Eric Liu, Co-Founder and Head of Research, Vanda Research

Hello, this is Dean Kernut and welcome to the Alpha Exchange, where we explore topics in financial markets associated with managing risk, generating return, and the deployment of capital and the alternative investment industry. Sketching out a business plan in 2012, Eric Liu and his co-founders saw an opportunity to create a product that simplified the world of macro for investors. Vanda Research was born, a firm that seeks to connect the top down with the bottom up, and in the process, fill a gap by providing clients with shorter term tactical research ideas. A decade later, the evolution of Vanda leans heavily on the collection of and analysis of unique and often high-frequency data sets. Making the point that, quote, "2020" was a year when alternative data sets went mainstream, Eric reflects back on the pandemic and the search for clues as to the speed of economic reopening, looking at various measures of supply chain disruption. With the notion that price moves result not just from how investors process new developments, but also by the stance of positioning, a large component of the Vanda product is looking for instances in which investors are either over or under exposed to assets. With respect to the latter, Eric cites palladium and platinum, both of which had substantially short positioning readings in late 2021. Combining data from dealerships, the team built a car inventory index that showed activities was bottoming about the same time, helping identify a trade in which palladium rallied by 80%. Much of our conversation also talks about the surge in retail activity in equity markets, and how individual investor behavior can be aggregated for clues on market direction. Asserting that nearly all of the moves in the S&P 500 in 2022 can be explained by retail, Eric cites positioning a bit less stretched now than it was late last year. And while he sees some risk that the Fed needs to hike rates further, a glass half full take is that the growth in profit environment that would motivate such moves would be a healthy one, giving further runway to the upside scenario in markets. I hope you enjoy this episode of the Alphax Change, my conversation with Eric Liu. My guest today on the Alphax Change is Eric Liu. He is the co-founder and head of research at Vanda Research, a boutique firm that's delivering strategy on the macro front to institutional clients. Eric, it's great to have you on the podcast today. Thank you for having me, Dean. Yeah, it had been a bit since we spoke, and I'm excited that we've reconnected here. I've always enjoyed talking to you about markets and just in reviewing some of your recent work, I was reminded about the differentiated approach to how you guys think about things here. So we'll have plenty to talk about in the here and now. We're just coming off CPI day today as we have this conversation. Can talk about how that fits into your process and how you think about markets. But let's go backwards a little bit first and learn a little bit more about you, your career history. I always ask the question around how you got involved. So tell us about the start for Eric Liu in terms of financial markets. I think like a lot of your guests, Dean, I had a somewhat non-traditional non-linear path to where I am right now. Both my father and my grandfather were PhD trained economists. And despite this, or maybe because of this, it was kind of the last thing I wanted to do in college. So I took one economics course during my time at Dartmouth. It was the foundations of microeconomics, I believe it was called. And to this day, it remains the only formal training I have in the field. This was never anything that I kind of imagined I would be doing with my career. I ended up majoring in political science. My thought I'd end up working for the government or going to law school or one of those typical tracks and ended up doing campus recruiting kind of on a large. I was a senior fall. I kind of wanted to enjoy the rest of my year and just wanting to kind of lock something down. And was lucky enough, really, the land was an opportunity at Morgan Stanley in their sovereign credit risk team. When you think about the spectrum of financial roles that I would have been sued for at that time, there weren't that many. And doing something with kind of the political science nature at Morgan Stanley was really one of them that was a great fit. So I ended up at Morgan Stanley working in the sovereign risk group. The remit of the team was essentially to help the firm set its sovereign risk levels and exposures to different counterparties on a firm-wide basis. And I remember, still to the say, my first business trip ever in my career was in 2005. And we went to, first we went to Athens and met with a bunch of government officials and then ended up in Rome. At the time, the very notion of a DM sovereign even being at risk of defaulting was fantastical. It wasn't really something that even came to mind for people. And I think even though we came back and it was pretty obvious that things were unsustainable and heading towards a pretty rough patch, it wasn't conceivable that a DM sovereign would really be at risk of defaulting. And so after that, I experienced in general the two years I spent there, I didn't really see a future in the space. And obviously I left a party just before the main event started. But in any case, I ended up doing a bit of a career pivot and became a tech analyst, a TMT analyst at a hedge fund. So I started out in Boston doing that for a couple of years. I ultimately ended up at Valley Asning in New York City where I started out and then we eventually moved to Hong Kong with Valley Asning to kind of help start up the initial adoration of their azure presence out there. This was back in '08, officially moved out to Asia in the summer of 2008, which was obviously just a few months before the World Cating Crashing Down everywhere, really, but especially in New York. I kind of had planned on spending about a year or two in Hong Kong and then coming back to New York. And eventually the financial crisis happened. New York was honestly something of a ghost town. And China was just on the verge of ingesting the equivalent of about 13% of GDP. And so I ended up staying out there. So I stayed out there with Valley Asning initially. And then I think '08 was like for many people, super influential for me. In large part because as a stock picker, I just didn't understand what was happening on a macro level, even though I'd come from that background previously. And made a decision to rejoin the macro world. I went back to Morgan Stanley this time in Hong Kong. I