Stig Brodersen  4:00  

Jim, I have to ask you what the bull case is. I hear like all the bear arguments and I think you have a lot of good arguments that are well-researched. I’m very, very curious now about why can you be wrong or why would Bitcoin be the currency for storage of value of the future?

Jim Rickards  4:17  

Well, you might outperform coal like the pet rock. I don’t have a good bull case or Bitcoin. See, if you say what’s the bull case, my question is, what’s the use case? Give me a use case that I can evaluate for better or worse. 

Now, I gave you a use case for Lumen, which is a cheap, efficient micropayments network in a bunch of countries that are four steps removed from the heart of the financial system. 

Some of the smart contracts have a use case, but Bitcoin doesn’t. The only use case I see is from criminals, terrorists and tax evaders, but even there, you’d rather be in Monero or Specter, one of the other coins that has a little more stealthiness to it. 

By the way, I’m not counseling people to do any of those bad things, just to be clear, but if you happen to be a criminal, you’re more likely to be in Specter or Monero than you are in Bitcoin. I see no use case at all.

It should *inaudible* placed in the Smithsonian, as the thing that increased consciousness and awareness of cryptocurrencies. It made an impact and was innovative. 

By the way, Nakamoto made another mistake. Look, I don’t want to be too critical. The guy was, the team or woman or whoever, we don’t know. But whoever it was, could have been a team from the NSA building backdoors with another shooter job, but who knows, right? 

But they didn’t know that much about monetary economics. There’s no reason why a really smart developer and really smart coder engineer should be an expert on monetary economics.

However, if you are an expert on monetary economics, you know that capping the number of coins is fatal. That will prevent it from being widely used as a currency. Here’s why, the criticism was the Fed printed $4 trillion QE. I mean, Nakamoto left us clues, whose code about bank bailouts… He or she clearly had an animus towards a lot of the bailouts, the money printing, QE, and things that were done in 2008. 

Okay, that’s a fair criticism, I share that criticism. 

However, the solution was we’re not going to be like a central bank. We’re not going to have all this quantitative easing. We’re going to cap the number of Bitcoin. I believe the number is 21 million and we’re getting closer to that number. 

Now, we’ll never get there, by the way, because of the exponential increase in the energy usage to make the next coin but there’ll be some natural level where a cap is set. That’s not good because it has an inherent inflationary bias. 

One of the attractions of gold, by the way, is that gold output expands at roughly the rate of the global economy, not exactly the global economy goes 2.93% a year. Gold output mining as a percentage of total stock above ground is about 1.6% a year. So it’s not perfect. Nothing is but it syncs up pretty well.

See the money supply has to be elastic. If the economy’s growing, and the money supply is not growing, you have a deflationary bias. The money becomes worth more because if you have a fixed amount of money and a growing economy, then each unit of money buys more of that growing economy which is deflationary.The money is more valuable. 

Now you don’t want the opposite when you have too much money. Now used on a deflation, you have inflation, and the money is less valuable. 

Deflation is bad, inflation is bad. What you want is elasticity and money roughly in sync with the capacity of the real economy to grow. That’s what Milton Friedman advocated. That was part of the beauty of the gold standard, etc. 

Now, what happens when you have deflationary money, which is what Bitcoin is because it can’t grow beyond a certain point? 

Well, you don’t have a credit market. Who in their right mind would borrow in money that is worth more when you have to pay back the debt? That means your debt is going up over and above interest. 

I borrow a certain amount of Bitcoin, but when I pay you back, the Bitcoin is worth a lot more. Well, that stinks for me, because my loan went up doubled or tripled. So you’ll never borrow in a deflationary currency, which means there’s never a credit market. You don’t have a credit market, you can’t have an economy. 

Your base money is important. M0 is important, M1 important, absolutely. But the economy is driven on credit. Credit is a high multiple of the base money. It’s credit in the form of bank credit, lending and increasing deposits. So a deflationary currency, which is what Bitcoin is, is doomed to fail, because it’s not elastic over and above all the other reasons I mentioned.

Stig Brodersen  8:36  

I know you have a lot of contacts in the central bank, not just in the US, but all over the world. What do they say about Bitcoin specifically or about cryptocurrencies? Is that something that they are concerned about in any way?

