Cem Karsan | Ex Market Maker - Options Trading Masterclass [Advanced Strategy]

Cem Karsan, Cem Karsan, Cem Karsan; he's the founder of Kai wealth, who some people describe as the "time traveler of trading."

The 13th of January, a tweet that you made, I believe at the 29th of November, if I'm not mistaken. When someone was asking in regards to the S&P Gap being filled post-election, how did you make such a direct and correct prediction?

For many years now, we've been very accurate in prediction, but really since February last year, we pretty much called every single major move and VA environment over the course of, um, I would say 10 months. His ability to make precise market predictions astounds even hedge fund managers. If you really look at why structural flows happen, and those flows are predictable, and they can help, uh, kind of delineate when certain types of things are more likely, compare that with some macro, and it can help to delineate very precise outcomes. So people think it's magic. There's a saying, I think, uh, "any future technology that is sufficiently sophisticated appears to be magic." The reality is that there is a lot of more information out there than they realize that allow you to trade with more accuracy.

What's the next January 13th call? I'm happy to lay out multiple, but they may seem to people as us saying up or down. Remember, we're not predicting up or down; we're [Music] predicting the number one podcast in the trading space, the fastest growing, and that's thanks to every single one of you. [Music]

Welcome everyone back to the Words of Rydom podcast. We are back once again and still the number one trading podcast in the world and the fastest growing, thanks to all of you and our incredible guests. We're currently on our US podcast tour, the biggest tour in podcast history. I've not seen one like it. We had ten states, over twenty podcasts, all with verified profitable and some of the very best traders in the world and talking with—which today we're in Chicago. So we're actually nine states into our podcast tour, but we thought this would be one of the very best just to highlight the levels to come. So make sure you have your notepads ready. And this tour wouldn't be possible without our sponsor, TradeZella, who helped us make this happen. They're the best tool in the market for backtesting, journaling, in-depth analytics, so make sure you check them out. And a huge thank you to them. But today we are here with a 26+ year trading veteran. He is a quant; he is actually the co-founder of Kai Volatility and Kai Wealth. During the great financial crisis, he was one of the largest market makers with 133% of the options volume. Is that correct?

And the S&P 500? Yep. Yeah. Okay. Well, it's the one and only—you've already seen Cem Karsan—and well, I was actually meant to add, apparently a time traveler too, is what I'm hearing on Twitter. Um, you—a lot of talk of the town recently. I know you probably—we had tons of requests, so I'm truly honored that we've been able to sit down today, uh, here in Chicago. I know it was last minute as well, so thank you very much for being here.

No thanks for having me. Looking forward to a kind of an extended conversation where we can kind of get through everything today.

Definitely, definitely. I'm very much looking forward to it, and I know we're going to have a lot of education when it comes to the to the audience on so many different factors. And I just want to kick off one question, and I know you've probably gotten this question a lot recently, but you know, the 13th of January, a tweet that you made, I believe at the 29th of November, if I'm not mistaken, when someone was asking in regards to the S&P Gap being filled post-election, how did you make such a direct and well, correct prediction?

Two months out almost? Yeah. I mean, it's, uh, it's always about probabilities, right? So, uh, when you get something right like that, which we've done, by the way, several times in the last three, four years publicly, uh, almost to the day or to the day, people's minds kind of explode. But the reality is there are dramatically more probable, uh, paths, um, in a distribution of outcomes than others. Um, and if you really understand flows and, uh, particularly, uh, market structure as it relates to derivatives, which have time components in them, uh, they really allow you to pinpoint what those higher, more likely paths are. Um, and that was, uh, just a great example. Like I said, we've done similar things. If you go kind of look back the last several years, uh, in early '22, we predicted the decline several months out, almost to the day, um, as well. Um, again, if you—we'll talk about it in this conversation, I'm sure—but if you really look at why structural flows happen, and a lot of those are—those flows are predictable, and they can help, uh, kind of delineate when certain types of things are more likely. And so, uh, pair that with some macro and some bigger picture, um, uh, things, and and it can help to delineate very precise outcomes. So, uh, yeah, people think it's magic. Uh, there's a saying, I think, uh, "any future technology that is sufficiently sophisticated appears to be magic," and I think the reality is markets are, you know, and traders are tuning into the realities that there is a lot of more information out there than they realize, um, that allow you to be more precise and trade with more accuracy.

What did the beginning of your trading journey look like? In terms of where did that interest come from?

Oh, wow. Uh, there's several different interests. Um, I think the derivatives, uh, interest really started, um, in college. Um, my mother and my father are both engineers. My dad's a PhD structural engineer; designs offshore oil platforms for companies. So, um, you know, my mind works much like an engineer. I, uh, very quantitative, always, uh, thinking about things in multi-dimensions, uh, trying to figure out how to fix and solve, uh, you know, things all the time. Um, but that paired with an upbringing where I was born in London, lived in Turkey as a child, grew up speaking multiple, you know, different languages on the border of cultures, and then living in Texas and then eventually going to boarding school on the east coast of the US while my parents were in Norway. So that kind of upbringing really, um, gave me, uh, you know, made me a third-culture kid, which was, you know, uh, almost like a third party looking into my own, uh, world and culture around me. The people in Texas seemed different; I was a bit of a square peg in a round hole. But my parents, you know, and the Turkish culture, what seemed, uh, you know, different, and I was a bit of a square peg in a round hole. And I think this happens to, to a lot of people who grow up on, on the border of, of different cultures. And I think that, um, interest that developed, uh, from being a square peg in a round hole and, and culture and policy and, you know, social relations, sociology, um, really came together with that engineer-ing and mathematical background, um, in the form of capital markets in college. I think the, the, the thing that unlocked, uh, kind of my path, uh, who I was into that world was I, you know, I got a PT scholarship to, to, uh, college, to university, and my freshman year, my parents gave me the money they had saved for my college education. Very fortunate, um, in that respect, but it was 1995, and this is how variance and, and probability comes into right outcomes, uh, you—I was talking in that and that, uh, kind of mindset. '95, if you're a student of markets, you know, markets made a hockey stick straight right for four years, uh, up into the tech bubble. And I was fortunate to be managing some money that my parents had given me, and I was reeking as a young, uh, uh, man, and, uh, I was quantitative. And the people I talked to about trading in college, uh, had a similar mindset based on the school I was going to, which is a more engineering kind of Rice University, which is a more engineering kind of focused school. And, uh, so I just got into options at an early age, got into quantitative finance. So my path was very different. You know, a lot of people come into trading or, or investing, which are two obviously different things but related, uh, from fundamentals or technical analysis, and I really came from a, a much more quantitative market structure perspective that drew me to Chicago. It's where we are now. Um, you know, I've been here for now twenty, uh, six years, um, and it led me to trading floors of Chicago and the options pits. Um, you know, 1997 there was an event called Long-Term Capital Management, which some people here I'll be aware of, um, that was a, a massive VA and options-centric event, uh, that was incredibly interesting to me. I knew people out here on the trading floors who had not only benefited and profited through that but, um, had followed it very closely. And that, that was kind of the final, you know, thing, the straw that broke the camel's back that I was—drove me to come out here and, and, and start experiencing that. But that's kind of—that's my origin story, I guess. That's how I got started. Um, I really didn't know a thing about options. I thought I did, and probably like a lot of people that are listening today, um, you know, there's a lot more back, uh, behind it, underneath, um, uh, their structure and how they affect markets. But, uh, had a trial by fire in those options pits in Chicago and eventually built my own market-making business and, uh, grew that to one of the biggest in, in the option space, like you mentioned. So that's how it all started, uh, you know, that eventually merged with my deep interest in macro and policy and, um, and so we'll—I'm sure talk about some of those things as well.

Definitely. Well, so much has happened in such a short space of time there, especially when you consider that you came in around '90, '97, right?

I came to Chicago '98. '98. So from '98, but then starting up your, your market maker, uh, business, going into the great financial crisis in that short space of time—time, all things considered—you—that's a lot of progression to happen in such a short space of time, especially that introduction into options as you say, with, with options at the time thinking it was probably, you know, surface level, but then obviously over that time so much more deeper that we're definitely going to get into, but coming in at the surface level, what was some of the, the trials that you had to go through in terms of learning those different dynamics?

