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Hello and welcome to another episode of the All Thoughts Podcast. I'm Traci Alloway. And I'm Joe Weisenthal. Joe, in your history of being a financial journalist, what are you most proud of in terms of coining phrases?
I gave you a hint just then because I assume it's mint the coin. Yeah. Oh, good one. Yes, sure. Mint the coin. That's right. Okay. Thank you. Yes. So I have a few. I have China's great ball of money. I like how you're like, what are you most proud of? And this is going to be my saying. It's what I'm proud of.
But keep going. Keep going. Thank you. You called me out. Europe's sovereign bank loop. Although no one believes that I invented that one. And flows before pros, which has come up quite a bit on this podcast. So the idea that, you know, when valuations are extremely high and everyone's buying everything, no matter what the price, that it's kind of momentum that matters more than fundamentals. Yeah.
Today, I'm very happy to say we are going to be speaking to a pro who knows flows. How's that? Extremely well done, Tracy. Thank you. Excellent setup. Of course, I appreciate all of the Tracy neologisms. Man, what a time in the market to be talking about flows, to be talking about momentum.
etc. I feel like I, you know, it's always chaotic. It's always uncertain. You'll never get an answer. But man, things feel really noisy right now. They feel super noisy. So we are recording this on January 30th. It is a week that has seen a very sharp sell-off in tech
stocks thanks to anxiety over deep seek coming out of China. We're going to talk about that. And more generally, we're just going to talk about what's going on in the market right now, how investors might be handling it and how the market structure might have changed.
over the years. And as I said, I'm very excited because we do have the perfect guest, the pro who knows his flows. He has a lot of nicknames, actually. Gandalf being one of them as well. Actually, I didn't realize one of our colleagues at Bloomberg kind of coined that name for him. But of course, we are speaking with Marko Kalinovich,
He is J.P. Morgan's former chief global market strategist and co-head of Global Research. And now he's with us to talk about the market. And Marco, thank you so much for coming on OddLots. Thank you so much for having me. I'm very excited. I know I've said that three times now, but I guess we should start with the recent sell-off, like the deep-seek, deep-stock sell-off.
There are all these superlatives that you can use to describe Monday's action, like the biggest single stock plunge in history in the form of NVIDIA and like eight of the top 10 biggest one day drops in the S&P 500, etc., etc. And actually, as we're recording this on January 30th, Microsoft is down 6% after earnings. So maybe the tech sell off isn't over yet, but
But one of the interesting things about this week is there hasn't really been broad contagion. So most stocks in the S&P 500 were kind of like, meh, we don't care. When would you expect some of the anxiety over deep seek
to maybe start having a bigger impact on the broader market. So, you know, as you said, there was not much contagion, you know, and if you look at the different stocks in S&P, many of them were actually up, you know, and many even in the tech sector were up. You know, if you look, for instance, you know, Facebook yesterday or...
today or a bunch of other names that sort of were perceived that they might be sort of benefiting from, you know, the sort of open architecture, you know, type of things that can come at a cheaper price, you know, and can be still, you know, implemented in their business model when it comes to AI models. So it was fairly contained, you know, I'm a little bit surprised just because there were like three or four names that really got hammered, you know, and that can only be explained with a
not just with the panic, but some of the sort of forced selling, you know, maybe coming from options. You know, if you are, you know, if you're selling Nvidia puts for the past few years, you could make a good living out of it. But then, you know, you'll have a day like we saw this weekend and basically you might get, you know, forced out of these positions and maybe have a catastrophic loss. So it was fairly limited. I'm,
I'm a little bit surprised, you know, just because we didn't really have a meaningful sell-off since last summer, you know, sort of when we were at the back of Bank of Japan, you know. So I do think we will see one. Perhaps it's a little bit too early in the year. There's still quite a bit of an optimism post-election. There's a little bit of seasonality in January. People put money to work, they get paid, you know, they allocate the capital. So maybe it's a little bit too early. I was somewhat inclined to see that we will see
a bit more, you know, at the back of it. It's perhaps not over. We still have a few important earnings to come. So it remains to be seen sort of what happens, you know, maybe another week of earnings, you know, whether there is any sort of follow-through, you know. But I do think that it's going to be, some investors will burn clearly and a little bit of a tarnish on the sort of this thesis that,
some of these stocks like Nvidia just go up, you know, every day you can't lose, you know, like, so I think people will think twice. If something can drop like 20% in a day, you got to also think of it, what it does to your risk. Well, so Tracy asked about contagion and the anxiety spreading across the market, but I guess I would flip the question, which is, as I've said before on the podcast, I'm a boring index investor. So like I look at a random American company that is like General Electric, it's doing fine.
I'm not very exposed to General Electric because they're a small part of the index. I am very exposed to NVIDIA and Microsoft and so forth. We did a recent episode about market concentration, but I'm sort of like curious your take on this fact that like so many of these flows, they go into broad market indexes, but we're really all very exposed to a few concentrated market bets.
