How to Build a Recession-Proof Investment Portfolio (w/ Danielle DiMartino-Booth & Chris Cole)

Imagine being furnished with generational wealth under one condition – you must choose only one asset allocation for your portfolio and stick with it for 100 years. Where would you even start? Chris Cole, CIO and founder of Artemis Capital Management, returns to Real Vision to answer that very question. He sits down with Danielle DiMartino Booth of Quill Intelligence to discuss the optimal portfolio construction for the long run, regardless of market condition. With uncertainty everywhere despite all-highs in the market, Cole discusses how to navigate Charlie Munger’s “death of the efficient frontier.” He explains the allegory of the Hawk and Serpent and breaks down the construction of his 100-year portfolio. Cole and Booth provide viewers with the tools to traverse the “incremental death of alpha,” and markets that are increasingly subject to the amplified volatility of increasingly passive investments. This piece is a much-watch for the pension fund or endowment that has no long-volatility exposure in their portfolio. Filmed on February 7, 2020 in Austin, Texas.

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00:02
DANIELLE DIMARTINO BOOTH: Well, hello.
00:03
This is Danielle DiMartino Booth with Real Vision, and today we’ve got a real treat.
00:07
We are bringing your Christopher Cole with Artemis Capital.
00:10
We’ve been waiting for over two years for a follow-up to his seminal paper.
00:14
It’s out there.
00:15
You have to read it.
00:16
Share it with people– maybe not people under 18.
00:18
They wouldn’t understand it.
00:20
But everybody needs to get a copy of this and read it.
00:23
We’re going to discuss what it’s all about today.
00:26
Welcome.
00:27
CHRISTOPHER COLE: Thank you.
00:28
It’s a pleasure to be here and back on Real Vision again.
00:30
DANIELLE DIMARTINO BOOTH: So I’m going to start with an anecdote.
00:34
Years ago, I was in Omaha, and I visited with Charlie Munger.
00:38
And he made the comment to me that the entire pension fund advisory business one day would
00:45
go out of business.
00:46
It would go the way of the dodo bird because of the group think that surrounded the industry
00:51
because of the way that the portfolios were being designed in a world where central banks
00:57
were effectively running the show.
01:00
And he made the comment to me that he saw in the future, he said, I might not live to
01:05
see, but you will, the death of the efficient frontier.
01:09
So I’m curious about your thoughts on portfolio construction, how it’s done, and how it that
01:18
evolution has changed basically the way this entire generation approaches investing.
01:24
CHRISTOPHER COLE: Well, beginning with that and looking at what Munger has said, as a
01:28
follow-up to my last letter, the Ouroboros letter that talked about the cycle of risk
01:33
and how volatility has been used as both a proxy for risk and also as a source of return.
01:40
I thought, how can I– what will disrupt that– what will disrupt that cycle?
01:46
I posed a question to myself saying, well, if we’re going to see what happens in the
01:50
future, we have to look to the past, and the distant past, not just the recent past, not
01:54
the last 10 years, not the last 40 years.
01:57
We need to look back 100, 200 years to understand the cycle of capital creation and destruction.
02:03
And I posed this question to myself.
02:06
I said, imagine that someone gives you generational wealth, enough money that you can live and
02:15
your children’s children can live at a high level.
02:19
But it’s subject to one question, one dynamic.
02:26
You have to choose an asset allocation and stick with that allocation over 100 years.
02:32
What allocation do you choose so that your children’s children will have prosperity?
02:41
And taking that cue, I went back and looked at 90 years of historical data, backtested
02:48
a wide range of popular financial engineering strategies, everything from risk parity, the
02:55
traditional pension portfolio, short volatility, long volatility strategies, commodity trending
03:01
strategies, and looked and how do these perform?
03:03
And what asset allocation is the allocation that’s going to provide wealth, not only consistently
03:11
over 90 years, but through every generational cycle, through both periods of secular growth
03:17
and secular decline?
03:19
And what I found surprised me, that echoing Munger’s statement, the allocation that the
03:28
majority of US pension systems and retirees are following, which approximately today is
03:33
about 70% equity-linked products- – that could be everything from stocks to private equity,
03:39
things that are the profit from secular growth– and about 20% bonds.
03:44
That portfolio has done incredibly well over the last 40 years.
03:49
But when you look at that portfolio over 90 years, you see a very, very different reality.
03:58
And that has a wide range of social, economic, and social ramifications that become quite
04:06
startling.
04:08
But looking at that, I say, what asset allocation can I find that will actually provide protection
04:17
over that 90 years consistently?
04:19
And that answer came not from a macro view.
04:24
It doesn’t come from me having an opinion about whether or not we’re going to go into
04:29
a recession or whether or not there’s going to be some continued economic prosperity.
04:33
It comes simply by looking at data, using mathematics, looking at data, and looking
04:39
at empirical data over a lifetime to come to that determination.
04:44
And I think the results are quite shocking.
04:46
And I think they run somewhat counter to the consensus knowledge as to what optimal portfolio
04:52
allocation should be.
04:53
DANIELLE DIMARTINO BOOTH: So Charlie Munger was right.
04:55
CHRISTOPHER COLE: I think he’s right.
04:57
DANIELLE DIMARTINO BOOTH: Take a step back to the October 2017 paper, if you will.
05:04
Back then, you drew the scope of the financialization of the markets of the economy.
05:13
You talked about risk parity, and share buybacks, and the massive effect that they had had on
05:20
the crowding in to certain asset classes.
05:25
So talk about what effect this herding instinct has had on the way this generation views investing.
05:37
CHRISTOPHER COLE: You and I have a very similar writing style.
05:40
I love metaphors.
05:42
I think visually.
05:43
I think I think you do too.
05:44
DANIELLE DIMARTINO BOOTH: Yours are better.
05:45
CHRISTOPHER COLE: Yours are– they’re very good.
05:48
But in that 2017 paper, I think I wanted to use the idea of an Ouroboros, this concept
05:55
of a snake devouring its own tail.
05:58
And what this was a metaphor for– what is now about $3 trillion in equity markets alone.
06:04
This is just equity markets, US equity markets.
06:08
The number is much larger if you expand that across asset classes.
06:13
But of strategies that use volatility as an input for taking risk, but also seek to generate
06:21
excess yield, either through selling volatility or through the assumption of stability.
06:29
So in this number, you have implicit and explicit short volatility strategies.
06:34
And I think there’s a lot of confusion as to what this means.
06:38
Explicit short volatility strategies are strategies that they will sell derivatives, so they’ll
06:44
sell options.
06:45
DANIELLE DIMARTINO BOOTH: So the easiest would be selling the VIX.
06:47
CHRISTOPHER COLE: Selling the VIX, that’s right.
06:48
So this paper came out prior to the XIV blow up, and it talked about how the VIX ETPs were
06:55
likely to have significant problems.
06:57
But that’s a very small component of that short volatility trade.
07:00
A much larger component of the short vol trade are strategies that replicate the risk parameters
07:10
of short volatility trades but may not actually be shorting volatility.
07:14
So strategies like this might be things like volatility targeting funds or some elements
07:20
of risk parity, for example.
07:21
DANIELLE DIMARTINO BOOTH: Risk parity is still something we don’t hear a lot about, even
07:25
though it’s massive.
07:26
CHRISTOPHER COLE: Yes, yeah.
07:28
And indeed, the framework there is– this could be anything between literally shorting
07:34
vol– literally shorting volatility, what I’ll call short gamma or being short trend–
07:38
and we could talk a little bit more about that– short correlations, short interest
07:44
rates.
07:45
These are risk factors of a portfolio of short options that various financial engineering
07:49
strategies will replicate, maybe not all of them, but certain aspects of them.
07:53
That doesn’t mean all these strategies are bad.
07:55
It just means that they are formulated to a world where interest rates are dropping,
08:00
assets are mean reverting, and that volatility is quite low.
08:05
And guess what has happened the last 40 years?
08:09
We are at generational lows in volatility across asset classes.
08:15
Asset trending– I think this is something most people don’t realize that, actually,
08:20
assets, equity for example, used to trend higher and lower.
08:25
You can measure that through something called autocorrelation.
08:28
All that means is that if today was down, it is likely that tomorrow will be up and
08:36
vice versa.
08:37
DANIELLE DIMARTINO BOOTH: Buy the dip.
08:39
CHRISTOPHER COLE: Buy the dip, that’s right.
08:40
So the assets for the greater part of a lifetime were autocorrelated in the sense that higher
08:49
prices resulted in higher prices, and lower prices resulted in lower prices.
08:54
That autocorrelation peaked right when Nixon delinked the dollar versus gold, or the US
09:01
dollar versus gold.
09:03
And we have underwent a multi-decade decrease in autocorrelations.
09:09
And now, we’re at really peak mean reversion markets.
09:13
So a lot of strategies make the assumption that mean reversion is implicit to asset price
09:20
behavior.
09:21
That’s definitely not always the case.
09:23
So to that point, one of the strategies we actually tested was buy the dip.
How would buy the dip perform going back 90 years?

09:33
This is very interesting.
09:34
Buying the dip, you don’t think of it as a short volatility, strategy but it is short
09:38
gamma, what’s short that autocorrelation effect.
09:42
Well, buy the dip has performed incredibly well over the last 10 years, and really over
09:50
the last 20 years, as central banks have been very reactive to market stress.

09:54
DANIELLE DIMARTINO BOOTH: That’s an understatement.
