PlanB (Pseudonymous Bitcoin Quant) rejoins me in this episode to talk about the response to his seminal work, Modeling Bitcoin’s Value with Scarcity. There have been some responses to his Stock to Flow (S2F) modelling work, so we talk:
- The response to his articles and translations
- What cointegration is and why it matters
- Responses to PlanB’s modelling work
- Cash and carry trade
- Where to from here
- Twitter: @100trillionUSD
- Medium Article: Modeling Bitcoin’s Value with Scarcity
- PlanB’s first interview: SLP67
- PlanB’s second interview: SLP86
- Phraudsta / Nick’s article: Falsifying Stock to flow as a model of Bitcoin value
- Hcburger1’s article: Bitcoin’s natural long term power law corridor of growth
Stephan Livera links:
Stephan Livera: Plan B, welcome back for the third episode.
PlanB: Thanks for having me back.
Stephan Livera: Ah, there’s been a lot happening since the first two episodes with you. Plan B, there’s been a big blow up in your following and you’ve had many more translations in your articles. How’s that been for you?
PlanB: It has been great. Again, I’m still waiting for the moment that all this stops or levels off, but I guess it’s not, not now yet. So indeed, a lot more people read the articles. I think the translations help enormously with that. For example, the Chinese translation was fun because I never knew that people in China cannot read medium articles cause it’s, it’s fenced off by the firewall. So everything has to be on WeChat. But since that article is on WeChat, I guess it opened the article and the content to to lots more people. Same with India. I never knew there were six languages in India and well there’s only one translation translated, I don’t even remember what the exact language was, but 60 million people living there that can now read the articles. So yeah, it has been phenomenal. And well I hope it goes on for a little while.
Stephan Livera: Yeah, that’s awesome. So let’s just set some quick context. So listeners check out the first two episodes. They are 67 and 86, but I suppose we can just give some high level context around the idea. So what you’re essentially doing is some quantitative modeling on this relationship that has existed between the stock to flow ratio of Bitcoin and the price of Bitcoin. And I suppose just like, let’s call out some of the high level numbers that you are in some sense predicting, or at least the model is predicting, let’s say. So there are a couple of different numbers and I believe you’ve got the 2020 halving, so approximately may 2020. Now, it doesn’t mean it’ll hit exactly that number then, but roughly, you’ve got $55,000 for then, I think it’s roughly 400,000 for the 2024 halving, and then 3 million for 2028. Is that a fair summary? You would say?
PlanB: Yeah, that’s correct. And that’s the numbers that were in the original article that I wrote in March this year. And maybe to go one step back. So I came to Bitcoin from a investor perspective. I am a traditional investor working at a large listed investment company in the Netherlands. So yeah, I approached this market and not from a technical point of view, but from a purely investment point of view. And I noticed the lack of quantitative models, econometric models, if you will. There was a lot of technical analysis. But yeah, the model that I made is actually quite simple. I’m trying to capture the price with a measure of scarcity and I found the stock to flow measure quite useful. The stock to flow measure that is mentioned by Trace Mayer but also by Saifedean Ammous of course, in his book.
PlanB: And the model I made is a a linear regression on the logarithmic values of both price and stock to flow. And then you come up with a very nice fit, a high R squared. But, but what’s even more important is the the co integration. We might talk later on that, but you’re right. So the model predicts or models the last 10 years, historical values of Bitcoin stock to flow and Bitcoin price. And because of the tight fit and the cointegration well, it’s at least reasonable to believe that it will hold for, for one or two more halvings. And the numbers you mentioned are absolutely correct,
Stephan Livera: Right, one clarification on that point. So I have seen some different numbers and it might be just good to clarify for the listeners. For example, for the next halving for may 2020, approximately, I’ve seen a $55,000 number. And I’ve also seen a $100,000 number. Could you just clarify for the listeners where those two different numbers are coming from? As I understand, one of them is only feeding the model the first few years of data. Is that correct?
