What is my take on these four: Bitcoin, Ethereum, Steem, and Golem?

My editorial on You Tube

I am (re)learning investment in the stock market, and I am connecting the two analytical dots I developed on in my recent updates: the method of mean-reversion, and the method of extrapolated return on investment. I know, connecting two dots is not really something I necessarily need my PhD in economics for. Still, practice makes the master. Besides, I want to produce some educational content for my students as regards cryptocurrencies. I have collected some data as regards that topic, and I think it would be interesting to pitch cryptocurrencies against corporate stock, as financial assets, just to show similarities and differences.

As I return to the topic of cryptocurrencies, I am returning to a concept which I have been sniffing around for a long time, essentially since I started blogging via Discover Social Sciences: the concept of complex financial instruments, possibly combining future contracts on a virtual currency, possibly a cryptocurrency, which could boost investment in new technologies.

Finally, I keep returning to the big theoretical question I have been working on for many months now: to what extent and how can artificial intelligence be used to represent the working of collective intelligence in human societies? I have that intuition that financial markets are very largely a tool for tacit coordination in human societies, and I feel that studying financial markets allows understanding how that tacit coordination occurs.

All in all, I am focusing on current developments in the market of cryptocurrencies. I take on four of them: Bitcoin, Ethereum, Steem, and Golem. Here, one educational digression, and I am mostly addressing students: tap into diversity. When you do empirical research, use diversity as a tool, don’t run away from it. You can have the illusion that yielding to the momentary temptation of reducing the scope of observation will make that observation easier. Well, not quite. We, humans, we observe gradients (i.e. cross-categorial differences and change over time) rather than absolute stationary states. No wonder, we descend from hunters-gatherers. Our ancestors had that acute intuition that when you are not really good at spotting and hitting targets which move fast, you have to eat targets that move slowly. Anyway, take my word on it: it will be always easier for you to draw conclusions from comparative observation of a few distinct cases than from observing just one. This is simply how our mind works.

The four cryptocurrencies I chose to observe – Bitcoin, Ethereum, Steem, and Golem – represent different applications of the same root philosophy descending from Satoshi Nakamoto, and they stay in different weight classes, so to say. As for that latter distinction, you can make yourself an idea by glancing at the table below:

Table 1

CryptocurrencyMarket capitalization in USD, as of April 26th, 2019Market capitalization in USD, as of April 26th, 2020Exchange rate against USD, as of April 26th, 2020
Bitcoin (https://bitcoin.org/en/ )93 086 156 556140 903 867 573$7 679,87 
Ethereum (https://ethereum.org/ )16 768 575 99821 839 976 557$197,32 
Steem (https://steem.com/ )111 497 45268 582 369$0,184049
Golem (https://golem.network/)72 130 69441 302 784$0,042144

Before we go further, a resource for you, my readers: all the calculations and source data I used for this update, accessible in an Excel file, UNDER THIS LINK.

As for the distinctive applications, Bitcoin and Ethereum are essentially pure money, i.e. pure financial instruments. Holding Bitcoins or Ethers allows financial liquidity, and the build-up of speculative financial positions. Steem is the cryptocurrency of the creative platform bearing the same name: it serves to pay creators of content, who publish with that platform, to collect exchangeable tokens, the steems. Golem is still different a take on encrypting currency: it serves to trade computational power. You connect your computer (usually server-sized, although you can go lightweight) to the Golem network, and you make a certain amount of your local computational power available to other users of the network. In exchange of that allowance, you receive Golems, which you can use to pay for other users’ computational power when you need some. Golems are a financial instrument serving to balance deficits and surpluses in a complex network of nested, local capacities. Mind you, the same contractual patterns can be applied to balancing any type of capacities, not just computational. You can use it for electric power, hospital beds etc. – anything that is provided by locally nested fixed assets in the presence of varying demand.

