In this episode I speak with Michael Green, Chief Strategist as Simplify ETFs.  In a first for the Flirting with Models podcast, we recorded this episode live at the ETF Exchange in Miami in early April 2022.  

Given Michael’s eclectic background, our conversation is wide ranging.  He has traded everything from small-cap value to commodities to housing derivatives to long volatility, and so we try to find the common elements and themes across his career.  One that sticks out is his quote that “it’s not enough to do the analysis: there needs to be a trade there as well.”

Michael has become well known for his view that passive investing may now represent a systemic risk to markets.  We discuss the origins for this view, how it has evolved, counter-points, and the trade that pairs with the analysis. 

Finally, we discuss the Simplify High Yield PLUS Credit Hedge ETF, the first strategy from Simplify that really has Michael’s fingerprints all over it.

I hope you enjoy my conversation with Michael Green.

Transcript

Corey Hoffstein  00:00

All right 321 Let’s do it. Hello and welcome, everyone. I’m Corey Hoffstein. And this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

Narrator  00:20

Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations, he will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of new found research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions and securities discussed in this podcast for more information is it think newfound.com.

Corey Hoffstein  00:51

If you enjoy this podcast, we’d greatly appreciate it. If you could leave us a rating or review on your favorite podcast platform and check out our sponsor this season. It’s well it’s me. People ask me all the time Cory, what do you actually do? Well, back in 2008, I co founded newfound research. We’re a quantitative investment in research firm dedicated to helping investors proactively navigate the risks of investing through more holistic diversification. Whether through the funds we manage the Exchange Traded products we power, or the total portfolio solutions we construct like the structural Alpha model portfolio series, we offer a variety of solutions to financial advisors and institutions. Check us out at www dot Tink newfound.com. And now on with the show. In this episode, I speak with Michael Greene, chief strategist at simplify ETFs. In a first for the flirting with models podcast, we recorded this episode live at the ETF exchange in Miami in early April 2022. Given Michael’s eclectic background, our conversation is wide ranging. He has traded everything from small cap value to commodities to housing derivatives to long volatility. And so we try to find the common elements and themes across his career. One that sticks out is his quote that quote, it’s not enough to do the analysis, there needs to be a trade there as well. Michael has become well known for his view that passive investing may now represent a systemic risk to markets. We discussed the origins for this view, how it has evolved over time, counterpoints to the view and the trade that ultimately pairs with the analysis. Finally, we discussed the simplify high yield PLUS credit hedge ETF, the first strategy from simplify that really has Michaels fingerprints all over it. I hope you enjoy my conversation with Michael Green. All right, well, this is going to be a special episode. For me flirting with models, I normally tell my guests that maybe the best thing about a podcast is that it’s pre recorded, no one’s watching. It’s not live, we can edit out all the mistakes. This is the exact opposite. We’re here at the exchange conference in Miami in front of a guest audience, I have a very special guest with me, Mike Green, I’m really excited to have this conversation. You and I have been talking back and forth. Or maybe I should rephrase that you’ve been educating me for two years now. But I got you in the hot seat. I’m excited about the opportunities I want to make. I’m excited

Michael Green  03:24

to be here. And that’s not at all a fair characterization. You’re one of the people that I call and regularly say, hey, is this completely crazy the way that I’m looking at this or presenting it and I personally have been thrilled with the way you’ve taken some of my work and run with it with your liquidity, cascades, paper, etc. So

Corey Hoffstein  03:40

thank you for all the flattery will get you everywhere with me like it. So let’s start off I would assume most of my listeners are probably the audience here knows who you are knows your background. But let’s just set the table. Because I’m a big believer, that experience ultimately influences beliefs. So let’s start the beginning of your career. You’ve had one of the widest ranging careers in finance of just about anyone I know. Talk me through it.

