Rapha Zagury CIO and Head of Research at Swan joins me to talk:
- Financial modelling flaws in the fiat financial world
- Putting things in terms that other people understand
- Open source research
- Nakamoto Portfolio
- Schrodinger Model
- Lump sum vs DCA
- Bitcoin vs altcoin
- Twitter: @alphaazeta
- Site: NakamotoPortfolio.com
- Blog: Why Bitcoin is the Ultimate Asset for your IRA
- Nakamoto Portfolio thread: https://twitter.com/alphaazeta/status/1640005261099139073
- Bitcoin vs altcoins thread: https://twitter.com/alphaazeta/status/1641534991556288532
- Swan Bitcoin
- CoinKite.com(code LIVERA)
Stephan Livera links:
- Follow me on Twitter @stephanlivera
- Subscribe to the podcast
- Patreon @stephanlivera
Stephan Livera 00:00:00
Rafael, welcome to the show.
Rapha Zagury 00:00:02
Thank you, Stephan.
Rapha Zagury 00:00:02
Great to be here.
Stephan Livera 00:00:05
And I found it really interesting. Obviously, you know you’ve recently joined with the team at Swan and I found it funny that we actually knew each other even before you joined. And I was looking back through our DMS, and I remember we were chatting back in, I think 2019 or maybe early 2020 back in those days about the risks of things like block fire. And rehypothecation so. Quite a funny story to see that you sort of come around and now here you are working at Swan and putting out some awesome research.
Rapha Zagury 00:00:35
Thank you. Thank it’s great to be at Swan, you know that this is actually a good way to start because I’ve been first active on Bitcoin Twitter for a while, right? But also been following Swan for a while. And of course you right. So it’s an honor to be here. I’ve listened to, I don’t know how many hours of your podcast. For the years. You know I love running and I always take one of your podcasts with me when I’m running out and it’s just great to be here. It’s a pleasure to be here and I can tell you a little bit about my story of how I ended up at Swan, but it has a lot to do with the story that you mentioned, a lot of Block 5 and the scams in, in the Bitcoin world and you know.
Rapha Zagury 00:01:16
Cory and I have been kind of the same way that we’ve been exchanging the ends. We’ve been exchanging the it ended up working really, really well so.
Stephan Livera 00:01:26
And let’s while we’re here, let’s talk a little bit about your role, because you are CIO and head of research, and I know you. Had some ideas about how research is being done in the, let’s call it norms or normal Fiat financial world versus how you think it should be done in let’s say the Bitcoin open-source ethos. So do you want to expand a bit on that?
Rapha Zagury 00:01:45
Yeah, absolutely. So I spent all my career in Wall Street pretty much, right? So I was 14 years working in Wall Street in New York, worked at Merrill, worked at Goldman, worked at Deutsche Bank. So a little bit about of everything, mostly on the trading side. So I did some fixed income trading, did some derivatives. But through those years, of course. And then when I moved back to Brazil, I decided to start my own company, started two companies. One was an investment banking company and the other one was a lending fintech which become became one of the largest fintechs in Brazil. But through the time in Wall Street, I saw a lot of things that you know, as you imagine I didn’t like. And one of them was exactly on research.
Rapha Zagury 00:02:29
You know how conflicted research was, how closed doors, everything was. And that always bothered me. Right. You know, you would see a price target for a stock, but even though the company, the usually the bank would provide some rationale how you got to those prices, it doesn’t really provide you the model. So you can’t. Check the calculations. You can check all their assumptions.
Rapha Zagury 00:02:51
So it’s very, very hard to critique and to really analyze anything that they’re doing. And the reality is that the vast majority of people, they will take for granted whatever they see. So Wall Street will send out the research to have a price target on the stock and the general population would just read that and say, OK, you know, so. Goldman thinks that stock XYZ is going through the moon. I should buy that stock, right? And that also really bothering me because it depends. First of all, it’s out of context. It’s not that stock may or may not be for everybody, right for that asset or whatever it is. But also just pinpointing a price for a specific stock. It’s just like throwing darts, right? And in fact, There’s actually an experiment that the Wall Street Journal conducted, you know, and you can Google this, you’ll see like many, many years ago where they got stock analysts. And they also got an ape, and the ape would pick out of a box, you know, different stocks for certain period of time. And guess what? The 8th bit was beating the analysts, right? So and there are different scrimmages like that.
Rapha Zagury 00:04:03
There’s like the dark spirit that the New York Times, I think they’re just threw darts and into like a panel of stocks as a way to. So there’s a random component to it that it’s massive. The other thing that you know, if you’ve been like me that you’ve been seeing these analysts come and go, you would see that some of them will have two or three or four years. They’re very good. And then they, you know, they start not being so predictable and they start having, you know, bad predictions for a while, which it’s expected. The other thing I’ve seen, you know, kind of like in parallel to that is traders. You see traders, they’re very good traders for a year for two years for. Years, but I think I can count like maybe you know in less than the fingers of a hand the traders that I think that that I’ve seen that were consistent across a very long period, right. So then if you see that you start to think, well, maybe these are just the guys that you know threw the dice and got six in a row 12 times in a row, which will happen.
Stephan Livera 00:04:59
Rapha Zagury 00:04:59
Stephan Livera 00:05:00
Were they lucky or were they skilled? And maybe the question is if they weren’t able to sustain it for a long period. Of time. Maybe they were just lucky.
Rapha Zagury 00:05:05
And I don’t think our lifespans are long enough for us to separate what’s left from, from what skill, right? So at least not on the professional side. So these are people that we have 10 maybe 20 years, as analysts and. In Wall Street. And in 20 years you may have 5:00 and. 10 years that are good just because you were lucky. Right, don’t get me wrong, there are people that are very competent and they’re I think they’re people that add a lot of value, but typically, unfortunately what would happen is that the ones that we’re lucky, you know. They’re actually getting predictions 345 ten years in a row. These are the ones that rise to the top right. And among these, they’re going to have the ones we’re lucky and also the ones that are competent. And my experience is that the ones that are lucky are actually the ones. That start rising to the top right.
Stephan Livera 00:05:54
Yeah, yeah. And the other point I wanted to, I wanted you to elaborate on is the models aspect, because it’s very feasible for people to based on the assumptions that you build in create all kinds of valuations and numbers, right? So as an example, you may be an analyst and I’m sure maybe you can elaborate on this, but as I understand you could be an analyst. Looking at a stock and there’s all kinds of subjectivity involved, right? You might be looking at, Okay.
What’s the discount cash flow analysis right known as DCF, you may be looking at that and based on the assumptions, this is how I’m going to build out a price estimate for what I think the future, you know the future value or the present value of this company is? So could you elaborate a little bit on the ways people could, let’s say, play with the numbers there to generate? A desired outcome.
Rapha Zagury 00:06:41
So yeah. So as you mentioned. Most of the analysts we would use something like, you know, either looking at multiples or looking at discounted cash flow stocks. What they will do, you know, very summarize that they will look at what their expected returns or the expected profits from their comma or whatever is the multiple that they’re using, right and they will discount that to today’s values. You’re gonna look Okay, expect this account. Company for the next 10 years to generate this amount of cash flow. You discount that to the present and then you come up with a number. Well, there are several problems with that. One of them is you have to estimate the cash flow. So there is uncertainty around how much how that company is gonna perform in the future, right? That’s already a huge uncertainty because as we’ve seen, their companies are doing really well this year, may do poorly 3 years down the road. A company that owned the opposite, the company may come up with a product that was completely unexpected. Take over market share and just. Explode in value rarely, we see analysts picking up discontinuities right as humans, we’re terrible in predicting discontinuity, so anything we always think that the next day or the next month are going to be just like the last day and the last month that we experienced, right. That’s kind of like the famous quote from Taleb, right. The Turkey never knows that. It’s Thanksgiving, right? So Thanksgiving come. And the Turkey didn’t expect that was a very unexpected day. And we’re exactly like that. So on none of these models. First of all, they will predict discontinuities. Also another thing you mentioned, which is really, really important and most people when they see the price and they don’t think about that, is that price that estimate it’s dependable and like a lot of.