09:56
CHRISTOPHER COLE: Right?
09:57
Well, it’s very interesting.
09:58
If you go back and you test buy the dip over 90 years, that strategy goes bankrupt three
times.

10:06
DANIELLE DIMARTINO BOOTH: Bankrupt’s a big word.
10:08
CHRISTOPHER COLE: Flat out loses all of its money three times over a 90 year history.
10:14
It is only really in the last 10 years where it’s compounded at about 10% a year where
10:19
we’ve seen that outperformance.
10:20
DANIELLE DIMARTINO BOOTH: I think that might– let’s see.
10:22
Is that the quantitative easing era?
10:23
CHRISTOPHER COLE: I think so.
10:25
It’s not a coincidence.
10:26
Yes, not a coincidence at all.
10:27
DANIELLE DIMARTINO BOOTH: So you tweeted out something a few days ago about long-term deflationary
10:35
trends.
10:36
CHRISTOPHER COLE: Yeah.
10:37
DANIELLE DIMARTINO BOOTH: It feels like we keep going there.
10:41
What in your mind could possibly ignite inflation?
10:46
Because it’s the one thing that nobody is expecting.
10:49
We’re all expecting wash, rinse, repeat.
10:52
More deflation next time there’s a disruption of any kind, and again, every central bank
10:57
comes riding into the rescue with more stimulus.
10:59
CHRISTOPHER COLE: More stimulus– so look at looking back at– there have been other
11:03
cycles across history that are like an Ouroboros eating its own tail.
11:08
If we take this beyond just short volatility, we can think of it as part of the entire debt
11:13
deflation cycle.
11:14
So this idea that you start out with something good, you start out with real economic growth,
11:19
technology, and demographics, and that leads to growth.
11:23
And fantastic– you’re growing.
11:25
The economy is growing.
11:26
It’s fundamental growth.
11:28
At a certain point in time, the fundamentals get stretched and we become reliant on fiat
11:36
devaluation and debt expansion.
11:38
DANIELLE DIMARTINO BOOTH: So think of the baby boomer generation generating genuine
11:42
economic growth, and then they’re starting to move to spending less.
11:48
And how do you fill that gap?
11:49
CHRISTOPHER COLE: Exactly.
11:50
So to this point, we start out in this framework.
11:53
It’s in the period of 1984 to 2007– one of the most incredible periods of asset price
12:02
growth and asset appreciation growth in not just American history, in history period.
12:08
90% of the returns of a 60-40 stock-bond portfolio came from the 22 years between ’84 and 2007.
12:17
Just 22 years drove 90% of the gains of that portfolio over 90 years.
12:21
DANIELLE DIMARTINO BOOTH: I probably couldn’t count on one hand the number of investors
12:24
who have been around since before 1984.
12:26
CHRISTOPHER COLE: Exactly.
12:27
The average investment advisor is 52 years old.
12:30
They were a kindergartener during the stagflationary period of the 1970s.
12:34
So you have all these baby boomers, 76 million baby boomers– largest generation in American
12:39
history.
12:40
They’re teenagers right into the devaluation of gold in the 1971.
12:45
That is driving a tremendous amount of inflation at that point in time.
12:49
Interest rates go up to 19%, and then these baby boomers, 76 million of them, enter the
12:54
workforce in the early ’80s.
12:56
And they start making money.
12:59
They start making money, and they start spending.
13:01
They start investing.
13:02
So you have baby boomers coming on in.
13:05
Then you have a trend towards globalization, so we’re able to export our inflation overseas.
13:10
You have a technology boom as well.
13:15
And then, interest rates begin dropping.
13:17
DANIELLE DIMARTINO BOOTH: Oh, yes.
13:19
CHRISTOPHER COLE: So and– DANIELLE DIMARTINO BOOTH: May he rest in peace, Paul Volcker.
13:22
CHRISTOPHER COLE: Exactly.
13:23
And as if that’s not enough, taxes start coming down.
13:28
So you have this once-in-a-generation, once-in-several-hundred-years economic boom, asset price boom that occurs,
13:37
driven as baby boomers come into the workforce, begin savings, enter into their prime earning
13:42
years.
13:43
But now, those boomers are going to be retiring.
13:46
They are going to be drawing $20 trillion dollars out of markets instead of putting
13:50
that into markets.
13:52
This, obviously presents a tremendous deflationary force.
13:56
So I’d like to think about this as a snake.
13:59
If we take the snake metaphor and we pull it out, it’s not just short volatility.
14:04
It is almost like a snake devouring its own tail as part of a business cycle.
14:08
The snake is eating prey and naturally compressing inwards through secular growth.
14:15
And that’s healthy.
14:16
But towards the end of the secular growth cycle, that snake relies on financial engineering,
14:23
excess leverage, and begins eating its own tail.
14:28
And that is where we’re at, I would say, in the cycle right now.
14:31
And you’ve written beautifully on this about some of the debt problems out there.
14:34
Currently, we’re at 48% debt to GDP, highest corporate debt to GDP, highest level in American
14:41
history.
14:42
DANIELLE DIMARTINO BOOTH: You tack on– you aggregate non-financial, we’re at 74%.
14:45
CHRISTOPHER COLE: 74%.
14:47
DANIELLE DIMARTINO BOOTH: Unheard of numbers.
14:49
CHRISTOPHER COLE: And what are we doing with this?
14:51
What are corporations doing with this debt?
14:52
They’re issuing debt to buy back their own shares at a trillion dollars a year.
14:56
And then institutions are funneling that in in order to– they need to find ways to generate
15:03
yield absent any fundamental growth.
15:05
So we had a year like last year, where there’s no actual earnings growth, but it’s all multiple
15:12
expansion driven by share buybacks and speculation.
15:15
So this is– we’re at this end of the cycle, where the snake is devouring its own tail.
15:19
Now, this can go on for a long time.
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DANIELLE DIMARTINO BOOTH: Clearly.
15:22
CHRISTOPHER COLE: Well, what breaks that cycle?
15:26
And this comes to the image in the paper of the allegory of the hawk and serpent.
15:31
And I was thinking about this.
15:33
Outside our offices here, we have a peregrine falcon that flies around.
15:36
DANIELLE DIMARTINO BOOTH: I saw that on Twitter.
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You need to tweet more often, by the way.
15:40
Can We.
15:41
Got on “Real Vision” thumbs up on that?
15:43
Thank you.
15:44
CHRISTOPHER COLE: I do a lot of research and work, but I’ll try.
15:49
I’ll try a little bit more.
15:50
I’m still getting used to it, by the way.
15:53
The whole retweet thing– DANIELLE DIMARTINO BOOTH: It gets tricky.
15:55
CHRISTOPHER COLE: It gets tricky a little bit.
15:59
But that hawk– I noticed the idea of hawk.
16:02
And there is an old symbol of a hawk fighting a serpent.
16:06
And this symbol has deep roots.
16:10
It’s actually on the great seal of the US.
16:13
It’s on the coat of arms of Mexico.
16:15
It has important ramifications across different traditions ranging from Aztec to Egyptian
16:23
to Indian.
16:24
But this idea to me, what it represented is the serpent represents the secular growth
16:29
cycle that becomes corrupted at a certain point in time, where the serpent begins devouring
16:35
its own tail.
16:36
It is unable to generate growth naturally and has to self-cannibalize.
16:41
And the hawk comes down and represents the disruption of that cycle.
16:46
But the hawk has two wings, which also work with the probability distribution.
16:50
On the left wing– DANIELLE DIMARTINO BOOTH: Wow, that’s deep.
16:52
OK.
16:53
CHRISTOPHER COLE: So the metaphor goes deeper.
16:56
On the left wing, we have debt deflation.
16:59
This is what Japan has experienced.
17:02
That’s one way you get out of this decaying growth cycle.
17:05
DANIELLE DIMARTINO BOOTH: Slowly.
17:06
CHRISTOPHER COLE: Slowly.
17:07
Painfully.
17:08
That’s what the US experienced in the ’30s.
17:10
But on the other end of it, you have fiat devaluation and reflation.
17:15
That is where you simply devalue your currency.
17:18
And that could be helicopter money, devaluation currency, money printing.
17:23
That is another way that you get out of that crisis.
17:27
This is as old as money itself.
17:29
And one wing can occur before the other.
17:33
You can have a deflationary crisis before you have a reflationary crisis.
17:38
So to get back to your original question, what will cause– what I see causing inflation.
17:43
You have a scenario today where the two largest blocks of the US population are baby boomers,
17:51
at about 22% of the population right now.
17:54
They’re rich.
17:55
They have a lot of money.
17:56
They’ve lived through one of the most incredible periods of asset price growth in history.
And they want to protect that money.
18:03
So they are going to– they’re going to support policies or are incentivized to, I should
18:09
say.
18:10
They don’t need to, but they’re incentivized to support policies that protect their retirement
18:14
and their entitlement benefits.
18:18
Now you have millennials, which are now the largest generation at 26% of the population,
18:23
and Gen Z following, are likely to be the first generation in American history to be
18:28
poorer than their parents.
18:29
DANIELLE DIMARTINO BOOTH: Remarkable.
18:30
CHRISTOPHER COLE: Remarkable, yeah.
18:32
Lower household creation rates– they have– the average millennial has substantial student
18:37
debt.
18:38
DANIELLE DIMARTINO BOOTH: Low savings.