PlanB: Yeah, that’s correct. And there’s some confusion about it. So I’m glad to explain it. So the $55,000 number for next hopping in may 20, 20, that was the number from the original model and the original March 2019 article. And, and that number of us was quite high. In my opinion, I thought, well, we’re now at, the time of writing it was three or $4,000. So $55,000 seemed like a very high number and I didn’t want people to get over optimistic about it. So I rounded the parameters a little bit. So if you read the article, you see the the formula is with nice round numbers 0.4 times stock to flow to the power three. And so 0.4 and three are the round numbers, but if you would take more digits or you would, you would get a little higher number.
PlanB: But then of course it becomes less, less easy to to explain and to communicate. So the model evolved, from March this year until we’re where it’s now. And of course more data was added. The data of the first $55000 model was until December, 2018. And now of course we have nine more months to take into account. Also, I have, I have more earlier numbers, so data archeology where I do have numbers from September, 2009 right now. So if we take that into account, just the new numbers you’d get to values somewhere between 60 and 90,000. And those are also the numbers that all the other teams that replicated my model come up with. So there’s, there’s one more model and it’s, that’s where the hundred thousand comes from. That’s the third model.
PlanB: And I personally prefer that. One I like it, not because it’s a higher number. But because that model was made on only pre November, 2012 data. So before there was any halving on only the first four years of Bitcoin data and yeah, that model predicted basically the 10X jump in price of 2012 halving and the 10 X jump in price from the 2016 halving. So I’m really fond of out of sample performance. So I’d rather take a model that’s done on less data but performs very well out of samples. So all new data, than taking all the data and fitting in as good as possible. So those are the three models, 55k, 60k to 90k, and 100k.
Stephan Livera: Fantastic. Thank you for that clarification. Also, it might be interesting and valuable to talk about the timing. So typically what we have seen looking historically is the first having was roughly mid 2012 and then 2013 was where we saw the big bull runs happen. Then the next one was in mid 2016 and then again, we saw the bull runs happen in 2017 rather one big bull run, let’s call it. So would you say then that if you were to copy paste that same pattern, the halving next time around will be mid 2020, but actually the run up will come in 2021. Would you say that’s a fair statement or what’s your view there?
PlanB: Yeah, I think that’s a fair statement but formally the model does not say anything about it. So there is no short term prediction. There is no prediction of the all time high or the next peak. There is also no prediction of the low. The only thing that you can take from the model is that you have the cointegration and it will stick very close to this stock to flow level. And so somewhere before say Christmas 2021 the price should be above 100,000. If we take that third last model or above 55,000, if you take the original model. So that’s in fact, the only thing that you can you can take from the model and where the next, all-time high is, yeah, it’s guessing, but the way you said it is correct. Normally, or at least last two halvings, the market didn’t react immediately on the halving, but it lagged a little bit. You can, you could even model that lag. But I wanted to keep the model simple, so yeah, that’s.
Stephan Livera: Fantastic, so let’s jump into a little bit around some of the responses to your model. And then one of those is around come integration as well. So as I understand there’s a gentleman named Phraudsta and I think his name is Nick. He wrote an article attempting to basically saying something like falsifying the stock to flow model. Could you help us break that down a little bit and just give us an overview on that?
PlanB: Yeah, he’s by the way, he’s an Australian.
Stephan Livera: Oh really? I should I should talk to him.
PlanB: Yeah, he’d like that. So Nick, indeed he did a very important study on the model and is an interesting guy because he is saying he’s very strict in learning and academic approach. So he’s saying like, the only way you can learn is by falsifying things. You cannot verify things. You can only falsify things. So it’s true until you falsify it. And that’s why he called his research falsifying to stock the flow model. Cause that was his intention and his aim. So he went after it and basically did the same exercise I did. It came with the same conclusions. But then also raised the same question I did like, okay. You can have a very high R-squared, especially in a logarithmic domain, but it could be spurious.
PlanB: It could be a false regression. It could be a correlation, not causation a meaningless correlation that, we measure. So he took one step more and crucially important. He checked for cointegration and cointegration is that two variables stock to flow and price in this case stick together. So the difference. So he’s actually checking and testing and modeling the difference between the two. And if that difference stays stationary that’s a statistical term, but if it stays around zero and doesn’t wander off too far all is good. And the two series are cointegrated which is very rare. So basically if you find that two series are cointegrated like he did that means that the relationship that you find that the high correlation that you find are not spurious. So that’s why he concluded that he could not falsify the Stock to flow model. So in that way, the title is a little bit misleading, but it was a very important moment also for me in the development of this model.
