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.

Partial outcomes from individual tables

My editorial on You Tube

It is time to return to my investment strategy, and to the gradual shaping thereof, which I undertook in the beginning of February, this year (see Back in the game). Every month, as I collect the rent from the apartment I own and rent out, downtown, I invest that rent in the stock market. The date of collecting the next one approaches (it is in 10 days from now), and it is time for me to sharpen myself again for the next step in investment.

By the same occasion, I want to go scientific, and I want to connect the dots between my own strategy, and my research on collective intelligence. The expression ‘shaping my own investment strategy’ comes in two shades. I can understand it as the process of defining what I want, for one, or, on the other hand, as describing, with a maximum of objectivity, what I actually do. That second approach to strategy, a behavioural one, is sort of a phantom I have been pursuing for more than 10 years now. The central idea is that before having goals, I have values, i.e. I pursue a certain category of valuable outcomes and I optimize my actions regarding those outcomes. This is an approach in the lines of ethics: I value certain things more than others. Once I learn how to orient my actions value-wise, I can set more precise goals on the scale of those values.

I have been using a simple neural network to represent that mechanism at the level of collective intelligence, and I now, I am trying to apply the same logic at the level of my own existence, and inside that existence I can phenomenologically delineate the portion called ‘investment strategy in the stock market’. I feel like one of those early inventors, in the 18th or 19th century, testing a new idea on myself. Fortunately, testing ideas on oneself is much safer than testing drugs or machines. That thing, at least, is not going to kill me, whatever the outcome of experimentation. Depends on the exact kind of idea, though.

What meaningful can I say about my behaviour? I feel like saying something meaningful, like a big fat bottom line under my experience. My current experience is similar to very nearly everybody else’s experience: the pandemic, the lockdown, and everything that goes with it. I noticed something interesting about myself in this situation. As I spend week after week at home, more and more frequently I tend to ask myself those existential questions, in the lines of: “What is my purpose in life?”.  The frame of mind that I experience in the background of those questions is precisely that of the needle in my personal compass swinging undecidedly. Of course, asking myself this type of questions is a good thing, from time to time, when I need to retriangulate my personal map in the surrounding territory of reality. Still, if I ask those questions more and more frequently, there is probably something changing in my interaction with reality, as if with the time passing under lockdown I were drifting further and further away from some kind of firm pegs delineating my personal path.

Here they are, then, two of my behavioural variables, apparently staying in mutually negative correlation: the lower the intensity of social stimulation (variable #1), the greater the propensity to cognitive social repositioning (variable #2). This is what monks and hermits do, essentially: they cut themselves from social stimulation, so as to get really serious about cognitive social repositioning. With any luck, if I go far enough down this path, I reposition myself socially quite deeply, i.e. I become convinced that other people have to pay my bills so as I can experience the state of unity with the Divine, but I can even become convinced that I really am in a state of unity with the Divine. Of course, the state of unity lasts only until I need to pay my bills by myself again.

Good. I need to reinstate some social stimulation in my life. I stimulate myself with numbers, which is typical for economists. I take my investment portfolio such as it is now, plus some interesting outliers, and I do what I have already done once, i.e. I am being mean in reverse, pardon, mean-reverting the prices, and I develop on this general idea. This time, I apply the general line of logic to a metric which is absolutely central to any investment: THE RATE OF RETURN ON INVESTMENT. The general formula thereof is: RR = [profit] / [investment]. I am going to use this general equation, together with very basic calculation of probability, in order to build a PREDICTION BASED ENTIRELY ON AN EXTRAPOLATION OF PAST EVENTS. This is a technique of making forecasts, where we make forecasts composed of two layers. The baseline layer is precisely made of extrapolated past, and it is modified with hypotheses as for what new can happen in the future.

The general formula for calculating any rate of return on investment is: RR = [profit] / [investment]. In the stock market, with a given number of shares held in portfolio, and assumed constant, both profit and investment can be reduced to prices only. Therefore, we modify the equation of return into: RR = [closing price – opening price] / [opening price]. We can consider any price observed in the market, for the given stock, as an instance of closing price bringing some kind of return on a constant opening price. In other words, the closing price of any given trading day can be considered as a case of positive or negative return on my opening price. This is a case of Ockham’s razor, thus quite reductionist an approach. I ask myself what the probability is – given the known facts from the past – that my investment position brings me any kind of positive return vs. the probability of having a negative one. I don’t even care how much positive gain could I have or how deep is a local loss. I am interested in just the probability, i.e. in the sheer frequency of occurrence as regards those two states of nature: gain or loss.

In the graph below, I am illustrating this method with the case of Bioton, one of the companies whose stock I currently hold in my portfolio. I chose a complex, line-bar graph, so as to show graphically the distinction between the incidence of loss (i.e. negative return) vs that of gain. My opening price is the one I paid for 600 shares of Bioton on April 6th, 2020, i.e. PLN 5,01 per share. I cover one year of trading history, thus 247 sessions. In that temporal framework, Bioton had 12 days when it went above my opening price, and, sadly enough, 235 sessions closed with a price below my opening. That gives me probabilities that play out as follows: P(positive return) = 12/247 = 4,9% and P(negative return) = 235/247 = 95,1%. Brutal and sobering, as I see it. The partial moral of the fairy tale is that should the past project itself perfectly in the future, this if all the stuff that happens is truly cyclical, I should wait patiently, yet vigilantly, to spot that narrow window in the reality of stock trade, when I can sell my Bioton with a positive return on investment.      

Now, I am going to tell a different story, the story of First Solar, a company which I used to have an investment position in. As I said, I used to, and I do not have any position anymore in that stock. I sold it in the beginning of April, when I was a bit scared of uncertainty in the U.S. stock market, and I saw a window of opportunity in the swelling speculative bubble on biotech companies in Poland. As I do not have any stock of First Solar, I do not have any real opening price. Still, I can play a game with myself, the game of ‘as if…’. I calculate my return as if I had bought First Solar last Friday, April 24th. I take the closing price from Friday, April 24th, 2020, and I put it in the same calculation as my opening price. The resulting story is being told in the graph below. This is mostly positive a story. In strictly mathematical terms, over the last year, there had been 222 sessions, out of a total of 247, when the price of First Solar went over the closing price of Friday, April 24th, 2020. That gives P(positive return) = 222/247 = 89,9%, whilst P(negative return) = 10,1%.

The provisional moral of this specific fairy tale is that with First Solar, I can sort of sleep in all tranquillity. Should the past project itself in the future, most of trading days is likely to close with a positive return on investment, had I opened on First Solar on Friday, April 24th, 2020.  

Now, I generalize this way of thinking over my entire current portfolio of investment positions, and I pitch what I have against what I could possibly have. I split the latter category in two subsets: the outliers I already have some experience with, i.e. the stock I used to hold in the past and sold it, accompanied by two companies I am just having an eye on: Medtronic (see Chitchatting about kings, wars and medical ventilators: project tutorial in Finance), and Tesla. Yes, that Tesla. I present the results in the table below. Linked names of companies in the first column of the table send to their respective ‘investor relations’ sites, whilst I placed other graphs of return, similar to the two already presented, under the links provided in the last column.      

Company (investment position)Probability of negative returnProbability of positive returnLink to the graph of return  
  My current portfolio
11BitP(negative) = 209/247 = 84,6%P(positive) = 15,4%11Bit: Graph of return  
Airway Medix (243 sessions)P(negative) = 173/243 = 71,2%P(positive) = 70/243 = 28,8%Airway Medix: Graph of return  
Asseco Business SolutionsP(negative) = 221/247 = 89,5%P(positive) = 10,5%Asseco Business Solutions: Graph of return  
BiotonP(negative) = 235/247 = 95,1%P(positive) = 12/247 = 4,9%Bioton: Graph of return  
Mercator MedicalP(negative) = 235/247 = 95,1%P(positive) = 12/247 = 4,9%Mercator: graph of return  
PBKMP(negative) = 138/243 = 56,8%P(positive) = 105/243 = 43,2%  PBKM: Graph of return
  Interesting outliers from the past
Biomaxima (218 sessions)P(negative) = 215/218 = 98,6%P(positive) = 3/218 = 1,4%Biomaxima: Graph of return  
Biomed LublinP(negative) = 239/246 = 97,2%P(positive) = 7/246 = 2,8%Biomed Lublin: graph of return  
OAT (Onco Arendi Therapeutics)P(negative) = 205/245 = 83,7%P(positive) = 40/245 = 16,3%OAT: Graph of return  
Incyte CorporationP(negative) = 251/251 = 100%P(positive) = 0/251 = 0%Incyte: Graph of return  
First SolarP(negative) = 10,1%P(positive) = 222/247 = 89,9%First Solar: Graph of return  
  Completely new interesting outliers
TeslaP(negative) = 226/251 = 90%P(positive) = 25/251 = 10%Tesla: Graph of return  
MedtronicP(negative) = 50/250 = 20%P(positive) = 200/250 = 80%  Medtronic: Graph of return

As I browse through that table, I can see that extrapolating the past return on investment, i.e. simulating the recurrence of some cycle in the stock market, sheds a completely new light on both the investment positions I have open now, and those I think about opening soon. Graphs of return, which you can see under those links in the last column on the right, in the table, tell very disparate stories. My current portfolio seems to be made mostly of companies, which the whole COVID-19 crisis has shaken from a really deep sleep. The virus played the role of that charming prince, who kisses the sleeping beauty and then the REAL story begins. This is something I sort of feel, in my fingertips, but I have hard times to phrase it out: the coronavirus story seems to have awoken some kind of deep undertow in business. Businesses which seemed half mummified suddenly come to life, whilst others suddenly plunge. This is Schumpeterian technological change, if anybody asked me.

In mathematical terms, what I have just done and presented reflects the very classical theory of probability, coming from Abraham de Moivre’s ‘The doctrine of chances: or, A method of calculating the probabilities of events in play’, published in 1756. This is probability used for playing games, when I assume that I know the rules thereof. Indeed, when I extrapolate the past and use that extrapolation as my basic piece of knowledge, I assume that past events have taught me everything I need to understand the present. I used exactly the same approach as Abraham De Moivre did. I assumed that each investment position I open is a distinct gambling table, where a singular game is being played. My overall outcome from investment is the sum total of partial outcomes from individual tables (see Which table do I want to play my game on?).   

Chitchatting about kings, wars and medical ventilators: project tutorial in Finance

My editorial on You Tube

I continue with educational stuff, so as to help my students with their graduation projects. This time, I take on finance, and on the projects that my students are to prepare in the curriculum of ‘Foundations of Finance’. The general substance of those projects consists in designing a financial instrument. I know that many students struggle already at the stage of reading that sentence with understanding: they don’t really grasp the concept of designing a financial instrument. Thus, I want to sort of briefly retake it from the beginning.

The first step in this cursory revision is to explain what I mean by ‘financial instrument’. Within the framework of that basic course of finance, I want my students to develop intellectual distinction between 5 essential types of financial instruments: equity-based securities, debt-based securities, bank-based currencies, virtual currencies (inclusive of cryptocurrencies), and insurance contracts. I am going to (re)explain the meaning of those terms. I focus on those basic types because they are what we, humans, simply do, and have been doing for centuries. Those types of financial instruments have been present in our culture for a long time, and, according to my own scientific views, they manifest collective intelligence in human societies: they are standardized parcels of information, able to provoke certain types of behaviour in some categories of recipients. In other words, those financial instruments work similarly to a hormone. Someone drops them in the middle of the (social) ocean. Someone else, completely unknown and unrelated picks them up, and their content changes the acquirer’s behaviour. 

When we talk about securities, both equity-based and debt-based, the general idea is that of securing claims, and then making those secured claims tradable. Look up the general definition of security, e.g. on Investopedia. If you want, in your project, to design a security, the starting point is to define the assets it gives claim on. Equity-based securities give direct, unconditional claims on the assets held by a business (or by any other type of social entity incorporated in a business-like way, with an explicit balance sheet), as well as conditional, indirect claims on the dividend paid out of future net income generated with those assets. Debt-based securities give direct, unconditional claim on the future cash flows, generated by the assets of the given business. The basic idea of tradable securities is that all those types of claims come with a risk, and the providers of capital can reduce their overall risk by slicing the capital they give into small tradable portions, each accompanied by a small portion of adjacent risk. Partitioning big risks and big claims into small parcels is the first mechanism of reducing risk. The possibility to trade those small parcels freely, i.e. to buy them, hold them for however long pleases, and then sell them, is the second risk-reducing device.

The entire concept of securities aims, precisely, at reducing financial risks connected to investing big amounts of capital into business structures, and thus at making that investment more attractive and easier. Historically, it literally has been working like that. Over centuries, whenever people with money were somehow reluctant to connect with people having bold ideas, securities usually solved the problem. You were a rich merchant, like in the 17th century-France, and your king asked you to lend him money for the next war he wanted to fight. You would answer: ‘Of course, my Lord, I would gladly provide you with the necessary financial means, yet I have a tiny little doubt. What if you lose that war, my Lord? Who’s going to pay me back?’. Such an answer could lead into two separate avenues: decapitation or securitization of debt. The former was somehow less interesting financially, but the latter was a real solution: you lend to the King, in exchange he hands you his royal bonds (debt-based securities), and you can further sell those bonds to whoever is interested in betting on the results of war.      

Thus, start with a simple business concept, e.g. something of current interest, such as a factory of medical ventilators. You have a capital base, i.e. some assets, and you finance them with equity and liabilities. Classical. You can skip the business planning part by going to the investors relations site of any company you know, taking their last financial report and simply simulating a situation when those guys want to increase their capital base, i.e. add to their assets. I mentioned medical ventilators, so you could go and check Medtronic’s investors relations site (http://investorrelations.medtronic.com/ ), and pick their latest quarterly financials. They have assets worth $92 822 mln, financed with $51 953 mln in equity and $40 869 in debt. Imagine they see big business looming on the horizon, and they want to accumulate $10 000 mln more in assets. They can do it either through additional borrowing, or through the issuance of new shares in the stock market.

You can go through the reports of Medtronic as well as through their corporate governance rules, and start by taking your own stance at the basic question: if Medtronic intends to accrue their assets by $10 000 mln, would you advise them to collect that capital by equity, or by debt, or maybe to split it somehow between the two. Try to justify your answer in a meaningful way.

If you go for equity-based securities (shares in equity), keep asking questions such as: what should be the nominal value (AKA face value) of those shares? How does it compare with the nominal value of shares already outstanding with this company? What dividend can shareholders expect, based on past experience? How are those new shares expected to behave in the stock market, once again based on the past experience?

If your choice is to bring capital through the issuance of debt-based securities, go for answering the following: what should be the interest rate on those corporate bonds? What should be their maturity time (i.e. for how long should they stay in the market of debt before Medtronic buys them back)? Should they be convertible into something else, like in the shares in equity, or in some next generation of bonds? Once again, try to answer those questions as if I were just a moderately educated hominid, i.e. as if I needed to have things explained simply, step by step.