18:39
CHRISTOPHER COLE: No savings, that’s right.
18:41
So the incentive of the average millennial, they’re incentivized in essence to pursue
18:45
policies that represent redistribution of wealth and seek to tax, redistribute, and
18:53
cause inflation.
18:55
So I think the time to look, and maybe what could cause inflation is the political sea
19:00
change towards– DANIELLE DIMARTINO BOOTH: At some point– we’re at $23 trillion now.
19:06
But to your point, at some point, you’re going to hit a level of debt if truly all of these
19:13
social spending initiatives are financed by printing money.
19:17
Theoretically, at some point, you will hit a limit.
19:20
I agree with you.
19:24
You talk about passive investing.
19:25
It’s a hot button.
19:27
90% of flows go into passive strategies.
19:32
Even pensions are in passive strategies.
19:35
Talk about the perfect– perfect liquidity of passive investing.
19:40
CHRISTOPHER COLE: The concept of passive– and now, we are at a point where passive investments
19:46
have eclipsed active for the first time in history.
19:50
And my friend Mike Green who’s a friend of “Real Vision” has a lot of fantastic research
19:53
on this.
19:54
DANIELLE DIMARTINO BOOTH: Yes, he has.
19:55
CHRISTOPHER COLE: And I’ve done some work, in essence, trying to replicate his assumptions
20:00
using some toy models and was able to do that.
20:07
His theory, at the end of the day, is that at a certain point, if the market is dominated
20:14
by passive actors, it not only amplifies volatility, which I completely agree with– if there is
20:21
no other incremental seller against a buyer or buyer against a seller, each incremental
20:32
buy or sell will result in massive movement in the underlying.
20:37
DANIELLE DIMARTINO BOOTH: It’s an amplifier.
20:38
CHRISTOPHER COLE: It’s an amplifier.
20:39
Because if you look at active investors, active investors are a volatility dampener.
20:44
Value investors will come into the market, and they will buy when there is a big collapse
20:49
in asset prices.
20:50
So they will in essence put a floor underneath asset prices.
20:53
And they’ll sell when asset prices go to high.
20:55
Well, you remove all the active investors, and that will amplify volatility.
21:00
The other factor that comes into play a lot of the time is this idea that it actually
21:04
reduces the alpha available to active participants.
21:07
DANIELLE DIMARTINO BOOTH: Clearly.
21:08
We’re watching one asset manager after another, one hedge fund after another go away.
21:12
CHRISTOPHER COLE: Because, in essence, passive is in its own right a systematic strategy.
21:18
It has elements of– it is a basic systematic strategy.
21:22
So it goes back to the soul of investing.
21:24
There are two different competing thought processes, I think, that are at war with one
21:30
another.
21:31
The one thought process is that assets should have a value, that there should be a value,
21:38
and that market participants are fighting to determine what that value is.
21:43
But there is, in theory, some intrinsic value to it.
21:46
DANIELLE DIMARTINO BOOTH: Price discovery.
21:47
CHRISTOPHER COLE: Price discovery.
21:48
There is a second school, which I think is gaining strength right now, which is forget
21:56
intrinsic value.
21:58
All that matters according to this school of thinking is the price momentum of the asset.
22:06
DANIELLE DIMARTINO BOOTH: You can burn your MBA.
22:08
You don’t need it anymore.
22:10
CHRISTOPHER COLE: That’s right.
22:11
So aspects of factor investing follow this principle, whether it’s momentum, quality,
22:18
whether it’s FANG, or whether it’s ownership of company management.
22:25
Whatever the factor is, as long as people believe in the factor, and keep buying, and
22:30
keep providing– as long there continues to be liquidity, that creates value.
22:37
I’m clearly in camp number one.
22:39
I clearly believe that there’s intrinsic value.
22:40
I believe– DANIELLE DIMARTINO BOOTH: Well, if you go back 100 years, there is.
22:44
CHRISTOPHER COLE: There is.
22:46
And I would like to quote Harley Bassman, who once had a fantastic quote.
22:51
He always says this, that pigs can fly if shot out of a large enough cannon.
22:55
They always return to earth as bacon.
22:59
He’s so right on the money with his usual wit.
23:04
With a large enough amount of central bank stimulus and enough ability to create debt,
23:10
you can create this illusion as to momentum in these factors that– so I actually think
23:16
passive investing is actually just a liquidity momentum trading.
23:19
DANIELLE DIMARTINO BOOTH: I would agree with you.
23:21
Look– well, October 2018, it was not pretty.
23:24
It acted as an amplifier, but on the downside.
23:27
But we haven’t seen a lot of that.
23:30
You put venture capital and private equity into your 70% slice of the pie.
23:40
Explain that.
23:41
Because I don’t think that if you went down to Texas teachers, for example, I don’t think
23:47
that they would say that– they would say it would be at the opposite end of the spectrum,
23:51
and it would be a diversification strategy against publicly traded equities.
23:56
CHRISTOPHER COLE: So one are the concepts on doing this paper is I wanted to find a
24:00
asset allocation that is a solution.
24:04
What asset allocation can work over 90 years that can protect you against the deflationary
24:11
elements of the left wing of the hawk and the reflation three elements of the right
24:15
wing of the hawk?
24:17
That led me to a very big conclusion, and it ties into the question about private equity.
24:23
Most people think that excess return– that you want to take to asset classes that both
24:30
have solid returns, and bring them together, and that you’ll get a better result from.
24:36
That they prioritize the search for yield and prioritize excess return.
24:41
And what I found is that, actually, what people should prioritize is secular diversification.
24:50
And what that means is that you should look to large asset– look to asset classes that
24:57
can perform on the left or the right tail, and boldly size them in your portfolio.
25:02
That means boldly sizing countertrend asset classes that perform when stocks and bonds
don’t.
25:10
DANIELLE DIMARTINO BOOTH: So gold’s not like the little 10% just in case?
25:13
CHRISTOPHER COLE: That’s right.
25:14
Gold shouldn’t be 1% or 2%.
25:16
It should be 20%.
25:20
Volatility should be 20%.
25:23
Commodity trend should be 20%.
25:25
And then stocks and bonds can make up the other remaining 20 and 20.
25:30
Well, so private equity– DANIELLE DIMARTINO BOOTH: That stands the conventional wisdom
25:33
on its head.
25:34
CHRISTOPHER COLE: It does, where many individuals have big problems trying to even allocate
25:38
3% of their portfolio to gold.
25:41
Well, this gets back to the private equity VC question.
25:45
Now, these are relatively new asset classes.
25:47
It’s tough to see their performance going back 100 years.
25:51
But Cambridge has fantastic data going back a good 20 years, 20, 30 years.
25:58
DANIELLE DIMARTINO BOOTH: When I was at DLJ, we had a merchant bank.
26:01
Private equity was this cottage industry.
26:04
Leon Black used to walk the halls.
26:06
This was way before– what, they’ve got $4 trillion?
26:09
CHRISTOPHER COLE: Yeah, it’s massive.
26:10
DANIELLE DIMARTINO BOOTH: Massive.
26:11
CHRISTOPHER COLE: Massive.
26:12
Well, it becomes very clear from looking.
26:15
You can just look at the return data from private equity NVCs to see that these asset
26:20
classes are secular growth asset classes.
26:23
They are correlated to the business cycle.
DANIELLE DIMARTINO BOOTH: So they move in concert with publicly-traded equities.
26:28
CHRISTOPHER COLE: They move.
26:30
Sure, you might get some excess return, but they are correlated to equities.
26:35
They will lose money in the event that there’s a widescale recession.
26:38
Well, I should say, they have historically lost money when that has occurred.
26:43
I cringe when I hear leaders of very large– and I’ve heard this.
26:51
Leaders of very large pension systems, huge, huge systems that have a lot of money, and
26:56
they say that private equity and venture capital are diversifies because they’re lagged.
27:05
This doesn’t work with the data in view.
27:10
DANIELLE DIMARTINO BOOTH: I’ve been harping on this issue for years and years.
27:15
When we went into the crisis, the baby boomers were still an actuarial accounting assumption
27:21
you could fudge with.
27:23
Heading into the next downturn, they’re going to be a cash flow issue for pensions.
27:29
And when you factor in the illiquidity aspect of the alternatives, it just makes no sense.
27:36
CHRISTOPHER COLE: No, it does not.
27:37
And this is what we’ve seen.
27:39
So I put about a post on Twitter.
27:41
And I had three asset classes.
27:43
And they were just sine wave graphs.
27:45
The two asset A and asset B were highly correlated with one another, and they were slightly offset
27:53
from one another.
27:54
And asset C, the last asset, was a countertrend asset.
27:58
It was an asset that didn’t make any money, but made money when all the other assets lost
28:04
money.
28:05
DANIELLE DIMARTINO BOOTH: Did it lose?
28:07
Lose money?
28:08
CHRISTOPHER COLE: It lost money, actually– lost a little bit of money.
28:10
It was flat.
28:11
DANIELLE DIMARTINO BOOTH: A little– OK, critical words.
28:12
OK, continue.
28:13
CHRISTOPHER COLE: And I posted to Twitter.
28:14
I said, which of these would you combine.
28:16
You can choose two assets to have the optimal portfolio.
28:18
And of course, everyone says, well, we’re going to choose the high returning asset and
28:22
the countertrend asset because that’s going to result in a dramatically better risk adjusted
28:29
return as opposed to combining the two assets that have similar return profiles, which results
28:35
in bigger gains, but bigger losses.