Stephan Livera: Great. Let’s break that down a little bit. So from Phraudsta, Nick’s article, he mentioned a few different, if you will, assumptions that must be true before you can do this technique. He mentioned the use of log scale and so he’s commenting that and as you mentioned, it’s log of stock to flow ratio and the log of BTC price. Can you articulate why that is?
PlanB: Yeah, you need a linear relationship if you do a linear regression fit. So obviously if you look at the stock to flow but also price is even more important. If you look at the price of Bitcoin on a, normal linear scale, it’s, it’s, it goes exponentially up and, and you don’t see any details before 2017 if you plot it that way. So to get more detail and to get more meaning, you have to transform the data to massage it into a linear line. So that’s massage might sound as as manipulate, but it’s just a transformation. So you transformed the data. You try to see if a low logarithmic regression is useful or an exponential function is more useful, or if it can be transformed in a linear shape.
PlanB: And, if you do that with stock to flow and the price and you get this beautiful linear shape that you can fit with a linear regression. So basically what that means is that the regression that you, fit on those data can be transformed in the power law. So that’s the function I use it that’s 0.4 times stock to flow to the power three. That’s a power law. And that basically and that has also to do with the logarithmic scale that models change. So not so much to the level, but that change of stock to flow and the change of price, they are proportional. So for example, if the stock to flow goes to X, the price goes 10 X and that’s what the function says, the power function. And that is true on a very small scale. So if it goes 3X or 0.3X, it doesn’t matter. This power law stays the same and that’s the, the power of this logarithmic transformation.
Stephan Livera: Excellent. Thank you for that. And there were some other terms that that were used in order to, you know, as he says, falsify this stock to flow model, he mentioned the terms are normality in error. Can you articulate what that is?
PlanB: The errors that those are the difference between the predicted values and the actual values and the study of the analysis of how those errors look is very important because there is all sorts of assumptions that are made about it. So they cannot be auto correlated, for example. And yeah, they should be normal. So if you have the ideal model, what’s left, so what’s not explained by the model should be white noise as we call it. So that’s normally distributed random noise all the other factors out there impacting the price. But on average leveling out to zero and if you find normal errors, normally distributed errors around zero, then you know, you have a good model. That’s it.
Stephan Livera: Great. And now one way I’ve heard this explained, speaking to the broader idea of cointegration is that it’s like a rubber band effect that as one moves further away from the other. So in this case, Bitcoin price moving further away from the model, then it rubber bands back to the model. Is that one way to think of this?
PlanB: Yeah. And and Nick has this nice, classic story about the drunk and his dog and you know, the drunk going out with his dog on the leash the drunk wanders in a sort of random fashion and the dog has to go with, cause he’s on the leash, but sometimes he will be on the right. Sometimes he will be on the left, but he can not go further than the leash and come closer. So you don’t know where the dog and the drunk are going. But you do know that they stay together. That is, yeah, it’s a classical example, but it exactly states what cointegration is and that is very useful from well multiple perspectives, but investment being one. And in this case it’s even more interesting because we know, of course, that the stock to flow isn’t wandering like a drunk. It’s pre-determined. So for predict prediction purposes, this is almost too good to be true. Yeah.
Stephan Livera: Right. Yeah. And he mentions at the end of the article, he’s saying in short, Bitcoin is the drunk and stock to flow is the road home.
PlanB: Yeah. That’s it. That’s it. And Oh, you should have him on your podcast cause, he can explain this a lot better than I do.
Stephan Livera: Oh, fantastic. Yeah, I might, I might have to do that. Let’s talk about some of the other responses to your work. So I believe there was BurgerCryptoAM who also did some similar sort of work. What was your take on his work?
PlanB: Yeah so, Burger, Nick and I sort of work together. So we were, we came into this separately, but our work was so related and, and so now we’re working with the three of us together. Yeah, Burger actually took the same path as, as Nick. He was very skeptical of the good fit and the high R squared. So he wrote an article, he verified the model, came with the same results, but really focused on the spurious, the possibility that it could be a spurious regression. Which of course is a good point. And, and later in a second piece just after Nick wrote about cointegration, he verified that cointegration as well, and even did some extra studies. So you can check cointegration with several different tests. He did like, like three or four and came with the same conclusion. So we all three now have a shared conclusion that we stand by. We all began with being very, very skeptical. And so I see a lot of people in that first stage right now where all three of us began a couple of months ago.