worked on what was called the Cross-Acid Desk at the time, which is kind of like an early iteration of what you see a lot of these days on equity desks. These kind of hybrid research sales macro equity desks. And so I worked there for a couple of years. Had a great time. I've moved on to Wellington. And again, Hong Kong as a macro analyst. All of this kind of happened in my 20s. So I distinctly remember there was kind of a point when I turned 30. I realized that if I wanted to do anything entrepreneurial, now was kind of the time right or wrong. I kept in touch with my old boss at Morgan Stanley in Hong Kong on the Cross-Acid Desk. I got a name of Jason Ambrose, who maybe with the exception of you Dean has the deepest role dex of anyone I've ever met. And he's just kind of a natural born salesperson, the kind of person that people just gravitate towards. And we kept in touch, we reconnected. We sketched out the business plan, DeVanda, in the summer of 2012. And I think we still have that. Isn't plan a little bit on a piece of paper framed somewhere. I guess the rest of history. I love it. It's a nice number one, a tour around the world. Across the NASA classes, you went from macro to micro, back to macro. There's a lot there. As you introduce yourself, I can't help but share that your father and grandfather have a slightly different path than my own. My grandfather was essentially a cowboy from Arizona. And his son, my dad, was the tour manager for Harrow Smith in the late 70s. No PhDs in that. It's more interesting now. Yeah. So this gives us a lot to go with. And I couldn't help but have interest in your first business trip in Europe. And as you said, this concept of a developed market default is really just not a thing. And boy, did it become something right? As you're formulating the business plan for Vanda, this is some of the most asymmetric moves in asset prices there. Typically pretty well behaved. The spiraling of spreads in Portugal and Italy and of course Greece. So what an interesting time for you guys to be contemplating launching a business. I'd love to learn a little bit more about the original business plan. Back then, if you can remember what you were hoping to deliver. How was it to be differentiated? And then maybe we can talk about the evolution of Vanda as a research product. I think at the time, and again, this was kind of back in 2012, 2013, I think generally speaking, there was kind of an understanding that macro is becoming more important. And it was still somewhat inaccessible at the time. Now, this has changed a lot in the past decade. But at the time, you kind of had your classic traditional macro research. And there was still a translation gap between those teams and let's say a stock picker or a NASH allocator. So translating that. And so that was one of the first, I think, goals was to simplify the macro world a little bit for equity folk or for non-macro folks. And then I think the other thing that we kind of realized is there was this gap in the market for really practical research, shorter term, practical research that looked at the world in kind of zero to three month time horizon rather than multiple quarters or multiple years and i think a lot of that was informed from my different experiences having spent time at a place like baleiazny where your hyper hyper tactical you know you're thinking about the next quarter sales or in the case of i invest in a lot of aging companies in places like tywan where you get the monthly data trying to predict or forecast for that monthly data is going to look like and so using that tactical approach but then also doing it on a broad macro level and looking at data that perhaps someone who was looking at the world in a longer term time horizon might not look at things like positioning and flows and sentiment and all the kind of things that i think matter much more in a short term time horizon so that was i think the initial idea and then we've kind of evolved since then i think as has the market to be honest with you we started out looking at positioning really on a fairly basic level just point things like c_f_c_c_ data e_p_f_ flows and things like that eventually as the market kind of all the systematic investors became more important we built out our resources in that area obviously as retail investors have become more important we've done a lot of work in that space as well that kind of culminated a couple years ago to launch a band of track which is our big retail data set it's a constantly evolving process and we are adjusting to all of these things are happening i think underneath the surface among market participants and that's been a really interesting ride well i'd love to get you to reflect a little bit more on the macro versus the micro i myself in the student of episodes of financial crisis i have predicted nine of the last four so i'm a false positive kind of guy but one that i remember getting very right was the summer of two thousand eleven we were really looking closely at as i referenced earlier the very unwelcome increase in risk premium in the southern sovereign countries the weaker periphery as they like to call it and that was an interesting time in the markets because if you were a bottoms up guy the market looked incredibly cheap and i remember having conversations with some folks who do a lot of work on the services this market is this is the s and p is as cheap as we've seen it in a long long time and i said there is a title wave of systemic risk that's building and it's going to be very nasty the market's cheap for a very good reason so maybe i turned out to be correct in that episode but i would love for you Eric to reflect on just this macro micro you were on this Morgan Stanley Cross acid desk and you've approached markets from both sides both with your single stock experience at bali and then more top-down at places like Morgan Stanley and then of course at vanda tell us just a little bit more about what you've come to appreciate i suppose in the way in which the macro and the micro communicate with each other the way they interact what are some things you might share on that front the worst thing you can do is be siloed in one or the other and there are lots of people with very successful careers we were able to do that but i don't think that that's necessarily kind of a typical or the easiest path success i think oftentimes what you'll see is micro investors will take information from macro and try to apply that to their approach you very rarely see the reverse which i think is still an opportunity to stay in markets i'll give you an example at the moment right now we're coming off inflation we're coming off this period where