See? Chitchatting, talking about kings, wars and medical ventilators, we have already covered the basics of preparing a project on equity-based securities, as well as on the debt-based ones.

If you want to go somehow further down those two avenues, you can check two of my blog updates from the last academic year: Finding the right spot in that flow: educational about equity-based securities , and  Unconditional claim, remember? Educational about debt-based securities.

Now, we talk about money, i.e. about a hypothetical situation when my students design a new currency in the framework of their project. Money is strange, to the extent that technically it should not have any intrinsic value of itself, as a pure means of exchange, and yet any currency can be deemed mature and established once its users start hoarding it a little bit, thus when they start associating with it some sort of intrinsic value. Presently, with the development of cryptocurrencies, we distinguish them from bank-based or central-unit-based currencies. In what follows immediately, I am focusing on the latter category, before passing to the former.

So, what is a bank-based currency, AKA central-unit-based currency? A financial institution, e.g. a bank, issues a certain number of monetary units (AKA monetary titles), which are basically used just as a means of exchange. The bank guarantees the nominal value of that currency, which, in itself, does not embody any claim on anything. This is an important difference between money and securities: securities secure claims, money doesn’t. Money just assures liquidity, understood as the capacity to enter into exchange transactions.   

When designing a new currency, step #1 consists in identifying a market with liquidity problems, e.g. we have 5 developing countries, which do business with each other: they trade goods and services, business entities from each of those countries invest in the remaining four etc. Those 5 countries have closed or semi-closed monetary systems, i.e. their national currencies either are not exchangeable at all against any other currency, or there are severe limitations on such exchange (e.g. you need a special authorization from some government agency). Why do those countries have closed monetary systems? Because their governments are afraid that if they make it open, thus when they allow free exchange against foreign currencies, the actual exchange rate will be so volatile, and so prone to speculative attacks (yes, there are bloody big sharks in those international financial waters) that the domestic financial system will be direly destabilized. Why any national currency should be so drastically volatile? It happens when this currency is not really exchanged a lot against other currencies, i.e. when exchange is sort of occasional and happens in really big bundles. There is not enough accumulated transactional experience. Long story short, we have national currencies which are closed because of the possible volatility and are so prone to volatility because they are closed systems. Yes, I know it sounds stupid. Yet, once you see that mechanism at work, you immediately understand. In the communist Poland, we had a closed monetary system, with our national currency, the zloty, technically being not exchangeable at all against anything else. As a result, whenever such exchange actually took place, e.g. against the US dollar, you needed to be a wizard, or a prime minister, to predict more or less accurately the applicable exchange rate.

Those 5 countries have two options. For one, they can use a third-country, strong currency as a local means of exchange, i.e. their governments, and their national business entities can agree that whenever they do business transnationally, they use a reference currency to settle their mutual obligations. The second option consists in creating an international currency, specifically designed for settling business accounts between those countries. This is how the ECU, the grandpa of the euro, was born, back in the day. The ECU was a business currency – you couldn’t have it in your wallet, you just could settle your international accounts with it – and then, as banks got used to it, the ECU progressively morphed into the euro. What you need for such a currency is a financial institution, or a contractually established network thereof, who guarantee the nominal value of that business currency.

If our 5 countries go for the second option, the financial institution(s) who step in as guarantors if the newly established currency need to bring to the table something more than just mutual trust. They need to assign, in their balance sheets, specific financial assets which back the aggregate nominal value of the new currency put in circulation. Those assets can consist of, for example, a reserve basket of other currencies. Once again, it sounds crazy, i.e. money being guaranteed with money, but this is how it works.

Therefore, step #2 in designing a new, bank-based currency, requires giving some aggregate numbers. What is the aggregate value of transactions served by the new currency? Let’s go, just as an example, for $100 billion a year. How long will each unit of the new currency spend on an individual bank account? In a perfectly liquid market, each unit of currency is used as soon as it has been received, thus it just has one night to sleep on a bank account, and back to work, bro’. In such a situation, that average time on one account is 1 day. Therefore, in order to cover $100 billion in transactions, we need [$100 / 365 days in the year] = $0,2739726 billion = $274 million in currency. If people tend to build speculative positions in that currency, i.e. they tend to save some of it for later, the average time spent on an individual account by the average unit of that new money could stretch up to 2 weeks = 14 days. In such case, the amount of currency we need to finance $100 billion in transactions is calculated as [$100 / (365/14)] = [$100 * 14 / 365] = $3,8356 billion.

There is a catch. I talk about introducing a new currency, but I keep denominating in US dollars, whence the next question and the next step, step #3, in a project devoted to this topic. The real economic value of our money depends on what we do with that money, and not really on what we call it. One of the things we do with an international currency is to exchange it against national currencies. In this case, we are talking about 5 essentially closed national currencies. For the sake of convenience, let’s call them: Ducat A, Ducat B, Ducat C, Ducat D, and Ducat E. Once again for sheer convenience we label the new currency ‘Wanderer’. So far, our 5 countries have been using the US dollar for international settlements, whence my calculations denominated therein. The issue of exchange rate of the Wanderer against the US dollar, as well as against our 5 national Ducats, is a behavioural one. Yes, behavioural: it is about human behaviour.

We have businesspeople doing international business in USD, and we want to convince them to switch to the Wanderer. What arguments can we use? There are two: exchange rate per se, and exchange rate risk. Whoever is a national of our 5 countries, needs to exchange their national Ducat against the US dollar and the other way around. As neither of the Ducats is freely convertible, exchange with the dollar takes place, most probably, in the form of big, bulk transactions, like once a month, mediated by the central banks of our 5 countries. Those bulk transactions yield an average exchange rate, and an average variance around that average.

We want to put in place an alternative scheme, where the national Ducats (A, B, C, D, E) are exchanged in real time against the Wanderer, and then the Wanderer gets exchanged against the US dollar. The purpose is to make the exchange {Ducat Wanderer USD} more attractive, average-rate-wise or variance-in-rate-wise, than the incumbent {Ducat Individual, National Central Bank USD} one. Some of you might think it is not realistically possible, yet it really is. If 5 central banks of developing countries gang up together to buy and sell US dollars, they can probably achieve a better price, and less volatile a price, as compared to what each of them separately could have. There is even an additional trick, and this is like really a trick: central banks of our 5 countries could hold some of their financial reserves in US dollars, more specifically the part devoted to backing the Wanderer. That’s the trick that our central bank in Poland, the National Central Bank of Poland, uses all the time. We are in the European Union, but we do not belong to the European Monetary Union, and yet we do a lot of business with partners in the eurozone. The National Bank of Poland holds important financial reserves in euros, and thus gives itself a better grip on the exchange rate between the Polish zloty and the euro.

Summing up the case of graduation projects focused on designing a new bank-based currency, here are, rephrased once again, the basic logical steps. Start with identifying a market with liquidity problems, such as closed monetary systems or very volatile national currencies. This is usually an international market made of developing countries. Imagine a situation, when the central banks of the countries in question place some of their financial reserves in a strong currency, e.g. the US dollar, or the Euro, and then the same central banks introduce a currency for international settlements in that closed group of countries. Keep in mind that the whole group of countries will need an amount of currency calculated as: [Aggregate value of international transactions done in a year * [Average number of days that one user holds one unit of currency / 365].  

The whole scheme consists, at the end of the day, in obtaining a better and less volatile exchange rate of individual national currencies against the BIG ONES (e.g. the US dollar) through aggregating their exchange transactions in the financial market.       

That would be all in this tutorial. I have covered three types of financial instruments that my students can possibly design for their graduation: equity-based securities, debt-based securities, and bank-based currencies. In the coming weeks I will try to write something smart on designing cryptocurrencies and insurance contracts. Till then, you can additionally read entry March, 26th, 2019 – More and more money just in case. Educational about money and monetary systems – and entry March 31st, 2019 – The painful occurrence of sometimes. Educational about insurance and financial risk.

Lettres de la zone rouge

Mon éditorial sur You Tube

Ça fait du temps depuis ma dernière mise à jour en français. Après une période de silence complet, suivie par une période de mises à jour exclusivement en anglais, je retourne à écrire en français aussi. C’était le rythme que je suivais dans le passé et j’aimais bien. Écrire en anglais et en français, sur les mêmes sujets, me donne comme une perspective sous deux angles différents. Oui, je sais, vu que nous avons des traducteurs automatiques, à présent, nous devenons habitués à l’idée que des langues différentes sont des façons alternatives de dire la même chose. À travers mon expérience personnelle, je peux dire que ce n’est pas vrai. Bien sûr, il y a dans chaque langue cette couche parfaitement traduisible, comme les jours de la semaine. Néanmoins, il y a plus : une langue est une structure cognitive que j’utilise pour appréhender la réalité. Mon polonais natal, l’anglais et le français, bien que pas très distants géographiquement, sont pour moi des structures cognitives distinctes.

Le fait de réalité que je suis en train d’appréhender à travers ces paires de lunettes différentes est le fait de perdre de l’argent en Bourse. Douloureux mais éducatif. Je veux faire cette expérience encore plus éducative en la discutant en des langues différentes. C’est le truc ninja que j’ai découvert en bloguant : lorsque je décris et discute par écrit ce que je fais je développe mes compétences dans ce champ d’activité spécifique.  Ça fait quelques semaines que j’avais décidé de retourner dans le jeu boursier, j’utilise mon blog pour développer ma stratégie d’investissement à long terme et j’ai déjà donné un compte rendu progressif de ce retour dans mes mises à jour en anglais : « Back in the game »  , « Fathom the outcomes », « Sharpen myself », « Bloody hard to make a strategy » et enfin « Rowing in a tiny boat across a big ocean ».        

J’avais décidé de retourner dans le jeu boursier juste avant la panique coronavirus. C’est comme dans ces films, où le personnage principal s’arrête dans un hôtel paisible et le jour suivant l’endroit est attaqué par des loup-garous ou bien un volcan explose dans la proximité. La panique explose dans les marchés financiers et moi, je me dis deux choses. Premièrement, bien sûr, je me dis : « Merde ! Fallait vraiment que ça arrive juste au moment où j’ai décidé de redevenir investisseur… ». Ensuite, lorsque je passe au-delà de ce gémissement de base, je me dis : « Des stratégies efficaces à long terme, ça se forge plus que ça s’invente. Il faut de l’adversité pour se faire une idée juste de ce que mes idées valent vraiment. Une bonne stratégie d’investissement sur plusieurs années – donc précisément ce que je veux développer – je ferais mieux de la tester dans un environnement difficile ».

Le contexte, c’est surtout du rouge. Dans mon portefeuille de valeurs, le sang coule abondamment. Cette métaphore veut dire que je note des pertes – habituellement affichées en rouge – sur presque toutes mes positions d’investissement. Presque toutes : je vois une petite tâche verte, donc un retour positif sur l’investissement. C’est Incyte Corporation où je note un gain de +1,01% sur le prix initial d’ouverture. La question intuitive est « pourquoi ? », donc pourquoi est-ce que je gagne sur cette position spécifique pendant que le reste sombre dans le bain de jus de betterave (je ne veux pas répéter le mot « sang » tout le temps ; la betterave, ça peut être tout aussi dramatique, sous certaines conditions). Les questions qui viennent à l’esprit en premier lieu ne sont pas nécessairement les plus pertinentes. La recherche scientifique, ça m’a appris qu’au lieu de demander pourquoi, il vaut mieux demander « comment ? ». Lorsque je développe une compréhension approfondie de la façon dont les évènements surviennent, je peux généraliser en forme des raisons et causalités.

Lorsque je veux comprendre le comment des choses, j’aime bien utiliser la comparaison, bien dans l’esprit des empiristes. Je compare donc ce qui s’est passé avec Incyte Corporation avec ce qui est arrivé à Virgin Galactic Holdings , qui est la perte la plus vertigineuse dans mon portefeuille : – 34,13% en moins de deux semaines. Je teste donc la qualité du service www.bloomberg.com/quote et je fraye mon chemin à travers la première couche du « comment ? » : l’analyse technique. J’étudie les prix transactionnels et les volumes des transactions, pour ces deux valeurs (actions d’Incyte Corporation et celles de Virgin Galactic), afin de trouver des régularités. Puisque je veux expliquer la différence en termes de mon retour sur l’investissement en ces deux positions, je couvre la période de celui-ci, depuis le 18 février 2020 jusqu’à la dernière cotation, vendredi le 6 mars. Les chiffres correspondants à mon analyse se trouvent dans les deux tableaux ci-dessous. Plus loin, donc en-dessous des deux tableaux, je développe mon analyse.       

Incyte Corporation
Date  Volume des transactions  Prix  Valeur totale des transactions
2020, Février 18                 1 209 877   $                     79,30  $       95 943 246,10
2020, Février 19                 1 895 420   $                     82,42  $     156 220 516,40
2020, Février 20                 2 653 348   $                     82,77  $     219 617 613,96
2020, Février 21                 1 413 961   $                     80,89  $     114 375 305,29
2020, Février 24                 1 366 786   $                     78,87  $     107 798 411,82
2020, Février 25                 2 174 593   $                     77,32  $     168 139 530,76
2020, Février 26                 1 442 275   $                     77,97  $     112 454 181,75
2020, Février 27                 1 641 737   $                     75,84  $     124 509 334,08
2020, Février 28                 2 525 548   $                     75,41  $     190 451 574,68
2020, Mars 2                 2 261 393   $                     79,06  $     178 785 730,58
2020, Mars 3                 1 939 411   $                     77,80  $     150 886 175,80
2020, Mars 4                 2 043 844   $                     80,16  $     163 834 535,04
2020, Mars 5                 1 430 221   $                     78,61  $     112 429 672,81
2020, Mars 6                 1 800 123   $                     76,04  $     136 881 352,92
Virgin Galactic
Date  Volume des transactions  Prix  Valeur totale des transactions
2020, Février 18             104 077 763   $                     30,30  $   3 153 556 218,90
2020, Février 19               84 891 132   $                     37,35  $   3 170 683 780,20
2020, Février 20               45 297 426   $                     37,26  $   1 687 782 092,76
2020, Février 21               45 297 426   $                     33,87  $   1 534 223 818,62
2020, Février 24               46 110 165   $                     34,29  $   1 581 117 557,85
2020, Février 25               44 092 654   $                     34,04  $   1 500 913 942,16
2020, Février 26               40 127 391   $                     28,75  $   1 153 662 491,25
2020, Février 27               47 987 693   $                     21,97  $   1 054 289 615,21
2020, Février 28               35 531 152   $                     24,60  $      874 066 339,20
2020, Mars 2               20 098 112   $                     25,98  $      522 148 949,76
2020, Mars 3               20 098 112   $                     24,71  $      496 624 347,52
2020, Mars 4               15 466 394   $                     23,76  $      367 481 521,44
2020, Mars 5               14 124 114   $                     24,09  $      340 249 906,26
2020, Mars 6               12 867 806   $                     21,67  $      278 845 356,02

Le truc de base à se mettre en tête est que ces chiffres représentent, en partie, mais seulement en partie, des comportements humains – des décisions complexes prises en des situations d’incertitude – et la partie non-comportementale (ou plutôt pas immédiatement comportementale) correspond aux transactions automatisées sur la base des logiciels d’intelligence artificielle. Je décris les décisions des logiciels IA comme pas immédiatement comportementales puisque à l’origine, leurs algorithmes sont basés sur une logique imposée par leurs créateurs humains. D’habitude, des logiciels d’investissement contiennent une partie génétique, où l’algorithme s’optimise lui-même en écrivant des lignes de code supplémentaires, donc une fois lâchés de leur laisse, ces trucs peuvent former leur propre logique. Encore, il faut se souvenir que la plupart de ces logiciels achèvent une optimisation linéaire tout à fait simple, du type « donne mois plus de retour que celui offert par l’indice boursier ».

