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Marko Kolanovic

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2025-02-03
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2025-02-03
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  1. I'll go like to Mars. There's no one there. So there's some limitations, right? And then there's some sort of also historical when you look at the weight of stocks in an index, right? So you take Navidia percentage weight in S&P and you run back history and you see that this basically never happened. Even if it happens, it never lasts forever, right? But to your point, it can last one or two or three years is enough to ruin a lot of investment strategies.

    2025-02-03 · Odd Lots · Marko Kolanovic Is Back With a Warning for Stocks · IDENTIFIED FROM THE TRANSCRIPT · source

  2. Big changes in the world, right? In technology. And also geopolitically, sort of, you know, capital move to US, and it moved into this sectors of innovation, right? And now 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. Certain type of quant strategies, a value-based strategies, right? The question is, how long, you know, how long can 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 Ameta, 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 go to the...

    2025-02-03 · Odd Lots · Marko Kolanovic Is Back With a Warning for Stocks · IDENTIFIED FROM THE TRANSCRIPT · source

  3. So, I think it butts the notion of a value as a factor, but value as a factor we've been struggling for a long time to sort of probably since. Know decline in interest rates post 2008. A lot of these and growth of indexation, right? Growth of indexation kind of sparked the momentum and changed the structure of the market. So some of these quants models or quant factors work less and less. There is also another aspect which is once when you put money to work in these strategies, you kind of squeeze out the alpha and these things are fully priced in so they stop working. So sort of growth of quantitational quant funds, you have quant ETFs, you have like broker dealers doing quant strategies, kind of squeezes out returns, right? 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

    2025-02-03 · Odd Lots · Marko Kolanovic Is Back With a Warning for Stocks · IDENTIFIED FROM THE TRANSCRIPT · source

  4. And same thing with a CTA stuff and vault targeting exposure. I was getting so much sort of critique in 2011, 12, 13. Now everybody has it CTA positioning percentiles. So you can derive some on your own based on your understanding of the markets.

    2025-02-03 · Odd Lots · Marko Kolanovic Is Back With a Warning for Stocks · IDENTIFIED FROM THE TRANSCRIPT · source

  5. So you have these stock specific data earnings derived, news derived, sentiment derived, and then also non-traditional ones. 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. It's a whole host of data. As a quant and there is 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 want to supplement that with all these other data. And then some of the data set you derive on your own. So for instance, gamma imbalance in S&P options plot minus call. So I was running that for 15, 20 years. And first people tell me what's that? That's you cannot know. But then now everybody has it actually.

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  6. Question. So there are all kinds of data, so there are price and volume data, all kind 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...

    2025-02-03 · Odd Lots · Marko Kolanovic Is Back With a Warning for Stocks · IDENTIFIED FROM THE TRANSCRIPT · source

  7. So it's kind of crazy. And we try to handicap it by looking at flows from Robin Hood, 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 exact

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  8. Probably some of these similar type of investors and similar type of people. So it changed. So there is less leverage in terms of borrowing money, 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 underwear suggesting how to trade options.

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  9. For most of it worked, you know, like, so speculative trading activity, especially on the long side, it worked. Then you also had in parallel sort of a 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.

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  10. So that's a very good question. And it started sort of 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 and short maturities, right? Options used to be sort of leaps and then maybe like a monthly options, you know, second and first, second and third month, quarterly options moved to weeklies and dailies, you know, and then in the single names, you know, like so you had sort of people locked, they got money, and they got these instruments, these extremely powerful instruments would leverage about 100 times leverage, you know, like, so you suddenly can make a bets of millions of dollars, even if you have like, you know, 10,000 or $5,000 to invest. So 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.

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  11. Or family feels good about investing, right? And they choose them to ignore, you know, like so on Monday. I was watching CMBC and every single guest was saying, oh, take your shopping list out, take your shopping list out, take you buy this, buy this, right? So, you know, it creates a little bit of a sentiment. It creates a sentiment. And people say, okay, I'll make a punt. I'll buy if it's 20% down. Maybe next day is going to that cat bounce. So people buy, right? Some people rotate. So, okay, like I'm getting rid of Navidia, but look, Apple has been 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.

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  12. So, you need to sort of put some scenarios what can happen in terms of taxes, regulation, tariffs, trade wars, geopolitical conflict, and then see how can they impact specific stocks and industries, countries, 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 you need to, what else you need to take into account. But you put some blueprints sort of what can happen. So, you know, I think you pointed very well. He was talking about Navidia, Taiwan export. 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. Most people think momentum. They don't calculate by the thing. They just feel good about market. They see good news about market. Their taxi driver or friend.