inflation and the fact that we're super important and so last year a big part of the story if not the only part of the story was kind of understanding the really really short-term fluctuations in prices and if you are going to approach that as a macro investor with a typical macro approach which might be looking at something like m2 or some lag-effective wages or payrolls or iosm or something like that you're going to have a really difficult time calling the individual turns and things like that and so last year was really i think a win for alternative data and being able to identify these individual little data sets that perhaps a sector analyst might be really really familiar with but that information hasn't percolated up to the macro level so i think that was an example of you know a time when the micro to macro flow was actually arguably just as important as the macro to micro flow having an appreciation for both components understanding how they all fit in in the broader picture i think it only kind of enriches the way that you approach things and nowadays we're kind of entering a phase where i think the questions have shifted from being one of inflation disinflation hyperinflation to recession soft landing no landing hard landing and the need to get really quality high frequency macro data is as high today as it's ever been you can't just rely on the data that comes out of the bls or the census bureau you kind of have to go out there and try to find a better real-time read on what's happening so that's kind of been a big part of what we're trying to do nowadays as well i'd love to learn a little bit more about the notion around being thoughtful in data collection and data aggregation one of the things that i think was prominent during that early pandemic period was this accessing and trying to harness unique data sets could have been miles driven restaurant reservations there were these efforts to try to gauge the pace of reopening in ways that we probably wouldn't have thought to try to do in years before or been able to do i think maybe the iPhone and making yourself trackable at all times is a part of this but walk us through just the last couple years with regard to the evolution of your process the kinds of data sets that you're interested in accessing and capable of accessing it'd be great to learn more about those unique data sets i think you could say that 2020 was really the year that a lot of these alternative data and data sets when mainstream and that's not just on a broad media general public sense i don't think you can underestimate how important was that bloomberg started putting up alternative data on this they have a function of vr us which kind of tracks all of these things that are happening with covid and case counts and things like that the ability for investors to access that via ticker was huge and so all of a sudden everyone's kind of an armchair expert in the alternative data now that brings up a pitfall which is arguably having a little bit of data not knowing what to do with it is a lot worse than having no data at all and we've seen that time and time again over the past couple of years but i think in general there's a much greater appreciation for acquiring the data finding differentiated data more importantly having a diverse enough set of different data sources that you're able to control for kind of one off and things like that and the data so we do a lot of that during the covid outbreak and obviously we're using the same hopkins data as everyone else and the open table data and the tsa throughput data and that kind of thing but then we got to the point where supply chains were a big issue and so the couple of our more talented data savvy young ranalysts went to work really just building a really robust and in-depth index to track the current progress of the supply chain ultimately i think the white house eventually came out with their own supply chain index and that was months later and we looked at that and said they're missing 95% of the available publicly available data that's out there and all you have to do is you just have to be willing to kind of go out there and get it and oftentimes it may have been an unstructured format you kind of have to go out and really dig for it and put in the legwork to understand the data and structure it and build the homo's egg that turned out to be a super fruitful project and ended up generating tons of investment ideas and particularly you know one thing that i think we were looking at i think this was back in twenty one was palladium and platinum what do we know about palladium and platinum first of all they popped up on our positioning screen as being super super on the road and that was in large part because palladium platinum are big inputs into cars into catalytic converters and because of the city supply chain issues you are having less car production which resulted in less demand for these metals and so you have super super short positioning i think it was palladium something like negative four standard deviations below average and platinum is negative two and this was kind of by the fall of twenty one and then we kind of went into the work and pulled a bunch of data around hyper concidator from car dealerships and were able to build this inventory index and metric and we were able to see that the carmetaries were really starting to bottom in the fall in october of twenty one really start to pick up since then and so combined with the positioning combined with the turning in the super high frequency data we're able to identify what was ultimately a really good trade i think palladium ended up rallying about eighty percent in the span of two or three months now obviously the rush you crane crisis was a tailwind as well but that thing was really moving pretty significantly even before then that's kind of evolved and so whatever the need has been last year was very much inflation driven as perfect example is earlier this week we're recording this in mid February we just had that January cp i print the week before the cp i print came out there was a report from manhine and manhine was the largest car auctioneer in the world and they came out and they said prices are going to rise a lot or they rose a lot in January and we kind of said well on But the data that we're looking at doesn't say that at all. The data that we're looking at, which is coincidental car price data also coming from dealerships, that was telling us that prices were falling again. We're able to go and actually investigate this catalyst that really got people anxious last week and to turn out that the data sets that we're looking at were much more accurate. I think you have