28:38
So Twitter got that answer correct.
28:40
80% of people chose the trend and the countertrend asset.
28:45
But what’s interesting is that the big institutions around the world are doing the exact opposite.
28:51
They’re taking equity exposure, and then they’re layering on more and more private equity exposure,
28:55
and more VC exposure, and more high yield credit exposure, and short volatility exposure,
29:01
and you name it, all because they have to reach the 7.25% return target.
29:07
And at the end of the day, what you have is a portfolio that is tilted to secular growth.
29:16
Will perform in secular growth, but in the event that we have any regime change, any
29:22
period of secular change, either on the left wing of the hawk with deflation or the right
29:28
wing of the hawk with reflation fiat devaluation, that portfolio will struggle and struggle
29:36
terribly.
29:37
DANIELLE DIMARTINO BOOTH: I wasn’t surprised about most of what you wrote.
29:40
But I was intrigued about how you view real estate as an asset class.
29:47
It’s got the highest return, but– CHRISTOPHER COLE: Yeah, so real estate is– real estate’s
29:53
quite interesting as an asset class, because I think most people don’t really think of
29:56
it as– it is a levered secular growth asset.
30:00
And your average person, I think, the average retiree– maybe not the institutions, but
30:05
the average retiree, they would never go lever their stock portfolio five times.
30:11
But you own a home, and that is a levered investment.
30:14
That’s not saying it’s a bad investment.
30:17
I’m not saying that.
30:19
But most people don’t look at it in that light.
30:23
So in the same way that you structure– that one should structure trend and countertrend
30:28
assets to balance the hawk and the serpent, the idea of including real estate in one’s
30:33
thinking about one’s personal portfolio, I think, is really important because, oftentimes,
30:39
your job is driven by the economic growth cycle.
30:43
Your home is driven by the economic growth cycle.
30:46
And then you’re Levering that exposure to the economic growth cycle.
30:49
And then you’re also adding stock exposure onto that.
30:52
So the average retiree with some– or the average working individual with a mortgage
31:00
has tremendous exposure to the secular growth cycle levered– DANIELLE DIMARTINO BOOTH:
31:04
And there’s an extraordinary percentage of baby boomers with mortgages.
31:08
CHRISTOPHER COLE: Yes, yeah.
31:09
DANIELLE DIMARTINO BOOTH: And the rest of their portfolio’s in an index fund.
31:12
CHRISTOPHER COLE: And very few people think about this.
31:15
And the concept at the end of the day that somehow that will be insulated– stocks dropped
31:23
86% in the Great Depression, and real estate dropped to the same degree.
31:29
Now, in prior cycles, when interest rates were at 19% and were able to be lowered, that
31:39
created a dynamic where real estate performed somewhat like a bond.
31:43
Every single time that rates went down, it increased the affordability for people to
31:48
buy bigger homes.
31:50
So that provided a cushion for real estate.
31:52
Well, when rates are at the zero bound, several bad things begin to happen.
31:58
First of all, your 60-40 portfolio can struggle in the sense that your bonds are not getting
32:03
as much benefit.
32:05
But on top of that, your hold price is not going to get as much benefit if rates can’t
32:10
be lowered.
32:11
DANIELLE DIMARTINO BOOTH: At the margin.
32:12
CHRISTOPHER COLE: At the margin, yeah.
32:13
So I don’t see people realize this.
32:15
Rates where they are today, for us to get the same benefit on a bond portfolio, on a
32:21
long-duration bond portfolio, or the same pickup in mortgages that we got after ’08,
32:28
the Fed would have to lower interest rates to negative 1.5%– DANIELLE DIMARTINO BOOTH:
32:32
Ooh, don’t say negative.
32:33
CHRISTOPHER COLE: –to get the same benefit as people got right based on where they lowered
32:38
in 2008.
32:39
I’ve never going to say that’s not feasible anymore, because God knows what is feasible
32:43
now.
32:44
But I will say there are major social ramifications if they pursue a course like that.
32:48
DANIELLE DIMARTINO BOOTH: Talk about one way that you would play volatility long.
32:57
Or if there is no way, one way, how do you– you said 20% long volatility.
33:05
How do you do that?
33:06
CHRISTOPHER COLE: Now, I take a very broad definition of what long volatility is.
33:09
So let’s start out with specifics.
33:12
I actually went back and I tested using very defensible assumptions.
33:17
What different traditional explicit volatility strategies, how they would have performed
33:23
over periods like the Great Depression, over the 1970s.
33:26
So for example, it’s very popular to do covered calls.
33:30
People will own stock and they’ll sell calls against that.
33:32
Large pensions do that as well.
33:34
Some people will do tail risk catching.
33:36
They’ll buy put options– various strategies.
33:38
So I tested all of these strategies using very realistic assumptions going back to the
33:44
1920s.
33:46
And those assumptions are laid held in very high detail in my paper.
33:52
So one of things I found, just to start out with– short volatility strategies, which
33:59
in equity markets, currently there’s upwards of about $200 billion of these strategies,
34:03
are very popular, have performed extremely well since the ’80s.
34:08
These mean reversion short vol strategies, pretty much every single one of them showed
34:14
complete annihilation of capital over 90 years.
34:19
And I would say that based on very defensible assumptions that people should not only avoid
34:24
these strategies, but also institutions that robotically and systematically apply them.
34:31
And I believe there is a place for these strategies if they’re used tactically.
34:36
Using human discretion, say, this asset has overpriced volatility.
34:39
We’re going to sell it as part of a trade.
34:42
That’s very different than what a lot of institutions are doing, which is they are constantly systematically
34:48
selling volatility for excess yield.
34:51
And this includes even collateralized short vol strategies.
34:55
So most people have come back and said, well what about something like a covered call strategy?
34:59
Why would that show impairment of capital.
35:02
And well, let’s take a look at that.
35:06
In the 1930s, the stock market dropped 80%.
35:10
Now, if you were selling calls on the way down, you would have done a little bit better
35:16
than someone who was just holding the stock.
35:20
But then, we had the deflationary left tail.
35:24
Then you have the right tail, where they do the 1932 Banking Act, and they devalue.
35:32
Lower rates– devalue, and also, devaluation versus gold.
35:39
At that point, you had a 70% rally that occurred over a month and a half.
35:44
So imagine that you’re selling calls, earning a little bit of money.
35:49
But you’re holding that against stock.
35:50
And you’re losing all the way down.
35:52
You lose 70% of your capital that way.
35:55
And then, you’re selling calls into a 70% rally that occurs over a month and a half.
36:01
And that wasn’t the only rally.
36:03
There was another rally that occurred in the ’30s, that over 80% over four months.
36:08
And that was the Roosevelt devaluation versus gold.
36:11
DANIELLE DIMARTINO BOOTH: Hard to pivot in that short period of time.
36:13
CHRISTOPHER COLE: That’s right.
36:14
DANIELLE DIMARTINO BOOTH: That’s your point.
36:15
CHRISTOPHER COLE: So these are political risks.
36:16
You have deflation.
36:17
And then, you all of a sudden have a political shift that causes reflation, either through
36:21
monetary or fiscal policy.
36:23
And if one thinks they can predict that, they’re wrong.
36:29
There’s just no way unless you’re psychic.
36:33
So with that same understanding how shortfall performed, we can look at how longfall has
36:39
performed.
36:40
Long volatility, truly buying a straddle, buying puts and calls, would have been positive
36:46
carry for decades.
36:48
It would have made money in giving you diversification over the 1930s all the way through the ’40s,
36:55
and also would have given you income in the 1970s.
36:59
So to this point, one of the things we’ve advised is something we call active long vol,
37:04
which is this idea that you forego the first movement in volatility.
37:10
You’re not looking to protect against exogenous risks.
37:13
But when the market moves a little bit, you catch the momentum of volatility.
37:17
And this is how we modeled it.
37:18
It is an attempt to model systematically what active long volatility managers seek to do,
37:24
which is provide portfolio insurance type of protection for lower cost security.
37:29
But there’s other long volatility strategies or countertrend strategies that are also really
37:35
effective.
37:36
Commodity trending is an example of a strategy that can be very effective.
37:40
Commodity trend has not been very popular in recent years, but was particularly effective
37:47
in the 1970s during that inflationary period and was effective in the 1930s.
37:52
And then finally, gold, is a long– I would say a long volatility asset because it plays
37:57
off of that fiat devaluation that occurs.
38:00
DANIELLE DIMARTINO BOOTH: Of course.
38:01
CHRISTOPHER COLE: So in this sense, by having parts of the portfolio, all of these asset
38:05
classes, all of these asset classes are countertrend to equities and are uncorrelated to bonds.
38:15
They show no correlation to equity and bonds.
38:18
So to the same point, instead of chasing excess yield, what people need to be doing, particularly
38:24
the large institutions need to be positioning portfolios boldly in asset classes that are
38:33
non-correlated to stocks and bonds, preparing for a period of secular change.
38:40
Danielle, the numbers are amazing.
38:42
The numbers are amazing.
38:43
In my portfolio, the replication portfolio going back 90 years that we show in the paper,
38:49
you’re able to achieve consistent performance above the 7.25% pension return target that
38:55
is consistent through every generational cycle.
38:58
DANIELLE DIMARTINO BOOTH: And that’s how pensions should be invested for the long haul.
39:01
CHRISTOPHER COLE: That’s right.