Stephan Livera: Great. And one other one I think might be interesting for you to touch on and this is more like a very outside of the the Ordinary Least Squares approach that you’ve taken. There was one by HCburger1 and it’s a basically a time based model. Now this one was a little interesting because he was basically based on this model that the price would reach 100,000 per Bitcoin no earlier than 2021 and no later than 2028. So in some ways it was a more conservative estimate. Have you had a chance to look at this modeling work and do you have any comments on that?
PlanB: Yes, it’s an interesting study as well. And we talked too. The time models are classic. So instead of stock to flow, you take a look at time as an explanatory variable for the price of Bitcoin. And the narrative, it’s like adoption that takes time to to play out. And that’s what you, what you see in the price which is very reasonable. And in fact, it’s where I began modeling as well before March this year. So the very first models I made were the exact same models as HCburger that with the time and we have the same parameters as well. So it’s definitely it’s an interesting model. The only thing is first there, there’s a big difference in predictions between stock to flow model and this time based model.
PlanB: Like you said, the time based models are lower in prediction. So it’s very interesting to see, maybe not this halving, but especially after 2024 halving the models really deviate from each other. So we’ll see which one is right. Very exciting. But more importantly now is that what I don’t like about the the time model and why I personally abandoned the model and jumped to stock to flow is two things. First the parameters are not stable. So the first time model is actually made in 2014 it’s a classical chart. I have it somewhere in my, I will tweet it out later. It’s a green line, green price line with a red curve, the logarithmic curve in it. And I think it was updated later by Tuur Demeester.
Stephan Livera: Ah, yes, this is a well known one Tuur, shared it I think.
PlanB: You shared it as well I think. And what you see there is the prediction that was made in 2014 and it was much higher than the actual price that we see right now. So the red prediction line is way above the green real price in the updated chart by Tuur. So, and then if you look at the model that was fitted on all the data, so including the, the data from 2014 till today that HCburger is using, then you see much model. So the model came down significantly and of course now it really fits well. But my prediction would be that it will have to be updated again after 2024 halving. And so the stability of the parameters is a key thing for me cause I don’t want to update the model every year cause I’d like to use it for prediction and investing. And like I said, that the model that I prefer to the third incarnation of the stock flow model, if you will is made only on 2012 data before there was any halving and it’s still working today. So without any change on parameters. So that’s for me is a crucial thing.
Stephan Livera: Yeah. That is a really fascinating thing. And it speaks to whether there is some kind of, again, it’s not praxeology, it’s not economic law but potentially in an Austrian term you might say something like Thymology, which relates more to psychological aspects that lead to human action. And as you were saying, this is before even there was a halving. Now I think it would be good to talk about one critique that I have seen where some people are laboring laying a critique against the stock to flow model idea by saying, well look the stock the flow model. It explains the price only by reference to supply and not by demand. What’s your thought on that?
PlanB: Yeah, I get that critique a lot and I understand it cause if you have an economic background, you are taught by with with the theory that that prices are are made on markets by buyers and sellers and there has to be a supply and demand and where they meet that’s where the price is. So a model only on supply is weird in that sense. And, that’s true. I mean, I understand that critique. But the thing is and Nick Szabo talked about it in the tweet this week as well.
Stephan Livera: The Veblen good idea, right?
PlanB: Exactly. So it could very well be that Bitcoin is a Veblen good. And a Veblen good, is diamonds or Rolex watches would be an example. It’s a good where demand increases where price increases. And that’s totally, that’s 180 degrees different than demand to supply laws normally work.
PlanB: That you are taught at, at school. Cause normally if prices rise, a demand will, drop and they’ll not rise. But there are some indication that Bitcoin is behaving like a Veblen good. And that would be, that would explain. So in that way, that demand is directly caused by the supply or the stock to flow, if you will. But, even more important than that. So that might be the case. But even more important than that’s a general point. Demand is not in the model, but lots of other factors are also not in the model that are important for the price. So for example, if we look at the past 10 years when China did this banning, Oh, China bans this, China bans that, yeah, it has an effect on price.