Michael Green  04:08

Well, I think that’s code phrase for you’re reasonably old. But like many in the industry, I got my start in the late 80s, early 90s. I went to university Pennsylvania’s Wharton School of Business for undergrad and came out of there actually deciding that I was much more interested in understanding how businesses work and how markets work, went into management consulting and developed an expertise in valuing business units for m&a purposes. That was a really interesting time because the technology that they taught you at Wharton back then things like discounted cash flow analysis, or NPV analysis or internal rate of return, etc was not really something I would describe as broadly distributed. I mean, even sitting down and aggressively learning how to program or build models in Excel. And actually back then you’re too young to remember what it was Lotus 123 that I originally learned on a lot of this stuff Microsoft Excel became the standard A couple of years later, that information was not broadly distributed. And so there was a tremendous comparative advantage that you had coming out of college being educated in that being able to run the discounted cash flow analysis that put you immediately into kind of the elite space, whether it was, you know, Goldman Sachs or Morgan Stanley analysts, etc. Recognize that there was an opportunity to codify some of the tools and techniques that had been built. And so relatively soon after going into management consulting, I bailed out and went into software development, which was not totally foreign, having come from Silicon Valley originally. And so we built a software package that was focused on valuation tools for use by corporations. And one of my mentors, Mitch jewelers, one of the founders of Canyon partners stumbled across our stuff in 1996, and said, hey, you know, this is a fantastic valuation engine you have could you link this to the public equity databases like compu stat, so that we could auto populate it and do valuation analysis on publicly traded equities that pushed me into the public equity space, the valuation of public equities, we built the software tool to facilitate that. And that in turn led to the sale of the company in 1999, to a firm called hold that in turn was acquired by Credit Suisse, and maintain those relationships. And that association each day, we’ll hopefully talk later about some of the products that we’ve done to simplify that rely on that type of stuff. But when we sold the software company, I had spearheaded the growth in the investment management business recognized that there was an opportunity to actually go and apply these tools and these insights into the investment management business. And so was hired by one of my clients, a firm called moody Aldrich partners to step in, initially as an analyst. And then ultimately, as a portfolio manager for their small cap value strategies. That was a choice on my part, you know, being young and naive and convinced that valuation was the golden insight, as many were back in that time period, recognize that the US small cap value had become extraordinarily cheap relative to the rest of the universe. And so when I made this transition in June of 1999, I was completely convinced that my path was in front of me, this is going to be very straightforward, we’re just gonna do a better job of analyzing the weighted average cost of capital to the third decimal point and forecasting out the cash flows, we’re gonna get all the answers in place. And I proceeded to get my butt kicked for the next give or take nine months, as I discovered that the theory behind valuation as a weighing mechanism, while the market is a voting mechanism was absolutely clear and unequivocal. In that time period, where the real threat to small cap value was not so much that you got the analysis wrong, as much as it is that nobody cared. There was just absolute neglect. One of the interesting things that’s changed over the past 25 years is the frequency with which investors rebalance their portfolios. And so if you imagine going back to that time period, you would typically have investment management investment committee meetings on an institutional side, they would make an allocation and then try not to touch that money for basically three years, that period from give or take 1998 until 2000, manifested in the.com cycle. And then as you came into March of 2000, a really interesting event occurred you’d had such dramatic outperformance from the technology names. And from the kind of speculative.com space that you began to see institutions reallocate away from it back into small cap value. And that was kind of one of the first insights I had on the magnitude and the impact of flows. On the day where the NASDAQ cracked and march 10 2000. It was one of these extraordinary things where I saw the NASDAQ falling 700 basis points and my portfolio is rallying 600 basis points is basically money tries to go from one place to another, discovering that the next offer is 7%, lower in the NASDAQ and 600 basis points higher and small cap and just to put the valuation extremes you know, while the s&p is theoretically trading at all time highs and valuations, homebuilders, were trading for less than half of book value. At that point, auto parts companies, many of the cyclical industries were trading at three and four times earnings. In some situations, banks were trading well below tangible value. You know, this is right before a big housing bubble. And the housing sector was completely in disarray, absolutely pricing in a recession. And nobody cared. And then when they tried to reallocate, everything changed, and that for me was a real turning point, all of a sudden, you know, went from being able to do nothing right to being able to effectively do nothing wrong, other than sell the small cap stocks from basically March of 2000 all the way through, irritate July of 2002. Really a little bit earlier than that. But you saw a tremendous run in the Russell 2000 value and the associated indices, they were up, you know, 150% While the NASDAQ was cratering and the broader markets were cratering and what we think of as the 2000, a 2003 recession, but was really a very isolated and short recession that occurred strictly in the aftermath of 911. So it has some similarities to what we saw with COVID the opportunity release out there was robust managed to take advantage of it actually got recruited to go then Manage mutual funds for a firm called Royce and Associates down in New York. And out of Royce, I was in rehab, I was hired out of Royce by the guys at Canyon partners again, reintroducing themselves, who hired me to help manage would then become a very large equity exposure, and they’re mostly credit focused book. And I saw this as a fantastic opportunity. Because all the analysis that I’ve done that said Small cap value was super cheap. I kind of flipped that on its head. And by March 2006, was pointing out to people that they were paying incredible premiums for relatively crappy companies, gave a speech at a conference, I think, was one of my first speeches at a conference in March 2006. Flagging for this is a Lehman Brothers small cap Conference, which of course, has tremendous foreshadowing, but highlighting that there was a bubble in small cap value, and it ended up being roughly correct. Again, you know, the people had basically gotten very lazy in their interpretation of the learnings from the.com cycle, and they become very focused on you know, well, value investing is easy. If I go long, the name at six times EBIT, da and short the name at nine times EBIT, da, I’m guaranteed to make money without much consideration for quality or anything else. With that, under my belt, transitioning over to Canyon partners was an amazing opportunity where they gave me the ability to invest across the capital structure. And that’s really where my macro strategies began to take shape. And so I was really fortunate in when I was at Roy as being very aware of what was going on in the housing market. Despite the fact that we had seen tremendous home price appreciation, we had seen something very different than what we see today, which is you know, there’s an incredible amount of second mortgages being taken out those incredible amount of zero money down. And actually you’d seen the equity in homes deteriorate even as the prices had exploded. And so you really had this very stressed financial condition that existed at that point. And it was clear that there was some elements of a housing bubble, although as I’ve clarified elsewhere, it was much more focused in the mortgage space. That was kind of the start of the next chapter is identifying trades around that sort of stuff, beginning to think in the global macro space beginning to be very focused on commodities, which I’d done very early on in my career, but then we’re reintroduced with the dynamic of the growth of China. And there’s all sorts of interesting stuff around that that have implications for where we are today. And this this sort of dynamic, but that was my transition over the hedge funds base. In 2013, I was approached by the team at Soros to try and hire me out of Canyon partners and go manage money for Soros. I turned them down, but used language that clued them into I was open to entrepreneurial ventures, they decided to seed a hedge fund called Ice farm advisors I founded and ran from 2014 through 2016. absolutely epic disaster, not on a returns basis. But on a business basis, I think that’s one of the things that’s most important for people to understand is that there’s a huge leap between managing money, and particularly managing money for other people. So you’re working as a cog in the machine. And the Kenyan partners are a Royce or moody holders partners where the infrastructure is set in place. And you can focus yourself strictly on the investing side. And the actual process of running a money management business that has all sorts of issues associated with it that you don’t romanticize or think about as you’re actually going through it. When I finally shut that down in 2016. I took a sabbatical for a while. And that’s where a lot of the interesting stuff that you and I will probably end up talking about in terms of the insights on what was happening in the markets. Because even at that point in time, I was still very much convinced that the general idea of valuation was really going to be the overriding theme. So people just were not paying attention that they lost track of what was going on in valuations. And it became really clear to me as that 14 1516 time period kind of rolled around, that there was something that had been deeply missed in the discussion around what had happened in the.com cycle. One of the more interesting things that happened in that time period was the behavior of the Shanghai stock market in the 2014 to 2015 time period when it basically went up 500%. And the narrative on the street became very much well, China’s react appetizing right there minute, they’re figuring out how to get rid of debt. We did an analysis where we realized there was something totally different happening, that what was actually occurring was that Western investors were trying to pile into China through futures in Singapore. And they were kicking off a really interesting dynamic where the structure of the Shanghai futures the Shanghai indices were that they were market cap weighted. Now again, most people don’t think that that has any difference from where we are today in the US markets. But that’s actually what we were in the 1990s. In 2003 2004, we changed the structure of indices in most regions around the world to be flow weighted. And China didn’t have that feature. And so what we were seeing in China was a really interesting dynamic where the gains that were occurring in the Shanghai index Were not in the large liquid names, they were actually occurring primarily in the small, illiquid, low float names are many names in the Shanghai stock market that had less than 5% of their float outstanding. And so what was occurring was a byproduct purely of market structure purely of index structure, money was trying to go into the futures, that money was then in turn being invested by futures brokers who are trying to create a non arbitrage condition replicating the index domestically. But when they did that, they were trying to go out and buy in proportion to the market cap, these super small thinly traded securities, and the rules of the Shanghai exchange were that individual securities had a 10% limit up feature. And so if they went up 10%, similar to like corn or sugar, etc, they just wouldn’t trade. But that price would then be entered into the index for replication purposes. And so we just saw this happen over and over and over again, where the actual gains that were occurring in the Shanghai stock market, were a function of stocks going limit up and never actually transacting. And they would repeat it the day after, and the day after the after the record was, at one point, something like 10% of the stocks in the Shanghai were limit up, in other words, not transacting at all. And the most extreme example that we saw was a single stock that was up 10% Every single day, with no transactions for 32 consecutive days, right, so 1.1 to the 32nd power, that’s an awful lot of compounding in a very short period of time. And it takes this company and turns it from completely irrelevant into functionally a market cap giant that is, then a bigger and bigger portion of the indices. And the minute the flows went the opposite direction, the behavior completely reversed, and suddenly, stocks were going limit down with no actual trading, information, whatever. That insight combined with a paper that came out in 2016 by Laci Peterson at AQR, who is a brilliant analyst, in my opinion, under recognized for the work that he’s done, even though he has a lot of recognition, he introduced a paper called sharpening the arithmetic of active management. And he introduced a really simple idea at that point, which was passive indices are not passive at all times, by definition in Bill Sharpes, 1991, the arithmetic of active management, the definition of passive passive is somebody who never transact. And what loss they recognized was that when indexes went through rebalancing, that that created the opportunity where they were forced to transact created the opportunity for active managers to outperform. Now, this has absolutely happened. And if you look at many of the more innovative and successful hedge funds like millennium, my understanding is about a third of their business is strictly tied to index arbitrage at this point. But there was another thing that last they didn’t hit on in that paper that jumped out at me, which is, wait a second, that’s a great insight, just the highlighting within bill, sharps paper that the definition of passive is somebody who never transact, because what I’m actually seeing is every single day inflows are happening to the vanguards Blackrock state Street’s it said of the world, which meant that they’re transacting every day. So they’re never passive. And that really began to form kind of the next leg of my journey in terms of beginning to think about what has changed. What are these assumptions mean? Or what are the violations of the assumptions mean in terms of how we would expect the market to behave. And from that point, it basically became a an all consuming passion to try to understand those dynamics and identify trading opportunities around them.

