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and join our amazing community. And with that, please enjoy this week's episode. What's up everybody? My guest today is one of the more creative
and curious macro minded investors that I've had on this program. His name is Mike Green and he's been a student
of markets and market structure in particular for nearly 30 years. His research and analysis of markets, specifically
the shift from actively managed portfolios and investment funds to systemic passive investment
strategies and their impact on market structure has been presented to the federal reserve,
the BIS, the IMF, and numerous other groups and associations with the intention of alerting
them to the clear and present danger that he feels these strategies pose to the stability
and viability of our capital markets. This conversation is broken into two parts. The second hour of which can be found on the
Hidden Forces Patreon overtime feed where we drill down into the specifics of Mike's
thesis regarding the implication of passive investment strategies that have ballooned
in popularity over the last 25 years, making up 47% and 27% respectively of assets under
management in equities and bond funds at the end of 2018, up from less than 5% in 1995. The first part that you're about to hear sets
the foundation for that conversation. I did my best to make it as accessible as
possible without dumbing it down, but there's going to be terminology used and references
made that many of you may not be familiar with. If so, don't worry. Hang in there. It's worth it. Mike is a wealth of information and this conversation
is one you will be hard pressed to find anywhere else. It's eye-opening and I want you all to hear
it. Lastly, because this conversation deals with
investing, I want to make absolutely clear that nothing I say on this episode or during
the overtime can or should be viewed as financial advice. All opinions expressed by me and my guests
are solely our own opinions and should not be relied upon as the basis for financial
decisions. And with that, please enjoy my conversation
with investor Mike Green. Mike Green, welcome to Hidden Forces. Thank you, Demetri. It's nice to be here. I've been chasing you for over a year to get
you on the show. Well, for various reasons as you know, I was
somewhat limited in my ability to appear, but I am super excited to be here. I'm a huge fan of the program myself. That's a great endorsement, Mike. I'm really excited to have you on. I'm not sure where to start. As you know, I did a lot of research as I
always do before this episode. You and I talked a bunch. We met once in New York about a few months
ago in September for a number of hours of hanging out, talking, geeking out about this
stuff. I did an episode with Shoshana Zuboff that
I was reminded of often when I was preparing for this because it's not exactly the same,
obviously it's a different subject matter. Her focus is on technology and the logic of
surveillance capitalism within the technological environment, and I have a, I think, better
understanding of this stuff natively than I did of that stuff to begin with. But the way that you talk about passive, the
way that you talk about the uncanny valley metaphor that you use has done the equivalent
for me. It's helped to click into place something
that I've been struggling to name. Like Shoshana talks about that we need to
be able to name it because if we can't name it, it's unprecedented. We don't know what it is. It feels very similar. So maybe you can start us off. How did you come to develop this thesis on
this subject that we're going to talk about today? Look, the thesis of the impact of passive
investing and this broader thesis, I think is a byproduct of having had the flexibility
to zoom in and zoom out on markets. There's lots of people that are traders. Who are really good at taking product A and
pricing it in one market better than it can be priced in another market and arbitrage
in the difference between those two. I'm a terrible trader. I really am, I'm a terrible trader. But what I'm actually really good at is figuring
out why people are being forced to do things that seem irrational. I've been fortunate to have been involved
in the market for an extended period of time. A lot of people don't have that flexibility. One of the refrains that I just keep hearing
from people in our space, when I say our space, I just want to clarify the difference between
active and passive. An active manager is somebody who trades with
discretion. They make their own choices about how they're
going to do something. They may have some restrictions on the products
that they trade. They may have some restrictions on the flexibility
that they have to either build a cash balance or to execute at a certain price, but they
tend to have qualitative discretion. The ability to choose to do one thing or another. Passive, at least in theory, is supposed to
be extraordinarily simple. It's supposed to take the aggregated behavior. So when you say passive, just for those people
that don't know what we're talking about here, you're talking about passive investing. Correct. And what is that? Passive investing actually means something
very different than what we see in the market. But typically what people are referring to
when they talk about passive is an S&P index fund or a Vanguard type fund but it's the
Vanguard total market which is trying to buy all of the stocks in the market in proportion
to the market capitalization or the float weighted market capitalization that supposedly
all of the discretionary, the active players have decided is the "right price." This was an output. Passive investing as a theoretical construct
was an output from the insights that were created in the 1960s from academics like Eugene
Fama and Bill Sharp and Harry Markowitz. The idea of market efficiency that markets
functionally represented all of the information that could be known in their totality. And the efficient market hypothesis, modern
portfolio theory, Markowitz. We covered this in an episode with Daniel
Paris on the history of financial theory. I definitely suggest listeners, check that
out. I think that was episode 73, that can help
you in this conversation. But please continue, Mike. Yeah. One of the interesting dynamics is, is that
the world tends to bifurcate in terms of skillsets. Somebody like myself who has historically
been a discretionary investor and has operated in markets ranging from equities to fixed
income to currencies to interest rates on global markets, that's somewhat unusual. Most people tend to focus or specialize in
a particular area. I would say that in part you're able to actually,
if you work across that very wide range, what's called macro investing, you can often get
a better perspective on the idea that maybe the earth isn't flat. It appears flat when we see a surface that
looks very flat to our eye. But once you understand the interconnections
across these markets, it becomes a little bit easier to see the curvature of that surface. That's a great observation. I want to interject and then please continue,
the fact that reality is not what it appears to be. That markets and prices are surface phenomena,
but there are something under the surface that is driving those changes. Well, and I think that's really important
because you used a couple of phrases there. You introduced the idea of markets. People tend to think of markets as fixed entities. So there's a stock market and there's a bond
market and it's as if these things have always existed. But most of these are actually very recent
phenomenon. The idea of a stock market on a continuous
basis really doesn't exist in the United States. We all know the fantasy of the Buttonwood
tree and the founding of the New York stock exchange. But the idea of tradable liquid securities
really is a relatively recent phenomenon. The fact that we can date that back to the
1790s. And the greater scale of human history, that's
actually a remarkably short period of time. And the index is even way newer than that. The index in the form of the S&P 500 which
everybody is super familiar with, was created in 1957 it was backdated through construction
techniques until the 1920s. But we're less than a hundred years into these
phenomenon. So when you have this type of dynamic where
everybody thinks of it as this giant fixed thing, it's easy to forget what markets actually
are. Markets are really designed to do one very
simple thing, take illiquid things and make them liquid. A place where buyers and sellers meet and
the more liquid they become, the closer the matches between buyers and sellers. So it's actually a very fragile emergent property
to have a market where you have to have participants who are coming in and having different perspectives
on worth, but able to transact relatively close to each other. The richness of these products, the richness
of markets, again, is a super, super reason phenomenon, the more esoteric the product
