Fire and ice. A real-life business case.

I keep going along the frontier between my scientific research, my small investment business, and my teaching. In this update, I bring you two typically educational pieces of content, one sort of astride educational and practical investment decisions of my own, and finally I give slightly educational an account of a current business decision I am taking.  

In the video entitled ‘My investment experience, my teaching and my science #3  BMW, Daimler and Volkswagen’ [ Invest 3 2020-08-26 14-02-22 ;  ], I discuss those three investment positions in my portfolio. Three German automotive companies. Same industry, same country, same macroeconomic environment, and yet three different performances in terms of return on investment. In this video, you can see me developing on the distinction between long term-trends and short-term variations, as well as trying to connect technical analysis of price trends with fundamental analysis of their half-annual reports.

I have place on You Tube two pieces of content in the stream of teaching designated as ‘Urban Economics and City Management’. ‘Urban Economics and City Management #1 Lockdowns in pandemic and the role of cities’ [ Cities 1 2020-08-27 08-57-15;  ] recounts and restates my starting point in this path of research. I browse through the main threads of connection between the pandemic of COVID-19 and the civilisational role of cities. The virus, which just loves densely populated places, makes us question the patterns of urban life, and makes us ask question as for the future of cities.

In ‘Urban Economics and City Management #2 Case study of REIT: Urban Edge and Atrium [Cities 2 2020-08-27 11-00-52 ; ], I study the cases of two REITs, i.e. Real Estate Investment Trusts, namely Urban Edge (U.S.) and Atrium (Central Europe), with two assumptions. Firstly, cities can grow and evolve, when the local humans master the craft of agglomerating in one, relatively tiny place, the technologies of construction, sanitation, transportation, energy supply etc., and to parcel those technologies into marketable goods. Secondly, rental and lease of real estate are parcelled, marketable urban technologies.

In the video ‘My investment experience, my teaching and my science #4 The Copernic project’, [ Invest 4 Copernic 2020-08-30 08-57-54 ;  ], I am developing on a topic exactly at the intersection of those three: the Copernic project. Honestly, this is complex stuff. I hesitated to choose this topic as educational material, yet I have that little intuition that good teachers teach useful skills. I want to be a good teacher, and the s**t I teach, I want it to be useful for my students. Life is complex and brutal, business is complex and brutal, and, as a matter of fact, each of us, humans, is complex and brutal. Fake simplicity is for pussies.

Thus, whoever among my students reads this update and watches the accompanying video material, is going to deal with real stuff, far beyond textbooks. This is a business which I am thinking about engaging in, and I am just starting to comprehend its patterns. This update is a living proof and test how good I am, or how I suck, at grasping business models of the digital economy.

In educational terms, I am locating the content relative to Copernic project in the path of teaching which I labelled ‘My investment experience, my teaching and my science’, as I am entertaining the idea of investing in the Copernic project. The subject cuts comprehensively across and into many aspects of economics and management. It can be considered as useful material for any educational path in these major fields.

It started when I reacted to a piece of advertising on Facebook. Yes, many interesting stories start like that, nowadays. It was an ad for the Copernic project itself. Here you have a link to Copernic’s website – – but keep in mind that it is only Polish version, at least for the moment. I will do my best to describe the project in English.

Copernic is both the name of the project, and the name of an LLP (Limited Liability Partnership), incorporated under Polish law, in Krakow, Poland. The commonly used Polish acronym for an LLP is ‘sp. Z o.o.’, however, as I write in English, I will keep using the name ‘Copernic LLP’. I checked this company in the Judicial Register (of incorporated entities) run by the Ministry of Justice of the Republic of Poland, under the link . A business story emerges. On December 6th, 2019, Copernic LLP is founded, under the register #817764, in Gdansk, Poland, technically by two partners: a physical person and another LLP, i.e. TTC Trade LLP (register #788023). Yet, after scratching the surface, the surface being the Judicial Register, I discovered that TTC Trade LLP is wholly owned by the same physical person who was its partner in Copernic LLP. Anyway, the physical person apported 1000 PLN and took 1 partner share, whilst her LLP paid in 4000 PLN in exchange of 4 partner shares. By the way, PLN stands for Polish zloty and it comes like PLN 1 = $0,27.

On May 6th, 2020, the physical person who founded Copernic LLP steps out of the partnership, and her own LLP, TTC Trade, sells two of its two partner shares in Copernic LLP, to Sapiency LLP (, register #789717) incorporated in Krakow, Poland, at their face value of 2000 PLN. On the same day, the partnership contract is being reformulated entirely and signed anew, including a change of headquarters, which move from Gdansk to Krakow, Poland. By the same occasion, another corporate partner steps in, namely Reset Sun Energy LLP (Konin, Poland, register #802147) and takes 2 partner shares in Copernic LLP, for a price of 2000 PLN. By the same means, the total partners’ equity in Copernic LLP moves from 5000 PLN to 6000 PLN.

On July 20th, 2020, TTC Trade LLP and Reset Sun Energy LLP both sell their partner shares in Copernic LLP to Sapiency LLP, at face value, i.e. 6000 PLN. We have an interesting legal structure, when one Limited Liability Partnership (Copernic) is wholly owned by another Limited Liability Partnership (Sapiency), which, in turn, is 50/50 owned by two gentlemen, one of whom I had the honour to meet in person. Cool guy. Fire and ice in one. A bit like me.   

Sapiency is mostly active in cryptocurrencies. They make Blockchain-based tokens for whoever asks, and I think their main technological platform is Ethereum ( The marketing model is membership-based, thus oriented on long-term relations with customers. The business model of Copernic LLP is logically connected to that of Sapiency LLP. Copernic builds solar farms in Poland, and markets Blockchain-based tokens labelled Copernic1, at a face value of 4 PLN apiece. Each such token corresponds to a share in the future leasing of solar farms, and those farms, by now, are under actual or planned construction. Later on, i.e. after the solar farms become operational, those lease-connected Copernic1 tokens are supposed to give their holders a claim on secondary tokens CopernicKWH, which, in turn, correspond to claims on electricity generated in those solar farms. The first attribution of CopernicKWH tokens to the holders of Copernic1 tokens is supposed to take place within 14 days after the first photovoltaic farm becomes operational with Copernic LLP, with a standing power of at least 1 MW. That day of operational capacity can be a movable feast, and thus the official statute of those tokens stipulates that the first attribution of CopernicKWH will take place not later than January 1st 2021. After the first attribution of  CopernicKWH, subsequent attributions to the holders of Copernic1 are supposed to take place at least once a week.

The CopernicKWH tokens can be used as means of payment at the Kanga Exchange ( ), which looks cool, on the whole, with one exception. According to Kanga’s own statement, ‘Kanga Exchange is operated by Good Investments Ltd, registered in accordance with the International Business Companies Act of the Republic of Seychelles, Company Number 192185’ ( ). Just for your information: I can incorporate a business in Seychelles without getting up from my desk, 100% online, for the modest sum of 399 British Pounds ( I am fully aware how bloody hard it is to set up any business structure connected to cryptocurrencies in the European legal environment, however… Seychelles? Seriously?

The average price of electricity in Poland, when I am writing those words, is around 0,617 PLN per 1 kWh. One Copernic1 token, with its current price of 4 PLN, corresponds to 4/0,617 = 6,48 kWh of energy. Assuming that every week, starting from the day 0 of operations at the solar farm, Copernic LLP attributes me 1 CopernicKWH token for each Copernic1 token in my possession, I break even after 7 weeks, and each consecutive week brings me a net profit.