Corey Hoffstein  18:31

Well, before we get into those dynamics, for those who are keeping score at home, I was counting small cap value, commodities, short vol, which you didn’t touch as much on but I know you’ve traded in the past, housing, trading long ball, just a few types of exposures that you’ve actually traded in your career, which is very unusual. Most investment professionals stay in a lane for a very valid reason, so many subtleties, right, that are required to learn an asset class. What do you think is really enabled you to make these leaps from one asset class to another?

Michael Green  19:06

Well, it says there’s two things one is, is that I always consider myself a value investor. So I’m trying to find a characteristic of a market that is under appreciated by other participants. And I would actually highlight that when you talk about spaces that tend to be dominated by specialists, one of the dynamics of specialists is that they become efficient within their market when they stop asking questions. Well, that’s just the way this works. I don’t need to worry about it anymore. You often find that where people stop asking those questions, if something unusual begins to happen, they try to basically ignore it or say, you know, we’re all familiar with the charts that get posted of you know, what looked like alligator Jaws right, where two indices that roughly tracked each other have now begun to diverge wildly. The general assumption is is that this is a function of some form of temporary insanity and that they’re going to immediately reverse. I would just say that I’m only He’s looking at those situations and saying, Well, why is this divergence happening? Is there something occurring here that is quite sustainable in a way that people are not paying attention to? And the other thing I would just say is, so if you’re constantly intellectually curious, I’d say that there’s two aspects of investing that you run into one, you get bored with what you already know. And so you go and you find other stuff to be interested in. And the second component is, is that it frees you up to ask those questions or apply things that you’re seeing from other spaces into an area that is totally naive, right. So in the housing market, for example, there were two separate stages of that investment. One was understanding that being long homebuilders in 2000 was not a terrible idea, even though the market was showing an inversion of the yield curve. Even though the conditions of a recession were very clearly in place, they had become so deeply discounted that they were effectively pricing in the worst housing recession we’d seen since the Great Depression, which is, of course ironic, because that’s what happened next. But digging into that, and understanding that was really critical to it. The second thing, though, that it exposed you to was the understanding of the assumptions that people were making as the financing bubble really took off. And one of the trades that I was involved with was The Big Short the dynamic of the ABX trade.

Corey Hoffstein  21:16

Well, that’s, that’s what I want to go into next, actually, because one of the things you said in a pre call preparing for this interview, and I wrote down the quote is that it’s not enough to do the analysis, there needs to be a trade there as well. Right? And I think there’s a lot of times a lot of macro discussion and analysis that goes on. And what’s ultimately missing to me is the trade without the trade, you have no real expression of has the market price something in or not, that’s a really important component. And so I wanted to walk through maybe a couple of examples before we get to maybe what everyone’s here to listen to you talk about, which is the passive thesis, but let’s go back to that sort of first derivatives trade you ever put on which was in the housing ABX