Thus, below we go further, a reminder: Bitcoins and Ethers pure money, Steem Payment for Work, Golems Payment for Access to Fixed Assets. A financial market made of those four cryptocurrencies represents something like an economy in miniature: we have the labour market, the market of productive assets, and we have a monetary system. In terms of size (see the table above), this economy is largely and increasingly dominated by money, with labour and productive assets manifesting themselves in small and decreasing quantities. Compared to a living organism, it would be a monstrous shot of hormones spreading inside a tiny physical body, i.e. something like a weasel.

In the following part of this update, I will be referring to the method of mean-reversion, and to that of extrapolated rate of return. I am giving, below, simplified summaries of both, and I invite those among my readers who want to have more details to my earlier updates. More specifically, as regards the method of mean-reversion, you can read: Acceptably dumb proof. The method of mean-reversion , as well as Fast + slower = compound rhythm, the rhythm of life. As for the method of extrapolated rate of return, you can refer to: Partial outcomes from individual tables .

Now, the short version. Mean-reversion, such as I use it now for financial analysis, means that I measure each daily closing price, in the financial market, and each daily volume of trade, as the difference between the actual price (volume), and the moving cumulative average thereof, and then I divide the residual difference by the cumulative moving standard deviation. I take a window in time, which, in what follows, is 1 year, from April 26th, 2019, through April 26th, 2020. For each consecutive day of that timeframe, I calculate the average price, and the average volume, starting from day 1, i.e. from April 26th, 2019. I do the same for standard deviation, i.e. with each consecutive day, I count standard deviation in price and standard deviation in volume, since April 26th, 2019.

Long story short, it goes like…

May 10th, 2019 Average (April 26th, 2019 –> May 10th, 2019), same for standard deviation

May 20th, 2019 Average (April 26th, 2019 –> May 20th, 2019), same for standard deviation

… etc.

Mean-reversion allows comparing trends in pricing and volumes for financial instruments operating at very different magnitudes thereof. As you could see from the introductory table, those 4 cryptocurrencies really operate at different levels of pricing and volumes traded. Direct comparison is possible, because I standardize each variable (price or volume) with its own average value and its own standard deviation.

The method of extrapolated return is a strongly reductionist prediction of future return on investment, where I assume that financial markets are essentially cyclical, and my future return is most likely to be an extrapolation of the past returns. I take the same window in time, i.e. from April 26th, 2019, through April 26th, 2020. I assume that I bought the given coin (i.e. one of the four studied here) on the last day, i.e. on April 26th, 2020. For each daily closing price, I go: [Price(Day t) – Price(April 26th. 2020)] / Price(April 26th. 2020). In other words, each daily closing price is considered as if it was bound to happen again in the year to come, i.e. from April 26th, 2020 to April 26th, 2021. Over the period, April 26th, 2019 – April 26th, 2020, I count the days when the closing price was higher than that of April 26th, 2020. The number of those ‘positive’ days, divided by the total of 366 trading days (they don’t stop trading on weekends, in the cryptocurrencies business), gives me the probability that I can get positive return on investment in the year to come. In other words, I calculate a very simple, past experience-based probability that buying the given coin on April 26th, 2020 will give me any profit at all over the next 366 trading days.

I start presenting the results of that analysis with the Bitcoin, the big, fat, patient-zero beast in the world of cryptocurrencies. In the graph below, you can see the basic logic of extrapolated return on investment, which, in the case of Bitcoin, yields a 69,7% probability of positive return in the year to come.

In the next graph, you can see the representation of mean-reverted prices and quantities traded, in the Bitcoin market. What is particularly interesting here is the shape of the curve informative about mean-reverted volume. What we can see here is consistent activity. That curve looks a bit like the inside of an alligator’s mouth: a regular dentition made of relatively evenly spaced spikes. This is a behavioural piece of data. It says that the price of Bitcoin is shaped by regular, consistent trade, all year long. This is like a busy market place, and busy market places usually yield a consistent equilibrium price. 

The next in line is Ethereum. As you can see in the next graph, below, the method of extrapolated return yields a probability of any positive return whatsoever, for the year to come, around 36,9%. Not only is that probability lower than the one calculated for the Bitcoin, but also the story told by the graph is different. Partial moral of the fairy tale: cryptocurrencies differ in their ways. Talking about ‘investing in cryptocurrencies’ in general is like talking about investing in the stock market: these are very broad categories. Still, of you pitch those probabilities for the Bitcoin and for the Ethereum against what can be expected in the stock market (see to: Partial outcomes from individual tables), cryptocurrencies look really interesting.