Rapha Zagury 00:08:18
Variables and small changes in some of these. Variables lead to massive changes in what the price is, right? So one of them is the discount rate. So you’re discounting all these future cash flows by certain discount rates. So if you have higher interest rates, for example, that is a problem because you know prices are going to go down on the present value basis. The other one is also as I just mentioned. They have to predict how those companies are going to be performing right. And when you have several variables together that each of them could impact the numbers, right? You have a lot of room to maneuver, and if we’ve seen there are stories about this, you know you can Google and not making this. You know, instances where investment banking, part of the company of the Bank right, was putting pressure on research because that company that they were doing research on was actually a client, right? Enron is great case about this right, and they couldn’t lose the business.
Rapha Zagury 00:09:16
So the analyst shouldn’t be coming out with an estimate that was bad and the analyst could just play around with two or three variables and get through to the number that was, you know, what the. Number they were looking at and that happened in Enron, right? There’s, like, massive lawsuits against Goldman against Merrow in terms of exactly the conflicts between and. You’re supposed to have what they call a Chinese wall, right? But yeah, that the, the walls are made in China for sure, because they break all the time and they, you know, OK they people. Come from one side to the other and that still happens today. Because the investment, the end of the day, you know you have a CEO in the bank, right? And the CEO overseas, both sides of the bank and you know, whether they like it or not, they may see, you know, and then others may say, oh, this company XYZ is doing a lot of business with and even if they don’t want to right and they are not notified, they are still influenced by that. As they know that you know if the if the bank loses that company at the end of the day, that’s gonna impact his bonus in a way or another, right? So it it’s very hard unless you are completely set, which there are a completely separate research independent research. Those inside banks, they are going to be conflicted, right? And these just became machines also to go out and put out reports and you know, you can’t be writing a report a day with different analysts and you know, expect that you are going to have quality all around. Take any bank that you want and you know, pick the Bitcoin is a great example. You’re going to have, you know. Inside, gold money have good research talking about Bitcoin and then you have somebody like Charmaine, which is the she is the CEO of their wealth management side that hates Bitcoin. And you know, and you read it and it’s everything is very superficial. It’s still a they are still that’s the bit blockchain, not Bitcoin phase, right? So these guys stopped like 10-15 years ago and they’re not interested in looking at it. I’m just using this as an example to show that you know research. Can be very wide and very diverse, even within one company, right?
Stephan Livera 00:11:22
And I think that’s also an often missed point, which is people will come out say, oh, Goldman said this, but actually it’s like 1 particular team and it’s like massive organization and these different teams all have different views on things.
Stephan Livera 00:11:34
And the individual analysts writing those reports and doing models and not financial modeling will have total. Different views one other point I wanted to touch on, as you mentioned, the discount rate, so as we mentioned often these models will be highly dependent on specific variables and in one notable case the discount rate. So for people who aren’t familiar, the finance, you know, the finance behind this, the general idea is this. Concept of the time, value of money So the idea is you may be estimating future cash flows and then we are discounting each of those years back to the present year to kind of come up with the NPV net present value and the interesting thing here is the discount. It is really influential and it can cause funny behaviors when that discount rate is either, you know is really high or really low and so in the recent environment where up until recently before we had the rates come up a lot in the US when the rates were really, really low, I think it’s fair to say people could justify. All kinds of crazy projects because the discount rate was so low. I’m curious if you have any thoughts or you wanna elaborate on the discount rate being low and how the? That can exaggerate or cause really crazy things to happen in a valuation context.
Rapha Zagury 00:12:54
And I think one of the results of that that we’ve seen is the massive financialization of different assets, right? Because capital has to go somewhere if the interest rates that you’re receiving, you know as a return are low, capital is going to go somewhere else, right? And may go to real estate and may go to different assets. And I’ll come back to that you know that has a lot to do with one of the models that that I wrote, but you are absolutely right. I think in an environment where you have very low interest rates, right, and we know that intuitively projects that historically wouldn’t be considered reliable and viable, right, they become viable because first of all the there’s capital. Available for them because the capital wouldn’t go anywhere else. Right, so this whole manipulation of lower interest rates that we’ve seen, you know, actually created a lot of the problems that we’re seeing right now, right? So we see manipulation. We’ve talked a lot about money, printing, money, printing, money printing, which is of course huge problem. But the manipulation of interest rates is also a massive problem, right? Because your governments are actually making. Financing of some of these projects that shouldn’t be around just being viable. So it’s an indirect subsidy to, you know, bad projects.
Stephan Livera 00:14:04
To crazy ideas, right?
Rapha Zagury 00:14:06
That’s exactly and people very rarely talk about that you know someone? If I told you like, you know, I tell everybody to start like I lived in Brazil and so I grew up in. Brazil in my early ages and I talk a lot about, you know, price controls and crazy things that I saw. And anytime that I talk about price controls, but that would never happen in the US, right? Well, that happens with interest rates, interest rates are price control, right? The interest rate is the price of the money and the government does price. Control on that every you know on every Fed meeting and we actually applaud that and that’s transmitted on CNBC and everywhere else. Right and it is a price control. They’re controlling the price that has massive implications in all of the assets. Directing money to one way or the other. And you know it’s impossible also for a group of people to know exactly where the capital should be going. They should let the market do that right?
And the money would have interest rates would be very different in one place than the other, as it should be, and it should be actually different for individuals and for companies. Right? And significantly different than it is today. As I said, one of the things we you know I built was a credit fintech. And we actually did like massive underwriting on the clients. We used like 40,000 data points to try to get what is the right interest rate for that individual because what we see is that you know, individuals usually, particularly in, in, in developing countries, they will go to their bank account or ask for, they’ll ask for some kind of loan. And the interest rate that you’re going to get, the interest rate that I’m going to get interest rate that pretty much anybody’s going. To get is similar in the same, and that has two effects. The first is that you attract people that no should be getting that loan anyway. They’re gonna be getting a cheaper loan. Than they did, but also on the other side they have people there, you know, very good quality that you I would be willing to lend them at very low interest rates but I actually can’t right because you have regulations have things in place that that do not allow you to to do that so that that’s a massive this alignment of interest. That, that, that again we have in in our system that need to be broken up. But in some sort of way, right.
Stephan Livera 00:16:18
Yeah, I think one point just to explain that for listeners who are newer to the concepts behind Austrian economics, a famous concept in the Austrian School of Economic thought is the Austrian business cycle theory. And that’s exactly what we’ve been speaking about as central banks and governments artificially push interest rates lower than what they otherwise would have been. What happens there? Is that Projects artificially look like they are viable when really they are not. Because of this problem with the discount rate as we’ve spoken about. So because entrepreneurs have been fooled in a sense, and one way I’ve heard, it’s kind of a pithy way to say it, it’s not that entrepreneurs all of a sudden become idiots and they’re doing all this mall investment.
Stephan Livera 00:16:58
It could also be that idiots become entrepreneurs, right? It’s that unfortunately, people have because we’ve lost all tether with the real world. There’s not that accountability that a real free market, some money. Would enforce. There’s no discipline being enforced by a free market sound money, so instead it’s being Fiat created. There’s artificial cheap credit, it just looks it makes projects look really viable and that’s why we see all these people running to do things like, you know, flipping houses, orthe.com bubble or in the 2008 case, there’s this idea of the ninja. Right. No income, no job, no assets. So all these loans are being issued for people who were simply not creditworthy. And so that was the problem in 2008, of course, you know, there are different issues that come up over time, but essentially. The Austrian cycle business cycle theory is showing us and explaining for us that we’re going to see all this mall investment. So I think bring it back to sort of the Bitcoin ethos of verifiability. I think that’s something that you’re trying to change with how you’re approaching modeling and being able to show people, OK, these are the assumptions. That you have made, if you have different assumptions, you can plug those in and you can see what numbers you come up with and it’s. I think that’s actually interesting because it’s more reproducible, it’s more verifiable, and that’s really much more aligned with the Bitcoin or ethos and a little bit about how financial. Modelling and financial estimation could be done. So do you want to just elaborate a little bit about how you’re viewing modelling?