I do my maths according to the logic of the capital balance sheet. First of all, I want to compute the book value of assets that corresponds to the planned solar farm of 1 megawatt in standing power. In a report published by the International Renewable Energy Agency (IRENA ), entitled ‘Renewable Power Generation Costs in 2019’ ( ), I can read that the average investment needed for 1 watt of power in a photovoltaic installation can be cautiously estimated at $0,38, thus PLN 1,40.

Building a solar farm of 1 MW, thus of a million watts in terms of electric power, means an investment of at least PLN 1,40 * 106 = PLN 1 400 000. To that, you need to add the price of acquiring land. At the end of the day, I would tentatively put a PLN 2 million capital tag on the project. Supposing that capital for the project comes from the sales of Copernic1 tokens, Copernic LLP needs to sell at least 2 000 000 PLN/ 4 PLN = 500 000 of them Copernic1.

Looks like a lot, especially for a Limited Liability Partnership with partner equity at 6000 PLN. Assets worth PLN 2 000 000 minus PLN 6000 in partner equity means PLN 1 994 000 = $ 538 919  in capital which is not clear at all where it is supposed to come from. The sole partner in Copernic LLP, namely Sapiency LLP could pay in additional equity. Happens all the time. Still, Sapiency LLP as a partner equity of PLN 5000. See what I mean? Another option is a massive loan, and, finally, the whole balance sheet could rely mostly on those Copernic1 tokens. Only those tokens are supposed to embody claims on the lease of the solar farm. Now, legally, a lease is a contract which gives to the lessee (the one who physically exploits), the right to exploit things or rights owned by the lessor (the one who graciously allows others to exploit). In exchange, the lessee pays a rent to the lessor.

There are two things about that lease of solar farms. A lease is not really divisible, as it is the right to exploit something. If you divide that something into smaller somethings, you split the initial lease into as many separate leases. If I buy one Copernic1 token and that token embodies claims derived from a lease contract, what specifically is the object of leasing? There is another thing. If I buy Copernic1 tokens, it gives me claims on the future CopernicKWH tokens. In other words, Copernic will pay me in the future. If they pay me, on the basis of a lease contract, it is as if they were paying me a rent, i.e. as if they were leasing that solar farm from me. Only I don’t have that solar farm. They will have it. Yes, indeed, WTF? This is the moment to ask that rhetorical question.

A few paragraphs ago, I wrote that I am entertaining the idea of investing in those Copernic1 tokens. I think the idea has become much less attractive, business-wise, whilst becoming much more entertaining. There is an important question, though. Isn’t it ethically advisable to invest in renewable energies, even if the legal scheme is a bit sketchy, just to push forward those renewables? I can give an answer in two parts to that question. Firstly, renewables grow like hell, both in terms of power supplied, and in terms of attractiveness in financial markets. They really don’t need any exceptional push. They walk, and even run on their own legs. Secondly, I worked through my own ideas for implementing new technologies in the field of renewable energies, and, notably, I worked a lot with a tool called ‘Project Navigator’, run by the same International Renewable Energy Agency which I quoted earlier. The link is here: . There is one sure takeaway I have from working with that tool: a good project needs a solid, transparent, 100% by-the-book institutional base. Wobbly contracts translate into wobbly financing, and that, in turn, means grim prospects for the project in question.     

Another doubt arises in my mind, as I do flows instead of balances. A solar farm needs to earn money, i.e. to make profit, in order to assure a return on investment. The only asset which can earn value over time is land in itself. In practical terms, as long as we want that solar farm to work, it needs to generate a positive operational cash flow. Photovoltaic equipment ages inexorably, by physical wear and tear as well as by relative moral obsolescence. That aging can assure substantial amortization, yet you need some kind of revenue which you can write that amortization off from. If all or a substantial part of energy produced in the solar farm is tokenized and attributed to the holders of Copernic1, lease-based tokens, there could be hardly any energy left for sale, hence not much of a revenue. In other words, the system of initial financing with tokens can jeopardize economic payoff from the project, and that’s another thing I learnt with the Project Navigator: you need a solid economic base, and there is no way around it.

The hopefully crazy semester

Another handful of educational material, for the apparently (hopefully) crazy semester at the university. Crazy because of the virus, stands to reason. Things are never crazy because we make them so, stands to reason, once again.

I am making a big, fat bottom line at my investment portfolio in the stock market, and I am using this opportunity to make some educational material. The point of using my experience in education. It is personal experience, important to enrich theory. It is a story of personal limitations in business decisions, and understanding those limitations is important for understanding microeconomics as the substance of decisions, macroeconomics as their context, and management as their execution.

I have successful experience, together with hindsight on the mistakes I made. I can utilize it as valuable material to share and to build some teaching on. Since January 2020, I have invested  $7 924,76 in the stock market, and today (August 25th, 2020), my investment portfolio is worth  $11 719,91. I have 47,89% of return on the cash invested, over a period of 7 months. Not bad for a theoretician, isn’t it? I am deeply convinced that personal experience is impossible to bypass in any true teaching. Whatever kind of story I am telling on the moment, I always tell the story of my own existence. I can make it genuine and truthful just as well. Here is the link to the first, introductory video in this path: ‘My investment experience, my teaching and my science #1’  [Invest 1 2020-08-25 11-54-58 ; ]

In the second video of the same series [Invest 2 2020-08-26 07-37-08; ], I focus on the presentation of my investment portfolio. I stress two points. Firstly, the portfolio which I hold now is the cumulative outcome of past trials and errors. Secondly, my portfolio shows many alternative scenarios of what could possibly have happened to my money, had I invested in just one among the 27 positions, thus if I had not diversified. I could have made +313% or -49%, instead of the 48% I had made as of August 25th 2020. I study more fundamentally the case of General Electric, which is one of my financial failures as for now. Turns out they have stakes in aviation, and that sucks in the times of pandemic.

In the third video of the series ‘Business Models in the Media Industry’ [Media BM 3 2020-08-26 08-24-42; ] I focus more in depth on studying the case of Netflix. You can have a glimpse of their transition from a streamer of externally made content to a business based on in-house made content. You can also see how strongly their business model is grounded in the assumption of constant growth in size.

In my second video devoted to Political Systems [PolitSys 2 2020-08-26 09-02-47; ] I use two cases, i.e. the constitutions of France and Finland, to give my readers, followers and students a first glimpse on forms of political power. You can see that general concept in the context of distinction between a presidential system (France) vs a Parliamentary one (Finland).  

Germany happens too, like all the time


I am experiencing an unusually long pause between consecutive updates on my blog. I published my latest update, entitled The balance between intelligence and the way we look in seasoned black leather, on June 23rd, 2020. This specific paragraph is technically in the introduction to a new update, yet I am writing it on June 30th, 2020, after having struggled with new writing for 6 entire days. There are two factors. Firstly, quite organically, we are having a persistent storm front over our part of Europe and with storms around, I have hard time to focus. I am in a bizarre state, as if I was sleepy and was having headaches in the same time. No, this is not hangover. There is nothing I could possibly have hangover after, like really, parole d’honneur. Sober as a pig, as we say in Poland.