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So started in '98 and, uh, you know, your experience definitely colors your perspective, and I think it was very fortunate—fortunate for me to see the tech bubble kind of firsthand, um, both from a sentiment, societal perspective, uh, I actually, before I came, uh, in '97, '98, uh, worked for a tech startup in, in Houston. So I experienced, you know, inside, uh, what that euphoria was like and what the flow of capital, uh, tied to kind of Greenspan's monetary policy was like. And then I got to come see the, the upside volatility, which was honestly in history—that, you know, at least in the United States, the biggest kind of right-tail event we've ever seen. And that, that really is important because very few people have that, uh, deep experience. I was able to see, uh, dramatic upside volatility, um, not just in financial markets but in derivatives, uh, and how that plays out and, and affects market structure. Um, and then, you know, those were early days, uh, and then in, uh, January of 2000, I was moved to a senior role, uh, running RBC's Equity Index Derivatives desk, um, and, uh, you know, March 2000, the tech bubble burst. And so then getting to see that right-tail event turned into a massive left-tail event. Um, I mean, the NASDAQ lost over 90% of its value; 95% of all tech companies went bankrupt, uh, during the tech bubble. Uh, these are numbers that if you weren't around during that time are almost unfathomable. Um, it was an incredible experience, um, and education in bubbles, how they form, how market structure affects them, um, and, and what, uh, you know, what they—what they mean, right, to, uh, broader liquidity as well. So, um, I have ever since that time been—had those things top of mind, right, um, and been gathering in the back of my mind data to help construct a better understanding of, of, um, how liquidity, uh, changes cycles, uh, how, how it drives outcomes. So, um, had a lot of success, uh, in, in that bubble burst, you know, whereas other people were facing liquidation, options markets were—were—the world was coming to hedge and, and get out of risk, um, and so, uh, that was an incredible, uh, trial by fire and education for me. Um, it also, you know, at a young age gave me an opportunity, uh, through kind of my success, to, to leave the bank and start my own market-making group in, in 2006. Um, and so I backed our group in '06, uh, started a more boutique, uh, index-based, um, uh, trading operation that eventually grew to, like we mentioned, one of the biggest. Um, through that process, an evolution—experience—not just the banking crisis firsthand, but also saw the evolution of options markets and, and their, um, their importance, their increasing importance to, uh, outcomes. Um, you know, that was—they were—it was important in 2000, but by the time I got to '08, um, uh, much more developed, much more important as a feedback loop to the outcome that we actually saw. Um, and that also obviously informs, uh, the way I look at the world today. Um, it really is what drove my deeper understanding. That period, being one of the biggest dealers, if you will, or, um, not just market makers but entities holding inventory and warehousing risk in the marketplace, um, I knew what the—what the positions of the world were because I held them. And, uh, when you know what those positions are, you begin to very clearly see how they are affecting outcomes, um, and see how they're driving market structure. That really gave me a deep insight early on, before maybe a lot of others, uh, into, to how options flows work—dramatically important to the distribution of outcomes. And that realization, uh, really informed, um, you know, once I sold my business, left, uh, you know, uh, that market-making space in 2010, it really drove a lot of my, uh, family office and personal and eventually fund investments that we now do, um, in terms of development models to help predict based on, uh, market structure and, and options involvement. Um, leaving that space, uh, you know, not that I've ever really left, but you know, uh, no longer being at the seat of a market maker, um, also allowed me, uh, to start investing more than trading. Again, people think they're the same thing often—obviously trading can be part of investing—but, uh, you know, really did a lot of the venture capital, private equity, real estate investing, uh, during that 2011 till, kind of to recent, over those periods as well, learning a lot about kind of the investing landscape. And the more I started doing that, the more it made me realize, um, how oddly those two sides rarely mix appropriately, and how people think about investing, um, really doesn't capture, um, risk management, um, nearly as much as it should. People think about investing as buying assets, uh, and, and buying them cheap and trying to hold on to them for long periods of times and capture appreciation assets, whereas trading is much more, uh, trying to get good risk-adjusted returns, um, and optimizing returns relative to the risk you're taking. And, uh, the way I started managing my family office was much more almost as a, a trader thinking about long-term investments, but in the context of risk management. And I think, um, you know, again, now integrating macro, my big picture, you know, view and understanding from being a person who grew up all over the world and has a deep interest in, in those realities as world well, and then combining risk management and trying to optimize risk-adjusted returns, um, has really been, uh, particularly adding in the flows of the market structure pieces we know have been what have finally led me here at 48 to, to, to kind of bring all that wisdom and knowledge together in a much, uh, kind of fuller, more kind of holistic way. Um, so yeah, been quite the evolution, um, uh, through time, but, um, you know, really feel like we're hitting, um, you know, I'm hitting my stride as a business and as investors and traders, we're really kind of at the best we've ever been at this point.

I love that, and there's just to take it back ever so slightly in terms of the importance of options and option flows, why do you think that is? And, and how can you help in terms of the audience and even myself to a degree of understanding why options hold such an importance over, say, other markets?

Yeah. So let's start at the beginning, I guess. People who start from a fundamental asset perspective, um, think that assets have one value; it's, it's a simple way to think, right? We should be able to value this asset, um, uh, and even options are considered a derivative of that, uh, expected value of that value of that company. Um, and having not started from that perspective but from a more derivative, quantitative, distributional, probability perspective, um, I'm here to tell you that, uh, an asset is much more complex than one value. Every asset has a rich distribution of probabilities of outcomes and potential values, um, uh, and that's what options, uh, price. They, they price every node, uh, in time, uh, every probability of a certain value, um, they are a three-dimensional representation of an asset, not a two-dimensional price. Um, and by arbitrage, these options must equal that expected, you know, must, uh, the, the total probabilities, the total distribution must have an expected value that equals that asset value. And when you come to the market, uh, understanding that, you begin to understand that there's much more importance to options and other things like structured products and all the different assets who have, uh, much different dimensionality than just two dimensions than just that stock or under what we call the underlying price. Um, nowadays people think options are the tail wagging the dog; you'll hear that saying again and again—that they are starting to affect the underlying dog, uh, by, you know, uh, in, in all kinds of incomprehensible ways. I'm here to tell you they are the dog; they are the better three-dimensional picture of the dog, whereas the asset value is simply a two-dimensional snapshot, um, which gives you much less information. People often talk about how, oh, the growth in derivatives, you know, 2020, 2021, people were actively saying, "Oh, it can't last; this is, this is just a blip; people are getting excited about the de-leverage they can get in options." What people miss, and I was very actively arguing for at the time, is options are like a technology; they're—it's a superior way to express, uh, any, uh, piece of information or any knowledge, um, uh, and, uh, accordingly, I've seen them grow in volume and, uh, and interest over my twenty-five, twenty-six years now, um, and that's not about to end. The options market is, um, essentially swallowing the, uh, the equity and bond and all asset markets because it's a superior way to bet on information on a risk-adjusted basis. So, um, so once you understand, you have to start there; you have to really look at the world a bit differently, um, uh, uh, and once you understand that any effect that happens in any part of the distribution to any asset or any positioning, even though it's not linear, has to, by definition, uh, equate to an, an underlying effect, uh, in the asset itself, directionally, but also in the volatility distribution, has knock-on effects as well, um, and, and so at the end of the day, you have to start with that perspective to truly understand, um, how options are driving price changes in the underlying, but also how the underlying can then have, have, have effects, um, on the other parts of the market as well. They're all integrally connected, um, and, and, uh, like I said, as somebody who held all the different parts, um, of the different distribution and the pricing, um, I had always a non-two-dimensional, uh, view on outcomes; it was really, uh, a view on probability outcomes and what paths were more likely, um, what, uh, how the removal of certain paths and certain information, uh, could then ultimately affect the probability distributions as well. Um, I gave you this example off-camera before, but you have to understand if you have two assets, right, at the end of the day, and you didn't know—two stocks, let's say—and you didn't know what they were—they came from the same industry, they were the same market cap, same exact price—you would look at those two and say they're the same asset, but then if you peel back the option chain and looked—might be incredibly right-distributed with a massively fat left tail, and the other one could be left-distributed with a massive right tail—completely different risks, completely different, uh, dynamics. You'd have a much better idea of the risks and returns and outcomes that are associated with each asset. You'd realize they're not the same assets at all, um, and I think that's a good way to kind of visualize what I'm trying to talk about here.

Yeah, and in terms of market structure, this is something that we talked about just before we started as well, is like the general retail definition of market structure in their perception is technically based, you know, like trending markets, higher highs, higher lows, and so on. But when we discuss that, from my understanding is market structure really looking at the actual structure of how the market is, is moving and actually operating.

