When the Market Crashes, How Do You Continue to Profit? (w/ Hari Krishnan)

HARI KRISHNAN: My name is Hari Krishnan. I’m a volatility macro-focused hedge fund
manager. I specialize in volatility. And I’m also the author of a book called The
Second Leg Down. And I’m in the process of writing a second
book called Market Tremors, which tries to identify the root causes of crises. Most investors hate hedging. They think it’s a waste of time. And the reason they think it’s a waste of
time is they view it as a line item on their list of manager allocations. And what happens is that most strategies tend
to make money most of the time. They go up like this with blips on the downside
so they’re negatively skewed, whereas hedging is kind of the reverse. It trundles along on a downward slope, but
should make you a huge amount of money if there is a material advance in the markets. So most investors hate hedging because markets
tend to be in a bullish phase most of the time. So hedging looks pretty much like a dog in
those phases. And the temptation is to cut out the hedges
when conditions are good, when the going is good. And then suddenly, when there’s a sell-off,
the conditions change. In my view, hedging is a necessity. Why? Because even though we have views, and as
money managers in general, we get compensated on the basis of our views, we don’t know for
sure. Now, if we did know for sure, life would be
easy. We could just cut our positions if we felt
that conditions were worsening. But the reality is that we don’t know for
sure what’s going to happen. So hedging is a very viable way to control
our downside in the absence of certainty. The way I think about hedging in general is,
I need to work with clients. Because hedging, ultimately, is a kind of
an interactive process. I need to know how bad the situation is for
the clients, what market conditions are, and how I might best respond to that combination. So that’s something that I focus on. Now, the way I think about hedging is that
hedging is expensive over the market cycle. So if you look over the market cycle, markets
tend to go up. At least, risky assets do. But there are times when volatility is cheap. And there are other times when you’re just
scrambling to survive. So the way I think about hedging is that it’s
regime-specific. In quiet markets, there are all sorts of wonderful
hedges you can do that are cheap, that are volatility-based, that look just like a private
equity investor would think about in aggregating positions in terms of finding real value at
a specific point in time. Now, you can do that by buying volatility
because, in my view, volatility tends to cycle, and it follows the path of fear and greed
in investors, in their mindset. But as conditions worsen, it’s necessary to
think of more efficient hedges that may not protect you all the way down but are not egregiously
expensive. Now, one of the key points in this is that
when there is a leg down in the markets, investors tend to scramble for insurance. They tend to overpay. And that’s exactly when you don’t just want
to be diving into the same trade everybody else is diving into. Now, a key point is that options trading is
quite different from Delta One or directional trading. And that needs to be taken into account, which
is what I do in the book. Yeah, there was a question about regime-specific
hedging. And I use an old phrase, which is that every
dog has its day. It’s kind of a dumb phrase. But it works really well for options. Why does it work well? Because you could come up with the most ridiculous
option strategy– buying certain strikes, selling other strikes, buying maturities,
selling other maturities. And you get this hopelessly complex, convoluted
structure. And most people would say that’s a waste. And it would be on average. But there must be some scenario where that’s
pretty good. So that dog, that ridiculous structure, has
its day. Even the stuff you come up with that you back
test rigorously, which is a big part of my book, my first book, which is that there are
certain structures that are very well conditioned to certain market regimes, even those have
holes. And so the way I think about hedging is that
you need to be very regimespecific. Basically, if I had to write what I’ve written
on the back of a postcard, I would say this. In the early phases of a crisis, buy volatility,
buy fear, that on the fact that the market may be increasingly scared after a very quiet,
upward sloping bull market. Now, once the fear gauges, once the fear indices
go up, whether it’s the VIX, the currency VIX, some measure of swap spreads, or whatever,
once that goes up, hedge the downside directly. Don’t focus on sentiment. Focus on direction. So the main theme of the book is to say, hedge
