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 9/11.12:24Tragic day, September 11, 2001, when the 9/11 attack took place.12:31And what happened then– there was insider trading in advance of 9/11.12:37In the two trading days prior to the attack, average daily volume and puts, which is short12:43position, put option buying on American Airlines and United Airlines, was 286 times the average12:50daily volume.12:51Now you don’t have to be an option trader, and I order a cheeseburger for lunch every12:55day, and one day, I order 286 cheeseburgers, something’s up.12:59There’s a crowd here.13:00I was tapped by the CIA, along with others, to take that fact and take it forward.13:06The CIA is not a criminal investigative agency.13:10Leave that to the FBI and the SEC.13:11But what the CIA said was, OK, if there was insider trading ahead of 9/11, if there were13:17going to be another spectacular terrorist attack, something of that magnitude, would13:22there be insider trading again?13:24Could you detect it?13:26Could you trace it to the source, get a FISA warrant, break down the door, stop the attack,13:30and save lives?13:31That was the mission.13:32We call this Project Prophecy.13:34I was the co-project director, along with a couple of other people at the CIA.13:39Worked on this for five years from 2002 to 2007.13:43When I got to the CIA, you ran into some old timers.13:47They would say something like, well, Al-Qaeda or any terrorist group, they would never compromise13:53operational security by doing insider trading in a way that you might be able to find.13:59And I had a two word answer for that, which is, Martha Stewart.14:03Martha Stewart was a legitimate billionaire.14:05She made a billion dollars through creativity and her own company.14:08She ended up behind bars because of a $100,000 trade.14:11My point is, there’s something in human nature that cannot resist betting on a sure thing.14:15And I said, nobody thinks that Mohamed Atta, on his way to Logan Airport, to hijack a plane,14:21stopped at Charles Schwab and bought some options.14:23Nobody thinks that.14:24But even terrorists exist in the social network.14:26There’s a mother, father, sister, brother safe house operator, car driver, cook.14:32Somebody in that social network who knows enough about the attack and they’re like,14:36if I had $5,000, I could make 50, just buy a put option.14:40The crooks and terrorists, they always go to options because they have the most leverage,14:44and the SEC knows where to look.14:47So that’s how it happens.14:49And then the question was, could you detect it.14:52So we started out.14:53There are about 6,000 tickers on the New York Stock Exchange and the NASDAQ.14:57And we’re talking about second by second data for years on 6,000 tickers.15:03That’s an enormous, almost unmanageable amount of data.15:06So what we did is we reduced the targets.15:08We said, well, look, there’s not going to be any impact on Ben and Jerry’s ice cream15:12if there’s a terrorist attack.15:14You’re looking at cruise ships, amusement parks, hotels, landmark buildings.15:18there’s a set of stocks that would be most effective.15:22So we’re able to narrow it down to about 400 tickers, which is much more manageable.15:26Second thing you do, you establish a baseline.15:28Say, what’s the normal volatility, the normal average daily volume, normal correlation in15:35the stock market.15:36So-called beta and so forth.15:37And then you look for abnormalities.15:39So the stock market’s up.15:42The transportation sector is up.15:43Airlines are up, but one airline is down.15:46What’s up with that?15:47So that’s the anomaly you look for.15:48And then the third thing you do.15:49You look for news.15:50Well, OK, the CEO just resigned because of some scandal.15:54OK, got it, that would explain why the stock is down.15:57But when you see the anomalous behavior, and there’s no news, your reference is, somebody16:03knows something I don’t.16:04People aren’t stupid, they’re not crazy.16:06There’s a reason for that, just not public.16:08That’s the red flag.16:09And then you start to, OK, we’re in the target zone.16:12We’re in these 400 stocks most affected.16:15We see this anomalous behavior.16:17Somebody is taking a short position while the market is up and there’s no news.16:21That gets you a red light.16:23And then you drill down.16:24You use what in intelligence work we call all source fusion, and say, well, gee, is16:28there some pocket litter from a prisoner picked up in Pakistan that says cruise ships or something16:34along– you sort of get intelligence from all sources at that point drilled down So16:38that was the project.16:40We built a working model.16:41It worked fine.16:42It actually worked better than we expected.16:44I told the agency, I said, well, we’ll build you a go-kart, but if you want a Rolls Royce,16:48that’s going to be a little more expensive.16:50The go-kart actually worked like a Rolls Royce.16:52Got a direct hit in August 2006.16:55We