is, whether it's Michael Milken with junk bonds in the 1970s, 1980s and early 1990s,
those just didn't exist. There was no capacity to create liquidity
around that. And liquidity suggests price discovery. Liquidity suggests price discovery, which
is really, again, the role of markets. It's facilitating liquidity. Your house, we talked about this example earlier,
but when you go to transact in your house, there's a high degree of uncertainty. What am I going to be able to sell it for? It could be 10% less than I listed for, it
could be 20% more than I listed for. If that were to happen with shares of Microsoft,
people would be nonplus. They have absolutely no idea what to do with
themselves. So we presume that these things function as
they always have and we treat them in that way, but they're very fragile. Going back to the efficient market hypothesis,
the idea is that all the necessary information is publicly available in the market and that
all the agents have access to that information and they are actively pricing and therefore
markets are efficient. It assumes that, and it actually goes even
a step further. What it presumes is, is that there's an incentive
which we call profit for every individual actor to put risk capital up based on the
information that they have. So if I happen to know based on my contacts
or my research that Microsoft is going to have better sales than people anticipate,
I have to put capital up to take the risk and reflect the idea that I think ultimately
that people will value Microsoft more highly in the aftermath of just the broad discovery
of that information. The process of putting up that risk capital
gives some form of probability weighted contribution to the existing price of Microsoft. One of the key criteria for efficient markets,
and it goes to the heart of the emergence of the impact of passive strategies and the
impact that passive strategies are having on the market, is the idea that each individual
player has a very small voice. Nobody can stand above everybody else and
shout, is the right price. But when players become so large that they
represent tens of percents of market, they can actually dramatically influence those
outcomes. We're seeing this, in my opinion, is what
we're seeing happen in the markets today. So there's been an evolution in passive that
incorporates the theory you talked about. Actually, I also want to bring in what we've
seen in the market for volatility in terms of the VIX, it's the insurance market for
the S&P, for equities. I want to bring that in too because that I
feel like from what I understand, having spoken with you and read your writings on this, this
is the canary in the coal mine for what you think we're going to see in equity markets
more broadly. So again, it goes back to the underlying theory
of markets. And the work of Kenneth Arrow highlights this
underlying dynamic that ultimately markets need to be complete. They need to have options for, and I don't
mean options in the form of derivatives, but they need to have products that allow people
to express functionally all possible desires and outcomes. So the desire to purchase insurance is a very
important one. But inevitably, the participants in an insurance
market are going to be smaller than the participants in the actual fundamental underlying. The emergence of products around what we call
volatilities. That would be the VIX inequity would be the
most common one that people are familiar with. VIX stands for volatility index exchange,
I believe. Just to drive that point home though for listeners,
the cost, I think this will make it more intuitive. The cost to purchase your house is by necessity
much, much greater than the cost to insure your home. And that's to do with probability. Exactly, correct. So when you think about your house, the risk
that your house is going to be lost to you through a fundamental event -- it burns down,
an earthquake occurs, flood happens, etc. I live in California, so all of those seem
to be increasing in probability, but that has to be a very small fraction of the underlying
value of the house. Otherwise, one, I can't purchase insurance
because it would represent such a sizable fraction of my overall outlay and two, that
would actually retard economic development because if I can't buy the insurance on the
house, I really can't take the risk of buying a nice house. I can't put that capital at risk except if
I'm extraordinarily wealthy. Exactly, and actually that would have an interesting
impact on the mortgage market and mortgage financing. And all of these innovations that have occurred
on a financing standpoint or a financial standpoint. Things like diversifying the risk of mortgages
or diversifying the risk in the form of insurance, all of those have facilitated the type of
economic activity that we very much take for granted. So when you have a market though, that emerges
in the form of volatility that is fundamentally insurance on the underlying, occasionally
it becomes very easy to mistake the behavior of those two markets as if they're interchangeable. And that's part of what we've seen in the
past couple of years where people have sought similar payoffs to things like the equity
market, but they've sought it with slightly better characteristics. So selling insurance against a house being
destroyed perversely has pay off characteristics that are similar to owning a house. So if you think about it in aggregate, if
a hurricane comes along, lots of homeowners lose their homes and insurance companies have
adverse consequences. They take losses. If you flip that on its head, if I've sold
insurance over a continuous period of time, I receive positive feedback in the form of
inflows of premium that looks an awful lot like the enjoyment that I get from living
in my house. You're being paid to carry the risk. I'm being paid to carry the risk. As an insurer. So I am capturing a portion of the positive
experience that the homeowners receive when I take their premiums. Whether they're happy or not in their house
is somewhat irrelevant, but they're generating a benefit from it. Paradoxically, the payoffs of those two look
actually very similar. Lots of lots of independent cash flows or
utility from living in your house. The avoidance of having to pay rental prices
for property looks an awful lot like what I'm receiving in terms of the inflow in terms
of premiums on the insurance policies. So it becomes possible to conflate the two
and to think that selling insurance is the same thing as having exposure to the underlying
asset. We'll get more into this because if people
feel lost, there are going to be a lot of concepts here that are going to be a little
weird. And for many people, probably the first time
they're hearing them. In terms of volatility, what does that mean? How do you explain to a lay person what it
means to buy and sell volatility? The easiest way to think about it is the vast
majority of volatility, meaning change in price per unit of time. That's what volatility is really telling you. The vast majority of the risks associated
with that are when prices go down. Because nobody is happy when their house blows
up. Most of the risks can be characterized in
the dynamic of you want to ultimately think of volatility as insurance on the market. When people are buying or selling volatility,
that's a very technical definition of what the VIX is. It is the price of the implied volatility
across all of the options that occur with a one month expiry. More accurately a 30-day expiry on the S&P
500. So the VIX is an index that represents that
30-day -- The 30-day expiry strip of options at all price points both high and low. Because that's only the front end of the volatility,
term structures. A volatility has a term structure, just like-
Interest rates. Interest rates. Yes, correct. That VIX front end and some of the financial
innovation that's occurred over the last decade has involved extending that term. So we can buy a VIX or the equivalent of a
VIX on a weekly basis, we can buy it on a monthly basis, we can buy it three months,
six months, nine months. And people are able to buy or sell exposure
to price movement in the front end of that curve. Actually along the curve, but in this case
represented by the VIX. And this also has a really interesting relationship
to how many of these models define risk. Risk has come to be defined as volatility,
which is actually fundamentally incompatible with what we know to be true. Well, it's an approximation. This is one of the challenges that we face. Anytime we attempt to build a model of something,
we're forced to discard features. Otherwise, we'd be modeling the entire world
and that's just an intractable problem. It takes you back to the beginning. So when you discard those features, you have
to make simplifying assumptions. One of the assumptions that's been made in
the modeling of risk is this idea that really what you're talking about is price volatility