Chaque décision individuelle – donc strictement, humainement comportementale – prise dans le jeu boursier ressemble à surfing. Il y a une force motrice prédominante, soit la tendance temporaire du marché. L’investisseur comprend quelque peu de cette tendance, mais cette compréhension est toujours partielle. Ce que moi je veux comprendre maintenant est la tendance du marché dans ces deux cas – Incyte Corporation et Virgin Galactic Holdings – ainsi que des fines déclinaisons de cette tendance, des déclinaison qui font la différence dans mon retour sur ces deux investissement.

Par vertu de la théorie économique de base j’assume que le comportement de base dans le  marché boursier est la triade sacrée : acheter, garder ou vendre. Moi, pour le moment, vu la panique « coronavirus » je garde mes positions d’investissement comme elles sont, sans acheter plus et sans vendre. Les décisions d’acheter et de vendre sont largement symétriques : elles influencent le prix d’une valeur boursière lorsqu’elles rencontrent l’une l’autre et lorsqu’une transaction est conclue. En termes de comportement, la variable la plus intéressante est le volume des transactions, soit le nombre des valeurs échangées, dans la première colonne numérique de chaque tableau.

Là, dans les cas respectifs d’Incyte Corporation et de Virgin Galactic Holdings, deux modèles se dessinent. Le volume des transactions sur Incyte Corporation oscille, en le dernier volume enregistré, vendredi 6 mars, est en fait supérieur au premier volume observé le 18 février. L’intensité d’échange sur cette valeur démontre quelque chose comme réflexion intense de la part des investisseurs. Ce volume n’est pas du tout corrélé avec les variations des prix : le coefficient de corrélation de Poisson tombe à r = 0,06. Vu la structure mathématique de ce coefficient, il y a probablement trop d’à-coups soudains dans les deux variables. Ça remue, quoi. Si je gagne ou je perds sur cette position dans le marché, c’est précisément parce que ça remue. Dans le cas de Virgin Galactic Holdings, le volume des transactions suit une trajectoire descendante sans équivoque – tout comme le prix – et les deux sont significativement, positivement corrélés, avec r = 0,59. En même temps, bien le volume des transactions strictement dit que la valeur agrégée de ces transactions étaient beaucoup plus élevées dans le cas de Virgin Galactic Holdings que dans celui d’Incyte Corporation.

Je vois donc que les investisseurs se comportent de façon différente vis-à-vis de ces deux valeurs. Le marché boursier est complexe, il marche largement sur anticipation faite sur la base d’information vraiment disparate, et ces différences peuvent être largement le résultat des facteurs autres que les traits individuels de ces sociétés. Ceci dit, les caractéristiques individuelles de ces deux business respectifs peuvent jouer un rôle important pour leur performance boursière. Je passe dont de l’analyse technique à l’analyse fondamentale et jette commence à feuilleter (figurativement, bien sûr, ce sont des documents PDF) leur rapports annuels.   

Incyte Corporation c’est du business bien ancré financièrement. Plus de deux milliards de dollars de revenu en 2019, avec presque 447 millions de bénéfice net, c’est du solide et du hautement profitable. En plus, lorsque j’observe leur compte d’exploitation sur la période 2015 – 2019, je vois in progrès constant et solide. En revanche, Virgin Galactic Holdings c’est plutôt du futur que du présent. Le modèle de business consiste surtout en des dépenses substantielles sur la recherche et le développement des nouvelles technologies (presque 133 millions de dollars) – il s’agit des technologies de voyage spatial commercial – orné ci et là avec des revenus symboliques de 3,8 millions de dollars. Bien sûr, les opérations courantes de ce business sont profondément déficitaires.

Je connecte ces deux observations à la théorie d’investissement formulée par James Tobin et William Brainard, linguistiquement intéressante pour les francophones puisqu’que dans le jargon économique elle est désignée comme la théorie du « q » (regardez, par exemple : Yoshikawa 1980[1]). Dans cette perspective théorique, les titres financiers sujets à l’échange boursier sont surtout et avant tout des titres de contrôle d’actifs productifs exploités par les sociétés émettrices de ces titres. Dans la longue perspective, le marché boursier est donc fortement connecté au marché d’actifs productifs – donc le marché des technologies et de l’immobilier industriel – et cette connexion est plus importante que la mécanique purement interne de la Bourse.

Je me pose la question suivante : comment cette connexion entre les actifs et l’échange boursier d’actions marche dans les deux cas étudiés ici ? Dans le cas d’Incyte Corporation , le 18 février les investisseurs avaient fait des transactions égales à 2,8% d’actifs totaux de la société et le 6 mars les transactions journalières avaient fait 3,99% de la valeurs comptable d’actifs. Avec Virgin Galactic Holdings , c’est une histoire différente : le 18 février le mouvement boursier sue leurs actions avait fait 520,78% de la valeur comptable de leurs actifs, pour descendre à 46,05% desdits actifs dans la journée du 6 mars. Je vois donc deux modèles comportementaux complètement différents dans les décisions d’investissement boursier dans ces deux sociétés. La différence entre ces deux modèles comportementaux peut être liée de la différence sectorielle : Incyte Corporation c’est de la biotechnologie bien tassée et Virgin Galactic Holdings c’est un rêve follement charmant d’organiser des vols orbitaux à l’échelle commerciale et ce rêve semble avoir un fort potentiel de générer des retombées technologiques substantielles.

Maintenant, j’applique la même méthode de comparaison – volume des transactions, valeur totale des transactions – aux deux autres valeurs dans mon portefeuille, toutes les deux dans le même secteur cette fois. Je parle du secteur des technologies photovoltaïques et là-dedans, j’ai investi dans les actions de First Solar (- 21,07% de perte dans mon portefeuille) et dans celles de Vivint Solar (perte – 7,96%). Bien que dans le rouge après vendredi dernier, ces deux valeurs c’étaient défendues longtemps contre la panique « coronavirus ». Encore mardi dernier, j’avais un retour positif sur ces deux positions. Toutes les deux démontrent une proportion similaire entre la valeur totale des transactions boursières et la valeur comptable des actifs. Dans le cas de First Solar , cette proportion était de 1,04% le 18 février et 0,84% vendredi 6 mars. En ce qui concerne Vivint Solar , on parle de 0,54% le 18 février et 0,80% le 6 mars.

Ci-dessous, dans deux autres tableaux, je présente les données détaillées à propos de ces deux sociétés. Je continue mon développement plus loin.  

First Solar
Date  Volume  Prix  Valeur totale des transactions
2020, Fevrier 18   1 407 357   $                     55,65  $       78 319 417,05
2020, Fevrier 19     1 945 614   $                     57,37  $     111 619 875,18
2020, Fevrier 20   4 156 485   $                     59,32  $     246 562 690,20
2020, Fevrier 21   9 774 901   $                     50,59  $     494 512 241,59
2020, Fevrier 24    3 700 847   $                     51,21  $     189 520 374,87
2020, Fevrier 25 2 807 226   $                 48,57  $     136 346 966,82
2020, Fevrier 26 2 670 436   $                     46,11  $     123 133 803,96
2020, Fevrier 27 2 699 288   $                  44,25  $     119 443 494,00
2020, Fevrier 28 2 950 667   $                     45,77  $     135 052 028,59
2020, Mars 2 2 730 409   $                     44,95  $     122 731 884,55
2020, Mars 3 1 569 455   $                     44,21  $       69 385 605,55
2020, Mars 4 1 340 145   $                     45,48  $       60 949 794,60
2020, Mars 5 1 259 142   $                     45,47  $       57 253 186,74
2020, Mars 6 1 448 658   $                     43,37  $       62 828 297,46
Vivint Solar
Date  Volume  Prix  Valeur totale des transactions
2020, Fevrier 18 1 319 176   $                    11,04  $        14 563 703,04
2020, Fevrier 19 2 290 068   $                    11,80  $        27 022 802,40
2020, Fevrier 20 4 279 615   $                    12,85  $        54 993 052,75
2020, Fevrier 21 3 125 854   $                    11,44  $        35 759 769,76
2020, Fevrier 24 2 476 290   $                    11,76  $        29 121 170,40
2020, Fevrier 25 2 004 507   $                    11,59  $        23 232 236,13
2020, Fevrier 26 3 798 572   $                    11,91  $        45 240 992,52
2020, Fevrier 27 3 563 184   $                    11,08  $        39 480 078,72
2020, Fevrier 28 2 526 386   $                    11,24  $        28 396 578,64
2020, Mars 2 3 188 036   $                    11,19  $        35 674 122,84
2020, Mars 3 2 886 017   $                    11,69  $        33 737 538,73
2020, Mars 4 1 539 854   $                    11,91  $        18 339 661,14
2020, Mars 5 1 478 135   $                    11,73  $        17 338 523,55
2020, Mars 6 1 990 593   $                   10,78  $        21 458 592,54

Voilà donc qu’une régularité se dessine. Dans la panique ambiante des marchés financiers, parmi les quatre valeurs que je viens d’analyser point de vue prix et volume, les deux gagnants – Incyte Corporation toujours dans le vert et Vivint Solar juste un peu dans le rouge – démontrent un trait commun intéressant. Dans les deux cas, entre le 18 février et le 6 mars, le coefficient « valeur totale des transactions boursières par jour divisée par la valeur comptable des actifs » démontre une tendance croissante, tout en restant relativement modeste.

Par ailleurs, dans le cas des sociétés du photovoltaïque, on peut remarquer les retombées des derniers développement aux États-Unis. Selon bloomberg.com, en janvier 2020, l’administration du président Donald Trump avait donné le feu vert pour la construction de la première méga-ferme solaire dans le désert californien et cette ferme va être construite précisément par First Solar. On peut voir qu’entre le 18 et le 21 février le volume des transactions en actions de First Solar avait bondi tout à coup, tout en faisant des vagues côté et Vivint Solar.    

Bon, c’est tout dans cette mise à jour. Vous pouvez me contacter à travers la boîte électronique de ce blog : goodscience@discoversocialsciences.com .


[1] Yoshikawa, H. (1980). On the” q” Theory of Investment. The American Economic Review, 70(4), 739-743.

Rowing in a tiny boat across a big ocean

My editorial on You Tube

On February 24th, after I posted my last update ( Bloody hard to make a strategy ), I did what I was declaring I would do: I bought 1 share of Invesco QQQ Trust (QQQ) for $224,4, 5 shares of Square Inc, at $76,85, thus investing $384,25 in this one. Besides, I have just placed an order to buy one share ($33) of Virgin Galactic Holdings, mostly because it is high tech.

These are the small steps I took, but now, as financial markets are freaking out about coronavirus, it is the right moment to figure out my endgame, my strategy. Yes, that’s surely one thing I have already nailed down as regards investment: the more the market is driven by strong emotions, the more I need to stay calm. Another thing I have learnt by experience is that it really pays off, at least for me, to philosophise about the things I do, would it be science or a business strategy. It really pays to take a step back from current events, sit and meditate on said events. Besides, in terms of scientific research, I am currently working on the ways to derive economic value added from environmental projects. I guess that fundamental questioning regarding economic value and decisions we make about it will be interesting.

When I philosophise about anything connected to social sciences, I like talking to dead people. I mean, no candles, no hand-touching, just reading and thinking. I discovered that I can find exceptionally deep insights in the writings of people labelled as ‘classics’. More specifically, books and articles written at an epoch when the given type of economic institution was forming, or changing fundamentally, are particularly insightful. It is a little bit as if I were an astrophysicist and I had a book, written by an alien who watched the formation of a planet. The classic that I want to have a word with right now is Louis Bachelier. I am talking about Bachelier’s ‘Theory of Speculation’ , the PhD thesis from 1900, originally published in French as ‘Théorie de la spéculation’ (Bachelier 1900[1]). Here’s how Louis Bachelier introduces his thesis: ‘INTRODUCTION. The influences which determine the movements of the Stock Exchange are innumerable. Events past, present or even anticipated, often showing no apparent connection with its fluctuations, yet have repercussions on its course. Beside fluctuations from, as it were, natural causes, artificial causes are also involved. The Stock Exchange acts upon itself and its current movement is a function not only of earlier fluctuations, but also of the present market position. The determination of these fluctuations is subject to an infinite number of factors: it is therefore impossible to expect a mathematically exact forecast. Contradictory opinions in regard to these fluctuations are so divided that at the same instant buyers believe the market is rising and sellers that it is falling. Undoubtedly, the Theory of Probability will never be applicable to the movements of quoted prices and the dynamics of the Stock Exchange will never be an exact science. However, it is possible to study mathematically the static state of the market at a given instant, that is to say, to establish the probability law for the price fluctuations that the market admits at this instant. Indeed, while the market does not foresee fluctuations, it considers which of them are more or less probable, and this probability can be evaluated mathematically.

As I see, Louis Bachelier had very much the same feelings about the stock market as I have today. I am talking about the impression to be rowing in a really tiny boat across a bloody big ocean, with huge waves, currents and whatnot, and a general hope that none of these big dangerous things hits me. And yes, there are sharks. A natural question arises: why the hell stepping into that tiny boat, in the first place, and why leaving shore? If it is that risky, why bother at all? I think there is only one sensible answer to that: because I can, because it is interesting, and because I expect a reward, all three in the same time.

This is the general, duly snappy reply, which I need to translate, over and over again, into goals and values. Back in the day, almost 30 years ago, I used to do business, before I went into science. For the last 4 years or so, I am thinking about getting back into business. The difference between doing real business and investing in the stock market is mostly the diversification of risk. I dare say that aggregate risk is the same. When I do real business, as a small businessman, I have the same feeling of rowing in that tiny boat across a big ocean. I have so little control over the way my small business goes that when I manage to get in control of something, I get so excited that I immediately label that controlled thing as ‘successful business model’. Yet, running my own small business is so time and energy absorbing that I have hardly any juice left for anything else. I have all my eggs in the same basket, i.e. I do not hedge my risks. With any luck, I just insure them, i.e. I share them with someone else.