were getting a flashing red signal on American Airlines three days before MI5 and New Scotland17:04Yard took down that liquid bomb attack that were going to blow up 10 planes in midair17:09with mostly Americans aboard.17:11So it probably would have killed 3,000 Americans on American Airlines and Delta and other flights17:16flying from Heathrow to New York.17:18That plot was taken down.17:20But again, we had that signal based on– and they made hundreds of arrests in this neighborhood17:26in London.17:27So this worked perfectly.17:30Unfortunately, the agency had their own reasons for not taking it forward.17:37They were worried about headline risk, they were worried about political risk.17:41You say, well, we were using all open source information.17:45You can pay the Chicago Mercantile Exchange for data feed to the New York Stock Exchange.17:48This is stuff that anybody can get.17:49You might to pay for it, but you can get it.17:52But the agency was afraid of the New York Times headline, CIA trolls through 401(k)17:58accounts, which we were not doing.18:00It was during the time of waterboarding and all that, and they decided not to pursue the18:06project.18:07So I let it go, there were plenty of other things to do.18:09And then as time went on, a few years later, I ended up in Bahrain at a wargame– financial18:14war game– with a lot of thinkers and subject matter experts from around the world.18:20Ran into a great guy named Kevin Massengill, a former Army Ranger retired Major in the18:27US army, who was working for Raytheon in the area at the time was part of this war game.18:32We were sort of the two American, little more out of the box thinkers, if you want to put18:36it that way.18:38We hit it off and I took talked him through this project I just described.18:42And we said, well look, if the government doesn’t want to do it, why don’t we do it18:45privately?18:46Why don’t we start a company to do this?18:47And that’s exactly what we did.18:49Our company is, as I mentioned, Meraglim.18:51Our website, Meraglim.com, and our product is Raven.18:55So the question is, OK, you had a successful pilot project with the CIA.19:01It worked.19:02By the way, this is a new branch of intelligence in the intelligence.19:06I-N-T, INT, is short for intelligence.19:08And depending on the source, you have SIGINT, which is signal intelligence, you have HUMINT19:14which is human intelligence, and a number of others.19:17We created a new field called MARKINT, which is market intelligence.19:20How can you use market data to predict things that are happening.19:24So this was the origin of it.19:26We privatized it, got some great scientists on board.19:30We’re building this out ourselves.19:31Who partnered with IBM, and IBM’s Watson, which is the greatest, most powerful plain19:38language processor.19:40Watson can read literally millions of pages of documents– 10-Ks, 10-Qs, AKs, speeches,19:47press releases, news reports.19:51More than a million analysts could read on their own, let alone any individual, and process19:57that in plain language.19:58And that’s one of our important technology partners in this.20:02And we have others.20:04What do we actually do?20:07What’s the science behind this.20:09First of all, just spend a minute on what Wall Street does and what most analysts do,20:13because it’s badly flawed.20:15It’s no surprise that– every year, the Fed does a one year forward forecast.20:22So in 2009, they predict 2010.20:24In 2010, they predict 2011.20:27So on.20:28Same thing for the IMF, same thing for Wall Street.20:30They are off by orders of magnitude year after year.20:34I mean, how can you be wrong by a lot eight years in a row, and then have any credibility?20:38And again, the same thing with Wall Street.20:41You see these charts.20:43And the charts show the actual path of interest rates or the actual path of growth.20:48And then along the timeline, which is the x-axis, they’ll show what people were predicting20:52at various times.20:54The predictions are always way off the actual path.20:57There’s actually good social science research that shows that economists do worse than trained21:02monkeys on terms of forecasting.21:04And I don’t say that in a disparaging way– here’s the science.21:07A monkey knows nothing.21:08So if you have a binary outcome– up, down, high, low, growth, recession– and you ask21:17a monkey, they’re going to be right half the time and wrong half the time, because they21:20don’t know what they’re doing.21:21So you’re to get a random outcome.21:24Economists are actually wrong more than half the time for two reasons.21:28One, their models are flawed.21:29Number two, what’s called herding or group behavior.21:32An economist would rather be wrong in the pack than go out on a limb and maybe be right,21:37but if it turns out you’re not right, you’re exposed.21:40But there are institutional constraints.21:42People want to protect their jobs.21:44They’re worried about other things than getting it right.21:47So