The next graph, further below, representing mean-reversion in price and volume of Ethereum, tells a story similar to that of the Bitcoin, yet just similar. As strange as it seems, the COVID crisis, since January 2020, seems to have brought a new breeze into that house. There had been a sudden spike in activity (volumes traded) in the beginning of 2020, and that spike in activity led to a slump in price. It is a bit as if a lot of investors suddenly went: ‘What? Those old Ethers in my portfolio? Still there? Unbelievable? I need to get rid of them. Jeeves! Please, be as kind and give those old Ethers to poor investors from the village.’. Another provisional lesson: spikes in activity, in any financial market, can lead both to appreciation of a financial instrument, and to its depreciation. This is why big corporations, and stockbrokers working for them, employ the services of market moderators, i.e. various financial underwriters who keep trading in the given stock, sort of back and forth, just to keep the thing liquid enough to make the price predictable. 

Now, we go into the world of niche cryptocurrencies: the Steem and the Golem. I present their four graphs (Extrapolated return *2, Mean-reversion *2) further below, and now a few general observations about those two. Their mean-reverted volumes are like nothing even remotely similar to the dentition of an alligator. An alligator like that couldn’t survive. Both present something like a series of earthquakes, of growing magnitudes, with the greatest spike in activity in the beginning of 2020. Interesting: it looks as if the COVID crisis had suddenly changed something for these two. When combined with the graphs of extrapolated return, mean-reverted prices and quantities tell us a story of two cryptocurrencies which, back in the day, attracted a lot of attention, and started to have sort of a career, but then it all went flat, and even negative. This is the difference between something that aspires to be money (Steem, Golem), and something that really is money (Bitcoin, Ethereum). The difference is in the predictably speculative patterns of behaviour in market participants. John Maynard Keynes used to stress the fact that real money has always two functions: it serves as a means of payment, and it is being used as a speculative asset to save for later. Without the latter part, i.e. without the propensity to save substantial balances for later, a wannabe money has no chance to become real money.   

Now, I am trying to sharpen my thinking in terms of practical investment. Supposing that I invest in cryptocurrencies (which I do not do yet, although I am thinking about it), what is my take on these four: Bitcoin, Ethereum, Steem, and Golem? Which one should I choose, or how should I mix them in my investment portfolio?

The Bitcoin seems to be the most attractive as investment, on the whole. Still, it is so expensive that I would essentially have to sell out all the stock I have now, just in order to buy even a small number of Bitcoins. The remaining three – Ethereum, Steem and Golem – fall into different categories. Ethereum is regular crypto-money, whilst Steem and Golem are niche currencies. In finance, it is a bit like in exploratory travel: if I want to go down a side road, I’d better be prepared for the unexpected. In the case of Steem and Golem, the unexpected consists in me not knowing how they play out as pure investment. To the extent of my knowledge, these two are working horses, i.e. they give liquidity to real markets of something: Steem in the sector of online creation, Golem in the market of networked computational power. Between those two, I know a bit about online creation (I am a blogger), and I can honestly admit I don’t know s**t about the market of networked computation. The sensible strategy for me would be to engage into the Steem platform as a creator, take my time to gain experience, see how those Steems play out in real life as a currency, and then try to build an investment position in them.

Thus, as regards investment strictly I would leave Steem and Golem aside and go for Ethereum. In terms of extrapolated rate of return, Ethereum offers me chances of positive outcomes comparable to what I can expect from the stock of PBKM, which I already hold, higher chances of positive return than other stock I hold now, and lower chances than, for example, the stock of First Solar or Medtronic (as for these considerations, you can consult Partial outcomes from individual tables ).   