and some super smart market commentators, guys like Howard Marks or others. Often Warren Buffet points this out. The true definition of risk is permanent loss. Taking yourself out of the game. If you didn't insure your house and your house
gets destroyed in a hurricane, that's really risk. But if you're insured, your real risk is not
that the house gets destroyed, but that you improperly read the contract and so you're
not actually protected against a hurricane or you improperly vetted the insurance company
and the insurance company goes bankrupt. 100%, and to the other point I was making,
which is it's fundamentally incompatible with what we know to be true. We know that some of the periods of the greatest
risk that we've seen in financial markets have been some of the least volatile. And this goes back to Minsky, stability begets
instability. Well, I would actually say that the precursor
to the most risky periods have actually been periods that have exhibited very low volatility. And that's what the Minsky hypothesis or framework
is working on. Low volatility environments can facilitate
the buildup of risk. They facilitate the buildup of risk because
among other things, they encourage people to take leverage. And leverage is actually a very unique animal
because leverage is actually a contract that says you will pay a fixed amount regardless
of the underlying characteristics. So when you experience adverse consequences,
people's net worth, effectively the difference between the value of what they own and the
contracts that they now have to pay with guarantee can collapse very quickly. The value of those guarantees, which is what
we call debt contracts can in turn collapse. And the most frightening thing about a lot
of the debt contracts is that people they are correct in thinking of these as assets,
but if they improperly analyze my ability to repay that debt on uncertain conditions
and they in turn lever that debt, then you can see a collapse of collateral in the system. That's what 2008 really was. And what's interesting is also there's a parallel
that in 2008 which is there was an insurance market in 2008 as well-known as credit default
swaps. Right, it's a market, I know very well as
we discussed the models that were built for the pricing of credit default swaps by AIG
and their financial products group was done by a guy by the name of Gary Gorton who's
a professor currently at Yale, but he was actually at Penn when I was there, at Wharton
when I was there and I was his TA. Which was, it was fun to watch the impact
of that and Gary just very quickly is absolutely brilliant and there's a very short list of
guys in the running for the Nobel economics prize and he is definitely on that list. Let's bring it back again. We're going to do this a few times, I think,
to try to get the most out of this as possible. But let's get back to the market for volatility
because there was a very important event in February of 2018, which was the blow up of
the XIV, the inverse VIX fund, short ETN. I want to throw that out there, but let's
go back to that and talk to me a little bit about how this market evolved. Because I think when most people think about
options and derivatives, they think about them in terms of hedges to the extent that
they even think about them at all. They think about them in terms of hedges as
part of a larger risk balancing for your portfolio. But this market radically changed over a very
short period of time. Talk to me about that so that we can get into
this climactic event of the XIV. In 2018, yeah. When you refer to the dynamic, most people
think about options as hedging. The first thing to recognize is that options
or derivatives are not a super complex topic. What they actually are, they're just things
that derive their value from some underlying -- Hence the name "derivative." Derivatives, that's exactly correct. In the period roughly around 2005-2006, the
market for volatility as an asset class really began to develop. And what that really means is what it was
referring to earlier where you have this dynamic of people begin to use the characteristics
of the insurance market to actually create an alternative payoff stream that looked at
a lot like being long equities. Pension funds and insurance companies began
searching for a less volatile payoff structure that offered equity like characteristics. The development of that market- Actually,
I want you to explain to our listeners what you really mean when you say "a less volatile
income structure that also has some of the benefits of equity or behaves a little bit
like equity." I think it's important to really drive that
point home. Sure. It's a super complex topic, but in really
simple terms, we are all familiar with the idea of dividends. That you get paid dividends on some stocks
or many stocks. Most stocks used to carry dividends. Yeah, fewer than -- Many fewer today. Many fewer, yeah. In part because of the emergence of these
types of alternate cashflow streams that have made that less attractive. And the idea that the dividend was effectively
embedded in the valuation of the stock, which is again, something we talked about in our
episode on the history of financial theory. And I do recommend listeners download that,
but go ahead, Mike. So this exactly the underlying feature is,
is that when you engage in the sale of insurance, you receive a consistent stream of profits
associated with the deterioration of the time period remaining in your insurance contract. And we call that theta or time decay. Or it can actually take the form of a stream
of income payments if you're continually writing insurance or continually writing periodic
components of insurance. Again, to use the house example, if I fail
to make a payment on my house insurance, the insurance company will say, guess what, you're
not covered. That continual stream of income, again, has
equity-like characteristics. It has a payoff feature that looks a lot like
owning that house, but it has greater stability to it. Yeah, because you gain exposure to that house
as opposed to, let's say, purchasing a fixed income security, like a bond where you're
getting a coupon but you're owning a bond. You're not owning a stock. Right. The difference between an equity and a bond
is that an equity has an uncertain payoff at the end and I receive additional compensation
for that uncertainty, and a bond has a fixed pay off at the end. That's why we call them fixed income instruments. I'm certain of what I'm going to get, assuming
I've correctly underwritten the credit risks. If I think about a US government bond, I'm
100% certain that I'm going to get back. If it's a par value or a price value bond
at 100 bucks, I'm definitely getting back 100 bucks. And my only uncertainty is how much can that
100 bucks actually buy; the inflation. These markets began to develop in the time
period from 2005-2006 and they actually introduced complimentary products, which we're familiar
with in the fixed income space. Things like, you've heard the term CDO or
CDS, credit default swaps. These were insurance products that were created
against the risks that fixed income products would not be able to return the principal
and coupons that you'd ultimately expected. So the development of those markets heralded
effectively the growth of these volatility markets. The failure of those markets in 2008, the
inability of those who had written insurance contracts to ultimately-- Most famously AIG
and AIGFP, Cassano's unit that blew up. Correct. That is the perfect example of it. What you ended up with was a situation in
which people had made promises to pay and they couldn't actually stick to them. Because the income stream of writing the insurance
was so lucrative and the fees associated with it as well. One of the most interesting things that actually
came out of 2008 was, it wasn't actually that the defaults and the outcomes were particularly
adverse relative to the uncertainty. But as we were in the heat of the moment,
the uncertainty as to how severe the outcomes were going to be required people to put additional
capital up in the form of collateral. And that was the fundamental mistake between
AIGFP. The actual defaults on investment grade bonds
and many other products were within the tolerances of what the products were designed for. But the uncertainty at that time and that
their prices had deteriorated that people needed to post collateral. It's the posting of collateral that killed
people. It's the point that insolvency and illiquidity
look very similar-- In the heat of the moment. In the heat of the moment. That's exactly correct. So when these markets began to develop, as
we came out of 2008, the rules changed quite sharply. The regulatory environment changed, the risk
that people had to demonstrate or the protection that people had to demonstrate to their clients,
to hedge funds at the time and other investors had to show their clients how they wouldn't
allow a 2008 repeat. And I was extraordinarily fortunate to be