When I invest in the stock market, I almost intuitively spread my equity over many financial assets. I hedge the business-specific risks. Here comes an interesting question: why did I choose to invest in biotechnology, renewable energies and IT? (see Back in the game ). At the time, one month ago, I made that choice very intuitively, following my intellectual interests. Now, I want to understand myself deeper. Logically, I turn to another dead man: Joseph Alois Schumpeter, and to his ‘Business Cycles’. Why am I knocking at this specific door? Because Joseph Alois Schumpeter studied the phenomenon of technological change with the kind of empirical assiduity that even today inspires respect. From Schumpeter I took that idea that once true technological change starts, it is unstoppable, and it inevitably drives resources from the old technologies towards the new ones. There are technologies in the today’s world, precisely such as biotechnology, information technologies, and new sources of energy, which no one can find their way around. Those technologies are already reshaping deeply our everyday existence, and they will keep doing so. If I wanted to start a business of my own in any of these industries, it would take me years to have the thing running, and even more years to see any economic gains. If I invest in those industries via the stock market, I can tap directly into the economic gains of the already existing businesses. Egoistic but honest. I come back to that metaphor of boat and ocean: instead of rowing in a small boat across a big ocean, I hook onto a passing cruiser and I just follow it.

There is more to the difference between entrepreneurship, and investment in the stock market. In the latter case, I can clearly pace myself, and that’s what I do: every month, I invest another parcel of capital, on the grounds of learning acquired in past months. In entrepreneurship, such a pacing is possible, yet much harder to achieve. Capital investment required to start the business usually comes in a lump: if I need $500 000 to buy machines, I just need it. Of course, there is the fine art of engaging my own equity into a business step by step, leveraging the whole thing with credit. It is possible in an incorporated business. Still, the very incorporation of a business requires engaging a minimum equity, which is way greater than the baby steps of investment.

Incidentally, the very present developments in the stock market make an excellent opportunity to discuss more in depth. If you care to have a look at the NASDAQ Composite Index , especially at its relatively longer time window, i.e. from 1 month up, you will see that what we have now is a typical deep trough. In my own portfolio of investment positions, virtually every security is just nosediving. Why does it happen? Because a lot of investors sell out their stock, at whatever price anybody is ready to pay for it.

Have you ever wondered how do stock prices plummet, as it is the case now? I mean, HOW EXACTLY? Market prices are actual transactional prices, i.e. prices that actual deals are closed at. When stock prices dive, people who sell are those who panic, I get it. Who buys? I mean, for a very low stock price to become a market price, someone must be buying at this specific price. Who is that, who buys at low prices when other people freak out and sell out? Interesting question. When the market price of a security falls, the common interpretation is: ‘it is ‘cause that security has become less attractive to investors’. Wait a minute: if the price falls, someone buys at this falling price. Clearly, there are investors who consider that security attractive enough to pay for it even though the price is likely to fall further.

When a whole market index, such as NASDAQ Composite, is skiing downhill, I can see the same phenomenon. Some people – right, lots of people – sell out whatever financial assets they have because they are afraid to see the market prices fall further down. Some other people buy. They see an opportunity in the widespread depreciation. What kind of opportunity? The one that comes out of other people losing control over their behaviour. Now, from there, it is easy to go into the forest of conspiracy theories, and start talking about some ‘them’, who ‘want to..’ etc. Yes, there is a rational core to those theories. The stock market is like an ocean, an there are sharks in it. They just wait patiently for the prey to come close to them. Yes, officially, the spiritus movens of the present trough in stock markets is coronavirus. Wait a minute: is it coronavirus as such, or what we think about it? Wait another minute: is it about what we think as regards the coronavirus, or about what we expect other people (in the stock market) to think about it?

When I look at the hard numbers about coronavirus, they look refreshing. When I divide the number of fatalities by the number of officially diagnosed cases of infection, that f**ker is, under some angle, less deadly than common flu. Take a look at Worldometer: 85 217 official carriers of it, and 2 924 of those carriers dead. The incidence of fatality is 2924/85217 = 3,43%. A study by the Center for Infectious Disease Research and Policy at the University of Minnesota finds an even lower rate: 2,3%. As reported by CNBC, just in the United States of America, during the on-going flu season, the flu virus has infected 19 million people, and caused 10 000 deaths. The incidence of death among people infected with flu is much lower, 10000/19000000 = 0,05%, yet the absolute numbers are much higher.       

Please, notice that when a real panic overwhelms financial markets, there is no visible fall in prices, because there are no prices at all: nobody is buying. This is when pricing is suspended, and the stock market is effectively shut down. As long as there are any prices in the market, some market agents are buying. Here comes my big claim, sourced in my recent conversation with the late Joseph Schumpeter: the present panic in the stock market is just superficially connected to coronavirus, and what is manifesting itself deep underneath that surface is widespread preparation for a bloody deep technological change, coming our way right now. What technological change? Digital technologies, AI, robotization, shift towards new sources of energy, and, lurking from the bottom of the abyss, the necessity to face climate change.

I am deeply convinced that my own investment strategy, should it demonstrate any foresight and long-range planning, should be most of all espousing the process of that technological change. Thus, it is useful to understand the process and to plan my strategy accordingly. I have already done some research in this field and my general observation is that technological change as it is going on right now is most of all marked by increasing diversity in technologies. What we are witnessing is not just a quick replacement of old technologies by new ones: that would be too easy. Owners of technological assets need to think in terms of stockpiling many generations of technologies in the same time, and the same place.

From the point of view of an entrepreneur it is what the French call “l’embarras du choix”, which means embarrassingly wide range of alternative technological decisions to take. I described it partially in ‘4 units of quantity in technological assets to make one unit of quantity in final goods’.  Long story short, there is a threshold speed of technological, up to which older technological assets can me simply replaced by newer ones. Past that threshold, managing technological change at the business level becomes progressively more and more a guessing game. Which specific cocktail of old technologies, and those cutting-edge ones, all that peppered with a pinch of those in between, will work optimally? The more technologies we can choose between, the more aleatory, and the less informed is the guess we make.

I have noticed and studied one specific consequence of that ever-widening choice of technological cocktails: the need for cash. Mathematically, it is observable as correlation between two coefficients: {Amortization / Revenue} on the one hand, and {Cash & cash equivalent / Assets} on the other hand. The greater a percentage of revenues is consumed by amortization of fixed assets, the more cash (in proportion to total assets) businesses hold in their balance sheets. I nailed it down statistically, and it is quite logical. The greater a palette of choices I might have to navigate my way through, the more choices I have to make in a unit of time, and when you need to make a lot of business choices, cash is king, like really. Open credit lines with banks are nice, just as crowdfunding platforms, but in the presence of significant uncertainty there is nothing like a nice, fat bank account with plenty of liquidity at arm’s reach.

When a business holds a lot of cash for a long time, they end up by holding a lot of the so-called ‘cash equivalents’, i.e. a low-risk portfolio of financial securities with a liquidity close to cash strictly spoken. Those securities are listed in some stock market, whence an inflow of capital into the stock market from companies holding a lot of cash just in case a breakthrough technology pokes its head from around the corner. Quick technological change, quick enough to go past the threshold of simple replacement (understood as straightforward shift from older technology towards and into a newer one), generates a mounting wave of capital placed on short-term positions in the stock market.

Those positions are short-term. In this specific financial strategy, entrepreneurs perceive the stock market as one of those garage-size warehouses. In such a place, you can store, for a relatively short time, things which you don’t know exactly what to do with, yet you feel could need them in the future. Logically, growing an occurrence of of short-term positions in the stock market induces additional volatility. Each marginal million of dollars pumped in the stock market via this tube is more restless than the previous one, whence increasing propensity of the market as a whole to panic and run in the presence of any external stressor. Joseph Schumpeter described that: when the economy is really up to a technological leap, it becomes overly liquid financially. The financial hormone gets piled up in the view of going all out.

I come back to thinking about my own strategy. Whatever kind of run we have in the stock market right now, the coronavirus is just a trigger, and the underlying tension it triggered is linked to technological change of apparently unseen speed and complexity.

In my portfolio, just two positions remain positive as for their return rate: Incyte Corporation and Square Inc. All the others have yielded to the overwhelming panic in the market. Why? I can follow the tracks of two hypotheses. One, those companies have particularly good fundamentals, whilst being promisingly innovative: they sort of surf elegantly on the wave of technological change. Two, it is more aleatory. In the times of panic such as we experience now, in any given set of listed securities, investors flock, in a largely random way, towards some businesses, and away from others. Mind you, those two hypotheses are mutually complementary (or rather they are not mutually exclusive): aleatory, panicky behaviour on the part of investors conjoins with a good mix of characteristics in specific businesses.     

Right, so I have the following situation. In my portfolio, I have two champions of survival, as regards the rate of return – the above-mentioned Incyte Corporation and Square Inc. – in the presence of all them other zombies that succumbed to the surrounding panic: First Solar Inc., Macrogenics, Norsk Hydro, SMA Solar Technology AG, Virgin Galactic Holdings, Vivint Solar, 11Bit, Asseco Business Solutions, AMUNDI EPRA DR (ETF Tracker), and Invesco PowerShares EQQQ Nasdaq-100 UCITS (ETF Tracker). I keep in mind the ‘how?’ of the situation. In the case of Incyte Corporation and Square Inc., investors are willing to pay for them more than they were ready to pay in the past, i.e. deals on those securities tend to be closed at a ramping up average price. As for all the others, displaying negative rates of return, presently investors pay for them less than they used to a few days or weeks ago. I stress once again the fact that investors pay. This is how prices are fixed. Whichever of those securities we take, some investors keep buying.

What I can observe are two different strategies of opening new investment positions. The first one, largely dominating, consists in buying into cheap stuff, and forcing that stuff to go even cheaper. The second one, clearly less frequently occurring, displays investors opening new positions in the market at a higher price than before. I am observing two distinct behavioural patterns, and I presume, though I am not sure, that these two patterns of investment are correlated with the intrinsic properties of two supposedly different sets of securities. I know that at this point I am drifting away from the classical ‘supply – demand’ pattern of pricing in the stock market, yet I am not drifting really far. I acknowledge the working of Marshallian equilibrium in that price setting, I just enrich my investigation with the assumption of diverging behavioural patterns.

In my portfolio, I hold securities which somehow are attached to both of those behavioural patterns. I have taken a position on other people’s possible behaviour. This is an important finding about my own investment strategy and the ways I can possibly get better at it. I can be successful in my investment if I make the right guess as regards the businesses or securities that incite the pattern of behaviour manifesting in growing price that deals get closed at.  What I can observe now, in the times of panic in the market, is a selective panic. As a matter of fact, even before that coronavirus story went western, securities in my portfolio were disparate in their rate of return: some of them positive, some others negative. What has changed now is just the proportion between the positive returns and the negative ones.  

Another question comes to my mind: when I open positions on the stock of businesses in some selected technological fields, like solar energy, do I participate in technological change, or do I just surf over the top of financial foam made by that change? There is that theory, called ‘Q theory of investment’ (see for example Yoshikawa 1980[2]), developed by James Tobin and William Brainard, and that theory claims that when I invest in the stock of listed companies, I actually buy claims on their productive assets. In other words, yes, listed shares are just financial instruments, but when I buy them or sell them, I, as an investor, I develop strategies of participation in assets, not just in equity.

When I think about my own behaviour, as investor, I certainly can distinguish between two frames of mind: the gambling one, and the farming one. There are moments, when I fall, unfortunately, into a sort of frantic buying and selling, and I use just small bits of information, and the information I use is exclusively technical, i.e. exclusively the price curves of particular securities. This is the gambling pattern. I do my best to weed out this pattern of behaviour in myself, as it: a) usually makes me lose money, like really and b) is contrary to my philosophy of developing long term strategies for my investment. On the other hand, when I am free of that gambling frenzy, I tend to look at my investment positions in the way I look at roses in my garden, sort of ‘What can I do to make them grow bigger and flourish more abundantly?’. This is my farming frame of mind, it is much less emotional than the gambling one, and I intuitively perceive it as more functional for my long-term goals.

Good, it looks like I should give some provisional closure and put this update online. I think that in the presence of a hurricane, it is good to stay calm, and to meditate over the place to go when the hurricane calms down. I guess that for the weeks to come, until I collect my next rent and invest it in the stock market, no sudden decisions are recommended, given the surrounding panic. I think the best I can do during those weeks is to study the fundamentals of my present portfolio of investment positions and draw some conclusions from it.

If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com .


[1] Bachelier, L. (1900). Théorie de la spéculation. In Annales scientifiques de l’École normale supérieure (Vol. 17, pp. 21-86).

[2] Yoshikawa, H. (1980). On the” q” Theory of Investment. The American Economic Review, 70(4), 739-743.

Bloody hard to make a strategy

My editorial on You Tube

It is weekend, and it is time to sum up my investment decisions. It is time to set a strategy for investing the next rent collected. Besides being a wannabe financial investor, I am a teacher and a scientist, and thus I want to learn by schooling myself. As with any type of behavioural analysis, I start by asking “What the hell am I doing?”. Here comes a bit of financial theory. When I use money to buy corporate stock, I exchange one type of financial instrument (currency) against another type of financial instrument, i.e. equity-based securities. Why? What for? If I trade one thing against another one, there must be a difference that justifies the trade-off. The difference is certainly in the market pricing. Securities are much more volatile in their prices than money. Thus, when I invest money in securities, I go for higher a risk, and higher possible gains. I want to play a game.

Here comes another thing. When I say I want to play a game, the ‘want’ part is complex. I am determined to learn investment in the most practical terms, i.e. as my own investment. Still, something has changed in my emotions over the last month. I feel apprehensive after having taken my first losses into account. Whilst in the beginning, one month ago, I approached investment as a kid would approach picking a toy in a store, now I am much more cautious. Instead of being in a rush to invest in anything, I am even pushing off a bit the moment of investment decision. It is like sport training. Sooner or later, after the first outburst of enthusiasm, there comes the moment when it hurts. Not much, just a bit, but enough to make me feel uncomfortable. That’s the moment when I need to reassess my goals, and just push myself through that window of doubt. As I follow that ‘sport training’ logic, what works for me when I am down on optimism is consistency. I do measured pieces of work, which I can reliably link to predictable outcomes.

Interesting. Two sessions of investment decisions, some 4 weeks apart from each other, and I experience completely different emotions. This is a sure sign that I am really learning something new. I invest 2500 PLN, and in my investments, I mostly swing between positions denominated in PLN, those in EUR, and those in USD. At current exchange rates 2500 PLN = €582,75 = $629,72. Please, notice that when I consider investing Polish zlotys, the PLNs, into securities denominated in PLN, EUR or USD, I consider two, overlapping financial decisions: that of exchanging money (pretty fixed nominal value) against securities, and that of exchanging zlotys against other currencies.

