things, to impersonate, to deceive,
Speaker 6: to manipulate. So there's gonna be a lot of a
Speaker 6: lot of interesting I would say, philosophical issues, you know,
Speaker 6: technological issues and investing investing issues. But I just don't
Speaker 6: think it's going to be as simple as like seven
Speaker 6: companies are gonna have P of fifty and everyone else
Speaker 6: will have P of ten and it's going to persist
Speaker 6: that way. Yeah, I don't think it's going to be
Speaker 6: like that in finance, at least him.
Speaker 2: The other thing I wanted to ask you is, you
Speaker 2: know you left JP Morgan in July and then pretty
Speaker 2: much a month later we had a very sharp sell off.
Speaker 2: When you look back at that particular sell off, you know,
Speaker 2: we never got to hear from you your thoughts on that.
Speaker 2: On that particular week in markets, what did you actually
Speaker 2: see and observe there because there are still differing opinions
Speaker 2: out there about what the proximate catalyst was for some
Speaker 2: of the moves and what was exacerbating what.
Speaker 6: No, So the catalyst was definitely moving rates related to
Speaker 6: Japan in the currency, right, that was a sort of catalyst.
Speaker 6: But you always have like a spark and a bucket
Speaker 6: of fuel, right, and the bucket of fuel will stretched
Speaker 6: CTAs stretched vault targeter systematic investors, too much optimism, you know,
Speaker 6: and then you start basically hitting the stops across these strategies, right,
Speaker 6: ctias hit their cell signals. Volt target is VIX goes up.
Speaker 6: What goes up they need to sell, you know, if
Speaker 6: you're selling puts on AI names, you suddenly to you know,
Speaker 6: you need to kind of close. So so VIX was
Speaker 6: very vixed. Behave most phenomenals. So it was a whole
Speaker 6: lot of all short wall covering as well, you know.
Speaker 6: But again I think it was what was missing for
Speaker 6: this to be the turn in the cycle was I
Speaker 6: guess you know, GDP employment still fine, right, still hope
Speaker 6: that FED is going to cut, right, you.
Speaker 5: Know, So it didn't. It didn't.
Speaker 6: There was a little bit of conflagration but didn't kind
Speaker 6: of burn everything down right, So it was a little
Speaker 6: bit of satisfaction. But in too long it is.
Speaker 1: Pretty markable because even we got a little sell off,
Speaker 1: but it's a very minor sell off. Or basically, the
Speaker 1: stock market is more or less at all time highs.
Speaker 1: This is despite a pretty big repricing of the expectation
Speaker 1: of the short end of the curve, where people were
Speaker 1: expecting deep cuts to continue through last year and to
Speaker 1: this year. We might not get any cut this year,
Speaker 1: and yet still the market is close to.
Speaker 4: All time highs.
Speaker 1: It must be nice on some level to be out
Speaker 1: of the game of having to come up with an
Speaker 1: end of your price, because that sounds like a job
Speaker 1: I would never want to take. But I also wonder,
Speaker 1: you know, do you wake up in the morning, it's
Speaker 1: still like your time to talk to us about you
Speaker 1: know what you have a market outlook for right now?
Speaker 1: Like give us some give us what's on your mind?
Speaker 6: Sure like so, so look, it's nice once in a
Speaker 6: while that you can be somewhere away and not look
Speaker 6: at the fact parst every single world word. Although I
Speaker 6: did it yesterday, you know, but a few months ago
Speaker 6: I didn't you know, like, so it's nice to make
Speaker 6: a break ord. Maybe it's necessary, you know, out markets
Speaker 6: are a little bit of a sort of compulsion thing
Speaker 6: of compulsion you when you feel like you need to
Speaker 6: understand what's going on in the world. You know, So
Speaker 6: I think it becomes part of your DNA if you
Speaker 6: do it for a long time. So so I do
Speaker 6: always think, and I do have an outcome, so give
Speaker 6: us so you know, you know, on a sales side,
Speaker 6: you kind of need to put a price target. And
Speaker 6: I and I think it's a kind of poor way
Speaker 6: to summarize everything into one number. It's basically almost you're
Speaker 6: telling you're trying to focus probabilities in the world, you know,
Speaker 6: because world real world actually works in terms of physics
Speaker 6: deep deeply works in terms of probabilities, not just superficially,
Speaker 6: you know, in a quantum physics. So you you need
Speaker 6: to sort of have a sort of hype proabailistic view
Speaker 6: and you're forced to have one view like one hundred
Speaker 6: percent or nothing. Right, So so it gets over simplifies.