Tough s**t makes tough people, and I when I experience struggle, I try to extract some learning therefrom. My learning from such episodes of intellectual struggle is that I can apply to my writing the same principles I apply to my training. Consistency and perseverance rule, intensity is an instrument. I can cheat myself into writing by short bouts. I can write better when I relax. I can write better when I consider pain and struggle as an interesting field of experience to explore and discover. By the way, this is something I discovered over the last 3,5 years, since I started practicing the Wim Hof method: that little fringe of struggle at the frontier of my comfort zone is extremely interesting. I discover a lot about myself when I place myself in that zone of proximal development, just beyond the limits of everyday habits. Nothing grand and impressive, just a tiny bit of s**t which I give to myself. When I keep it tiny, I can discover and study my experience thereof, and this is real stuff, as learning comes.   

The other reason I am struggling with my writing for is the amount of information I need to process. I am returning to studying my investment strategy, as I do every month, or so. There is a lot going on in the stock markets, and in my own decisions about them. I have hard times to keep up with my writing. Besides, I am really closing on the basic structure of my book on the civilizational role of cities, and I am preparing teaching content for online learning the next academic year. Yes, it looks like we go almost entirely distance learning, at least in the winter semester.

All in all, this update for my blog is a strange one. Usually, writing helps me put some order in my thinking and doing. This time, I have hard times to keep up with what’s going on. Once again, having hard times just means it is difficult. I keep trying and going. By trying and going, I have almost painfully come to the realisation what kind of message I want to convey in this update, when I finally end up by publishing it. Before I develop on that realisation, a short digression as regards the ‘end up by publishing’ part of the preceding sentence. I work in a rhythm of intuitively experienced intellectual exhaustion: I publish when I feel I have unloaded an intelligible, well rounded portion of my thinking into my writing.

What I am experiencing right now is precisely the feeling of having made a closure on a window of uncertainty and hesitation in many different fields. This update is specifically oriented on my strategy for investing in the stock market, and therefore this is the main thread I am sticking to. Still, that feeling of having just surfed a large wave of uncertainty sort of generally in life. I know it sounds suspiciously introspective in a blog post about investment, but here is another thing I have learnt about investment: being introspective pays. It pays financially. When I put effort into studying my own thoughts and my own decision making process, I learn how to make better, more informed decisions.  

My financial check from last month financial check is to find in ‘The moment of reassessment’. As I repeat that self-study of my own financial strategy, I find it both hard and rewarding. It is much harder to study my own decisions and my own behaviour (self-assessment) than to comment on sort of what people generally do (social science).

I feel as if I were one of those old-school inventors, who would experiment on themselves. Anyway, let’s study. Since ‘The moment of reassessment’ I made a few important financial decisions, and those decisions were marked by an unusual injection of cash. Basically, every month, I invest in the stock market an amount of PLN 2500, thus around $630, which corresponds to the rent I collect monthly from an apartment I own in town. I take the proceeds from one asset. i.e. real estate, and I use them to create a collection of financial assets.

As I have been practicing investment as a real thing, since the end of January, 2020 (see Bloody hard to make a strategy), I have learnt a lot in social sciences, too, mostly as regards microeconomics. I teach my students that fundamental concept of opportunity cost: when you invest anything, i.e. capital or your own work, in thing A, you forego the possibility of investing in thing B, and thus you choose the benefits from investment A to the expense of those from investment B. Those benefits B are the opportunity cost of investing in A. This is theory from textbooks. As I invest in the stock market, I suddenly understand all the depth of that simple rule. The stock market is like an ocean: there is always a lot that remains out of sight, or just out of my current attention span, and the way I orient my attention is crucial.

I have acquired a very acute feeling of what is called ‘bound rationale of economic decisions’ in textbooks. I have come to appreciate and respect the difference between well-informed decisions and the poorly informed ones. I have learnt the connection between information and time. Now, I know that not only do I have a limited bandwidth as regards business intel, but also that limited bandwidth spreads over time: the more time I have to decide, the more information I can process, and yet it would be too easy if it was that simple, since information loses value over time, and new information is better than old information.

That whole investment story has also taught me a lot about business strategies. I realized that I can outline a lot of alternative wannabe strategies, but only a few of them are workable as real sequences of decisions and actions of a strategy.

Good. Time to outline the situation: my current portfolio, comparison with that presented a month ago in ‘The moment of reassessment’, a short explanation how the hell have I come there, assessment of efficiency, and decisions for the future. Here is the thing: at the very moment when I started to write this specific update on my blog, thus on June 24th, 2020, things started to go south, investment-wise. I found myself in a strange situation, i.e. so fluid and changing one that describing it verbally is always one step behind actual events.

When I don’t have what I like, I have to do with what I have. In the absence of order and abundance of chaos, I have to do with chaos. Good chaos can be useful, mind you, as long as I can find my way through it. Step one, I am trying to describe chaos to the extent of possible. I am trying to phrase out the change in itself. There is some chaos in markets, and some in myself.

Good. Now I can start putting some order in chaos. I can describe change piece by piece, and I guess the best starting point is myself. After all, the existential chaos I am facing is – at least partly – the outcome of my own choices. After I published in ‘The moment of reassessment’, I began with taking non-routine decisions. That end-May-beginning-June period was a moment of something like a shake-off in my personal strategy. I was changing a lot. For reasons which I am going to explain in a moment, I sharply increased the amount of money in my two investment accounts. Now, as I look at things, I am coping with the delayed effects of those sudden decisions. The provisional lesson is that when I do something sudden in my business activity (I consider investment in the stock market as regular business: I put cash in assets which are supposed to bring me return), it is like a sudden shock, and ripples from that shock spread over time. Lesson number two is that any unusually big transfer of cash between into or from any of my investment accounts is such a shock, and there are ripples afterwards.

I think it is worth reconstructing a timeline of my so-far adventures in stock-market investment. End of January 2020, I start. I start investing shyly, without really knowing clearly what I want. I didn’t know what exact portfolio I wanted to build. I just had a general principle in mind, namely that I want to open investment positions in renewable energies, biotech, and IT.

From February through March 2020, I experiment with putting those principles into a practical frame. I do a lot of buying and selling. From the today’s perspective, I know that I was just experimenting with my own decision-making process. It had cost me money, I made some losses, and I intuitively figured out how I make my decisions.  

Over April and May 2020, I was progressively winding down those haphazard, experimental investments of mine. Step by step, I developed a reliable sub-portfolio in IT, and I rode an ascending market wave in Polish biotech companies.

At the end of May 2020, two things happened in my personal strategy of investment. First of all, I had the impression (and let’s face it, it was just an impression, devoid of truly solid foundation) that growth in stock prices across the almost entire Polish industry of biotech and medical supplies was just a short-term speculative bubble. I sold out part of my investment positions in the Polish stock market – mostly those in biotech and medical supplies, which proves to have been a poor move – and I transferred $1600 from my Polish investment account to the international one. Besides, my employer paid me the annual lump compensation for overtime during the academic year, and I decided to use like ¾ of that sum, thus some $3 125 as investment capital in the stock market, splitting it 50/50 (i.e. 2 times $1562) between my two accounts.

See? That was the first moment of chaos in me. First, I transferred $1600 from one account to another, and then I paid two times $1562 into both accounts, and all that like days apart. As a results, my Polish investment account noted a net cash outflow of – $1600 + $1562 = + $38 (very clever, indeed), and my international account swelled by $1600 + $1562 = $3 162.

