JIM RICKARDS: I’m Jim Rickards, writer, author
of number of books, all on the international monetary system. Currency Wars, The Death of Money, The Road
to Ruin. I have a new book coming out and of October
called Aftermath. And these four books together are what I call
the International Monetary quartet, or almost the four horsemen of the monetary apocalypse. My view is that the world has been in a depression
since 2007, and will remain so for an indefinite period of time. And when you say that people are, wait a second,
we know the definition of a recession. The technical definition of a recession is
two consecutive quarters of declining GDP, rising unemployment. There’s a few other bells and whistles, there’s
a little bit of subjectivity to it. The National Bureau of Economic Research are
the unofficial but widely followed referees on when you’re in a recession and when you’re
not. Well, we’re in the ninth year of an expansion. The expansion started in June 2009. Here we are in the summer, 2018, nine years
behind us, in our 10th year. So wait a second, how can we be in a depression
if we’re in the 10th year of an expansion? And the answer is, that people don’t really
understand the definition of a depression. They assume intuitively, well, if a recession
is two quarters of declining GDP, and a depression sounds worse, it must be quarters of declining
GDP. Well, we haven’t had that. As I say, we’re in the 10th year of an expansion. But that’s not the definition of a depression. The definition of a depression is a sustained
period a below trend growth with no particular tendency to collapse or getting back to trend. In other words, if trend growth– I’ll use
the United States, but you can apply this more broadly to the world– if trend growth
is three 3%, 3.5%, and that’s probably the long term potential of the United States,
higher nominal growth with inflation, but we’re talking about real growth– if trend
is three 3%, 3.5%, and you’re running at 2%, that gap between say 3.5% and 2%, that’s depressed
growth. So yes, you have growth, you’re not in a technical
recession. But you’re in a depression because you’re
not getting back to that trend. And people say, well, 2%, 3%, 1 percentage
point, who cares? No, that gap is huge. And because of the compounding effect, think
of it as a wedge. There’s the trend line. Here’s the actual line. That wedge gets bigger. So we’re 10 years out. We’ve left $5 trillion of potential growth
on the table. That’s the output gap or the growth gap, the
difference between depressed growth and trend growth. That’s how much wealth has been lost because
of this depressed growth. By the way, that definition I gave, a sustained
period of below trend growth, that’s not my definition. John Maynard Keynes came up with a definition
in the 1930s. It was good enough for him, it’s good enough
for me. I think it’s accurate. But being objective using the numbers I mentioned,
we’re in a depression, we’re going to stay that way. The United States is Japan. You know, Japan had the famous lost decade. Well, the lost decade was 20 years ago. Started in 1990 through 2000. Japan is now almost at the end of their third
lost decade. The United States has had a lost decade from
2007 to now 2018. If something doesn’t change either in terms
of policy or a collapse, something gets worse, but absent that, we’re going to remain in
this kind of pumped 2% growth as far as the eye can see. People say, well, second quarter GDP, Atlanta
Fed predicts 4.5%. Yeah, but we’ve had 4% and 5% quarters in
the last nine years. They don’t last. You get these spikes. You get it real good you know 4% print, and
then the next quarter’s 2%, and the one after that is 0.5% or maybe even negative quarter. So a year and a half into the Trump administration,
he’s producing the same kind of growth as Obama, and I don’t think it’s policy driven. I’m not saying one guy is a good guy, one
guy’s a bad guy. What I’m saying is that the headwinds, demographic,
technological, productivity, psychological, et cetera, haven’t changed, and there’s no
reason to expect they’ll change. So combine this world depression, because
what I just described is true of Japan, it’s true in Europe- – not so true in China, but
China is a special case. They cook the books. Maybe we can talk about that. Their 7%, 8% growth that they’ve been printing,
cut that in half, because 45% of that growth is infrastructure, most of which is wasted. So if you apply generally accepted accounting
principles, made them write off all that wasted investment, they’d be a lot lower than it
appears. But the whole world is caught in this trap. Meanwhile, debt is going up faster than the
growth. Is debt good or bad? Well, it depends debt can be good, if you
can afford it, if you can pay it off, and you can use for productive purposes. It’s bad if you can’t afford it, it’s not
sustainable, you’re using it as a substitute for real growth, and it’s all going to crash
and burn. So you can’t understand debt in isolation. You have to understand debt relative to income. And that debt to GDP ratio, which is something