in a seed that allowed me to dispassionately look at the insurance that people were buying. Many people are familiar with the work of
Nassim Taleb in the black Swan. In the aftermath of 2007, the desire to purchase
this insurance exploded. And the pricing of the insurance did exactly
what you would anticipate in an environment in which the demand rises and the supply because
of the changing regulatory framework decreased, the pricing exploded for the insurance. The price to insure against multi-standard
deviation order events exploded. What we call black swans exploded. So, when we have that type of dynamic emerge,
you need time for people to build the ability to sell that insurance. And I was extraordinarily fortunate to be
able to do that in the seat that I was in. But as the adverse events failed to materialize-
So, your point, just to clarify, because I think it's important, you were saying that
there was a period of time between 2007 and 2013 or so where you were able to sell insurance. Insurance because you felt that the risk to
reward was sufficiently low that it made sense. It was a profitable decision. Right, that it's absolutely correct. Mechanically, you can think about it in the
framework that people were trying to buy investment products that they thought were attractively
priced. So in the heat of the aftermath of 2008 if
we go back to roughly March 2009, the yields that were available on very high quality investment
grade bonds approach nine to 11%. But in order to convince their investors that
they were protected, they had to go buy insurance. And because the regulatory framework had changed
to limit the ability of that insurance to be sold particularly by the historical providers
of it, most famously, Warren Buffet was actually a huge provider of that insurance. But also, the investment banks themselves
had significantly underwritten that insurance and AIG disappeared. All sorts of players that had been able to
provide it disappeared. The pricing of that insurance rose to the
point that it wasn't uncommon to see an implied yield, an implied return on selling that insurance
that was 35%, 40%, 50%. I was extraordinarily fortunate to be in a
seat where I was able to take advantage of some of those opportunities. But it also gave me an interesting place to
sit and watch the evolution as that disappeared. And so-- It has now actually gone the other
direction, which we will talk about. So, now it's completely flipped in the opposite
direction. In particular, there's been a growth in the
desire to sell insurance because people are concerned about generationally low interest
rates. As people get older, they need to find sources
of yield so that the assets that they've accumulated over their investment lives can actually fund
their future consumption in retirement. When you face that type of demand and central
banks choose to stimulate the economy by trying to lower interest rates, people are suddenly
faced with a mismatch. The assets that they had accumulated no longer
offer the returns that they had anticipated when they were saving. And that they need. And that they need. That's a very important to articulate that. We've developed all sorts of alternative ways
for people to generate that yield. One of which is selling insurance and most
people don't understand that that's what they're doing. Many of these products are marketed in a manner
that doesn't really attempt to minimize the risks explicitly, but people really don't
fully understand that what they're engaged in is selling disaster insurance to the markets. You touched on one of the two major drivers
of this, this one that you touched on is one of the more well established, well understood,
which has to do with the search for yield, the reach for yield driven by the very low
interest rates, which I do want to talk about right now. But I want to tease this other point about
automation that you and I've talked about. I think this is more of an original contribution
on your part in terms of how you think about it, which I think is super interesting. And that gets into the conversation about
passive approaches, where they were talking about in volatility or inequities, but this
point about needing the yield is really powerful. It is, and it's one that I think is very,
very hard for people to understand. Because the fact that I need that yield, it
doesn't mean that I get it. But it does mean that I'm going to seek out
behavior that tries to deliver it to me. When we talk about automation, and this is
actually a natural segue, if you ask me what are the risks associated with writing insurance,
it's very natural to look back over the history and say, well, here's how frequently 100 years
storms occur that wipe out beach front property in Miami. So if I'm going to write insurance for hurricanes
in Miami, I want to have that information. The availability of datasets in financial
markets has exploded and has given people the ability to try to articulate that by looking
back at historical data. And the minute you start doing that, the minute
you start providing these very robust datasets that contain all of the available information,
and I specify available because I think it's important, you start to develop a degree of
certainty that says, Hey, I can do this with an algorithm. I can construct a model. We've talked about the pricing of real estate. Increasingly, when I speak to friends that
are in real estate, they'll tell me the frustrations that they're dealing with, where their clients
will come to them and say, well, here's what Zillow says it's worth. That must be what it's actually worth. It's the same underlying dynamic. There's an algorithm that underpin Zillow
that says, this is what we think the house is worth, but the reality is that's what the
algorithm says it thinks the house is worth. It's not what it's going to transact for. Yeah, 100%. When you have that type of phenomenon and
it spreads broadly across the markets, it actually has a perverse feedback loop because
if people become increasingly confident that that answer is the right answer, they become
increasingly confident about transacting at that price. Regardless of whether it's the right price. It's a similar model about the mark-to-model
accounting of the CDOs pre-crisis. No, it's not similar. It's identical. So, when you have that type of dynamic, when
you have people becoming increasingly confident with the models that the historical information
embeds, the future distribution of outcomes, the future potential outcomes, they start
to ignore something really important that we saw in the housing market and we saw on
the dot com cycle and that we've seen in functionally every financial crisis that's ever existed. Which is that when people become overly confident
in these types of things, when they begin the process of actually providing liquidity
or providing insurance based on a presumed certainty about what that distribution is,
they start to influence the markets themselves. Well, actually to that point and to a fix
this to what we were talking about earlier, the purchase of insurance against collateralized
debt obligations actually made it so that the market for trading those was less active
or less liquid. Because if you could buy insurance on a product
that you were carrying, you were able to carry that product having adjusted the risk without
actually having to sell it if you felt that your risk models were out of whack. Yeah, that's exactly correct. You were actually able to hold to the underlying
asset and trade the derivative. Or you use the pricing of the derivative to
presume that you actually had a product that was valued at X. [crosstalk 00:35:07]. Which is one way in which the insurance market
actually has a tangible impact on the underlying. Correct. And it's exactly the same thing. If you, again, take yourself back to your
house, which is the insurance that people are most familiar with, if the insurance company
comes back to you and says, Hey, your house is worth X and therefore we're going to ensure
it at that value, well that gives you confidence that your house is worth X. You become increasingly tied or in the behavioral
component, you frame or you reference that underlying dynamic. Yeah, my house is worth X because the insurance
company tells me. They're an expert. They've got an algorithm that tells them what
my house is worth. It also makes me feel better. Makes me feel better to know there's an algorithm. This is what Zillow tells me. It makes me feel good. And the more people that say that to me, the
better I feel because now there are people that agree with me. Again, it creates a feedback loop. Because the more people that subscribe to
the idea that Zillow has the right price, the more likely they are to transact at that
price. So, it actually has a self-validating mechanism. Again, this is the underlying framework of
a Minsky dynamic. If I become more confident in that, well then
I'm increasingly willing to actually leverage that exposure. If I'm told that my houses are going up in