Yeah, so the concentration is the highest, you know, sort of 60s or 70s. So we're looking at 50 years, half of the century history and concentration is sort of at the highest point. It's been a while for staying there at this level, like maybe past year. So it's a weird market. You know, this concentration came for two reasons. You know, one is clearly...
thematic investing in technology. Then you also have investing in large company. You have a theme of momentum sort of that is basically self-fulfilling. You know, more something goes up, more money it attracts, becomes bigger in index, you know, all the passive flows into it. So there's a technical aspect, there's a thematic aspect, there's even geopolitical aspect
A lot of money went outside of the other parts of the world. Europe is doing worse when it comes to sort of economy. China has been, we've been sort of at the brink of this Cold War with China. So money has left there. You know, Latam has its own share of sort of issues. So money has been also geopolitically moving. So it's moving in the US. It's moving into indices. It's moving into tech, you know. And then you end up with these, give or take 10 stocks that really sucked up all the capital and valuations got cut.
got very, very high. Now, you know, tech investors, they do have a sort of their rationalization. So what's going to happen in the future? These things just grow and grow and grow. And that's when we saw with the deep seek, we saw a little bit of a dent in that thesis. But these stocks didn't go up for the sake of, for the thesis. They went up some of these other flows, you know. So,
unprecedented concentration is not going to stay there. You know, the big question is when will we see that rebalance? Do we need to see, you know, some cyclical downturn first to purge and to normalize some of these valuations? You know, because here's
Historically, these PEs were never this high. I mean, in 2000, they were this high and we know how it ended, you know. But a lot of people got burned, myself included, with some of the cold. We were more negative last year, you know, and basically, you know, market has this tremendous momentum. So the timing is going to be a challenge. Yeah.
What does it take to, I guess, turn momentum at this point? Like what are the catalysts that actually work here? Because it does feel looking at the market, so much of it is now technical or systematic in some way. There's a lot of option selling, as you mentioned, lots of multi-strat funds that basically, you know, just have to sell or buy to rebalance their exports.
exposure, what actually changes directions? Like, how do you get enough to change a trend? So, you know, so there's a technical aspect, sort of the mechanical aspect of it, and there's a sort of catalyst or more fundamental angle of things, you know. So,
To get things to start moving or to stall, you usually do need to have some fundamental driver of it. So perhaps there is concern about economy slowing down. Perhaps there is a concern about something geopolitical, maybe trade war with China or some sort of blockade of Taiwan's trades or something like that. So first you need to have a little bit of a catalyst. But if the market is technically very strong, the catalyst is not going to change in momentum.
what that means in, you know, more specifically, you know, so when you look at the trend investors, you know, they have a range of signals, you know, like, so they can look at a one month price momentum, three month price momentum, six month price momentum, 12 month price momentum, maybe 18 months, but that's about it, you know, and there are some very short term momentum players that look intraday or on a daily basis, but,
Most of these signals are concentrated around 12 months and 200-day moving averages, right? So that's why when people look at the 200-day moving average, first, 20 years ago when someone told me, I said, like, what is this magic? Why would this work, right? But it's realities that many models, systematic models primarily, it's computer-driven, but also psychological investors look at these things and become self-fulfilling. So you need to actually come close enough to these levels to break them, right? Right.
So for instance, earlier this week, S&P got below around 6,000 or a bit below. We were close to breaking 20-day moving average and 50-day moving average. So you can think of it as about one month and three month price momentum. So that's about a third of a signal. The big signal is really 12 months, you know, or 200-day moving average, right? But when you start moving things, you can actually unravel. It's like a little bit like a snowball. So I thought like if the market is going to stay below 20 and 50 days, it
at the end of the day, you may get enough selling from CTAs or de-risking from CTAs that they may get you to another leg lower, right? So it's basically, you need to have a setup that you're close enough to these triggers on the downside to move it. And again, I think this week we got very close, but there was also other flows like rotation. You saw like selling on Nvidia, but Apple and Meta went up, you know, like, so at the end, Nasdaq dropped, but did not drop a lot. Hmm.
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Joe, it kind of reminds me of like structured credit notes where there's that knockout level and then you get this massive cliff risk and the whole thing kind of unwinds. Right. You have all these rules-based investors and something happens like, oh, the model is just to sell. It is worth noting that as we are talking about
10, 19 a.m. January 30th. NVIDIA is basically right at its 200-day moving average. I know this isn't the broader market, but it's a big part of the broader market. Might as well be. Yeah, it might as well be. One thing that struck me on Monday, which I was a bit surprised about in the sell-off,
is that even on Monday, you mentioned that Meta actually closed green. And they are a maker of a competitor to DeepSeek. They have their open source Lama. But they're seen by the market as a consumer of AI services because they don't sell their product. They use it to do things like better ad targeting, etc.,
Were you surprised that even on this day in which there was this sort of exogenous shock to the market, everyone wakes up to some new thing that actually investors showed a fair amount of discrimination in terms of what they dumped?