Rapha Zagury 00:18:33
So a little bit of background first. So as I said, you know, when I when I was growing up, I always loved technology. So I’ve been involved in even though I went to work in financial sector, I always loved technology. I actually ran, you know, your listeners are probably not going to know. What this is, but the a BS. Bulletin board system like back in the in. The 90s. So these were this was the infancy of communication of the. Internet, as we saw we used dial-up. So we use, you know, wouldn’t be asked to call the other and we would users would call in right and access with their mode issue, you know chat and download games and different things. Like that, right? So I I’ve always been involved in technology in some sort of way. So even when I was working at Wall Street, I as a hobby I would do other projects, right. And I always love to be involved in in the open source community, so I’ve contributed to some projects along the way. I’ve created my own projects, right? And there are challenges with open source code and the open source system, but I think the benefits for the community far outweighs all of these challenges, right? So I’ve always been a huge proponent of open source. And so when I joined Swan we was talking to cars like, oh, we need to start thinking about. Different things in research, what we’re going to be putting out right and kind of like with that ethos in mind of having transparency and with everything that I just mentioned that you know, I wasn’t going to definitely. I told him, like I’m definitely not going to have any price predictions for Bitcoin, you know, predicting price is of losing proposition right, at least in the short term. I can tell the direction the price is going, you know. For 5-10 years, which I think it’s.
Rapha Zagury 00:20:13
Up if it’s going to be up 10 percent, 20%, you know 1000%. I have no idea. No one has any ideas as well, right? So if anybody tells you that they know where price is going, I can tell you they’re wrong. Right. And this is one of the reasons why I don’t like also the stock to flow model, which is basically it’s predicting price at different places right. There are other issues with that. That any model that I see that is trying to predict price and. The time I already kind of like don’t like that because again, you have to be very right and you have to have so many variables that are right in order to get to that price. So this was the first thing we had in mind is that, you know, it was going to be open source. And the other thing is that we’re going to make it available so that anybody can run the numbers. So not only on the open source. So they can replicate the code and can inspect. The code and see what We’re doing cuz we’re also gonna make mistakes, right? We’re gonna have, I can guarantee you that, you know, on our website there’s something there. That it’s wrong So this gives an option to whoever is looking to the numbers to redo the calculation and get through their own numbers, which may be in line with, not with others. The second thing is for people that do not code like, we know their vast majority of people. Don’t code right. So if we couldn’t expect to just put the code out there and expect people to read the code and understand what’s happening, you know there’s a fraction of the users that will do that. So we actually provided web apps where users can go there and then just change the assumption. So the model is baked in, but the assumptions are not and they can plug in their own assumptions, right?
Rapha Zagury 00:21:44
So for example, we have a model that at the end of the day, it’s looking at the probability of demonetization of different assets. We have a base case you know, we have a bear case we have a bookcase, but don’t take those for granted.
Getting there, run the model and then you can change all the assumptions, right? So the same assumptions that we got that we created to get to these. Those you can do the same thing. The other thing is that you know, we talked about models, models, models, but a lot of the thinking is not in creating models. It’s actually in creating frameworks, so when we build this. Model the end goal is not to have a price prediction. The end goal is try to explain why different things happen. Right. So in this model that I just mentioned, for example, it’s very easy to see why Bitcoin has massive volatility because small changes in some of these variables would lead to significantly different prices in Bitcoin, right? And as we as a collective group right of investors of people looking at Bitcoin are making our own assumptions, they’re going to have the ultra Bear that thinks Bitcoin is going to go to zero and have the Ultrabook that think Bitcoin is going to go to $100 million, right? And everything in between. So this gives you an idea of like, OK, if the guy that says goes through zero, this is an easy one, whether it’s going to go right, but also the guy that is talking about, you know 100 million or whatever the number is what his assumptions need to be to get in that place, right. And then try to do the OBS and say OK, given these and given what we know about the model, how is the current? Price pricing these probabilities right? Is this a high probable event? Are we early? In adoption or relating adoption and I think with these things it’s very easy to see you know first that as I said, price can be very volatile and 2nd we are very extremely early in a lot of senses, right.
Stephan Livera 00:23:37
Yeah, and let’s chat a little bit about some of the modelling that you’ve started and this idea of Nakamoto portfolio theory. So what, what is that? So this is the first big piece of research that we’re putting out, right.
Rapha Zagury 00:23:52
So based on everything that I said on how we’re going to be doing. The first area of focus for us is portfolio allocation and how Bitcoin should be part of any portfolio. At the end of the day, right? So what I did is I went back to all the models, all the theories that you know I’ve seen through, you know, all this time and I either adapted them to work with Bitcoin or just in most of the case I just ran them as they are so that we can see. If Bitcoin could fit or not in this part, so another way to think about this is a challenge I always had is when I went to my colleagues and talked about Bitcoin, right? It’s too foreign for them. There’s too many things that they need to know in order to really grasp Bitcoin, right? They need to know about, you know, a little bit about Austrian economics. They need to know a little bit about technology. And you need to know a lot about game theory, and it’s hard for them to break out of some of the silos that they’ve had in the. Best So what I’ve tried to do and this is again a lot of the work that I have and that I prefer, is part of this framework that I’ve been doing. It’s actually go back to their models, right. So portfolio allocation for example, everybody talks about modern portfolio theory, which is not very modern. It’s a theory that was written in the 50s right by Markovitz. That basically looks at assets. Look at historical returns and then take the tries to see how do you optimize risk for the amount of return that you want to take or the opposite right to tell how much risk you want to take and then you find the amount of return that the best weighting that you.
Rapha Zagury 00:25:24
Can do with these assets, right? So I got this and I included Bitcoin and the result, which again we can get the details that Bitcoin should be part of pretty much any allocation that we look at. It’s an asset that has low correlation, right? That has of course good returns. It has very good returns. By the amount of volatility that that we see, right? So everybody says, oh, it’s volatile. It’s volatile Yeah, it’s volatile, but it also has, you know, good returns in the in the long term. So that’s the nature of the bee So in a sense, I’m trying to replicate again a lot of the things that I’ve seen and bring it back to now. I’ll, give you a story a very quick story. There is a colleague of mine who’s a bit became a bitcoiner today, but he was very skeptical of Bitcoin for a long period of time and this is a major investor in hedge funds. It’s one of the guys that started investing in hedge funds, like really early and of course made, you know, good amount of money with hedge funds. And so you know, I told him about Bitcoin a while back and he couldn’t break it out. So he kept asking these questions about, you know, how it works. I can’t verify the code I can’t see anything right how transparent it is like the typical the government is going to ban it. Right, like the typical. And he could never get across it so, but this is a good friend. But like I need this guy to see that this is more than actually he’s saying right? So I did something a little tricky, so I called him up. I got a Bitcoin historical returns, right? This was early, I think 2018 or something like that. So I got Bitcoin historical returns right? I printed in a sheet that looked exactly like a hedge fund manager to him. So I had the monthly returns had the historical distribution right. Again, a lot of this work that I have a Nakamoto portfolio was born from that story, right? And I sent it to him and I said listen, I’m investing and I, you know, just like he knows that he knows. I also invest in in headphones By the way, this guy invested in Madoff, right? Which is a completely different schedule. But keep that aside because I’m going to come back. And I sent you here. And I said investing with this with this fund, and I don’t know this this manager looks very good in the long term, but he has some very bad ears. So what?