Speaker 6: I think media, you know, and not referring to you,
Speaker 6: but media does a bad job. They say, oh, what's
Speaker 6: your price?
Speaker 5: Starty?
Speaker 6: They just want to talk about that, and they say, oh,
Speaker 6: you're right, you're right. Yeah, so so no, so I
Speaker 6: would say, like, you know, if if I can move
Speaker 6: away from price, sorry, I do think we'll go back
Speaker 6: down in five thousands this year. Sometimes I think at
Speaker 6: that point in time, we will we will see whether
Speaker 6: the cycle is still strong or it's not. You know,
Speaker 6: I think we need to see the whole new political
Speaker 6: climate whether it will lead to turmoil, and I believe
Speaker 6: more likely than not it will, you know. Like so
Speaker 6: those things I think will get us lower, right, you
Speaker 6: know at that time, whether whether it becomes an end
Speaker 6: of a cycle and we go much lower into four thousand,
Speaker 6: that I don't know. I think there's some probability of that,
Speaker 6: you know. And then conversely, on the upside, is everything
Speaker 6: goes if really this is what they call it Golden age,
Speaker 6: the Golden age of America, you know, then market will
Speaker 6: stay in six thousands, it can go a bit higher.
Speaker 6: I just see, I'm hard pressed to see it going
Speaker 6: much much higher, right because evaluations are there, Positioning is
Speaker 6: already there. As you said, FED is not cutting, right,
Speaker 6: So it's a little bit of a chicken and egg.
Speaker 6: I mean, I have been scratching my head, like at
Speaker 6: these level of rates, which I do think is that
Speaker 6: are restricted rates for now now more than two years
Speaker 6: with the commercial real estate here and there, we saw
Speaker 6: a few hiccups, you know, like, but you know, I
Speaker 6: do think that is sort of under the hood of
Speaker 6: economy some damage is being sort of built up and done.
Speaker 6: So so I don't think like market really you know,
Speaker 6: going to seven thousand or you know, sixty eight hundred
Speaker 6: or something like that. So I would say, maybe it
Speaker 6: can go sixty five, stay range bound, you know, Like,
Speaker 6: so I would sort of formulate the view in terms of, okay,
Speaker 6: you perhaps want to sell ups, give yourself a little
Speaker 6: bit of a room for some more excitement first few
Speaker 6: years of the of the first few months of the year,
Speaker 6: but then also be ready to assess once when you
Speaker 6: go in, you know, let's say fifty five hundred or
Speaker 6: fifty seven hundred, to assess is the cycle potentially ending,
Speaker 6: you know, or that's going to be buying opportunity. And
Speaker 6: I wouldn't want to sort of say, hey, like it's
Speaker 6: going to first stay at sixty one hundred, then it's
Speaker 6: going to pull to fifty five, and then you buy
Speaker 6: with both hands at fifty five and like that'll be
Speaker 6: too predictive, you know, but I think some variation of
Speaker 6: that path will be whereby sort of the depth of
Speaker 6: a pullback will depends on trade war China, domestic political
Speaker 6: situation rates, and like one off things.
Speaker 7: Like we had a Monday.
Speaker 2: I'm glad you mentioned domestic politics because one of the
Speaker 2: other weird things about this week when we had the
Speaker 2: deep seek cell off was everyone was focused on that,
Speaker 2: and you know, tech stalks went down, as we mentioned,
Speaker 2: But then on Tuesday everything started rebounding. Even though we
Speaker 2: had headlines coming out of the White House about cutting
Speaker 2: what amounted to a pretty big chunk of federal spending,
Speaker 2: the entire market seemed to look through that, which is
Speaker 2: kind of ironic because one of the things we've heard
Speaker 2: for the past four years or so is this idea
Speaker 2: that you know, deficit spending is driving the entireomy and
Speaker 2: stuff like that. I feel like political risk is one
Speaker 2: of those things that investors really struggle to price in
Speaker 2: because there's so much uncertainty. A lot of it seems
Speaker 2: very binary. How do you deal with that?