OK, so let’s suppose I stay with the portfolio I already hold –11Bit, Airway Medix , Asseco Business Solutions, Bioton, Mercator Medical, PBKM – and I consider diversifying into Ethereum, First Solar , and Medtronic. What can I expect? As I look at the graphs (once again, I invite you to have a look at Partial outcomes from individual tables ), Ethereum, Medtronic and First Solar offer pretty solid prospects in the sense that I don’t have to watch them every day. All the rest looks pretty wobbly: depending on how the whole market plays out, they can become good investments or bad ones. In order to become good investments, those remaining stocks would need to break their individual patterns expressed in the graphs of extrapolated return and engage into new types of market games.

I can see that with the investment portfolio I currently hold, I am exposed to a lot of risk resulting from price volatility, which, in turn, seems to be based on very uneven market activity (i.e. volumes traded) in those stocks. Their respective histories of mean-reverted volumes look very uneven. What I think I need now are investment positions with less risk and more solidity. Ethereum, First Solar , and Medtronic seem to be offering that, and yet I am still a bit wary about coming back (with my money) to the U.S. stock market. I wrapped up my investments there, like one month ago, because I had the impression that I cannot exactly understand the rules of the game. Still, the US dollar seems to be a good investment in itself. If I take my next portion of investment, scheduled for the next week, i.e. the rent I will collect, transferring it partly to the U.S. market and partly to the Ethereum platform will expose just some 15% of my overall portfolio to the kind of risks I don’t necessarily understand yet. In exchange, I would have additional gains from investing into the US dollar, and additional fun with investing into the Ethereum.

Good. When I started my investment games by the end of January, 2020 (see Back in the game), I had great plans and a lot of doubts. Since then, I received a few nasty punches into my financial face, and yet I think I am getting the hang of it. One month ago, I managed to surf nicely the crest of the speculative bubble on biotech companies in the Polish stock market (see A day of trade. Learning short positions), and, in the same time, I had to admit a short-term defeat in the U.S. stock market. I yielded to some panic, and it made me make some mistakes. Now, I know that panic manifests in me both as an urge to act immediately, and as an irrational passivity. Investment is the art of controlling my emotions, as I see.

All I all, I have built an investment portfolio which seems to be taking care of itself quite nicely, at least in short perspective (it has earnt $238 over the last two days, Monday and Tuesday), and I have coined up my first analytical tools, i.e. mean-reversion and extrapolation of returns. I have also learnt that analytical tools, in finance, serve precisely the purpose I just mentioned: self-control.

We’d better make that change liveable

My editorial on You Tube

I continue developing my ideas. Most people do, all the time, actually: they keep developing their own ideas, and other people’s ideas, and, on the whole, we just develop our ideas.

Good. Linguistic warm up done, I go to work. I continue what I started in my last update ( Steady inflow of assets and predictable rules ): a workable business concept for restarting local economies after COVID-19 lockdowns, and during the ongoing pandemic. Last time, I studied the early days of the Bitcoin, in the hope of understanding how a completely new economic scheme emerges. As hope crystalizes into something more structured, ideas emerge. I am going to make a quick sketch of what I have come up with, and then I will try give it some shine by using my observations as regards the early infancy of the Bitcoin.  

As I observe the present situation, I can see that local communities both need and accumulate some typical goods and assets. The most immediately needed, and semi-instinctively accumulated goods are those serving personal protection and hygiene: gloves, facial protections (masks, covers, googles etc.), scrubs and aprons, bonnets, soap, ethanol-based sanitizers. I wonder, and, honestly, I would gladly do with the consultation of an epidemiologist, to what extent an abundant use of those hygienic goods can be substitute to social distancing. I mean, to what extent can we restart social interactions with adequate protection?

Anyway, I am quite confident that local communities will be accumulating what I provisionally call ‘epidemic assets’. The challenge consists in using that phenomenon, and those assets, so as to give some spin to economies brought down by lockdowns.

Now, I am using basic laws of economics. Whenever and wherever some stock of medical supplies will be accumulated, it will be inventories, i.e. circulating assets subject to storage and endowed with direct economic utility, but not to amortization. Sooner or later, substantial inventories of anything attract the company of some fixed assets, such as buildings, equipment, and intellectual property, on the one hand, as well as the company of other circulating assets (e.g. receivable claims on third parties), and, finally, the company of JOBS, which are the key point here.   