the forecasting market is pretty bad.21:48The reasons for that– they use equilibrium models.21:52The capital markets are not in equilibrium system, so forget your equal equilibrium model.21:57They use the efficient market hypothesis, which is all the information is out there,22:01you can’t beat the market.22:02Markets are not efficient, we know that.22:05They use stress tests, which are flawed, because they’re based on the past, but we’re outside22:12the past.22:13The future could be extremely different.22:16They look at 9/11, they look at long term capital management, they look at the tequila22:20crisis.22:21Fine, but if the next crisis is worse, there’s nothing in that history that’s going to tell22:25you how bad it can get.22:27And so they assume prices move continuously and smoothly.22:30So price can go from here to here or from here to here.22:34But as a trader, you can get out anywhere in between, and that’s for all these portfolio22:38insurance models and stop losses come from.22:41That’s not how markets behave.22:42That go like this– they just gap up.22:44They don’t hit those in between points.22:45Or they gap down.22:46You’re way underwater, or you missed a profit opportunity before you even knew it.22:51So in other words, the actual behavior of markets is completely at odds with all the22:56models that they use.22:57So it’s no surprise the forecasting is wrong.23:00So what are the good models?23:01What are the models that do work?23:03What is the good science?23:04The first thing is complexity theory.23:07Complexity theory has a long pedigree in physics, meteorology, seismology, forest fire management,23:13traffic, lots of fields where it’s been applied with a lot of success.23:18Capital markets are complex systems.23:20The four hallmarks of a complex system.23:24One is their diversity of actors, sure.23:26Two is their interaction– are the actors talking to each other or are they all sort23:30of in their separate cages.23:31Well, there’s plenty of interaction.23:33Is there communication and is there adaptive behavior?23:37So yeah, there are diverse actors, there’s communication.23:40They’re interacting.23:42And if you’re losing money, you better change your behavior quickly.23:45That’s an example of adaptive behavior.23:47So capital markets are four for four in terms of what makes a complex system.23:51So why not just take complexity science and bring it over to capital markets?23:55That’s what we’ve done, and we’re getting fantastic results.23:57So that’s the first thing.23:58The second thing we use is something called Bayesian statistics.24:03It’s basically a mathematical model that you use when you don’t have enough data.24:08So for example, if I’ve got a million bits of data, yeah, do your correlations and regressions,24:14that’s fine.24:15And I learned this at the CIA, this is the problem we confronted after 9/11.24:19We had one data point– 9/11.24:20Janet Yellen would say, wait for 10 more attacks, and 30,000 dead, and then we’ll have a time24:26series and we can figure this out.24:28No.24:29To paraphrase Don Rumsfeld, you go to war with the data you have.24:33And so what you use is this kind of inferential method.24:37And the reason statisticians dislike it is because you start with a guess.24:41But it could be a smart guess, it could be an informed guess.24:44The data may be scarce.24:45You make the best guess you can.24:47And if you have no information at all, just make it 50/50.24:50Maybe Fed is going to raise rates, maybe they’re not.24:53I think we do better than that on the Fed.24:55But if you didn’t have any information, you just do 50/50.24:58But then what you do is you observe phenomena after the initial hypothesis, and then you25:05update the original hypothesis based on the subsequent data.25:07You ask yourself, OK this thing happened later.25:10What is the conditional correlation that the second thing would happen if the first thing25:14were true or not?25:16And then based on that, you’d go back, and you either increase the probability of the25:19hypothesis being correct, or you decrease it.25:21It gets low enough, you abandon it, try something else.25:24If it gets high enough, now you can be a lot more confident in your prediction.25:27So that’s Bayesian statistic.25:29You use it to find missing aircraft, hunt submarines.25:32It’s used for a lot of things, but you can use it in capital markets.25:36Third thing, behavioral psychology.25:38This has been pretty well vetted.25:40I think most economists are familiar with it, even though they don’t use it very much.25:43But humans turn out to be a bundle of biases.25:48We have anchoring bias, we get an idea in our heads, and we can’t change it.25:52We have recency bias.25:54We tend to be influenced by the last thing we heard.25:57And anchoring bias is the opposite, we tend to be influenced by something we heard a long26:01time ago.26:03Recency bias and anchoring bias are completely different, but they’re both true.26:07This is how