Let’s focus for a moment, on the strictly speaking money game. If I swing between three currencies, it is a good move to choose one as reference. Here comes a practical rule, which I teach to my students: your reference currency is the one you earn the major part of your income in. My income comes from my salary, and from the rent, both in Polish zlotys, and thus the PLN is my denominator. A quick glance at the play between PLN, USD, and EUR brings the following results:

>> PLN to EUR: February 1st 2020, €1 = 4,3034 PLN  ; February 23rd, 2020 €1 = 4,2831 PLN ; net change: (4,2831 – 4,3034) / 4,034 =  -0,50% 

>> PLN to USD: February 1st 2020, $1 = 3,8864 PLN ; February 23rd, 2020 $1 = 3,9623 PLN; net change: (3,9623 – 3,8864) / 3,8864 =  1,95%

For the moment, it seems that the euro is depreciating as compared to the US dollar, and I think it would be better to invest in dollars. Since my last update on this blog, I did something just opposite: I sold in USD, and bought in euro. That would be it as for consistency. February 21st – decided to sell Frequency Therapeutics, as I was losing money on it. I consistently apply the principle of cutting losses short. I had a look at short-term trend in the price of Frequency Therapeutics, and there is no indication of bouncing back up. Question: what to invest that money in? Canadian Solar? No, they are falling. SMA Solar Technology AG? Good fundamentals, rising price trend, equity €411,4 mln, market cap €1 251 mln, clearly overvalued, but maybe for a reason. Bought SMA Solar Technology, and it seems to have been a bad move. I have a slight loss on them, just as I have one on First Solar. I consider selling them both, still they both have interestingly strong fundamentals, yet both are experiencing a downwards trend in stock price. Hard to say why. Hence, what I have just done is to place continuous ‘sell’ orders with a price limit that covers my loss and gives me a profit. We will see how it works. For First Solar, I placed a ‘sell’ order at minimum 54$, and regarding SMA Solar Technology I did the same with the bottom limit at €37.

I found another interesting investment in the industry of renewable energies: SolarWinds Corporation. Good fundamentals, temporarily quite low in price, there is risk, but there is gain in view, too. I would like to explain the logic of investing in particular sectors of the economy. My take on the thing is that when I just spend my money, I spend it sort of evenly on the whole economy because my money is going to circulate. When I decide to invest my money in the equity of particular industries it is a focused decision.

Thus, I come to the issue of strategy. I am completely honest now: I have hard times to sketch any real strategy, i.e. a strategy which I am sure I will stick to. I see three basic directions. Firstly, I can keep the present portfolio, just invest more in each position so as to keep a constant structure. Secondly, I can keep the present portfolio as it is and invest that new portion of money in additional positions. Thirdly, and finally, I can sell the present portfolio in its entirety and open completely new a set of positions. My long-term purpose is, of course, to earn money. Still, my short-term purpose is to learn how to earn money by financial investment. Thus, the first option, i.e. constant structure of my portfolio, seems dumb. Firstly, it is not like I have nailed down something really workable. That last month has been a time of experimentation, summing up with a net loss. The third option sounds so crazy that it is tempting.  

I think about investing the immediately upcoming chunk of money into ETF funds, or so-called trackers. I have just realized they give a nice turbo boost to my investments. The one I already have – Amundi Epra DRG – performs nicely. The only problem is that it is denominated in euros, and I want to move towards dollars, at least for now.

Trackers sectorally adapted to my priorities. Trackers (ETFs) are a bit more expensive – they collect a transactional fee on the top of the fee collected by the broker – yet my experience with Amundi Epra, a tracker focused on European real estate, is quite positive in terms of net returns. I think about Invesco QQQ Trust (QQQ), a tracker oriented on quick-growth stock. Another one is Microsoft. OK, I think about Tesla, too, but it is more than $900 one share. I would have to sell a lot of what I already have in order to buy one. Maybe if I sell some of the well-performing biotechs in my portfolio? Square Inc., the publicly-listed sister company of Twitter, is another interesting one. This is IT, thus one of my preferred sectors. I am having a look at their fundamentals, and yes! They look as if they had finally learnt to make money.

I think I have made my choice. My next rent collected will go 50% into Invesco QQQ Trust (QQQ), and 50% into Square Inc..

My blog is supposed to be very much about investment, and my personal training therein, still I keep in mind the scientific edge. I am reworking, from the base, my concept of Energy Ponds, which I have already developed on for the last year or so (see, for example ‘The mind-blowing hydro’). The general background of ‘Energy Ponds’ consists in natural phenomena observable in Europe as the climate change progresses, namely: a) long-term shift in the structure of precipitations, from snow to rain b) increasing occurrence of floods and droughts c) spontaneous reemergence of wetlands. All these phenomena have one common denominator: increasingly volatile flow per second in rivers. The essential idea of Energy Ponds is to ‘financialize’ that volatile flow, so to say, i.e. to capture its local surpluses, store them for later, and use the very mechanism of storage itself as a source of economic value.

When water flows downstream, in a river, its retention can be approached as the opportunity for the same water to loop many times over the same specific portion of the collecting basin (of the river). Once such a loop is created, we can extend the average time that a liter of water spends in the whereabouts. Ram pumps, connected to storage structures akin to swamps, can give such an opportunity. A ram pump uses the kinetic energy of flowing water in order to pump some of that flow up and away from its mainstream. Ram pumps allow forcing a process, which we now as otherwise natural. Rivers, especially in geological plains, where they flow relatively slowly, tend to build, with time, multiple ramifications. Those branchings can be directly observable at the surface, as meanders, floodplains or seasonal lakes, but much of them is underground, as pockets of groundwater. In this respect, it is useful to keep in mind that mechanically, rivers are the drainpipes of rainwater from their respective basins. Another basic hydrological fact, useful to remember in the context of the Energy Ponds concept, is that strictly speaking retention of rainwater – i.e. a complete halt in its circulation through the collecting basin of the river – is rarely possible, and just as rarely it is a sensible idea to implement. Retention means rather a slowdown to the flow of rainwater through the collecting basin into the river.

One of the ways that water can be slowed down consists in making it loop many times over the same section of the river. Let’s imagine a simple looping sequence: water from the river is being ram-pumped up and away into retentive structures akin to swamps, i.e. moderately deep spongy structures underground, with high capacity for retention, covered with a superficial layer of shallow-rooted vegetation. With time, as the swamp fills with water, the surplus is evacuated back into the river, by a system of canals. Water stored in the swamp will be ultimately evacuated, too, minus evaporation, it will just happen much more slowly, by the intermediary of groundwaters. In order to illustrate the concept mathematically, let’ s suppose that we have water in the river flowing at the pace of, e.g. 45 m3 per second. We make it loop once via ram pumps and retentive swamps, and, if as a result of that looping, the speed of the flow is sliced by 3. On the long run we slow down the way that the river works as the local drainpipe: we slow it from 43 m3 per second down to [43/3 = 14,33…] m3 per second.  As water from the river flows slower overall, it can yield more environmental services: each cubic meter of water has more time to ‘work’ in the ecosystem.   

When I think of it, any human social structure, such as settlements, industries, infrastructures etc., needs to stay in balance with natural environment. That balance is to be understood broadly, as the capacity to stay, for a satisfactorily long time, within a ‘safety zone’, where the ecosystem simply doesn’t kill us. That view has little to do with the moral concepts of environment-friendliness or sustainability. As a matter of fact, most known human social structures sooner or later fall out of balance with the ecosystem, and this is how civilizations collapse. Thus, here comes the first important assumption: any human social structure is, at some level, an environmental project. The incumbent social structures, possible to consider as relatively stable, are environmental projects which have simply hold in place long enough to grow social institutions, and those institutions allow further seeking of environmental balance.

Some human structures can be deemed ‘sustainable’, but this looks rather like an exception than the rule. As a civilization, we are anything but frugal and energy-saving. Still, the practical question remains, how can we possibly enhance the creation of sustainable social structures (markets, cities, industries etc.), without relying on a hypothetical moral conversion from the alleged ‘greed’ and ‘wastefulness’, to a more or less utopian state of conscious sustainability. The model presented below argues that such enhancement can occur by creating economic ownership in local communities, as regards the assets invested in environmental projects. Economic ownership is to distinguish from the strictly speaking legal ownership. It can cover, of course, property rights as such, but it can stretch to many different types of enforceable claims on the proceeds from exploiting economic utility derived from the environmental projects in question.

Any human social structure generates an aggregate amount of environmental outcomes EV, understood as reduction of environmental risks. Environmental risk means the probable, uncertain occurrence of adverse environmental effects. Part of those outcomes is captured as economic utility U(EV), and partly comes as freeride benefits F(EV).  For any human social structure there is a threshold value U*(EV), above which the economic utility U(EV) is sufficient to generate social change supportive of the structure in question. Social change means the creation of institutions and markets, which, in turn, have the capacity to last. On the other hand, should U(EV) be lower than U*(EV), the structure in question cannot self-justify its interaction with natural environment, and falls apart.

The derivation of U(EV) is a developmental process rather than an instantaneous phenomenon. It is long-term social change, which can be theoretically approached as evolutionary adaptive walk in rugged landscape. In that adaptive walk, the crucial moment is the formation of markets and/or institutions, where exchange of utility occurs as stochastic change over time in an Ornstein–Uhlenbeck process with a jump component, akin to that observable in electricity prices, i.e. (Borovkova & Schmeck2017[1]). It means that human social structures become able to optimize their environmental impact when they form prices stable enough to be mean-reverted over time, whilst staying flexible enough to drift with jumps. Most technologies we invent serve to transform environmental outcomes into exchangeable goods endowed with economic utility. The set of technologies we use impacts our capacity to sustain social structures. Adaptive walk requires many similar instances of a social structure, similar enough to have common structural traits. Each such instance is a 1-mutation neighbour of at least one other instance. By the way, if you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com


[1] Borovkova, S., & Schmeck, M. D. (2017). Electricity price modeling with stochastic time change. Energy Economics, 63, 51-65. http://dx.doi.org/10.1016/j.eneco.2017.01.002 

Sharpen myself

My editorial on You Tube

On February 17th, I sold my position in ATM Grupa. I managed to strike a deal at PLN 4,9 per share, which, after transactional fees, gave me a two-week rate of return at 1,74%. Once again, I broke the rules I declared I would follow. I was supposed to take investment decisions once a month, and here I made one half-way through that period. I wonder what exactly is at work in me, when I suddenly do things like that. The simplest answer would be: ‘Lack of discipline’ etc. Yes, it was lack of discipline, and it occurred in a person – me – who is prone to compulsive discipline, like really. In many other fields of my life, I tend to be overly consistent. I am like one of those golems in Terry Pratchett’s novels. When you tell me to dig a hole in the ground, I will keep digging until you tell me to stop, and if you forget to tell me to stop, well… I will keep digging. People around make me understand, every now and then, that it would be a good thing to accept a bit of chaos into my order.

Thus, what makes me suddenly less consistent when it comes to financial investment? Would it be about a subjectively new type of match between information and decision? Looks like… Another hypothesis is that what I see, for the moment, as lack of consistency, is precisely the right amount of consistency. Maybe my initial assumptions – making investment decisions once a month, as I collect my rent from real estate once a month – were wrong. This is a tricky one: on the one hand, investing capital at the same pace it comes to me is pretty intuitive, and yet, on the other hand, financial investment is supposed to have liquidity, and my own investment strategy should bring me more than just the average rate of return based on market indexes.

One thing is certain: given my ordinary schedule of work, it takes many days, even weeks, to plough through the information I need to make really informed investment decisions. Maybe I can use a strategy in two steps: once a month big shopping with the freshly received rent, and mid-month a correction of the course.

For the moment, I have just taken into account the advice that professionals give: cut your losses short. After having sold ATM Grupa, I have just decided to sell the positions I was losing money on: OAT, Aprea Therapeutics, Vir Biotechnology, Aston Martin, Black Diamond Group, PGE, Cyfrowy Polsat. I sold them all at market price. With the proceeds from selling and the Polish CDM platform, thus with proceeds from selling ATM Grupa, Cyfrowy Polsat, PGE, and OAT, I bought into one position, a gaming company: 11 BIT Studios. This particular egg in my basket is partly what I initially outlined as my strategy, and partly completely not. I guess something similar can be said about most things I do in life. Anyway, with an equity of roughly PLN 106,07 mln, and a market capitalization of PLN 255,93 mln, the company is clearly overvalued in the market. Still, they have good fundamentals, and their stock price growing like hell.

There is an interesting hypothesis to ponder, like generally: the financial count of equity, in a business, can be more or less accurate. The financial value of equity is an expression of underlying economic value in assets net of debt, and the interplay between financial value and economic value can take different forms. Debt is debt, and as long as I don’t want to make that debt tradable in the form of securities, it has a clear nominal value. Assets are a different story. When assets are being valued for the purposes of a balance sheet, two methods can (and should) be used concurrently: the book accounting, and the market valuation. I take the value of assets from the last time I counted it, I subtract depreciation from the current period, and I get the book value net of depreciation. From time to time, I can ask myself what price I could get for my assets if I decided to sell them. This is market valuation, and this is supposed to estimate quite closely the implicit economic utility of my assets, net of any subjective calculations of mine. It is possible that book valuation goes a long way above or below the market valuation.

Financial markets, such as the stock market, have a peculiar property, which was noticed, apparently, hundreds of years ago. When instead of trading assets in big chunks, like whole factories, we just trade small participatory titles in those assets, the financial market yields very sharp valuations of economic value. What? It is just about expectations? Hell, yes. The value of productive assets is all about expectations. Do you buy a factory in order to live inside? I guess it is for having some future business outcomes: it is about expectations. Anyway, in cases like 11BIT Studios, when equity is overvalued, but the stock price keeps flying high, and with good fundamentals, it can be hypothesised that some of their assets have greater an economic value than the official book valuation in their balance sheet. I know, I know: assuming that I see things that other people can’t see is tricky. Still, when that bloody price keeps growing, I guess other people see the same ghosts which I see, and this is reassuring.

I return to my corrective investments. In the DEGIRO platform, after having sold my positions in Aprea Therapeutics, Vir Biotechnology, Aston Martin, and Black Diamond Group, I decided to strengthen the ‘energy’ component of my portfolio. My assets of choice have been: Vivint Solar, and Norsk Hydro.  Vivint Solar’s fundamentals are sort of intriguing. On the one hand, they still lose money. On the other hand, they lose much less than they used to, and they seem being terribly resilient. I remember I spotted their financial reports in early Spring 2017, for the first time, and I was like: ‘What a sad story… Another ambitious bunch of innovators going bankrupt soon’. Still, they haven’t gone bankrupt. They are still there, they keep their head above the water, and they develop, step by step, their technological concept of small smart grids based on renewable energies. As for Norsk Hydro, these guys are fundamentally solid, period. I consider that position as a stabilizer.     

Now, once again, drums: I am drawing a bottom line under my so-far investment decisions. I sum up the state of my possessions as for today, i.e. February 19th, 2020, and I compare with the initial values on February 3rd. My account on the Polish platform CDM comes first. Starting point: February 3rd, cash 2693 PLN. Action #1: buying Cyfrowy Polsat, OAT, PGE, and ATM Grupa. Action #3: selling ATM Grupa at a negotiated deal price. Action #4: selling PGE, OAT, and Cyfrowy Polsat at market price. Action #5: buying 11 Bit at market price. Current status: cash PLN 584,73 + position on 11BIT PLN 1 936 = PLN 2520,73. Net loss of PLN -172,27, or -6,4%.