Yeah. So, so technicals, um, and the reasons they work other than the second-order effect, which is people watching technicals again themselves somehow affecting distributions, but the real core driver of why the major—the ages—the things coming—going—from seasonality to momentum to, uh, looking at Bollinger Bands, understanding how those things work are primarily a function of structural effects that, that exist, you know, macro flows that are not tied to anything other than, um, uh, these—the positioning in the market, uh, calendar effects that are tied to expirations, uh, end-of-the-month effects that are tied to rebalancing and re-leveraging effects, um, and, uh, again, the positioning effects of, of options were large. So, uh, people are essentially sitting on the other side of a door with technical analysis, trying to listen to what's happening, trying to, to, to, you know, from a distance understand what trends and, and, and, and rhythms they're hearing to try and predict what might be happening on the other side of the door, um, whereas, you know, if you really understand what's happening, uh, there's—you have a keyhole and understand how positioning is affecting things, you, you can actually see what's driving a lot of those technical factors. That's the true market structure; technical analysis is, in a sense, trying to sense what those things are that you don't really truly understand. So I think, um, you know, technical analysis is, is still useful, uh, if you understand what's...

Actually, happening through that keyhole, because you may not see everything that's happening. Right, you're still only have a keyhole; you can't see what's out of frame. All kinds of things are happening. Um, and and understanding trends and why they happen and relating those to what you're seeing then can help you project and predict. So I'm not saying technical analysis is useless, um, but uh, but on its own, you can often make very false assumptions, uh, because you don't have a clear understanding of the drivers and structural realities that are driving that technical trend or or breakdown or or uh different things.

Where do you think uh a retail trader should start to learn more about you actual market structure and actually what's going on behind that door? Um, so it's a new technology, right? And much like a new technology, when I talk about options, not that new, but you new in the sense that it's becoming more readily available, um it's affected by network effects, right? Um, a new technology maybe exist for a long time before it gets adopted more widely. When I started in the business in '98, we everything was written on a card uh in the pits. Um, we had only quarterly expirations, so you know, know four expirations a year. Uh, the options were on approximately, you know, we had a 25-point in the S&P when the S&P was trading, you know, about 400, right? So you know you're dealing with like almost 1% gaps. Uh, no single-list options. Um, you know, uh, the multipliers on them were 250. Uh, you know, now you have 150, 101.1, right? Uh, you have every day to expiration. Uh, you have uh every uh strike. Everything we used to trade in in eights, uh, you know, now we trade in pennies, right, or fractions of pennies. Um, so uh those network effects of of U more structure uh have also been paired with education access. Uh, you said you were talking to Tastytrade recently. You know, entities like them educating, providing access. Um, uh, you know, um, people like ourselves sitting on here educating, right? So where do you start? You know, there's there's an incredible uh richness of information and education out there. I'd say we're tip of the spear because we're fortunately, you know, in in the depths of those markets and understand these things maybe better than uh than many others. So you know, we feel free to follow us and and listen to what we have to teach, but uh but there's a a lot of uh, you know, literature out there, a lot more um uh education. But similar to myself, sometimes learning isn't just read in a book; sometimes you have to get in there, uh, watch, understand, listen, and participate. Um, and like I said, there's more ability, more liquidity uh out there to do those things uh than ever.

One thing that I found interesting from watching some of the recent interviews that you've done is prediction. I know is a is a big thing that a lot of people are interested in, um and hearing your thoughts, and I know that you've set out sort of your prediction for the next year to two years uh in some of these interviews, but where does the the sort of confidence and the the mindset come from in terms of being able to make those predictions on a regular basis? As you said, you've already been doing it for the last, you know, 3-4 years uh publicly, where does that come from? Is it from the the mathematical, you know, equations and those distributions that you talk about and then removing certain information based on uh certain outcomes no longer being in play, if that makes sense?

Yeah, I I think uh it's actually I don't want to say easy, but it's a lot easier to draw distributions, to to to get close to a broad set of probabilities and draw distribution statistically, you know, it's done in lots of different places in the world, right? Um, given a large number of outcomes, um, you know, people can predict distributions based on understanding the physics, if you will, the actual uh supply, the demand, the pressures on the system. Um, you can draw very reliable distributions. Predicting a single outcome, M, um uh is uh with with infinite accuracy is impossible. Um, that said, um, I have great confidence in our ability, understanding how markets work, to predict distributions. And so once you know the distributions better than other people, you can lay those out with some clarity. Um, and people uh, you know, sometimes those probability distributions that you lay out are not a 1%, which people might think based on a log normal outcome, but maybe 30-40%. Um, and then given x uh y given uh y ZF, right? Once you start to have these two paths out of maybe a thousand paths, um uh you can more reliably put a couple things out there that are incredibly valuable. Now again, people people focus on that January 13th uh uh prediction because it's uh two-dimensional, uh because it's so specific U, and again we've done that again and again as highlight really high probabilities and then hit the high probability uh bets, but understand the real value is understanding the paths because again you don't have to play in two dimensions; you don't have to go pick the gap-filled day; you can go bet in the options markets on multiple different paths and outcomes. Right now I can go pick the two, three, four most likely paths on a distribution for the next three months, um and uh in particular the ones that are priced quite differently maybe than than our our priced in the market and then have laid out not a long bet or a short bet or a long Vol bet or a short Vol bet, but a series of distributional path bets that are incredibly low-risk, high-probability, great bets. Um, and uh if you can do that again and again and again in the marketplace, that's an incredibly profitable way to trade. So I think the the right way on a risk-adjusted basis to become long-term profitable, successful is to remove yourself from just betting two-dimensional and trying to pin single outcomes in the market, but really start thinking again in probabilities and distribution.

In terms of uh probabilities, it's easy to look at—well, well I say it's easy—well, a lot, majority of retail audience is looking at is just statistics based on their strategy, meaning they're a lot of the time technical based, maybe some macro in there to a small degree, but the majority has just got the statistics based on their strategy and how that edge plays out. You, how important do you think it is for people to go beyond that? And that that's really what you've been highlighting is that's thinking more two-dimensional. How do you think people can make that transition to thinking differently, thinking more three-dimensional?

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Yeah, I mean, it's hard if you are not familiar with statistics, right? I mean, if you're not familiar with what a distribution is or what probabilities are, um, it's harder. Um, so I think you have to—everybody's got to step back first of all and uh get that. Doesn't mean you have to be a statistical saon; you don't have to know; you don't have to be a statistician, right? Um, but the reality is our brain, bra, it becomes intuitive once you learn because our brains work in probabilities. You know, uh, you reached out; you reached out to me, and you were like, hey, you want to come on this podcast? You know, my brain probably did some very quick probabilities of uh, you know, well, he's not going to be here that long; will we be able to do it some other time? Uh, you know, how much time do I have? Is it worth my time? Uh, you know, what's important to me? You know, how does this mean? You know, and it drew some set of, you know, optimal outcome for myself to make me decide on expected value of yes or no. Um, we all make decisions day in day out managing um, you know, preferences and and probability outcomes. Uh, when you fall, you know, when you when you walk before you fall, you're judging in real time; your mind is judging whether you should put your arm out to protect your head or if that's more dangerous for your arm than it is for your head. We do these things naturally; that's how our minds work. Um, and in in a sense it's a series of ones and zeros, but those ones and zeros are nodes on that distribution, right? Um, much like an option. So so I know this is blowing people's—some people's minds out there—but this is a way of thinking and living and and and and managing our lives and coming actually more in tune with what we naturally do, and I think it's an incredible education outside of markets that everybody should uh should have. Um, but I think the more you know that and you look through the world and markets through those lens, the more uh success you'll have.

Taking that that same mindset, you know, for yourself personally, what's the time horizon looking like in terms of your positions? Are you primarily just long-term and thinking more long-term and sort of participating long-term or is it a a mixture or is it more short-term?