Now, let’s imagine the following scenario. A local community, e.g. local hospital plus local city council, need to have a given amount of ‘epidemic assets’ stored and ready to use, just to keep the local epidemic situation under control. They need those epidemic assets, yet, as the local economy is stricken by epidemic lockdown, they don’t have enough money (or no money at all) to pay for those assets. Here starts the gamble. The local community offers the suppliers of epidemic assets to be paid in tokens of a virtual currency, where each token corresponds to a futures contract with claims on a future stock of epidemic assets.

The central idea is that with the virus around, everybody will have a keen interest in having enforceable claims on epidemic assets. That keen interest will be driven by two motives. In the first place, many people will need to use those epidemic assets like directly and personally. Secondly, those assets will be valuable, and futures contracts on them will have monetizable, financial value. It should be possible to create a circulation of those tokens (futures), where the direct supplier of epidemic assets can use those tokens to pay their own suppliers of intermediate goods, as well as to pay a part of the payroll. Those whom he pays will either consume those futures to grab some epidemic assets, or make those futures circulate further.

As those tokenized futures contracts on epidemic assets get developed and put in circulation, we can use the relatively recent invention called ‘smart contract’. A complex contract can be split into separate component parts, like LEGO blocks, each endowed with a different function. Users can experiment with each part separately, and the actual contracts they sign and trade are compound legal schemes. For now, I can see 3 principal LEGO blocks. The first one is the exact substance of the claim incorporated in the tokenized contracts. Futures contracts have this nuance in them: they can embody claims on a certain quantity of specified goods or assets, e.g. 100 kg of something, or on a nominal financial value of those goods or assets, like $100 worth of something.     Maturity of the claim is another thing. Futures contracts have a time horizon in them: 1 month, 6 months, 12 months etc. In this specific case, maturity of claims is the same as the lifecycle of one tokenized contract, and, honestly, if this scheme is applied in real life, we will be sailing uncharted waters. Those tokens are supposed to keep local economies going, and therefore they’d better have a long lifecycle. Hardly anyone would trust quasi – monetary tokens with a lifespan of 3 months. On the other hand, the longest futures I have seen, like those on coffee or wheat, stretch over 6 months, rarely longer. Here comes the third building block, namely convertibility of the claim. If we want the system to work smoothly, i.e. inspire trust in exchange, and be realistic in the same time, we can make those tokens convertible into something else. They could convert into similar tokens, just valid over the next window of trade, or into something else, e.g. shares in the equity of newly built local hospitals. Yes, we are certainly going to build more of them, trust me.  

Building blocks in hand, we start experimenting. Looking at the phases I distinguished in the early infancy of the Bitcoin (once again, you can look up Steady inflow of assets and predictable rules ), I see three essential steps in the development of this scheme. The first step would consist in creating a first, small batch of those tokenized contracts and test them in deals with whoever would like to try. The experience of the Bitcoin shows that once the thing catches on (and IF the thing catches on), i.e. once and if there are any businesspeople interested, it should spread pretty quickly. Then comes the second phase, that of building large portfolios of those tokenized contracts in a relatively small and select community, sort of Illuminati of medical supplies. In that phase, which is likely to be pretty long, like 1,5 year, said Illuminati will be experimenting with the exact smart structure those contracts, so as to come up with workable, massively reproducible patterns for the third phase, that of democratization. This is when the already hammered and hardened contractual patterns in those tokens will spread to a larger population. Individual balances of those tokens are likely to shrink in that third phase and become sort of standardized. This could be the moment, when our tokenized contracts can start being used as a vehicle for saving economic value over time, and it looks like a necessary condition for driving it out of its so-far autonomous, closed market into exchangeability against money.

That would be all for today. If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com . If anyone wants to bounce this ball off their bat, you are welcome. I am deeply convinced that we need to figure out some new s**t. Our world is changing, and we’d better make that change liveable.