PlanB: Price goes down, China bans are not in my model. So when the government, the SEC or the CFTC or whatever cracks down on on Bitcoin or, or puts a FUD article in the media, it has an effect on price. Is it in my modal? No, it’s not in my model. So there’s like tens of variables that are really important that are, but that are not in my model. And still that means that doesn’t mean the model is wrong or the model is not useful because it’s just a model. It’s, it’s a model and it’s really a simple model and I don’t know who the quote is from, but the saying is “All models are wrong but some are useful”. And yeah, and I think that’s true. I mean if we look at the power law and that’s why I think it’s interesting to have a non spurious power law here.
PlanB: There’s, Bitcoin obviously is a complex system, very nonlinear, very dynamic with at least seven network effects miners, investors, merchants, developers, all and regulators and countries. Everything has an impact on price. So to model that would be impossible. And in complex systems, what you see is sometimes there is an underlying structure that is very simple. So there is a complex reality, but a simple underlying structure. And that is what a power law can grasp. So in a way, I think the stock to flow, I don’t know how exactly of course, but in a way, that’s what I measure. The stock to flow captures all this underlying complexity and including demand for whatever reason.
Stephan Livera: Right, maybe another way to frame that might be something like there are many factors, but it’s just that stock to flow happens to be the dominant one right now. And that is not to say that it will always be the dominant one. As you mentioned, maybe the model breaks down after 2028.
PlanB: Yeah, exactly. Yeah. And there’s one more thing the demand because if something has a high stock to flow ratio, there’s not much things that have a high stock to flow ratio. Gold has it, Bitcoin has it, diamonds have it. Basically it could be a definition of money, right? People use high stock to flow things for the function of money. And the demand for money of course, is unlimited. There’s always demand for money. So, you could assume that to be there. It’s a very rough assumption, but you could assume in a money model, which my model is that demand is there, or at least that it’s captured through these stock to flow.
Stephan Livera: Another point that I’ve seen you make is this idea that, and it’s, I guess harkening back to Nick Szabo with unforgeable costliness, but as you were mentioning, you were saying it’s all about energy and ultimately it’s how much energy does it cost you to make Bitcoin versus any substitutes, right? So even in the case of gold and platinum and palladium and so on, there are some elements of substitutability amongst those other metals. But fundamentally it’s about how much energy does it cost you to make them.
PlanB: Yes. And there are some other critiques that also go in this direction. I think it’s Steven Barbour or I forgot his name, but so he’s also very much into pricing Bitcoin in watts. And that’s a very interesting concept. Which I think is correct as you said, like gold is scarce because it’s very expensive to mine it. And the same with bitcoin same with diamonds. And we can also see the opposite of that, right? If a money is very easy to make like our current fiat money, then it goes wrong. And, and we saw that in in Zimbabwe very clearly and more recently in Venezuela. You can just print the money as a government and spend it, but in the end that will not last long and it will not end well. And, and of course this is, well actually the reason why I came to Bitcoin, the whole quantitative easing experiment that is done by central banks today nobody knows how that will end and some fear it will not end well. Me being one of those people, of course,
Stephan Livera: Of course. I think so. One other big critique that might be good to address at this point is the, “Why is it not priced in?” idea. Now, one suggestion could be that most or everyone who knows about it is already invested as much as they reasonably can. But what’s your view there?
PlanB: Yeah. If I were to give a Steelman argument against the model and I’m constantly looking for good arguments against the model cause that’s how I learn. That’s how I know I’m investing, yeah the correct way. If I were to give it a steel man argument, it would be this argument. If the relationship is true and if the information is out there as it is since since March or even earlier about the halvings should be priced, in a reasonably efficient market. So this would be my ultimate argument against the model. And it’s the one thing I do not understand why it’s not priced in the half things. And there was also Nick Szabo’s ultimate argument against the stock to flow model in his tweet last week. So yeah. Why, why that is? I don’t know, but I can guess and I learned something this week about this.