Speaker 6: So you need to sort of, you know, put some scenarios,
Speaker 6: you know, what can happen in terms of taxes, regulation,
Speaker 6: you know, tariffs, trade wars, geopolitical conflict you know, and
Speaker 6: then see what can how can they impact specific stocks
Speaker 6: and industries, countries and maybe overall market sentiment, you know,
Speaker 6: and maybe put some scenarios. That's the kind of a blueprint.
Speaker 6: And market never goes by that blueprint, but at least
Speaker 6: gives you some framework to try to understand if it
Speaker 6: doesn't go by your sort of assessment, what have you
Speaker 6: missed and what you need to what else you need
Speaker 6: to take into count but you put some blueprints sort
Speaker 6: of what can happen? So you know, I think you
Speaker 6: pointed very well. He was talking about Navidia, Taiwan export sortiz.
Speaker 6: Like so those type of things, right. So market that's
Speaker 6: then market has its minds of its own, which is
Speaker 6: tied to sentiment, you know, and it's tied to momentum.
Speaker 7: You know.
Speaker 6: Most people think momentum, you know, They don't calculate by
Speaker 6: the thing. They just feel good about market. They see
Speaker 6: good news about market. They their taxi driver or friend
Speaker 6: or family feels good about investing, right and and they
Speaker 6: choose them to ignore, you know, Like so on Monday,
Speaker 6: I was watching CNBC and every single guest was saying, oh,
Speaker 6: take your shopping list out, take your shopping list out,
Speaker 6: take you buy this, buy this right. So you know,
Speaker 6: it creates a little bit of a sentiment. You know,
Speaker 6: it creates a sentiment, and people say, okay, you know,
Speaker 6: I'll make a punt. I'll buy if it's twenty percent down,
Speaker 6: maybe next day is going to that can bounce them,
Speaker 6: So people buy right. Some people rotate it say okay,
Speaker 6: like I'm getting rid of no video, but look Apple
Speaker 6: has been you know, underperformance, so maybe I put my
Speaker 6: money there. So the sentiment overall was still pretty strong.
Speaker 6: There's this aura of momentum, psychological momentum that it's harder
Speaker 6: to break. You know, you don't need to have a
Speaker 6: few punches for it to break, for people to sort
Speaker 6: of give up.
Speaker 2: So one of the other things I want to ask you,
Speaker 2: you know, just again looking back at the sort of
Speaker 2: long term changes in the market. And we touched on
Speaker 2: this earlier, but we've just seen an hour absolute explosion
Speaker 2: in different types of options trading and volatility trading, and
Speaker 2: now you even have TikTok influencers who are like pitching
Speaker 2: options investing as passive income, like, oh, don't buy a
Speaker 2: US Treasury bond, do you an options? But which is
Speaker 2: kind of crazy. How has that impacted the market and
Speaker 2: how have you seen people, you know, trying to handle
Speaker 2: some of that new I guess dynamic that's been introduced.
Speaker 6: So so that's that's a very good question. It started
Speaker 6: sort of with around the COVID time. People were locked in,
Speaker 6: they got these stimulus checks, they started trading, right, proliferation
Speaker 6: of these online brokers, no commission fees, options being traded
Speaker 6: as a sort of very short, short and short maturities, right.