Yeah, so, I mean, that was, again, you know, January sentiment is still pretty positive. Economic data are strong. So people are saying, okay, this is not the beginning of economic downturn. This is isolated sort of event whereby some companies will get hit, you know, their sort of revenues will get hit. Yeah. And some will be able to do things for cheaper, you know, like, so you have the Salesforce, I believe, as well. And
So it ended up not a macro day, but more of a rotational day. You know, there is also a so-called quant factors where, you know, even within technology, some stocks are higher multiples, some stocks are lower multiples. You know, some stocks are more momentum, less momentum, you know, like, so there was a bit of rotation. So Apple, which was a laggard, also kind of caught a bit, although I don't think there was much fundamental change.
stuff going on for Apple. It was probably just rotation. So market kind of held up, you know, and I was sort of a defense whether these like technical levels 2050 will get broken and we'll go lower or not. We didn't, you know, but I do think that sort of, you know, valuation, positioning and some of these technicals are a bit stretched, you know, like so I don't,
I don't see like a huge, huge upside for the market. So maybe I'm switching the topic a little bit. No, no, that makes sense. Can I ask you a sort of procedural question, which is, you know, you were at J.P. Morgan for 20 years and you were in the industry even before that. How did your sort of research and forecasting process change throughout those years?
Oh, thank you. And it did change, and it's an interesting, good question. So I got my PhD in physics, in theoretical physics. So there was a lot of coding, there was a lot of modeling. There was a lot of sort of trying to understand why...
one thing leads to another. You know, what is the cause? What is the concept? What's causality? You know, and what's the noise? What's statistically important? What's statistical not important? You know, like, so in physics, you build this type of models. You try to understand what's significant, what's not, what you can neglect, which factors you can, you have to take into account. And most importantly, how to simplify the complex. You know, market is extremely complex system. You know, many is physical system. So you need to sort of
move the noise on one side and drivers on the other side and try to recognize those patterns, right? Like, so, although I really never used any formula from the physics in my, almost never really in the finance, but the way of thinking is similar. So I started Merrill Lynch in derivatives, in derivatives research, where I started looking, you know, interestingly, we are now in earnings season. So my first models were impact sort of of earnings on a stock price and what can you read from options market, you know, like, so I published some papers and
you know, come up with some formulas and we were kind of backing out, okay, what the option market is saying. And then we would go to analysts and say, hey, do you think it makes sense or doesn't make sense, right? And then if you think that options are saying too much of a move or too little of a move, you could trade these options and stuff like that. So that was,
one example of, okay, how do you sort of, you know, you have a catalyst, you look at different markets, you see, are these markets aligned? You put some model together and then you find a discrepancy with the model. It's not always going to work, but if you do it for a hundred stocks, maybe an average in a portfolio level, you'll be fine. You know, like, so that derivatives research and quant research, I did a lot of quant research. So you try to process the data, you try to look at the measures. And at that time, like 20, 25 years ago, it was beginning really of trading data
of VIX, of volatility swaps, of correlation, of dispersion. So you kind of try to see, okay,
You look at the VIX, you look at the market volatility, what's driving market volatility? Well, people say, well, it's a panic or it's not, but let's be more quantitative. You can see how correlated stocks are, what individual volatility of each stock is, which part is due to the macro factor, the market, what's idiosyncratic. So you can kind of break this down. Then you can look at the sector. What's correlation between sector? What's correlation within sector?
So you can kind of quantify these things and analyze and get some insight. You know, like 2008, for instance, we look at 2007, 2008, I look at how the hedging of options impacts the market. You know, like, so you basically need to look at how many options are out there in index, let's say. You try to assess what's the positioning from the flows, from the sort of knowing of industry, hedging flows.
And then you see, okay, what are the hedging requirements in the end of the day? And 2008 and then 2011, and there's like a low hanging fruit. You could see sometimes these flows would be bigger than market can absorb, you know, and they would all go same way from lever ETFs, from options, you know, so you see like, okay, there's like $20 billion to sell and market can't absorb it. So, you know, market towards last 10 minutes will drop. And that's,
You mentioned sort of Gandalf. That's where some of these things... Because if you look from the outside, you say, oh, how can he get that? But it is really understanding a bit of technicalities, which is flows, option, convexities, liquidity, and how they sort of interfere. But after...
2015, 16, people figure it out, you know, and then people put it in their models. They create like a structured product. After 2018, everyone sort of woke up to the VIX, especially, right? Oh yeah, February 2008, Volmageddon, yeah. And, you know, stuff like that. So you kind of analyze causes and consequences in the market, something that is new, that's not been yet looked at, you know, and I have focused on
things that were new in the market, like products, options, futures, CTAs, those type of things. You know, this is it's not particularly like technical, but another thing that seems to be true about the market right now is at least maybe up until a week ago. And there's just an incredible amount of optimism in any sort of measure. So if you ask, you know, there's consumer measures. Do you think stocks are going to be higher in a year from now?