Michael Green  21:57

indices? Well, this is a fantastic example of being absolutely right, and nailing it and doing it for all the right reasons, but making money for the reasons that you didn’t actually anticipate, right. So when I was a canyon partners in early 2006, it became very clear to me that there was a housing bubble, I was negatively disposed to the housing sector and as you know, kind of the lead equity analyst was able to stay out of that position. Another senior analysts really a portfolio manager had identified that Carl Icahn was investing in WCI communities, right and WCI communities was primarily a Florida based housing builder. homebuilder primarily focused in three large developments that were multifamily condos effectively down here in Florida, really in this area. And it did become distressed because of the increasing fears of a dramatic housing slowdown. So this is, you know, late 2006 sort of environment. The argument was made by this analyst that nobody walks away from $200,000 deposits on condos. And so the fears of this underlying business being damaged, were misplaced. And my analysis was 180 degrees in the opposite direction, like not only are people going to walk away from these contracts, and the people that are already in there are going to in many situations allow their homes to be repossessed, and sold by the banks. And so you’ll end up seeing this completely collapse, that felt somewhat laughable at the time, ended up being exactly what happened. But the irony was, was that we decided we were going to make this $200 million investment if we could get comfortable with the hedge. And so as a hedge, I proposed that we would do this new ABX index that had been presented to me by Goldman Sachs, that I had become familiar with the mechanics of it in some initial presentations by Morgan Stanley and Merrill Lynch. And so this is one of the great ironies is that the research was all out there, it was all being shared, the dynamics that you see in the movie, The Big Short of people going around and talking about it and sharing the information. A lot of people just didn’t want to hear it. They just did not want to hear it. And so what we did was we hedged the position with an exposure and ABX that we figured would compensate us if the ABX deteriorated to a historically low level, which was in the low to mid 90s, basically, right. So doing the math on a $200 million position. If you’re going to hedge that position, largely against a give or take 10% decline, you want to put somewhere in the neighborhood of a billion and a half to $2 billion on right. We actually ended up deciding that we were going to be really conservative, and we ended up putting like $3 billion worth of ABX on with the idea being well, 5% decline and that index would compensate us for the risk in the homebuilders. Not only did the homebuilder go bankrupt, but the ABX went to 60. Right, which then translates, of course, to about a billion and a half dollars of gain. And the perception that you nailed the trade. You got it absolutely right. But I missed sighs the hedge, you know, et cetera, right. So that was one of these really interesting situations where the trade was just better than you could have ever imagined. You didn’t realize the, to the extent to which that was going to deteriorate. And so a lot of people that ended up getting that trade, right, like, I’ll just lay it out there, like most people did not expect the triple ABX to go into the low 60s, they expected it to go into the 90s. And what had occurred, there was a foreshadowing of what ended up happening with products like the XIV, where the relationship and the assumptions that existed in the market in particular, it didn’t have anything to do with Bernanke or Greenspan hiking interest rates, and people not being able to afford the teaser rates when they flipped over. It literally was a function of an assumption in something called the Gaussian distribution Coppola that around the correlation of defaults in mortgages and price behavior. The assumption was that the correlation was about 30%, it ended up being about 95%. And that relationship was enough to kick off at waterfall of defaults that just blew all these products to smithereens.

Corey Hoffstein  25:57

You just alluded to another major, barely well known trade in your career, which is the XIV blow up. You pretty famously spoke at a conference in 2017. I think Chris Cole was on the panel with you and Nick journey of velocity shares. And you and Chris both emphatically said that XIV had a very large risk of going to zero. Nick took the other side of that history is written, you know, you’ve proven you were proven correct. And I don’t want to sort of beat the logic of that trade to death. I think in hindsight, it’s very obvious. What I’m more curious about is what was the catalyst for even looking into that trade? How did you get there?

Michael Green  26:38

So the super low vol environment of 2017, is what actually had led me to become very directly involved. And so if you remember, if this happened in 2014, and 2015, as well, you entered into this regime where you would have days where the s&p would move at an annualized rate of volatility in the 234 percent sort of range, right, something you had never seen before in history. And in understanding that trade, it actually became very clear that there was a structural feature that occurred in the market that was a function of regulatory arbitrage. And so just very specifically, something called CCAR. capital adequacy ratios is a byproduct of the Volcker rules. Those had an arbitrage difference between short volatility positions in long equity positions, the capital that was required to carry a long equity position, you had to ensure effectively hold capital against that equity long as a bank for an immediate 30% decline, effectively repeating the events of 1987. The analogous position for a short volatility position was a 10 point jump in the VIX, right now, that’s off of whatever level that you started with. And quite frankly, that’s an absurd comparison, because most, you know, estimates of the VIX behavior in 1987, that it went from somewhere around 16 to somewhere in the neighborhood of 140. So, not at all comparable at all. But what it did created was conditions were for capital efficiency reasons, everybody on the street would prefer to hold their positions as a short volatility expression. This is occurring simultaneously alongside the growth of the VIX ETFs and in particularly the inverse VIX, ETFs. And so you were creating conditions where the volatility surface and the volatility market was increasingly driving the behavior in the equity markets. And you could mechanically see this through the hedging activity that was occurring. I’ve you know, shared pretty broadly a video that shows the dynamics of what an algorithmic balancing of Greek exposures looks like. It’s like an inverted pendulum, you know, where you’re trying to balance your your delta hedging to hold your Greek exposures. If you do that, with algorithms, you can bring it to fantastically low realized volatility levels. That’s exactly what we’re seeing in the markets. So as I was trying to understand this, it became clear that this supply of volatility coming out of the retail space in products like XIV was a critical contributor. And then I became aware of a second component which is a competing product. So it wasn’t actually XIV, it was SV xy, which was offered by pro shares, had long dated, leap, put options against it. What had become painfully clear to anyone who was paying attention to it was that this had become very much a tail wagging the dog dynamic. And the behavior of those indices had led to effectively a rising beta of the VIX, relative to the s&p. And so historically, and today, you would think about the beta of the VIX in terms of its move as a multiple of the move of the s&p 500. It’s normally about four to 6x. So a 1% decline in the s&p typically translate to a four to 6% Jump in the VIX in point terms, typically more often by 2017. That beta had picked up to the point that when I got into my argument on stage and by the way, it was Chris Cole and I presenting and highlighting this short evolved dynamic that existed in the market. We were both very attuned to it. And Nick Cherny, from the audience pipes up, because we’ve flagged XIV is a critical dynamic. Karen says you don’t understand the product at all, which was fun, because we ended up you know, we did understand the product. But that beta had gone from, you know, basically minus four to minus 22. And XIV had a very particular structure to it, it had a force majeure clause at 85% decline, that if the market if it fell by 85%, in a single day, Credit Suisse, the sponsor had the ability, and actually the obligation to shut it to avoid taking any losses, it was actually an ETF, and as compared to an ETF. And so very, very simple calculation was, well wait a second, if you’re running a 22 beta, and then there’s an 85% minus 22 beta and there’s a an 85% shutdown, all you need is a 4% decline in the s&p for this to occur. How frequently does a 4% decline in the s&p happen? Well, guess what the answer turns out to be this roughly 95% probability that that will occur over any given two year period. We’ve seen multiples of those in the past couple of years. And that formed the basis of my push back to Nick and saying, like, look, I think there’s like a 95% chance your product goes to zero at some point over the next two years. His belief was because they had modeled, you know, the relationship between the futures and they’d assumed a stable beta that this would survive the crash of 87. Long story short, on a 3.9% decline in the s&p on February 5 2018, the product went to zero. We ended up doing extraordinarily well, at teal macro, where I was managing Peter Till’s capital. At that point, we take in a you know, somewhere in the neighborhood of $250 million notional short against the SPX. Why that ended up working out very well. And the other funny part of that story, and it’s just a good indication of why you should always take into consideration your counterparties is when we put that trade on, I made a point of explaining to my counterparties at Merrill, and JP Morgan and a couple of other places, I know you want to hold this risk, I know you think I’m crazy for doing this, let me walk you through this, and explain why you do not want to be left holding this bag. And of course, afterwards, in a form of gratitude, they basically released all the details to the press. And that’s why I became relatively famous, if I’m