I spent a lot of time looking at, the GDP is kind of chugging along, not going up very
much. But the debt is going like this, the debt
to GDP ratio is getting worse. It looks like we’re heading for a global debt
crisis. Not quite there yet, but it could happen sooner
than later. Very little doubt that Fed is going to tighten
in September. The baseline scenario for the Fed is straightforward. They’re going to tighten four times a year,
every March, June, September, and December, 25 basis points each time, until they get
interest rates up to you know 3.75%, 3.5%, somewhere in that range. Unless one of three pause factors applies. The pause factors are disorderly decline in
the stock markets. Employment starts to go down, they basically
lose jobs, unemployment’s going up. The third one, disinflation or deflation spins
out of control. Meaning core PCE goes down to 1.4%. Right now, it’s at 2%. Right now, none of those three pause factors
applies. The stock markets, they’re going sideways,
but they’re not crashing. The Fed doesn’t care if the stock market goes
down 15% in six months. They do care if it goes down 15% in six days. That’s disorderly and that’s the kind of thing
where you would see the Fed pause, but that’s not happening right now. Job growth is strong. Inflation is ticking up. I don’t think it will spin out of control,
but we’re out of that disinflation danger zone. So none of the three pause factors applies. Therefore, you should expect the Fed to just
keep raising. So my forecast for September would be yes,
and then at this point, December, more likely than not. But there’s no doubt that Fed is over tightening
because in addition to trying to get interest rates back to normal, they’re also reducing
the balance, they’re trying to normalize the balance sheet. But now the Fed has a dilemma, which is, what
are they going to do if the US economy goes into a recession. As I said, we’re in the 10th year of an expansion. The old cliche, expansions don’t die of old
age is true, but they do die. And history shows that it takes about four
percentage points of cuts, 400 basis points, in other words, to put the Fed to get the
economy out of a recession. Well, how do you cut interest rates 4% if
you’re only at 2%? The answer is, you can’t. You cut them to zero, and then you’re stuck. You’re at that zero bound and the evidence
is good that negative rates don’t work. So then what do you do? Well, then you go to QE 4, we’re going to
print some money again. But there, if you had the balance sheet at
$4.5 trillion, how high can you go before you destroy confidence? $5 trillion, $6 trillion, $7 trillion? Well, the modern monetary theorists would
say, yes, I disagree, and I think the Fed disagrees, as shown by their own actions in
trying to reduce the balance sheet. So what the Fed is doing. They’re trying to raise rates to 3% or 4%. They’re trying to get the balance sheet down
to maybe $2 trillion, a little bit less so that when the recession hits, they can run
the playbook again. They can cut rates, and if necessary, do QE. But here’s the dilemma. Can you normalize interest rates and normalize
the balance sheet without causing the recession that you’re preparing to cure? That’s the conundrum. I think the answer is, no. I think that actually in trying to tighten
to get ready for the next recession, they’re probably going to cause the recession. There’s no data, no time series that tells
you how this is going to play out. Except during QE, what did we see? We did not see a lot of inflation, but we
saw asset prices blow up, stocks, real estate. Other asset categories. They all went up a lot. So it seems at least the kind of first order
intuitive that if you print money, asset prices go up. If you destroy money, asset prices are going
to go down. So what the Fed is doing, they’re destroying
money, reducing the money supply. So they’re really double tightening. In addition to the four rate hikes a year,
this reduction in the balance sheet is probably equivalent– this is an estimate– probably
equivalent to four more rate hikes per year. So they’re actually tightening on a tempo
of about 2%. Probably going to throw the economy into a
recession. The Fed has never forecast a recession. They have a terrible forecasting record. This will happen before they know it. Of course, we all know that stocks go down. So based on that, I’m not bullish on growth
and bearish on stocks. Bullish on the euro. Gold is actually doing fairly well, considering
the headlines. People said, well, gold has gone up a lot. It’s true. I’m surprised it hasn’t gone down more given
the tightening environment that we’ve described. So gold has actually performed fairly well,
given the environment. Now what I do expect is in time, as the signs
of a recession emerge, yield curve inverts, growth slows. Job creation slows down. Not a full scale recession or crash, but just
enough warning signs, and the Fed reverses course, that all of a sudden, they do pause
at one of these meetings, gold’s going to skyrocket. Because that’s an admission. At that point, the Fed is throwing in the