to obviously respect the alternative data that's coming in, but also realize that not all of it is going to be a silver bullet and you can't really do the legwork to verify that the stuff. Well, I think you almost answered my next question with what you just said about doing the legwork, but I'd love to ask this question anyway, especially as you think about things like positioning. If you say something is four standard deviations away from its average, of course, that begs a real close look. When we look at averages, we're looking backwards in time. My question is, especially insofar as a time period as impactful and unsettling as the pandemic, how do you balance the idea that, okay, this metric that I'm looking at is so far out of bounds, but so is the time period. In other words, if I'm comparing something to the pre-pandemic period, how do I balance the, well, this is a totally unique time. I've got to be careful around my comps that use pre-pandemic data. That's a great question. I think the answer is multi-fold. One is, at the end of the day, positioning is one input and is one input that we use. Everyone on my research team comes from a more traditional macro background. We've all done the GDP forecast and worked at the institutions that really value the fundamentals of macro. So, being able to meld the fundamental narrative with what you're seeing in the positioning, that really is the secret sauce. No individual data point is going to be the silver bullet there. 2020 is a great example of this. The best example of this really is the surge in retail activity since 2020. When you're looking at the retail data and you're trying to compare it to prior instances, how do you do that post 2020? Because what we've seen is a 5, 6, x increase in the amount of retail activity that's happening in the market. In fact, last week, we saw the largest amount of retail, weekly buying ever, across our entire data set. How do you control for that kind of stuff? Well, it does turn out that there are some repeatable patterns. Even though, for example, we saw a huge influx of retail money really start coming in in late 2019, early 2020, that was the Tesla-led increase in retail activity. That was building up, building up, building up, building up, and then the pandemic hit, markets crash. You saw all this retail money come in. Again, it was 4, 5, x anything that we've ever seen before. That was really unusual. But when you actually break down some of the behavior that we were seeing over that two or three-week period when markets were really starting to find the bottom, it was very, very typical. It was typical of things that you saw in the summer of 15 after the CNY devaluation. In early 16, you saw it throughout 18 after the VIX spike and then after the Fed-driven decline in late 18 as well. This really typical pattern occurred where you had this large amount of retail buying initially. You had the fast money guy selling, you had the hedge fund selling, the CTA selling, all that stuff is fairly typical and all the very well-tried and path. Then sometime around the last week of March, you saw this huge retail capitulation. To despite the fact that these flows are much bigger than they used to be and the types of things that people are buying are very different, it was blatantly obvious that you had this retail capitulation in the last week of March. Our work has really showed us over time is whenever you see that very large retail puke after an initial drawdown, that tends to mark the bottom of that move and that work this time as well. The backgrounds are different, the policy situations are different, the macro environments are different, but human behavior is pretty typical and pretty standard and pretty repeatable and that's the sort of thing that we try to forecast. You had pointed to this opportunity on the long side really that might have been enhanced by the vast under-investment in something like palladium. You mentioned the VIX implosion, so that's really right around five years ago. I like to celebrate anniversaries of these things and that was really more about over-investment. People were way long this essentially trade in which they were selling front-month VIX measures and increasingly thin spreads. Does positioning as a follow-through metric, if the fundamental thesis turns out to be right, positioning, I assume you're thinking, is it a better amplifier from an under-investment standpoint or better in terms of it was crowded on the long side and if there's a disappointment, the asset can sell off a lot, which is better or are they equally interesting to look at. That's a fantastic question and I'll be honest with you, I've been doing this for 10 years. No one has asked the question in that manner because that's ultimately one of the biggest lessons that you learn when you kind of go through the data is positioning is useful and almost all context, but it's extraordinarily useful when people are short. It's the less useful when people are long. The idea of being, when people are long, they can always take out more leverage. The market can stay irrational longer than you can stay solvent and all of that kind of stuff. What you see when people are very short is it doesn't take a lot to force these squeezes. The signal that we get from positioning is much better on the short side than it is on the long side. Where we see that I think consistently is across the commodity space. In general, commodities are fairly ecstatic. They don't tend to trend that significantly. If you were to buy some random XYZ commodity today, the likelihood that you would be in the green in three months time would be 50/50. It's a coin flip essentially. If you were to buy that commodity based only on positioning being short, the likelihood of being up in three months time is 70%. You increase your probabilities initially very, very fast. Your meeting returns are going to be a lot higher. The distribution of potential returns is going to be essentially there will be no left tail there. That's how I think we like to think about things as well. Like I said, positioning isn't going to tell you what event is going to drive that drawdown. Positioning was super-stretch on the long side of the inequities at the start of 2020. We came out and said you want to be selling equity here. Did we know that a global pandemic was going to come in and shut down the world? I don't think any of us had any clue that that was going to happen. The situation was rife for that kind of move. When we think about positioning, it's really we're not necessarily directly trying to predict that something is going to turn. We are saying that if and when that turn happens, this is what that