PlanB: So for my first reaction would be, okay, markets are not efficient. Bitcoin is small with its 150 billion market cap. It’s small. So maybe it’s not efficient. But on the other hand, if you look at currencies and Bitcoin, it’s very efficient. So you cannot buy Bitcoins with dollars and then convert it, sell them in euros and then sell the euros for dollars and then make a profit or something that could be done in the early days. But now you have exactly the foreign exchange rates on those Bitcoin prices. So markets are pretty efficient. Yeah, I don’t think that’s the thing. The other thing would be, okay, the information is not out there. For everybody. For example, new investors won’t know immediately about the stock flow model, so they don’t know about it and they don’t price it in. And maybe some of the current investors don’t know yet about the model, although the model spreads like wildfire.
PlanB: So I think most of the investors right now, know, but then the, the other argument, and that’s the simplest argument, and I think that’s the true one, is that there’s lots of people that do not believe in the model and the stock to flow relationship. So I put a tweet out yesterday with three well-known Bitcoin people that are against the stock to flow model with good arguments as well. I don’t think they hold, but there are good arguments that you can, that you can follow and believe and then you would be against the stock to flow model and not invest into a, a stock to flow price relationship. And in that view, the halvings would be less important than what I think they will be. So I guess it’s just a normal market situation where there is not much people that really believe in the model, which is very interesting and a nice investment opportunity.
Stephan Livera: Yeah. Well for everyone who is a believer, I guess that’s their opportunity. Let’s turn now. I, one interesting topic that you were discussing was also this concept of downward difficulty adjustment or stated in other words, Bitcoin bull markets seem to start at difficulty bottoms. So what’s your take there?
PlanB: Yeah, so what I learned from, from my career, in investing and so I’m a traditional investor. We do mortgages and bonds and those are billions of dollars, so the big deals, what I learned there that it’s very interesting to look at what the big buyers and sellers are doing. So the people that move billions of dollars have very good research very smart teams, great access to low prices. They have it all and they’re at the top of the spear, if you will. So looking at what they are doing might give you an edge just by following them. And the same is true with Bitcoin. In my opinion. And miners are the ones that, that are really invested in Bitcoin because they invested in hardware. And, mining these days is really only profitable if you’re a professional industrial miner.
PlanB: So you’d need access to very cheap electricity below five, four, three cents, almost free electricity. So you need to be next to a water dam or something else where there’s excess energy, otherwise you cannot make Bitcoins profitably and you need the latest of the latest hardware. So the seven nanometer chips, specific chips. Yeah. So, so the, the miners are big players and of course they are big sellers, so they make Bitcoins and once they make them, they have to sell at least part to, to make good for the electricity costs and and the capital expenditure on the, on the miners. So it is interesting to see what the miners are doing. And in bull markets you see and I’m not talking causation here, but just an observation. You see the hash rate and the difficulty go up.
PlanB: So and that means that miners are being added. Mining hardware is being added to the network or the latest chips are being bought by miners and are increasing the hash rate and thereby the security by the way of the network. But so miners are investing and the opposite is true in down markets. So in the latest bear market and you saw that in last two bear markets as well that, the two bear markets before this one you see at a certain point that that miners are switching off their old equipment because the price of Bitcoin is too low and their miners are not profitable anymore. So it costing them more electricity than it gives them in the in revenue and that’s why they switch them off and you can measure that so you can see a drop in difficulty and it’s very rare.
PlanB: It only happened like well a couple of times in the last 10 years. And each, if you look at the lowest point of that difficulty decline, I call that the difficulty bottom and mark that point from where it starts going up again. So minors are getting positive and investing again. That point in time has been the start of bull markets in the last three times. So it’s very interesting to look at the big guys. It’s by no means a statistical 100% verified and secure thing like the stock to flow model, but it’s just a very, I find it a very interesting observation.
Stephan Livera: Excellent. And just for the listeners, the prior difficulty downward adjustments as you point out, they were late 2011, early 2015 and most recently December 2018. So just for context for the listeners there. Turning now to your mission of trying to merge the worlds of Bitcoin and professional money management. Now some of your work online, you’ve shown things like the Nassim Taleb influenced barbell portfolio of holding say 1 to 5% of Bitcoin and then the rest in cash and showing that this has a much better return versus risk profile and obviously a much higher Sharpe ratio than the traditional 60 40 stocks and bonds or other traditional investments. Have you had any luck convincing professional money managers on that point?