towel to say, you know, we really can’t escape the room. We can’t get back to normal. Because when we try, we sink the economy. And we have to back off from that. And that’s when gold will shine, no pun intended,
because it’ll be very clear that the Fed cannot get out of this easy money mode. One thing I never do– I never make a forecast
or make a claim without backing it up. Maybe there are people out there that are
a dime a dozen, this is going to crash, or this is going to go up, or growth is great,
or it’s so whatever. That’s fine, but you need to back that up
with some kind of analytics, some kind of data, some kind of analysis that you can do. And I always do that, I’m always happy to
kind of drill down on that. So the methodology I use is quite different
from what Wall Street forecasters use. But actually, recently, with some partners,
and some scientists, and other investors formed the company to do this. This is a third wave artificial intelligence. First wave is just big data crunching, correlations,
and regressions. That’s fine. Second wave is what they call machine learning. So as the machine is running these, it actually
gets data itself, and begins to, in effect, reprogram itself based on what is learned
from the correlations it’s finding. Third way. So we do the first two. Third wave, which is what we’re doing, is
actually closer to cognition. It’s teaching the computer to actually kind
of think, use inferential method. You know, the frequentist statisticians–
and Janet Yellen is a classic example– they say, more data, more data, more data. Well fine, but what do you do when you don’t
have the data? You’re trying to solve hard problems. You don’t have the data. You use various inferential methods, and machines
can do that in what’s called a fuzzy way. People don’t like the word fuzzy. Cognition sounds soft, but I always say, I’d
rather be approximately right than exactly wrong. So fuzzy is better than the alternative because
it’s at least pointing you in the right direction. My company is called Meraglim, I’m the chief
global strategist. Our product is called Raven. Raven is a little bit easier to say than Meraglim,
but Raven is our third wave AI predictive analytic product. This has roots that go back to 9/11. Tragic day, September 11, 2001, when the 9/11
attack took place. And what happened then– there was insider
trading in advance of 9/11. In the two trading days prior to the attack,
average daily volume and puts, which is short position, put option buying on American Airlines
and United Airlines, was 286 times the average daily volume. Now you don’t have to be an option trader,
and I order a cheeseburger for lunch every day, and one day, I order 286 cheeseburgers,
something’s up. There’s a crowd here. I was tapped by the CIA, along with others,
to take that fact and take it forward. The CIA is not a criminal investigative agency. Leave that to the FBI and the SEC. But what the CIA said was, OK, if there was
insider trading ahead of 9/11, if there were going to be another spectacular terrorist
attack, something of that magnitude, would there be insider trading again? Could you detect it? Could you trace it to the source, get a FISA
warrant, break down the door, stop the attack, and save lives? That was the mission. We call this Project Prophecy. I was the co-project director, along with
a couple of other people at the CIA. Worked on this for five years from 2002 to
2007. When I got to the CIA, you ran into some old
timers. They would say something like, well, Al-Qaeda
or any terrorist group, they would never compromise operational security by doing insider trading
in a way that you might be able to find. And I had a two word answer for that, which
is, Martha Stewart. Martha Stewart was a legitimate billionaire. She made a billion dollars through creativity
and her own company. She ended up behind bars because of a $100,000
trade. My point is, there’s something in human nature
that cannot resist betting on a sure thing. And I said, nobody thinks that Mohamed Atta,
on his way to Logan Airport, to hijack a plane, stopped at Charles Schwab and bought some
options. Nobody thinks that. But even terrorists exist in the social network. There’s a mother, father, sister, brother
safe house operator, car driver, cook. Somebody in that social network who knows
enough about the attack and they’re like, if I had $5,000, I could make 50, just buy
a put option. The crooks and terrorists, they always go
to options because they have the most leverage, and the SEC knows where to look. So that’s how it happens. And then the question was, could you detect
it. So we started out. There are about 6,000 tickers on the New York
Stock Exchange and the NASDAQ. And we’re talking about second by second data
for years on 6,000 tickers. That’s an enormous, almost unmanageable amount
of data. So what we did is we reduced the targets. We said, well, look, there’s not going to
be any impact on Ben and Jerry’s ice cream if there’s a terrorist attack. You’re looking at cruise ships, amusement