distribution of returns looks like. It's going to be especially left-hills, going to be especially right-hills. That's at the base level what we're trying to do. I want to get a little more granular on your process of determining positioning. Everyone's got a different process. As you mentioned, short squeeze, I can't help but ask you to just reflect on the meme that craze. It was roughly two years ago, the granddaddy of the mall from a short squeeze standpoint, very retail-centric guys going on chat boards and ganging up on stocks. What was that like for you and your team in terms of data indicators and the process that you execute to look at things? First of all, that was the timing with the buzz for two of us, at least as it could have been. For some hedge funds, it was not because of the patterns that we talked about. Our retail being a really good signal of the market trough. We've been tracking some form of retail positioning or activity for the better part of the last five years prior to the meme stock phenomenon. There's data coming from Charles Schwab, AII, you could pull up low cost ETF data to try to look at flows. There are other alternative data sets that you can look at. The gold standard ultimately emerged in the form of RobinTrack. It's a RobinTrack was a web-based platform that was essentially pulling the data, the publicly available data from Robinhood around daily retail flows and daily retail activity into all of the stocks that were being traded on the Robinhood platform. If you're in this really small ecosystem of positioning nerds, you saw this really, really on and it became a really important input, I think, for us as far back as 2019 when it really started kicking off. In the summer of 2020, possibly in response to the rise in retail activity and the increased focus that Robinhood was getting at the time, they essentially shut the pipes. They stopped making the data publicly available. I think for a lot of us, you had what was at the time one of the most important positioning indicators out there, just go dark. For us, we really just started scrambling to build an alternative dataset, build an alternative approach. And Jockamo, who runs our data team, basically went away for, I think, two months. He was a good amount of time at the end of 2020, where he kind of just went away. I don't know where he was. Trying to source data, trying to build an alternative dataset, trying to find an alternative to this RobinTrack data. And then ultimately by the end of the year, he was able to build this really phenomenal dataset where he was able to pull up 9,000 U.S. listed stocks in ETFs and look at daily flow. And importantly, it back-tested phenomenally well with the data that was coming out of Robin Track. And so we knew that we'd found gold at that point and just coincidentally rolled that out at the start of 2021. Not really even knowing that this name stock phenomenon would top off around then. And we just kind of got lucky and then you could see the flow is coming in really, really strong in the data. That's one of the things I think when we were first publicizing the data set and first going on marketing, it wasn't difficult because so much was happening. And you could see that there was a point I think on the first or second week of January of 2021, where retail buying of GameStop, and this was before really the huge source quiz, which was kind of I think it's the last two weeks of January. Even before then, you were seeing retail activity in GameStop come in at a larger magnitude than retail activity in all of the things combined. And so it was pretty obvious that this flood was starting and that turned out to be I think a really useful tool for us for our clients. And I think it continues to be so today. The conversation around retail has died down a lot. And yet when you actually look at the influence of retail, not just on individual stocks, not on the Russell 2000, but on the F&P 500. On the overall level of these big indices, it's never been more important. If you kind of look back at what flows look like for the second half of last year, you could explain almost all of the moves in the F&P by moves in retail. Essentially what you saw is the long short community, they'd be risked at the start of last year, Q1, Q2. They didn't do a lot after that. Systematic community same thing. They were essentially super low exposure for most of the second half of the year. And then most of what you saw between the rallies in the summer and the clients in the fall to the decline in December, all of that was really, really evident in the retail activity. And thus far what you've seen this year is retail is coming in but at a larger amount than we've ever seen. Even going back to the meme stock era or going back to March of 2020. This is by far the most that we've seen like I said, peak last week. And so I think retail data has never been more important than this today, even though it's receiving less press. That meme stock episode was so fascinating. You mentioned GameStop and there's a couple of things in my career that I'll not forget. And I think one of them is I go back 25 years in 1998. I'm staring at the screens in August of '08 and two year implied vol on the S&P is 42 bid as LTCM is imploding. And then on Jan 27th of 2021, GameStop, Front Month Options, the implied vol was 884. Those are extremely expensive options and people couldn't buy them quick enough. And I'll tell you, we were pretty close to some version of a systemic risk event. The VIX got the 37 on that day and it had no business being there based on the actual movements in the S&P. I think the realized vol over the preceding month was about 15. So 22 vols is just a giant spread and it's basically people saying, look, this thing could get worse in a hurry. We could be in for a real market malfunction. And then the other thing, I just wanted to pass along as we talk about positioning and crowdedness. It was sort of a pass at a long concept. What's the next one that they're going to go for? And one of them that came up in the message boards or on Reddit was Silver, SLV. And so it had a big spike and the option prices went absolutely haywire. Stocks, when they typically go up, the vol goes down. Well, in this case, for Silver, as happened with GameStop, SLV went way up and the vol just went through the roof. And I just remember some very intelligent people saying at 75 implied vol, these options are extremely unsustainable from a premium standpoint. And it didn't last very long when they realized they couldn't really corner the SLV. So a fascinating time to look back on. You mentioned something I wanted to get your thoughts on, which is this systematic