Let’s go downstream. When I did all those cash transfers, I settled for a diversified portfolio. In Poland, I decided to keep my IT positions (11 Bit and Asseco Business Solutions), and to create three other branches: energy, retail, and restaurants. I know, I know: energy sounds cool, but retail and restaurants? Well, I decided to open positions in those two: the shoe retailer CCC, and a restauration giant Amrest, essentially because they were unusually cheap, and my own calculations, i.e. the moving average price, and mean-reverted price, indicated they were going to go up in price. As for two Polish energy companies – Tauron and PGE – my reasoning was the same. They were unusually cheap, and my own simulations allowed expecting some nice bounce-up. Out of those four shots on the discount shelf, two proved good business, the two others not really. Tauron and PGE brought me a nice return, when I closed them a few days ago, the former almost 79%, the other 28%. As for CCC and Amrest, they kept being cheap, and I closed those positions with slight losses, respectively – 4,3% and – 11,7%. Lesson for the future: don’t be daft. Fundamentals rule. This is my takeaway from the last 3 months of learning investment in practice. I need to look at the end of the market lane, where the final demand dwells for the given business.         

Question: why did I close on Tauron and PGE, if they were bringing me profit? Because it looked like they had a temporary rise in price, and then it seemed to be over.

I have already learnt that I make real money on accurate prediction of something, which, fault of a better expression, I call ‘market waves’, and by which I understand a period of many weeks when the price of some specific stock grows substantially for largely fundamental reasons. In other words, something important is happening in real business and these events (trends?) provoke a change in investors’ behaviour. As for now, and since January this year, I have successfully ridden three market waves, got washed under by one such wave, and I am sort of in two minds about a fifth one.

The wave that maimed me was the panic provoked in the stock market in the early weeks of pandemic. At the time, I had just invested some money in the U.S. stock market. I had been tempted by its nice growth in the first weeks of 2020, and, when the pandemic started to unfold, and market indexes started to tremble and then slump, I was like: ‘It is just temporary. I can wait it out’. Well, maybe I could have waited it out, only I didn’t. I waited, I waited, and my stock went really down, like to scrambling on the ground, and then I went into solid, tangible panic. I sold it all out, in the U.S. market (see Which table do I want to play my game on?). On the whole, it was a good decision. I transferred to the Polish stock market whatever cash I saved out of that financial plunge in U.S. and I successfully rode the wave of speculative interest in Polish biotech companies.

I noticed that I got out of the Polish biotech market wave too early. As I cast a casual glance at their performance in the stock market, I can see they have all grown like hell over the last month. I decide to get back into Polish biotech, plus one gaming company: CD Projekt. The biotechs and medical I take on are: Mercator Medical, Biomed Lublin, Neuca, Synektik, Cormay, Bioton. I am taking some risk here: those biotechs are so high on price that I am facing a risk of sudden slump. Still, their moving cumulative average prices are climbing irresistibly. There is a trend.

What do I do with my U.S. assets? I think I will hold. I don’t want to yield to panic once again. Besides, they diversify nicely with my assets in Poland. In Poland, I took a risk: I jumped once again on the rising wave of investment in biotech and medical business, only this time I jumped on it at a much more elevated point, as compared to the beginning of April 2020. The risk of sudden downturn is substantially bigger now than in April. In the U.S. market, I am holding assets which are clearly undervalued now, with all that panic about social unrest and about a second spike in COVID-19. Possibly overvalued assets in one market and undervalued assets in another market: sounds familiar? Yes, this is a form of hedging, which, in plain language, means that I spread my assets between several baskets, and I hand each basket to a different little girl in a little red riding hood, in the hope that at least some of those girls will outsmart those big bad wolves. Girls usually do, by the way.

On the whole, so far, I have invested $6 674,76 in cash into my two investment accounts. With the current value of my assets at $7 853,30, I have a total return on cash invested around 17,65%. It has decreased slightly over the last month: by the end of May 2020, it was 23,2%. 

I think I need to explain the distinction between two rates of return which I quote as regards my investment: return on the currently open positions vs return on the total cash invested in my investment accounts. Any given moment, I hold cash and open positions in securities. The cash I hold is the sum total of two components: past cash transfers into my investment accounts from my other financial accounts, on the one hand, and cash proceeds from the closure of particular investment positions. When I compare the total value of financial assets (i.e. cash + securities) which I currently hold, to the amount of cash I had paid into my investment accounts, I get my total return on cash invested. When I split my financial assets into cash and securities, and I calculate the incremental change in the value of the latter, I get the rate of return on currently open investment positions, and this one is swinging wildly, those last days. This might be the reason why it took me so long to hatch this update for my blog. Last Thursday it was 12,9%, and today it is 5,5%. What happened? United States happened to be in social unrest, for one, and they keep doing so, by the way (c’mon, guys, pull your pants up, I have money in your stock market). Germany happens too, like all the time, and I have some open positions in their automotive sector.

One thing that happens more or less as I expected is the incremental change in stock price as regards the logistics sector. My positions in Deutsche Post, UPS, and FedEx are doing well.       

I have already learnt that I make real money on accurate prediction of something, which, fault of a better expression, I call ‘market waves’, and by which I understand a period of many weeks when the price of some specific stock grows substantially for largely fundamental reasons. In other words, something important is happening in real business and these events (trends?) provoke a change in investors’ behaviour. As for now, and since January this year, I have successfully ridden three market waves, got washed under by one such wave, and I am sort of in two minds about a fifth one.

The wave that maimed me was the panic provoked in the stock market in the early weeks of pandemic. At the time, I had just invested some money in the U.S. stock market. I had been tempted by its nice growth in the first weeks of 2020, and, when the pandemic started to unfold, and market indexes started to tremble and then slump, I was like: ‘It is just temporary. I can wait it out’. Well, maybe I could have waited it out, only I didn’t. I waited, I waited, and my stock went really down, like to scrambling on the ground, and then I went into solid, tangible panic. I sold it all out, in the U.S. market (see Which table do I want to play my game on?). On the whole, it was a good decision. I transferred to the Polish stock market whatever cash I saved out of that financial plunge in U.S. and I successfully rode the wave of speculative interest in Polish biotech companies.

I noticed that I got out of the Polish biotech market wave too early. As I cast a casual glance at their performance in the stock market, I can see they have all grown like hell over the last month. I decide to get back into Polish biotech, plus one gaming company: CD Projekt. The biotechs and medical I take on are: Mercator Medical, Biomed Lublin, Neuca, Synektik, Cormay, Bioton. I am taking some risk here: those biotechs are so high on price that I am facing a risk of sudden slump. Still, their moving cumulative average prices are climbing irresistibly. There is a trend.

OK. I need to end it somewhere. I record my video editorial on You Tube, I attach it to this piece of writing, and, que sera sera (or What The Hell!), let’s publish those uncombed thoughts.  

Discover Social Sciences is a scientific blog, which I, Krzysztof Wasniewski, individually write and manage. If you enjoy the content I create, you can choose to support my work, with a symbolic $1, or whatever other amount you please, via MY PAYPAL ACCOUNT.  What you will contribute to will be almost exactly what you can read now. I have been blogging since 2017, and I think I have a pretty clearly rounded style.

In the bottom on the sidebar of the main page, you can access the archives of that blog, all the way back to August 2017. You can make yourself an idea how I work, what do I work on and how has my writing evolved. If you like social sciences served in this specific sauce, I will be grateful for your support to my research and writing.

‘Discover Social Sciences’ is a continuous endeavour and is mostly made of my personal energy and work. There are minor expenses, to cover the current costs of maintaining the website, or to collect data, yet I want to be honest: by supporting ‘Discover Social Sciences’, you will be mostly supporting my continuous stream of writing and online publishing. As you read through the stream of my updates on , you can see that I usually write 1 – 3 updates a week, and this is the pace of writing that you can expect from me.