parks, hotels, landmark buildings. there’s a set of stocks that would be most
effective. So we’re able to narrow it down to about 400
tickers, which is much more manageable. Second thing you do, you establish a baseline. Say, what’s the normal volatility, the normal
average daily volume, normal correlation in the stock market. So-called beta and so forth. And then you look for abnormalities. So the stock market’s up. The transportation sector is up. Airlines are up, but one airline is down. What’s up with that? So that’s the anomaly you look for. And then the third thing you do. You look for news. Well, OK, the CEO just resigned because of
some scandal. OK, got it, that would explain why the stock
is down. But when you see the anomalous behavior, and
there’s no news, your reference is, somebody knows something I don’t. People aren’t stupid, they’re not crazy. There’s a reason for that, just not public. That’s the red flag. And then you start to, OK, we’re in the target
zone. We’re in these 400 stocks most affected. We see this anomalous behavior. Somebody is taking a short position while
the market is up and there’s no news. That gets you a red light. And then you drill down. You use what in intelligence work we call
all source fusion, and say, well, gee, is there some pocket litter from a prisoner picked
up in Pakistan that says cruise ships or something along– you sort of get intelligence from
all sources at that point drilled down So that was the project. We built a working model. It worked fine. It actually worked better than we expected. I told the agency, I said, well, we’ll build
you a go-kart, but if you want a Rolls Royce, that’s going to be a little more expensive. The go-kart actually worked like a Rolls Royce. Got a direct hit in August 2006. We were getting a flashing red signal on American
Airlines three days before MI5 and New Scotland Yard took down that liquid bomb attack that
were going to blow up 10 planes in midair with mostly Americans aboard. So it probably would have killed 3,000 Americans
on American Airlines and Delta and other flights flying from Heathrow to New York. That plot was taken down. But again, we had that signal based on– and
they made hundreds of arrests in this neighborhood in London. So this worked perfectly. Unfortunately, the agency had their own reasons
for not taking it forward. They were worried about headline risk, they
were worried about political risk. You say, well, we were using all open source
information. You can pay the Chicago Mercantile Exchange
for data feed to the New York Stock Exchange. This is stuff that anybody can get. You might to pay for it, but you can get it. But the agency was afraid of the New York
Times headline, CIA trolls through 401(k) accounts, which we were not doing. It was during the time of waterboarding and
all that, and they decided not to pursue the project. So I let it go, there were plenty of other
things to do. And then as time went on, a few years later,
I ended up in Bahrain at a wargame– financial war game– with a lot of thinkers and subject
matter experts from around the world. Ran into a great guy named Kevin Massengill,
a former Army Ranger retired Major in the US army, who was working for Raytheon in the
area at the time was part of this war game. We were sort of the two American, little more
out of the box thinkers, if you want to put it that way. We hit it off and I took talked him through
this project I just described. And we said, well look, if the government
doesn’t want to do it, why don’t we do it privately? Why don’t we start a company to do this? And that’s exactly what we did. Our company is, as I mentioned, Meraglim. Our website, Meraglim.com, and our product
is Raven. So the question is, OK, you had a successful
pilot project with the CIA. It worked. By the way, this is a new branch of intelligence
in the intelligence. I-N-T, INT, is short for intelligence. And depending on the source, you have SIGINT,
which is signal intelligence, you have HUMINT which is human intelligence, and a number
of others. We created a new field called MARKINT, which
is market intelligence. How can you use market data to predict things
that are happening. So this was the origin of it. We privatized it, got some great scientists
on board. We’re building this out ourselves. Who partnered with IBM, and IBM’s Watson,
which is the greatest, most powerful plain language processor. Watson can read literally millions of pages
of documents– 10-Ks, 10-Qs, AKs, speeches, press releases, news reports. More than a million analysts could read on
their own, let alone any individual, and process that in plain language. And that’s one of our important technology
partners in this. And we have others. What do we actually do? What’s the science behind this. First of all, just spend a minute on what
Wall Street does and what most analysts do, because it’s badly flawed. It’s no surprise that– every year, the Fed
does a one year forward forecast. So in 2009, they predict 2010. In 2010, they predict 2011. So on. Same thing for the IMF, same thing for Wall