Rapha Zagury 00:27:43
What do you think about that? Should I invest with that and it takes like 10 minutes the guy calls me up, right? And he’s like, who’s that? Manager, I want to invest with him. You know, this is very good. I will take the bad years. Right? You know, this is not a problem because look at his historical returns. Right? And I told him like, oh, this is Mr. Nakamoto, right? and Mr. Nakamoto, in Japan, he’s very secluded, right? I don’t know if he’s you’re going to be able to get in his fund. You know, it’s not for everybody to see. There’s a lot of volatility and he’s like, well, try to get me a meeting with him because I really want to get to meet this guy. So we hang I don’t tell him anything, so he picks it up. Like and he calls me like 10 minutes later. He’s like these are Bitcoin returns, right? Yeah, yeah. It was like yeah, man. Maybe I’m overthinking this right, because again, if it was a manager where I have no transparency, I don’t know what they’re doing right. I would probably invest and knowing that Bitcoin is transparent, I’m actually asking all these questions right that a manager would never answer to me, so I may be overthinking this and he ended up. That’s how he ended up investing a little bit in Bitcoin. To start with and then a little bit becomes a little bit more and you start to learn right and he comes back and he mentioned he’s like, yeah, I invested in Madoff, right. The guy wouldn’t tell me anything about his returns. Right? And I was happy for a long period. Until I wasn’t So again, I’m overthinking this. I should invest I’m telling you this story because as I said, the Nakamoto portfolio was born from these kind of conversations that I had in the past.
Rapha Zagury 00:29:19
It’s a little bit of a Trojan horse. The front, you know that I’m going out there and putting it out there in the language that a lot of traditional financial advisors that other people can read and can see that, you know, Bitcoin has a place in the. But I’m talking I’m speaking their language, right if I just. Go to them and say yeah, you know. But this is decentralized technology and you know it can verify the code. It doesn’t resonate to a lot of these people, right? It resonates to us right, because we know again technology and we love this stuff, right. But then we have a little bit of an Austrian school mindset, right, a lot of these guys don’t and they don’t have time, also financial advisors. Thinking about the thousand different things he’s looking at real estate investment. He’s looking at day-to-day cash management. His client, he’s looking at, you know, new funds that the clients investing, right. He’s looking at how they, how he they’re gonna do inheritance. So they don’t have time to dig to do the deep dive into some of the assets. So what they will do usually is that they will receive a fact sheet just like this, right? They look at the returns and like yeah, this fits or doesn’t fit my overall portfolio. And again I you know, I’m very happy with what we’ve done because and this is one of the advantages of open sourcing it and then you start to get feedback, right. And I’ve seen everything in these last few weeks. It’s been a week and 1/2 that we launched this thing and it’s like, you know the. Website breaks all the time because people are really running the numbers. And if people are coming back and saying, oh, I added in a portfolio of corporate bonds, for example, right and here the results and Bitcoin should be there, right? I shouldn’t have 50% Bitcoin, but I should have maybe 1% Bitcoin in that portfolio, right? And I ran this on a portfolio of commodities and then they’ll come back and they’ll show the results as well. I encourage everybody to just go to nakamotoportfolio.com and then they can. They can run the numbers, right, they can see how their own portfolio or fictional portfolio would actually perform compared to Bitcoin. And there we have several you know, I got all this analytics that we’ve had in terms of statistics. You can be as basic and or as advanced as you want, and we’re going to be adding more things to that with time as well.
Stephan Livera 00:31:30
That’s fantastic. And I think 1 interesting point that came to me from your story, which is a good story. It’s that when people are new to something, they often impose an even higher bar for that new thing than what they’re currently using and comfortable with. So it’s kind of funny that, you know, your friend was imposing this incredibly high standard for Bitcoin, but that standard was much higher than the standard he was already. Playing for things that he would. Already put money and did already put money into and so I think. A funny parallel and it just shows to some extent it’s about people who are open minded and to some extent it’s people who are willing to think for themselves and being willing to challenge the herd, right, because there are some people out there who would rather be wrong. But with the tribe, let’s say, then be correct and on their own. And I think that sort of, yeah and that sort of shows, you know, people who get into Bitcoin early and not all of them, of course, some of them were, you know, maybe some of them were lucky, not skilled as we were talking about.
Stephan Livera 00:32:33
Earlier, but the ones who were skilled, they were getting into it because they were open minded, but they also were able to have some kind of thesis about why they held for such a long period of time because as many people have mentioned, right, you, you know, people might have bought Bitcoin at $2 or whatever, but did they hold it? That’s the important question because many of them did not or they would have sold for a 10X because if you don’t have a thesis about where you think it’s going in the long run, you’re not going to be able to hold for the long run. And so that’s why this Nakamoto portfolio theory actually is an important idea.
Rapha Zagury 00:33:04
Just one point on that, it’s it goes back to one of the reasons why we are trying to do research differently, because if you actually was looking at this this week get I think actually put this on Twitter, look at the analysts and the predictions that they had for Silicon Valley bank up until a few a month ago, right. And what you’ll see is that There’s a chart you can see like the where the expectations, the price expectations are from the most bullish analysts to the most. Analyst and the difference is like none, right? So there are no outliers on this, right? Everybody, why exactly? For what you just said, right, guy, that goes out there and has a price prediction that is much different than his peers. He’s gonna have a target on his back, right? It could be good It could be bad most of the times it’s bad because, you know, he took. A chance if they’re all wrong It doesn’t matter everybody was wrong You were at the pack, right, which they were and you see that? You know, as things start to collapse, all of the analysts expectations are. Hey, son together, right So everybody’s with the hurt and the for us as investors that has no value, right? I mean, I’m not going to be reading that because you know, what kind of signal I’m getting out of that. There’s no signal I would look at the price chart, it’s easier and it has probably more signal than looking at the analyst expectations, right. As the guy that made you know the wild prediction and he was making wild predictions for many years, he got fired. He’s not there anymore, so there’s negative selection also.
Stephan Livera 00:34:32
It’s like a survivor bias, maybe a recency bias as well as that kind of tribal aspect that, you know, maybe there’s still a bit. The biological or cultural elements that maybe programmed into us, right? Because maybe historically, if you were, let’s say not in line with the tribe, that might have been a death sentence, right? Like you might be literally not eating if you weren’t in line with the tribe, so maybe you can sort of understand. Maybe there’s some psychological or biological. Reason for that? But in today’s world, maybe it’s maladaptive in a sense, or we’re maladapted for, you know, the modern world and Mal adapted to understand why Bitcoin is a better money and it’s going to take time, right? That’s kind of my thesis as well as that. I believe it’ll take, you know it may it may you know hyper virtualization as much as you know people pumping it on Twitter or whatever. And yeah, it’s gonna happen next year or whatever. No, I think it’s, it’s a long process of people slowly shifting and understanding. And it’s only that small percentage, let’s say 15 or 20% of society, who might be capable of thinking on their own. And the 80% of people are just going to have to be dragged kicking and screaming, screaming into understanding and using Bitcoin and maybe they won’t consciously be thinking about why it works or why it makes sense. They’ll just be using it because, OK, the tribe uses it, you know? So maybe there’s a little bit of that, but I’d love for you to touch a little bit on. We’re talking about modern portfolio theory. And you know, portfolio optimization, could you just explain a little bit about that for you know for the neophytes and people who are new what that is and why Bitcoin should be a part of people’s portfolio?
Rapha Zagury 00:36:10
Yep, absolutely. So, you know, think about it this way. You start with baskets of assets, right? Let’s say we pick S&P, we pick bonds, pick commodities, we pick different. Let’s say we pick ETF’s right. Exchange traded funds, which are easy to track, their price daily. And you know there’s a market on them there. So we pick a basket of different ETFs and stocks. Say we pick Apple, we pick Google, we put. All of this in a Right? And we put Bitcoin there as well, right? or for now, let’s leave Bitcoin apart. We come back to Bitcoin.
Rapha Zagury 00:36:41
So the first question that you’re going to have is like, OK, I know I’m gonna buy all these assets, but how much should I buy of any of them, right. And the answer lies in different things, right? It lies also, first of all. In your risk preference, so you might be somebody that is very risk averse that don’t want to take too much risk or maybe a corporation where this is cash for your corporation and can’t be risking it too much. So you can’t take too much so let’s say that’s investor one and an investor 2 maybe an investor that it’s very, it’s young, could take a lot of risk, right. And he’s OK in having very large volatility in his portfolio because he’s gonna huddle for the long term, right? So these are two kind of investors so modern portfolio what it. Tries to do is optimize for a given variable. So in this case it may be risk. So the first investor is gonna say. I’m very low risk I’m trying to achieve. Let’s put a number trade. I’m trying to achieve a volatility of 3% in My Portfolio because I’m very, very risk averse.