Very high levels. If you look at things like the Bank of America sell side analyst sentiment, very close to euphoric levels. If you look at fund cash levels, extremely low right now. Everyone is overweight, long tech levels.
How do you ingest this information? Because on the one hand, you say, oh, everyone's all in. This is negative. On the other hand, people have been very optimistic for a while. How should we as consumers of this information think about what it says about the fragility of the bull market? So,
Clearly in the markets, things are mean reverting. So when things reach some very high levels, eventually they're- It can go on a long time. Exactly. So there is this mean reversion, but there's also trend, right? So figuring out the timing of that is hard, right? I mean, if there is a sort of a limited set of drivers, like in some of these technical markets, so for instance, CTAs, you know once when all the levels are positive, all the signals are positive,
and then volatility drops a little bit, you know they're maxed out, you know, so you know they're not going to buy more, right? You know, like, and so you can, that's a self-contained isolated system where you can say, okay, optimism is too high, so there's only downside, right? With a, with a,
the entirety of market, right? You know, with, you know, you have a crypto, you have a fiscal measures, you have like monetary stimulus, you have a sentiment shifts. It's hard to handicap all of those, you know? So it's hard to say that for all of the investors to be able to know exactly when this thing is going to stall, right? Sure. And there are developments that is hard, like, you know, this whole AI, you know, and I see the...
I wrote a book in 2017 about AI with my colleague Rajesh, 2018. So we were early on on AI and, you know, six years ago, right? Nobody was talking really about it at that time. So it's not that I don't understand it, but I'm a little bit cynical about it now. I think it's too hyped up, right? But it's hard to assess how long people will be excited about it, right? You know, and yeah, so, and then you have a changes. You have political changes that can bring deregulation, that can be changing tax regimes. So you have like a wild cards.
So it's hard. Your question, I started like there is a reversion always, but where you're going to pick it can be very frustrating and very sort of, you can be wrong for a long time. Wait, can I just press you on that point about AI? Because I think the difficulty that investors are having is AI has a great story right now. And there's this idea out there that it's this
revolutionary technology that's going to change the world. Joe keeps referring to it as inventing God when it comes to AGI, at least. And at the same time, there is also a feeling that people are maybe getting a little too optimistic about it. There's too much hype in the market. You've started seeing companies that, you know, just put out a press release going, oh, we're looking into AI and their stock price goes up. How should investors handle
handle their exposure to AI? Like, how do you actually play it at this point in time, given that you were early to the topic? And now cynical. You know, I look at it from a theoretical side. I look at it more how to apply it sort of in finance, in quantitative trading, you know, how to use a large language model to assess the sentiment, changes in sentiment and those type of things, right? So, you know, how to read quickly things and summarize them and derive some signals out of it. So
There is obviously a bigger question of AI, you know, which you said, it's kind of philosophical questions like, are we going to be replaced? You know, at what, at which point, what's going to be role of human once when, when we can, you know, kind of break down our way of thinking and effectively train it and replace it, you know? So there are a whole host of other questions, you know, like, so I'm, I'm not skeptical that this is going to be hugely important and it is a hugely important, you know, it's not very, very different of what people have been doing
you know, five years ago or 10 years ago or 20 years ago, obviously big progress in computing power, big progress in the models as well. You know, like, so it's, but I see it like more as an evolution, you know, than something that changed with chat GDP in 2023, like two years ago as a kind of like a step function. I see it as an evolution, always important, right? Like 10 years ago, when we use our smartphone to take a picture, it's like, you know, camera would recognize the face, it would zoom into face, it would kind of,
do the proper focus and stuff like that. So that's also, you know, that's also AI and things are advancing, right? And we'll keep on advancing. Now, the question is going to be winners, losers, how to monetize, you know, does that suddenly re-rates all of equity market multiple, you know, like suddenly, okay, people are not going to work. These companies go to all the work. So we're just going to value them. Like,
Who am I to say that? And also, who am I to say that that's wrong as well? You know, but there's a lot of speculation and atrust. You know, people often tell me, well, imagine just how my way to search internet has changed. You know, like, okay, like we were searching internet for 25 years the same way. I used to use like a Netscape like 25 years ago, right? And the same thing, you type in a bar and you find something. So for Christ's sake, of course it's going to change. Of course, at some point it's going to be we're going to tell something to computer users
computer will have its own ways of parsing and finding what's relevant and giving us back information. So I'm not as excited about that change. I think it's a way overdue change, you know, like, but there is a lot of optimism now. Well, I was wondering because I was looking at your LinkedIn and you mentioned you have a PhD in physics, graduating from NYU in 2003, theoretical high energy physics, cosmology, string theory and finance. I guess a two part question. A,
Would you would you think there's a world in which if you graduate today, you would have gone into AI instead of going into finance? Because I imagine they would have hired you at those skills. But be like, would you think about and Tracy mentioned, you know, like the true AGI? Do you think that the current AI research is on a path to that sort of AGI inventing God that a lot of the proponents believe?