Corey Hoffstein  32:15

not mistaken, was sort of this trade that actually served as a bit of a catalyst towards your overall passive thesis that I want to dive into now, right? This sort of almost was a microcosm of a bigger picture of what you see going on today. Again, I don’t want to make any assumptions about what people in the audience know or what future listeners know about your thesis. So maybe high level, briefly, you can sort of introduce what dynamics you’re seeing in the marketplace today, and why you’re concerned about them. So

Michael Green  32:47

that trade was kind of the one of the interesting things about doing that trade was actually went back and read Paul Tudor Jones his letters to clients in 1987. Because I felt the two were very similar that I was seeing a dynamic of a systematic strategy that had become too large for the market that it was participant in roughly two thirds of the volume on any given normal day were flowing through the VIX complex, and it was just too large, so that again, that outsize day there is going to be demand for liquidity that didn’t exist. It’s the exact same analysis that Paul Tudor Jones did for the 1987 crash. And if anyone can get their hands on his letters from this is a fascinating reading. It has nothing to do with the PBS special where he is tracking with Peter Borash. You know, the day to day movements. The validation behind the XIV trade created the impetus that then allowed me to start saying, Well, wait a second, if I was right about that, what are the broader implications of it? And really began to push into this area of what is the impact? And what can I find in terms of the impact of the massive growth of passive investing. And there was a very clear outgrowth from the lacI Peterson stuff, right? So I had started down that path. And losses were changed the game for me because it made it very clear to me that this was not passive investing. This was active investing with the world’s simplest algorithms. Did you give me cash? If so, then buy Did you ask for cash? If so then so. And once you recognize that there has to be an impact from that, then it becomes a question of how can I find that impact? And how can I explain why it’s not as obvious to everybody else? And so little steps along that process were things like introducing the dynamics of not just correlation, but also what I refer to as CO movement. So developing mathematical techniques that allowed me to track much further back in time was I beginning to see securities move together in a way that you would expect under passive investing? What happened if I did things like adjust for the realized volatility recognizing the type of volatility dampening that I was seeing in the volatility space, has a natural depressive impact on things like correlation beginning to build a theoretical model to think about how should valuations change did a research project that I was kind of shocked that no academics had done before, we’re actually went out and surveyed active investors. And I asked them a really simple question. You’re a portfolio manager, you have 5% cash in your portfolio, this little survey went out to 450 investors at the end, and ask them, your portfolio manager, you have 5% cash in your portfolio, what is your marginal propensity to buy or sell based on inflows or outflows establishing Marginal Propensity curves, right marginal propensity to buy marginal propensity to sell curves, that doesn’t exist in the academic literature, nobody had thought to kind of build supply demand curves in terms of securities, the insights that came out of that were incredibly interesting, because what you discover is that valuation actually is an endogenous feature of a market in which the participants discount and when you begin replacing those discounting, and by just kind of I mean, less willing to buy it 30 times earnings, and you are one times earnings, for example, right? Once you begin to change the character of the universe, and introduce players who don’t have that response function. Alright, so again, the passive response function is not, did you give me cash? Is it a good time to buy, therefore buy it? Did you give me cash, if so, then buy there’s no filtration, it’s 100%, propensity to buy or sell, began building agent based models to simulate what the implications of that were. And as you know, you’ve seen in my research, the theoretical models just fit the empirical with astonishing fidelity. And it became very, very clear to me that something quite important was happening. I pulled together all of my research and began traveling around the world, I contacted people like yourself that are thoughtful in the space, and basically put it in front of peers that I respect and said, Look, this is what my data is leading me to tell me how I’m wrong. And you know, the frightening thing was first, other asset managers couldn’t explain to me why I was wrong. And then when I began to go to the regulators and talk to the Fed, and the IMF and the BIS, etc, nobody had a push back. Everyone’s reaction was some variant of, well, I don’t see how this can possibly be correct, but I can’t find anything wrong with your math. And so that really is kind of what kicked it off is that you know, broad detective work. The Insight is very straightforward. If you change the rules by which participants behave, the market is going to change, just imagine yourself, you’re in a Moroccan suit, and you change the hours. So you shorten or lengthen the hours, well, what is the frenetic activity that’s going to be occurring in Russia, it’s going to be different if they have fewer hours to sell in the day, right? If you place limitations on, can you use cash? Or do you have to use credit cards right, you’re gonna see very different behavior. So we know that a market has to change based on the behavior of its participants. But it wasn’t until last Peterson’s piece that I really actually realized that going back to the very definition that Bill sharp introduced in his paper, passive in Bill, sharps paper is defined as somebody who never transact, oh, my God, how do you get into the market, then? If you can’t transact, you can’t get into the market. If you can’t transact, you can’t get out of the market, right? I mean, State Street has a exhibit here at the ETF conference, you know, highlighting, be careful what you get into and make sure you can get out right? Well, if you never transact, you don’t have to worry about that. So you have this really weird dynamic where the model we know can’t be true. And yet, roughly 50% of managed assets are now managed under the assumptions of a model that we empirically know is false.

Corey Hoffstein  38:39

One of the sort of armchair critiques I see all the time, people who throw this one at you on Twitter, I see it quite frequently is they’ll point to a big mega cap stock, like Facebook Mehta, down 20% after earnings, and they’ll say see, passive isn’t supporting this. This is clearly an active repricing. Yeah. How do you respond to that?