Jim Rickards  8:50  

Well, it’s what I described in the closed door is becoming more of an open door because they have to act. It goes to your point precedent. If they wait too long, this is cats out of the bag, so to speak. 

There are some real Bitcoin millionaires out there, and there are some Bitcoin billionaires out there. The math is simple. If you bought your Bitcoin for five bucks, and *inaudible* 100,000 coins for half a million bucks and it went to 20,000. You sold out? I mean, you’re a billionaire. 

So I don’t dispute that those cases exist, but the point I make is that how did you make the money? You took it from suckers who were paying you $20,000 a coin. 

South Korean auto mechanics who hock their inventory, guys in West Virginia who took out a home equity loan and bet the ranch at crazy prices because they thought this was easy money or a way to retire early. That’s how you made your money. 

Is that the economic model you want? There’s no value created. There was no wealth created. There was no ingenuity. There was no Bill Gates. There was no Warren Buffett. It was just a wealth transfer from early adaptors to suckers.

Preston Pysh  9:53  

Jim, you’ve been working as a Chief Global Strategist at Meraglim Holdings and you’re working on a thing called Raven. Can you tell us a little bit more about what you’re doing here?

Jim Rickards  10:04  

Sure. Thank you. It’s a company I started with a partner Kevin Massengill about a year and a half ago. Kevin, like you, has an army background. He was an Army Ranger Mountain Division and Army Intelligence. We’re off to a good start. 

The company is Meraglim and the product is Raven. That was in honor of the Raven of Zurich, which, if you read my book, “The Road to Ruin,” it started with the little bio and history of Felix Somary who is Austrian born, but lived most of his life in Switzerland. He had an uncanny track record of predicting all the great events of the 20th century in advance. 

He saw World War One coming. He saw all these customer securities converted to gold, and moved the gold to Norway. When the war was over, everyone else was ruined. His clients were still rich. He saw the inflation of the 1923 period. He saw the collapse of the Gold Exchange standard of the 1930s, and so forth throughout the century, up until his death in the mid century. 

He was called The Raven of Zurich because the raven is associated with prophecy, prognostication and omens, and so forth. So that was his nickname. 

We call our product Raven and then what we’re doing is we are building a system for predictive analytics and capital markets. You need to let that sink in a little bit, because capital markets are the biggest markets in the world.We’re going to tell you what’s going to happen. That’s what predictive analytics are, using our technology. 

A couple of things about it. First of all, a lot of people will tell you that’s impossible. They’ll go, “Oh, that’s interesting, Jim. Don’t you know that that cannot be done?”

Well, the answer is it cannot be done using the mainstream technologies and using the mainstream methodologies because those methodologies are badly flawed.

It can be done using the right technology and the right branches of science. So that’s one of our innovations. We look all the time at competition, a firm like Morgan Stanley, they spend $2 billion a year on R&D, and the other big banks spend comparable amounts. 

You see Paul Jones of Tudor Investments and Steven Cohen of Point72, these guys with their practically unlimited resources, they’re all, “We’re getting into this artificial intelligence.”

I look at that very closely because I’m like, man, if they’re doing what we’re doing, I’m not sure I want to be in this business, because we’re not spending $2 billion. They’re not. So we’re very satisfied that we are unique in applying new branches of… not science that we invented, but some of the sciences that are a couple 100 years old, but the application of it to problems in capital markets. 

A lot of this in the case of myself, Kevin and Terry Rickard, who’s our chief scientist, and just absolutely brilliant, groundbreaking, applied mathematician, we all have a background in intelligence. 

Kevin worked in military intelligence. Terry works for the Navy. I did a lot of work for the CIA. We have that kind of mindset of how do you solve problems when you don’t have all the data? I always say, “If you have all the data, a smart high school kid can solve the problem.”

How do you solve a problem? How do you proceed? How do you make forecasts about, in my case, the next terrorist attack with very limited data? Well, there are ways to do it. 

One of the main ways to do it is something called Bayes’ theorem. So that’s one of our inputs. The other one is complexity theory. We’ve talked a lot about that. It’s amazing. The applications of complexity theory all over the place in meteorology, seismology, volcanology, forest fire management, traffic management, so many areas where you see complex dynamic systems. You can use this science to get better results. I’m dumbfounded that no one’s applied it to capital markets, but we are. 