PlanB: Yes, I think I have. And you might have noticed indeed that I’m shifting my focus a little bit towards investors and investor podcast as well. Like the investor podcast with the Preston Pysh or the interview I did with Realvision and Raoul Paul lately the Gold versus Bitcoin thing. So I’m focusing on investors because that’s also, my background. And they are very skeptical. At least they were last year, the year before that and maybe a couple of months ago. But something is starting to change. And you see the very forward looking guys, the hedge funds, and let’s say the group that Raoul Paul with Realvision is targeting that group is getting it. If you listen to Raoul, he’s also saying that most of the people, almost all the people that he knows, the CEOs of these are the CIOs of these hedge funds are already invested in Bitcoin personally.
PlanB: So that’s where it all starts. So you see it there. You see also Anthony Pompliano of course with his company getting customers in the more traditional domains, the pension funds and the endowments. So that’s a step. And you’ll also see if you look at stock to flow model, which was discussed but on, on CNBC the other day you see a real turn in thinking there, which is remarkable. And the other day you had this, this German Landesbank, the Bayern Landesbank who verify the stock to flow model, and actually publish the results to the clients. So you can see traditional parties that used to just tell all the the mainstream media FUD, Bitcoin is for criminals, Bitcoin is for frauds.
PlanB: Bitcoin boils the oceans. You’re seeing that turn slowly but surely. And I can give one example of my own, a company where I work, right? Nobody except the quants of course that do the work, but nobody of the investors really wanted to know anything about Bitcoin. But the example that’s really well received is the following. The Bitcoin futures markets are very interesting at the moment. If you look at the price of futures at the CME and Chicago or the ICE Bakkt system in New York physically settled you see that the future market is in contango. That means that the future prices, the prices next month and over two months and even over three to six months are higher than the spot prices. So the current prices and that opens the door to a classical carry trade construction.
PlanB: It’s not complex. I’ll explain it. It’s like you have a Bitcoin position, you do buy Bitcoin and have a Bitcoin position, and then you sell that same position for delivery over one month or two months or six months. So you buy it and you immediately sell it against this higher future price. So you lock in a certain profit, guaranteed profit. So in a way that’s a risk free profit if you there are some risks, but they’re small. So you can do the same thing with gold. Buy gold and future, sell the gold and rinse and repeat every month. You would make 1% every year in return, which is low, but which is consistent with gold being very stable and risk-free. But if you do it with Bitcoin, like the way I described on CME or Bakkt, you can make like 12% per year and that almost risk-free. So if you pitch that example to a traditional money manager, his first reaction will be “That’s not possible”. And then when you show him the quotes, the actual quotes, he’ll be like, “Okay, I have to know more about this”.
PlanB: And that’s the example I use nowadays for people with a traditional investing mindset. And that really gets them thinking and into Bitcoin. Actually, I know three guys, who personally bought Bitcoin after this. This example who are professional money managers.
Stephan Livera: So the proliferation of the carry trade idea, and I think we touched on this in some of our earlier episodes as well. One question I’ve got around that is the contango as you mentioned, the future price being higher than the spot price, part of that may just be driven by people having this future expectation of a rise in Bitcoin’s price. So they’re buying it. And is that part of what’s driving that overall contango scenario?
PlanB: Yes. yeah, there’s two components. There’s the actual cost components. So with gold for example, you see the same thing. If you have physical gold, then you have to store it and you have to insure it and that costs money. So that’s why the future price of gold should be a little bit higher. You know, buyers of gold in the future will like the idea of not having to pay for storage and insurance next month or two. So that real cost of storage and insurance explains part of it. The other part of speculation of what, like you say, so if there’s lots of people that expect the prize, of bitcoin to go to, well let’s say 55,000, then they can take on leverage multiply their investing results by buying futures and and they will play the futures game.
PlanB: So yeah, in a way. And at that, of course, if they buy a futures that drives up the price of the, of the futures and also creates contango. So in a way it’s the carry trade that I’ve just described describes is funded by the people that are very, very optimistic by and are willing to carry that risk of Bitcoin. In a way. It’s, like the prelude to an option market, which I think Bakkt is also introducing, which is very logical. So the people that think about 55,000 Bitcoin in one or two years like me if they’re professional money managers, they think of it like an option that they think they have a certain probability that that 55,000 scenario is happening. Probability might be low, might be like 10% or 20%.