you have to get your mind around all these contradictions.26:11But when you work through that, people make mistakes or exhibit bias, it turns out, in26:16very predictable ways.26:18So factor that in.26:19And then the fourth thing we use, and economists really hate this, is history.26:23But history is a very valuable teacher.26:26So those four areas, complexity theory, Bayesian statistics, behavioral psychology, and history26:33are the branches of science that we use.26:35Now what do we do with it?26:37Well, we take it and we put it into something that would look like a pretty normal neural26:41network.26:42You have nodes and edges and some influence in this direction, some have a feedback loop,26:47some influence in another direction, some are influenced by others, et cetera.26:51So for Fed policy for example, you’d set these nodes, and it would include the things I mentioned26:55earlier– inflation, deflation, job creation, economic growth, capacity, what’s going on27:02in Europe, et cetera.27:03Those will be nodes and there will be influences.27:05But then inside the node, that’s the secret sauce.27:08That’s where we have the mathematics, including some of the things I mentioned.27:12But then you say, OK, well, how do you populate these nodes?27:15You’ve got math in there, you’ve got equations, but where’s the news come from?27:19That’s where Watson comes in.27:20Watson’s reading all these records, feeding the nodes, they’re pulsing, they’re putting27:24input.27:25And then we have these actionable cells.27:26So the euro-dollar cross rate, the Yuandollar cross rate, yen, major benchmark, bonds, yields27:36on 10 year treasury notes, bunds, JGBs, et cetera.27:41These are sort of macro indicators, but the major benchmark bond indices, the major currency27:46across rates, the major policy rates, which are the short term central bank rates.27:50And a basket of commodities– oil, gold, and a few others– they are the things we watch.27:55We use these neural networks I described, but they’re not just kind of linear or conventional28:06equilibrium models.28:07They’re based on the science I describe.28:09So all that good science, bringing it to a new field, which is capital markets, using28:13what’s called fuzzy cognition, neural networks, populating with Watson, this is what we do.28:19We’re very excited about it, getting great results.28:21And this is what I use.28:23When I give a speech or write a book or write an article, and I’m making forecast.28:28This is what’s behind it.28:32So we talked earlier about business cycles, recessions, depressions.28:37And that’s conventional economic analysis.28:40My definition of depression is not exactly conventional, but that’s really thinking in28:44terms of growth, trend growth, below trend growth, business cycles, et cetera.28:50Collapse or financial panic is something different.28:52A financial panic is not the same as a recession or a turn in the business cycle.28:57They can go together, but they don’t have to.28:59So let’s talk about financial panics as a separate category away from the business cycle29:04and growth, which we talked about earlier.29:06Our science, the science I use, the science that we use with Raven, at our company, Meraglim,29:12involves complexity theory.29:14Well, complexity theory shows that the worst thing that can happen in a system is an exponential29:21function of scale.29:23Scale is just how big is it.29:24Now you have to talk about your scaling metrics.29:27We’re talking about the gross notional value derivatives.29:28We’re talking about average daily volume on the stock market.29:31We’re talking about debt.29:33We could be talking about all of those things.29:35This is new science, so I think it will be years of empirics to make this more precise.29:39But the theory is good, and you can apply it in a sort of rough and ready way.29:43So you go to Jamie Dimon, and you say, OK, Jamie, you’ve tripled your gross notional29:50value derivatives.29:51You’ve tripled your derivatives book.29:53How much did the risk go up?29:54Well, he would say, not at all, because yeah, gross national value is triple, but who cares?29:59It’s long, short, long, short, long, short, long, short.30:01You net it all down.30:02It’s just a little bit of risk.30:04Risk didn’t go up at all.30:06If you ask my 87-year-old mother, who is not an economist, but she’s a very smart lady,30:10say, hey mom, I tripled the system, how much did the risk go up?30:14She would probably use intuition and say, well, probably triple.30:17Jamie Dimon is wrong, my mother is wrong.30:21It’s not the net, it’s the gross.30:22And it’s not linear, it’s exponential.30:24In other words, if you triple the system, the growth went up by a factor of 10, 50,30:28et cetera.30:29There’s some exponential function associated with that.30:32So people think, well gee, in 2008, we learned our lesson.30:36We’ve got debt under control, we’ve got derivatives under control.30:39No.30:40Debt is much higher.30:41Debt to GDP