Steady inflow of assets and predictable rules

My editorial on You Tube

Clink! The coin dropped… I have been turning that conceptual coin between my synapses for the last 48 hours, and here it is. I know what I have been thinking about, and what I want to write about today. I want to study the possible ways to restart business and economy in the midst of the COVID-19 pandemic.

There is a blunt, brutal truth: the virus will stay with us until we massively distribute an efficient vaccine against it, and that is going to take many months, most probably more than a year. Until then, we need to live our lives, and we cannot live them in permanent lockdown. We need to restart, somehow, our socio-economic structures. We need to overcome our fears, and start living in the presence of, and in spite of danger.

Here come three experiences of mine, which sum up to the financial concept I am going to expose a few paragraphs further. The first experience is that of observing a social project going on in my wife’s hometown, Starachowice, Poland, population 50 000. The project is Facebook-named ‘The Visible Hand’ (the original Polish is: Widzialna Ręka), and it emerged spontaneously with the COVID-19 crisis. I hope to be able to present the full story of those people, which I find truly fascinating, and now, I just give a short glimpse. That local community has created, within less than two weeks, something like a parallel state, with its supply system for the local hospital, and for people at risk. They even go into developing their own technologies of 3D printing, to make critical medical equipment, such as facial masks. Yesterday, I had a phone conversation with a friend, strongly involved in that project, and my head still resonates with what he said: ‘Look, the government is pretty much lost in all that situation. They pretend a lot, and improvise a lot, and it is all sort of more pretending than actually doing things. Our local politicians either suddenly evaporated, or make clumsy, bitchy attempts to boost their popularity in the midst of all that s**t. But people… Man, people are awesome. We are doing together things that our government thinks it is impossible to do, and we are even sort of having fun with it. The sense of community is nothing short of breath-taking’.

My second experience is about the stock market. If you have been following my updates since the one entitled ‘Back in the game’, you know that I decided to restart investing in the stock market, which I had undertaken to do just before the s**t hit the fan, a few weeks ago. Still, what I am observing right now, in the stock market, is something like a latent, barely contained energy, which just seeks any opportunity to engage into. Investors are really playing the game. Fear, which I could observe two weeks ago, has almost vanished from the market. Once again, there is human energy to exploit positively.

There is energy in people, but it is being locked down, with the pandemic around. The big challenge is to restart it. Right now, many folks lose their jobs, and their small businesses. It is important to create substantial hope, i.e. hope which can be turned into action. Here comes my third experience, which is that of preparing a business plan for an environmental project, which I provisionally call Energy Ponds (see Bloody hard to make a strategy and The collective archetype of striking good deals in exports for latest developments). As I prepare that business plan, I keep returning to the conclusion that I need some sort of financial scheme for situations when a local community, willing to implement the technology I propose, is short of capital and needs to sort of squeeze money out of the surrounding landscape.

Those three experiences of mine, taken together, lead me back to something I studied 3 years ago, when I was taking my first, toddler’s steps in scientific blogging: the early days of the Bitcoin. Today, the Bitcoin is the big, sleek predator of financial markets, yet most people have forgotten how that thing was born. It was an idea for safe financial transactions, based on an otherwise old concept of financial law called ‘endorsement of debt’, implemented in the second year of the big financial crisis, i.e. in 2009, to give some liquidity to small networks of just as small local businesses. Initially, for more than 18 first months of existence, the Bitcoin was a closed system of exchange, without any interface with any established currency. As far as I know, it very much saved the day for many small businesses, and I want to study the pattern of success, so as to see how it can be reproduced today for restarting business in the context of pandemic.

Before I go analytical, two general remarks. Firstly, there is plenty of folks who pretend having the magical recipe for the present s**t we are waist-deep in. I start from the assumption that we have no fresh, general experience of pandemics, and pretending to have figured the best way out is sheer bullshit. Still, we need to explore and to experiment, and this is very much the spirit I pursue.

Secondly, the Bitcoin is a cryptocurrency, based on the technology designated as Blockchain. What I want to take away is the concept of virtual financial instrument focused on liquidity, rather than the strictly spoken technology. Of course, platforms such as Ethereum can be used for the purpose I intend to get across, here below, still they are just an instrumental option.  