Speaker 6: You know, options used to be sort of leaps and
Speaker 6: then maybe like a monthly options, you know, second and first,
Speaker 6: second and third month quarterly options moved to weeklies and
Speaker 6: dailies you know, and then in the single names you know,
Speaker 6: Like so you had sort of people locked they got money,
Speaker 6: and they got these instruments, these extremely powerful instruments with
Speaker 6: leverage about one hundred times leverage, you know, like so
Speaker 6: you suddenly can make a bets of you know, millions
Speaker 6: of dollars even if you have like ten thousand or
Speaker 6: five thousand dollars to invest, you know, Like so that changed,
Speaker 6: that changed a lot and for most of these people
Speaker 6: actually it worked, right because since twenty twenty we had
Speaker 6: that pullback when the FED started hiking, But for most
Speaker 6: of it it worked, you know, Like so speculative trading activity,
Speaker 6: especially on the alongside, it worked. Then you also had
Speaker 6: in peril sort of crypto markets growing, right, you know,
Speaker 6: Like so if you think of it, like, you know,
Speaker 6: a few trillions of dollars of wealth was created there
Speaker 6: with probably some of these similar type of investors and
Speaker 6: similar type of people, you know, like so so it changed,
Speaker 6: you know, so there is less a leverage in terms
Speaker 6: of borrowing money with interesting but more a lot more
Speaker 6: leverage in terms of option trading activity.
Speaker 7: You know.
Speaker 6: So, as you said, I'm always also surprised. You go
Speaker 6: on some of these social media and then you see
Speaker 6: all kinds of strategy is that can't lose money, that
Speaker 6: making like tens of thousands every day. You just need
Speaker 6: to follow him, and it becomes really kind of bizarre.
Speaker 6: You have like these people who are at the same
Speaker 6: time performer or like women who are like you know,
Speaker 6: in the like underwear, suggesting how to trede options, like yeah,
Speaker 6: it's all.
Speaker 4: Big fans of mind.
Speaker 1: They follow me on Twitter ADM very flattered.
Speaker 6: So it's it's kind of crazy, you know, like and
Speaker 6: we try to handicap it by looking at flows from Robinhood,
Speaker 6: see which names are being sort of bold, which names
Speaker 6: are being sold, try to see where the retail may
Speaker 6: be forced out or something like that. So we did
Speaker 6: some quantitative work. We did a lot of the sort
Speaker 6: of language large language models sentiment wise, like from Twitter
Speaker 6: and those type of other social medias which we could
Speaker 6: we could get permission to do. So we're trying to
Speaker 6: incorporate it. But I think overall it's hard to one
Speaker 6: hundred percent handicap it. But for sure it added leverage
Speaker 6: to the market, added speculative element to the market, and
Speaker 6: at some point it's not going to probably end up
Speaker 6: well right some point, you know, but it's hard to
Speaker 6: say when exactly right.
Speaker 2: It's again one of those things that can go on
Speaker 2: for longer than you think. Since you mentioned getting data
Speaker 2: from robin Hood just then, this is the other thing
Speaker 2: I always wanted to ask an equity derivative strategist because
Speaker 2: you alluded to this earlier. There's a lot of I guess,
Speaker 2: misunderstanding or lack of understanding of what an equity derivative
Speaker 2: strategist actually does, and exactly what data they're looking at
Speaker 2: in order to make some of their conclusions. Can you
Speaker 2: maybe give us like a quick one oh one in
Speaker 2: where your data comes from? How much of it is
Speaker 2: from official sources like I don't know an EPFR or
Speaker 2: someone like that, versus like color that you're getting from
Speaker 2: the market question.
Speaker 6: Yeah, no, so sore. They're all kinds of data. So
Speaker 6: there are price involume data or kind of technical data
Speaker 6: that can be derived from from from those type of things,
Speaker 6: which can also be a different time horizons, you know,
Speaker 6: like they can be daily. Mostly they are daily, right,
Speaker 6: you know, but increasingly you so want to look at
Speaker 6: the intra day data, you know, intraday correlations, interramormentum volumes,
Speaker 6: large blocks that are traded. So there's also high frequency
Speaker 6: one day, but most of it is daily, I would say.