It's a mixture. Uh, different uh things matter over different uh horizons. Uh, so when we draw a distribution, it's not—you can draw a one-day distribution; you can draw a one-hour distribution; you can draw a one-week, one-month, one-quarter, one-year, one a decade distribution. Um, and we really try and do all those things to the best of our ability. Um uh those distributions are constantly evolving and changing based on information and changing on change and what we know to be true, but obviously um easier to predict one day than it is 10 years in some ways, actually I guess, right? In uh in terms of um I guess I shouldn't say that. In some ways, a 10-year total outcome might be easier than a day, uh but very different information that that that drives those uh you know, famous Benjamin Graham quote, you know, in the short-term the markets are a voting machine and in the long-term markets are a weighing machine. And and what what Benjamin Graham meant by that is in the short term it's a function of supply and demand; people come to market much like they did in the Gora and you know, Greek and Roman times, and they have a there, you know, the function of who how many buyers are in that market in that day or sellers; it's not a matter of what that that uh that mule might be worth per se, right? But over the long run, you know, uh with more liquidity, with more information, with you know, markets that the the value that that mule trades for will be more in line with its pure uh value in terms of what people can use that mule for. Um uh so the weighing machine is essentially the outcomes and the true valuation, right? We saw we talked about the tech bubble, right? Over uh short period times, even sometimes years, valuations can be completely out of line with true value, right? Uh, as a function of liquidity and mismatch of supply and demand, but over the long term uh things come into line with some type of value that's fundamental, if you will. Um, and uh you know, the question is what's long-term? What's short-term? Um, and and uh fundamental value itself changes over time, but to your point, we we manage distributions in different ways over different periods, and we do predict, try and predict to our best of our ability based on the information we have all of those, and all of them, as you might imagine, that 10-year outcome does affect the one-year outcome, right? Those fundamentals do matter in a year, much like the flows really matter for a year; those 10-year outcomes probably don't matter that much for one day, but they still have some effect.

I think the biggest problem in media broadly is everybody attributes one-day, one-week, one-month, one-year outcomes uh to that 10-year reality uh to valuation, and so few understand the structural macro, the structural uh flows that go on on a day-to-day basis that have almost nothing to do with that 10-year um outcome. So um we do both; we we uh make trades on an hourly basis based on uh morning and opening flows, closing flows, how things are happening; we make bets on a daily basis based on um, you know, like I mentioned, options structure, expiration cycles, uh you know, positioning outside of options markets, uh uh sentiment, uh you know, liquidity rebalancing based on end-of-month flows, um but also in the context of uh events, um Federal Reserve meetings, unemployment meetings, uh you know, presidential elections, uh you know, um uh all kinds of different things that have very short-term but also long-term um effect. So uh I think the beauty of of investing and trading and doing all these things holistically like we try and do now is we try and integrate all the macro information, but really in the context of probabilities for the long term, in the context of macro uh information uh flows—I'm sorry, information and and uh you know, news that may come out— um and how they that affect short-term outcomes and then try and integrate them all um uh you know, mathematically and otherwise um in larger models.

Would it be possible uh the January 13th as an example, regardless of the accuracy of the the prediction, could we maybe talk about what the actual you know, feis and the the distribution thought process was towards getting towards that date?

Yeah, sure, absolutely. I'm happy to walk through it. Um, so November 29th, uh you know, when when the the question was posed, uh you know, in in my mind uh several things were clear in terms of probability distribution. Um, one um that uh we sat uh their post-Thanksgiving and pre-Christmas and end of the year um that markets were up uh 20% plus uh that we had uh in front of us the biggest uh annual um uh options expiration for indexes um uh every year and uh you could argue the biggest options expiration ever in history because they're always going up at this point. Um, not to mention the quarterly are have become particularly the end of the year become even more important. Uh, why is that the biggest expiration uh the December expiration by the way, which sits in the middle, the third Friday of um of of every month, but the quarterly uh you know was I think December 20th. Um, why was that the most important ever? Because structured product issuance has gone from two years ago 500 billion globally to a to a trillion dollars per year per year. Um, that issuance is bigger than it's ever been, um and those are tied to a December opet. I also uh understood uh based on pricing and looking at option skew in the marketplace how how high the skew was in those expirations, how big the positioning was uh in those expirations. Um, and so you took that information together and uh what was obvious to us is there was massive structural flows, which by the way we've been talking about since October. Uh, we had talking about the event and the election and how that outcome was also going to lead to the big spike up. So something we also were very out in front of and have been talked about. So we knew that also people were aware of this and were out in front of this. We knew based on positioning and underlying assets that um that that positioning and underlying assets was was very uh uh long and sentiment based on many IND indicators were also overstretched. Um, yet we knew these flows were bigger than uh than than the importance of that uh during this period and would likely lead to to continued up movement but with compressing volatility. Um, so you know, before that made that call, we were very uh an October very press in saying that the market would get a nice spike that volatility would compress throughout November and December, that you would have a lot of a lot slower move in markets because it was uh once that big spike happened. So you know, again, distributions, understanding what the pressures are. So once you understand that November 29th that there's all these massive flows coming in that people are, but yet people are very, very long and historically long speculatively, you you see that that this market will continue to glide higher until it hits a point, which we thought, you know, back in October, which would probably start a little bit later, so something closer to um a uh you know, early January to mid-January, but we knew that before January 13th that that uh decline would cut—well, knew was a strong word—that we knew that the probably were very high at that point they would come. Um, so January 13th being the Monday of Opex uh was a time after those Rel leveraging effects from the end of the year were off the table, the D Opex um uh effects uh supportive flows were off the table, that the January expiration um biggest flows from that that Friday into Monday uh would be coming off the table, uh and January again is the biggest expiration for single-list options, um and that all of these massive flows had one by one come off the table, and the one thing that would be left was very long positioning in the market without supportive flows to support it. Um, and now would that happen? Would that decline be begin in late December because uh the flows from uh D Opex were already off the table and all that was left was the Rel leveraging? Would it happen after Jan uh 2nd after those initial uh Rel leveraging effects and start January 3rd? We we laid out different scenarios and where it would start, but the gap would most likely be filled on that that Monday as a function of that, um and there were many different paths that could happen. The more we talked about it publicly because there are a lot of institutions that follow us and that are also trying to track these things and understand what we're talking about, um you know, we we uh in mid-December were very vocal and went out and said, look, uh the the the top will likely not happen in January, but is going—we're probably going to top here going into the Wednesday or Thursday of uh viir and D Opex—and so we called that literally on the FED meeting day and the day before that that fed meeting was the likely beginning. So we actually called that, you know, more important than the Gen 13; we we called the top in the market and the the rollover um and and also the stair-step nature of it given that the flows were still there supporting uh and then we also obviously um so my point is that there's much more information there than just the Jan 13th, and if you follow and you listen to what we're saying, there's there's many trading opportunities is much far beyond that just that Jan 13th call. Um, but that's how that that that date came about, and again uh some people think it's a lucky guess; it's it's no time machine. Yeah, no time machine Le—not that I can yet talk about—but you know—is one part of that or a huge part of that as well, like the knock-on effect to prior what you would—what some may call predictions—but prior information that you then you know, led you to a particular distribution that then takes place and then for that next one. So for the January 13th one, something would have happened before that to reinforce that one.

Yeah, it's a it's a constantly evolving. The the part that is increasingly difficult, um but that we definitely model as well are are two things: one, uh you know, people increasingly aware of these effects and uh you know, knowledge is all dampening we say, right? Like the more that people understand them, the more efficient markets become. Uh, those flows are immutable to be clear; those flows are still there, but there is a counterbalancing set of flows on people, and that's part of why things have becoming been a little bit earlier. We actually called for initially for a September decline that came in August on that big August decline after a big slow summer. Uh, a lot of that was again people aware, out in front of it, and then when things actually there's a catalyst like there was in the Japan, you know, the Yen carry trade that can then break things a little bit earlier. Um, you have a different set of interaction actions. That said, the market structure is the market structure, and understanding the different path that then comes out of that is a function of that um that other positioning. This was the December reality that happened as opposed to early January that little bit, you know, was also similarly understanding market structure, but also understanding that people are positioned in a way that now you have to account for in your probability distribution. So um that and increasingly, you know, the more people listen to me and what we're doing, uh seeing the the efficacy and importance of of it, I have to be very cognizant about what I'm saying when now in my and predictions. So um there's also, you know, it's crazy to think that we're having direct effects on market outcomes and structures, but at this point obviously given the accuracy of of what we're doing, um we are. So so we're we're redrawing probability distributions based on what I say, uh which means I have to be careful about what I say or or when I say it, at least remodel a distribution accordingly. Um and so so it's uh it's complicated.

When you uh say flows, could we just break that down for the audience?