value and they're going up on a consistent basis, well maybe I should own two of them. Maybe I should get three. Well, let's go for four. That's the underlying framework and it's the
same thing that we're seeing in the markets today. Let's go back to the market volatility because
I want to cover XIV before we get into passive. We were talking a little bit about how this
market began to change around 2005-2006 or some of these pension and insurance companies
that were entering the market that we're looking for income flows while still having exposure
to equity-like products. The emergence of those facilitated the 2008
event. In the aftermath of 2008, the desire for insurance,
the need to buy these products to show investors that they were protected emerged. That's actually where the very first volatility
ETFs emerged. This is super esoteric description. We're talking about markets-- This is where
the demand emerged from [crosstalk 00:37:16]. This is where the demand emerged. The demand for the insurance emerged. So, in the aftermath of 2008, the demand for
what we call black Swan insurance or funds that were designed to protect people's portfolios
exploded. Most people don't have access to a hedge fund
like Nassim Taleb's universe or others that attempt to protect their portfolio against
super esoteric outcomes. Most hedge funds have access to products like
CDS or what are called OTC, over the counter derivatives contracts that are designed to
allow them to protect their portfolio. Doesn't mean that they're pricing them correctly,
but they have access to them. What type of accounting exists to actually
ensure that the people that are writing these securities or these products actually have
the income or the assets to actually make good on the insurance? The quick answer is that in the regulated
space there is capital that has to be held against it, but it's one of the perverse dynamics
of selling insurance in an unregulated environment, which is what's emerged on the markets. We talked about XIV. XIV emerged as a direct analog of people's
desire to purchase insurance. The purchasing insurance product was called
VXX. VXX is something that many registered investment
advisors, many brokers recommended to their clients in the period of 2000, I believe it
emerged in early 2009. They recommended that they buy some form of
insurance associated with it. The product was so disastrous in its performance,
that people very quickly realized that, Hey, I don't want to actually be long this thing. I want to be short this thing. I want to bet against the markets. I want to bet against the insurance markets,
I want to offer insurance. Again, I was very fortunate in doing that
in the immediate aftermath of the regulatory environment that emerged in 2008. But by 2013, the markets had completely flipped
and as we came into 2014 and 2015, the demand to bet against that, to offer insurance to
the markets began to explode. That's where the popularity of products like
XIV really began to take off. They were competing products, some offered
by velocity shares, which is a credit Swiss dynamic, some of which were offered by a firm
called ProShares. The offerings of these exploded and it hit
its peak excitement as we came into 2017. AM I remembering this correctly, that not
only was it cheap to issue, but the left tail or the right tail was being dramatically underpriced
relative to the left tail? In other words, the fear of the recency bias
of the deflationary anxiety was causing the price of insuring that side of the distribution
to be much more expensive than the risk of a hyper inflationary event. Yeah. Chris Cole, who I believe you've interviewed,
actually wrote a phenomenal piece called Volatility At World's End. Again, it's super esoteric and- I think it's
actually digestible though. Well, Chris has a better gift with words than
I do, but I would encourage anyone who is interested in reading this stuff to prepare
themselves for some pretty esoteric topics. But in really simple terms, what Chris had
identified, Chris and I are good friends. It's something that I had identified as well,
is this idea that the right tail was undervalued and the left tail was very richly valued. To give you some idea of how extreme that
was, at the peak of the disconnect between the two in the summer of 2012 and it was very
particular dynamics around that, I'm not going to go into it. You could sell a contract that provided insurance
to the market only in the event that the market fell by 50% over the next two years and then
your insurance was only offering the remaining ability to fall. For the sale of that contract, you could purchase
100% of the upside in the market for the next decade. It was just a complete mismatch. I watched Chris deliver that presentation,
I believe, in the fall of 2012 at Grant's Conference. I was in New York scouting some guests and
then I had him on capital account shortly thereafter talking about exactly this. But I took you off, you're talking about velocity
shares for example, which created an XIV, the ETN. Where did I cut you off? No, you didn't cut me off at all, but the
underlying dynamic of what emerged in that 2014 to 2017 time period was that people became
aware of these products that were betting against it. Betting against the insurance in the market
and the performance of these products, at least on their surface, appeared spectacular. So XIV, which is the most famous of these
and became the largest of these, its performance was in the neighborhood of 40% to 50% a year
for several years. I don't remember the number off the top of
my head, but I want to say it's somewhere in the neighborhood of the price of the XIV
went from somewhere around five dollars to, at its peak in January of 2017 was about $125
over the course of give or take five years. Huge returns. Huge returns. In 2017, if you go back and you read the popular
press, there was tons of references to target managers leaving their jobs after having made
millions trading XIV and launching investment vehicles to offer these types of products. Were you also getting, at the same time if
you were invested in XIV, were you also getting an income flow? You weren't receiving an income flow, but
what you were receiving is an appreciation associated with the term structure-- With
being wrapped into the income flow. Right. What's called "carry." The difference between buying low and selling
high thing. That was embedded in the term structure of
volatility. The underlying options that were purchased. Those yielded an income flow that were incorporated
into XIV's price. Those would yield an income that was incorporated
into performance of XIV. That's exactly correct. They just held XIV. Simple. Boom, boom. I make money from appreciation. Hence, the attraction for the retail public. What people failed to understand about this
product. What was so interesting about is because it
was an inverse product. So as XIV is rising, the underlying VIX is
falling. So the really critical insight, and again,
I was fortunate-- As XIV is rising, volatility is dropping on the short end. It is dropping on the short end. And so, this setup dynamic in which people
were under appreciating this inverse characteristic. So when volatility gets very low, when the
VIX gets very low-- Implied volatility. Implied volatility, it takes very little price
change to actually cause a spike in volatility. And as it super low, the potential for it
to double actually begins to rise. It's all relative. Because it's all relative. That's exactly right. If you have an inverse product, something
that is betting in the opposite direction, when something doubles then it has to go down
100%. Meaning what you would expect to see is a
risk of the product not falling in price but instantaneously going to zero. And that's exactly what we saw on February
5th, 2018. What happened in February 5th, 2018? There's a lot of things that happened on February
5th, 2018. But the XIV product had become large enough
in its share of the overall market, particularly in the volatility space that when the underlying
product, the S&P faced a period of instability. When something began to go wrong in the S&P,
the price of options, the volatility exploded. Now, this is not particularly well documented
and I certainly can't present it as proof, but it actually appears that what happened
is that the Federal Reserve changed the characteristics of how they monitored or how they measured
risk that was being held in volatility terms by the investment banks. They changed what are called the CCAR provisions
associated with volatility. That change in the regulatory framework closed
an arbitrage where it had been cheaper to hold risk in the form of short volatility
from a regulatory standpoint than it was to be long an underlying equity index. And the impact that that had rippled through
the market and created a bid for volatility that rapidly escalated and again, caused the