Street. They are off by orders of magnitude year after
year. I mean, how can you be wrong by a lot eight
years in a row, and then have any credibility? And again, the same thing with Wall Street. You see these charts. And the charts show the actual path of interest
rates or the actual path of growth. And then along the timeline, which is the
x-axis, they’ll show what people were predicting at various times. The predictions are always way off the actual
path. There’s actually good social science research
that shows that economists do worse than trained monkeys on terms of forecasting. And I don’t say that in a disparaging way–
here’s the science. A monkey knows nothing. So if you have a binary outcome– up, down,
high, low, growth, recession– and you ask a monkey, they’re going to be right half the
time and wrong half the time, because they don’t know what they’re doing. So you’re to get a random outcome. Economists are actually wrong more than half
the time for two reasons. One, their models are flawed. Number two, what’s called herding or group
behavior. An economist would rather be wrong in the
pack than go out on a limb and maybe be right, but if it turns out you’re not right, you’re
exposed. But there are institutional constraints. People want to protect their jobs. They’re worried about other things than getting
it right. So the forecasting market is pretty bad. The reasons for that– they use equilibrium
models. The capital markets are not in equilibrium
system, so forget your equal equilibrium model. They use the efficient market hypothesis,
which is all the information is out there, you can’t beat the market. Markets are not efficient, we know that. They use stress tests, which are flawed, because
they’re based on the past, but we’re outside the past. The future could be extremely different. They look at 9/11, they look at long term
capital management, they look at the tequila crisis. Fine, but if the next crisis is worse, there’s
nothing in that history that’s going to tell you how bad it can get. And so they assume prices move continuously
and smoothly. So price can go from here to here or from
here to here. But as a trader, you can get out anywhere
in between, and that’s for all these portfolio insurance models and stop losses come from. That’s not how markets behave. That go like this– they just gap up. They don’t hit those in between points. Or they gap down. You’re way underwater, or you missed a profit
opportunity before you even knew it. So in other words, the actual behavior of
markets is completely at odds with all the models that they use. So it’s no surprise the forecasting is wrong. So what are the good models? What are the models that do work? What is the good science? The first thing is complexity theory. Complexity theory has a long pedigree in physics,
meteorology, seismology, forest fire management, traffic, lots of fields where it’s been applied
with a lot of success. Capital markets are complex systems. The four hallmarks of a complex system. One is their diversity of actors, sure. Two is their interaction– are the actors
talking to each other or are they all sort of in their separate cages. Well, there’s plenty of interaction. Is there communication and is there adaptive
behavior? So yeah, there are diverse actors, there’s
communication. They’re interacting. And if you’re losing money, you better change
your behavior quickly. That’s an example of adaptive behavior. So capital markets are four for four in terms
of what makes a complex system. So why not just take complexity science and
bring it over to capital markets? That’s what we’ve done, and we’re getting
fantastic results. So that’s the first thing. The second thing we use is something called
Bayesian statistics. It’s basically a mathematical model that you
use when you don’t have enough data. So for example, if I’ve got a million bits
of data, yeah, do your correlations and regressions, that’s fine. And I learned this at the CIA, this is the
problem we confronted after 9/11. We had one data point– 9/11. Janet Yellen would say, wait for 10 more attacks,
and 30,000 dead, and then we’ll have a time series and we can figure this out. No. To paraphrase Don Rumsfeld, you go to war
with the data you have. And so what you use is this kind of inferential
method. And the reason statisticians dislike it is
because you start with a guess. But it could be a smart guess, it could be
an informed guess. The data may be scarce. You make the best guess you can. And if you have no information at all, just
make it 50/50. Maybe Fed is going to raise rates, maybe they’re
not. I think we do better than that on the Fed. But if you didn’t have any information, you
just do 50/50. But then what you do is you observe phenomena
after the initial hypothesis, and then you update the original hypothesis based on the
subsequent data. You ask yourself, OK this thing happened later. What is the conditional correlation that the
second thing would happen if the first thing were true or not? And then based on that, you’d go back, and
you either increase the probability of the hypothesis being correct, or you decrease