Yeah. I mean, flows are just simply supply and demand, buyers and sellers of underlying for predicting that, for predicting volatility; it's buyers and sellers of all contracts or things tied to all compression. Um, there flows in different uh vectors of the market, but I think for the most part people are thinking asset, right, underlying asset and and so when I'm saying flows, I'm talking about um the the buying or selling, and I guess uh you know, if you want to measure outcome to market, it's a pretty straightforward equation: how much liquidity is there in the marketplace? How much incremental uh buying versus selling needs to happen for the market to move X or Y, right? Um and then measuring what you know in terms of a mutable supply, buying or selling of that asset um in the context of that. Now, to be clear, we don't know all the flows; if we did, we would not be predicting distribution; we predicting every day to the day every time. You know, I didn't say we have the door open and we're watching everything happening or that we're in that room; we're looking through a keyhole. Um, but if you have um 30 to you know, I would say 20 to 50% of the flows uh and you know what those are, and I say 20 to 50 because on some days it's 20% of the flows and on some days it's 50% of the flows, um you know, you may not have the whole story, but the beautiful thing is if you know 50% of what's happening, um at the end of the day that gives you a lot of information based on what's happening to price about what's happening to the other 50% of flows, right? And so the the beauty here is we may know only 20 to 50% of the flows, but we've learned a lot, and we are constantly uh learning a…

Ton about what's happening to the other things we don't know, even though we don't necessarily know what those flows are. Right, we can model what we don't know as a factor and as flows as a function of what we do. So when I say flows, I'm talking about structurally not just the things that we know, the things that I mentioned: options flows, which we could dig into a little bit more like the gamma, the vanna, the charm flows, or the rebalancing, re-leveraging flows that are not option-centric. But what I can also look at is, you know, what are these flows that exist that I don't even know what they are—the supply and demand—and what can I discern mathematically about those based on what I do know? Um, and that's where technical analysis or structural trends, uh, and and looking at those, you know, framing out a framework to understand those in the context of, you know, provide value as well.

What are your thoughts in terms of the average retail trader out there? Would they be able to have access to such information, or is that something more for your sort of position? Um, they're varying degrees; it's not like one or zero—either you have it or you don't. Uh, you can understand trends in those flows, which is kind of a step beyond technical analysis, um, based on um what tends to happen to the flows. You may not have um transparency into the actual positioning, but there are trends in that positioning, uh, on on big quarterly expirations, for example. As I mentioned, you tend to have big positioning and bigger effects from vanna and charm and gamma effects. Um, end of the year tends to be bigger, um, and those positions tend to be, you know, long put short call for customers, short put long call for dealers, and the re-hedging effects tend to be generally predictable given those trends. So you can use some of this without actually fully knowing what positioning is or understanding exactly where things are. So there's a value, um, understand that these things exist and they have tend to have certain types of positioning under certain scenarios. Um, so, uh, but the next level is to then, you know, try and gather information and get more basic assumptions based on where it might be relative to normal. Is this a lighter positioning time, a heavier, or maybe there's no flows coming this time based on a very anomalous type of structure? Um, you know, the the thing is you have to be, uh, you know, have some humility when you're doing that because you you're focusing on trends, and uh that trend may be wrong this time, uh, based on not having more detail. So to do it the best possible, you want to have as much information as possible, but and best, and not just as much information but as much understanding as possible, how to take that information and equate it to outcome. Um, but there are varying degrees of education and knowledge and ability to use these frameworks, um, without having all the information.

Yeah, in terms of uh one key theme across this tour so far has been edge, the topic of edge, and I would love to ask you like what would your definition of edge be for you? Yeah, I mean edge is uh knowledge; it's having it's having a a a a understanding um of something um better than the rest of the market. Um, that would be my first definition of edge, but I think a second corollary to that is, uh, being able to take that knowledge and then in a very uh precise way, you benefit from that knowledge to capture profit. You need both; you need the knowledge and then the ability to structure trades to capture that knowledge. Well, I think there are lots of people who are really good at isolating information and extracting a value from markets, and there are people who are very good at, uh, uh understanding markets, understanding having information and knowledge, and executing that. I think it tends to be quite rare to have people who do both really well. Um, I do think options, a deep understanding of options and using them is a is an incredible tool to isolate information and and capture edge. Um, so I encourage people to really dive in and understand how to use options and what different positioning means. Um, but I also um encourage people to dive into understanding market structure and the flows and the knowledge of truly how markets work at their core, um, because then if you can do both, um, you can really uh, you know, make an incredible amount of money and uh not, you know, really capture increasing amounts of edge, as you said.

Yeah, what is the source of, whether it's passion for the markets, what is the the sort of driving force for you to continue to do what you do? Because if it was money, no doubt you probably could have stopped a very long time ago. No doubt, but what is that that drive? Let's take a break for a minute there, guys, cuz I want to tell you about one of our sponsors, Alpha Capital. Now, without our sponsors, it's not possible for us to host such incredible podcasts around the world and get the level of guests that we are getting. So again, thanks to Alpha Capital for sponsoring the podcast. Now Alpha Capital is one of the best prop firms in the industry. So far this year alone, they have done over $50 million in payouts, which is absolutely incredible. They have the very best infrastructure in place for longevity, from an in-house broker so they can offer the very best trading conditions and platforms that all traders love to use. They're still able to offer services to the US as well, so the US traders can still trade with them on particular platforms. They have institutional experience, so they know how to manage a P&L correctly and have such an incredible team on hand. On top of which they have both a pro and swing plan, so depending on your style and strategy, you can choose which one is best for you. Now you can use and get the highest discount available at anywhere using riz25, so that's riz25 for 25% off all challenges. The links in the description below. So let's get back to the episode. Why do we do anything, right? Uh, I mean, uh, you, we live a very short—it really is a a split second in in the grand picture of the world and uh the universe—and you know, quite quite honestly, it's uh it's having some effect. It's the same reason I educate and I do all this; it's uh having some real impact um on on uh the distribution of outcomes for the world, for lack of a better term. You know, uh um it's not about fame or or fortune. At the end of the day, we don't leave with either of those things, but we can, you know, my knowledge is built on the shoulders of all kinds of other uh people who have left a legacy. Um, we all live in a world—it's not just knowledge, but we live in a world where we depend on, you know, we plug our outlets into, you know, the wall, and we take everything for granted. But all of that knowledge, all of that development, all those things that we we stand on the shoulders of giants, and to get to be one of those people um who, you know, affects the world around us is what a what an honor, what a privilege. Um, so that's the real reason.

Defin. I love that. I think legacy is a very a concept that's almost lost in in this generation. I I think in terms of uh trying to have that impact in whichever way it is—doesn't matter the industry—uh so I absolutely love hearing that. And in terms of the deeper options flows that you you mentioned, should we go into those now? Yeah, absolutely. So, um, I mentioned there probably be a couple of second-order Greeks here. You know, a conversation with with me would probably not be complete without at least uh referencing uh, you know, gamma, vanna, charm, right. Um, I want to make sure people don't get lost in the math or, you know, in in the Greeks, right. Um, but the reality is to understand the flows that come off of options markets and um, you know, this three-dimensional surface um of outcomes and positioning that are not linear, that are not two-dimensional, um, you really have to think about um uh effects of other things other than just buying and selling of the underlying asset. You have to think about the effects of time—again, the third dimension that we're talking about when we say three dimensions is time, right—and you also have to think about the effects of uh the distribution. Again, outcomes are not linear outcomes; it's probabilistic. Um, so these realities of the uh, you know, called the volatility surface or the distribution of outcomes um uh lead to um these Greeks, okay, and these effects. What do I mean? Let's take one; let's take uh charm. Uh, it is, you know, the measure of change in under supply and demand on the underlying market, the the the delta, right, um based on change in time, based on one day forward. Uh, so if there is positioning in the marketplace—uh, it's always is on this distribution—let's say we're looking at the S&P 500, and you move time forward, that positioning in the marketplace changes based on the change of time. A two-day option becomes a one-day option, right? So as you move that distribution forward, that expected value of that distribution changes, right, and the positioning in the market changes; it needs to be rebalanced. And you have to understand that the dealers—or when I say the dealers, I mean anybody, the whole world—that is warehousing the net risk of the marketplace, somebody has to warehouse all of this trading that's happening; all of that warehouse risk has to change; it changes its risk. And so whether you're a bank or a market maker, your goal in warehousing this risk is to extract an edge, and in order to do that, you need to lock down your risk and and take your risk deck down to zero. That change in time changes leads to a buyback or selling of deltas in the S&P 500, and it has to do it that day. And by the way, it doesn't happen uh, you know, every minute of the day equally, right, because we have an overnight session; markets aren't always open, right? People come in, rebalance their books, end a day, beginning a day, particularly banks; entities are subject to regulatory requirements that are end-a-day, beginning-a-day. These things are market structure; it is not a continuous, right, distribution; it's disjointed with certain acts and certain incentives uh tied to regulatory uh reporting, end of month, beginning month, like we're saying, options expirations and contracts. And so as time moves forward, these deltas change, but they change based on different intervals and different effects. And so charm is simply as a Greek, the per one-day change and the underlying delta of your position. But when I talk about, I'm talking about the changing of the warehouse risk of dealers, and that flow—to get back to flow—is a demand or supply on the market that can be measured, and that can be measured relative to liquidity and other supply and demand in the marketplace, both on an hourly basis, a daily basis, weekly, monthly, quarterly, annual basis. Um, so that's the charm.