Michael Green  39:01

Oh, so I say that’s absolutely true. But what they don’t appreciate is one the magnitude of inefficiency that exists in a market that can take a large cap name like Facebook, and cause it to lose 200 billion or $300 billion in market cap and a single event speaks to a broader issue of our markets really efficient. I mean, nothing that dramatic came out in that news. And the theory of information based markets suggests that any number of people were incentivized to figure that out. What’s interesting about that is it’s actually an argument for my theories, because what’s actually happening there is Vanguard receives no incremental information when Facebook has a terrible quarter or when Cisco reports that you know there’s a slowdown in customer order activity. There’s nobody even paying attention to that. What they’re assuming is is that the last price is the correct price. And once they have exhausted their set their buy or sell orders for the day There’s nothing left to do, right. So if an excess of sell orders comes in, and in particular, if it comes in after the market is closed, when Vanguard or others are not going to be particularly active, will suddenly the market that you had assumed as an active manager was liquid with a deep bid to it turns out to be very illiquid. And very the correct term is inelastic, capable of huge changes in price for relatively small changes in supply and demand. And it’s that inelasticity that I focus people on or try to focus people on effectively, the market is becoming more and more brittle. That incremental buy order that comes in has no natural seller anymore. That incremental sell order that comes in has no natural buyer anymore. And so on the individual stock, you get these crazy outsized moves. And yet for the indices themselves, you experience this bizarre, super high Sharpe ratio marching upwards sort of dynamic that’s characterized effectively by what I described as a mindless board receiving flows every single day that lead it to buy, what do you buy, you buy in proportion to the float adjusted market cap? What’s the right price to pay for it? Whatever the last price was? If the price goes up, should you hold off on your purchases? Absolutely not. Because that would be contrary to your investment thesis, all of these things are coming together to create the behavior that we’re seeing in markets today.

Corey Hoffstein  41:22

There’s this idea that to get to the root cause of something you have to ask why five times? Right. And I think when I think that first time we spoke was maybe summer 2019. and presented to me, and I think my gut reaction is this guy is absolutely insane. But still your gut reaction? Absolutely. But the bug you put in my head was instead of asking why five times, sort of asking who five times or even how, and you see this in financial media, they doing something, you start asking, who knows? Who is that? Who is transacting? How are they transacting and two big who’s that have emerged over the last 20 years, our target date funds, and at the risk of maybe being a little bit of heretical at this conference ETFs? What are your views as to sort of how you see these new who’s impacting markets.

Michael Green  42:12

So ETFs are actually a very interesting component, because that in some ways, they’ve made the data analysis dramatically richer, we know the daily flows associated with ETFs. We know the composition of them on a continuous basis in a way that we have not known about traditional products. And so very large, actively managed ETF complex here that it has been actually quite instructive in terms of your ability to, to disaggregate the flows and the behavior in a very particular way. That’s actually been extraordinarily helpful. And I’ll come back to talk about the challenges on ETFs. In a second, target date funds, just I know, everybody kind of knows what they are. But they are a relatively recent innovation. It is a systematic allocation mechanism that’s used primarily in 401, k’s and for retirement vehicles, that is a fund of funds allocating on the basis of how old you are right? Target Date, funds didn’t exist prior to 2003. They were introduced in 2003, became part of a staple inclusion into 401. K’s in the aftermath of the Pension Protection Act of 2006, and actually became the default investment vehicle for about 85% of 401k, starting in 2012. In 2006, and 2012 was introduced, the concept was called a qualified default investment alternative. This was part of the whole nudge framework, one of the real problems with 401 K’s was relatively low adoption rates, and relatively low investment rates. So if you got a job, you had to opt into a 401k, which meant you were receiving less than your paycheck, right, then you normally would in 2006, that change from opt in to opt out which dramatically improved participation rates. And then the second thing that was a concern prior to 2006 was, even if you did participate, the selection of funds was left up to the investor. And oftentimes they would just leave it in a money market fund. So you would accumulate cash over time. In 2006, they introduced the concept of acuity ie a qualified default investment alternative. And in 2012, that que dia became a target date fund. And so what this means is, the HR manager at your corporation basically selects the fund that you’re gonna go into an 85% of the time people just stick with that, right? That’s led to explosive growth. So target days have gone from non existing to slightly larger than hedge funds in the space of about 12 years,

Corey Hoffstein  44:36

from less than 10 billion in 2003 to three and a half trillion today.

Michael Green  44:40

Correct. And about 85 cents of each 401k dollar now coming in comes through target date funds, which is pretty remarkable. When you consider that the highest earning individuals those 55 and above who are still participating 401k is and making those allocations. They tend to be underrepresented in things like target date funds, right because they’re they’ve got less legacy investments etc. So it’s basically 95% of, you know, the 25 to 35 year olds out there are investing almost exclusively in target date funds. Vanguard has this statistic that by 2025, I think it is they expect 85% of all of the accounts that they touch through the 401k space will have a single asset, the target date fund, right now, that’s a huge deal, because that means that none of the mutual funds that I grew up managing, none of the active manager platforms etc, are even being considered for people’s retirement purposes. And so everything is flowing into these vehicles. Now, that creates a bunch of interesting dynamics. One, it means everybody’s buying the same thing. So if I go into a Vanguard target date fund, whether I’m 35 years old, or I’m 65 years old, my equity exposure is going to come through the Vanguard Total market index, and it’s going to come through the Vanguard Total international index, those two components have a bond allocation through the equivalent of BND, the ETF or the Bloomberg Barclays Bond Index, which is 70% US Treasuries and mortgages, 30% investment grade credit, and then an international expression on the same thing. And then once you cross I believe it’s 45 years old, they start to introduce an element of inflation protection into it. But literally, those five funds. Now define the investment landscape for something like 60% of 401k investors, one that creates an impossibly high hurdle for people to enter into the market, because there’s just this huge advantage of flow that is going to the Vanguard, Blackrock capital groups, etc. T Rowe has a reasonable target date fund business. The second thing that that’s doing is it’s creating a systematic rebalancing. And so I’d mentioned early in my career, you would have these three or Windows. Now most target date funds rebalance on a monthly basis. And we’re seeing a distinct pattern emerge where if stocks outperform bonds through the course of a month, that leads to allocation away from stocks and into bonds, because you want to end up in that balanced position, the reverse is true. And if you look at major market bottoms for the past several moves, they’ve inevitably occurred with basically a week left in the month or a week left in the quarter, which is when you start to hit the manual rebalance. And these target date funds and academic by the name of Jonathan Parker at MIT has written a paper on financial innovation, the case of target date funds, and identifies that this is actually a primary stabilization mechanism that exists in the market that is slowly driving the correlation of things like bonds and equities towards each other, right, they effectively are becoming much more stable because the flows are balancing themselves, right.