against sentiment changes early on because people tend to be overconfident in the early
phases of crisis. And then hedge directly and cheaply as the
crisis gets worse. There’s been a question that’s been raised,
which is, how do crises develop. And I’ve been working on this a lot. And that’s sort of the headline theme of my
second book. And the way I think about it is that there
are different time horizons where crises develop. There are kind of flash crashes over short
horizons. And there are credit unwindings over longer
horizons. So let me focus on the longer horizons. A lot of people say that when the central
bank, whether it’s the Fed, or the ECB, or somebody else, when they expand their balance
sheet, that’s bad for equities because there is a debt overhang. That’s far from the truth. The truth is that the more debt that is out
there in the markets, in the economy, the more deposits there are in banks for corporations
and individuals. Now, when that’s the case, the so-called economic
theory of spending power or aggregate demand goes up. So that should be good for equities. That should be good for risky assets. The danger is when that debt overhang contracts,
which is exactly the scenario we’re in now. We’re in a scenario where the central banks,
like the Fed, which may be operating on close to $4 trillion of balance sheet expansion
at this point, has created huge amounts of deposits in the system. And those deposits have chased risky assets
in the absence of yield. As that changes, as that contracts, that’s
got to be bearish. That’s structurally bearish. And we’ve seen that. And what you can say is that, even though
not every credit crisis is the same, every one rhymes. They might not be identical, but they rhyme. And the way that they rhyme is that credit
contraction from a high level tends to be a leading indicator of crisis. Why? Because all of that demand that is piling
into things that have yield, or that have momentum, even at the margins, if it disappears,
it’s bearish. That’s where we are now. It’s not about the Fed funds rate in isolation. What it’s about is the balance sheet contraction. And as you see those $50 billion or whatever
number– maybe it’s a bit lower than that– peeling off the Fed balance sheet, that’s
kind of a punishment, or that’s a compression on aggregate demand. So that should be bearish for risky assets. So you have that structural headwind. It doesn’t matter what the Fed funds rate
is in isolation. What matters is how much credit is available
in the system to chase yield and to chase risky assets. So that’s the long horizon issue. Over short horizons, you kind of move from
balance sheet dynamics, which, sadly, but in truth, are only available once a quarter
or once a month, to credit spreads. And it’s well known that when credit spreads
are trending up, that tends to be somewhat bearish for risky assets because it’s a suggestion
that something’s going on in the system that’s creating a reduction in demand for risky assets. Over shorter horizons, you see stuff that’s
a little bit more subtle. Maybe it has a lower Sharpe ratio, or maybe
not a Sharpe ratio, but a lower level of conviction from a common sense perspective, which is
that, typically, when markets start to have decreasing volatility, when they start to
go into these kind of channels or whatever, a move in a given direction tends to be meaningful. Why? Because in particular for markets trending
up, it starts shrinking in the trend, and then it shanks on the downside. You know that a lot of people have their stops
in place. There’s a lot of late money, late hands in
the market. And they’re going to be forced out if anything
dramatic happens. So you have situations where a contraction
in volatility is actually indicative of rising risk. So while I’m not a technician, and I will
not present to you technical ideas, what I will say is that contracting ranges are not
necessarily indicative of reduced risk, but of greater pressure given a move of certain
size in the markets. What causes a market crisis? Of course, you need to qualify it by another
question, which is a crisis over what horizon. But let’s forget about that for a moment. In general, the two preconditions for a crisis,
in my opinion, are over ownership of certain assets and a change in leverage provision. So leverage and sentiments. Over long horizons, you can look at things
pretty easily. And QE fits into that paradigm really nicely,
which is that what happened in QE? Well, the central bank expanded its balance
sheet. Maybe it went from $800 billion to $4 and
1/2 trillion over a period of some years. Now, what does that mean? That there was a huge amount of new debt that
was issued. So the relative quantity of debts vis-a-vis
equities, which have a relatively fixed supply, at least in share terms, grew dramatically. So for people who had fixed allocation schemes,