The other branch we’re using is behavioral psychology. Now this one has had a lot more take up on Wall Street and not as much as it should. It’s more in kind of government policy. Cass Sunstein, Richard Thaler, *inaudible* all this stuff, seen in the public policy around… 

It really goes back to Stanley Milgram in the 1950s, prominently Daniel Kahneman and Amos Tversky in the 1970s and 1980s. doing very simple but ingenious experiments. They’ll go to a group of subjects and say, “I’m going to give you two choices. I’ve got $3 in my hand and I’ve got $4 on the other hand, you can have the $3 with 100% certainty. You can just come and take $3. You can have the $4 with an 80% probability. Some risks you won’t get.”

Well, Janet Yellen would do the math and say, “$4 and 80% probability. The expected value is $3.20. Do an inequality, $3.20 is greater than three. I’ll take the $4.’

When you do this experiment, overwhelmingly, people take the $3. So why do people take the lower expected return to what mathematicians would call the lower expected return? 

The answer is they don’t like to lose. That was the value of the possible loss even a 20% outweighs the smaller gain you’re going to get by going with the sure thing. 

These are examples of what efficient market theorists called irrationality, but they’re actually rational if you put humans back in the Ice Age and the risk is that not that you won’t get the four bucks, but that you’ll be eaten by a saber toothed tiger. 

There are many biases like confirmation bias, recency bias, anchoring bias. Last time I looked, there are 180 of these cognitive biases. We’ve taken a close look at that. 

The fourth branch of science for us is history. You won’t find any economists using history. You find economic historians who are experts, but to say that we can look at the past and gain valuable lessons about what policymakers will do in the future. that’s rare. 

So we are using Bayes theorem, complexity theory, behavioral psychology, and history. These are our Applied Sciences. Now, how do we combine them? 

Well, we combine them in a neural network, which, again, by themselves are not that unusual. We’ve all seen maps of neural networks, but you have nodes, edges and some are output and input nodes, some are recursive functions, where it’s an output and then it becomes an input. Some of them are actionable. Some of them are exogenous. 

What would neural networks look like for whether the Fed is going to raise interest rates in June? You’d put in an employment report. You put in disinflation, PC core deflator, and what’s going on in the currency wars and who’s the new Fed Chairman, you have all these nodes. 

We got four branches of science. We process them through a neural network. Who’s around this? We’ve teamed with IBM. Watson can read 200 million Twitter feeds in real time with plain language comprehension or every page of every 10Q or 10K. Fed speech. I’m a geek. I read a lot of them, but I can only read so many, but Watson can read them all and we’re working with a team of cognitive linguists in Finland. Bless them. Absolutely cutting edge.

The simple word association is trying to find your website. I’m like, “Well, if I just put in Preston and Stig, I know I’ll find it. lGoogle’s that smart, right?” But okay, so that’s word association. 

However, there are more sophisticated grammatical, syntactical ways of doing that. Watson speaks about eight languages. 

We’ve got the four branches of science. We’ve got the way of combining them in neural networks. We can populate what’s in the neural network with billions of pages of plain language comprehension by Watson. Then of course, human *inaudible*, humans are important. They never go away. We tweak these things continually. 

What we’re doing is we’ll tell you where the Euro is going to be in six months, relative to the dollar. Now, this sounds funny, but believe it or not, it’s actually easier to forecast six months than one day. I don’t know what the Euro is going to do tomorrow. It could go up or down. There’s a lot of noise in the short, I could have a view. 

However, my forecasting abilities, six months out is much, much greater, particularly with the tools we’re talking about. I will tell you where the Chinese yuan is going to be, where the Euro is going to be, where the return units are going to be, etc. Again, with a three or six month horizon using the method we described, it’s much more accurate. Not 100%. Nothing’s 100%. 

If you can get to 70 to 75, you are way ahead of the pack. Now, if you’re a day trader, our systems are not interesting to you. I mean, if I tell you, the Euro is going to be 130 by the end of the year, and you bet the ranch on that then it goes down tomorrow, you just lost all your money. So it doesn’t really work for day traders. 

Hedge funds have a tough time with this because they’re on a mark to market basis. Again, you have to allow for the fact that you can be writing a long run but wrong in the short run. That can be costly. 

However, if you’re an institution, if you’re a sovereign wealth fund, if you’re a college endowment, if you’re an insurance company, if you are one of these very, very large portfolios, where most of your money is actually managed by third parties, that view that we offer you is very valuable. This is because if we’re telling you the Euro is going to be higher at the end of the year, and that is one of our outputs right now. 