Besides the continuous stream of writing which I provide to my readers, there are some more durable takeaways. One of them is an e-book which I published in 2017, ‘Capitalism And Political Power’. Normally, it is available with the publisher, the Scholar publishing house ( ). Via , you can download that e-book for free.

Another takeaway you can be interested in is ‘The Business Planning Calculator’, an Excel-based, simple tool for financial calculations needed when building a business plan.

Both the e-book and the calculator are available via links in the top right corner of the main page on .

You might be interested Virtual Summer Camps, as well. These are free, half-day summer camps will be a week-long, with enrichment-based classes in subjects like foreign languages, chess, theater, coding, Minecraft, how to be a detective, photography and more. These live, interactive classes will be taught by expert instructors vetted through Varsity Tutors’ platform. We already have 200 camps scheduled for the summer.

The moment of reassessment


For a few days, I am turning into a different thread of my writing: my investment in the stock market. In winter, I decided to come back into the game of active investment in the stock market, and to use my blog as a tool of self-teaching, in the view of sharpening my game (see, for example: Fathom the outcomes and a few subsequent updates). Those of my readers who have been following this thread know that my basic strategy consists in investing in the stock market, every month, the rent I am collecting from an apartment in town. This is a monthly decision, and, whilst I appreciate a day of quick trade on short positions, every now and then, I generally like that slow, monthly paced cycle of investment.

My updates in this specific thread of thinking and writing have a triple function. Firstly, they make me think what I am doing, and by that virtue they help me sharpen myself as an investor. Secondly, this is educational material for my students, especially in Finance and in Economics. Thirdly, for all the other readers of this blog, it is shared experience, seasoned with some science and mathematical rigour.

The time of collecting another instalment of rent approaches, and I am bracing for a new set of decisions. This time, i.e. in this update, I strongly focus on summarizing my so-far experience, since the end of January. I follow the same principle that sport coaches do: if we want to be more efficient, we need to own our past experience, both our mistakes and our successes. I can tell you: it is hard. Like really. I have already past the point of devising my own analytical tools for financial investment (see for example Partial outcomes from individual tables), and, whilst I am aware of the immense wealth of human invention in this field, it is relatively easy. It is modelled. On the other hand, telling my own story, even a short and selective one, is hard in a different way. It requires taking a step back from my own actions, figuring out a rational way of comprehending them, collecting information and putting it all together. When I was doing it, I discovered that my own behaviour is much more difficult to study than the behaviour of other people in the stock market.

Long story short, I did it. I summarized my own story, in a form interpretable for coining up a strategy for the future. First of all, I summarize the journey, which you can see in Graph 1, below. Over the last 4 months, I invested a total of $3 519,42 in my two investment accounts: the domestic one, which I hold with the PeKaO Bank, for buying and selling stock in the Polish stock market, and the international one, which I hold with the Degiro platform. The details of that strictly financial cash flow are to find in Graph 2, further below. Interestingly, the biggest single cash transfer in this thread of my investment story is the transfer from international account to domestic account, in the first days of April. I described my dilemma of the moment in the update from April 5th, 2020, entitled ‘Which table do I want to play my game on?’. I was panicking about the huge slump in the U.S. stock market, and, in the same time, I was having an eye on the speculative bubble swelling on biotechs in the Polish stock market. The first important observation as for my strategy is therefore the following: my cash flows tend to be regular and systematic, unless I go emotional about the market and then I am able to make sudden twists and turns.

Graph 1

Graph 2

The whole chain of deals I made with the cash I paid in has led me, as for May 27th, 2020, to a capital account worth $4 335,79. Over 4 months, I have added $816.37, or 23.2%, to the cash invested, in a total of 36 deals, 10 of which remain open at the moment of writing those words (see Graph 6, much further below) and 26 are closed. My biggest gains are somehow paired with my biggest losses so far. I lost the most money in the U.S. stock market, when it was all just surfing down over the top of the collapsing wave of COVID-19-related panic. I made the most money on the mounting wave of short-term fascination with biotech businesses in the Polish market, right after. Three companies – Biomed Lublin, Airway Medix, and Mercator Medical – were my vessels to ride that wave. Graph 5, further below, shows the profits and losses I made on each of the 20 stocks, which I have been playing with in those 36 deals I opened. Graph 4 illustrates, in the form of a Pareto curve, the relative importance of the deals I opened by the end of March and the beginning of April. Right after the extraordinary, and, let’s face it, abnormal profits I made by riding crest of that speculative bubble, come the much more normal profits I made on Polish IT companies. The one named 11Bit, a gaming business, brought me the most profit as for now. On the whole, and at the condition of having a good look at the fundamentals, IT businesses seem to be a must in a sensible investment portfolio. Graph 6 shows the profit I am currently making on the open financial positions, with those IT guys, i.e. 11 Bit, Asseco Business Solutions, and Talex, clearly sticking out and up above the lot.   

Graph 3

Graph 4

Graph 5

Graph 6

As I observe the timeline of my cumulative profit (Graph 3), a pattern emerges. Up until the end of March, I had been losing money. I suppose it was the price to pay for learning: the price of my early mistakes. Starting from the beginning of April, my cumulative profit on all deals up to date began to poke its head above the zero line. I began making money: what I had paid for my mistakes started bringing fruit. Question: is it a once-and-for-ever pattern, i.e. have I simply paid my entrance ticket to the game and now I will just ride that wave? It is tempting to believe, and yet it is foolish to rely on. I would rather expect a recurring cycle, likely to take place in moments of turbulence. I need a few weeks (like 8?) to make some reconnaissance in the market around me, and then I can target a wave to ride.  

Interestingly, when I started making money, I also started to make sense of the whole process, in the form of analytical tools (see e.g. Acceptably dumb proof. The method of mean-reversion ). Did I start to make money because I developed more formal an understanding of market trends? It might have been exactly the other way around: I might have gone explicitly analytical as, intuitively, I felt I make money. I am serious. I know myself. I know that when I start thinking recurrently about something, to the point of writing consistently about it, those thoughts manifest something going on at a deeper, subconscious level. It is possible that my writing about mean-reversion in financial analysis was expressing the fact that I was getting acquainted with the really observable variance in stock prices.

I can formulate a tentative description of my own strategy as regards investment. This time, by strategy I mean recurrent behavioural patterns in me rather than a set of goals with a plan. First of all, I am strongly intuitive. It seems that what I consciously think I do is usually one step behind what I really do. Probably a lot of people are like that, and what is interesting is to see that pattern manifest in myself. I intuitively look for relatively short-term opportunities for quick gain, and I jump into the game as soon as I see them. I tend to jump a bit too quickly, though. As I study those 26 closed deals I made since January, sometimes I am like: ‘What? Really? I did THAT? Aston Martin? Virgin Galactic? Seriously? What the hell was I thinking?’.

Even with that propensity to uncontrolled fascination with the prospects of quick gain, I am clearly attached to some specific sectors in my investment. So far, it is IT industry, biotechnology and medicine, as well as renewable energy. I declared such a span of interest in the very beginning (see Back in the game) but, in all honesty, when I was making that declaration, by the end of January, I had no idea how consistent I was going to remain. Looks like I am pretty consistent in my sectoral scope of investment.  