they had to go into equities. Whether equities were overpriced or not, whatever
the p to e, or p to revenue, or p to dividends, or whatever ratio you wanted to look at, they
needed to meet their asset allocation mandates, their strategic mandates. So that was very bullish for equities. Now, what do you have happening when the supply
of treasuries goes down? The reverse. Basically, bonds and cash are in scarce demand. Equities are maintaining a constant supply. Now, suddenly, they look overvalued. So you have this overhang, which is structurally
bearish for equities. Now, that’s an important point. The other point I would mention is that it’s
not only a function of relative levels of ownership, but also a function of the time-relative
amount of supply of credit. The more credit that’s available, the more
excess credit that’s available, the greater the level of deposits in the system. Whenever somebody on the other side of the
table– I don’t know who he or she might be, or it might be– takes a loan, the person
who took the loan spends it on something, and that results in an additional deposit. That deposit has the potential to be spent
in some way, namely, toward a risky asset. So that creates additional aggregate demand. Now, when you pull that out of the system,
that demand goes away. So that should be bearish structurally for
equities. So I think we’re in a phase where there are
headwinds over longer horizons on the equity market. And you can use that paradigm very directly. Well, there was a question about what do you
foresee, what do you envisage as the outcome of this huge proliferation of exchange-traded
products, whether they’re funds, notes, or whatever. Now, what we have seen– and I’ll try and
be as timely as I can be– is a sharp increase in the quantity of these products, followed
by a slight decline in the final quarter of 2018, which coincided with a sell-off in the
markets. Now, what’s my view on these? Well, is it the devil or the angel that you
wish to refer to within me? I’ll start with the devil. I hate those things. And when I was a macro manager, I used to
have dedicated short basket of badly designed exchange-traded products. And there were so many of them. It was a wonderful area for examination, for
looking at things. And one of them was the levered ETF. So if you have an asset– it could be any
asset. It could be corn. It could be anything, really. If that asset tends to mean revert over daily
horizons, so of they tend to be choppy dynamics from day to day, a levered product is the
worst thing you could possibly invest in. Why? Because every day that it goes up, the position
grows in size. And every day it goes down, it contracts in
size. If the asset is mean reverting, that’s the
worst strategy you can invest in because you’re over-scaling at the worst times and you’re
under-scaling at the worst times. That was a good case in point. The other case is kind of a ownership-related
one, which is a point that I talked about previously, which is that there have often
been studies, there’ve been various analysis of bubbles. And Didier Sornette– and my French is not
great, so my pronunciation is pretty bad. But he’s focused on bubbles where there’s
an acceleration in the price. So the price starts going super linear or
super linear even in logarithmic terms. So it’s just kind of scooting up at an exponential
rate. And we’ve seen that with Bitcoin, which subsequently
had a crash. We’ve seen that in China, which Sornette pointed
out as well, which had a crash, and various other markets. Now, those are great. Those are great markets. But there are other markets where you get
this kind of– for lack of a better phrase– Madoff-like straight line dynamic, where there
is no exponential growth, but the Sharpe ratio is hurtling toward infinity over time. And you know something’s up. Because it’s not possible, at least in my
view, to generate such outrageously high Sharpe ratios, such outrageously high risk-adjusted
returns, without high frequency type trading algos over longer horizons. The overarching point isn’t that you see necessarily
exponential increases in price, but that you see sharp increases in, or radical increases
in Sharpe ratio– no pun intended. And those are indicative of potential bubbles. We saw that with the mortgage-backed securities
markets in 2008. And we may well be seeing that in the leveraged
loan markets in the current time, where those markets have become exaggerated in their size. They are covenant-lite, which means they don’t
require a lot of intermediate checks. And they had the same bubbly dynamics that
maybe Sornette would not identify. But anybody who looks at outrageous Sharpe
ratios would scratch their heads and say, something maybe afoot. How do I deal with increased automation in