You look around at your managers and short the Euro, you better pick up the phone. You could lose a lot of money in the next six months. Forget about the daily mark to market. 

So this is a tool that will be at the most value to the largest buy side institutions in the world and the largest money managers in the world. We’re not going to pick stocks. We’re looking at the big macro ticker. 

Yield to maturity on tenure now on German *inaudible*, JGB, cross rate euro, US dollar euro, Swiss franc, Yuan, US dollar, yen, ruble, etc. Some of the big commodities: oil, gold, copper. A few others, and sorry, central bank policy rates. So what’s the Fed going to do and what’s the ECB going to do? That’s our universe of tickers. 

I’ve described the science. No one else is doing this. We’re very far along. I couldn’t be more excited. I got a lot of the investor uptake. For anyone’s interested, they can just go to our website meraglim.com to learn more. 

By the way, this is a continuation of the work that my partner and I did at the CIA. This is Project Prophecy, third generation. So Project Prophecy was the predictive analytic engine that we built for the CIA to predict terrorist attacks based on a strategic study that was done after 9/11.

It works so well that we were getting chastised by the General Counsel because we kept finding insider trading that was not a terrorist relay. We were looking for terrorists, but we kept finding like normal cruxes. We were telling the SEC and they said we can’t do that. The CIA is a law enforcement agency so we serve our catch and release program. We let the sleazeballs go but kept looking for terrorists. But the system worked very well. 

This is way beyond that. This is third generation AI, again, combined with the power of Watson, so we’re just having a lot of fun with it.

Preston Pysh  20:53  

I’m curious, do you guys already have a working prototype with this? This is absolutely fascinating, by the way.

Jim Rickards  20:58  

Well, the answer is yes. I was the one who stood in one of the capsules on the London Eye on June 20, 2016, three days before Brexit in front of a camera telling people that the UK was going to vote for Brexit. The pound is going to collapse. Gold was going to soar.

In the days leading up to the 2016 election saying Donald Trump was going to win. I got laughed at and ridiculed. These were not lucky guesses. This was using exactly the science that I’m describing to you now. 

I’ll give you a simple example like the polling. Hillary was always ahead in the polls. Well, you look at the polling methodology The first thing you saw was that they were oversampling Democrats. So there are more Democrats and Republicans. A fair poll would be about 53%, Democrat and 47%. Republican. That would be an honest poll, because there are more Democrats.

They were sampling kind of 58-42 or 57-43. So they’re oversampling Democrats. Then within the oversample, they were over sampling African-Americans who have a much higher capacity to vote for the Democratic candidate 90%, as opposed to maybe 70% or 80%. That counted for another point. 

Once you made those adjustments, Trump was always ahead. He’s always had to take the poll results adjusted for the two things that I just mentioned. There’s a lot else in the analysis, but so we called Brexit. We called Trump in January. In February 2017 using Fed Funds Futures and high probability rate hike, the market was giving a 30% probability that the Fed would hike rates in March of 2017. Our system was giving it an 80% probability.

The Fed freaked out *inaudible*. Wait a second, the market doesn’t believe us. This was after, remember, they went through all of 2015 with one hike. All in 2016 with one hike. Then in December 2016, the Fed says, “We’re going to hike three or four times.” Our system said, “Yeah, you’re right. You’re going to hike in March.” The market said, “Well, we don’t believe you.”

So the Fed freaked out. In three days at the end of February, Yellen, Dudley and *inaudible* all went out and gave speeches and practically yell, “Hey, we’re going to raise rates. Wake up.” 

The market implied profitability went from 30% to 80%, in three trading days at the end of February 2017. It converged. We were already at 80%. Market was 30%. In a couple days, the market converged at 80%. Then by the meeting date, which I think was March 13, or somewhere around there, everybody was at 100%. These are real world cases.

Preston Pysh  23:36  

Where are you looking forward to specific things? Is the dollar relative to other currencies? How is that looking in the next six months and then also oil? What’s that looking like in the next six months?

Jim Rickards  23:47  

We’re looking at a Euro, kind of 130 by the end of the year. That’s a significant increase in the Euro, a significant diminution in the value of the dollar. That’s one of the ones you’re mainly focused on. We’ve definitely got the Fed hiking rates in June. 