Another pattern I noticed in myself is that I like dividing my portfolio in two categories: the no-brainers, on the one hand, and the waves to ride, on the other hand. I like holding some ETF trackers – this is what I mean by ‘no-brainers’ – sort of having someone else doing some of the thinking for me. Yet, I abhor the idea of investing all my money in one investment fund, and allow other people do to all the thinking for me. I want to stay somehow in the middle, i.e. to hold some balanced investments embodied in structured instruments, such as ETFs, and to do active thinking as for other deals.

Summing (provisionally) up, I make money when I acquire a good understanding of the market environment as for the possible occurrence of sudden slumps and sudden rises. I think it is time for me to develop such understanding now. I made some money on one financial wave (biotechs in Poland), and I want to repeat the experience. I want to spot interesting opportunities in a broader context. Intuitively, I feel that I am entering another phase of searching and learning, similar to the one observable in the left half of Graph 3. An intuition is burgeoning in my brain: the capital market is going into another phase. Why do I think so? Well, the last 4 months were mostly marked by the outbreak of the COVID-19 pandemic and by the resulting lockdowns in most economies. Now, lockdowns are being progressively loosened up and I think they are going to stay loosened up, whatever local, epidemic surges appear. Lockdowns are simply unsustainable on the long run: they are a softened, and overly extended transformation of military protocols applicable in the case of a biological attack. I remember those protocols from high school. I was born and raised in the communist Poland, and at the time, we were being indoctrinated that we are supposed to fight an ever-lasting war for peace. We would even crack jokes, like ‘we will keep fighting for peace even after there is nothing left to be at peace with’. Anyway, at school, we had classes called Preparation for National Defence. In the theoretical part, among other things, we would study the rules to follow in the case of attack with mass-destruction weapons, including bio-attacks. The rules I was being taught were to be played out over days, weeks at the worst, not over months. From a long-range perspective, lockdowns are like an attempt to regulate air traffic with fighter jet planes indicating the available flight corridors: theoretically feasible, maybe even spectacular, yet a tiny little bit unpractical.       

Anyway, lockdowns are becoming the past and the new present requires new business models, new markets, and new public policies. My gut feeling is that a lot is going to change in the coming months and years, technology-wise and business-wise. This is why I think I need to reassess the economic context of my investment in the stock market. I start with reassessing the prospects conveyed by my current portfolio of 10 open positions: 11 Bit Studios, Asseco Business Solutions, Talex, Airway Medix, PBKM, Bioton, SMA Solar, First Solar, Medtronic, and Amundi Asset Management. I want to understand the economic and financial alternative scenarios for this specific portfolio.

By my recent experience, I know that it is important to phrase out my intuitions, in order to utilise them fully. As Frank Knight would probably say, if he was still alive, ‘it is important to know how you think about what you think’. I need to understand what is it exactly that I cover with my intuition when I think about the economic context. In my previous analytical updates, I was very technical, in the sense that I was very much focused on short-term interpretation of stock prices (see for example: Partial outcomes from individual tables ). This time, I want to be more oriented on the long term, and therefore I focus on a different set of metrics. For 9 out of the ten investment positions I hold, I am following the same method (the Amundi ETF tracker is in the category ‘no brainer’).

I want to understand, most of all, what do those companies do with the trust expressed by investors. Are they investing in their future, or are they just riding the waves of capitalism? All those 9 companies have benefited from some amount of trust expressed actively by investors who have acquired and hold their shares. I want to understand how this trust has been used in the view of building a future, and therefore I am focusing on assets in those companies’ balance sheets. I am interested in their assets, because this is where I look for future-oriented decisions. If the given company has more assets than it had at the end of the last reporting period, it means, most of all, that the business is accumulating capital. They are investing into being able to make stuff in the future. Next, I want to know what kind of assets is the most variable in their balance sheets.

An insight into each company’s balance sheet allows me to compare changes observable at this level with their market capitalization, and with stock market indexes which I can take as the closest general context. I consider market indexes as a background, informative about general attitudes in investors. Then, I calculate a simple coefficient, that of elasticity, in those companies’ assets, when denominated over market capitalization, and over the market index I chose. Elasticity is calculated as, respectively: ‘∆(assets) / ∆ (market capitalization)’, and ‘∆(assets) / ∆(market index)’. I want to discover to what extent those companies respond, in their capital base, to the signals they receive from the stock market.

On the top of that I add a long-term analytical tool of the stock price strictly spoken. From the general formula of mean-reverted price (see We really don’t see small change), I extract the component of moving average price, calculated cumulatively over the last 12 months of trade, since May 27th, 2019. For every day of trade between May 27th, 2019 and May 26th, 2020, an average closing price is being calculated, for all the daily closing prices between May 27th 2019 and the given date. This form of moving average is probably one of the simplest forms of artificial intelligence. It is a function which learns a long-term trend as it advances in time, and it answers the question about the probable shape of long-term changes in this specific price, based on past experience.

The remaining part of this update is structured in two parts. At first, I bring up a written account of my observations, as I applied the above-described method to the 9 businesses in my portfolio. Then, a series of tables and graphs is provided, with the source numbers, to use at your pleasure and leisure as analytics. I used market indexes specific to the corresponding markets and sectors. As regards 11 Bit Studios, an IT and gaming company listed in the Warsaw Stock Market, I used three indexes: the WIG-GAMES Index, the WIG-INFO Index, and one more general, the WIG Tech index. The two other Polish IT firms, namely Asseco Business Solutions and Talex are being benchmarked against two of those three indexes, i.e. WIG Info, and WIG Tech. The three companies from the broadly spoken medical and biotech sector –  Airway Medix, PBKM, and Bioton – all three listed in the Warsaw Stock Exchange as well, have been benchmarked against the WIG Pharmaceuticals index. First Solar and Medtronic are both listed in the NASDAQ, and the closest index I can find is NASDAQ Industrial. Finally, the German company SMA Solar is compared with the DAX Performance metric.

As I run those analyses, a first observation pops out: Airway Medix has not published yet any financials for 2019. It is impossible to assess the current balance sheet of that company. I have just read they have postponed until mid-June 2020 the publication of ALL their financials for 2019. This is odd and makes me think of something like a ticking bomb. They must have the hell of a mess in their financials. For the moment, they show an interesting short-term trend in their price, and so I hold this position. Yet, I know I need to stay alert. Maybe I sell shortly.

Generally, like across all those 9 firms, I can notice an interesting pattern: when their assets change, it is almost exclusively about current assets, not the fixed ones. As for their state of possession in terms of productive assets, they all have been staying virtually at the same level over the last year. What changes is most of all cash and financial instruments, and in some cases inventories and receivables (Talex). They build up strategic flexibility without going, yet, into any specific avenue of technology. It looks as if all those businesses were poised, up to something. My own gut feeling, and the theory of business cycles by Joseph Alois Schumpeter, allow expecting a big and imminent technological change.      

Now, I am going to exemplify the details of my approach with the 11 Bit Studios. It’s an IT, gaming business, and thus I connect it to three market indexes in the Warsaw Stock Exchange, namely the WIG-GAMES Index, the WIG-INFO Index, and one more general, the WIG Tech index. In Table 1, below, you can see a quick, half-fundamental and half-technical study of 11BIT Studios. Its market capitalisation had shrunk, between the end of 2Q2019 and 1Q2020, yet, currently, its stock price has been growing nicely those last weeks.