So, you know, I think eventually it will get there in a sense that it will sort of address some very important, you know, questions which are kind of deeply what every person fears or wonders or sort of seeks.
you know, kind of meaning of our lives, like, you know, future after we die and stuff like that. So there are definitely interesting things there that can be done. I mean, people are doing with these like assistants, right? Like you train, and I believe really this AI will have to be a lot more personalized, you know, like so...
You will train it really on your life experience. So if AI can see every image I saw, if it can read every email, I believe AI will be able to tell me when did I make a mistake? When should I do something different? Did you overreact in this life situation? Did you not, right? And going further, right? Like that will stay. And my kids can, after I pass away, they can say, hey, what would that say in this situation, right? You know, like what would, maybe I'll be able to in some way talk to them, right? So you'll blur all these things which were,
which were sort of not blurred in the past, right? You know, so you will start having these like very, very interesting developments, you know, but I would kind of not look at them from the sort of P&L perspective, earnings perspective. There is also going to be a lot of issues as we have already now. I mean, sometimes AI can give you wrong answers. Sometimes it can be used to do bad things, to impersonate, to deceive, to manipulate. So there's
It's going to be a lot of interesting, I would say, philosophical issues, technological issues and investing issues. But I just don't think it's going to be as simple as like seven companies are going to have P of 50 and everyone else will have P of 10 and it's going to persist that way. I don't think it's going to be like that in finance, at least. Yeah.
The other thing I wanted to ask you is, you know, you left JP Morgan in July and then pretty much a month later, we had a very sharp sell-off. When you look back at that particular sell-off, you know, we never got to hear from you, your thoughts on that particular week in markets. What did you actually see and observe there? Because there are still differing opinions out there about what the proximate catalyst was for some of those moves and what was exacerbating what.
So the catalyst was definitely moving rates related to Japan and the currency, right? That was a sort of catalyst. But you always have like a spark and a bucket of fuel, right? And the bucket of fuel will stretch CTAs, stretched markets.
vol targeters, systematic investors, too much optimism, you know, and then you start basically hitting the stops across these strategies, right? CTAs hit their sell signals, vol targeters, vol VIX goes up, vol goes up, they need to sell. If, you know, if you were selling puts on AI names, you suddenly need to, you know, you need to kind of close. So VIX was very, VIX behaved most phenomenally. So it was a vol, a lot of vol, short vol covering as well, you know. But again, I think it was,
What was missing for this to be the turn in the cycle was, I guess, you know, GDP, employment, still fine, right? Still hope that Fed is going to cut. Right. You know, so it didn't, it didn't, there was a little bit of conflagration, but it didn't kind of burn everything down, right? So it was a little bit of satisfaction, but it didn't last too long. It is pretty remarkable because even with, we got a little sell-off, but
It's a very minor sell-off. We're basically, the stock market is more or less at all-time highs. This is despite a pretty big repricing of the expectation of the short end of the curve, where people were expecting deep cuts to continue through last year and to this year. We might not get any cut this year, and yet still the market is close to all-time highs.
it must be nice on some level to be out of the game of having to come up with an end of year price target. Because that sounds like a job I would never want to take. But I also wonder, you know, do you wake up in the morning and still like... Yearn to give end of year targets. So like talk to us about, you know, what you have a market outlook for right now. Like give us some, give us what's on your mind. Sure, sure. Like, so look, it's nice once in a while that you can be somewhere away and not look at the fact, parse every single word out.
Although I did it yesterday, you know, but a few months ago I didn't. So it's nice to make a break or maybe it's necessary. You know, ultimately markets are a little bit of a sort of compulsion thing of compulsion when you feel like you need to understand what's going on in the world. You know, so I think it becomes part of your DNA if you do it for a long time. So I do always think and I do have an outlook. Yeah, so give us a... So, you know...
On a sales side, you kind of need to put a price target. And I think it's a kind of poor way to summarize everything into one number. It's basically almost you're telling... You're trying to forecast probabilities in the world. Because real world actually works in terms of physics. Deeply works in terms of probabilities, not just superficially in a quantum physics. So you need to sort of have a sort of that hyper-probabilistic view. And you're forced to have one view, like 100% or nothing, right? So it gets oversimplified. I think media...
you know, and not referring to you, but media does a bad job. They say, oh, what's your price target? They just want to talk about that. And they say, oh, you're right, you're wrong. That's okay, Joe just did that. Yeah, so no. So I would say like, you know, if I can move away from price target, I do think we'll go back down in the 5,000s this year. Sometimes. I think at that point in time, we will see whether the cycle is still strong or it's not.
I think we need to see the whole new political climate, whether it will lead to turmoil. And I believe...
More likely than not, it will. You know, like, so those things, I think, will get us lower, right? You know, at that time, whether it becomes an end of a cycle and we go much lower into $4,000, I don't know. I think there's some probability of that, you know? And then conversely, on the upside is everything goes, if really this is what they call it, golden age, golden age of America, you know, then market will stay in $6,000. It can go a bit higher. I just see...