product to instantaneously, nearly instantaneously go to zero. What's really interesting about that is it
brings up the point that regulations are so important. In fact, regulations play such an important
way down the vector of behind the scenes role in all of this stuff, not just in this conversation
that anybody in markets, period. Again, when I talk about studying market structure,
I talk about the dynamics of market structure. Almost inevitably when you find somebody doing
something that on its surface seems foolish, the answer is they're either doing it because
they're trying to evade taxes or they're doing it because they have to for regulatory purposes. So when people talk about the impact of the
Fed on markets, I'm much less interested in the idea that low interest rates somehow stimulate
activity. I'm much more interested in what are the regulatory
frameworks or what are the constraints on people's behavior that is created by a regulator? Whether they're taking away the prospect of
earning an adequate return on a bond by lowering interest rates or whether they're changing
the CCAR provisions in an esoteric piece of paper that was released on February 2nd, 2018. Also, another question that this brought up
to me is, how effective is zero to 30-day volatility options? How effective is that as a hedge? Well, when you describe it as a hedge-- As
a way of protecting yourself against the risk of volatility? The quick answer is it depends on the price. In the same way that description of how effective
is insurance on your house. You can be living entire life and pay insurance
on your house every year and never have your house burn down and you're very happy about
that outcome. But you look back on it in hindsight and you
say, boy, that insurance wasn't very effective. But a single fire or an earthquake or any
sort of adverse event that takes away your house, you're pretty happy you had that insurance. So the underlying demand for that protection
is valid. How it's priced and a truly random outcome
in terms of whether you needed it or not is a very different statement. So XIV was becoming a larger and larger player
in this market, correct? Correct. So the demand for this product, because of
its simplicity, because it felt like a buy it, set it, forget it, make fantastic money. Again, if you go back and you read the contemporary
reports, you'll see descriptions of taxi drivers explaining to people that XIV was this company
that just made money. And it wasn't a company at all. Where was this? In the wall Street Journal there was actually
an article at one point where somebody was in a taxi and the taxi driver started explaining
to them that he was investing in this company called XIV. [laughter] How do these things
always pop up? [laughter] I wonder how often these things actually happen, or they're just
so paradigmatic that we focus on them. I think the quick answer is, is that they're
always happening in one form or another. People have misperceptions about things, but
they tend to resonate with us when you experience things behaving in a manner that they probably
shouldn't. One of the things that I think people often
confuse, I know that I often do, is correlation with volatility. What is correlation? How does that play a role in what we're just
talking about today? Correlation is really just a measure of how
much the movement of two things are related to each other. The benefit of a diversified portfolio is,
is that you may have- Going back to Markowitz, MPT. Going back to Markowitz, is that you have
a series of assets that have their own individual risk characteristics, but those risks are
not perfectly correlated. You generate a diversification benefit from
taking a series of different risks. If I simultaneously insure houses in Miami
and houses and Alaska, the prospect of me being simultaneously wiped out by a hurricane
in Miami is reduced. But if I concentrate all of my risks in Miami
Beach Front properties, I have a much greater risk. That correlation dynamic, it's just a measure
of saying how likely or how correlated are the underlying payoff structures in my portfolio. It's typically measured in price terms. And in markets, we think again about that
risk, those risks in terms of payoffs of the day to day price movements of these events. The covariances between equities. Correct. Which, not to go too far off, but to bring
it back to Markowitz and MPT, the emergence of beta, the idea of investing in the broader
index came out of the fact that it was computationally impossible at the time to actually measure
all the different covariances to actually create a risk adjusted portfolio according
to how Markowitz had originally envisioned it. Right. In purely mathematical terms, what we call
beta is actually just the slope of a line between two items. It's how much of the movement of Y can be
explained. Both an amplitude and timing can be explained
by the movement of X. If two things are very highly correlated,
you basically get a 45 degree line with the lines set up perfectly in no variance around
it. If things are weakly correlated, you get weakly,
not as in the time period, but in terms of a poor relationship, you get something that
looks like a cloud where there is no apparent relationship between the two. Literally, what we could talk about in linear
regression terms or the solving of that line through a linear regression, it's called R
squared, the explanatory power for it, also just known as the correlation coefficient. This is actually really great because when
you were talking there about how do we explain a correlation, what we have been seeing or
what the consensus view was that correlation has been falling. But you actually put together some, I have
some of the charts in the rundown here. You actually use something called co-movement
and you use that to basically replicate correlation data going back before 2011. I think because the data doesn't go until
2011, and what you found is actually that correlation has been rising. And to bring it back to that point about movement,
how does that relate to the fact that correlation seems to have been dropping when in fact in
your view, the data suggests completely otherwise that it's actually been doubling roughly? Again, This sounds like a really esoteric
point. But in really simple terms, because these
products, these insurance type products really only emerged in the aftermath of the global
financial crisis. Much of the data in terms of analyzing it,
the importance of this data really only emerged in the aftermath of that. So publicly available data series on things
like correlation, particularly as we're discussing it here, where it's measuring how much of
the behavior in the stocks in the S&P 500 are correlated. How much they move together. That data really only began to emerge post
2009, 2010, '11. That time period broadly is a period of decreasing
risks. Or what's perceived as decreasing risks. We're coming out of the Global Financial Crisis. Out of the Big Bang. Exactly. We're out of the dynamic associated with the
global financial crisis. We had a period of excitement associated with
European crises, but by and large, it has been in a period that has felt safer and safer
over time. Now the irony is, is that if you think about
that dynamic of people feeling safer and safer over time, they start selling more and more
insurance. So as people feel safer and safer, they sell
more insurance. Perversely, that begins to impact the behavior
of the market because that willingness to sell insurance facilitates other people taking
more risks. And if people are facilitating and taking
more risk, then actually the market itself exhibits less volatility. Under the conditions of less volatility, you
would expect there to be less of a systemic factor, less of an aggregate economic factor
that would show up as declining correlation. That concealed something that was really interesting. Which is if you look inside the market and
you look inside the behavior of the market, and I adjust for how is the market moving
on each individual day? So was the market moving less than 25 basis
points, less than 0.25% on a day or 25 to 50 basis points, 50 to 100 basis points? A very, very different pattern emerges, which
is when I hold those types of movements constant, I see a pattern of sharply rising correlation. So instead of a decline in correlation, we
actually have an underlying feature in which correlations have roughly doubled over the
last 25 years. So the perception that correlation has been
dropping is actually a result of the fact that there has not been sufficient movement
in variable X to provide the data, the information to show the correlation if it were to have
existed. Exactly right. And because we all tend to behave in a very
localized fashion, it's a little bit like saying, well, the earth is clearly flat because
I can see it with my eyes. But if you pull back and look at it from outer
space, you can see something very, very different. So the critical insight from my standpoint