Speaker 6: And then there are longer term data, you know, when
Speaker 6: you look at the sort of you know, some like
Speaker 6: a monthly statistics on positioning or up to the sort
Speaker 6: of filings you know, thirteen F filings like holdings and stuff
Speaker 6: like that. You know, like so so different sort of
Speaker 6: frequencies of positioning volume data price data. Then so directly
Speaker 6: market observed data like open interest, you know, options volume
Speaker 6: and options those type of things. Right, then you have
Speaker 6: sort of fundamental data, you know, and fundamental data. You
Speaker 6: have fundamental data for stocks, you know, which are related
Speaker 6: to earnings, but increasingly you have a data which are
Speaker 6: derived from non traditional sources, you know, so called big
Speaker 6: data that can be sort of sentiment measures, but quantitatively
Speaker 6: derived measures, objective not sort of a guesswork to some
Speaker 6: very specific niche data like you know, satellite you know,
Speaker 6: all kinds of like data that you know, we didn't have,
Speaker 6: you know.
Speaker 3: How many cars are parkeding parking lots.
Speaker 5: And in front of Walmart and those type of things.
Speaker 6: Right, So you have these stock specific data, earnings derived,
Speaker 6: news derived, sentiment derived, and then also non traditional ones
Speaker 6: you know, and then you have like micro data, you know,
Speaker 6: typically lower frequencies, but increasingly also with some of these
Speaker 6: alternative data sets, big data sets, you can try to
Speaker 6: figure out like you know, shipping and again sort of
Speaker 6: storage and oil tank tanks how full they are and
Speaker 6: stuff like that.
Speaker 5: So it's a whole.
Speaker 6: Host of data, you know, like as a quant and
Speaker 6: there at this person you probably focus most on the
Speaker 6: market data, you know, so open enters, price volumes and
Speaker 6: old stuff that is derived from that, but you also
Speaker 6: want to want to supplement that with all these other
Speaker 6: other data. And then and then some of the data
Speaker 6: set you derive it on your own, you know. Like
Speaker 6: so so, for instance, got my imbalance in SMP options
Speaker 6: put minus call. So I was running that for fifteen
Speaker 6: twenty years. And first people tell me to say, you know,
Speaker 6: what's that. That's you cannot know what's you know. But
Speaker 6: then now everybody has it, actually, you know, and the
Speaker 6: same thing with like a CTA stuff, you know, I
Speaker 6: and volt targeting exposure. I was getting so much sort
Speaker 6: of critique in twenty eleven, twelve thirteen. You know, now
Speaker 6: everybody has it, you know, kind of CTA positioning percenta.
Speaker 6: So you can derive some on your own based on
Speaker 6: understanding with markets.
Speaker 1: All right, I just have one take and I bring
Speaker 1: it up a lot, and I sort of feel like
Speaker 1: the kool aid man. It's like every conversation I have
Speaker 1: to jump it through the wall and interject this. But
Speaker 1: you know, there's all sorts of like quant techniques, and
Speaker 1: there's the definition of quant and changes over time, and
Speaker 1: obviously there's an incredible amount of data that we can
Speaker 1: use now. And then there's sort of like old fashioned
Speaker 1: quant where you're just like, we're gonna buy you know
Speaker 1: that I sort of associate with like AQR from years ago,
Speaker 1: where like we're gonna buy the cheap stocks that are
Speaker 1: exhibiting momentum, right, and we're going to short the expensive
Speaker 1: stocks that are declining momentum. And why doesn't this work anymore?
Speaker 1: And all these sort of hand ringing and the traditional
Speaker 1: quant industry, why has it?
Speaker 4: Why haven't things mean reverted?
Speaker 1: How much is the fact that, like so many of
Speaker 1: these sort of ideas about how the market should work
Speaker 1: have been broken by the simple fact that a handful
Speaker 1: of American companies that are very big exhibit year over
Speaker 1: year earning his growth that are truly remarkable. And this
Speaker 1: is a fact not about the market world, but about
Speaker 1: the real world that, for whatever reason, these big tech
Speaker 1: companies just keep getting bigger despite their size.
Speaker 2: Wait, Joe, you have to end that by saying the
Speaker 2: kool aid man catchphrase.
Speaker 5: What did he say?
Speaker 3: Oh yeah, oh yeah.