I'm hard-pressed to see it going much, much higher because valuations are there, positioning is already there. As you said, Fed is not cutting. So it's a little bit of a chicken and egg. I have been scratching my head at these level of rates, which I do think are restricted rates for now more than two years with the commercial real estate. Here and there, we saw a few hiccups, but I do think...
that is sort of under the hood of economy, some damage is being sort of built up and down. So I don't think like market really, you know, going to 7,000 or, you know, 6,800 or something like that. So I would say maybe it can go 65, stay range bound, you know, like, so I would sort of formulate the view in terms of, okay, you perhaps want to sell upside, give yourself a little bit of a room for some more excitement first few years of the first few months.
the year, but then also be ready to assess once when you're going to, let's say 5,500 or 5,700 to assess is the cycle potentially ending or that's going to be buying opportunity. And I wouldn't want to sort of say, hey, it's going to first stay at 6,100, then it's going to pull to 55, and then you buy with both hands at 55. That would be too predictive. But I think some variation of that path will be whereby sort of the depth of a pullback will depend on
trade war, China, domestic political situation, raids, and like one-off things like we had on Monday.
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I'm
I'm glad you mentioned domestic politics, because one of the other weird things about this week when we had the deep seek sell off was everyone was focused on that. And, you know, tech stocks went down, as we mentioned. But then on Tuesday, everything started rebounding, even though we had headlines coming out of the White House about.
cutting what amounted to a pretty big chunk of federal spending. The entire market seemed to look through that, which is kind of ironic because one of the things we've heard for the past four years or so is this idea that deficit spending is driving the entire economy and stuff like that.
I feel like political risk is one of those things that investors really struggle to price in because there's so much uncertainty. A lot of it seems very binary. How do you deal with that? So you need to sort of, you know, put some scenarios, you know, what can happen in terms of taxes, regulation, you know, tariffs, trade wars, geopolitical conflict, you know, and then see how can they impact specific stocks and industries, countries, right?
And maybe overall market sentiment, you know, and maybe put some scenarios. That's a kind of a blueprint and market never goes by that blueprint, but at least gives you some framework to try to understand if it doesn't go by your sort of assessment, what have you missed and what else you need to take into account. But you put some blueprint sort of
what can happen. So, you know, I think you pointed very well. He was talking about NVIDIA, Taiwan export sort of thing. So those type of things, right? So market, then market has its minds of its own, which is tied to sentiment, you know, and it's tied to momentum. You know, most people think momentum, you know, they don't calculate it by the thing. They just
feel good about market. They see good news about market. Their taxi driver or friend or family feels good about investing, right? And they choose then to ignore, you know, like, so on Monday, I was watching CNBC and every single guest was saying, oh, take your shopping list out, take your shopping list out, take your buy this, buy this, buy this, right? So, you know, it creates a little bit of a sentiment, you know, it creates a sentiment and people say, okay,
you know, I'll make a punt. I'll buy if it's 20% down, maybe next day it's going to, that can bounce. So people buy, right? Some people rotate. So, okay, like I'm getting rid of Nvidia, but look, Apple has been, you know, underperforming. So maybe I put my money there. So the sentiment overall was still pretty strong. There's this aura of momentum, psychological momentum that is harder to break. You know, you do need to have a few punches for it to break for people to sort of give up.
So one of the other things I want to ask you, you know, just again, looking back at the sort of long term changes in in the market. And we touched on this earlier, but we've just seen an absolute explosion in different types of options trading and volatility trading. And now you even have TikTok influencers who are like pitching options investing as passive income, like, oh, don't buy a U.S. Treasury bond. Do an options bet, which is kind of crazy. Yeah.
How has that impacted the market and how have you seen people, you know, trying to handle some of that new, I guess, dynamic that's been introduced? So that's a very good question. And it started sort of with around the COVID time, people were locked in. They got this
stimulus checks, they started trading, right? Proliferation of these online brokers, no commission fees, options being traded as a sort of very short, short and short maturities, right? You know, options used to be sort of leaps and then maybe like a monthly options, you know, second and third, first, second and third month, quarterly options moved to weeklies and dailies, you know, and then in the single names, you know, so you had sort of people
People were locked, they got money, and they got these instruments, these extremely powerful instruments with leverage, about 100 times leverage. So you suddenly can make bets of millions of dollars, even if you have $10,000 or $5,000 to invest. So that changed everything.