I pass to my account on the DEGIRO platform, for international investment positions. Starting point: February 3rd, 2020, cash: PLN 2 550. Action #1: I buy into Black Diamond Group, Macrogenics, Incyte, Vir Biotechnology, Amundi Epra (tracker fund), Frequency Therapeutics, Aprea Therapeutics, Aston Martin. Action #2: I buy into First Solar Inc. Action #3: I sell Black Diamond Group, Vir Biotechnology, Aprea Therapeutics, Aston Martin. Action #4: I buy into Vivint Solar and Norsk Hydro. Current status: cash €37,43 + € 512,54 worth of positions on Amundi Epra, First Solar, Vivint Solar, Norsk Hydro, Incyte, Frequency Therapeutics, Macrogenics = € 549,97 = PLN 2 348,37. Net loss: PLN – 201,63, or – 7,9%.   

In the first blog update in this fresh cycle (see Back in the game ), I wrote I am aware how humbling this learning will be. Well, it is humbling. Those losses are the cost of my learning. I understand why those tracker funds are so popular. Many people try what I am trying, i.e. learn by trial and error to invest profitably, and if one is not prepared to pay the price of learning, it is really frustrating. Yet, I am ready to pay the price, and I need to get the most value for that price. I need to learn as much as I can. In my plan, the moment of the next big shopping approaches. By the very end of February, or in the first days of March, I am supposed to invest the next rent, the PLN 2500. I have a few days to sharpen myself for that next step.

Fathom the outcomes

My editorial on You Tube

Here comes the next update in my process of self-learning about investment in financial markets. In the last update ( Back in the game ), I briefly sketched my starting point, i.e. my first handful of financial positions, and my long-term goals. According to a pace I set for myself, once a month I make investment decisions. Why once a month? Because once a month I collect the rent from an apartment I have in town. My basic concept is to invest the rent I collect on one form of capital – real estate – into another form of capital.

What should be my next steps in investment? What should be my strategy? I start by studying my expectations. What do I expect? Shortly and honestly: I expect to beat the index. In the jargon of investment, it means that I expect to achieve abnormally high returns on investment, higher than the returns offered by composite indexes for the stock markets where I invest. Lots of people expect to beat the index, most of them fail, so what makes me expect that I can do it? Well, I can quite clearly pin down situations, in my own past experience as investor, when I managed to beat the index by many lengths, and other situations when I failed lamentably.

I had one success, which gives me some confidence. My success name was Bioton, a Polish biotech company.  I had an eye on them for many years, I worked on their case with my students, and I knew they have good foundations. Innovative enough to launch an original substance of their own – synthetic insulin – and conservative enough to diversify their business into good old generics like basic vitamins, antibiotics or basic vaccines. My investment story with them began in January 2014. At the time, the founder of the company, Mr Krauze, suddenly sold out all of his participations and essentially disappeared from the business landscape of Poland in quite mysterious circumstances. There was a healthy business in a reputationally shitty spot. Their stock price hit an all-time low: 0,03 PLN (less than $0,01) per share. WTH, I thought. At this price, there is not even much to lose. I bought. Two and a half years later, by the end of September 2016, I sold those shares at 10,04 PLN (something like 3 dollars). Yes, I made 33367% of profit on that one. Had I been less timid in opening my initial investment position, I could have bought some real estate with the proceeds. By the way, as I have a fresh look at Bioton, they seem to be back almost to the point where I invested in them. Ever since Autumn 2016, their stock price has been falling. Now, it is at 3,5 PLN per share, which is pretty low, and as I study the graph of their price, they are likely to hit, quite soon, that bottom plateau between the horns of the bull. I need to study their fundamentals, but it looks like the next good opportunity to open an interesting position. I check, and it looks tricky.

I mean, it usually looks tricky. At Bioton, they are losing money: at the end of the 3rd quarter of 2019 they had an operational loss of more than $30 mln. Those fundamentals look bad in the context of their business history. Their stock price tends to nosedive, and there are fundamental reasons to that. Still, they remain undervalued as regards the ‘market to book’ ratio. Calculated as ‘market capitalization divided by equity’, it makes PLN 300,52 mln / PLN 621 mln = 0,483851232. In other words: there is room for making money on this position, at least to a ‘market to book’ coefficient of 1,00, i.e. up to a stock price of about PLN 7,2 ÷ 7,3, which would give a gain of more than 200%. 

Miracles happen quite unfrequently, whence their reputation. Still, I can say ‘Gotcha’!’. That’s the strategy for my investment, which I have been turning and sniffing around for during the last 2 weeks. I had to recapitulate those past events in order to bring those thoughts into daylight. What I am looking for are companies with healthy fundamentals, i.e. with reasonably good financial results and prospects for just good a near future, which, for some reason, are deeply undervalued.

The next step is to run all my present investment positions with the same test. One, check their fundamentals.  Two, check their relative market valuation, denominated over their equity. Three, check the price curve and try to locate the present price of my investment position, so as to check opportunities for growth. I start with OAT – OncoArendi Therapeutics. I am having a look at their quarterly financials for Q3 2019. They lose money, as typical R&D-focused biotechs frequently do. They have very little revenue and much bigger operational costs. They lose cash, too, which is more worrying. When I sum up their operational cash-flow (-2,04 PLN mln) with the investment-related one (-7,3 PLN mln), and with the financial one (6,69 PLN mln), the bottom line is negative, by almost 3 millions of Polish zlotys. I have a look at OAT’s stock price, and I see that I behaved dumbly: I bought their stock right before a local peak, which is now being followed by a descent. I bought on the back of the bear, in the stock-market jargon. Their ‘Market to Book’ coefficient is 164,04/80,08 = 2,05. I see I really need that slow, grinding learning investment by writing about my own investment. That’s what I can call a perfect mistake: bad financials, supposedly overappreciated stock.

I move forward with APREA THERAPEUTICS. The fundamentals are weak and look a bit foggy. They certainly lose money, with a net loss of $6,25 mln in Q3 2019, twice the net loss one year earlier. Technically, the company has no equity in the strict sense of the term. What I bought are some convertible securities. Still, those convertibles, apparently not burdened with any liability, amount to $47 393 333,00. The market capitalization, according to Yahoo Finance, is $664,35 mln, which gives one of those crazy ‘Market to book’ ratios: 14,02. Well, if I was looking for an undervalued biotech company, this is quite the opposite of what I should have bought. Next, FREQUENCY THERAPEUTICS INC.  An interesting case. They lose money, but just a bit. In Q3 2019 they had a net loss of – $575 000, whilst in Q3 2018 it was – $5,14 mln. In 2019 they started to have operational revenues, some $24 mln as for end of September 2019. They have an equity of $92 mln, and a market capitalization of $743,45 mln, which once again gives a huge ‘market to book’ ratio: 8,08.   

The next position in my portfolio is INCYTE CORPORATION . Those guys, they are dutiful: they have already published their annual 10-K report for the fiscal year 2019. The fundamentals look nice. They have a steady profit, second year on end: $402 mln of operational profit, out of revenues amounting to $1 775 mln. The market to book ratio is hilariously high: $16,9 bln of market cap, denominated over $2,6 bln of equity makes 6,5. Still, the trend in market price is interesting: looks like halfway the ascending horn of the bull. This investment position is maybe not the most illustrative for my successful strategy from the past, yet it seems to be offering some promise. I pass to MACROGENICS INC. The latest financial report available is Q3 2019, which shows a clear financial deterioration as compared to Q3 2018: less revenues, deeper loss. The market appreciates this company moderately, with a market cap of $497,42 mln, denominated 1,95 times over the company’s equity, amounting to $255,2 mln. With all that, the price trend looks moderately promising: even over 1 month there seems to be room for a nice jump up.

Finally, my last investment position in biotech: VIR BIOTECHNOLOGY INC. Their fundamental operations don’t look well: a shrinking revenue, and a deepening operational loss. Still, their cash flow is positive: investors seem to be trusting them, and the whole show was over $46 mln on surplus in terms of cash. Over the last weeks, i.e. since I bought their shares, the price has been decreasing, and yet, on Friday 14th, there seems to have been some bounce-back. In terms of market valuation, this company stands $1,75 bln, which, denominated over their nominal equity of $355,8 mln, spells 4,92. This business is to watch with caution. It looks like a big balloon: a lot of confidence from investors with little apparent substance in the business, but what do you want, that’s biotech.

That would be about all as regards my investment positions in biotech, and so I pass to my two Polish crown jewels at the frontier of digital and show business: CYFROWY Polsat and ATM Grupa. CYFROWY Polsat has excellent fundamentals. In Q3 2019 they made PLN 459 mln ($117 mln) of quarterly operational profit. By the way, in businesses involved with media and cinematic production, the most important operational metric is EBITDA, or operational profit plus amortization, and this one stands at more than PLN 1 bln in Q3 2019. Their equity was PLN 14 155 mln, which, when serving as denominator for their market cap of PLN 5 311 mln gives a market to book ratio of 0,38. Interesting: finally, an investment position with clear undervaluation in the stock market. The long-term trend in their stock price is generally steady growth with temporary bumps. As regards ATM Grupa, they have good operational fundamentals, i.e. a comfortable operational profit, yet they seem to be losing cash at the level of financing activities. Anyway, they accumulate equity at a steady pace, and their market capitalization values that equity at 1,59 of its nominal value.

I pass to my investment positions in energy, and I start with FIRST SOLAR INC. Their fundamentals are sort of hesitantly good: they had positive operational margin in Q3 2019, still it had to offset a deeply negative one in Q1 2019. I found a piece of news at Yahoo Finance, which allows some optimism as for the immediate prospects of their business.  Their market capitalization is $5,83 bln, and denominated over book value of equity ($5,2 bln), stands at 1,12. As for the Polish PGE , they seem to be attaching a lot of importance to that EBITDA metric, as if they were running some show business, not power plants. That EBITDA looks substantial, more than PLN 6,6 bln in 2019 (provisional, unaudited results), yet it is a bit less than in 2018. Their equity – PLN 48,7 bln – is deeply undervalued in the stock market: in terms of market capitalization, it stands at 0,23. Looks interesting for a long-term position.   

Thus I come to the two investment positions, which I can’t help calling but my follies:  ASTON MARTIN, and BLACK DIAMOND GROUP LTD . The fundamentals of ASTON MARTIN look a bite like the beginning of trouble. They spike with their sales in the Americas, and in China, but everywhere else, their sales fall. In 2018, they had a positive operational margin, but in 2019 not anymore. Although their assets keep growing, including their fixed assets, they accumulate debt even faster, whence a decrease in nominal equity: £361,9 mln. With a market capitalization at £961,7 mln, they are overappreciated by the market at 2,66. BLACK DIAMOND makes me understand why I bought it. It is stupid. What I wanted to open a position on was Black Diamond Therapeutics. Black Diamond Group, which I actually bought, was just next on the list. I clicked on the wrong one, and I didn’t notice my mistake. Still, just as in a neural network, errors can lead to something interesting. Here comes the first interesting fact: that company is undervalued in the stock market. Their market cap, at CAD 102,87 mln, is just 0,47 of their nominal equity at the end of Q3 2019. Fundamentally, they display a loss before taxes, still it is a loss after amortization. When we kick amortization out of the formula, Black Diamond made a solid CAD 10 mln of operational profit in Q3 2019.

Just as an additional explanation: I do not perform the same kind of check for my last financial position, the tracker fund Amundi Epra DR ISIN LU1437018838. This is a fund calibrated so as to reflect the performance of listed real-estate companies across Europe, in Borsa Italiana,

Nyse Euronext Paris, Nyse Euronext Amsterdam, and London Stock Exchange. It cannot really be undervalued or overvalued.  

Now, the drums. I mean, the drums start drumming, so as to build up some tension, and I compute the rate of return I had on those investments of mine over the first 2 weeks since opening. In the table below, I sum up the descriptive remarks developed earlier, and I give it a bottom line with the rates of return.

Company Market to book Fundamentals Remarks about price trend – opportunity for growth Rate of return as of February 14th, 2020 (after 2 weeks since opening)
OAT Onco Arendi Therapeutics 2,05 weak no -13,78%
Aprea Therapeutics 14,02 weak possible -21,1%
Frequency Therapeutics 8,08 weak, but improving problematic -1,24%
Incyte Corporation 6,5 strong possible 6,36%
Macrogenics Inc 1,95 weak problematic 8,19%
Vir Biotechnology 4,92 weak possible -29,01%
Cyfrowy Polsat 0,38 strong possible but moderate -1,86%
ATM Grupa 1,59 strong not much, looks more like stable 0,08%
First Solar 1,12 mixed, uncertainty possible 3,09%
PGE 0,23 good, slightly deteriorating possible -13,54%
Aston Martin 2,66 deteriorating no -13,79%
Black Diamond Group 0,47 strong possible -7,82%
Amundi Epra – tracker fund n.a. strong possible 5,21%

A few observations emerge out of that table, as regards my learning by writing about doing things I am supposed to learn how to do. I fail more often than I succeed. Out of the 13 positions I opened, 5 are successful (i.e. bring positive return), and the remaining 8 are failures, at least for the moment. Of course, it is just 2 weeks, and I want to invest on the long range. Longer a time perspective might change things. Still, the science of financial markets says that any given moment, I face a certain aggregate volatility of said markets, and my personal art of survival in those markets consists in keeping myself on the positive fringe of that volatility. The useful assumption is that I never have a full knowledge of the financial market, and my strategy is always the one of trial and error. I need to have many takes at the thing, and my overall success depends on the percentage of those attempts, which I derive positive outcomes from. Right now, my rate of success measured across investment positions (i.e. I do not weigh them yet with the respective amounts of money I invested in each of them individually) is 5/13 = 0,3846. In order to even fathom the outcomes I want to reach, my first practical improvement should be to drive that coefficient above 0,5. When I say ‘drive’ it means that my incidence of success should depend on my choices, not on the volatility of the market. Hence, I need to hedge. I can already see that trackers, such as the Amundi Epra one I already have, are a good way to hedge.

Back in the game

My editorial on You Tube

I am starting a new log of activity: investment. After some 18 months of pausing in active investment in financial markets, I am going back into the game, and I want to do it as rationally and as artfully as possible, using all the science I have in order to achieve three consecutive goals: a) achieving predictable, attractively positive growth of market capitalization in my portfolio b) adding positive cash flow to that growth of value (i.e. turning my portfolio into a source of cash revenue, and c) creating an investment fund, i.e. a fund where I manage capital entrusted by third persons. One word of explanation as for that last one, as it could seem overly pretentious. I simply want to develop my skills in investment up to a point, where a group of other people – probably a relatively small group – would trust those skills of mine enough to coordinate their capital investment in businesses of interest common to all of us. I want to develop at least one business connected to renewable energies, and to tackling climate change. Becoming a trusted fiduciary for other people’s investment, and standing up to the corresponding promises, could be quite a good step on that way.    