Three years ago, I used data from https://www.quandl.com/collections/markets/bitcoin-data,  which contains the mathematical early story of what has grown, since, into the father of all cryptocurrencies, the Bitcoin. I am reproducing this story, now, so as to grasp a pattern. Let’s walse. I am focusing on the period, during which the Bitcoin started, progressively acquired any exchangeable value against the US dollar, and finished by being more or less at 1:1 par therewith. That period stretches from January 3rd, 2009 until February 10th, 2011. You can download the exact dataset I work with, in the Excel format, from this link:

https://discoversocialsciences.com/wp-content/uploads/2020/03/Bitcoin-Early-days-to-share.xlsx .

Before I present my take on that early Bitcoin story, a few methodological remarks. The data I took originally contains the following variables: i) total number of Bitcoins mined, ii) days   destroyed non-cumulative, iii) Bitcoin number of unique addresses used per day, and iv) market capitalization of the Bitcoin in USD. On the basis of these variables, I calculated a few others. Still, I want to explain the meaning of those original ones. As you might know, Bitcoins were initially mined (as far as I know, not anymore), i.e. you could generate 1 BTC if you solved a mathematical riddle. In other words, the value you had to bring to the table in order to have 1 BTC was your programming wit plus computational power in your hardware. With time, computational power had been prevailing more and more. The first original variable, i. e. total number of Bitcoins mined, is informative about the total real economic value (computational power) brought to the network by successive agents joining it.  

Here comes the first moment of bridging between the early Bitcoin and the present situation. If I want to create some kind of virtual financial system to restart, or just give some spin to local economies, I need a real economic value as gauge and benchmark. In the case of Bitcoin, it was computational power. Question: what kind of real economic value is significant enough, right now, to become the tool for mining the new, hypothetical virtual currency? Good question, which I don’t even pretend to have a ready-made answer to, and which I want to ponder carefully.

The variable ‘days destroyed non-cumulative’ refers to the fact that Bitcoins are crypto-coins, i.e. each Bitcoin has a unique signature, and it includes the date of the last transaction made. If I hold 1 BTC for 2 days, and put it in circulation on the 3rd day, on the very same 3rd day I destroy 2 days of Bitcoins. If I hold 5 Bitcoins for 7 days, and kick them back into market on the 8th day, I destroy, on that 8th day, 5*7 = 35 days. The more days of Bitcoin I destroy on the given day of transactions, the more I had been accumulating. John Maynard Keynes argued that a true currency is used both for paying and for saving. The emergence of accumulation is important in the shaping of new financial instruments. It shows that market participants start perceiving the financial instrument in question as trustworthy enough to transport economic value over time. Note: this variable can take values, like days = 1500, which seem absurd at the first sight. How can you destroy 1500 days in a currency born like 200 days ago? You can, if you destroy more than one Bitcoin, held for at least 1 day, per day.

The third original variable, namely ‘Bitcoin number of unique addresses used per day’, can be interpreted as the number of players in the game. When you trade Bitcoins, you connect to a network, you have a unique address in that network, and your address appears in the cumulative signature that each of the Bitcoins you mine or use drags with it.  

With those three original variables, I calculate a few coefficients of mine. Firstly, I divide the total number of Bitcoins mined by the number of unique addresses, on each day separately, and thus I obtain the average number of Bitcoins held, on that specific day, by one average participant in the network. Secondly, I divide the non-cumulative number of days destroyed, on the given day, by the total number of Bitcoins mined, and present in the market. The resulting quotient is the average number of days, which 1 Bitcoin has been held for.

The ‘market capitalization of the Bitcoin in USD’, provided in the original dataset from https://www.quandl.com/collections/markets/bitcoin-data, is, from my point of view, an instrumental variable. When it becomes non-null, it shows that the Bitcoin acquired an exchangeable value against the US dollar. I divide that market capitalization by the total number of Bitcoins mined, and I thus I get the average exchange rate of Bitcoin against USD.