it. It gets low enough, you abandon it, try something
else. If it gets high enough, now you can be a lot
more confident in your prediction. So that’s Bayesian statistic. You use it to find missing aircraft, hunt
submarines. It’s used for a lot of things, but you can
use it in capital markets. Third thing, behavioral psychology. This has been pretty well vetted. I think most economists are familiar with
it, even though they don’t use it very much. But humans turn out to be a bundle of biases. We have anchoring bias, we get an idea in
our heads, and we can’t change it. We have recency bias. We tend to be influenced by the last thing
we heard. And anchoring bias is the opposite, we tend
to be influenced by something we heard a long time ago. Recency bias and anchoring bias are completely
different, but they’re both true. This is how you have to get your mind around
all these contradictions. But when you work through that, people make
mistakes or exhibit bias, it turns out, in very predictable ways. So factor that in. And then the fourth thing we use, and economists
really hate this, is history. But history is a very valuable teacher. So those four areas, complexity theory, Bayesian
statistics, behavioral psychology, and history are the branches of science that we use. Now what do we do with it? Well, we take it and we put it into something
that would look like a pretty normal neural network. You have nodes and edges and some influence
in this direction, some have a feedback loop, some influence in another direction, some
are influenced by others, et cetera. So for Fed policy for example, you’d set these
nodes, and it would include the things I mentioned earlier– inflation, deflation, job creation,
economic growth, capacity, what’s going on in Europe, et cetera. Those will be nodes and there will be influences. But then inside the node, that’s the secret
sauce. That’s where we have the mathematics, including
some of the things I mentioned. But then you say, OK, well, how do you populate
these nodes? You’ve got math in there, you’ve got equations,
but where’s the news come from? That’s where Watson comes in. Watson’s reading all these records, feeding
the nodes, they’re pulsing, they’re putting input. And then we have these actionable cells. So the euro-dollar cross rate, the Yuandollar
cross rate, yen, major benchmark, bonds, yields on 10 year treasury notes, bunds, JGBs, et
cetera. These are sort of macro indicators, but the
major benchmark bond indices, the major currency across rates, the major policy rates, which
are the short term central bank rates. And a basket of commodities– oil, gold, and
a few others– they are the things we watch. We use these neural networks I described,
but they’re not just kind of linear or conventional equilibrium models. They’re based on the science I describe. So all that good science, bringing it to a
new field, which is capital markets, using what’s called fuzzy cognition, neural networks,
populating with Watson, this is what we do. We’re very excited about it, getting great
results. And this is what I use. When I give a speech or write a book or write
an article, and I’m making forecast. This is what’s behind it. So we talked earlier about business cycles,
recessions, depressions. And that’s conventional economic analysis. My definition of depression is not exactly
conventional, but that’s really thinking in terms of growth, trend growth, below trend
growth, business cycles, et cetera. Collapse or financial panic is something different. A financial panic is not the same as a recession
or a turn in the business cycle. They can go together, but they don’t have
to. So let’s talk about financial panics as a
separate category away from the business cycle and growth, which we talked about earlier. Our science, the science I use, the science
that we use with Raven, at our company, Meraglim, involves complexity theory. Well, complexity theory shows that the worst
thing that can happen in a system is an exponential function of scale. Scale is just how big is it. Now you have to talk about your scaling metrics. We’re talking about the gross notional value
derivatives. We’re talking about average daily volume on
the stock market. We’re talking about debt. We could be talking about all of those things. This is new science, so I think it will be
years of empirics to make this more precise. But the theory is good, and you can apply
it in a sort of rough and ready way. So you go to Jamie Dimon, and you say, OK,
Jamie, you’ve tripled your gross notional value derivatives. You’ve tripled your derivatives book. How much did the risk go up? Well, he would say, not at all, because yeah,
gross national value is triple, but who cares? It’s long, short, long, short, long, short,
long, short. You net it all down. It’s just a little bit of risk. Risk didn’t go up at all. If you ask my 87-year-old mother, who is not
an economist, but she’s a very smart lady, say, hey mom, I tripled the system, how much
did the risk go up? She would probably use intuition and say,