Rapha Zagury 00:37:43
So what you do is that you start with the probability that you know it’s three the volatility which you know is 3%. And then you look at the volatility of all of these assets, you combine them together and you try to get what would be the optimal allocation to all of these assets. So you get the maximum amount of return possible, right? And I’ll back to that, the other investor is the same thing, but the difference is that this is an investor that is more pro risk. So he’s gonna end up with assets that are much more risky than the other one. One thing I didn’t mention is that it’s not only when. You put all of these together, it’s not only the individual assets. But it’s also how they work with each other so that when we talk about correlation, correlation is important exactly because of that. Right? Let’s say for example, we have two assets, both very volatile, right? Let’s say you have both assets, as you know they go, they could go up or down.
Rapha Zagury 00:38:40
You know, they could go up 50% in one year, they could go down 30% in one year. So very volatile assets, right? But let’s say that when asset a goes up, asset B goes down and when asset B goes up asset. It goes down, right. So what happens there is that one asset actually behaves a little bit like an airbag to your portfolio, cushioning a lot of these volatiles, SO2 assets are very volatile. You put when you put them together. There you may actually end up with very little volatility because your overall return is good. Your overall volatility is going to be much lower. So when I say, well, you know I have a lot of issues with mark of its motto, you know and we can get into them. But one of the advantages that it has is that. It easily you can easily visualize how these things work when they are together right and when you. Construct your phone. So optimization is the process of finding what would be the the the ideal allocation of these assets that you already. It for to satisfy a given condition, in this case is your risk tolerance. How much risk you want to take, so just very quickly I’ll go back to some of the the the issues that the mark of its model has, which I think are important on this context.
Rapha Zagury 00:40:00
So the first one is that it’s based a lot on historical returns. Right? So it will look at the best of these assets and with that it’s going to try to get an understanding of what kind of return and what kind of risk each of the asset. So when you look at historical returns, you of course have issues because you know the future. As we said, the future probably is not going to be like the past. So it’s a model that is very bad in predicting discontinuities again. So if you have an asset that you know had very low volatility, but then all of a sudden start having more volatility or an asset. Which I’ll get to it that it doesn’t have the distribution of returns you know on a normal. No way it this model doesn’t work that well, right? So going to this point. So it also assumes that the returns are normally distributed. What does that mean? That means that they have that that bell curve shaped that we know, right. So you have most of the returns concentrated in the middle and you have a lot of tails, right? Guess what Bitcoin is and said it’s, you know, exactly the opposite. We have fat tails, so we have very little. We have actually have a lot of returns in the middle. We have very little returns in between and I have these extremes right in terms of returns in any period of time that you pick. The other thing that the model assumes is that investors are rational, right? And that information is across everybody in the in the same way. Well, we’ve seen a lot of irrationality in the markets. Recently, right? Meme stocks all of that in a sense it is rationality because people are trying to optimize something. But it is irrational for the this model, right. And the way that the model will see. And the other thing is that access to information is widespread and I would say that probably the market, it shouldn’t have written that it’s widespread because it is widespread. There’s a lot of information that anybody that wants could read about Bitcoin for, you know, hundreds of thousands of hours today. There’s more than ample with that information out there, the problem is that there is no Wheeling in us to actually access that information, right.
Rapha Zagury 00:42:04
So even though information is out there, it’s not distributed equally yet. As I mentioned the example right, my friend, he had all the information out there to learn about Bitcoin, right? I actually so I could take the horse to the water, can make a drink, right? So I took him to the water. He didn’t drink I had to put a little bit of sugar in the water. Then the horse drink the drink, the water. Right, so information is widely distributed, it’s just not accessed. I think in the in the right way yet because it is a novel concept, it is something different. As to your point that what we we’ve seen in the past, right, so with these in mind, I brought Bitcoin in to actually show how these things work within the context of portfolio allocation. The other thing that we see and again you know anybody can run the numbers on a computer portfolio and then they can see this. But we have a tab there for optimization, so I optimize you put in the assets that you have and then you go to this optimization and then it would spit out like with different variables. So let’s say we optimize for risk return, right? So having the highest amount of return. For any risk that you take, let’s say you optimize to have equal risk contribution. So every asset I kind of like adds the same amount of risk to the portfolio and we run all of these. Is in regards to certain levels and what we’ve seen is that a Bitcoin allocation is present pretty much everywhere and in every different model, right?
And it is also present in every level of risk from the less averse investor to the extreme risk tolerant investor. All of them should have some kind of Bitcoin. Location to their portfolio.
Stephan Livera 00:43:41
Yeah. So Basically, adding Bitcoin to your portfolio, even if it’s only a small percentage, improves in most portfolios in terms of we would say risk adjusted return. Yeah, right? I think that’s probably the that’s how we could summarize that. I guess actually, would you mind explaining what is risk adjusted return just for people to who aren’t familiar with?
Rapha Zagury 00:44:00
Yep, absolutely. So the way typically risk adjusted returns are measured is what we call the sharp ratio. So the sharp ratio does very simple. It will get your return right. It will take out the risk free and it will divide by some measure of volatility or risk, right? So what you’re seeing is how much you’re getting of return in terms of the risk that that you are taking. So you want to optimize that, you want to have. As much return as you can. For the same amount of risk, right? So let’s say we have two baskets, right? And they all have 20% volatility, but one has a historical return of 40 and the other has a historic return of 20, right? One is going to have a sharp ratio of two, the other is going to have a sharp ratio of 1. Forget about risk free for. Now, right. But risk free is also important because it goes back to what we were discussing before, right?
Rapha Zagury 00:44:49
Which is if you have a risk free rate that is manipulated in some sort of way, you are going to end up with sharp ratios that are also indirectly manipulated, right? The results are not exactly what you see, and we see a lot of this in like for example this this investor was running short term bonds. He saw that the sharp ratio you know. Depending on the risk free that because here talking about very low returns, right? So if you increase your risk free rate a little bit or reduce a little bit. You end up either having very good sharp ratio or negative sharp ratio in some cases, right? So manipulation also has an effect here, but the point is exact. That, that you’re trying to adjust the level of the return that you could take by the level of risk that you do? So one of the things that will come out of this is what we call the efficient frontier, right. So what the efficient? Frontier is you’re going to have on the X axis different risk tolerances, so on the zero you’re going to have somebody that doesn’t want to take. Any risk whatsoever, and of course, you know, he should invest in anything. And then on the other spectrum, on the end of the X axis, you’re going to have somebody that may would be willing to take, you know a volatility off 50 or. 100% of whatever it and then you draw a curve that would actually find for every given level of return of risk that you want the at the level the optimal level of return that you can achieve by mixing these different assets, right? So let’s pick Bitcoin and bonds as extreme examples. Right. So the investor that doesn’t want to take. Any risk he’s gonna have 99 points. 99% of his portfolio of bonds and .01% of Bitcoin. Right on the other spectrum, the investor that you know is young, as I said, wants to take a lot of risk, right? Has a long time horizon. He may say screw bones. I’m only going to buy Bitcoin has 100% Bitcoin. So these are true, easy extremes. But what about the guy that’s in the middle right? That says, OK, I want to take 10% annualized volatility. So what the efficient frontier does is that it spits out. What the right combination between Bitcoin and bonds would be so that he can optimize and have the maximum amount of return as possible for that risk that that he’s taking?
Stephan Livera 00:47:01
And the risk tolerance conversation also comes up when people are thinking about. About DCA versus lump sum, right? And as I, I mean the way I sort of explained it for people is I’ll say typically you might think about taking an initial lump sum and then set up an automated purchase after that or just regularly accumulate. And I think that’s probably that’s what makes sense for a lot of people. But you have to think about your own risk tolerance and deeply think about that because a lot of people. Can be very gung ho. They can say Oh yeah, I’m willing to take 80%. And drops, but actually once an 80% drop happens, if you end up selling that could be disastrous, right? So I and I know you’ve written and spoken about this also. So do you want to elaborate a bit on your thoughts around lump sum versus regular accumulation? Absolutely, this is, you know, Sam and I when I when Sam Callahan, when I when I joined the first long conversation that we had was around this like.