I can distinguish four phases in that early history of the Bitcoin. The first one is the launch, which seems to have taken 6 days, from January 3rd, 2009 to January 8th, 2009. There were practically no players, i.e. no exchange transactions, and the number of Bitcoins mined was constant, equal to 50. The early growth starts on January 9th, 2009, and last just for 3 days, until January 11th, 2009. The number of Bitcoins mined grows, from 50 to 7600. The number of players in the game grows as well, from 14 to 106. No player destroys any days, in this phase. Each Bitcoin mined is instantaneously put in circulation. The average amount of Bitcoins per player evolves from 50/14 = 3,57 to 7600/106 = 71,7.

On January 12th, 2009, something changes: participants in the network start (timidly) to hold their Bitcoins for at least one day. This is how the phase of accelerating growth starts, and will last for 581 days, until August 16th, 2010. On the next day, August 17th, the first Bitcoins will get exchanged against US dollars. On that path of accelerating growth, the total number of Bitcoins mined passes from 7600 to 3 737 700, and the daily number on players in the network passes from an average around 106 to about 500 a day. By the end of this phase, the average amount of Bitcoins per player reaches 7475,4. Speculative positions (i.e. propensity to save Bitcoins for later) grow, up to an average of about 1500 days destroyed per address.

Finally, the fourth stage of evolution is reached: entry into the financial market, when we pass from 1 BTC = $0,08 to 1 BTC = $1. This transition from any exchange rate at all to being at par with the dollar takes 189 days, from August 17th, 2010 until February 10th, 2011. The total number of Bitcoins grows at a surprisingly steady rate, from 3 737 700 to about 5 300 000, whilst the number of players triples, from about 500 to about 1 500. Interestingly, in this phase, the average amount of Bitcoins per player decreases, from 7475,4 to 3 533,33. Speculative positions grow steadily, from about 1500 days destroyed per address to some 2 400 days per address.

Below, you will find graphs with a birds-eye view of the whole infancy of the Bitcoin. Further below, after the graphs, I try to give some closure, i.e. to guess what we can learn from that story, so as to replicate it, possibly, amid the COVID-19 crisis.  

My first general conclusion is that the total number of Bitcoins mined is the only variable, among those studied, which shows a steady, quasi linear trend of growth. It is not really exponential, more sort of a power function. The total number of Bitcoins mined corresponds, in the early spirit of this cryptocurrency, to the total computational power brought to the game by its participants. The real economic value pumped into the new concept was growing steadily, linearly, and to an economist, such as I am, it suggests the presence of exogenous forces at play. In other words, the early Bitcoin was not growing by itself, through sheer enthusiasm of its early partisans. It was growing because some people saw real value in that thing and kept bringing assets to the line. It is important in the present context. If we want to use something similar to power the flywheels of local markets under the COVID-19 restrictions, we need some people to bring real, productive assets to the game, and thus we need to know what those key assets should be. Maybe the capacity to supply medical materials, combined with R&D potential in biotech and 3D printing? These are just loose thoughts, as I observe the way that events are unfolding.

My second conclusion is that everything else I have just studied is very swingy and very experimental. The first behavioural transition I can see is that of a relatively small number of initial players experimenting with using whatever assets they bring to the table in order to generate a growing number of new tokens of virtual currency.  The first 7 – 8 months in the Bitcoin show the marks of such experimentation. There comes a moment, when instead of playing big games in a small, select network, the thing spills over into a larger population of participants. What attracts those new ones? As I see it, the attractive force consists in relatively predictable rules of the game: ‘if I bring X $mln of assets to the game, I will have Y tokens of the new virtual currency’, something like that.  

Hence, what creates propitious conditions for acquiring exchangeable value in the new virtual currency against the established ones, is a combination of steady inflow of assets, and crystallization of predictable rules to use them in that specific scheme.

I can also see that people started saving Bitcoins before these had any value in dollars. It suggests that even in a closed system, without openings to other financial markets, a virtual currency can start giving to its holders a sense of economic value. Interesting.

That would be it for today. If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com .