By the way, it’s not that we’re always out of consensus. We’re not contrarians. Sometimes the market is right with us. Sometimes they’re not. We don’t care. I mean, we look at the market. We care in that sense. Though we’re going to go with our methodology because it’s very solid, very proven. 

I said with our team, we’re building out these what are called maps or neural networks. The engineering and the math inside is an awful lot of work in and of itself. It’s all proprietary, but just getting the subject matter expertise to identify the key nodes, the key factors and inputs, that sort of work as well, because it’s easy to miss some.

We’ve developed them for geopolitical events, as well as the economic events I described. We’ve got the President pulling out of the Joint Comprehensive Plan of Action. This is our deal with Iran on nuclear weapons. It’s not a treaty. I think everyone knows that it’s not a treaty. I’m not sure what it is. I don’t think it was ever signed. I don’t think… 

It was never ratified by any legislature. There was some backdoor ratification by the US Senate, but it was never ratified by the Iranian parliament. It was never signed by anybody. It’s just a piece of paper that John Kerry and Rafsanjani cooked up. It’s kind of a handshake deal between two parties who don’t trust each other, but look for Trump to pull out of that in the months ahead.

We’re getting a lot of output. but just to be clear, this is still in development. We’ve got a really cool team. The people who are doing our user interface,the UI, and the UX, which is the user experience, that’s the new name for the user interface. But these are the folks who did Iron Man 2. They did Black Panther title sequence. They’re absolutely brilliant. We’re very happy to be working with them. We just got a lot of good people on the team and we are having a lot of fun with it.

Stig Brodersen  25:55  

Jim, it’s really interesting studying the work that you guys do know. It’s a lot of different fields. It’s actually kind of interesting how you talked about Keynes and Tversky in the 1970s. People are just puzzled that they would combine finance, economics with psychology. They even got a Nobel Prize for that, because it was just mind blowing for the establishment. 

Can you tell us your personal story about taking the road less traveled? Because this seems, I guess, for most people like a very unorthodox way of thinking and perhaps not being more tempted to use conventional methods. You said you were laughed at when you said Trump would win. 

So I hope I’m not saying something wrong, but when you put yourself out there and say something like this, and are so ambitious in the way that you combine your fields, where do you really get your drive from?

Jim Rickards  26:51  

I was kind of a lawyer minding my own business. I worked at a major commercial bank. I worked at Citibank for 10 years. I worked at one of the major investment banks, primary due on government securities. I worked for what at the time was one of the biggest, most successful hedge funds in the world, Long Term Capital Management.

Then along came 1998, and Long Term Capital Management not only failed spectacularly. If it had just failed and we just lost $4 billion in one month, which we did, that would have been a pretty big story. 

We took global capital markets to the brink of collapse. That is not an overstatement. I was in the room. We had the Treasury and the Fed. We were waking up the Italian finance minister in the middle of the night, because Long Term Capital Management was the biggest trader and holder of Italian government bonds outside the Italian Treasury, 

Italian Treasury was one of our investors, as well as the Kuomintang, the Taiwanese army. We were networked and we were in every market in size, like you wouldn’t believe. I said, if we had failed. We lost money, obviously, but failed in the sense of actually going bankrupt, I would have just slept in the next day. My job would have been over, but our $1.3 trillion of derivatives would have been instantaneously transferred to Wall Street and with the counterparties. We were no longer good for it. They would have had to cover…

All of a sudden, two-sided positions become one-sided of positions when your counterparty goes away and you have to cover… Imagine we had $15 billion in equity positions. We were the largest player in risk arbitrage on Wall Street, bigger than Goldman Sachs or any of the hedge funds have specialized in this. We were in every deal. We were in Citigroup Travelers, Lockheed, Boeing, MCI, WorldCom. You name it. 

We and the cascade that would have followed the other banks, that would have failed in our way, would have collapsed the entire global financial system. 

So as a lawyer, I negotiate that bailout. We got through it. Closed up shop. People went on. Actually, some of the guys went on to make a billion dollars doing other things. 

Our back office became a hedge fund servicing company called *inaudib;e* that was sold for close to a billion dollars some years later. So everybody got back on their feet. 