39:02
DANIELLE DIMARTINO BOOTH: Absolutely.
39:03
We’re going to go in the weeds, and then we’re going to pull back out.
39:07
Describe the evolution of cross-asset volatility.
39:11
There used to be an order of things– FX, rates, equity.
39:16
Has that been destroyed in this era of all– you name it– VIX, move, every gauge of volatility
39:25
is at a record low.
39:26
CHRISTOPHER COLE: That’s right.
39:28
Actually, equity vol, US equity vol is actually relatively expensive comparative to other–
39:32
comparative to like currency vol, for example, which is truly at all-time lows right now.
39:37
DANIELLE DIMARTINO BOOTH: And that’s a massive market that nobody ever talks about.
39:39
CHRISTOPHER COLE: I think one of the things that’s really– we talk about the short volatility
39:43
trade.
39:44
And I say, OK, it’s close to $3 trillion in equity markets right now.
39:49
The portfolio insurance was only 2% of US equity markets, but in 1987.
39:56
And that, now, these short volatility strategies of all of their styles are now closer to 10%
40:02
of the market.
40:03
That same trade is being replicated across multiple different asset classes.
40:07
so we’re seeing it replicated across multiple different asset classes.
40:10
And of course, you have the, which is something you’ve written quite brilliantly about, the
40:14
reaction function of central banks.
40:17
And that’s something I also talk about in a 2015 paper, where they are preemptively
40:23
getting in front of– DANIELLE DIMARTINO BOOTH: Yes, this is– I’ve tried to communicate this.
40:30
And I don’t think that the market quite understands Jay Powell and how different he is because
40:36
he does understand credit volatility, and he does understand what’s at stake.
40:41
So he’s unlike his three predecessors.
40:43
He’s actually trying to get out in front of what’s happening.
40:47
And that– it truly changes– it’s not reaction function right now.
40:52
He’s trying to proactively get out in front of this.
40:54
CHRISTOPHER COLE: That’s right.
40:55
Preemptive– very similar to the way that the Bush administration sought to do preemptive
41:00
strikes against terror.
41:02
They are doing preemptive strikes on financial stress.
41:06
And I think we saw this– we have different models that look at thousands of different
41:14
economic indicators.
41:16
But this last– economic and technical indicators.
41:20
And a lot of the drivers of volatility were there in the fourth quarter of last year.
41:25
We saw CCC yields begin exploding higher.
41:28
DANIELLE DIMARTINO BOOTH: They still haven’t come back in, a lot of them, though.
41:31
CHRISTOPHER COLE: They still haven’t come back in, yeah.
41:33
We saw value begin to outperform momentum stocks– very interesting.
41:38
We saw, obviously, a re-steepening of the yield curve after an inversion.
41:41
That’s a bear signal.
41:42
And then, finally, the granddaddy of them all, we began to see blow outs in the repo
41:47
market.
41:48
Of course, what that will do is, inevitably, if that continues, you have a deleveraging
41:54
of various hedge fund strategies that will impact asset markets.
41:57
All of these things were big risk-off flex.
42:00
However, I think the Fed obviously saw the same thing.
42:06
I don’t think people fathom this.
42:09
They created $400 billion worth of liquidity to inject into the repo system, the largest
42:17
expansion of the balance sheet since 2009.
42:20
DANIELLE DIMARTINO BOOTH: Well, it was only $85 billion when it was QE3.
42:25
So this is bigger.
42:26
CHRISTOPHER COLE: Bigger.
42:27
It’s remarkable.
42:28
DANIELLE DIMARTINO BOOTH: It is bigger.
42:29
And I understand what J Powell is trying to do.
42:34
I get it.
42:35
Because he saw the credit volatility genie start to come out of her bottle in the fourth
42:39
quarter of 2018, and it scared the Dickens out of him.
42:42
Public pensions had the worst returns for that quarter.
42:46
It’s anarchy, and we’ll get to that in just a minute.
42:50
So he understands the gravity of the situation.
42:53
But it seems like the market has begun to play him.
42:56
For every 100 decline– 100 point decline in the Dow, you have 1 basis point of rate
43:01
cut immediately priced in.
43:04
You can follow it on your Bloomberg terminal.
43:05
It’s like clockwork.
43:06
CHRISTOPHER COLE: It’s the moral hazard of the problem.
43:09
DANIELLE DIMARTINO BOOTH: And they’re playing the Fed.
43:11
The market players are playing the Fed.
43:13
And I don’t think people– this is the last thing that Jay Powell wanted.
43:17
CHRISTOPHER COLE: Yeah.
43:18
It absolutely has become this point where it appears that they’re really between a rock
43:24
and a hard place.
43:25
Because on one aspect, you are risking a complete melt up in markets, which is already occurring.
43:31
You look at the behavior of Tesla, for example.
43:34
It’s fun to try to watch the media justify it, but there’s no justification.
43:40
I think Tesla’s vol term structure was dramatically steeper than the vol term structure of the
43:46
VIX the other day.
43:47
DANIELLE DIMARTINO BOOTH: Yeah you tweeted that out.
43:49
I was like, wow.
43:50
CHRISTOPHER COLE: It’s really– DANIELLE DIMARTINO BOOTH: There’s so many different ways to look
43:51
at it.
43:53
But the main is there.
43:54
This is like 1999 and 2007.
43:56
You walk into a bar and hook up.
43:59
Sorry, that probably wasn’t very politically correct, but you’ve got the leverage and you’ve
44:02
got the mania.
44:04
You’ve got the two of them together.
44:05
CHRISTOPHER COLE: I could not put it any better myself.
44:08
I think you’re right.
44:09
And these are the two realities.
44:12
And maybe they’re trying to keep it together until the election.
44:16
DANIELLE DIMARTINO BOOTH: We don’t have to go there.
44:20
But I don’t I think Jay Powell is probably the least political fed chair since Paul Volcker,
44:25
but he also understands credit volatility, and he talked about it in October 2012 specifically.
44:31
CHRISTOPHER COLE: And this ends up– it’s interesting how this ends up impacting so
44:38
many different things, because not only is there market expectations built on this, this
44:43
results in the enhancement of that mean reversion effect that we talked about.
44:49
I think one of the reasons why volatility worked for 70 years in all of its forms is
44:54
because there was not mean reversion in markets.
44:58
It had less to do, sometimes, about the absolute spikes or the big down days or up days in
45:05
markets and more to do with the fact that markets would trend.
45:08
Well, now, because people anticipate this reaction function, the mean reversion is so
45:14
baked into markets, and then that incentivizes people to follow financial engineering strategies
45:20
that profit from that mean reversionary expectation.
45:23
And today, there’s a whole cottage industry in the vol world about gamma hedging.
45:29
That’s something that people talk a lot about now.
45:33
And it’s a complicated issue, but effectively, when big institutions come out in short volatility,
45:40
the hedging of those volatility shorts reinforces mean reversion to markets.
45:46
It’s a little like a rubber band, the gamma hedging.
45:51
And what I mean by that is that the rubber band stretches out, and you have a down day
45:55
or an up day.
45:57
And the hedging of all the short volatility products results in it coming back in.
46:02
So people will buy the dip or do the opposite of what the market’s doing.
46:07
The dealers will do this to hedge.
46:08
But if you get a big enough shock where that rubber band stretches too far, it could snap
46:13
in either direction.
46:14
It can snap on the left tail or the right tail in either direction.
46:19
So in essence, it’s not just the human beings that are now anticipating what the Fed– anticipating
46:27
this behavior pattern from the Fed.
46:28
But now you have machines that are being attenuated– DANIELLE DIMARTINO BOOTH: That’s why it feels
46:34
so systemic.
46:35
CHRISTOPHER COLE: That’s right.
46:37
DANIELLE DIMARTINO BOOTH: Speaking of systemic, let’s end this– I could talk to you for hours,
46:43
by the way.
46:44
This is just fascinating.
46:45
But let’s wrap this up today with where you conclude this wonderful paper.
46:53
Richard Fisher and I met years ago when I was still inside the Fed.
46:56
We had lunch there were riots in the streets of Athens at the time.
47:01
And I said, I said, Richard, what do you make of this?
47:07
What can we draw from this?
47:09
I’d been writing about pensions for 20 years.
47:13
And he said, Danielle, I fear that we’ll have those riots in our streets one day, that the
47:23
public pensioners and the people who are paying for the public pensionersif you’re Joe
47:29
Q with an IRA or 401(k), and you lose most of the value of your equity holdings, and
47:36
you’re told that your property taxes or your income tax, state income taxes are going to
47:41
have to go up to top off the pension that’s just lost as much– you talk about these things
47:47
in public forums, and individuals go at each other.
47:53
Talk about the societal implications of where we are today what you see potentially happening,
48:01
because you used the word systemic.
48:03
CHRISTOPHER COLE: So the way that the average institutional entitlement portfolio is structured
48:09
today- – and this is not an opinion.
48:12
I’m looking back across history.
48:14
There is a recency bias.
48:16
This is constructed for the last 40 years of unprecedented asset price growth.
48:22
But if you look beyond that 40 years and look at how that portfolio will perform, at a best
48:28
case, you’re looking at a 5% type of return.
48:32
In a worst case, given where debt levels are and where leverage is, you’re looking at something
48:38
much, much worse.
48:40
So if we just assume the best case, it makes 5% or 4% over the next 20 years, these entitlement
48:50
programs.