the markets? What’s its impact? And is it still possible to make money in
that regime? Let me first give you the doomsday scenario. The big hedge funds, the big asset managers
who use a lot of machine learning are using it well relative to what it’s supposed to
do. They’re applying sophisticated algos. They’re applying it to high frequency data. They’re applying it to heterogeneous data,
which means all sorts of different types of data, whether it comes from a satellite, from
sentiment, from price action, or whatever. And they’re doing it better than the average
Joe, which includes me, could do. So that’s creating a lot of compression in
alpha in the standard strategies. Now, the people out there– and there are
many people out there, and many of them are very talented– who focus on risk premia,
they’ve been beat to the punch. They’ve been beaten to the punch. Because the algos know all of the risk premia
that are out there. They know all of the sustainable sources of
alpha that are out there because they’re looking for structurally stable sources of return. Let me think of myself as a machine learning
algorithm, which I’m not. But let’s imagine that I was. Now if I were, what I would say is this. If a lot of data is coming in, and it’s confirming
what I know, or what my inference rules are, what my inference engine is saying, I’m super
solid in my next bet. And the more data that comes in that confirms
my view, the better off I am, and the more confident I am, and the bigger the sizing
of my bets. Whether it’s Bayes’ rule or any other inference
engine rule, it’s all the same. The danger is that if all this data is coming
in at this polynomial or exponential rate, and it all confirming what I already know,
given that I’m this inference engine– I’m not a human. I’m an inference engine– it would take a
lot of data to change my view. And humans are not as good at being accurate
as computers. But they’re better at smelling rats, smelling
out rats than computers are. So if something has changed dramatically,
the computer won’t know. They’re just saying, this is an outlier, but
it’s two or three standard deviations away. It’s OK. It’s within my distribution rule. I won’t chuck out my main modus operandi. I’ll just keep going until I get more negative
signals, whereas humans might say, well, a computer is really good at trading 10 lots
every time really steady. I might trade nine. I might trade 11. But I’ll never trade a million lots, if that’s
my budget, whereas a computer might not be able to distinguish between something that’s
radically different from what it’s done and common sense. I think that’s one issue with computational
problems, which is that if you have this overlay of regimes where we’ve been in a very quiet,
calm environment until October 2018, and now, we have some rockiness, some jaggediness,
well, the computers have been latching on to 2017, which was straight line, happy days,
bull market times, into 2018– oh, February was a blip. But it came back as quickly as it went down. Happy days again. So the computer, is or the algo is like, life’s
still good. All the stuff I latched on to in the bull
market, it’s still valid. Whereas humans might say, well, I mean, look
over longer periods. There have been times when markets were risked
off. And in fact, based on your calibration regime
or conditional on your calibration regime, maybe a third of the time, markets are risk
off. 2/3 of the time markets are risk on, which
anecdotally translates to the idea that markets tend to go up more than they go down, but
the down periods tend to be sharp, whatever. The human has a memory that maybe incorporates
some of those things that are outside the Bayesian inference engine of the algo that’s
latching on to the most recent events. So the problem that I see, or the area where
machines can be exploited, is if their regime shifts. We saw a regime shift in October. We saw a more severe one in Dec, in December. And then we’ve seen a sharp reversal in Jan,
in January. But I would expect that we’ve reached a new
baseline level of higher vol from what we saw in 2016 and 2017, where, using a crude
case study, the VIX was trading at a handle of 12 or 13. Now, it’s trading closer to 18 or 20. And it’s not going down as quickly as it did
in February of 2018. So one could argue, potentially, that the
algos have latched on to a huge quantity of data, but the data is very specific to one
tiny regime. So the richness of the data that the machine
might think it’s dealing with is far less than it thinks. What happened in December 2018? Well, if you look at what happened in volatility
markets, it had all the hallmarks of a standard bear market movement. So if you looked at how much the VIX moved,