Rapha Zagury 00:47:52
You know, because this is a question, as you can imagine, we get a lot from our clients because Swan is has a product that, you know, incentivizes clients to buy Bitcoin as they go, they can DCA monthly, daily. However, they want, right? So it is a question that we get a lot at one.So first the definition, right? So when I’m thinking about. DCA versus lump sum. I’m thinking about the following scenario. You have capital is available, so you have cash in your Fiat account you have, you know $10,000 in your free account. At that point you need to make a decision. Are you going to buy the 10,000 Bitcoin or are you going to log in? You know the 10,000 along a week, a month, a year, whatever it is. So when we’re talking about the same lump sum, that’s it’s different than the client like we have a lot that every month is going. To have or every two weeks going to hit his paycheck right into his account, and then he buys some Bitcoin. So this guy is just doing a series of lumps. Terms, he’s not exactly, at least in our definition, he’s not exactly dollar cost averaging, right? So when you talk about Ohh dollar cost average cause I’m actually buying every week but you have to first define is that OK is your amount at risk is the amount that you had available the amount that you are allocating or you had more available and you decide not to allocate. So the first question investors.
Rapha Zagury 00:49:10
Which you have is the one that you mentioned. Is like from the amount that I have available and that. I’m going to have available how much I’m going to be putting Bitcoin. And this answer is critical so that you can withstand the drawdowns after you make that decision of what is the right amount of capital that I’m going to be deploying. But then and then, given the time right then you need to make a decision or should I? Allocate that right away, or should I? Do that through time. Right? The answer is and I think it’s obvious is during bull markets, of course you need to allocate as quickly as possible during bear markets. It’s probably better to DCA along different time, but then I told you I don’t predict price, so I don’t know if we are in a bear market in the bull market. I don’t know if we’re going to turn right and I don’t think anybody can because, you know, we may have another. FDX tomorrow we have a, you know, a large sovereign wealth fund that just comes out and declares that they have Bitcoin. And then. Now, as their exposure right and two things would have wild consequences in one side or the other and we can predict that. So on very long period of time lump sum beats DCA there and that was in the beginning for many people when we came out with that conclusion was a little, you know surprising.
Rapha Zagury 00:50:22
Like you know, they say it’s why averaging in is probably better because Bitcoin has these wild drawdowns. This wild volatility, right? So why is that happening? And you know? And so we dig into the numbers and another thing that it’s again, it’s almost a little intuitive, but it doesn’t come out when you have the discussions they say because it’s different, it’s Bitcoin does, you know pretty much two things. It’s either sideways or going down right, which is 80% of the time, or it’s going up very quickly and very explosively. So if you actually look at the returns of Bitcoin, remember we talked about. The bell curve Sort of bell curve, right? You have these wild. Mainly on the upside, which is, you know surprising like. So we have these explosive moves. We did some analysis like looking at very small blocks of time.
Rapha Zagury 00:51:09
So you feel like you’re excluding a period of five years, right? You take out the top 10, three days move instead of having a positive return, you actually have a negative return on Bitcoin, right? Because these moves were really large very quickly. Which is something that we also tell clients. They’re like, you know, we’ve had particularly I think earlier this year, a lot of people should be sitting on the sidelines. That should weigh. Bitcoin is gonna go to XYZ Price, right? It’s gonna drop a lot, and then I’ll buy. You’re not right, because first of all, if it moves out, it’s going to move very quickly in your face, and if it does, you’re not going to get into that there. You’re actually gonna wait again for it to drop, right? So there’s a lot and going back to your point, there’s a lot, I think, of human behavior that needs to be managed on this. So going back to DCA Versus lump sum, the first thing is determine what is the amount that you can put at risk. That you can live with it, that you know you’re not going to be needing it in six months, that you’re not going to be. Needing it in one year and after that, probably the best thing to do is allocate that quickly, right? and then if you have more capital available in in a week, in three weeks in a year, whatever it is because you sold the business because you know you’re receiving your paycheck, whatever it is, then you make that decision again of how much you’re going to allocate. At that point, but historically, going back to the conclusions, if you actually look in a very long period of time, lump sum tends should be much better than DCA, and it is significant. It’s not, it’s not even close, right? But again, don’t trust me. Verify that’s another thing that you know we’ve built on the motor portfolio website.
Rapha Zagury 00:52:44
People can go in and simulate different time frames, different DCA strategies. They can say I’m gonna buy weekly. I’m gonna buy monthly. I’m gonna buy during three weeks. I’m gonna buy during. You know, I don’t know. 20-4 months, whatever it is. And then they can compare. What would have happened? And then it can start they can say, oh, let’s say I did that one year ago. Let’s say I did that five years ago, right? And they can compare and come to their own conclusions of what they. Can withstand in, in level of risk. There’s the flip side of that. As I said, DCA tends to perform better in bear markets and a very good illustration of. This is if you look, somebody that actually bought Bitcoin, you know, on a daily basis from the all time high until today, they’re actually making money, they’re up something like 15%, right? So think about that. I bought at 66,000, kept buying every day, right? He’s actually making money now. While somebody that lumps some at the all time high is down. You know more than 50%. So you know that the answer is that there is no magic formula here. You have to analyze.
Stephan Livera 00:53:39
It’s down, yeah.
Rapha Zagury 00:53:47
Numbers and come to your own conclusion of what kind of risk you are going to be taking in terms of, you know, either because there is a risk, right, either lump summing or decaying at. The end of the day. And I think the other way to motivate it is. Because some of bitcoins returns are so dependent on the top ten days in the year, it’s important to be allocated to have an allocation, because if you’re not in at that point, you miss the upside. So I think that’s probably the other easy way to motivate that point, but. I think the main thing is if you’re going to be in here, you need to have a stomach for the long haul. You need to have a stomach to kind of take some draw drawdowns. As many of us have been around for a while, have just had to you’ve had to withstand and, but I think you build. That over time, but I think, yeah. It’s very hard, Stefan, because hardly, you’re gonna have, you know, days of glory. And you’re gonna have months and years of pain, right. That’s the nature of oddly, you’re gonna have, you know, we know we had, like, you know, a year and a half ago, we had months of glory, right. It felt good. You read Bitcoin, Twitter, go back, read Bitcoin. Twitter at the messages at that time, right? Everybody’s super euphoric hyper virtualization is happening right. And then we have this whole period where people capitulate, get out of Bitcoin. You know are not very happy.
Rapha Zagury 00:55:03
It’s the nature of the acid they have to live with it. Right and they have to have. The stomach to live with that. I think I think that’s right you just have to build your conviction over time.
Stephan Livera 00:55:11
I think one other aspect of that, and I think we’re gonna get into this now is around the Schrodinger model because part of that is having a thesis about what you think Bitcoin will someday be. So do you want to explain what’s the Schrodinger model that you have come up with and just explain a bit about that?
Rapha Zagury 00:55:28
So one of the things that bothered me a lot when I created like because in terms of tools that I created first I created the what we just discussed, the portfolio optimization. Those tools were done, you know, from the past, from other things that I’ve done, right. But one things that always bother me is that it’s backward looking. So we’re always looking at the back. What happened in Bitcoin price return What happened? You know, if these assets really and when you’re trying to optimize for the past again we’ve we’ve discussed there are several issues with that, right?
Rapha Zagury 00:55:56
So I’ve kept thinking about, you know, what can I look that it’s a little bit, it’s not ideal, but it’s a little bit more forward-looking, right. And then I was listening to Michael Saylor, one of the podcasts that he did.I think was late last year. Here Bitcoin has property right? But he compares Bitcoin to real estate and sees what could you know. He kind of like tries to explain what could happen if we capture part of the monetary premium that it’s now sitting in real estate. Right.