community. I've seen CTAs referenced in your work. People wake up every day and read the newspaper and decide to do things. There's tactical traders, but there's also very mechanistic trading strategies that just they respond to a trading decision that comes at them. They don't make the decision. The data makes the trading decision. I'd love to learn a little bit more about how those come into your process or how you look at entities like CTAs and how they're part of your overall mosaic. The last word that you said is the most important part, which is the analysis and conversation around systematic investors, which really became popular after the VIX blow up in '18. That's one of these instances where I think you can have a lot of noise and the data is changing every day, and so therefore forecast could change every day. And if you don't know how to use the data, it can be dangerous. Thinking that a CTA trigger or a vault targeting trigger, something like that, is a silver bullet for determining asset prices is really really dangerous. You have to use it, I think, in the context of everything else that's happening. We follow this stuff really closely. You know, there are a couple of different ways that you can do it traditionally the way that you would try to estimate positioning from a CTA, for example, as you would take a CTA performance index, and then you'd kind of run a regression against it. That's still an approach that's useful. The newer approach that I think a lot of people are doing is building essentially kind of an in-house theoretical, hypothetical CTA, and saying, you know, when price gets to XYZ, this is what a traditional rules-based model would do. And so both of those approaches, I think, oftentimes approximate one another, and they're useful in the broader mosaic of positioning, but I would caution against necessarily using them on the row. And bear in mind, the signal that you give from CTA is often the opposite of the typical signal that you want to get from positioning, which is to say, usually you want to be leaning against positioning. If positioning is heavily short or something like that, you want to be going in the opposite direction. With CTAs, you kind of want to follow them. And so you really kind of have to be nuanced about how you use all the data into the PALITY. But ultimately, I think the CTA example is a great one because it reflects why positioning is most important and where it's most important. I think one of the things, oftentimes, we will do a presentation to a prospective client about the tactical approach, about the use of positioning, and some of the more clued in investors will kind of ask the, I guess, seemingly obvious question, if someone's buying something, someone's selling something at the same time, isn't that going to net out to zero and why is positioning matter in that context? And that's absolutely true. Ultimately, what you want to find is not necessarily just where people positioned, but who is the actor that's going to be forced to do something? Who is the actor that's going to be forced to buy something because their models indicate that? Who is the actor that's going to be forced to buy something? Because their prime broker needs them to cover a short position. Who is the actor that's going to be forced to buy something for institutional reasons that may have nothing to do with fundamentals? That's the Holy Grail is trying to figure out who is going to be forced to do something and that's going to work for squeezes to happen. Yeah, I think that's a great framework. I typically utilize something very similar, which is just the degree of market sensitivity is one way of thinking about that. And you might also just say the degree of price and difference. If I'm a Delta headger chasing around an option position and re-heaging according to the impact of gamma, I'm price and different. I'm just executing a robotic trading strategy. We might say something similar about CTAs and volatargeting as well. And I think the big question, at least in the options market, is trying to find those instances where some sort of reinforcement back into prices, either muting them in the case of the market being stuck long-vall or amplifying them if the market's kind of chasing its Delta hedges higher or lower. And that certainly seemed to be the case very much in single stocks and names like Tesla and Vidya. And there were just some just giant moves that definitely seemed to be accentuated by, as you mentioned, the retail appetite for people like to call them zero DTE. Zero days to expiration options is kind of all the rage, at least in the listed options market these days. Well, let's do this. I'd love to learn about the out of how you and Vanda frame out the kind of big picture of risk. And I think in order to do that, it just would be helpful to learn a little bit more about what was on your mind throughout 2022. A really interesting year, the stock bond correlation conundrum certainly reared its head. The dollar was just the anti-SMP vastly negatively correlated, very trending, at least for long period of time. What were some of the things that were an active part of your research process last year and then we can kind of fast forward into how you see things in the here and now? I think it is a good segue from your prior comment around price-insensitive buyers because last year we saw the largest price-insensitive market participant in the world, which is the Fed, really coming to move markets. So this is kind of one of those examples where you got to be nuanced about the positioning data for a lot of time last year, positioning in bonds was super short, positioning in equities was super short. You couldn't really rely on the signals in the same way that you would have over the past 12 years, in part because if the Fed decides that the Fed funds rate is at 2% or 4% or 6%, rates are going to move in that direction. It doesn't matter how short the market is in that space. So I think you have to kind of have a healthy respect of that last year and be able to balance the other things that go into the price action task. price action other than positioning. Last year was difficult in the context of he was a really unusual period, not just because of the inflationary issues and the Fed and things like that, but arguably more important, everyone was looking at one data point last year and that was CPI. And what made it so frustrating was CPI last year was essentially on a month-to-month basis, it was essentially non-forecastable. You go back and you look at the last 10 CPI prints. We're having this conversation on CPI day, so maybe this is super relevant at the moment. But you go back and