if you looked at the VIX beta relative to the S&P, it was pretty standard. It was less than 1. What you typically see is that, in flash crashes,
in sudden moves from a bull market setup, the VIX tends to explode. And you didn’t see that in December. What you saw was the VIX going from, say,
mid-teens to 30, but not that much higher, in spite of a very significant trending down
move in the S&P 500 and various other US indices and abroad. So this, to me, at least if you look at the
interaction between volatility markets, or equity indices vol and equity index movements,
nothing out of the ordinary happened. This was not a vol squeeze. This was a somewhat predictable credit-contraction-oriented
sell-off. Now, if you operate on that assumption, what
you can say is that, in the absence of some exogenous shock, which I can never control
for, this has all the hallmarks of a choppy, perhaps bearish market going into the next
year. Now, there’s been as strong, if not super
strong rally since Christmas Eve to the present, which is mid to late January. That may persist. But it’s occurring at a higher level of volatility,
of equity vol. Now, you don’t have confirmations from the
volatility in other markets. Currency vol is still pretty low. Rate vol has not exploded. But rates are in kind of an awkward situation
where there’s a twoforce action taking place, where short rates are going up. At least they’ve been signalled to go up at
least a little bit in the next 12 months. But at the same time, there’ve been some fluctuations
in risky assets. Equity vol has gone up, so that’s kind of
compressing rates, at least at the longer end, in the mid to longer end. So you have this kind of tug of war going
on in rates, which compresses realized vol, and hence, implied vol. But equities seem to be operating in a regime
that’s quite different from 2016 and 2017 and much of 2018. Namely, they’re choppier. And so what I would expect is that the vol
of vol will be higher. The opportunity set for relative value traders
will be higher. But if I were an investor, if I were an asset
allocator in hedge funds or an active manager, I would focus on ones that can carve out their
edge without using too much leverage, without too much borrowing, because that’s always
a risk. And when the vol of vol goes up, it’s great. But it’s not a time to get greedy. It’s a time to get good returns with modest
leverage. And I think that’s where we are now. OK, so the final question was, how would I
recommend hedging given the current market environment? Well, as I’ve said before in my book, the
way I think about hedging is that there’s a good hedge or good hedging strategy for
any stage of the market. At the end of a bull market cycle, volatility
tends to be underpriced, insurance tends to be underpriced. So it’s a good time to just stockpile long-dated
options, let’s say puts on the euro stocks, or the S&P, or whatever. As conditions worsen, which they have now,
the absolute level of vol, or the equilibrium level, if there is one, tends to go up. So hedging isn’t that easy in terms of just
buying out of the money insurance, because the level of risk aversion has gone up. So what I would recommend in this phase is
to be a little bit more relative-value-oriented. And what I’ve said in the book, and what I
would say to many potential investors who are clients, is that in the beginning of a
sell-off, investors get a little bit scared over the short term, but they’re not that
scared over the long term. And they’re not that scared about extreme
down moves. So there are good places to still buy insurance. One good place is to go further out of the
money. If I were to buy a 10% out of the money one
month to go S&P put, you might say, well, it’s not going to go down there. I don’t care whether it goes down there because,
as a hedger, I’m not worried about the terminal payout for the insurance that I buy. I’m worried about the repricing of insurance. So it’s like the old saying goes. If there is a storm in Tokyo, the price of
hurricane insurance goes up even though the hurricane may never occur. So I’m trying to make money on overreactions
in sentiment. If conditions were to worsen, which I predict
that they will in the next year– although there’s no saying whether it will be sustained
or not– I would move more to gamma type hedges. And the final frontier of gamma hedging is
just following the trend down. But there are lots of relative value options
strategies that you can get into that sell the skew, that sell extreme fear, but still
provide a range of protection that I would recommend. And I can go into that in great detail. I’m happy to respond to any questions you
may have. But a lot of it’s in the first book that I
wrote, which is called The Second Leg Down.


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