Why is that? Let’s look.  The coefficient of market-to-book, i.e. market capitalization divided by the book value of assets, had been decreasing consistently, from the really unsustainable level of 7,17 down to the touch-and-go level of 4,81. It had happened both by a downwards correction in market capitalization (investors collectively said: ‘it is too expensive’), and by ramping up the company’s assets. As I can read in the company’s quarterly reports, the financial strategy they seem to be pursuing, and which manifests in the value of their assets, consists in keeping a baseline reserve of cash around PLN 3 ÷ 3,5 mln, which they periodically pump up to somewhere between PLN 5 million and PLN 6 million, and right after ‘Boom!’, their fixed assets get a pump. It is a sequence I know from observing many tech companies. Over the last few years, tech companies started to behave like banks: they accumulate substantial piles of cash, probably to have flexibility in their investment decisions, and then, suddenly, they acquire some significant, productive assets.

All that development takes place in the context of a capricious market indexes. Yes, they are growing, but the price of growth is increased volatility. The more they grow, the more variance they display. To the extent that anyone can talk about behaviour of a company vis a vis its investors, 11BIT Studios seems to be actively demonstrating that no, they are not an artificially inflated financial balloon, and yes, they intend to invest in future.

Now, you can go to the graphs and tables below.

Table 1 – 11 BIT Studios, selected financial data

Market cap (PLN mln)908,02902,30914,88823,39
Assets (PLN mln)126,62138,76155,67171,25
Equity (pln mln)100,42106,07119,74136,27
Market cap to assets7,176,505,884,81
WIG Games index18,3418,4518,5515,67
WIG Info Index2 396,242 387,552 834,292 619,12
WIG Tech Index9 965,259 615,8110 898,6610 358,61
Elasticity of assets to market cap(2,12)1,34(0,17)
Elasticity of assets to WIG Games Index110,36169,10(5,41)
Elasticity of assets to WIG Info Index(1,40)0,04(0,07)
Elasticity of assets to WIG Tech index(0,03)0,01(0,03)

Table 2 – Asseco Business Solutions, selected financial data

Market cap (PLN mln)935,71915,66949,081 035,96
Assets (PLN mln)384,11391,12422,64433,87
Equity (pln mln)272,74288,43316,11331,62
Market cap to assets2,442,342,252,39
WIG Info Index2 396,242 387,552 834,292 619,12
WIG Tech Index9 965,259 615,8110 898,6610 358,61
Elasticity of assets to market cap(0,35)0,940,13
Elasticity of assets to WIG Info Index(0,81)0,07(0,05)
Elasticity of assets to WIG Tech index(0,02)0,02(0,02)

Table 3 – Talex, selected financial data

Market cap (PLN mln)              31,80               38,85               40,80               41,10 
Assets (PLN mln)              81,06               83,34               78,79               81,69 
Equity (pln mln)              54,89               54,59               50,82               51,37 
Market cap to assets                 0,39                  0,47                  0,52                  0,50 
WIG Info Index       2 396,24        2 387,55        2 834,29        2 619,12 
WIG Tech Index       9 965,25        9 615,81     10 898,66     10 358,61 
Elasticity of assets to market cap                 0,32                (2,33)                 9,66 
Elasticity of assets to WIG Info Index               (0,26)               (0,01)               (0,01)
Elasticity of assets to WIG Tech index               (0,01)               (0,00)               (0,01)

Table 4 – Bioton

Market cap (PLN mln)281,63326,28364,06355,48
Assets (PLN mln)890,60881,42914,18907,17
Equity (pln mln)587,84582,00621,10626,59
Market cap to assets0,320,370,400,39
WIG Pharma index3 432,335 197,435 345,735 410,86
Elasticity of assets to market cap(0,21)0,870,82
Elasticity of assets to WIG Pharma index(0,01)0,22(0,11)

Table 5 – PBKM, selected financial data

Market cap (PLN mln)543,06355,68352,27375,00
Assets (PLN mln)n.a.455,59427,00425,20
Equity (pln mln)n.a.188,39181,36179,54
Market cap to assetsn.a.0,780,820,88
WIG Pharma indexn.a.5 197,435 345,735 410,86
Elasticity of assets to market capn.a.8,39(0,08)
Elasticity of assets to WG Pharma indexn.a.(0,19)(0,03)

Table 6 – First Solar

Market cap ($ mln)3 819,015 926,566 143,676 955,98
Assets ($ mln)6 949,147 515,697 054,697 137,81
Equity ($ mln)5 168,625 096,775 182,485 135,12
Market cap to assets0,550,790,870,97
NASDAQ Industrial Index5 785,706 807,706 371,606 559,20
Elasticity of assets to market cap0,27(2,12)0,10
Elasticity of assets to NASDAQ Industrial0,551,060,44

Table 7 – Medtronic

Market cap ($ mln)120 856,18152 041,85145 568,85130 518,78
Assets ($ mln)91 053,0091 268,0089 694,0088 730,00
Equity ($ mln)50 719,0050 497,0050 212,0049 941,00
Market cap to assets1,331,671,621,47
NASDAQ Industrial Index5 785,706 807,706 371,606 559,20
Elasticity of assets to market cap0,010,240,06
Elasticity of assets to NASDAQ Industrial0,213,61(5,14)

Table 8 – SMA Solar

Market cap (€ mln)954,251 199,23902,89887,63
Assets (€ mln)1 031,471 107,321 014,86970,56
Equity (€ mln)415,35416,89411,39406,72
Market cap to assets0,931,080,890,91
DAX Performance Index9 935,8413 249,0112 428,0812 398,80
Elasticity of assets to market cap0,310,312,90
Elasticity of assets to DAX Performance0,020,111,51

Discover Social Sciences is a scientific blog, which I, Krzysztof Wasniewski, individually write and manage. If you enjoy the content I create, you can choose to support my work, with a symbolic $1, or whatever other amount you please, via MY PAYPAL ACCOUNT.  What you will contribute to will be almost exactly what you can read now. I have been blogging since 2017, and I think I have a pretty clearly rounded style.

In the bottom on the sidebar of the main page, you can access the archives of that blog, all the way back to August 2017. You can make yourself an idea how I work, what do I work on and how has my writing evolved. If you like social sciences served in this specific sauce, I will be grateful for your support to my research and writing.

‘Discover Social Sciences’ is a continuous endeavour and is mostly made of my personal energy and work. There are minor expenses, to cover the current costs of maintaining the website, or to collect data, yet I want to be honest: by supporting ‘Discover Social Sciences’, you will be mostly supporting my continuous stream of writing and online publishing. As you read through the stream of my updates on , you can see that I usually write 1 – 3 updates a week, and this is the pace of writing that you can expect from me.

Besides the continuous stream of writing which I provide to my readers, there are some more durable takeaways. One of them is an e-book which I published in 2017, ‘Capitalism And Political Power’. Normally, it is available with the publisher, the Scholar publishing house ( ). Via , you can download that e-book for free.

Another takeaway you can be interested in is ‘The Business Planning Calculator’, an Excel-based, simple tool for financial calculations needed when building a business plan.

Both the e-book and the calculator are available via links in the top right corner of the main page on .

You might be interested Virtual Summer Camps, as well. These are free, half-day summer camps will be a week-long, with enrichment-based classes in subjects like foreign languages, chess, theater, coding, Minecraft, how to be a detective, photography and more. These live, interactive classes will be taught by expert instructors vetted through Varsity Tutors’ platform. We already have 200 camps scheduled for the summer.

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 ( )93 086 156 556140 903 867 573$7 679,87 
Ethereum ( )16 768 575 99821 839 976 557$197,32 
Steem ( )111 497 45268 582 369$0,184049
Golem ( 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 ( ), 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.