Over the last 3 years, starting in Spring 2017, I used a scientific blog as a tool to boost my scientific creativity and I think it worked: I reached a level where I make true discoveries, and I feel I bring a real contribution to the development of social sciences. For the time being, my biggest scientific achievement is research published: “Energy efficiency as manifestation of collective intelligence in human societies”. I intend to stay humble and consider what I do just as a contribution to the development of social sciences, not as personal glory. I also intend to develop on that science I already have, Still, achievement is achievement, I know I have gone a path of personal development as a scientist, I think I understand how I have accomplished what I have accomplished, and I want to repeat the experience with a practical application of social sciences, i.e. with financial investment.

The method I intend to use consists in keeping a log of exhaustive, written auto-analysis of what I do, publishing that analysis in the form of updates on my blog “Discover Social Sciences”, and using the insights I develop in the process so as to develop my skills as professional investor. In other words, I know that if I write a lot about what I think I do in a specific line of activity, it makes me think about what I do, and true insights appear. It is a long, laborious process, still it has one advantage from my point of view: I already know the pace of that work, and I know how to structure it, because I have already done it in another line of work, namely in science.

Publishing that investment writing on my blog will be painfully humbling. I will certainly make laughable mistakes in my investment decisions, and many a professional broker will have good times mocking how stupid and pretentious I am if I think I can become proficient enough to create an investment fund of my own. Yes, that’s the desert to walk through, and I know that walking it through brings a reward.     

I start with the account – and the analysis – of what I have done as my first steps, during those last two weeks. I am investing via two digital platforms: a Polish brokerage house CDM PeKaO, and DEGIRO for investment in foreign financial instruments. I put 2500 PLN thus some €585 in each of them, and I did some very intuitive, quick shopping. Precisely, I just did a bit of thinking before buying my first basket of financial instruments. I am learning the way I observe it in neural networks I use. Error is learning, and more error means more learning, as long as I can sustain the consequences. I make errors, I study my errors, I try to understand how exactly I make errors, and this understanding will help me to make more and more informed decisions in the future.

The way I intend to pace myself in that learning of investment is precisely based on the sequence: decision >> analysis and definition of errors >> outline of a strategy free of those errors >> implementation of the new strategy, i.e. next investment decision(s) >> analysis etc. My plan is to practice that loop on a monthly basic, i.e. I invest once a month, in a relatively short window of time, and I spend the rest of the month on studying my own decisions.   

Before I go into describing the details of those first investments of mine, which I made in the first days of February 2020, one thing as sort of popped into my mind. When I was starting to use my account with DEGIRO, I was dealing with something new. DEGIRO is not exactly a brokerage house: it is a transactional platform, something like a very fluid investment fund, where I decide what exact assets I want to have for the money I pay into my DEGIRO account. It was new for me, and I made a series of, if my memory is correct, 3 consecutive transactions where I just paid money onto the DEGIRO account and then withdrew it back onto my main bank account. Why was I doing that? Good question. When I look back at those specific decisions of mine, they were quite emotional and impulsive. I remember being sort of vibrant in my thinking, as I was stroking an unknown animal, wondering whether it is going to leap to my throat. Lesson #1: when I do a new type of financial operation, and I use a new type of financial instrument, I experience strong emotions and those emotions tend to blur my rational thinking. In this case, I was probably afraid that once I pay my money onto the DEGIRO account, there could be problems with recouping it, e.g. very high fees. As a matter of fact, none of that happened. Conclusion: I can make irrational, possibly erroneous investment decisions out of fear. I need to understand what I am afraid of, in my investment, in order to make as informed decisions as possible.

It is interesting to understand my own fears. What exactly was I afraid of, in connection with investment through the DEGIRO platform, and what triggered those fears? In the first place, I freaked out because I did not quite do my homework as for the functionalities available. When I invest with DEGIRO, my account has a few metrics. Among them, I have the amount of cash available, and the so-called ‘free space’, or the exact amount of cash I can use to buy financial assets. The default currency of DEGIRO is the euro, and my balances appear in euro. Right after I effectively transferred Polish zlotys on that account, i.e. right after they became visible as the cash balance as euros, they mirrored in the ‘free space’ account. Still, when I tried to use them for buying financial assets, the platform blocked me and an error message of ‘Insufficient free space’ was displayed. Then I freaked out. ‘They stole my money!’, I thought. I know this was stupid. DEGIRO is a licenced operator, and they are legit as for financial reliability. However, seeing that I have no full access to my cash made me extremely nervous and irrational. This is when I suddenly withdrew, back onto my main bank account, all the cash I had paid onto the DEGIRO account.

Only after having done that, I did my homework in the FAQ section of the DEGIRO page and found out what exactly happened to my money. The default currency at DEGIRO is the euro, but display in euros is not exactly the same as conversion into euros. When I do my transfer in Polish zlotys, they are recalculated into euros in real time, for the needs of display on the cash account, and in the free space account. Yet, they are not immediately converted into euros, and thus not immediately available for transactional purposed. Conversion into euros takes place automatically once a day (technically, once a night) or I can do it manually whenever I want. Thus, when I want to use the zlotys I have just transferred, I need to convert them manually into euros, or I have to wait until the next day, i.e. wait for automatic conversion.

That’s the homework I neglected to do before starting with DEGIRO, and the lack thereof made me do those frantic transfers back and forth between my basic bank account and the DEGIRO account. What exactly was I afraid of? As I try to deconstruct my behaviour, I was anxious because I thought I haven’t immediate control over my money. Fear #1: loss of control, possibly loss of immediate liquidity. It’s funny, but John Maynard Keynes wrote about the same thing: many people truly feel they have money when they are convinced they can spend it whenever they want. He called it propensity to conserve liquidity or something in these lines. Lesson #2: when I invest, I tend to be afraid of losing liquidity, i.e. immediate transactional control over my money.

My fear #2, as I think about the situation, is reputational. I was afraid that someone I know could like have a look on what I do and say: ‘You have done something stupid and you lost money’. Here comes a nice paradox: I invest online, privately, and yet I tend to be very concerned about the possible opinions of other people. A part of me want to look 100% professional in my investment decisions. I know, that’s stupid. A few paragraphs earlier I stated that I want to learn how to be a pro. Lesson #3: when I invest, I need to define my true ‘free space’, i.e. the amount of money I am ready to put on stake without being afraid of losing immediate control over its liquidity. Lesson #4: I need to work through the classical question of personal development, namely ‘How will other people know I am successful in my investment decisions?’. Good question, once again, and I think it is the right moment to describe my first investment decisions.

My general idea was, and still is, to focus on three sectors: biotechnology, renewable energies, and IT. Why? The most honest explanation is that I am interested in those fields of technological change. As it is frequently the case with general ideas, they remain sort of general. As I was sailing the ocean of investment opportunities, and as I took a total of 13 investment positions, 6 among them are biotech businesses: OAT – OncoArendi Therapeutics, APREA THERAPEUTICS , FREQUENCY THERAPEUTICS INC, INCYTE CORPORATION , MACROGENICS INC, VIR BIOTECHNOLOGY INC. Those names of companies are hyperlinked to their respective ‘Investor relations’ sites.  As for IT, I have one investment position, namely CYFROWY Polsat. They are not exactly the type of innovative IT company. It is more of a generalist in telecommunications. Why have I bought their stock? Because I have studied their case with my students, in the class of management. The same applies to a TV production company:  ATM Grupa. Lesson #5: I tend to invest in companies, which either I have an intellectual interest in, or I have discussed their cases in class.

As for renewable energies, I have one position open: FIRST SOLAR INC, a photovoltaic business. I have one more energy business, a Polish one – PGE – yet, in all honesty, you wouldn’t really call them a renewable energy-based business. It is mostly your (well, our) basic coal, with some timid glimpses of RES here and there. As for the First Solar case, I am somehow familiar with them: they were one of the first topics I discussed on my blog, back in 2017. As for PGE, kill me (figuratively), I don’t know why I opened that position. I think I was intuitively looking for something big sort of next door.

There is one position which I opened like a real dumb f**k, i.e. after having read a piece of news. I am talking about ASTON MARTIN WI, ISIN GB00BFXZC448. I read that a new big investor decided to acquire a significant portion of their stock, and I made quite stupid a move of following the grizzly bear all the way into its feeding grounds. Now, you are going to have fun. There is one company, which I haven’t the faintest idea why I opened an investment position on: BLACK DIAMOND GROUP LTD. They are in industrial real estate. WTF? Why did I do it? Go figure.  

The last investment position I took is a so-called ‘tracker’: an investment fund supposed to reflect, as closely as possible, the structure of a big stock market index. I chose Amundi Epra DR ISIN LU1437018838 . As I am deconstructing my way of thinking about it, I a pretty sure I wanted some kind of stabilizer, sort of independent from my own judgement.

As I have a look at those investments of mine, I re-ask myself the question: namely ‘How will other people know I am successful in my investment decisions?’. Two assumed factors of recognition seem to emerge from my decisions: coherence, and sort of a general alertness. I want to feel that I am following some sort of coherent strategy, and, intuitively, I want to save some of my money to investments outside that strategy, as if I wasn’t entirely trusting my own judgement.

That’s all in this update. I hope to keep a nice pace in the months to come. Thank you for your attention.  

We, the average national economy. Research and case study in finance.

 

My editorial on You Tube

 

I am returning to a long-followed path of research, that on financial solutions for promoting renewable energies, and I am making it into educational content for my course of « Fundamentals of Finance ». I am developing on artificial intelligence as well. I think that artificial intelligence is just made for finance. Financial markets, and the contractual patterns they use, are akin endocrine systems. They generate signals, more or less complex, and those signals essentially say: ‘you lazy f**ks, you need to move and do something, and what that something is supposed to be you can read from between the lines of those financial instruments in circulation’. Anyway, what I am thinking about is to use artificial intelligence for simulating the social change that a financial scheme, i.e. a set of financial instruments, can possibly induce in the ways we produce and use energy. This update is at the frontier of scientific research, business planning, and education strictly spoken. I know that some students can find it hard to follow, but I just want to show real science at work, 100% pure beef.

 

I took a database which I have already used in my research on the so-called energy efficiency, i.e. on the amount of Gross Domestic Product we can derive on the basis of 1 kilogram of oil equivalent. It is a complex indicator of how efficient a given social system is as regards using energy for making things turn on the economic side. We take the total consumption of energy in a given country, and we convert it into standardized units equivalent to the amount of energy we can have out of one kilogram of natural oil. This standardized consumption of energy becomes the denominator of a coefficient, where the nominator consists in the Gross Domestic Product. Thus, it goes like “GDP / Energy consumed”. The greater the value of that coefficient, i.e. the more dollars we derive from one unit of energy, the greater is the energy efficiency of our economic system.

 

Since 2012, the global economy has been going through an unprecedentedly long period of expansion in real output[1]. Whilst the obvious question is “When will it crash?”, it is interesting to investigate the correlates of this phenomenon in the sector of energy. In other terms, are we, as a civilisation more energy-efficient as we get (temporarily) much more predictable in terms of economic growth? The very roots of this question are to find in the fundamental mechanics of our civilisation. We, humans, are generally good at transforming energy. There is a body of historical and paleontological evidence that accurate adjustment of energy balance was one of the key factors in the evolutionary success of humans, both at the level of individual organisms and whole communities (Leonard, Robertson 1997[2]; Robson, Wood 2008[3]; Russon 2010[4])

When we talk about energy efficiency of the human civilisation, it is useful to investigate the way we consume energy. In this article, the question is being tackled by observing the pace of growth in energy efficiency, defined as GDP per unit of energy use (https://data.worldbank.org/indicator/EG.GDP.PUSE.KO.PP.KD?view=chart ). The amount of value added we can generate out of a given set of production factors, when using one unit of energy, is an interesting metric. It shows energy efficiency as such, and, in the same time, the relative complexity of the technological basket we use. As stressed, for example, by Moreau and Vuille (2018[5]), when studying energy intensity, we need to keep in mind the threefold distinction between: a) direct consumption of energy b) transport c) energy embodied in goods and services.

One of the really deep questions one can ask about the energy intensity of our culture is to what extent it is being shaped by short-term economic fluctuations. Ziaei (2018[6]) proved empirically that observable changes in energy intensity of the U.S. economy are substantial, in response to changes in monetary policy. There is a correlation between the way that financial markets work and the consumption of energy. If the relative increase in energy consumption is greater than the pace of economic growth, GDP created with one unit of energy decreases, and vice versa. There is also a mechanism of reaction of the energy sector to public policies. In other words, some public policies have significant impact on the energy efficiency of the whole economy. Different sectors of the economy respond with different intensity, as for their consumption of energy, to public policies and to changes in financial markets. We can assume that a distinct sector of the economy corresponds to a distinct basket of technologies, and a distinct institutional outset.

Faisal et al. (2017[7]) found a long-run correlation between the consumption of energy and real output of the economy, studying the case of Belgium. Moreover, the same authors found significant causality from real output to energy consumption, and that causality seems to be uni-directional, without any significant, reciprocal loop.

Energy efficiency of national economies, as measured with the coefficient of GDP per unit of energy (e.g. per kg of oil equivalent), should take into account that any given market is a mix of goods – products and services – which generate aggregate output. Any combination “GDP <> energy use” is a combination of product markets, as well as technologies (Heun et al. 2018[8]).

There is quite a fruitful path of research, which assumes that aggregate use of energy in an economy can be approached in a biological way, as a metabolic process. The MuSIASEM methodological framework seems to be promising in this respect (e.g. Andreoni 2017[9]). This leads to a further question: can changes in the aggregate use of energy be considered as adaptive changes in an organism, or in generations of organisms? In another development regarding the MuSIASEM framework, Velasco-Fernández et al (2018[10]) remind that real output per unit of energy consumption can increase, on a given basis of energy supply, through factors other than technological change towards greater efficiency in energy use. This leads to investigating the very nature of technological change at the aggregate level. Is aggregate technological change made only of engineering improvements at the microeconomic level, or maybe the financial reshuffling of the economic system counts, too, as adaptive technological change?

The MuSIASEM methodology stresses the fact that international trade, and its accompanying financial institutions, allow some countries to externalise industrial production, thus, apparently, to decarbonise their economies. Still, the industrial output they need takes place, just somewhere else.

From the methodological point of view, the MuSIASEM approach explores the compound nature of energy efficiency measured as GDP per unit of energy consumption. Energy intensity can be understood at least at two distinct levels: aggregate and sectoral. At the aggregate level, all the methodological caveats make the « GDP per kg of oil equivalent » just a comparative metric, devoid of much technological meaning. At the sectoral level, we get closer to technology strictly spoken.