That changed a lot. And for most of these people, actually, it worked, right? Because since 2020, we had that pullback when the Fed started hiking. But for most of it, it worked. So speculative trading activity, especially on the long side,
It worked. Then you also had in parallel sort of crypto markets growing, right? You know, like, so if you think of it, like, you know, a few trillions of dollars of wealth was created there with probably some of these similar type of investors and similar type of people, you know, like, so it changed, you know, so there is less leverage in terms of borrowing money.
interest rates, but a lot more leverage in terms of option trading activity. So as you said, I'm always also surprised you go on some of these social media and then you see all kind of
strategies that can't lose money, that are making like tens of thousands every day. You just need to follow him. And it becomes really kind of bizarre. You have like these people who are at the same time performer or like women who are like, you know, in like underwear suggesting how to trade options. Buy zero day options. They're all big fans of mine. They follow me on Twitter and then they DM me. I'm very flattered. So it's kind of crazy, you know, like, and we try to handicap it by looking at flows from Robinhood.
see which names are being sort of bought, which names are being sold, try to see where the retail may be forced out or something like that. So we did some quantitative work. We did a lot of the sort of language, large language models sentiment-wise, like from Twitter and those type of other social medias, which we could get permission to do. So we're trying to incorporate it, but I think overall...
It's hard to 100% handicap it, but for sure it added leverage to the market, added speculative element to the market. And at some point, it's not going to probably end up well, right? At some point, you know, but it's hard to say when exactly. Right. It's, again, one of those things that can go on for longer than you think. Yeah.
Since you mentioned getting data from Robinhood just then, this is the other thing I always wanted to ask an equity derivative strategist, because you alluded to this earlier. There's a lot of, I guess, misunderstanding or lack of understanding of what an equity derivative strategist actually does and exactly what data they're looking at in order to make some of their conclusions. Can you maybe give us like a quick 101 in where your data comes from? How much of it is from
official sources like, I don't know, an EPFR or someone like that versus like color that you're getting from the market? Good question. Yeah, no, so there are all kinds of data. So there are price and volume data, all kinds of technical data that can be derived from those type of things.
which can also be a different time horizons. They can be daily. Mostly, they are daily, right? But increasingly, you also want to look at the intraday data, intraday correlations, intraday momentum, volumes, large blocks that are traded. So there is also high frequency one day, but most of it is daily, I would say.
And then there are longer term data, you know, when you look at the sort of, you know, some like a monthly statistics on positioning or up to the sort of filings, you know, 13F filings, like holdings and stuff like that, you know, like so. So different sort of frequencies of positioning volume data.
price data. Then, so directly market observable data, like open interest, you know, options, volume and options, those type of things, right? Then you have sort of fundamental data, you know, and fundamental data, you have fundamental data for stocks, you know, which are related to earnings, right?
But increasingly, you have a data which are derived from non-traditional sources, you know, so-called big data that can be sort of sentiment measures, but quantitatively derived measures, objective, not sort of guesswork, to some very specific niche data like, you know, satellite, you know, all kind of like data that, you know, we didn't have, you know. How many cars are parked out in shopping malls. Correct, parking lots in front of Walmart and those type of things, right? So you have these...
stock-specific data, earnings-derived, news-derived, sentiment-derived, and then also non-traditional ones, you know. And then you have, like, macro data, you know, typically lower frequencies, but increasingly also with some of these alternative data sets, big data sets, you can try to figure out, like, you know, shipping and
again, sort of storage and oil tanks, how full they are and stuff like that. So it's a whole host of data, you know, like as a quant and derivatives person, you probably focus most on the market data, you know, so open interest price volumes and all stuff that is derived from that. But you also want to supplement that with all these other data. And then some of the data sets, you derive it on your own, you know, like so for instance, gamma imbalance in S&P options, put minus call. So I was running that for
15, 20 years and first people tell me, "What's that? You cannot know what's..." But then now everybody has it actually. And same thing with the CTA stuff and vault targeting exposure. I was getting so much sort of critique in 2011, 12, 13. Now everybody has it, kind of CTA positioning percentiles. So you can derive some on your own based on your understanding of the markets.
All right. I just have one take and I bring it up a lot and I sort of feel like the Kool-Aid man. It's like every conversation I have to jump through the wall and interject this. But, you know, there's all sorts of like quant techniques and there's the definition of quant and changes over time. And obviously there's an incredible amount of data that we can use now. And then there's sort of like old fashioned quant where you're just like,
We're going to buy, you know, that I sort of associate with like AQR from years ago. We're like, we're going to buy the cheap stocks that are exhibiting momentum, right? And we're going to short the expensive stocks that are declining momentum. And why doesn't this work anymore? And all these sort of hand-wringing in the traditional quant industry.
Why haven't things been reverted? How much is the fact that like so many of these sort of ideas about how the market should work have been broken by the simple fact that a handful of American companies that are very big
exhibit year-over-year earnings growth that are truly remarkable. And this is a fact not about the market world, but about the real world, that for whatever reason, these big tech companies just keep getting bigger despite their size. Wait, Joe, you have to end that by saying the Kool-Aid man catchphrase. What did he say? Oh, yeah. Oh, yeah. As long as the Metas and the Googles and the NVIDIAs and maybe the Apples of the world just keep growing earnings like crazy every year,
How much does that bust any sort of notion of mean reversion in markets? So I think it busts the notion of a value as a factor, but value as a factor has been straggling for a long time now. So sort of probably since...
in interest rates post-2008, a lot of these, you know, and growth of indexation, right? Growth of indexation kind of sparked the momentum and changed the structure of the market. So some of these quant models or quant factors work less and less, you know?