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  13. I'm hard pressed to see it going much, much higher, right? Because valuations are there, positioning is already there. As you said, Fed is not cutting, right? So it's a little bit of a chicken and egg. I mean, I have been scratching my head like at these level of rates, which I do think are restrictive rates for now more than two years with the commercial real estate. Here and there we saw a few hiccups, you know, like, 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 going to 7,000 or 6,800 or something like that. So I would say maybe it can go 65, stay range bound. 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.

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  14. Yeah, so no, so I would say if I can move away from price starting, 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'm 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 whether it becomes an end of a cycle and we go much lower into 4,000 that i 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 six thousands it can go a bit higher i just see

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  15. 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, you know, because real world actually works in terms of physics, deeply works in terms of probabilities, not just superficially, you know, in a quantum physics. So you need to sort of have a sort of hyperbolistic view and you're forced to have one view, like 100% or nothing, right? So it gets oversimplifies. I think media, 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 then say, oh, you're right, you're wrong.

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  16. Sure. So look, it's nice once in a while that you can be somewhere away and not look at the fate. Parse every single word. Although I did it yesterday, you know, about a few months ago, I didn't, you know, like, so it's nice to make a break, or maybe it's necessary. 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. So I think 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.

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  17. So 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.

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  18. Thought 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 Voltargeter, systematic investors, too much optimism. And then you start basically hitting the stops across these strategies, right? CTAs hit their cell signals, Voltarget is Vix goes up, Vol goes up, they need to sell. If you were selling puts on AI names, you suddenly need to kind of close so Vix was very Vix behaved most phenomenally. So it was a lot of all shortfall 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.

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  19. Way talk to them, right? So you 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. 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 answer. Sometimes it can be used to do bad things, to impersonate, to deceive, to manipulate. So there's going to be a lot of interesting, I would say, philosophical issues, you know, 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. Yeah, I don't think it's going to be like that in finance, at least.

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  20. So, you know, I think eventually it will get there in a sense that it will sort of address some very important questions which are kind of deeply what every person fears or wonders or sort of seeks kind of meaning of our lives. So there are definitely interesting things there that can be done. I mean, people are doing with these assistants, right? 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, you know, 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 inside?

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  21. Am I to say that that's wrong as well? You know, but there's a lot of speculation and I trust people often tell me, well, imagine just how my way to search internet has changed. 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. Excited about that change. I think it's way overdue change, you know, like, but there is a lot of optimism now.

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  22. Very, very different of what people have been doing five years ago or 10 years ago or 20 years ago. Obviously big progress in computing power, big progress in the models as well. So it's, but I see it like more as an evolution than something that changed with ChatGDP 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, question is going to be winners, losers, how to monetize, does that suddenly rerates all of equity market multiple? 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.

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  23. I look at it from theoretical side. I look at it more how to apply it sort of in finance, in quantitative trading, how to use large language model to assess the sentiment changes in sentiment and those type of things. So how to read quickly things and summarize them and derive some signals out of it. So there is obviously bigger question of AI, which you said is kind of philosophical questions, like, are we going to be replaced? At what point, what's going to be role of human once when we can kind of break down our way of thinking and effectively train it and replace it? So there are a whole host of other questions, you know, like, so I'm not skeptical that this is going to be hugely important than it is a hugely important.

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  24. Are developments that it's hard, like, you know, this whole AI, and I see, I wrote a book on 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? And then you have a change is you have political changes that can bring deregulation, that can be changing tax regimes. So you have like a wildcards. 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.

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  25. Entirety of market, right? You know, with you have a crypto, you have a fiscal measures, you have monetary stimulus, you have a sentiment shift. It's hard to handicap all of those. 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.

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  26. Exactly. So there is this mean reversion, but there's also a trend, right? So, you know, 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, 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. So you know they're not going to buy more, right? So that's a self-contained isolated system. And you can say, okay, optimism is too high. So there's the only downside, right?

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  27. So, you know, clearly in the markets, things are mean reverting. So when think reached some very high levels, eventually they go along.

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  28. yeah 20 february 2008 vome again on yeah and uh 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 product options futures ctas those type of things

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  29. Sometimes these flows would be bigger than the market can absorb. And they would all go the same way from leverage ETFs, from options. So you see like, okay, there's like $20 billion to sell and market can't absorb that. So you know market towards last 10 minutes will drop. And that's, you mentioned Soro Gandov, that's where some of these things, because people, you know, if you look from the outside, you say, oh, how can he get that, right? But it is really understanding a bit of technicalities, which is flows, option, convexity is liquidity, and how they sort of interfere. But, you know, after. 2015 16, people figure it out, you know, and then people put it in their models, they create a structured product.