And then he also goes through the fundamental arguments of why Bitcoin is better. Than real estate And again, why this demonetization of real estate should happen, right? So I kind of sat with that and like, yeah, but this is not only true in real estate. We see that in other assets, right? We see that in stocks. We see that in bonds We see that in cryptocurrencies, right, we see. That across the more and then I gave one step more and said OK, So what happens if Bitcoin demonetizes all of these assets or some of these assets and let’s say it also doesn’t happen today, but it happens at some time in the future, right? How can I think about that? So that was the first inspiration that. I got the second one was. As I said, you know, I traded a lot of derivatives. There is a model in derivatives that is very well known. The black shows model where basically what you’re doing, so it’s a model to price call options and put options on different assets, right? So you have an option to buy the S&P in one month or in two. How do you know if that option should be worth a dollar two dollars $10.00. So what? The Black Scholes model is that? Try to find what the right price would be for. For that option, should they and what it does. Very simplified is that it will look at the probability of that option being in the money being exercised and also discounted by time. You know, I got that same concept and brought into a valuation methodology for Bitcoin, right. But before that, let’s talk about.
Rapha Zagury 00:57:54
You know the monetization of assets and why? That’s a very important component. So we talked about rates being low for a very low period of time and that created all kinds of wrong incentives and all kinds of, I think wrong price signals in the market. Let’s stick with real estate. We can talk about other as well. Let’s stick with real estate because there’s ample evidence that this happened. In real estate, right? So the amount of homes that are out there that are second homes, they’re investment homes that people are just buying not for the their social utility of homes of being in a home, right, what they’re buying because of speculation or storing their value of thinking about you know way to store value in the long term, it has increased through the last decades and continues to increase which you know I don’t know if 10% of the real estate market is you know attributed to kind of like monetary premium. I don’t know if it’s 90%. It’s somewhere in between, right, but for now, let’s keep that as a concept and the model we can change the assumption. Later, I’ll give some numbers. Like you know, we know that 40% only of the homes in the US, our owner occupied, right? So that gives an idea that there is large amount of properties are not also the volumes that we see in real estate investment trust in the US keep going up, which again is evidence that people use these as a way to store. Right in the is a whole separate discussion around is really real estate, a good store of value in the long term, which I don’t think it is, but let’s leave that aside for a SEC. I think you know real estate is a shift client, right? You know, you don’t own it and there’s like several problems with that. Maintenance is I was a real estate investor in the past. You know I owned You know more than a few properties and some of them gave me headaches that I really don’t want to have anymore in in my life. The only thing I need to worry about is you’ll see if you know. Check my notes from time to time, see if the blocks are coming through right. You know, it doesn’t call me at 3:00. O’clock in the morning with a water leak much.
Rapha Zagury 01:00:02
The government is not going to be coming after it with more taxes, right? Or maybe the day. But you know, that’s again a separate discussion.
Real estate, I think there’s massive issues and I think people are kind of blinded because first of all, if you imagine so when you head on your house right, you get you walk to your house every day and then your front door, you have a ticker price ticker of how much your house is worth that day.Versus the day before, right? And people can bid and ask that all day long, they can buy a piece of your property. They can sell a piece of your property right I guarantee you wouldn’t be able to withstand that volatility, right? Because the instance where I had like a water leak and you know the house was full of mold, right, the price would have. Collapsed right. People will only be willing to buy that house. It’s like half of the price that actually the house next door was being sold and I would see that in my door. I would panic, right? But people don’t see it like prices are actually not shown on the day-to-day basis. They’re also on aggregates, right, which are that. It’s going to masquerade a lot of the factors in terms of what you’re seeing, but if they did, it’s very liquid, right?
Stephan Livera 01:01:04
Right. It’s illiquid. Yeah.
Rapha Zagury 01:01:07
And there’s also that part of the liquidity of the problem. If the liquidity is exactly that. But we know that there is, you know it’s a massive market, $320 trillion globally, right, estimated. And again that number. And can change up and down a little bit depending on what you read, but it’s a massive market. It’s one of the probably the one of the largest markets in in the world right along with. So I look at that as, OK, So what happens? It’s a very large number. What happens even with a very small probability, right, let’s put small probabilities on this and let’s see what happens if Bitcoin captures, maybe with a 10% probability it captures 10% of this market, right. And then run that number, you see what it wants, right? And then I did this. Across all different asset classes I did it for stocks, for bonds as I mentioned for real. Gold, silver and you can add any acid you want. You know, if you think that oil, for example, is monetized bad example, but any other commodity you could put it in the model and you can you? Can run the Numbers as well and then A couple of things that are interesting. When we run this this first of all is that you can put very low probability. This is also being a Trojan horse for conversations with norms because you know a lot of people say, I don’t think this is going to work and it’s always people talking about absolutes, right? What bother me a little bit about, you know? The talk that even sailor, that is that it’s an absolute he says, oh, this is going to happen. We are SharePoint we are going to go into hyper victimization. We are this is all going to happen right and then you also have on the other spectrum somebody like Dan Pena that keeps yelling Bitcoin is going to zero, Bitcoin is going. To 0, right? What this model does like forget about the extremes, let’s put a probability into it. So we put a probability right and what you find out is that even at very low probabilities, even if very low.
Rapha Zagury 01:02:56
So very high time horizon thinking, oh, I think real estate is going to be demonetized, demonetized by Bitcoin with a 10% probability in 20 years time that still results in very large numbers. For Bitcoin, our base case with what we ran there and again I can guarantee our base case is wrong. Right. But anybody can look at that and then they can get to their own estimates of what, what the probabilities are, what the time horizons are and also what the discount rate is. But our base case puts Bitcoin at $380,000 today, so the fair value would be today around $380,000.
And as Bitcoin captures more of that demonization, long time price should be going up to what the end price in this base case is a little bit more than $3 million, but anyway, people. And can the bullish case is Bitcoin should be worth $402 million today and it should be worth like $8 million in in the future, which means it will demonetize vast majority of these assets. So I was saying it’s kind of like a Trojan horse because I go to these to the advisors for example. And I say, OK, you don’t think Bitcoin is? Going to work out. But some scenarios will. What do you think? It’s the probability. And it’s amazing to see that these guys will come up with probabilities. There are even more bullish than mine, they would say, oh, I think it’s 510%. And then you plug that into the model and say, OK, if you think that Bitcoin should be worth today $500,000, right?
Rapha Zagury 01:04:23
And it’s not. So how do you explain that? And then they start to change the sense that so maybe it’s not 10%, maybe it’s a little bit less, but what that shows is that you know I think people are very hard time in conceptualizing what’s the, you know, the good and the extreme. Outcomes really are, so the outcome of hyperbolization, if it happens even if low probability, it represents a Bitcoin should be worth significantly. I’ve we’ve heard a lot that Bitcoin is the, you know, the most asymmetric bets that you have today. This proves it like you know, because it is very asymmetric. If you’re wrong, you know your downside. It’s gonna hurt you down, you know. Could go to 0, but there’s so much upside on this asset right through what could happen of the demonetization of other assets that it’s not even close, right. And everybody again, everybody shouldn’t have a piece of Bitcoin in their portfolio just because of that. Because it does have the you know the potential to capture a lot of the premium in in other markets. And if it does that, you know 1% that 2% that you have in your portfolio is really going to make a difference long term?
Stephan Livera 01:05:33
Right. and then as people start even paying a small percent typically? What that does is as lots more people are coming into Bitcoin as we see it grows the network effect and then it sort of increases, even the likelihood that it does it go to that level. So you know it’s kind of a funny reflexivity there.
Rapha Zagury 01:05:50
And I just remember something like we launched the site last week, right? So it wasn’t a Twitter spaces with. A lot of people and then I mentioned it and you know the thing just blew up, but Greg Foss was on the audience. So Foss gets. Of course this is pure math, right? So he gets really excited about it and he comes into. Space and it’s like man, I’m running the number and I ran the numbers and said, oh, listen, if you add Bitcoin to this portfolio, it actually reduces your risk, reduces your drawdown. Right. And then he comes back. He’s like, I ran the numbers and I don’t know. I’m still seeing a 90% drop down. I don’t know what’s happening. Right. And I could, I couldn’t reconcile my numbers and I said OK, let’s after the call, we’ll talk about that. We’ll see what’s happening, right, so. Finish up the call. I run the numbers and what happened was the following. He actually he created a simulation where he included the 1% allocation to Bitcoin in 2014, but never rebalanced that. So that 1% actually became the portfolio after a while. And of course now that you know that 1% became close to 100%.