There is also another aspect, which is how, you know, once when you put money to work in these strategies, you kind of squeeze out the alpha, you know, and these things are fully priced in. So they stop working. So sort of growth of quant funds, traditional quant funds, you have quant ETFs, you have like broker dealers doing quant strategies, kind of squeezes out returns, right? You know?
on your question, sort of like these big companies that keep on delivering, that's also a very good point. You know, quant strategies are designed for sort of steady state situation when kind of things are fluctuating around something which is in a steady state. And we had sort of, you know, big sort of
big changes in the world, right? In technology. Yeah. And also geopolitically sort of, you know, capital move to US and it move into this sectors of innovation, right? You know, and now, so you may sort of, you know, you may constantly be out of equilibrium, right? You know, where some of these mean reversion or quant strategies would work. So,
certain type of consequences, value-based strategies, right? Question is how long, you know, how long can, you know, going back to the concentration, right? How long can it go? My question becomes like, let's say if you have like a social media company, like a meta, right? I mean, once when they have all the users in the world, I mean, like, you know, they can't go much further, right? They can't go to the... Right. There's limits. There's limits, you know, but number... They have to start creating fake AI. Fake AI, or go like to Mars, like there's no one there. So there's some limitations, right? Like...
And then there's some sort of also historical when you look at the weight of stocks in an index, right? So you take NVIDIA percentage rate in S&P and, you know, you run back history and you see that this basically never happened. And even if it happens, it never lasts forever, right? But to your point, it can last, you know, one or two or three years is enough to ruin a lot of investment strategies. All right, Marko Kalinovich, thank you so much for coming on OddLots. A real treat for both of us. Yeah, that was great. Thank you so much. Thank you so much. Thank you.
Joe, that was really fun. I'm so glad I finally got to ask him a bunch of
questions about just being an equity-derived strategist that I had on my mind for a while. I love those questions so much because they're this sort of like dark fiber or the dark matter of how this industry actually works, but everyone just abstracts over them. It's like, oh, you look at the data and then you do this. Right, like what data? Where did the data actually come from? Like, I could listen forever to someone, and we should do more of that, just like talk about these aspects, like paying for data and data costs and
all that stuff. I really like Marco. It was a nice conversation. The one thing I would say is, you know, you asked that question about like, well, the S&P 500 overall didn't do too bad on Monday. And like, you know, if I'm an index investor, I have exposure to GE. And so that's good because I don't have to worry about AI. But actually,
But I think the question really is, like, how much is GE exposed to AI in very indirect ways? Yeah, that's correct. Right. And you do. So, I mean, you know what? I think I saw a tweet about this, so I'm just going to say it. And it may not even be true. But if it's not true, it's truthy. Someone tweeted that apparently there was like a Sherwin-Williams earnings call. And someone asked about how much paint data centers were going to need to paint their walls.
I don't know if it's true, because I just saw the tweet. But if it's not true, it doesn't really matter because it is true. Like every company is like, are you doing something that could supply? Do you sell some product that someone building an AI data center is going to need at some point? But my point about GE, though, is kind of the opposite, which is like GE did fine on that day, but I wish I had more exposure to GE. What I really have is a bunch of NVIDIA and Microsoft exposure through my index fund.
Right. But you don't know how much exposure, indirect exposure you have to Nvidia and Microsoft through GE. This is true. This is true. Skanda wrote that excellent column in the Oddlots newsletter about how more and more of the economy is being driven by AI. We actually saw on Monday the treasury market move a little bit. Yeah, yeah, for sure. Which, you know, okay, treasuries will go up when there's a market sell-off, but a lot of people were saying, well, this impacts
growth expectations as well. And so that's why you're getting this reaction. No, totally. I think the degree, I mean, especially, you know, you go back and like Trump announced the half a trillion dollar Stargate project. And I don't know what's really going to become of that. But like, I believe the data center's
And AI, specifically through the data center channel, is a meaningful, important, is a growing important part of the real economy right now. And if suddenly they're like, you know what, this is a dead end or suddenly like we don't need this because we can get AGI so cheaply that it's just like on our laptop, that would raise some real econ concerns. Yeah. Shall we leave it there? Let's leave it there. Oh, yeah. Yeah.
This has been another episode of the All Thoughts Podcast. I'm Traci Allaway. You can follow me at Traci Allaway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our guest, Marco Kolonovic. He's at Marco in NY. Follow our producers, Carmen Rodriguez at Carmen Armin, Dashiell Bennett at Dashbot, and Kale Brooks at Kale Brooks. For more OddLots content, go to Bloomberg.com slash OddLots where we have transcripts, a blog, and a newsletter.
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