ratios are much worse.30:44Total notional value, gross notional values of derivatives is much higher.30:47Now people look at the BIS statistics and say, well, the banks, actually, gross national30:52value derivatives has been going down, which it has, but that’s misleading because they’re30:57taking a lot of that, moving it over to clearing houses.30:59So it’s never been on the balance sheet, it’s always been off balance sheet.31:02But even if you use the footnotes, that number has gone down for banks, but that’s only because31:07they’re putting it over clearing houses.31:09Who’s guaranteeing the clearing house?31:10The risk hasn’t gone away, it’s just been moved around.31:12So given those metrics– debt, derivatives, and other indices, concentration, the fact31:21that the five largest banks in America have a higher percentage of total banking assets31:26than they did in 2008, there’s more concentration– that’s another risk factor.31:31Taking that all into account, you can say that the next crisis will be exponentially31:37worse than the last one.31:38That’s an objective statement based on complexity theory.31:41So you either have to believe that we’re never going to have a crisis.31:44Well, you had one in 1987, you had one in 1994, you had one in 1998.31:49You had the dotcom crash in 2000, mortgage crash in 2007, Lehman in 2008.31:55Don’t tell me these things don’t happen.31:56They happen every five, six, seven years.31:59It’s been 10 years since the last one.32:02Doesn’t mean it happens tomorrow, but nobody should be surprised if it does.32:05So the point is this crisis is coming because they always come, and it will be exponentially32:11worse because of the scaling metrics I mentioned.32:14Who’s ready for that?32:15Well, the central banks aren’t ready.32:17In 1998, Wall Street bailed out a hedge fund long term capital.32:23In 2008, the central banks bailed out Wall Street.32:25Lehman– but Morgan Stanley was ready to fail, Goldman was ready to fail, et cetera.32:31In 2018, 2019, sooner than later, who’s going to bail out the central banks?32:35And notice, the problem has never gone away.32:37We just get bigger bailouts at a higher level.32:40What’s bigger than the central banks?32:42Who can bail out the central banks?32:43There’s only one institution, one balance sheet in the world they can do that, which32:46is the IMF.32:48The IMF actually prints their own money.32:51The SDR, special drawing right, SDR is not the out strawberry daiquiri on the rocks,32:55it’s a special drawing right.32:56It’s world money, that’s the easiest way to think about it.32:58They do have a printing press.33:00And so that will be the only source of liquidity in the next crisis, because the central banks,33:07if they don’t normalize before the crisis– and it looks like they won’t be able to, they’re33:11going to run out of runway, and they can expand the balance sheet beyond the small amount33:17because they’ll destroy confidence, where does the liquidity come from?33:20The answer, it comes from the IMF.33:23So that’s the kind of global monetary reset, the GMR, global monetary resety.33:28You hear that expression.33:31There’s something very new that’s just been called to my attention recently, and I’ve33:36done some independent research on it, and it holds up.33:39So let’s see how it goes.33:42But it looks as if the Chinese have pegged gold to the SDR at a rate of 900 SDRs per33:51ounce of gold.33:52This is not the IMF.33:53The IMF is not doing this.33:55The Federal Reserve, the Treasury is not doing it.33:58The ECB is not doing it.33:59If they were, you’d see it.34:00It would show up in the gold holdings.34:02You have to conduct open market operations in gold to do this.34:05But the Chinese appear to be doing it, and it starts October 1, 2016.34:11That was the day the Chinese Yuan joined the SDR.34:15The IMF admitted the Yuan to the group was four, now five currencies that make up the34:21SDR.34:22So almost to the day, when the Yuan got in the SDR, you see this a horizontal trend where34:29first, gold per ounce is trading between 850 and 950 SDRs.34:37And then it gets tighter.34:38Right now, the range is 875 to 925.34:41Again, a lot of good data behind this.34:44So it’s a very good, it’s another predictive indicator.34:47If you see gold around 870 SDRs per ounce, that’s a strong SDR, weak gold.34:54Great time to buy gold, because the Chinese are going to move back up to 900.34:58So that’s an example of science, observation, base and statistics, inference, all the things35:04we talked about that can be used today in a predictive analytic way.35:08A crisis is coming, because they always do.35:10I don’t have a crystal ball, this is plenty of history to back it up.35:13It’ll be exponentially worse.35:15That’s what the science tells us.35:16The central banks will not be prepared, because they haven’t normalized from the last one.35:20You’re going to have to turn to the IMF, and who’s waiting there but China with a big pile35:24of gold.