PlanB: And the other scenario being it goes to zero, 80%. So that’s sort of an option structure, but still they would, they would buy that future and, and someone else is not willing to take that even that 10 or 20% chance of a 55 K scenario. So they rather have the cash and carry where they make a certain let’s say 10% per year. And it’s, yeah, it’s a great way of futures markets bringing together speculators and more risk averse investors.
Stephan Livera: Excellent. PlanB do you have any other related projects, other things that you’re working on at the moment?
PlanB: Yeah, there is lots of projects and there’s some I could mention. So in the summer I was busy with all the translations of the article, which is great, and there’s 24 now, but right now lots of people are replicating the models and building a realtime version of models.
PlanB: And on websites, there’s digitalik, a Swiss guy who made a very nice website. Now we’re sort of working together and I’m thinking of not making my own charts anymore. I’m just using his charts because they are evolved and even better charts than I can make. And they are real time. So that’s very nice. You might also have seen the art thing that I I’m involved in. With the charts that I make inspire me, but also some of my followers. So they want high resolution prints and stuff. And that’s great. I have that same desire. And so I decided to take it one step further and, and a commissioned an artist that is by the way, one of my followers and approached me on Twitter.
PlanB: So we met face to face and, and she is now making an artwork of one of the charts. And, and I think that’s, that’s important goes Bitcoin is more than just numbers and investments. It’s a movement. And, and it’s very important that it’s not only programmers and investors are involved, but also the art world. So science has to meet art and yeah, it’s something I’m really excited about. The other thing you might have seen that is pure science is chain analysis. So we talked about miner capitulation and difficulty drops and that kind of stuff. You can extract a lot more data from the blockchain than only difficulty or stock to flow ratio. You can, yeah. You have 300 gigabytes of data that you can analyze and extract and yeah, I’m diving into that with new computers and new ideas and yeah, more high frequency trading point of view and the things that you find are actually very, very interesting. I have not tweeted or written about it. I’m sure that that will come and and yeah, that absorbs a lot of my attention at the moment, but it’s very interesting what you can, what information you can get from from the chain.
Stephan Livera: That’s awesome. That sounds great. I’m really looking forward to hearing about what you’ve got coming up next. I suppose just as a final comment for the listeners, can you just let them know what should they be watching and thinking about just to understand if the model has broken down or is there anything else there? Any other people who you would like to hear from?
PlanB: Yeah, the model is it’s very simple. So ifthe cointegration is gone, if it doesn’t show in the next one or two years, if it’s gone, then the model breaks down. And so my point is a bit conservative. I’d like to see it earlier than that. But if a Bitcoin is not above a hundred thousand or the 55,000 depending on the model you’d like to use, if it’s not above that number before Christmas 2021, then yeah, the model is in real trouble. And I’m probably so am I because I made it, but yeah, it is a possibility. Of course. Let me finish with that. It’s just a model. It’s not a guarantee for quick profits. And it can be wrong, so yeah, please watch that.
PlanB: And in the meantime, there’s lots of things going on around the world. So, especially also in the investment world that slowly but surely starts to understand Bitcoin and starts to invest in Bitcoin. And I expect a lot more investors piling in. And the halving around May, 2020 might of course help with that if prices do indeed rise like they did last two times. And like it’s predicted by my model that would create the setting where at least the investors that believe in Bitcoin, I want to pitch it to their investment committees. At least those people will have better charts to show there and have a, somewhat better story than they have now. Because now you could still argue that Bitcoin is dead and it will never go to the all time high of 20,000 again. But once that point is reached, I think we’ll see. Yeah. An exponential rise in the interest of investors.
Stephan Livera: Fantastic. So plan B, just to let the listeners know where they can find you and follow you as well.
PlanB: Yeah, so I’m on Twitter @100trillionUSD. And my article is on medium. It’s called modeling Bitcoin’s value with scars. But most of the time you can find me on Twitter and all the DMs are open. So please, please reach out. I like it and I still can manage to answer at least all DM’s so, yeah.
Stephan Livera: Thank you very much. It’s been a fascinating conversation.
PlanB: Thank you Stephan.