As a lawyer, I felt why I’d done my job. There were no enforcement actions. There shouldn’t have been. There were no penalties, no lawsuits, nothing. wW all moved on. But I was very intellectually unsatisfied. 

I said wait a second. We really did have 16 finance PhDs on our executive committee. We did. We actually got complaints from deans of business schools who said we were depriving academia of the next generation of economic scholars, because we were hiring so many bright PhD candidates. You’re hiring all the geniuses. Who’s going to be the faculty? That was a serious complaint from one of the Ivy League schools. We also had two Nobel Prize winners. 

They actually were that smart. Well, it wasn’t like fake smart. They actually did have 161-165 IQs. I knew them all were good guys. 

So you get the finest, the absolute finest minds in finance. Some of the founders and inventors of modern financial theory, two Nobel Prize winners, and you fail that spectacularly. 

My takeaway was there must be something wrong with the theory. They were not *inaudible*, they weren’t venal. They weren’t bad people, there must be something really wrong with how they think about risk, because otherwise this couldn’t have happened. 

I then set out on a personal Odyssey to find the answers to what went wrong. And I did. The main thing I found out was that the reliance on the normal distribution, the so-called bell curve, efficient market hypothesis, assumptions about risk free rate assumptions about prices moving continuously from point A to point B so you can transact smoothly at every point along the way, that every single one of those things was untrue. 

Markets are not efficient. Risk is not a dewey distribution. It’s not a normal distribution. It’s a power curve. 

Markets do not move spooling continuously. They gap up and gap down. Things come out of nowhere.

Nassim Taleb was doing similar work at the same time. He came out with his book, “Black Swan.” I met him. He’s a very funny guy, nice guy.

Where I parted ways with Taleb, Taleb demolished the bell curve. He took a baseball bat and just bludgeoned it to like dust. That needed to be done, but then he threw up his hands. He walked away, said, “Hey, and by the way, you can’t quantify this. It’s just just going to be long volatility. I’m a philosopher. Have a nice life.”

So he criticized the existing paradigm, which is a good thing, but he didn’t take it any further. I wasn’t satisfied with that. I thought that now I know what doesn’t work, but what does work? There must be some quantitative scientific way of understanding this phenomena. 

That’s when I was introduced to complexity theory. I spent a long time doing complexity theory. That was kind of out of bad reputation, because a lot of people on Wall Street, you go back to the 90s, they were trying to use chaos theory. There’s a lot of confusion about the difference between chaos theory and complexity theory. 

Chaos theory really doesn’t work on Wall Street, but chaos theory is like a little branch of complexity theory. Complexity theory is a much bigger field. It works extremely well, when you set the dials in certain places. 

What became apparent is that capital markets were complex systems. If you went to the University of Michigan Physics Department and took a course in complexity, from a professor like Scott Page, who doesn’t know anything about finance. He’s a brilliant physicist who doesn’t know anything about finance. He’s just going to teach you physics. 

What is his definition of a complex dynamic system? He will say it has four characteristics. One is diversity, meaning the agents in the system have diverse views. If we all think alike, it is not complex, it’s boring. But if we have different opinions, pretty interesting. 

The second one is they have to be connected. What difference does it make, if you have diverse views, if you’re not connected to some channel, then that’s not going to be an interesting system. 

Three, there has to be interaction. So what good does it do to have a connection like we were connected right now over the Internet, but if we weren’t talking to each other, this wouldn’t be much of a podcast so you have to interact. 

The fourth is adaptive behavior that I based on what I’m hearing, learning and seeing. I might change my behavior or other people might change their behavior based on what I’m doing. 

Well, looking at capital markets, diversity, absolutely. Bulls, bears, long, short, fear, greed, short term, long term. You have lots and lots of diversity.

Connectedness, we got Bloomberg, Thomson Reuters, iPhones, email, CNBC, Fox Business. Probably over connected.

Interacting? Big time, trillions of dollars a day in stocks, bonds, derivatives, currencies, etc. 

Adaptive behavior, Sarah Palin would say, “You betcha.”

If you’re a hedge fund losing money, and you don’t adapt your behavior, you’re going to go out of business. So yes, we do respond to what we see or other people respond to us.

Capital markets or four for four. Once you realize the capital markets are complex, dynamic systems, you can now import this whole body of physics that has been around for 60 or 70 years into capital markets and get enormous insights.