48:51
DANIELLE DIMARTINO BOOTH: Which is not enough.
48:52
CHRISTOPHER COLE: Not enough, because they’re targeting 7.25%.
48:58
That will cause an expansion of the unfunded liabilities in just the state systems alone
49:04
to about $3 trillion.
49:07
If we end up getting a lost decade, that could go as high as $10 trillion.
49:10
That $3 trillion number, that is the cost of four bank bailouts.
49:15
It is the entire tax revenue of the US government over the next year.
49:22
That is your base case.
49:24
These entitlement programs, which right now, based on the 7.25% assumption, will go from
49:30
70% funded to under 50% funded, and a third of them will be under 30% funded.
49:36
And this is not including corporate programs and other personal retirement programs.
49:42
This issue of asset allocation is an issue of systemic risk.
49:51
It is an issue of social stability, because we will be at a point where these entitlement
50:00
programs will go belly-up and face insolvency unless we can think differently about the
50:07
portfolio construction.
50:09
I could see a lot of different things happening.
50:12
I could see a day where the Fed prints money to buy pension obligation bonds.
50:17
DANIELLE DIMARTINO BOOTH: Chris, if I can tell you something, during the crisis, when
50:19
I was inside the Fed, it was debated.
50:22
CHRISTOPHER COLE: Wow.
50:24
That’s amazing.
50:26
DANIELLE DIMARTINO BOOTH: The idea– if you tech logic to the end game, the idea of the
50:32
Fed buying municipal bonds is perfectly feasible.
50:36
CHRISTOPHER COLE: That’s right.
50:37
And that will be a backdoor bailout of Wall Street if that happens.
50:41
You could see a radical progressive dynamic, where we shift to seize capital, and where
50:47
there’s– it causes massive inflation.
50:50
There’s numerous ways.
50:51
But the question at the end of the day is the average portfolio is only attenuated to
50:58
this last 40 year period of growth.
51:00
It’s not about being afraid.
51:03
It’s about being prepared.
51:04
So my parents, during the great financial crisis, they came out ahead because they had
51:11
allocations to volatility in gold, and that saved them and allowed them to retire on time.
51:20
I think the institutions, the large institutions and the average investors, if they can find
51:26
ways to invest large portions in countertrend assets, not only will they get a better overall
51:34
return profile and more safe return profile, but this will be a way for these institutions
51:40
to be able to prosper during a period of secular change, rather than suffer.
51:45
So I think this is a major– it is more than a financial issue.
51:51
It’s a social issue.
51:54
That these defensive assets, they’re not for a rainy day.
They’re for a rainy decade.
52:02
The problem that we face is not a problem of financial management or economics.
52:09
It’s a problem– it’s a social problem.
52:12
It’s an emotional problem.
52:15
It takes a lot of social discipline and to think differently.
52:20
Many of our leaders would rather fail conventionally with the herd than succeed unconventionally.
52:26
DANIELLE DIMARTINO BOOTH: They’re not Genghis Khan.
52:28
CHRISTOPHER COLE: That’s right.
52:29
That’s absolutely right.
52:31
DANIELLE DIMARTINO BOOTH: It was great talking to you today.
52:34
Thank you so much– CHRISTOPHER COLE: Thank you.
52:35
DANIELLE DIMARTINO BOOTH: –for being with Real Vision.
52:36
CHRISTOPHER COLE: I had a great time.

Why the Trends of Income Inequality & Redistribution of Wealth Could Reverse (w/ Trevor Noren)

Trevor Noren, managing director at 13D Global Research and Strategy, discusses how the concentration of wealth and corporate power is shaping his macro perspective. He sees the past three decades of industry consolidation as root causes of the problems that the American economy currently faces: stagnant growth, increasing wealth inequality, and a QEdependent stock market. Noren predicts that this trend of consolidation will reverse, and he sees significant investment potential in gold, small cap stocks, and companies leading the decentralization movement. Filmed September 26, 2019 in New York.

Predicting Terrorism with Market Intelligence: Stock Options

Jim Rickards explains why there’s a financial crisis coming, and in so doing, reviews the unusual origins of his predictive analytics tool. He also explores complexity theory and Bayesian statistics. Jim Rickards is a renowned author and the chief global strategist at Meraglim. Filmed on July 12, 2018 in New York.

 

This has roots that go back to 9/11.
12:24
Tragic day, September 11, 2001, when the 9/11 attack took place.
12:31
And what happened then– there was insider trading in advance of 9/11.
12:37
In the two trading days prior to the attack, average daily volume and puts, which is short
12:43
position, put option buying on American Airlines and United Airlines, was 286 times the average
12:50
daily volume.
12:51
Now you don’t have to be an option trader, and I order a cheeseburger for lunch every
12:55
day, and one day, I order 286 cheeseburgers, something’s up.
12:59
There’s a crowd here.
13:00
I was tapped by the CIA, along with others, to take that fact and take it forward.
13:06
The CIA is not a criminal investigative agency.
13:10
Leave that to the FBI and the SEC.
13:11
But what the CIA said was, OK, if there was insider trading ahead of 9/11, if there were
13:17
going to be another spectacular terrorist attack, something of that magnitude, would
13:22
there be insider trading again?
13:24
Could you detect it?
13:26
Could you trace it to the source, get a FISA warrant, break down the door, stop the attack,
13:30
and save lives?
13:31
That was the mission.
13:32
We call this Project Prophecy.
13:34
I was the co-project director, along with a couple of other people at the CIA.
13:39
Worked on this for five years from 2002 to 2007.
13:43
When I got to the CIA, you ran into some old timers.
13:47
They would say something like, well, Al-Qaeda or any terrorist group, they would never compromise
13:53
operational security by doing insider trading in a way that you might be able to find.
13:59
And I had a two word answer for that, which is, Martha Stewart.
14:03
Martha Stewart was a legitimate billionaire.
14:05
She made a billion dollars through creativity and her own company.
14:08
She ended up behind bars because of a $100,000 trade.
14:11
My point is, there’s something in human nature that cannot resist betting on a sure thing.
14:15
And I said, nobody thinks that Mohamed Atta, on his way to Logan Airport, to hijack a plane,
14:21
stopped at Charles Schwab and bought some options.
14:23
Nobody thinks that.
14:24
But even terrorists exist in the social network.
14:26
There’s a mother, father, sister, brother safe house operator, car driver, cook.
14:32
Somebody in that social network who knows enough about the attack and they’re like,
14:36
if I had $5,000, I could make 50, just buy a put option.
14:40
The crooks and terrorists, they always go to options because they have the most leverage,
14:44
and the SEC knows where to look.
14:47
So that’s how it happens.
14:49
And then the question was, could you detect it.
14:52
So we started out.
14:53
There are about 6,000 tickers on the New York Stock Exchange and the NASDAQ.
14:57
And we’re talking about second by second data for years on 6,000 tickers.
15:03
That’s an enormous, almost unmanageable amount of data.
15:06
So what we did is we reduced the targets.
15:08
We said, well, look, there’s not going to be any impact on Ben and Jerry’s ice cream
15:12
if there’s a terrorist attack.
15:14
You’re looking at cruise ships, amusement parks, hotels, landmark buildings.
15:18
there’s a set of stocks that would be most effective.
15:22
So we’re able to narrow it down to about 400 tickers, which is much more manageable.
15:26
Second thing you do, you establish a baseline.
15:28
Say, what’s the normal volatility, the normal average daily volume, normal correlation in
15:35
the stock market.
15:36
So-called beta and so forth.
15:37
And then you look for abnormalities.
15:39
So the stock market’s up.
15:42
The transportation sector is up.
15:43
Airlines are up, but one airline is down.
15:46
What’s up with that?
15:47
So that’s the anomaly you look for.
15:48
And then the third thing you do.
15:49
You look for news.
15:50
Well, OK, the CEO just resigned because of some scandal.
15:54
OK, got it, that would explain why the stock is down.
15:57
But when you see the anomalous behavior, and there’s no news, your reference is, somebody
16:03
knows something I don’t.
16:04
People aren’t stupid, they’re not crazy.
16:06
There’s a reason for that, just not public.
16:08
That’s the red flag.
16:09
And then you start to, OK, we’re in the target zone.
16:12
We’re in these 400 stocks most affected.
16:15
We see this anomalous behavior.
16:17
Somebody is taking a short position while the market is up and there’s no news.
16:21
That gets you a red light.
16:23
And then you drill down.
16:24
You use what in intelligence work we call all source fusion, and say, well, gee, is
16:28
there some pocket litter from a prisoner picked up in Pakistan that says cruise ships or something
16:34
along– you sort of get intelligence from all sources at that point drilled down So
16:38
that was the project.
16:40
We built a working model.
16:41
It worked fine.
16:42
It actually worked better than we expected.
16:44
I told the agency, I said, well, we’ll build you a go-kart, but if you want a Rolls Royce,
16:48
that’s going to be a little more expensive.
16:50
The go-kart actually worked like a Rolls Royce.
16:52
Got a direct hit in August 2006.
16:55
We were getting a flashing red signal on American Airlines three days before MI5 and New Scotland
17:04
Yard took down that liquid bomb attack that were going to blow up 10 planes in midair
17:09
with mostly Americans aboard.
17:11
So it probably would have killed 3,000 Americans on American Airlines and Delta and other flights
17:16
flying from Heathrow to New York.
17:18
That plot was taken down.
