Predicting Terrorism with Market Intelligence: Stock Options

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

 

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

Donald Trump’s Pick for Health Secretary Traded Medical Stocks While in House

Rep. Tom Price has sponsored and advocated legislation that could affect the companies’ share prices

Mr. Price’s trading is likely to be a significant issue during his Senate confirmation hearings. Allegations of abusive trading by members of Congress in recent years led to a 2012 law—the Stop Trading on Congressional Knowledge (STOCK) Act—that bars members and employees of Congress from using “any nonpublic information derived from the individual’s position…or gained from performance of the individual’s duties, for personal benefit.” The law also requires members to report their trades within 45 days.
.. The health-care industry is Mr. Price’s biggest patron. In 2015 and 2016, he received about $730,000 in campaign donations from health professionals, insurers and drug companies, more than from any other industry
.. Aetna and others are set to benefit from certain provisions of the 2010 health law that Mr. Price’s bill would replace.
.. The largest shareholder of Mr. Price’s biggest stock buy, Australia’s Innate Immuno, is Rep. Chris Collins (R., N.Y.), with a 17% stake, according to the company’s website. Mr. Collins, a member of the Trump transition team, also sits on the company’s board.
.. Mr. Collins said in his hometown newspaper, the Buffalo News, that his authorship of legislation wasn’t a conflict of interest, but rather an instance of bringing his business knowledge to the Congress.
.. The closing price on the Australian Exchange on the day Mr. Price made his purchase was A$0.41, and it closed on Thursday at A$0.85. That means that Mr. Price’s holdings have paper gains of between $50,000 and $100,000.