Speaker 1: As long as the metas and the Googles and the
Speaker 1: videos and maybe the Apples of the world just keep
Speaker 1: growing earning is like.
Speaker 4: Crazy every year.
Speaker 1: How much does that bust any sort of notion of
Speaker 1: mean reversion in market?
Speaker 6: So I think it busts the notion of a value
Speaker 6: as a factor. But value is the fact that we've
Speaker 6: been straggling for a long time. Yeah, so sort of
Speaker 6: probably since you know, decline in interest rates post two
Speaker 6: thousand and eight, a lot of these, you know, and
Speaker 6: growth of indexation, right, growth of dxation kind of spark
Speaker 6: them momentum and change the structure of the market. So
Speaker 6: some of these quants, quants models or quant factors work
Speaker 6: less and less.
Speaker 7: You know.
Speaker 6: There is also another aspect, which is, you know, once
Speaker 6: when you put money to work in these strategies, you
Speaker 6: kind of squeeze out the alpha, you know, and and
Speaker 6: and and these things are fully priced in so they
Speaker 6: stop working. So sort of growth of quant funds, traditional
Speaker 6: quant funds, you have quantity tfs, you have like broker
Speaker 6: dealers doing quant strategies kind of squeezes out returns, right,
Speaker 6: you know, on your question, sort of like these big
Speaker 6: companies that keep on delivering what that's that's also a
Speaker 6: very good point. You know, quant strategies are designed for
Speaker 6: sort of steady state situation when kind of things are
Speaker 6: fluctuating around something which is in a steady state. And
Speaker 6: we had sort of you know, big sort of big
Speaker 6: changes in the world right in technology, Yeah, and also
Speaker 6: geopolitically sort of you know, capital moved to us and
Speaker 6: then moved into these sectors of innovation, right you know,
Speaker 6: And now so you may sort of you know, you
Speaker 6: may con simply be out of equilibrium, right, you know,
Speaker 6: where some of these mean reversion or quand strategies would work.
Speaker 6: A certain type of con strategises have value based strategies.
Speaker 5: Right, question is.
Speaker 6: How long you know, how long can you know, going
Speaker 6: back to the concentration, right, how long can it go?
Speaker 6: My my question becomes like, let's say, if you have
Speaker 6: like a social media company, like a Meta right, I
Speaker 6: mean once when they have all the users in the world,
Speaker 6: I mean, like you know, they can't go much further, right,
Speaker 6: they can go to the.
Speaker 3: Right, there's limits, there's limits.
Speaker 4: You know, but numbers are creating fake ai FA.
Speaker 6: Or go like to Marcel, there's no one there, so
Speaker 6: there's some some limitations, right, like and then there's some
Speaker 6: sort of also historical when you look at the weight
Speaker 6: of stocks in an index, right, so so you're taking
Speaker 6: a video percentage rat in SMP, and you know, you
Speaker 6: run back history and you see that this basically never happen,
Speaker 6: and even if it happens, never lasts forever. Right, But
Speaker 6: to your point, it can last. You know, one or
Speaker 6: two or three years is enough to ruin a lot
Speaker 6: of investment strategies, you know.
Speaker 2: All right, Marco Kolonovitch, thank you so much for coming
Speaker 2: on odd lots of real treat for both of us.
Speaker 4: Yeah, that was great, Thank you so much, Thank you.
Speaker 2: So much, Joe, that was really fun. I'm so glad
Speaker 2: I finally got to ask him a bunch of questions
Speaker 2: about just being an equity derive strategist.
Speaker 4: I've I love those.
Speaker 1: I love those questions so much because they're this sort
Speaker 1: of like dark fiber or the dark matter of how
Speaker 1: this industry actually works. But everyone just abstracts over them.
Speaker 1: It's like, oh, you look at the data and then
Speaker 1: you do.
Speaker 3: They but right, like what data?
Speaker 4: Where where do you data actually come from?
Speaker 1: Like I could listen forever to someone and we should
Speaker 1: do more of that, just like talk about these aspects
Speaker 1: like paying for data and data costs and all that stuff.
Speaker 1: I really like Marco, It's a nice conversation.