was that I was able to isolate this dynamic by basically saying, can I control for this
market's increased willingness to provide insurance and the liquidity that that creates? And what that identified was this dynamic
overall rising correlation. We have rising correlation when events become
more correlated. Or when underlying components of an index
become more correlated. The prospect of hundred year storms becomes
far more frequent. Explain that for listeners because this is
super important. Why is correlation so important in the context
of turning what would be a five year storm into a hundred year storm? Yeah. It's not actually that the storm itself is
increased. Rather the damage of the storm. The damage of the storm. Exactly, correct. So if you think about it, again in the context
that I gave of Alaskan waterfront property in Miami waterfront property. Because those two are so geographically distant,
the prospect that I'm going to incur simultaneously as tsunami in the Pacific that strikes my
Alaskan properties that I've insured and a hurricane in the Atlantic strikes the Miami
properties that I've insured is fantastically low. Functionally, we can dismiss it and we can
say, it only happens when a giant asteroid hits the earth and causes a worldwide tsunami
of one form or another. So functionally I can disregard that and that's
what we call a hundred year storm. It's not a risk that is really worth factoring
in because it's going to happen largely with frequency outside of our observable lifetime. But if what I don't understand is that there's
a feature that's actually causing those to become increasingly correlated, and I don't
know what they are. But in really simple terms, you could think
of it as Alaska geographically migrates to be on the Gulf of Mexico. The prospect of Texas beach front property
in Miami beach front property being hit at the same time or in the same hurricane season,
that actually is much, much higher. That increase in underlying fundamental correlation
takes the dynamics of the frequency of events that I would expect to see and compresses
them dramatically. We've talked about the dynamic of roughly
a doubling in correlation. You can think of it as taking a hundred year
storm and turning into a 15 year storm. There are two important points you made. I want to just double down to the point about
the storm. Another way to think about it I think is that
if a hundred year flood were to occur today in parts of India, that would have a much
more devastating effect on the population today than it would have had a thousand years
ago because there are cities along the coast are much higher levels of population density. So the impact of the same storm has a very
different effect. Well, it's interesting you say that because
in many ways it would have a much less devastating impact for each individual that's there. Because you can fly in resources from outside,
Americans and others can contribute to relief efforts. There's technology that allows people to deal
with the ramifications of that. I mean like the monetary impact, let's just
say like there's more property than more people there to be damaged as a result of the rising
sea levels than would otherwise. I would actually say though that part of the
reason why those people are there and why people have that behavior is because of the
dynamics that I was referring to. Which is the mitigating factors are higher. So people can actually take those risks. Yeah, including climate insurance. Including flood insurance, which is subsidized
across multiple geographies. Including the ability to rescue people, including
the ability to receive early warning, which says, Oh, okay, I'm not that worried about
living on the beach because I can get back from the beach as quickly as possible. So the presumption of that, again, this goes
back to the Minsky type framework, the presumption of that actually encourages the concentration
of those risks. Yeah, and the herd behavior. To go back, we talked about interest rates
that I called the yield imperative automation, and actually I would throw in herd behavior
there too. So let's go back to correlation because there
is something that's driving the correlation. It's deep down in the vector. Deep down in the vector. That's one of these things. And to be clear, to my knowledge, nobody else
has really highlighted this dynamic of rising correlation because the experience that people
have is that the world is flat and I'm saying it's curved. So we've established that correlation is important. Your contention is that it is rising. Why is correlation rising? Empirically, I can show that it's rising if
you make these adjustments, if you isolate for the underlying behavior. Then you have to start asking yourself why
you're seeing this. And this is where I make the link to passive
investing because as passive investing grows, as people increasingly make the decision not
to buy Coca-Cola or Microsoft, but increasingly make the decision to buy
the S&P 500 as an aggregate through an ETF or through an index fund, the propensity of
those stocks to move together should naturally rise. If the only vehicles that were available to
trade were index funds, we would expect correlations to be one. We would expect everything, the only reason-
Because everything is investing using the same economic logic to go back to Shoshana
Zuboff. The same logic is being used. That's exactly correct. So when you have that type of dynamic, we
should naturally be expecting this to rise. And the fact that it hasn't appeared has created
the conditions for people to by and large say, I'm not going to spend my time thinking
about it. I'm not going to spend my time thinking about
these risks. It's facilitated by the regulatory environment. The increasing concentration in our industry,
the power that is accruing to these vehicles that are attracting capital. The ability to change the underlying structure
of the regulatory framework to create conditions that force people to go into these products
by default. That's actually facilitating this dynamic. The way that we measure performance of managers
in our industry is increasingly influenced by these underlying type dynamics. And again, I started this off by saying that
markets are fragile, that they're a phenomenon that by and large on a continuous basis. We only have about a hundred year\'92s worth
of experience with. We're presuming that the very, very simplified
models under which passive vehicles are supposed to work are an accurate representation of
the world. And my contention is, is that they very clearly,
are not. Absolutely. Another really interesting thing, this is
something that I wrote out in the top of the rundown because I was thinking about it. There has been a flourishing of interdisciplinary
thinking that has entered the field of economics from behavioral sciences, from complexity
sciences and other fields that have enriched, I think, our understanding of economics post-crisis. And yet this higher level of intellectual
understanding has been impotent in the face of these larger forces that we've talked about. One is the search for yield, the yield imperative
that it isn't just that yields are low and people want higher yields. It's that they need them. By the way, this is going to touch on the
demographic. The demographic just is enormous because these
passive strategies invest in some cases with only the question of how old are you in mind
in the case of target funds, which you've actually done a lot of work on and focused
on. But then there's also the point about automation,
and that's something that you've really thought a lot about. How does that fit into this? Well, so automation is a byproduct-- And by
the way, when we're talking about automation, we're not talking about automation in the
sense that you hear about it in terms of machine automation. We're talking about automation in the much
more basic, original sense of the word. Yeah, so these are literally algorithms. Automation is a byproduct of certainty. If you know that you are going to be making
the exact same product in the exact same way, then you can, the first thing you do is you
build a, if you're thinking in carpentry terms, you build a jig that facilitates the improvement
in productivity associated with making the same thing over and over again. The same underlying dynamic plays into an
investment world. Where the access to financial advice, the
access to allocation strategies for where do I put my money, is a byproduct of more
and more people needing to answer that question. It becomes increasingly prohibitively expensive
to provide them with that type of advice on a customized basis. It's also incredibly hard to look forward
into the distribution of potential outcomes and have any form of certainty about how good
that advice that you're receiving is. So when you have that type of construction,
it's very easy for the industry to naturally gravitate to saying, well, what has worked. And again, to go back to this idea that as