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  30. 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, no hedging flows. And then you see, okay, what are the hedging requirements at the end of the day? And in 2008 and then 2011, and like low-hanging fruit, you could see some.

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  31. We are now in earning season. So, my first models were impact sort of of earnings on a stock price. And what can you read from options market? So I published some papers, 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 more, 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 100 stocks, maybe on average in a portfolio level, you'll be fine. So that derivatives research and quantum research, I did a lot of quantum research. So you try to process the data. You try to look at the measures. And at that time, like 20, 25.

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  32. 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. What is the cause, what is the concept, what's causality, you know, and what's the noise, what's statistically important, what's statistically not important, you know, like, so in physics, you build these type of models, you try to understand what's significant, what's not, what you can neglect, which factors you have to take into account, and most importantly, how to simplify the complex. You know, market is extremely complex system as many physical systems. 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 Mary Lynch in derivatives research where I started looking interesting.

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  33. Stuff going on for Apple. It was probably just rotation. So the market kind of held up. I was sort of a defense whether these technical levels 2050 will get broken and will go lower or not. We didn't, you know, but I do think that sort of valuation positioning and some of these technicals are a bit stretched. So I don't see a huge, huge upside for the market. So maybe I'm switching the topic a little bit.

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  34. Yeah, so I mean, that was again January's 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, their sort of revenues will get hit. And some will be able to do things for cheaper. So you had Salesforce, I believe, as well. So it ended up not a macro day, but more of a rotational day. There is also so-called quantors where even within technology, some stocks are higher multiple. Some stocks are lower multiple. Some stocks are more momentum, less momentum. 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.

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  35. 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 month 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 if the market's going to stay below 20 and 50 days 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 set up 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.

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  36. Months and 200 day moving averages, right? So that's why when people look at the 200 day moving average, first when I, you know, 20 years ago and someone told me, I said, like, what is this magic? Why would this work? But it's reality is that many models, you know, systematic models primarily, you know, computer dream, 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? So for instance, earlier this week, S&P got below around 6,000 or a bit below.

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  37. 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. 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 geopolitically maybe trade war with China or some sort of blockade of Taiwan's traits or something like that, right? So first you need to have a little bit of a catalyst. If the market is technically very strong, the catalyst is not going to change the momentum. So what that means more specifically, you know, so when you look at the trend investors, they have a range of signals. So they can look at a one-month price momentum, three-month price momentum, six-month price momentum, so 12 month price momentum, maybe 18 months. But that's about it. There are some very short-term momentum players that look intraday or on daily basis, but most of these signals are concentrated around 12.

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  38. China has been, we've been a sort of the brink of this Cold War with China, so money has left there. Latam has its own share of sort of issues. So money has been also geopolitically moving. So it's a moving in the US, it's a moving into indices, it's moving into tech. And then you end up with these give or take 10 stocks that really sucked up all the capital and valuations got very, very high. Tech investors, they do have a sort of their rationalization. So what's going to happen in future? This thing is 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 the thesis. They went up some of these other flows. So unprecedented concentration is not going to stay there. The big question is when will we see that rebalance? Do we need to see some cyclical downturn first to purge and to normalize some of these valuations?

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  39. Yeah, so the concentration is the highest 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. This concentration came for two reasons. 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. more something goes up more money it attracts becomes bigger than index you know all the passive flows into it so there's a technical aspect there's 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

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  40. 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, whether there is any sort of follow through. But I do think that it's going to be some investors got burned clearly and a little bit of a tarnish on the sort of this thesis that Some of these stocks like Navidia just go up every day. You can't lose. 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.

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  41. So it was early contained. 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 not just with a panic, but some of the sort of forced selling, you know, maybe coming from options, 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 forced out of these positions and maybe have a catastrophic loss. So it was fairly limited. I'm a little bit surprised because we didn't really have a meaningful seller since last summer, you know, sort of the back of Bank of Japan. 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. They allocate the capital. So maybe it's a little bit too early. I was somewhat inclined to see that we will.

    2025-02-03 · Odd Lots · Marko Kolanovic Is Back With a Warning for Stocks · IDENTIFIED FROM THE TRANSCRIPT · source

  42. So, you know, as you said, there was not much contagion, you know, and if you look at the different stocks in SP, many of them were actually up, you know, and many even in the tech sector were up. If you look, for instance, Facebook yesterday or today or a bunch of other names that sort of were perceived that they might be sort of benefiting from the sort of open architecture type of things that can come at a cheaper price and can be still implemented in their business model when it comes to AI models

    2025-02-03 · Odd Lots · Marko Kolanovic Is Back With a Warning for Stocks · IDENTIFIED FROM THE TRANSCRIPT · source