you look at the last 10 or 11 CPI prints and in at least eight of the situations, the actual number came out somewhere between two or three standard deviations outside of what the analyst's economy community was thinking. So you have 60, 65 plus highly educated economics teams out there trying to take a stab at the single most followed macro metric in the entire world. And in most, the vast majority cases last year, no one was coming within spinning distance. And so again, you have to kind of have a bit of humility around what can you actually forecast and not forecast last year. And so I think those are all parts of the lessons that we learned last year. You have to be able to evolve with how the market is evolving. And start thinking about if you say that the most important macro do is to some extent non-forecastable, well, can you find asymmetries elsewhere rather than, as I like to say, playing hero ball rather than trying to call every single print, can you think about assets that perform really well in one scenario and just kind of bad in the other scenario? So finding these asymmetric opportunities, I think, was a big part of at least protecting yourself last year. And then I suppose being able to really adjust the narrative. One of the things that you still see today is positioning in the front end of the U.S. race curve today to be super, super short. I think if you go back and you look at the data, we've kind of been somewhere around here with some fluctuations more or less for most of the last two years. And you'll remember, even back in 2021, when inflation was really starting to kick off and you had that 4%, 5% seed guy print by the summer, the global macro community was heavily short, fixed income. And at some point in the summer of 2021, that kind of blew up. And rates fell, and a lot of these positions ended up getting shot voluntarily or involuntarily. And you have to be able to maneuver these different environments. And a big lesson toward the end of last year was being able to get out of this mindset that we're in the 70s. And so everything is going to look like the 70s. And so every down move in inflation is going to be a head fake. And just respond to the data, which was telling us that some of these improvements in inflation actually are legitimate. I think they still are to this point. And so being able to, I guess, move along alongside that. It's really hard sometimes to square market prices with, he used the term narrative, the set of facts on the ground or the dialogue among participants. We spend a lot of time, all of us trying to do it. And I think we have this burning itch for it all to make sense. That's kind of why we're here. I'm recalling, as you mentioned, that summer of 2021, I believe the tenure in August traded as low as 1.2%. Of course, year over year, CPI was still elevated. But I think it was in the sixes at the time, you know, on its way to nine. But still so far north of nominal rates, it was to me a real confusing one. And it just shows you how hard this stuff is. When we look now at the current set of prices, I'd love for you to just frame out what you see. Things like the degree of yield curve inversion. That gets a lot of play. There are people that think it's a hard one to see the Fed hiking. I think we're at actually almost three more times now. If you look at that WIRP page, that June number is really crept up. It's almost at three. But we'll certainly squeeze two more 25s out. But then the question is, okay, as soon as you do that, then we're letting them go. That's kind of what the curve tells us. We reverse course pretty quickly. When you stare at whether it's the three-month two-year part of the yield curve or two-stens, there's just some interesting inversions. How does that fit into your process? How do you think about those relationships? I think you have to respect the signal that the inversion gave us last year, which is to say that a recession is impending. Now, the problem is what that kind of statement is, you can say that with some degree of certainty over an 18 to 36-month time horizon. But I mentioned at the top of the call dean, we're looking at markets from a zero to three-month time horizon. You have to be able to understand what's happening and understand the context of that in the impending recession, but also understand that if a recession happens in December of this year or the summer of next year, there's still a long way to go. One of the analogs that I like to use nowadays a lot is 2018-19. If you think about how the market came in in early '19, most recession indicators were at 100%. The Bloomberg economics has a recession indicator where they throw all the very typical metrics in their two's, ten's, ISMs, all the kind of stuff that anyone worth their salt is going to be looking at. And that indicator was flashing in January of 2019 and was flashing 100% probability of recession. Obviously, it was correct. But the market still rallied, the equity market rallied 25-30% during the course of the year. You can have these fundamental backdrops in the background, but that's not going to stop people from doing their day jobs. I think even more than normal in a time like this where the narrative is so ominous. I think it pays to narrow down your focus a little bit. We all know what the world's going to look like in 12 months or in 18 months. It's really, really hard. But do we have a better chance at knowing what it's going to look like in six months, in three months, in two months, in one week? Yeah, I think the probability that you're going to get that right is much higher than knowing what the world's going to look like in 12 months time. And so I think in these sorts of environments, you really, really have to reign it in and try to predict what you can predict. So it seems that some version of what you're doing is trying to almost front and run near-term changes, where maybe the market's gotten in over its skis on the long side. Positioning is crowded long, the margin of error, or maybe the margin of safety is a better way to say it is finned out. Or vice versa, sentiment is awful. Positioning is really light. And you see a set of whether it's indicators or a set of events that can materialize where maybe you use this term before this kind of more ways to win than just one. As you look at markets right now, I think you've got a bullish take on the next couple of months, at least as it might play out. Can you talk through that? Yeah, that's for the sake. I think positioning today in US equities is less attractive than it was a couple of months ago. Our indicators were pretty stretched by November, December, last year, and then as I mentioned, you have that