I started working with people at the Applied Physics Laboratory outside of Washington, DC and Los Alamos. What I would say to the physicists is let’s crack the code. Here’s what we need to do. It’s what we call team science. This is what we’re doing at Meraglim. 

Let’s get physicists. Let’s get an applied mathematician. Let’s get a developer as an engineer, a behavioral psychologist, a lawyer, an economist, and a few other folks, and let’s team up and crack the code. 

The physicists will say, “That’s great. What a great idea. Let’s get the team, let’s get some funding. Let’s do it.”

I will talk to PhD economists. They will say, “Why would we do that? You have nothing to teach us. We know everything about economics. Why would we work with a physicist?” 

In other words, physicists were more open to advancing the science of economics than economists were. So that was one of my discoveries. We just solved that problem by putting our own team together. 

The journey was as a lawyer, I felt that I did my job at Long Term Capital. I brought the team through that. Everyone emerged without a scratch. People were back on their feet. Reputations intact, back in the business.

However, as a person and as just an intellectually curious person, I was very, very dissatisfied that the smartest people in the world didn’t get it right. That led me on an intellectual journey through the branches of science. I really learned Bayes’ theorem at the CIA. Complexity theory I learned on my own. I’m a little bit of an autodidact, as you can probably tell you. I teach myself a lot of stuff. 

Bayes is something I learned as CIA, because we were trying to predict the next terrorist attack after 9/11. Well, how many data points do we have? One, there’s never been an attack like 9/11 that led to 1000 Americans dead? And so if you’re Janet Yellen, you’d say, “Well, okay, let’s wait for 10 more attacks, 30,000 dead and then we’ll have a time series, and then we can look for correlation.” 

No. When it’s life or death, you can’t do that. When you’re in the intelligence community, you don’t have that luxury. You have to go tackle the problem with what you have, however, scant. 

That’s what Bayes will let you do. You form a hypothesis based on whatever you have. Is it enough to satisfy a frequentist statistician like Janet Yellen? No, but it’s the best you can do. 

Sometimes when you have nothing to go on, you make a guess, but you’re honest with yourself. You say this is the guess and it’s 50-50. I could be wrong. But then what you do, you look at subsequent data. This is why it’s called causal inference or inverse probability, because what you’re doing is you’re updating the hypothesis with subsequent information. 

So when subsequent information comes along, you ask yourself a question: What is the probability that that subsequent thing would or would not have happened if my original hypothesis were true or false?

In other words, what’s the conditional probability of the second thing being true, if the first thing was true? Well, if it’s high, then you’ve now strengthened the original hypothesis with the subsequent data. If it’s slow, you lower the odds. So you might take that 50% that guess, and you might upgrade it to 60-70% as this new stuff comes in. Or you might lower it. You might abandon it. It might go to zero. You’ll think none of these other things would be happening if I were right, so I’m probably wrong. So discard that and keep going.

Preston Pysh  37:06  

Jim, what a pleasure chatting with you. I mean, this was just fascinating stuff. We’re just so thankful that you come back on the show. It’s always so much fun to hear what you’re up to, because it’s always something so fascinating.

Jim Rickards  37:19  

Well, I always say great questions make for hopefully great answers. So this is absolutely one of my favorite shows. It’s the only one I’m doing and like I say, I’m just absorbed in this book, but I was given the opportunity to be with you guys.

Preston Pysh  37:30  

Well, thanks so much for joining us today, Jim. We just really appreciate it and if people listening to this want to learn more about you or find you on the web, give them some information so that they know where to find you.

Jim Rickards  37:41  

Sure, for anyone interested in predictive analytics, that’s meraglim.com. That is our company website. I’m very active on Twitter at @JamesGRickards. Also if you want to have a look at Collide. I have my own channel over on Collide and I do weekly interviews. I have, of course, my books and the new one coming out on October 30th.

Preston Pysh  38:05  

Alright, so that concludes our episode for today and we’ll see everyone next week.

Outro  38:09  

Thanks for listening to TIP. To access the show notes, courses or forums, go to theinvestorspodcast.com. To get your questions played on the show, go to asktheinvestors.com and win a free subscription to any of our courses on TIP Academy. This show is for entertainment purposes only. Before making investment decisions, consult a professional. This show is copyrighted by the TIP Network. Written permission must be granted before syndication or rebroadcasting.