well, probably triple. Jamie Dimon is wrong, my mother is wrong. It’s not the net, it’s the gross. And it’s not linear, it’s exponential. In other words, if you triple the system,
the growth went up by a factor of 10, 50, et cetera. There’s some exponential function associated
with that. So people think, well gee, in 2008, we learned
our lesson. We’ve got debt under control, we’ve got derivatives
under control. No. Debt is much higher. Debt to GDP ratios are much worse. Total notional value, gross notional values
of derivatives is much higher. Now people look at the BIS statistics and
say, well, the banks, actually, gross national value derivatives has been going down, which
it has, but that’s misleading because they’re taking a lot of that, moving it over to clearing
houses. So it’s never been on the balance sheet, it’s
always been off balance sheet. But even if you use the footnotes, that number
has gone down for banks, but that’s only because they’re putting it over clearing houses. Who’s guaranteeing the clearing house? The risk hasn’t gone away, it’s just been
moved around. So given those metrics– debt, derivatives,
and other indices, concentration, the fact that the five largest banks in America have
a higher percentage of total banking assets than they did in 2008, there’s more concentration–
that’s another risk factor. Taking that all into account, you can say
that the next crisis will be exponentially worse than the last one. That’s an objective statement based on complexity
theory. So you either have to believe that we’re never
going to have a crisis. Well, you had one in 1987, you had one in
1994, you had one in 1998. You had the dotcom crash in 2000, mortgage
crash in 2007, Lehman in 2008. Don’t tell me these things don’t happen. They happen every five, six, seven years. It’s been 10 years since the last one. Doesn’t mean it happens tomorrow, but nobody
should be surprised if it does. So the point is this crisis is coming because
they always come, and it will be exponentially worse because of the scaling metrics I mentioned. Who’s ready for that? Well, the central banks aren’t ready. In 1998, Wall Street bailed out a hedge fund
long term capital. In 2008, the central banks bailed out Wall
Street. Lehman– but Morgan Stanley was ready to fail,
Goldman was ready to fail, et cetera. In 2018, 2019, sooner than later, who’s going
to bail out the central banks? And notice, the problem has never gone away. We just get bigger bailouts at a higher level. What’s bigger than the central banks? Who can bail out the central banks? There’s only one institution, one balance
sheet in the world they can do that, which is the IMF. The IMF actually prints their own money. The SDR, special drawing right, SDR is not
the out strawberry daiquiri on the rocks, it’s a special drawing right. It’s world money, that’s the easiest way to
think about it. They do have a printing press. And so that will be the only source of liquidity
in the next crisis, because the central banks, if they don’t normalize before the crisis–
and it looks like they won’t be able to, they’re going to run out of runway, and they can expand
the balance sheet beyond the small amount because they’ll destroy confidence, where
does the liquidity come from? The answer, it comes from the IMF. So that’s the kind of global monetary reset,
the GMR, global monetary resety. You hear that expression. There’s something very new that’s just been
called to my attention recently, and I’ve done some independent research on it, and
it holds up. So let’s see how it goes. But it looks as if the Chinese have pegged
gold to the SDR at a rate of 900 SDRs per ounce of gold. This is not the IMF. The IMF is not doing this. The Federal Reserve, the Treasury is not doing
it. The ECB is not doing it. If they were, you’d see it. It would show up in the gold holdings. You have to conduct open market operations
in gold to do this. But the Chinese appear to be doing it, and
it starts October 1, 2016. That was the day the Chinese Yuan joined the
SDR. The IMF admitted the Yuan to the group was
four, now five currencies that make up the SDR. So almost to the day, when the Yuan got in
the SDR, you see this a horizontal trend where first, gold per ounce is trading between 850
and 950 SDRs. And then it gets tighter. Right now, the range is 875 to 925. Again, a lot of good data behind this. So it’s a very good, it’s another predictive
indicator. If you see gold around 870 SDRs per ounce,
that’s a strong SDR, weak gold. Great time to buy gold, because the Chinese
are going to move back up to 900. So that’s an example of science, observation,
base and statistics, inference, all the things we talked about that can be used today in
a predictive analytic way. A crisis is coming, because they always do. I don’t have a crystal ball, this is plenty
of history to back it up. It’ll be exponentially worse. That’s what the science tells us. The central banks will not be prepared, because
they haven’t normalized from the last one. You’re going to have to turn to the IMF, and
who’s waiting there but China with a big pile of gold.