big retail capitulation in December, which is typically a pretty good green light. And so I think you've gotten a lot of the easy moves. So the easy moves being the short covering from the long-shore community, like I said, retail buying has been red hot over the past six weeks. You kind of have questions about how long back in sustain. But at the same time, when I listen to the bearers, talk to the bearers in the street about why they're so bearish. Generally speaking, some version of the high inflation, fed hiking type narrative tends to come up. And so what I like to say is, you know, you think about where's the top side here? Where's the real estate top side on race or fed funds? Maybe inflation runs a bit hot for a couple of months, and you're talking about 6% fed funds, right? Which equates to a real yield that, let's say, is 50 basis points, 75 basis points higher than it is today. What does that mean for equities? The math basically works out such that if you look at how equities responded to real yields last year, essentially for every one basis point move in real yield and the 10-year real yield, the S&P moved 10 basis points. In other words, a 100 basis point move in real yields is also 10% move in the S&P. And so if you think that the real yield upside here is another 50 basis points, which means 5% down the S&P, that's not nothing. That's nothing to sniff at. But you got to ask yourself, why would that happen? If we were in an environment where the fed was hiking 6%, it's probably because the underlying macro environment is strong enough to say in that. And it's probably because the likelihood of a recession is going to push further and further into 2024. And so if you kind of ask the average long short investor, average fundamental investor, on the one hand, the S&P is going to be down 5% because of this rate hike. On the other hand, you're going to get another 12 to 18 months of runway in the economy. Are you willing to take that trade off? And I think 9 out of 10, if not 10 out of 10, equity investors would be willing to take that trade off? And so that's one of the reasons I think we are still constructive here is all the data tells us that activity really, really picked up in January to a large degree that still hasn't come out in the official data. And so I think the price discovery there is going to happen of the next couple of weeks, next couple of months. And given kind of where the upside is around rates and given that positioning is still under owned at the moment, I think that's a pretty good formula for success in the next couple of months. Yeah, it's hard to know where we're going to get to on the policy rate. And I think you're like me, you have a vast respect for [BLANK_AUDIO] I like to say markets are never say never business, but it does feel like we're reasonably far along. It just look at metrics like the Move Index, you know, it peaked at 160, which by the way, that was in 2022. That was as high as we saw it in 2020 in March. So just to show you how volatile rate markets were last year, but that's come down 60 normal basis points. So circling 100, it just tells you we're further along the market kind of sees the end game here. And I think then the question is how damaging is that? Can the economy duly withstand a policy rate in the fives? I don't know. It's really hard to know. Well, Eric, I'd love to finish off by just asking you an open-ended question just on Vanda. You guys are constantly thinking about new ways to approach the research process. You're intellectually curious. You're trying to find new data sets. What are some of the projects? Just open-ended areas of investigation that have got you and team excited, things that you're in the lab building. Can you share some of the work that you're doing now or at least thinking about? We're doing it both on the research and data side as well as the business development and corporate side of things. On the research side, our data team right now and data software team are building out our platform that we want to roll out to the public. So I mentioned earlier, I'm not sure if I mentioned this, we essentially house all of our position data internally on these dashboards and these web-based platforms. And so every Monday when I get in the office, I come in and I look at where positioning is, I kind of get context for what the week's going to look like. We want to make it's all available to clients. And so our team is working pretty hard right now and getting this rolled out, hopefully by the end of Q1 or really Q2. And so hopefully all of our clients will be able to access all the data that we look at internally, pull it via API, integrate into their workflow. That really is, I think, one of the more exciting things that we're working on right now. To the point that we kind of talked about earlier, really boning up on the high-frequency macro data, that's another area that we're spending a lot of time on is to try to think about how to kind of enrich the data sets around economic activity, not just in the US, but in Europe, in China, in the UK, in Japan, really giving a good sense of when the cycle is going to turn, because we all know it's going to turn at some point. So being able to kind of call that, I think, is really important. From a broader business perspective, I think the work that we do in the early days, it was a lot of education and convincing and things like that. Nowadays, I think your average macro fund, bank trading desk, equity hedge fund, they kind of understand why you want to use positioning and why you want to look at the world and at least have a tactical overlay. I think what we are trying to do more and more nowadays is really providing offering to the wealth community, the asset allocation community, the pension fund community. And so we've recently hired the ex-chief strategist at the Ordeal wealth management. That's kind of an area that I think is really growing and where we're trying to put some of our resources nowadays. Fantastic. Well, Eric, it's been great to chat with you over the course of this hour and great to hear about the investment and progress in Vandus. So thanks for taking the time, our guests will enjoy listening to this discussion. It's been great to be on being back for having me. You've been listening to the Alpha Exchange. If you've enjoyed the show, please do tell a friend. And before we leave, I wanted to invite you to drop us some feedback. As we aim to utilize these conversations to contribute to the investment community's understanding of risk, your input is valuable and provides direction on where we should focus. Please email us at [email protected]. Thanks again and catch you next time.