Vanna uh is very similar; it's measuring the change in implied volatility. So per percent change in implied volatility, the the volatility expectations of the market, per percent change in that, it's the amount of supply and demand. And those two things—the reason I often talk about vanna and charm together—is they're integrally related because as time passes and nothing happens, implied volatility comes down, also based on market structure; there's a roll down. So as time passes, the whole curve rolls down, and that has V effects. So V and charma have—vanna and charm have to be looked at together. Um, you can look at time effects, but you have to look at time effects as a function of V effects as well, and and vol effects are not only tied to that roll down in the market structure, but they're also tied to if nothing happens over this time, over some period of time, V declines at a certain type of trajectory, which then feeds back to these delta effects. So these are the day-to-day ones that very few people talk about—vanna and charm—that I think are so important for prediction because they are structural flows that happen reliably day after day at certain intervals; they're higher, stronger, bigger over different periods with bigger positioning, um, uh, and and those are so valuable for prediction, way more than anybody talks about. What gets the uh the the focus tends to be gamma uh as it relates to options because it's sexier; it's a function of—as market—it's more convex as as markets uh move, uh, you know, it's it's it's the it's the derivative of delta. So per change in underlying move, it's the change in the delta. So again, if there's positioning in the marketplace, uh we know what that positioning is; if we get a certain percent move, the amount of flows that need to come out to rebalance for those moves, either uh, you know, reinforcing if it's short gamma or dampening if if the market is warehousing long uh gamma, um are incredibly important effects too. Now you can see how those interact too now, right? Because vanna, like the V compresses if V is well supplied and gamma effects are compressing, right? So market goes up; it's going to be met with sellers; market goes down; it's be met by buyers because there's the the warehousing of risk is long V that then is going to reflexively lead to V compression over time, which is then going to lead to a supportive set of flows in a world that's short put, long call in general, which then reinforces vol dampening, which then reinforces more long gamma, and you can see how it's important to look at all these things together and how the flows are interacting. Um, so when we're looking at flows and using those for predictive kind, particularly short-term distributional modeling, uh we're really looking at um all these effects, but it's tied to understanding what the positioning in the in the marketplace is and how the warehousing of risk is affecting those directional flows in the end line.

Uh, processing all of that information, is that something you do individually? Is that something that you have technology for? Is that something you're doing as a team? Yeah, obviously we we have these things called computers that do math a lot better than than even I do here. Um, so um, yeah, we we uh we use technology uh to the to the best of our ability to model these distributions um um with as much predictability as we can. Now, um, I think to say it's all quantitative with with no qualitative inputs would be uh dishonest and not true. The reality is that uh certain information and certain effects cannot be modeled. Uh, we can look at what we know for positioning, uh and uh what we uh, you know, what we model in terms of certain aspects, um but how the the inputs of information and uh particularly when we're looking at the macro pieces and the news um and other things um uh that are that are less easily measurable um need almost like a human input to uh to gauge and put those in. But we are, even with those, we are putting our own kind of quantitative measure uh qualitatively into a quantitative model.

Love that. I did, in preparation for our interview, reach out to some of my friends to see if they had any sort of input in terms of uh questions, things, and there was actually one who stood out and said, I've been following J for so long, yeah, and I love—so if it's okay, I would love to go some of these—of course, I love this. What is a myth or thought most by people out there about options and or or V volatility that you would like to dispel? Oh wow, there's so many, um, but no, good question. I think one of the biggest is that uh I kind of referenced this, but I want to be direct, that that V is not an asset class. People think as volatility actually—even endowments and funds of funds were like at for some time; they're starting to change a little bit—we're allocating to V as an asset class. Um, you know, again, V or options are, as I mentioned before, the distribution that underlies all asset classes; they are a tool, right, as we mentioned, to uh to position more precisely. I guess I mentioned the tail wagging the dog again, that big idea that options are in a sense the underly—they're a a a clear three-dimensional picture of the true asset itself. Um, so I think those are the biggest things. Um, you know, the VIX as a as a representation of V would be another big one. Like VIX is a representation of one kind of V; it's a representation of floating V, but I think to say that is somehow a fear index is a completely incorrect uh thing. Uh, the fact that the VIX, seeing the VIX as some type of a hedge to uh equity markets is also an incorrect way to to use it or think about it. Um, the it is not a measure of supply and demand uh in the V markets, uh which in theory would be what a fear index is. If people are raising their their view of what the risks are in the marketplace and buying more uh V or options, um that is a good fear index, and and we can measure that, by the way, and that's very valuable for us; that's something that I encourage people to look at, which is fixed strike vol. It's actually looking at the volatility surface and understanding that that rich data, rich data volatility surface in each product, how that's moving uh uh in its full picture and not trying to summarize all of this in one floating term that is uh itself a subject to kind of skew and other factors in the maret market. So uh people say the VIX is broken; it's not broken; it's doing what it was always intended to do; people's understanding of what the VIX is is broken. Um, and so that would be another myth I would really kind of focus on.

Here's one as well. If if any of these have been answered already, by the way, we um you don't have to do it again. Fine, please. Yeah, but in the past you've nailed many moves from a V perspective, with this year being an inaugural year and coming off making fresh all-time highs not too long ago, how do you see this year playing out and any key drivers to look out for? So, um, part of the reason that, you know, really since February of last year, we were—again, for many years now, we've been very accurate in prediction, but really since February last year, we pretty much called every single major move and V environment over the course of um I would say 10 months. Um, I I think people can back that up and take a look; it's all there to see, and um um, you know, on public, um, but the reason we were able to do that is, you know, it's easier to do when you have uh a Federal Reserve and a uh Administration that's consistent; you know what their incentives are; you know how they're going to react broadly to different outcomes; it makes the prediction easier because those are the things that we otherwise have a very hard time predicting. You can look at incentives and try and predict those things, but when those are more reliable, prediction for markets is more reliable. Um, so the problem, as you get to a new Administration, particularly at one that it's a vast departure from uh where you were uh and itself uh, you know, we don't have a lot of—even though again, Donald Trump in this Administration—obviously we have some history to look at—it is in our view a very different Administration this time around, the second time around, with very different um players uh and a very different set of incentives. Just as an example, I mean the last time around, 2016, 2020, you know, Russia had yet to invade Ukraine. Yeah, Russia itself had yet to join China in a joint communic and and opposition to Western influence. Uh, these are massive things. Trump's uh uh relationship with uh uh Putin uh in 2016, 2020 uh was very strong, uh for lack of a better term, uh uh and uh, you know, since then we have had a very obviously adversarial uh um relationship with not just, you know, Putin obviously given what's happened, but with Xi as well. Last time around, Trump was very strong against China; I think you can say that with terrorist, but also not just in rhetoric but in actual actions. This time, how is he going to react? How's this Administration going to act towards China in the context of still what seems to be a very um healthy working relationship with a Russian government? Um, these are big questions that are unknown and lead to a lot of volatility to uh certain asset classes and and and the broader structural prediction. The flows are the flows, but these bigger picture things matter as well. How is Donald Trump going to interact with the Federal Reserve? Powell himself is going to no longer be, you know, uh heading the Fed come next January, um, well, at least that's what's likely based on what we understand, um, but how will he influence and how will his Treasury and bent influence, right, um uh the the Federal Reserve going forward? So all this uncertainty um uh really makes the paths that we're now predicting—we now have multiple paths; we know what the flows are going to do, but the reaction to those makes a single prediction harder. That doesn't mean, by the way, having multiple paths is still very valuable. As you mentioned, that there's not a lot of value, but I think if you're looking for more—what's the next January 13th call?—I'm happy to lay out multiple, but they may seem to people as us saying up or down. Well, again, remember we're not predicting up or down; we're predicting paths that are highly probable relative to thousands of paths, millions of paths honestly. Um, and so we're happy and willing to do that; we've been talking about that much like we have for the last four years, um, but uh, you know, people who are kind of new to hop in the boat and look at things more linearly need to appreciate that uh things will become clearer and have more certainty and more path, maybe not as much as the last Administration in general, but the more we know and and currently we're gaining every a dramatically more inside information about what's probable or likely um as related to that uh let's dive into some of that prediction. Um, you know, we were out about a month ago uh starting to hammer the table on that China, the the path where China, Chinese mean reversion to uh to US and and global equities, which is at a historic width; US assets versus global assets in general, but particularly China, um are at dramatic width, and we we've been very adamant that there's a much higher probability; we're getting more information to that direction as well recently, but that Trump, who everybody has been thinking is going to be very tough on China, much like a Nixon—you know, it took somebody like Nixon to go open up China—we believe that Trump, uh counterintuitively, is likely to be much uh more cooperative with China and uh have a bit of a détente, if you will, um with with that, and that should open up uh some inflation, you lower some inflationary pressures uh in the medium to longer term if he's successful, um, but also um can can really uh create a massive push in in Chinese equities, um which is a very convex and mispriced path. And again, no need to buy Chinese stocks; I'm not saying they're going up; I'm saying the probabilities, which people were pricing at 5, 10% just um, you know, a month or ago so or so ago now, which they're now pricing at 15, 20 as they continue to rally here, especially in the last week, as really getting a lot of the news that that there seems to be some um interest in working with China, um could very well come up to our probability of 40% or so that there's a real uh attempt to do that, which should drive uh dramatic performance, what otherwise a very convex trade. If you can go from a 5% probability to 40%, that's a huge trade with convex outcomes. So that was one thing we've talked about a month ago, which again is it seems to be uh again coming in line with our probabilities and what we've said, um, but there's all kinds of prediction, right, uh uh that that we're looking at. When we look at prediction, not just the direction of the S&P 500, uh but the