You are going to face the large drawdowns of Bitcoin and I thought you’re like Greg in this situation, you’re going to have a 90% drop down and you’re going to be happy that you had it because it just means that your. Profile is doing much better so just that.
Stephan Livera 01:07:07
Yeah, I mean it. It’s right I think anyone who was around in the early days and did not rebalance. Bitcoin became a large percent of their liquid net worth let’s say, and I one other area to touch on is alt coins or, as I prefer to call them, shitcoins. What’s the problem of diversifying into alt coins? Because I know this is another area that you actually ran some of the numbers, right? Because a lot of people are coming to this, maybe they’re not thinking about the tax consequences. They’re not thinking about fees. They’re not. They’re just looking at kind of very specific outliers and. And hanging their hopes on that. So what? What does your research say about, quote UN quote diversifying into altcoins?
Rapha Zagury 01:07:48
Yeah, you’re not really the first. Fine, right, so Yeah, that’s Dewey, let’s say. Your worst is fine, that’s the TLDR. TLDR, right. But so you tell what we ran, because that’s also a question that I got a lot, right and it really hurts like, every second guarantee we’re going to see, we see this this cycle again, right is people go straight. I’m like being a no coin others to thinking they are experts in in crypto currencies or shift coins right and buying like the most obscure things that you could see and that happens like in a matter of months depending on some. Some of these guys, right, and that always bother me. So what I did is I went back to 2016 and downloaded the historical data from all of these different. Shitcoins right. Imagine like from the most obscure ones to some that we know, right, like dog coins all of that and ran the numbers. So OK, let’s compare them with Bitcoin and let’s see, you know, if you start in 2016, look at the performance of Bitcoin. This is on Twitter, and I have a Twitter on that and there’s a chart showing this. I think it’s good to visualize on the chart so the result? The end result is the following. So from 2016 until now, if you bought it’s around 8000 different coins, right? 5000 roughly don’t have price anymore, so you can’t find the price. They’re price. There’s they. Yeah. They they’re either so illiquid that there isn’t a price out there or just they just died. Right. So they went to nowhere right.
Stephan Livera 01:09:08
So you got zeroed.
Rapha Zagury 01:09:17
From that also the let me talk. Just tell you a little bit about the top ones. So the top ones, they’re 40 ones that actually outperform Bitcoin through that time period, right? And these 41 if you bought them, you got 46% more Bitcoin. So for every one Bitcoin that you had, they had. Now we have 1.46 Bitcoin on average right from these four. It’s also crazy that, you know, I put this out there first question that somebody asked like. So what are the four there? Because I and I’m not going to tell you what the 40s are, because some of these are really again obscure names that you never heard. And I guarantee you’ve never invested on that, but the other 2000 something right, they are in between. But if you bought. These two thousand that actually have a price now, right? They’re taking out the ones that went. Zero on average for every Bitcoin that you had you have 0.04 Bitcoin, so you lost 96% of your Bitcoin. So people that are diversifying, I can’t guarantee you they’re not gonna buy if they think, oh, I’m gonna divorce.
Fine to Shitcoins they’re not gonna buy the 40 that outperform. They may buy 80 from those 80, they’re gonna have the 40 that I’ve maybe they’re gonna have the 40 that outperform. They were very like they picked the four that outperformed, but the other four that underperform are going to crush them.They’re going to give away their bitcoin and I don’t think because we’ve seen so many of these things, you know, have a cycle of coming out, you know being the latest thing saying that they have something that is revolutionary, that is going to take over the world, right. And they skyrocket, and then they collapse on the chart. They can actually see the ICO crage very easily, you see. You know all of these tokens like outperforming Bitcoin, you know through 2017, 2008. And then on the right side of this, when Bitcoin collapsed, you see all of these going to 0 pretty much on 9. And then one thing that gives me hope is that if we actually look at this last cycle, you see that the craze is actually you see less of these shitcoins actually going much higher.
Rapha Zagury 01:11:14
You see a lot of them just being already like in the bottom, not even taking off at any period of time. But then the most interesting part of this chart is looking at the right side. The bottom it’s like all the shitcoins are dying there. Like you have this dense amount that’s the Shitcoin cemetery there, like they’re all going to die at that corner there of that chart, right. So there is no diversification there. I also like to talk about the fundamentals. Yeah, because a lot of people forget that all of these, right, if they are going to be in this market, they need to be open source. There’s no there isn’t a single coin that is going to be out there is not open source cause people are not gonna buy it, right? Or maybe they will, but the vast majority of them are open source, so the code is open to anybody to review and to see what. Is if there is something that is so good in the code right that came up with something that is so revolutionary is so good. We have very good developers in Bitcoin. There’s a lot of incentives in Bitcoin to adapt that technology right. So eventually that technology is going to be adopted and it’s going to just plug in. My laptop here so and it is going to have an impact on Bitcoin cause that again if it is a good technology that can be copied and it makes sense to be copied, it will be copied. And unless something happens very quickly overnight, something that is absolutely out of the blue revolutionaries, again, if it’s not in any of these coins right, there is no reason to buy them up until then. They are just test Nets for Bitcoin at the end of the day, right? We’ve seen this happen like you know, there are some examples like Segways when it happens in light. Mine was great to see that it actually worked, and then it could be implemented in. Bitcoin later, right? So a lot of these, if you think are you’re going to be early enough to determine that. You know, there is really a technology there. There’s revolutionary, there is something there that it’s not going to be able to be copied into bits.
Rapha Zagury 01:13:03
I think people are just wrong. That’s not going to happen, right. We will need to change things to change significantly from where they are today and who knows, maybe in the future, Bitcoin becomes so ossified that it’s hard to implement changes and something comes up. But you’re not there. At this point you’re not even close, right? So just keep that in mind. As well, because the fundamentally there is no reason to invest in these coins, they are gonna have extremely high correlation to and you see that to Bitcoin like, you know, Bitcoin price comes down, they all collapse and they go down significantly more. So they’re not only leverage plays, it’s worse because when they come down, they come down significantly more than when they go up, right? So there’s no diversification in outgoing, you’re actually gonna end up with something that is worse than just buying Bitcoin.
Stephan Livera 01:13:52
Yeah, unfortunately, a lot of people fall for this and I think what happens is it’s similar to when people are gamblers and they love to tell you about their big wins, but they haven’t been diligently tracking every single thing and they’re not telling you about all those losses. And really, in reality, for most people now, OK, maybe there’s a few shitcoin insider shitcoin elite people who maybe they’re making money they’re making bank. But the most people just end up losing. Making money on these things, and we’ve also have to think about for some people there could be tax implications, there could be fees, there could be all these other implications that make it even worse. So the reality is, for a lot of people, they end up de worser fying and not diversifying. So that’s probably the short version there. But we should probably wrap up here. So Rafael, where’s the best place for people to find you? And to find your work.
Rapha Zagury 01:14:39
Yeah, so most of the stuff I mentioned is on Nakamoto portfolio. Come on Twitter I’m alpha Zeta with two a so alpha Zeta so they can find me there as well. I’m always posting no the way we do these, any of these research that it usually goes 1st through Twitter. So I’ll make a Twitter thread, I’ll post it out there and of some of the highlights and get some feedback and then make adjustments and then launch it on the website. Launch a research report and then launched the code as well. But again, people can find me on Twitter and I’m happy to answer any questions. DMS here any kind of inputs on the models, that’s what we want. This is part of the day this is all through the community, right and. Change these modify send the PR to the GitHub whatever you want. That’s what we want to see?
Stephan Livera 01:15:29
And that’s the ethos we wanna see. So let’s just make sure you go and follow Rafa. He’s been posting about Bitcoin for years, a lot of great stuff, so make sure you follow him and Rafa. Thank you for joining me.
Rapha Zagury 01:15:38
It’s my pleasure. Thanks, Stephan. Great to be here.