Speaker 2: The one thing I would say is, you know you
Speaker 2: asked that question about like, well, the S and P
Speaker 2: five hundred overall didn't do too bad on Monday, and like,
Speaker 2: you know, if I'm an induct investor, I have exposure
Speaker 2: to GE and so that's good because I don't have
Speaker 2: to worry about AI. But I think the question really is,
Speaker 2: like how much is GE exposed to AI? In very
Speaker 2: indirect way?
Speaker 1: Yeah, that's correct, right, and you do sort of I mean,
Speaker 1: you know what, I think. I saw a tweet about this,
Speaker 1: so I'm just gonna say it, and it may not
Speaker 1: even be true, but if it's not true, it's truth y.
Speaker 1: Someone tweeted that apparently there was like a Sherwin Williams
Speaker 1: earnings call and someone asked about how much paint data
Speaker 1: centers were going to need to paint their walls. I
Speaker 1: don't know if it's true, but if it, because I
Speaker 1: just saw the tweet. But if it's not true, it
Speaker 1: doesn't really matter, because it is true, Like every company
Speaker 1: is like are you doing something that could supply do
Speaker 1: you sell some product that someone building an AI data
Speaker 1: center is going to need at some point? But my
Speaker 1: point about GE though, it was kind of the opposite,
Speaker 1: which is like GE did fine on that day, but
Speaker 1: I wish I had more exposure to GE. What I
Speaker 1: really have is a bunch of video Microsoft exposure through
Speaker 1: my index.
Speaker 2: Fund, right, But you don't know how much exposure indirect
Speaker 2: exposure you have to Microsoft through GE.
Speaker 4: This is because this is true.
Speaker 2: Sconda wrote that excellent column in the au Thought's newsletter
Speaker 2: about how more and more of the economy is being
Speaker 2: driven by AI. We actually saw on on Monday the
Speaker 2: treasury market move a little bit, which you know, okay,
Speaker 2: treasuries will go up when there's a market sell off,
Speaker 2: but a lot of people were saying, well, this impacts
Speaker 2: growth expectations as well, and so that's why you're getting
Speaker 2: this reaction.
Speaker 1: No, totally, I think the degree. I mean, especially if
Speaker 1: you go back and like Trump announced the half a
Speaker 1: trillion dollar Stargate project, and I don't know what's really
Speaker 1: going to become of that, but like I believe the
Speaker 1: data centers and AI specifically through the data center channel,
Speaker 1: is a meaningful important, is a growing important part of
Speaker 1: the real economy right now. And if suddenly they're like,
Speaker 1: you know what this is a dead end or suddenly
Speaker 1: like we don't need this because we can get AGI
Speaker 1: so cheaply that it's just like on our laptop.
Speaker 4: That would raise some real econ concerns.
Speaker 3: Yeah, shall we leave it there?
Speaker 4: Let's leave it there.
Speaker 3: Oh yeah, this has.
Speaker 2: Been another episode of the Authoughts podcast. I'm Tracy Alloway.
Speaker 2: You can follow me at Tracy Alloway.
Speaker 1: And I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 1: Follow our guest Marco Kolonovich, He's at Marco in and Why.
Speaker 1: Follow our producers Carbon Rodriguez at Carmen Arman, dash Ol
Speaker 1: Bennett at Dashbot, and kill Brooks at Kilbrooks. From our
Speaker 1: odd Lots content, go to Bloomberg dot com slash odd Lots,
Speaker 1: where we have transcripts of blog in the newsletter and
Speaker 1: you can chat about all of these topics twenty four
Speaker 1: seven in our discord Discord dot gg slash od Loots.
Speaker 2: And if you enjoy Oddlots, if you like it when
Speaker 2: we discuss the dark magic of equity derivatives, then please
Speaker 2: leave us a positive review on your favorite podcast platform.
Speaker 2: And remember, if you are a Bloomberg subscriber, you can
Speaker 2: listen to all of our episodes absolutely ad All you
Speaker 2: need to do is find the Bloomberg channel on Apple
Speaker 2: podcast and follow the instructions there. Thanks for listening.