Predict the projection of constituents relative to the index. Uh, what kind of rotation are we seeing? What areas of the market are going to outperform? What's happening in the bond market? What's the likely outcome there? Um, and we have a whole set of predictions that we've been putting out. I'll give you a few more outside of China, just broad market action. You know, um, you have to understand that, um, you know, the market now, which has run 50% in two years, um, is priced on a valuation relative to interest rates at historic highs. We talked earlier about my experience doing the tech bubble; that's the type of bubble we're talking about. Maybe not an outright valuation; the PE is significantly lower, but you have to understand tenure yields are at a place and are going to a place which are driving a, you know, relative to interest rates, which is always what's critical, a historic valuation.

Those valuations don't matter in the short term, um, but they do have—kind of given this metaphor before—it's kind of like a plane. You know, the trajectory of that plane doesn't matter how far off the ground it is, but it's the liquidity; it's the fuel that's driving it. But how far off the ground you are does really matter when that liquidity starts to put, put, put—all that matters is how far off the ground you are. And so we know our elevation; we know we've been climbing dramatically for two years, and we also know that liquidity, the engines are running a bit low, um, and that's what that 10-year yield is doing. The inflationary structures are tying the Fed's hands a bit, um, at least in theory, um, because they have a mandate to control and manage inflation, which is growing. The question is, despite all that, is the Federal Reserve, with a Trump Administration, um, despite that, likely to lean into the Fed and push still very hot economic policy?

If we think those are the incentives and that's the likely—what's happening, about to happen—the engines, they're about to get a bit more of a fuel. And we know that there's a lot of short positioning in the market given a rational understanding of valuation and realities, um, and we're about to see a liquidity burst on a monetary perspective. If we're about to see a potential détente with China, which is also stimulative, um, if we're about to see massive stimulation in the face of what has a lot of, you know, speculative short positioning, particularly a low beta on the upside in these markets and understanding market structure, one obvious path is a right-tail event similar to 2000. Right? I wouldn't say it's going to be a multi-year, but we're already at two years in, 50%, and could we see a blowoff top? I think that's a much higher probability than the market's pricing.

Um, I do think that this doesn't end well, and that would exacerbate structural inflation, which is a whole another issue we've talked about, not on this pod, but populism, protectionism—all the things that have driven that structural inflation, which is generational, by the way, and it's not going away. Away against structural things we know. In the face of that, if we see a liquidity burst, um, that won't end well. That will, you know, we could push off a bigger decline, but you're going to exacerbate inflation, and the 10-year in that situation, um, will likely go from five or 4.75 this year to six and a quarter, um, six and a quarter 10-year in the face of a screaming equity market, um, increased liquidity. Again, we already mentioned how markets themselves create liquidity. Imagine now the Federal Reserve creates liquidity; we make some type of deal with China; you have protectionism still as part of that with other entities driving inflation; populism and fiscal policy driving more inflation; now with a market that rallies another 10% here and creates another 10 trillion dollars of liquidity, um, things can get incredibly hot and incredibly, um, convex one way and then the other. And there's market structure in the terms of vol will increase into that, which will remove liquidity from the system, which will exacerbate volatility. You'll have more potential energy in the sense that the market will then be 10, 15% higher into what valuation gap that needs to be filled long term, um, that then will exacerbate and create a different set of paths going forward.

So that is definitely one of the big things we've been thinking about, talking about. We are actively watching what is Trump doing with the Federal Reserve? What is he doing with China? Um, how are those going to play out and measuring incentives and probabilities tied to that because that is interacting with everything else in the structure. We know the other big path, um, and by the way, in that situation, I think we get to 66 to 6700. Put a number 6666 out there, which is just like the perfect, you know, little spot for things to kind of go weird for, because technically and all kinds of other reasons, there's a confluence of things connecting there. It's also provocative, um, but also the other big path is we don't get that liquidity, which again, there are clues that we will, but you know, if the tenure continues to go from 4.75 here to—on these now expectations of potentially, oh, we're going to, we're going to run here in the market, and they're going to provide liquidity; we're going to have a deal with China—if the tenure now runs to five and a half quickly and then they don't provide the liquidity that's expected because they feel like they can't or they wait and see, and the Pows and academics in that space say, listen, we have a mandate of inflation; we cannot, you know, they push back against Trump or for whatever reason he doesn't stimulate early, um, and there isn't a deal with China for any number of reasons or even as much of an attempt to, and I think in that situation, I think there's another major path, which is I think, you know, we see a mid-year beginning of a structural decline in markets.

Again, I want to emphasize that one thing that's least understood by market participants and everybody should understand as markets is the reflexive nature of markets. There's a reason markets themselves lead recessions. People think it's—and this is wrong—that it, markets are smart, that they think six months ahead; they know six months ahead, so they're predictive because participants are wise. No, it's actually the fact that liquidity—the majority of liquidity, even more than the Federal Reserve—is created by the market itself. Again, 100 trillion dollars in equities globally, give or take, never mind all the things that are tied to global equities; a 20% decline in markets removes 20 trillion of collateral and liquidity out of the market. This market is such that if you remove that liquidity of the market, it'll be too late at that point for the Federal Reserve to push against it; the amount of effect to counteract that would be dramatic, and they're likely not to be aggressive enough, I mean, in an initial 10, 15% decline. And so I think under that scenario, especially given the size of volatility positioning and the potential, you know, convexity in the marketplace given how, how, how much position in the nonlinear parts of the market there are, would likely, in our view, in the back half of this year into early next year, lead to a 30 to 40% decline in markets.

And then that would be, again, at first, here, first six months of the year, be choppy; people trying to short it, not working; options EV decaying, calendars expanding; but eventually would likely lead to a sell-off that started at a very counterintuitive time, tied to some counterintuitive catalyst that we're not even sure what it is yet, but likely tied to geopolitics, um, or some type of governmental outcome. And again, the perfect time for that to happen would be in Q4 when liquidity is worse, Q3, Q4, and then exacerbated by some of these structured products that are sit in December where the risk is the biggest; now no longer fall dampening and supportive through these V and charms but now exacerbating through gamma effects and all the positioning in that December and early January effects, so would be most focused, counterintuitively, and is very what people think is a very supportive December, January of this coming year, um, but but likely turns out to be a very convex and dangerous window. So two very kind of specific outcomes, yeah, to somebody who's just looking at up, down; they seem almost like you're betting.

Yeah, I think what's important that you highlighted very early on into our conversation from the start, but also the question is there's multiple—it's like a multiverse, right? It's like there's so many—there's a spider's web, and what you're doing is really nailing it down based on information to only a few things that are probable, and then based on that, dependent on information that then becomes available to you, then, you know, really gets focused down to one or two, and then down to one eventually when that information and all more information from the markets comes through. Y, there are things we know that are not going to change; right, those are structural flows; how they, how they interact. Now those structural flows can, those positioning can have different effects: gamma effects, vanna charm effects based on these other things happening, but we know that those exist, um, and to varying degrees, um, the once you have the equation there, things we don't know, you have to fill in those other unknown variables. Yeah, and those unknown variables will change your path.