There is empirical evidence that at the sectoral level, the consumption of energy per unit of aggregate output tends to: a) converge across different entities (regions, entrepreneurs etc.) b) tends to decrease (see for example: Yu et al. 2012[11]).

There is also empirical evidence that general aging of the population is associated with a lower energy intensity, and urbanization has an opposite effect, i.e. it is positively correlated with energy intensity (Liu et al. 2017[12])

It is important to understand, how and to what extent public policies can influence the energy efficiency at the macroeconomic scale. These policies can either address directly the issue of thermodynamic efficiency of the economy, or just aim at offshoring the most energy – intensive activities. Hardt et al. (2018[13]) study, in this respect, the case of United Kingdom, where each percentage of growth in real output has been accompanied, those last years, by a 0,57% reduction in energy consumption per capita.

There is grounds for claiming that increasing energy efficiency of national economies matters more for combatting climate change that the strictly spoken transition towards renewable energies (Weng, Zhang 2017[14]). Still, other research suggest that the transition towards renewable energies has an indirectly positive impact upon the overall energy efficiency: economies that make a relatively quick transition towards renewables seem to associate that shift with better efficiency in using energy for creating real output (Akalpler, Shingil 2017[15]).

It is to keep in mind that the energy efficiency of national economies has two layers, namely the efficiency of producing energy in itself, as distinct from the usage we make of the so-obtained net energy. This is the concept of Energy Return on Energy Invested (EROI), (see: Odum 1971[16]; Hall 1972[17]). Changes in energy efficiency can occur on both levels, and in this respect, the transition towards renewable sources of energy seems to bring more energy efficiency in that first layer, i.e. in the extraction of energy strictly spoken, as compared with fossil fuels. The problematically slow growth in energy efficiency could be coming precisely from the de-facto decreasing efficiency of transformation in fossil fuels (Sole et al. 2018[18]).

 

Technology and social structures are mutually entangled (Mumford 1964[19], McKenzie 1984[20], Kline and Pinch 1996[21]; David 1990[22], Vincenti 1994[23]; Mahoney 1988[24]; Ceruzzi 2005[25]). An excellent, recent piece of research by Taalbi (2017[26]) attempts a systematic, quantitative investigation of that entanglement.

The data published by the World Bank regarding energy use per capita in kg of oil equivalent (OEPC) (https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE ) allows an interesting insight, when combined with structural information provided by the International Energy Agency (https://www.iea.org). As one ranks countries regarding their energy use per capita, the resulting hierarchy is, in the same time, a hierarchy in the broadly spoken socio-economic development. Countries displaying less than 200 kg of oil equivalent per capita are, in the same time, barely structured as economies, with little or no industry and transport infrastructure, with quasi-inexistent institutional orders, and with very limited access to electricity at the level of households and small businesses.  In the class comprised between 200 kg OEPC and approximately 600 ÷ 650 kg OEPC, one can observe countries displaying progressively more and more development in their markets and infrastructures, whilst remaining quite imbalanced in their institutional sphere. Past the mark of 650 OEPC, stable institutions are observable. Interestingly, the officially recognised threshold of « middle income », as macroeconomic attribute of whole nations, seems corresponding to a threshold in energy use around 1 500 kg OEPC. The neighbourhood of those 1 500 kg OEPC looks like the transition zone between developing economies, and the emerging ones. This is the transition towards really stable markets, accompanied by well-structured industrial networks, as well as truly stable public sectors. Finally, as income per capita starts qualifying a country into the class of « developed economies », that country is most likely to pass another mark of energy consumption, that of 3000 kg OEPC. This stylized observation of how energy consumption is linked to social structures is partly corroborated by other research, e.g. that regarding social equality in the access to energy (see for example: Luan, Chen 2018[27])

The nexus of energy use per capita, on the one hand, and institutions on the other hand, has even found a general designation in recent literature: “energy justice”. A cursory review of that literature demonstrates the depth of emotional entanglement between energy and social structures: it seems to be more about the connection between energy and self-awareness of societies than about anything else (see for example: Fuller, McCauley 2016[28]; Broto et al. 2018[29]). The difficulty in getting rid of emotionally grounded stereotypes in this path of research might have its roots in the fact that we can hardly understand what energy really is, and attempts at this understanding send us to the very foundations of our understanding as for what reality is (Coelho 2009[30]; McKagan et al. 2012[31]; Frontali 2014[32]). Recent research, conducted from the point of view of management science reveal just as recent an emergence of new, virtually unprecedented, institutional patterns in the sourcing and the use of energy. A good example of that institutional change is to find in the new role of cities as active players in the design and implementation of technologies and infrastructures critical for energy efficiency (see for example: Geels et al. 2016[33]; Heiskanen et al. 2018[34]; Matschoss, Heiskanen 2018[35]).

 

Changes observable in the global economy, with respect to energy efficiency measured as GDP per unit of energy consumed, are interestingly accompanied by those in the supply of money, urbanization, as well as the shift towards renewable energies. Years 2008 – 2010, which marked, with a deep global recession, the passage towards currently experienced, record-long and record-calm period of economic growth, displayed a few other interesting transitions. In 2008, the supply of broad money in the global economy exceeded, for the first documented time, 100% of the global GDP, and that coefficient of monetization (i.e. the opposite of the velocity of money) has been growing ever since (World Bank 2018[36]). Similarly, the coefficient of urbanization, i.e. the share of urban population in the global total, exceeded 50% in 2008, and has kept growing since (World Bank 2018[37]). Even more intriguingly, the global financial crisis of 2007 – 2009 took place exactly when the global share of renewable energies in the total consumption of energy was hitting a trough, below 17%, and as the global recovery started in 2010, that coefficient started swelling as well, and has been displaying good growth since then[38]. Besides, empirical data indicates that since 2008, the share of aggregate amortization (of fixed assets) in the global GDP has been consistently growing, after having passed the cap of 15% (Feenstra et al. 2015[39]). Some sort of para-organic pattern emerges out of those observations, where energy efficiency of the global economy is being achieved through more intense a pace of technological change, in the presence of money acting as a hormone, catabolizing real output and fixed assets, whilst anabolizing new generations of technologies.

 

Thus, I have that database, which you can download precisely by clicking this link. One remark: this is an Excel file, and when you click on the link, it downloads without further notice. There is no opening on the screen. In this set, we have 12 variables: i) GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent) ii) Fixed assets per 1 resident patent application iii) Share of aggregate depreciation in the GDP – speed of technological obsolescence iv) Resident patent applications per 1 mln people v) Supply of broad money as % of GDP vi)

Energy use per capita (kg of oil equivalent) vii) Depth of the food deficit (kilocalories per person per day) viii) Renewable energy consumption (% of total final energy consumption) ix) Urban population as % of total population x) GDP (demand side) xi) GDP per capita, and finally xii) Population. My general, intuitive idea is to place energy efficiency in a broad socio-economic context, and to see what role in that context is being played by financial liquidity. In simpler words, I want to discover how can the energy efficiency of our civilization be modified by a possible change in financial liquidity.

 

My database is a mix-up of 59 countries and years of observation ranging from 1960 to 2014, 1228 records in total. Each record is the state of things, regarding the above-named variables, in a given year. In quantitative research we call it a data panel. You have bits of information inside and you try to make sense out of it. I like pictures. Thus, I made some. These are the two graphs below. One of them shows the energy efficiency of national economies, the other one focuses on the consumption of energy per capita, and both variables are being shown as a function of supply of broad money as % of GDP. I consider the latter to be a crude measure of financial liquidity in the given place and time. The more money is being supplied per unit of Gross Domestic Product, the more financial liquidity people have as for doing something with them units of GDP. As you can see, the thing goes really all over the place. You can really say: ‘that is a cloud of points’. As it is usually the case with clouds, you can see any pattern in it, except anything mathematically regular. I can see a dung beetle in the first one, and a goose flapping its wings in the second. Many possible connections exist between the basic financial liquidity of the economic system, on the one hand, and the way we use energy, on the other hand.

 

I am testing my database for general coherence. In the table below, I am showing the arithmetical average of each variable. As you hopefully know, since Abraham de Moivre we tend to assume that arithmetical average of a large sample of something is the expected value of that something. Thus, the table below shows what we can reasonably expect from the database. We can see a bit of incoherence. Mean energy efficiency is $8,72 per kg of oil equivalent in energy. Good. Now, I check. I take the energy consumption per capita and I multiply in by the number of capitae, thus I go 3 007,28 * 89 965 651 =  270 551 748,43 tons of oil equivalent. This is the amount of energy consumed in one year by the average expected national society of homo sapiens in my database. Now, I divide the average expected GDP in the sample, i.e. $1 120 874,23 mln, by that expected total consumption of energy, and I hit just $1 120 874,23 mln / 270 551 748,43 tons = $4,14 per kilogram.

 

It is a bit low, given that a few sentences ago the same variable was supposed to be$8,72 per kg. This is just a minor discrepancy as compared to the GDP per capita, which is the central measure of wealth in a population. The average calculated straight from the database is $22 285,63. Cool. This is quite a lot, you know. Now, I check. I take the aggregate average GDP per country, i.e.  $1 120 874,23 mln, and I divide it by the average headcount of population, i.e. I go $1 120 874 230 000 / 89 965 651 =  $12 458,91. What? $12 458,91 ? But it was supposed to be is $22 285,63! Who took those 10 thousand dollars away from me? I mean, $12 458,91 is quite respectable, it is just a bit below my home country, Poland, presently, but still… Ten thousand dollars of difference? How is it possible?

 

It is so embarrassing when numbers are not what we expect them to be. As a matter of fact, they usually aren’t. It is just our good will that makes them look so well fitting to each other. Still, this is what numbers do, when they are well accounted for: they embarrass. As they do so, they force us to think, and to dig meaning out from underneath the numbers. This is what quantitative analysis in social sciences is supposed to do: give us the meaning that we expect when we measure things about our own civilisation.

 

Table 1 – Average values from the pooled database of N = 1228 country-year observations

Variable Average expected value from empirical data, N = 1228 records
GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent) 8,72
Fixed assets per 1 resident patent application (constant 2011 PPP $) 3 534,80
Share of aggregate depreciation in the GDP – speed of technological obsolescence 14%
Resident patent applications per 1 mln people – speed of invention 158,90
Supply of broad money % of GDP – observed financial liquidity 74,60%
Energy use (kg of oil equivalent per capita) 3 007,28 kg
Depth of the food deficit (kilocalories per person per day) 26,40
Renewable energy consumption (% of total final energy consumption) 16,05%
Urban population as % of total population 69,70%
GDP (demand side; millions of constant 2011 PPP $) 1 120 874,23
GDP per capita (constant 2011 PPP $) $22 285,63
Population 89 965 651

 

Let’s get back to the point, i.e. to finance. As I explain over and over again to my students, when we say ‘finance’, we almost immediately need to say: ‘balance sheet’. We need to think in terms of a capital account. Those expected average values from the table can help us to reconstruct at least the active side of that representative, expected, average economy in my database. There are three variables which sort of overlap: a) fixed assets per 1 resident patent application b) resident patent applications per 1 mln people and c) population. I divide the nominal headcount of population by 1 000 000, and thus I get population denominated in millions. I multiply the so-denominated population by the coefficient of resident patent applications per 1 mln people, which gives me, for each country and each year of observation, the absolute number of patent applications in the set. In my next step, I take the coefficient of fixed assets per 1 patent application, and I multiply it by the freshly-calculated-still-warm absolute number of patent applications.

 

Now, just to make it arithmetically transparent, when I do (« Fixed assets » / « Patent applications ») * « Patent applications », I take a fraction and I multiply it by its own denominator. It is de-factorisation. I stay with just the nominator of that initial fraction, thus with the absolute amount of fixed assets. For my representative, average, expected country in the database, I get Fixed Assets = $50 532 175,96 mln.

 

I do slightly the same with money. I take “Supply of money as % of the GDP”, and I multiply it by the incriminated GDP, which makes Money Supplied = 74,60% * $1 120 874,23 mln =  $836 213,98 mln. We have a fragment in the broader balance sheet of our average expected economy: Fixed Assets $50 532 175,96 mln and Monetary Balances $836 213,98 mln. Interesting. How does it unfold over time? Let’s zeee… A bit of rummaging, and I get the contents of Table 2, below. There are two interesting things about that table.

 

 

Table 2 – Changes over time in the capital account of the average national economy

Year Average fixed assets per national economy, $ mln constant 2011 PPP GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent), in the average national economy Supply of broad money in average national economy, $ mln constant 2011 PPP Money to fixed assets
1990 2 036 831,928 8,08 61,526 0,0030%
1991 1 955 283,198 8,198 58,654 0,0030%
1992 2 338 609,511 8,001 61,407 0,0026%
1993 2 267 728,024 7,857 60,162 0,0027%
1994 2 399 075,082 7,992 60,945 0,0025%
1995 2 277 869,991 7,556 60,079 0,0026%
1996 2 409 816,67 7,784 64,268 0,0027%
1997 2 466 046,108 7,707 71,853 0,0029%
1998 2 539 482,259 7,76 77,44 0,0030%
1999 2 634 454,042 8,085 82,987 0,0032%
2000 2 623 451,217 8,422 84,558 0,0032%
2001 2 658 255,842 8,266 88,335 0,0033%
2002 2 734 170,979 8,416 92,739 0,0034%
2003 2 885 480,779 8,473 97,477 0,0034%
2004 3 088 417,325 8,638 100,914 0,0033%
2005 3 346 005,071 8,877 106,836 0,0032%
2006 3 781 802,623 9,106 119,617 0,0032%
2007 4 144 895,314 9,506 130,494 0,0031%
2008 4 372 927,883 9,57 140,04 0,0032%
2009 5 166 422,174 9,656 171,191 0,0033%
2010 5 073 697,622 9,62 164,804 0,0032%
2011 5 702 948,813 9,983 178,381 0,0031%
2012 6 039 017,049 10,112 195,487 0,0032%
2013 6 568 280,779 10,368 205,159 0,0031%
2014 5 559 781,782 10,755 161,435 0,0029%

 

This is becoming really interesting. Both components in the capital account of the representative, averaged economy had been growing until 2013, then it fell. Energy efficiency has been growing quite consistently, as well. The ratio of money to assets, thus a crude measure of financial liquidity in this capital account, remains sort of steady, with a slight oscillation. You can see it in the graph below. I represented all the variables as fixed-base indexes: the value recorded for the year 2000 is 1,00, and any other value is indexed over that one. We do that thing all the time, in social sciences, when we want to study apparently incompatible magnitudes. A little test of Pearson correlation, and… Yesss! Energy efficiency is Pearson correlated with the amount of fixed assets at r = 0,953096394, and with the amount of money supplied at r = 0,947606073. All that in the presence of more or less steady a liquidity.

 

Provisional conclusion: the more capital we accumulate, we, the average national economy, the more energy efficient we are, and we sort of dynamically adjust to keep the liquidity of that capital, at least the strictly monetary liquidity, at a constant level.

 

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

 

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