the data has become more and more available, we have greater and greater certainty to say,
Hey, this worked in the past. And while it's very explicit in financial
regulation that you're not able to say, Hey, this worked in the past, this is the results
in the past. We actually are required in the financial
industry to put a very specific disclaimer that says past performance is not a predictor
of future outcomes. Functionally, everyone discards that. Because what do we do? We look at the historical performance, and
this is explicitly true when you think about things like quantitative strategies or systematic
strategies. You look at prior price behavior and you say,
this is what I think is going to happen in the future on that basis. This embeds into a larger philosophical point. Again, something that you've observed and
this whole thing of automation embeds into that, which is that we all feel increasingly
overwhelmed. You and I have talked about this choice is
a fundamental feature of capitalism. The abundance of choice is something that
famously we advertised when Nixon met with Khrushchev on national television back in
the 1950s and you could see what an American home looked like and you could see what a
Soviet home will look like. Of course, the Americans live with greater
abundance, but today choice has become a detriment. It's become something that overwhelms us and
we look to offload decisions. We look to offload decisions when it comes
to driving. We look to offload decisions when it comes
to planning our day, Siri, Alexa, everything else. And one place that we have done it has been
in markets. 100%, and I would even go so far as to say
that we have increasingly begun to fetishize not the accumulation of different products,
materialism. We're increasingly fetishizing people's ability
to shed the need to make those decisions. Elizabeth Holmes with her black turtleneck
and black tire that she wore every single day to us represented a higher form of intelligence,
a higher form of certainty, that I don't want to be distracted by the minutia of selecting
my outfit for everyday because I've got more important things to do. Cognitive load. Right, exactly. I'm reducing my cognitive load. We talk about this. It's the language of the millennial, candidly,
like what are the life hacks? What are the things I can do to take the shortcuts? Well, the simplest one of those is, I'm not
going to worry about my investments. I'm going to buy an S&P index fund. It's my time, put in. Right, exactly. If it's that time of the month, put it in. Stocks always go up in the long-term. That's exactly correct. And you see this in the language that people
have adopted. Jeremy Siegel most famously with stocks for
the long-run. The idea that somehow the returns associated
with roughly 150 years that we have of market data represent the forward return distribution
of the next 100 years or the next 50 years and the next 10 years is on its face, absurd. Things that happened in the last 150 years
have very little relevance to what happens in the next 10 or the next 50 or the next
100. But because we're trying to shed this decision
making because we feel so inexpert in making those decisions for ourselves because there's
so little ability in advance to express certainty of what those outcomes are going to be, whether
I'm an expert and you're a moron. And that wasn't an association, that came
out wrong. But- I feel like one pretty often. As my wife will claim regularly that I inhabit
that space as well. But the underlying uncertainty about the validity
of the advice that you receive encourages people to default to the machines and hand
it over. And I encourage everyone to look at their
own behaviors in this dynamic. You receive a paycheck every two weeks. Do you stop and think, do I actually want
to put money into the market? Almost no one does. So I want to move us into the overtime for
the second half of this conversation, Mike, but I really want to hammer a point home before
we get in there because we're going to drill down into the details. I think you alluded to it earlier, which is
that the role of markets is to provide liquidity to be a source of discretionary buying. Part and parcel of that is the idea that independent
agents competing over a finite amount of information can arrive to the most accurate price of a
particular asset or security, whatever. These passive funds impart part of this, is
they don't just depend on the liquidity. They also depend on the pricing. They expect that others are going to do the
work to determine what the price of whatever it is they're going to purchase is, and then
they come into the market with their flows and they buy it, they free ride. That makes sense if you're small, if you're
a parasite on a large elephant, that can work. But as you get larger and larger, those dynamics
change. I think what you've highlighted and what we're
talking about today is that this ecosystem is basically in danger of collapse. To another point too, we can think about it
in terms of variables. These models that passive investment strategies
are using do not take themselves into account in the model. They do not see themselves as you have said,
I think you've used the term that they're incentivized to demonstrate that they do not
exist. Well, the entities themselves very clearly
have that from a regulatory framework. Their goal is to say, we are not impacting
the markets. That's not, to be fair to the participants,
that's not actually saying that the people at these index funds, the Vanguards, et cetera,
are mal intentioned because in many situations they actually truly believe what they're saying. But I would also, the fact that you believe
something doesn't make it true. So if your goal is to demonstrate that you're
not impacting the markets, if your goal is to demonstrate that you're ultimately helping
people, the easiest thing to do is to point to the historical performance and say, look
what's happening. Markets are all time highs, therefore everything's
great. Americans have made more money investing in
index funds than they have with active managers or with other vehicles. That's very easy to demonstrate in the past. But there's very little ability to actually
detect the emergent phenomenon that are being created because of this. That's some of the stuff we've talked about. Yeah, and before we go, I want to highlight
that point. In the case of these funds, they have a very
simple model, which is that they expect markets are mean reverting. They also expect that over the long-term,
being invested in equities is a good proposition. So they're not discriminating by price or
value. When we think about investing, we tend to
think about it as, okay, well, Howard Marks said this on my show. Like, if I come to you and I tell you I want
to sell you my car, the natural question is how much? I'll buy any piece of crap you have if it's
cheap enough. It all depends. But what you've highlighted and what we're
going to get into is that increasingly, because of the presence of these large non-discriminatory
buyers, their flows, their cash flows, their incremental dollars are becoming increasingly
the dominant variable that's driving price, not the underlying value, not anything else. It is the systematic flow of dollars from
these funds that grow larger as they become more successful and they reaches a tipping
point and that is ecosystem decline. To take us full circle back to Shoshana Zuboff's
observation, the underlying dynamic that we're actually experiencing is the success of these
vehicles is actually leading to self-validation that the algorithms themselves are correct. 100%. So, Mike, stick around. We're going to drill into all the details
on the other side of this in the overtime. If you're new to the show and you haven't
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to this week's episode where you can find a link to our Patreon page as well as information
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that alongside the regular episodes seamlessly downloaded straight through your phone. You can also get access to a transcript of
today's conversation with me and Mike as well as to my illustrious rundown full of graphs,
links, materials, much of which Mike provided me with and that I incorporated into this
rundown. Mike, thank you so much and stick around. I'm looking forward to it. Today's episode of Hidden Forces was recorded
at Creative Media Design Studio in New York city. For more information about this week's episode,
or if you want easy access to related programming, visit our website at hiddenforces.io and subscribe
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every episode, check out our premium subscription available through the Hidden Forces website
or through our Patreon page at patreon.com/hiddenforces. Today's episode was produced by me and edited
by Stylianos Nicolaou. For more episodes, you can check out our website
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