Corey Hoffstein  47:33

I mean, systematically enforcing a negative correlation in a way systematically enforcing

Michael Green  47:37

because of the bonds go up, and stocks are down and then you’re going to sell bonds, and you’re gonna buy stocks, right, it is building this into the market. It’s one of the push backs that I have, to a certain extent against fears that the correlation between bonds and equities are going to go completely haywire. Now, the problem with all of these is, this is great as long as money is flowing in, because you have a combination of super aggressive players again, I mean, there’s nothing more aggressive than adopting the mantra, did you give me cash, if so, then by, you have these super aggressive players that are taking share from what I would describe as moderately aggressive players. So as a discretionary active manager, in the space that I was it, I could carry up to 40% Cash, that would not be uncommon. But for most active managers, you’re looking at somewhere in the neighborhood of 5% Cash, that means an inflow can be greeted with me saying, Thanks, I’m gonna hold that cash for future opportunities. But if I take away that flexibility, and I say, No, you have to deploy that capital, changes the market structure significantly. And again, you’ve seen the simulations I run, where the really interesting is, is the historical model where the individual participants discounted. Valuation was mean reversion. It was endogenous to the market. And actually, you don’t really need to have that much information about interest rates or anything else in order to come to those conclusions. But the minute you start changing that character, and introducing players who buy regardless of valuations, it causes valuations start to rise, and it matches almost perfectly with the empirical data we’ve seen.

Corey Hoffstein  49:04

So to bring your own quote back around against you, right? It’s not enough to do the analysis. There needs to be a trade there as well. What’s the trade for all this?

Michael Green  49:14

So for me the trade and by the way, I think many people can have different conclusions about what the trade should be. That’s one of the wonderful things about markets is that there’s always a premium on trade expression. For me, it’s twofold. One is there’s an element of you can’t fight the Fed, you can’t fight the flows is part of the point that I would make. In the last year we saw roughly a trillion dollars net flow into US equities in 2021. That sounds like an awful lot of money and everyone should be celebrating but the reality is, is that was actually 1.3 trillion flowing into passive vehicles and 300 billion flowing out of active vehicles, right. So even in a market that exploded upwards and everybody benefited from the increase in aum. We continue to see these outflows from active have investors. That’s a problem regardless, because it effectively is amplifying these types of dynamics that we’re talking about. You’re firing people who are thoughtful, and you’re hiring mindless drones. The second thing that I would just highlight in terms of flipping it around and saying, then what’s the trade is, if that’s the case, you’re foolish to try to not invest in the indices as your base exposure, you should have low cost index exposure. But the dynamics of this and this is one of the things I really encourage everyone to do, just run the daily return series for the s&p 500. And look at what’s happened to the skew in terms of the behavior, you’re entering into a regime in which the majority of days are now more positive than they should be. So effectively the center of the distribution, if you think about that normal distribution, which we all know doesn’t fit. And then we imagine the Nassim Taleb variant, which is a large left tail. If we replace that with what we actually are experiencing today, it’s that the distribution has been shifted to the right, the median effectively is now really powerfully positive, but the left tail has grown in size. That means you want to maintain your equity indexed exposure, but you have to protect against the downside. And I’ve used the analogy elsewhere. It’s like playing roulette at a roulette wheel that has become imbalanced, red and black no longer come up 5050 Red, meaning you win comes up 70% of the time, it’s a great game to be playing as long as the house is giving you odds of 5050. But the downside isn’t when Black comes up, they shoot you, like you need to have some form of insurance against that type of event. And so that’s really what we’ve tried doing. It simplifies building products that take advantage of that. And just the simplest form, I’ve identified a dynamic of drift in the markets. And if that dynamic of drift is correct, the irony is, is somebody’s using a risk neutral arbitrage approach, the way that you actually make markets in options assumes a risk neutral arbitrage, where put call parity allows you to synthetically create any position, right? So you can have open positions that don’t necessarily have somebody on the other side, the pricing on those is wrong if there’s drift in the market, but the irony is, is if you’re hedging with the underlying in that market, you effectively have a convex product that you’re hedging with, you don’t realize it. And so this is one of these weird situations where both the market makers can make money. And those buying options can make make money. It really just depends on the underlying configuration. But the danger scenario is people deciding that they’re going to do some form of uncapped short volatility. And this has been one of the byproducts We saw this in March 2020. We saw in February 2018. Strategies that had survived for decades, suddenly blew up in totally unexpected ways. And I would expect that we’re going to see more of that,

Corey Hoffstein  52:41

you have a couple expressions of this idea. One of the products that was recently launched by simplify, definitely had your fingerprints all over. And this will actually take us all the way back to the beginning of our conversation where you mentioned Credit Suisse and hold the simplify credit hedge ETF, I might have gotten that name slightly wrong ticker CD X. It’s a basically a high yield exposure, plus a credit hedge that you actually expressed with a long short equity trade. And I was hoping you could maybe walk me through that because at first glance, it’s not an obvious pairing.

