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  • 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.

JIM RICKARDS: I'm Jim Rickards, writer, author of number of books, all on the international

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