Yeah, we had another one here, and this was there, they said, have to ask this question, which is, uh, when it comes to skew on intraday positioning when looking at zero DTE, let's say we are trading at local highs, what is something we should be looking at/for for continuation or a reversion back to mean, specifically for zero DTE? So zero DTE has so little Vega in it, so little V, so the vanna effects are actually unreliable, harder to measure, so little time, so the charm is also—again, it all matters, but it, the gamma does really matter, um, in those situations, um, and because it's almost like a zero DTE is almost like an island. I think of it like a GME type product. Like anything could happen in GameStop because you couldn't hedge GameStop with anything but GameStop, right? That's why the gamma effects there mattered so much; that's why you heard all about the gamma squeeze and everything that was everybody was talking about because if you're in a corner of the market that is so on the edge of a market structure, it's completely subject to its own liquidity, um, and so the thing about zero DTE that people don't appreciate and part of the reason I think quite, you know, explosive and dangerous to market structure in because—and market makers say this candidly—is like they only hedge zero DTE with zero DTE. You know, the exchanges come out very vocally and say, look, there are all these, all this data that shows that market makers, you know, are net net flat zero DTE; there's not a bunch of one-sided trading that's happening. But the reason is there is a lot of one-sided trading happening, but the reason is when it happens, the market makers have to get out within their own zero DTE to hedge out the risk or at least parts of the distribution, zero DTE, um, and so because of that it's this closed loop, and when you have a closed loop, like I said, a, a, what happens if somebody hits the market with 10,000 zero DTE, you know, 2% out of the money puts? We've actually seen it happen, not 10,000, but, and what you do, what you get is the market can't handle that because all of the dealers have to hedge that all at the same time, and you get a cascade that pushes the market down to that strike.

So there's this ability to manipulate and push market action through zero DTE. So I guess my one response to that is that, you know, it's not always the case because a lot of times the size and scale of those types of trades and positioning don't exist, but if, if you want one piece of information that's incredibly valuable to predicting outcomes, um, on a very short-term basis, if there are big trades that go down, uh, those are going to not only have delta effects in terms of the hedging of it but the convexity of that can be dominant and self-fulfilling, um, so I would be really, really watchful of trades in real time and volume and zero DTE. And if you're not watching that stuff and you're just playing it somehow technically or you're exposing yourself to some dramatic significant effects. If someone from a—who's less capitalized would like to trade like yourself, what is a way they could do so? Well, if you're less capitalized, well, one of the first things—it's beautiful now that nowadays that you can, um, you can play, you know, fractional options; you can size positions all the way down to the smallest of sizes. Used to not be able to do that. Um, I think one of the keys for is you have to be in the most capital-efficient structure, you know, you have to be portfolio margin if you're trying to trade options with reg T, like you're gonna, you blow yourself up, and you're at an automatic disadvantage. So I would strongly recommend against it. So I think to have a portfolio margin account nowadays is like 50k, 100k. I would be—if you can't do that, right, I would at least go under some umbrella that can do that for you, um, so, you know, again, I think if you're just forced to post dramatically more capital, you really put yourself in a tough spot, and it's going to be really difficult.

That said, uh, you know, how do you take advantage of it? You understand statistics, understand probability, look at a distribution, map out your paths, understand based on that what the options are generally pricing, you know, come to understand what options, how options pricing works. You don't—doesn't mean you have to understand, you know, how Black-Scholes came to be; it's your history. I mean, if you do, great, but like it's not essential. It's really understanding probability and what options delta mean in terms of probability, what how they work and how they change in pricing. I think an education on options that understands how they are volatility, how what implied volatility is and what that, what what probabilities are, and then simply mapping that relative to what you think markets are going to do will allow you a really nice framework to, um, to profit over time. Just like anything, it takes experience, and it takes practice, but you can start really small in a portfolio margin account, um, and and going to start replicating. With the rise of zero DTE volume, how do you think that affects things moving forward? Exacerbating moves or making the waters more muddy? Yeah, I think that's a good question, actually. I think, um, reflexively positioning does the exact opposite or, uh, it provides the same impact on the market as what the instrument itself does. So zero DTE, um, in in some good ways, or or the movement of trading to zero DTE in some good ways will dampen volatility over the, over periods longer than a day because it will leave less positioning posts that day that can, you know, can implode and make create a bigger structural problem. Think Long-Term Capital Management; there's the reason Long-Term was in Long-Term Capital Management; they were out in options a year, two years out. If you're in an option a year, two years out and things start to go badly and that value goes up and up, those are marked to market; you're not getting those back for a year or two, and you have to at some point buy them back, and that creates a bigger structural problem. If you're in zero DTE, you bet today; it's gone tomorrow, right? So in that way it's good. So when people say, oh, these are, these are going to blow up the system, now that said, it is that point, as we mentioned, that so far out on the distribution, on the very one day left, that the gamma effects are unhedgeable by anything else, and it's a closed loop, and so similar to a GME, which can just—and the edge of a distribution just go—and there's, you know, it's a, it's a, it's a closed circle, um, the same thing can happen in zero DTE. So it exacerbates one-day crash risk; there's no doubt in my mind, uh, it can, it can, it within a day, uh, create a dramatic, you know, volatility to the volatility, um, and that's, you know, that V of all, I think, is definitely enhanced, increased by zero DTE in general.

What I would love for us to finish on, really, from yourself, because I know that you do, as you mentioned, loads of, you know, especially now being reached out by almost everyone, I imagine, um, but this I think is a unique opportunity more so for the retail side, for the retail audience, and I would love for two points, really: one being, um, you know, what would be your advice to them trying to enter this space if they haven't already, but secondly, just your general advice to the retail traders out there who haven't experienced a bubble before and generally just navigating these very volatile, uncertain markets, what your advice for them would be just from your experience and and from having such a long-term career so far. Yeah, um, I mean, there's so much wisdom to impart. Um, I think risk management, that concept, um, is critical. So when you think about, you know, what is a market maker? A market maker absorbs liquidity and tries to neutralize all risk from every vector and extract a yield or some type of return. Um, in the option space, those vectors of risk are much more than just how long am I, how short am I, how much exposure to V do I have per change in day—all the multi-dimensional aspects. And so I think thinking about risk management, um, in a in a very multi-dimensional framework, um, I think it's critical. I think managing, um, kind of nonlinear risks and thinking about things in nonlinear ways, I think is critical. Um, and again, I think if you learn to use options, you can not only manage risk but position in ways that are very risk-adjusted, risk-adjusted very specific, and really, uh, will will lower your max potential loss while giving you significant potential upside. So options, right, we've talked about it at length, um, are an incredible way to create incredible positive outcomes, but you have to understand and size and scale appropriately, and you have to, um, really begin to think about them again in terms of laying out a path and trying to capitalize on that the risk, you know, of that path, isolating that path in the best possible way. So I think if you take that approach, which we've kind of referenced throughout this conversation, um, you'll really find over the long term you'll lose a lot less on things that you're really not betting on. Again, if you're long an asset or short an asset, you're betting on all kinds of, um, you know, outcomes that you probably don't really care or know about, um, and and you'll capitalize more on whatever edge you develop, um, or whatever knowledge you may have, um, and over time I think that you'll become, you know, dramatically better trader.

J, it's been an absolute pleasure and honor to host you today, and hopefully we'll get to catch up in the future as well. Yeah, wonderful. Yeah, post-2025 predictions potentially, uh, but yeah, absolutely wonderful to meet you. Well, everyone, make sure you drop a comment below with your biggest takeaway from this episode. I know there was so much—probably the most in-depth episode we've ever had—so make sure you rewatch it a bunch of times and make as many notes as possible so you really ingrain all the lessons that Gemma has dropped for us today. Now links for Gemma will be in the description below, so make sure you check those out as well as Kai Wealth, which is wealth advisory, so check that out if that is suited towards you. As I said, huge thank you to TradeZella for helping make this US tour possible. You know, the most in-depth analytical tool as well as automated journaling as well as backtesting as well as so much more. Links for them are in the description below also. Now make sure you hit subscribe; other episodes are on screen; be ready for the rest of the episodes from the tour, and until next time, everyone, take care.