17:20
But again, we had that signal based on– and they made hundreds of arrests in this neighborhood
17:26
in London.
17:27
So this worked perfectly.
17:30
Unfortunately, the agency had their own reasons for not taking it forward.
17:37
They were worried about headline risk, they were worried about political risk.
17:41
You say, well, we were using all open source information.
17:45
You can pay the Chicago Mercantile Exchange for data feed to the New York Stock Exchange.
17:48
This is stuff that anybody can get.
17:49
You might to pay for it, but you can get it.
17:52
But the agency was afraid of the New York Times headline, CIA trolls through 401(k)
17:58
accounts, which we were not doing.
18:00
It was during the time of waterboarding and all that, and they decided not to pursue the
18:06
project.
18:07
So I let it go, there were plenty of other things to do.
18:09
And then as time went on, a few years later, I ended up in Bahrain at a wargame– financial
18:14
war game– with a lot of thinkers and subject matter experts from around the world.
18:20
Ran into a great guy named Kevin Massengill, a former Army Ranger retired Major in the
18:27
US army, who was working for Raytheon in the area at the time was part of this war game.
18:32
We were sort of the two American, little more out of the box thinkers, if you want to put
18:36
it that way.
18:38
We hit it off and I took talked him through this project I just described.
18:42
And we said, well look, if the government doesn’t want to do it, why don’t we do it
18:45
privately?
18:46
Why don’t we start a company to do this?
18:47
And that’s exactly what we did.
18:49
Our company is, as I mentioned, Meraglim.
18:51
Our website, Meraglim.com, and our product is Raven.
18:55
So the question is, OK, you had a successful pilot project with the CIA.
19:01
It worked.
19:02
By the way, this is a new branch of intelligence in the intelligence.
19:06
I-N-T, INT, is short for intelligence.
19:08
And depending on the source, you have SIGINT, which is signal intelligence, you have HUMINT
19:14
which is human intelligence, and a number of others.
19:17
We created a new field called MARKINT, which is market intelligence.
19:20
How can you use market data to predict things that are happening.
19:24
So this was the origin of it.
19:26
We privatized it, got some great scientists on board.
19:30
We’re building this out ourselves.
19:31
Who partnered with IBM, and IBM’s Watson, which is the greatest, most powerful plain
19:38
language processor.
19:40
Watson can read literally millions of pages of documents– 10-Ks, 10-Qs, AKs, speeches,
19:47
press releases, news reports.
19:51
More than a million analysts could read on their own, let alone any individual, and process
19:57
that in plain language.
19:58
And that’s one of our important technology partners in this.
20:02
And we have others.
20:04
What do we actually do?
20:07
What’s the science behind this.
20:09
First of all, just spend a minute on what Wall Street does and what most analysts do,
20:13
because it’s badly flawed.
20:15
It’s no surprise that– every year, the Fed does a one year forward forecast.
20:22
So in 2009, they predict 2010.
20:24
In 2010, they predict 2011.
20:27
So on.
20:28
Same thing for the IMF, same thing for Wall Street.
20:30
They are off by orders of magnitude year after year.
20:34
I mean, how can you be wrong by a lot eight years in a row, and then have any credibility?
20:38
And again, the same thing with Wall Street.
20:41
You see these charts.
20:43
And the charts show the actual path of interest rates or the actual path of growth.
20:48
And then along the timeline, which is the x-axis, they’ll show what people were predicting
20:52
at various times.
20:54
The predictions are always way off the actual path.
20:57
There’s actually good social science research that shows that economists do worse than trained
21:02
monkeys on terms of forecasting.
21:04
And I don’t say that in a disparaging way– here’s the science.
21:07
A monkey knows nothing.
21:08
So if you have a binary outcome– up, down, high, low, growth, recession– and you ask
21:17
a monkey, they’re going to be right half the time and wrong half the time, because they
21:20
don’t know what they’re doing.
21:21
So you’re to get a random outcome.
21:24
Economists are actually wrong more than half the time for two reasons.
21:28
One, their models are flawed.
21:29
Number two, what’s called herding or group behavior.
21:32
An economist would rather be wrong in the pack than go out on a limb and maybe be right,
21:37
but if it turns out you’re not right, you’re exposed.
21:40
But there are institutional constraints.
21:42
People want to protect their jobs.
21:44
They’re worried about other things than getting it right.
21:47
So the forecasting market is pretty bad.
21:48
The reasons for that– they use equilibrium models.
21:52
The capital markets are not in equilibrium system, so forget your equal equilibrium model.
21:57
They use the efficient market hypothesis, which is all the information is out there,
22:01
you can’t beat the market.
22:02
Markets are not efficient, we know that.
22:05
They use stress tests, which are flawed, because they’re based on the past, but we’re outside
22:12
the past.
22:13
The future could be extremely different.
22:16
They look at 9/11, they look at long term capital management, they look at the tequila
22:20
crisis.
22:21
Fine, but if the next crisis is worse, there’s nothing in that history that’s going to tell
22:25
you how bad it can get.
22:27
And so they assume prices move continuously and smoothly.
22:30
So price can go from here to here or from here to here.
22:34
But as a trader, you can get out anywhere in between, and that’s for all these portfolio
22:38
insurance models and stop losses come from.
22:41
That’s not how markets behave.
22:42
That go like this– they just gap up.
22:44
They don’t hit those in between points.
22:45
Or they gap down.
22:46
You’re way underwater, or you missed a profit opportunity before you even knew it.
22:51
So in other words, the actual behavior of markets is completely at odds with all the
22:56
models that they use.
22:57
So it’s no surprise the forecasting is wrong.
23:00
So what are the good models?
23:01
What are the models that do work?
23:03
What is the good science?
23:04
The first thing is complexity theory.
23:07
Complexity theory has a long pedigree in physics, meteorology, seismology, forest fire management,
23:13
traffic, lots of fields where it’s been applied with a lot of success.
23:18
Capital markets are complex systems.
23:20
The four hallmarks of a complex system.
23:24
One is their diversity of actors, sure.
23:26
Two is their interaction– are the actors talking to each other or are they all sort
23:30
of in their separate cages.
23:31
Well, there’s plenty of interaction.
23:33
Is there communication and is there adaptive behavior?
23:37
So yeah, there are diverse actors, there’s communication.
23:40
They’re interacting.
23:42
And if you’re losing money, you better change your behavior quickly.
23:45
That’s an example of adaptive behavior.
23:47
So capital markets are four for four in terms of what makes a complex system.
23:51
So why not just take complexity science and bring it over to capital markets?
23:55
That’s what we’ve done, and we’re getting fantastic results.
23:57
So that’s the first thing.
23:58
The second thing we use is something called Bayesian statistics.
24:03
It’s basically a mathematical model that you use when you don’t have enough data.
24:08
So for example, if I’ve got a million bits of data, yeah, do your correlations and regressions,
24:14
that’s fine.
24:15
And I learned this at the CIA, this is the problem we confronted after 9/11.
24:19
We had one data point– 9/11.
24:20
Janet Yellen would say, wait for 10 more attacks, and 30,000 dead, and then we’ll have a time
24:26
series and we can figure this out.
24:28
No.
24:29
To paraphrase Don Rumsfeld, you go to war with the data you have.
24:33
And so what you use is this kind of inferential method.
24:37
And the reason statisticians dislike it is because you start with a guess.
24:41
But it could be a smart guess, it could be an informed guess.
24:44
The data may be scarce.
24:45
You make the best guess you can.
24:47
And if you have no information at all, just make it 50/50.
24:50
Maybe Fed is going to raise rates, maybe they’re not.
24:53
I think we do better than that on the Fed.
24:55
But if you didn’t have any information, you just do 50/50.
24:58
But then what you do is you observe phenomena after the initial hypothesis, and then you
25:05
update the original hypothesis based on the subsequent data.
25:07
You ask yourself, OK this thing happened later.
25:10
What is the conditional correlation that the second thing would happen if the first thing
25:14
were true or not?
25:16
And then based on that, you’d go back, and you either increase the probability of the
25:19
hypothesis being correct, or you decrease it.
25:21
It gets low enough, you abandon it, try something else.
25:24
If it gets high enough, now you can be a lot more confident in your prediction.
25:27
So that’s Bayesian statistic.
25:29
You use it to find missing aircraft, hunt submarines.
25:32
It’s used for a lot of things, but you can use it in capital markets.
25:36
Third thing, behavioral psychology.
25:38
This has been pretty well vetted.
25:40
I think most economists are familiar with it, even though they don’t use it very much.
25:43
But humans turn out to be a bundle of biases.
25:48
We have anchoring bias, we get an idea in our heads, and we can’t change it.
25:52
We have recency bias.
25:54
We tend to be influenced by the last thing we heard.
25:57
And anchoring bias is the opposite, we tend to be influenced by something we heard a long
26:01
time ago.
26:03
Recency bias and anchoring bias are completely different, but they’re both true.
26:07
This is how you have to get your mind around all these contradictions.
26:11
But when you work through that, people make mistakes or exhibit bias, it turns out, in
26:16
very predictable ways.
26:18
So factor that in.
26:19
And then the fourth thing we use, and economists really hate this, is history.
26:23
But history is a very valuable teacher.
26:26
So those four areas, complexity theory, Bayesian statistics, behavioral psychology, and history
26:33
are the branches of science that we use.
26:35
Now what do we do with it?
26:37
Well, we take it and we put it into something that would look like a pretty normal neural
26:41
network.