Michael Green  53:14

So when you think about credit, credit is the equivalent to being short of put in the capital structure, you are putting up a fixed amount of capital, and you’re agreeing that you will take none of the upside other than the interest expense that you’re receiving the coupons that you’re receiving. And you’ll take all of the downside, right, that’s the equivalent of being short put, as I indicated, like those puts can be deeply mispriced in this type of environment. And so the question is, can I recreate that put structure in a way that cost me significantly less money? And the answer is because passive creates this right side bid, right? So this underlying bid for all types of securities that are in the indices, I can actually express a relatively unique approach where I’m going along quality equities quality being defined as strong balance sheet, strong profitability, stable underlying business components, etc. So it’s not as simple as saying, find a company with a lot of cash on the balance sheet, for example, which has, you know, very idiosyncratic risks. By going long that I’m effectively taking advantage of the the dynamic that equity is the residual security. So I’m buying a very deep in the money call, on the other side of the extreme are highly levered companies who within the capital structure effectively are sitting very close to the strike price of that debt contract. And so they have a unique characteristic where they spend basically their you know, waking days and nights trying to figure out how to survive and they’re constantly forced to tap the capital markets. So if you construct a long short portfolio, what you’ve done effectively as you’ve gone long and deep in the money call, you’ve gone short and at the money call, you’ve beta adjusted the two and you’ve industry normalize them so you don’t have Other idiosyncratic factors. But that call spread that you’ve created. Also, you can just flip it using put call parity into, you’ve now created a put spread. And that put spread behaves almost perfectly inverse to credit exposures. There’s a theoretical underpinning behind it, which is totally consistent the design of these indices, I actually tapped into my old firm hold. To work with me, this is actually a strategy I’ve used for a number of years to improve credit exposures. And so this is actually a product that we’ve created that takes advantage of that, I would add, there’s one additional layer of protection in there, which is we also have crash equity puts, because that equity long short overlay is basically a put spread, we don’t want to leave the extreme tail exposed. And so we have that. But the real advantage here is, is that we’re not paying the premium to hedge credit. And so if you choose a credit expression that is long hyg, for example, and buying hyg puts, are protecting that portfolio through continuous purchases of CDs contracts, you’re going to lose money, you’re effectively going to get the risk free rate. And you’ll probably end up actually underperforming that once you’ve adjusted and accounted for the bid ask. We believe that by targeting this through the equity long short overlay, we’re able to not only hedge that credit exposure, but to add an additional what’s referred to as volatility drag premium, because the securities that we’re buying, they don’t need to issue shares, they tend to buy back shares, they don’t need to issue new debt and constantly struggle with the risks associated with that. And so this is this is what underpins that product. The other thing that I think people miss about credit, and we all are aware of the Tina type dynamic, but with high yield. In particular, in this last cycle, we got to the point where the coupons were so low, that the duration of exposure of high yield was incredibly high. And what’s happened in the high yield sell off so far, where high yield has actually outperformed investment grade in a lot of ways, because investment grade has even more duration exposure than high yield. But almost all the losses we’ve seen in high yield, over the past six months have been a function of risk free rate moves, very little of the move is actually tied to spread widening, that creates an opportunity, it suddenly becomes interesting to invest in high yield. Using a credit overlay, you’re protecting against that credit risk while taking advantage of the much higher yields that you’re achieving. And then as you know, there’s one further wrinkle we add, which is we’re synthetically accessing the hyg exposure, through a total return swap, again, taking advantage of the infrastructure that simplify is built to offer is the type exposures. So we’ve access that through a total return swap, which allows us to pick up basically another 175 basis points of yield. So this is a genuine high yield product that actually has significantly lower volatility characteristics than the underlying which is completely consistent with the approach that we’re hoping to target at simplify

Corey Hoffstein  57:59

with at the risk of this sort of sounding like a little bit of product sponsorship here and full disclosure, I’m not a holder of this personally, or in the funds I manage. But I do think there’s really two clever things that I wanted to highlight and have you discuss a little more, you just sort of brought up the first which is the synthetic exposure, instead of just buying the underlying bonds or using an ETF, you do go through this more complicated process, hopefully is a value added hoping you can talk about that. And the second is for people coming from the traditional equity factor world to their use to sort of this idea of long high decile, short, bottom decile, right long quality short junk. And that’s not actually what you’re doing here. And you’re you actually have two very distinct and different baskets not coming from the same characteristic sort. And that’s a really important part of the portfolio construction. I want you to draw. Yeah,

Michael Green  58:49

no. So it’s helpful to highlight both of those. So this synthetic exposure is caused because so many credit investors choose to, if they’re running a long, short exposure, they’re trying to minimize their overall market factor exposure. And they will short hyg Is the convenient vehicle to allow themselves to X out the high yield market, right, so they’re left with their individual selection. But that demand is short. hyg has actually created conditions under which the borrowing rates, the basis has flipped negative. And so we’re actually getting paid to take the other side of that trade. So the synthetic exposure is effectively a street broker who is building up their overall book of exposure and saying, Okay, you’re now going to face us as compared to outright owning this. And so there is an introduction of a little bit of counterparty risk that exists there. But for the most part, this is a very tried and true approach. It’s just not one that has traditionally been employed. The second thing is in terms of the factor exposures, and I really think that’s an important component. So a lot of people that do this, for example, sock, Chen has a strategy that uses this. They kind of try to maximize the returns by going exactly as you’re describing for the day. sale exposures. So junk in many of these types of exposures not only has a refinancing component to it, but it’ll have a sectoral component to it. So it tends to be heavily exposed to things like natural resources. It also tends to take the form of John that is very narrowly defined in terms of terrible balance sheets. So you know, negative cash balances significant net debt exposure, we’re trying to focus our junk on something slightly different, which is explicitly exposure to the refinancing characteristic. If you have a company that has relatively near term, maturing liabilities, they’re going to be more important to this index than something that has an equally credit balance sheet, but it’s managed to term itself out. On the long side, we’re not flipping to the other side of that junky balance sheet. And instead, we’re going for a much more stable multifactor quality definition for the very explicit reason that the volatility between those two factors if I go top decile, bottom decile on something like balance sheet. Well, the irony is, is that the top decile in balance sheet is basically companies that just have cash, right? They have no operating business whatsoever, for all intensive purposes. You know, Michael, Sailor prior to him buying a bunch of Bitcoin looks very much like a quality company on a balance sheet basis, because they have so much cash. Now, well, it would have been fun to be long MicroStrategy in some ways. The reality is, is that’s not a quality company. And so what you actually want to do is you wanted to find that quality term, using things like profitability using the balance sheet strength, using things like the consistency and durability of those margins, to try to reduce that volatility. And then the second thing is, as I mentioned, that we do is, if you strictly run in s&p 500, which has a quality bias to it, because of the profitability characteristics, you can also find yourself wildly out of whack in your sector exposures. And so we try to balance those two to give a cleaner expression. It may not be perfectly optimized for outperformance based on some historical framework, but it’s a much lower volatility relationship.

Corey Hoffstein  1:02:02

Oh, Mike, we’re coming to the end of the episode here. And the way I’m closing my season this year is with the question to all my guests, what is the luckiest thing the luckiest break you’ve had or the most serendipitous moment in your career?

Michael Green  1:02:19

If you were gonna say in my life, I’d said meeting my wife. But since the focus is on my career, I would honestly say that it was probably running into Mr. Lewis on 57th Street in New York, in late 2005, and rekindling the romance before I joined Kenyan partners. But broadly speaking, I think, you know, this concept of luck. We should all be very clear that we’re incredibly lucky to be living in a country that values capital allocation skills at a time that valid values, capital allocation skills, were raised in an environment in which there was relative stability, even though it doesn’t feel like we’ve had it for the past couple of years. We’re in a unique time and a unique space in history. And I would suggest that all aspects of it have been pretty lucky. Mike, this has been great. Thank you for joining me. Thank you. I appreciate it, Cory. Really well done.

1:03:11

Thank you.