26:42
You have nodes and edges and some influence in this direction, some have a feedback loop,
26:47
some influence in another direction, some are influenced by others, et cetera.
26:51
So for Fed policy for example, you’d set these nodes, and it would include the things I mentioned
26:55
earlier– inflation, deflation, job creation, economic growth, capacity, what’s going on
27:02
in Europe, et cetera.
27:03
Those will be nodes and there will be influences.
27:05
But then inside the node, that’s the secret sauce.
27:08
That’s where we have the mathematics, including some of the things I mentioned.
27:12
But then you say, OK, well, how do you populate these nodes?
27:15
You’ve got math in there, you’ve got equations, but where’s the news come from?
27:19
That’s where Watson comes in.
27:20
Watson’s reading all these records, feeding the nodes, they’re pulsing, they’re putting
27:24
input.
27:25
And then we have these actionable cells.
27:26
So the euro-dollar cross rate, the Yuandollar cross rate, yen, major benchmark, bonds, yields
27:36
on 10 year treasury notes, bunds, JGBs, et cetera.
27:41
These are sort of macro indicators, but the major benchmark bond indices, the major currency
27:46
across rates, the major policy rates, which are the short term central bank rates.
27:50
And a basket of commodities– oil, gold, and a few others– they are the things we watch.
27:55
We use these neural networks I described, but they’re not just kind of linear or conventional
28:06
equilibrium models.
28:07
They’re based on the science I describe.
28:09
So all that good science, bringing it to a new field, which is capital markets, using
28:13
what’s called fuzzy cognition, neural networks, populating with Watson, this is what we do.
28:19
We’re very excited about it, getting great results.
28:21
And this is what I use.
28:23
When I give a speech or write a book or write an article, and I’m making forecast.
28:28
This is what’s behind it.
28:32
So we talked earlier about business cycles, recessions, depressions.
28:37
And that’s conventional economic analysis.
28:40
My definition of depression is not exactly conventional, but that’s really thinking in
28:44
terms of growth, trend growth, below trend growth, business cycles, et cetera.
28:50
Collapse or financial panic is something different.
28:52
A financial panic is not the same as a recession or a turn in the business cycle.
28:57
They can go together, but they don’t have to.
28:59
So let’s talk about financial panics as a separate category away from the business cycle
29:04
and growth, which we talked about earlier.
29:06
Our science, the science I use, the science that we use with Raven, at our company, Meraglim,
29:12
involves complexity theory.
29:14
Well, complexity theory shows that the worst thing that can happen in a system is an exponential
29:21
function of scale.
29:23
Scale is just how big is it.
29:24
Now you have to talk about your scaling metrics.
29:27
We’re talking about the gross notional value derivatives.
29:28
We’re talking about average daily volume on the stock market.
29:31
We’re talking about debt.
29:33
We could be talking about all of those things.
29:35
This is new science, so I think it will be years of empirics to make this more precise.
29:39
But the theory is good, and you can apply it in a sort of rough and ready way.
29:43
So you go to Jamie Dimon, and you say, OK, Jamie, you’ve tripled your gross notional
29:50
value derivatives.
29:51
You’ve tripled your derivatives book.
29:53
How much did the risk go up?
29:54
Well, he would say, not at all, because yeah, gross national value is triple, but who cares?
29:59
It’s long, short, long, short, long, short, long, short.
30:01
You net it all down.
30:02
It’s just a little bit of risk.
30:04
Risk didn’t go up at all.
30:06
If you ask my 87-year-old mother, who is not an economist, but she’s a very smart lady,
30:10
say, hey mom, I tripled the system, how much did the risk go up?
30:14
She would probably use intuition and say, well, probably triple.
30:17
Jamie Dimon is wrong, my mother is wrong.
30:21
It’s not the net, it’s the gross.
30:22
And it’s not linear, it’s exponential.
30:24
In other words, if you triple the system, the growth went up by a factor of 10, 50,
30:28
et cetera.
30:29
There’s some exponential function associated with that.
30:32
So people think, well gee, in 2008, we learned our lesson.
30:36
We’ve got debt under control, we’ve got derivatives under control.
30:39
No.
30:40
Debt is much higher.
30:41
Debt to GDP ratios are much worse.
30:44
Total notional value, gross notional values of derivatives is much higher.
30:47
Now people look at the BIS statistics and say, well, the banks, actually, gross national
30:52
value derivatives has been going down, which it has, but that’s misleading because they’re
30:57
taking a lot of that, moving it over to clearing houses.
30:59
So it’s never been on the balance sheet, it’s always been off balance sheet.
31:02
But even if you use the footnotes, that number has gone down for banks, but that’s only because
31:07
they’re putting it over clearing houses.
31:09
Who’s guaranteeing the clearing house?
31:10
The risk hasn’t gone away, it’s just been moved around.
31:12
So given those metrics– debt, derivatives, and other indices, concentration, the fact
31:21
that the five largest banks in America have a higher percentage of total banking assets
31:26
than they did in 2008, there’s more concentration– that’s another risk factor.
31:31
Taking that all into account, you can say that the next crisis will be exponentially
31:37
worse than the last one.
31:38
That’s an objective statement based on complexity theory.
31:41
So you either have to believe that we’re never going to have a crisis.
31:44
Well, you had one in 1987, you had one in 1994, you had one in 1998.
31:49
You had the dotcom crash in 2000, mortgage crash in 2007, Lehman in 2008.
31:55
Don’t tell me these things don’t happen.
31:56
They happen every five, six, seven years.
31:59
It’s been 10 years since the last one.
32:02
Doesn’t mean it happens tomorrow, but nobody should be surprised if it does.
32:05
So the point is this crisis is coming because they always come, and it will be exponentially
32:11
worse because of the scaling metrics I mentioned.
32:14
Who’s ready for that?
32:15
Well, the central banks aren’t ready.
32:17
In 1998, Wall Street bailed out a hedge fund long term capital.
32:23
In 2008, the central banks bailed out Wall Street.
32:25
Lehman– but Morgan Stanley was ready to fail, Goldman was ready to fail, et cetera.
32:31
In 2018, 2019, sooner than later, who’s going to bail out the central banks?
32:35
And notice, the problem has never gone away.
32:37
We just get bigger bailouts at a higher level.
32:40
What’s bigger than the central banks?
32:42
Who can bail out the central banks?
32:43
There’s only one institution, one balance sheet in the world they can do that, which
32:46
is the IMF.
32:48
The IMF actually prints their own money.
32:51
The SDR, special drawing right, SDR is not the out strawberry daiquiri on the rocks,
32:55
it’s a special drawing right.
32:56
It’s world money, that’s the easiest way to think about it.
32:58
They do have a printing press.
33:00
And so that will be the only source of liquidity in the next crisis, because the central banks,
33:07
if they don’t normalize before the crisis– and it looks like they won’t be able to, they’re
33:11
going to run out of runway, and they can expand the balance sheet beyond the small amount
33:17
because they’ll destroy confidence, where does the liquidity come from?
33:20
The answer, it comes from the IMF.
33:23
So that’s the kind of global monetary reset, the GMR, global monetary resety.
33:28
You hear that expression.
33:31
There’s something very new that’s just been called to my attention recently, and I’ve
33:36
done some independent research on it, and it holds up.
33:39
So let’s see how it goes.
33:42
But it looks as if the Chinese have pegged gold to the SDR at a rate of 900 SDRs per
33:51
ounce of gold.
33:52
This is not the IMF.
33:53
The IMF is not doing this.
33:55
The Federal Reserve, the Treasury is not doing it.
33:58
The ECB is not doing it.
33:59
If they were, you’d see it.
34:00
It would show up in the gold holdings.
34:02
You have to conduct open market operations in gold to do this.
34:05
But the Chinese appear to be doing it, and it starts October 1, 2016.
34:11
That was the day the Chinese Yuan joined the SDR.
34:15
The IMF admitted the Yuan to the group was four, now five currencies that make up the
34:21
SDR.
34:22
So almost to the day, when the Yuan got in the SDR, you see this a horizontal trend where
34:29
first, gold per ounce is trading between 850 and 950 SDRs.
34:37
And then it gets tighter.
34:38
Right now, the range is 875 to 925.
34:41
Again, a lot of good data behind this.
34:44
So it’s a very good, it’s another predictive indicator.
34:47
If you see gold around 870 SDRs per ounce, that’s a strong SDR, weak gold.
34:54
Great time to buy gold, because the Chinese are going to move back up to 900.
34:58
So that’s an example of science, observation, base and statistics, inference, all the things
35:04
we talked about that can be used today in a predictive analytic way.
35:08
A crisis is coming, because they always do.
35:10
I don’t have a crystal ball, this is plenty of history to back it up.
35:13
It’ll be exponentially worse.
35:15
That’s what the science tells us.
35:16
The central banks will not be prepared, because they haven’t normalized from the last one.
35:20
You’re going to have to turn to the IMF, and who’s waiting there but China with a big pile
35:24
of gold.

Defensive Investing & the History of Recession (w/ Victor Sperandeo) | Real Vision Classics

Victor Sperandeo, President & CEO of EAM Partners, sits down with Adam Rodman, founder and portfolio manager at Segra Capital Management, to break down the relationship between shifting political tides and macroeconomic trends. Sperandeo provides his view on the history of recessions in the United States and on the current inflationary environment. Filmed on January 3, 2019 in Dallas.