Fast + slower = compound rhythm, the rhythm of life

My editorial on You Tube

I am continuing and expanding my so-far line of thinking and writing, into something both more scientific and more educational (we are still in full distance learning mode, at the university). I want to develop on that simple model I have recently presented in the update entitled ‘Acceptably dumb proof. The method of mean-reversion’. I am going to develop and generalize on its cognitive and behavioural implications. By the way, I have just used it (it is April 10th, 15:40 p.m.) to buy a bit into Asseco Business Solutions and to open a position on a company active in stem cells: PBKM. I spotted a moment, when their mean-reversed stock price was passing the 0 point and going up. According to this method, there is very likely to be an upcoming spike, with an opportunity to sell at a profit.

Good. The behavioural context. When I trade in the stock market, with my own money, emotions grow strong. After a few years of pause in investing, I had actually forgotten how strong those emotions can unfold. The first thing which I already know this method has given me is emotional step-back, and the capacity to calm down. This is the mark of a good strategy: it is simple (this model of mine is really simple, as financial forecasts come), thus workable, and it gives that special sort of calm flexibility in decisions.

The capacity to step back from the emotions of the moment, to get some perspective, and make more informed decisions is based on one essential assumption: the distinction between the normal and the alarming. There is a state of things, which I accept as ‘normal’, when I just can do something, but I don’t need to. By opposition, I define a state of things-which-consist-in-me-experiencing-reality, where my perception urges me to take action.

This is about my perception of reality, right? In the stock market, reality is made of numbers, right? I mean, there is much more in trade, there are people, for example, yet the reality which I am most of all supposed to pay attention to is made of numbers: the stock prices. Prices change. This is their normal way of being in the stock market. By the way, some of you might think that stationary a price, in a security, is the best way of being for a long-term investment. Not really. When you try and do some trade, one day, you will see that durably stationary prices can frighten the s**t out of you. It is like a frozen reality: scary. When prices swing, their ebb and flow gives information. When they stop moving, there is no more information. You are in a dark room.

Good, to the numbers that make my reality in the stock market – prices – change constantly and they’d better keep changing. What I observe, thus, is change in prices rather than prices themselves. Mathematically, I observe the values of a function (stock prices), and the values of its derivatives (change in prices, and coefficients calculated thereupon). It is the old intuition of Isaac Newton: what we really perceive is change and difference rather than absolute states of reality.

I define two classes in all the possible types of change I observe in reality. Class #1, the relax-bro type, covers normal change and allows me to sit back and watch what happens next. I can do some action, if I really feel like, yet it is all up to me. Class #2, the c’mon-do-something one, jumps into being when change becomes somehow abnormal, like highly stimulating. There is normal change and abnormal change, then, and I want to define these two states of reality with the toolbox of mathematics. From there on, it is highly subjective. Mathematics provide many ways of defining what’s normal. In my model, I go for a classic: the normal distribution. The normal state of change, seen through the lens of normal distribution, is acceptable oscillation around the expected value of price. The expected value is arithmetical average of prices observed over a given period of time. Seen under this angle, the average price is something like an immediate projection of my past experience: I expect to see, here and now, something aligned with the states of reality I have experienced so far.

The ‘so far’ part is subjective. Do I expect the current change in prices to be somehow in line with what has been happening over the last year, over the last 3 years, or maybe just over the last week? You can see a glimpse of that choice when you go and check stock prices online, with a graph. Most online utilities give you the choice between snapshotting the current day, the last 2 weeks, the last month etc. People have different temporal frames of reference as for what is normal to them. In my personal model, the one I hinted at in ‘Acceptably dumb proof. The method of mean-reversion’, I set my frame of reference at the last month, or, to me more specific, at the last 30 trading days, which actually makes a little more than a calendar month.

Subjectivity is scalable and measurable. I am going to focus on two ramifications of this principle. Firstly, I can make typical change my unit of measurement. Secondly, I can shift between different time frames and see what kind of change it brings in terms of strategic behaviour. Before I walk down these two paths, I am reminding the general mathematical frame of what I am talking about (see picture below).

What happens, mathematically, when I follow the old Newtonian intuition of observing change rather than stationary states of nature? Logically, a given magnitude of change becomes my unit of measurement. In basic statistics, i.e. as long as we stay in the safe realm of Gaussian distributions, standard deviation, i.e. mean expected deviation from the mean expected average, can be such a Sevres-meter of my perception. Let’s keep in mind it is deep in our human perception: there are differences and variations large enough for us to notice, and the remaining part of all the chaos happening in that stuff we call reality passes essentially unnoticed to us.

When standard deviation becomes my gauge, and it serves me to assess whether anything is worth my attention, I can interestingly decompose the basic equation of mean-reversion, as residual difference between the actual value observed (price, in this case) and denominated in its own standard deviation, and the expected average value, denominated in the same way. In other words, mean-reversed price is the residual difference between the locally observed deviation from what I call ‘normal and expected’, and the general variability of what I observe (average divided by standard deviation).  

There is a simply and technically useful aspect of that approach. When standard deviation becomes the unit of measurement, I can directly compare the actions I should take on many investment positions, when they are in very different price ranges. Let’s study it on two different cases in my portfolio: Airway Medix, and 11Bit. The former is market-priced at less than PLN 1 per share, the latter is currently around PLN 380. When I mean-reverse their prices, I drive them both to the same scale, like inside the interval -3 ≤ x < 3. The local magnitude of mean reversed prices is directly comparable between the two.  

As I talk about comparisons, let’s compare these two – Airway Medix and 11Bit – in different time frames. My basic one is the last 30 trading days, but what if I look differently at time and change? What if I take a shorter view over the timeline, or a longer one? In tables below, I show four alternative temporal perspectives on those two stocks: last 30 days, 7 days, 14 days, and finally the past 6 months of trade.

 Mean-reversed price of Airway Medix
Trading dayWindow 30 daysWindow 7 daysWindow 14 daysWindow 6 months
 Mean-reversed price of 11Bit
Trading dayWindow 30 daysWindow 7 daysWindow 14 daysWindow 6 months
01.04.2020(0,47)0,490,62         (0,90)
02.04.2020(0,42)0,300,57         (0,90)
03.04.2020(0,31)0,730,90         (0,82)
06.04.20200,522,513,01           0,06 
07.04.2020(0,23)(0,10)0,25         (0,83)
08.04.2020(0,04)0,210,61         (0,68)
09.04.20200,411,021,46         (0,31)
10.04.20200,440,791,27         (0,30)

As I study the two tables above, my first question is: what do I actually see? What the differences between those numbers are actually informative about? Positive numbers tell me that the current price is sort of high as compared to the moving average, and negative say the opposite. As I look at the last days of trade before Easter, 11Bit appears as being kind of moderately positive in the 30-day view, and it means: rather hold than sell, unless you strike a really good deal. A timeframe of 7 days tells me more or less the same. When I set my timeframe at 14 days, it says: definitely look for a good sell, the price is abnormally high. Still, when I take a really long step back and look at the whole thing from the perspective of a 6-month temporal horizon, it says: ‘no, you dumb f**k, don’ even think about selling; if you feel the urge to do something, go and buy some of these’.

You can see empirically that my subjective perception of what is a long time, as opposed to what is just a moment impinges directly on the strategy I am supposed to adopt. It is a deep, general principle of human action. Farmers look at life differently from stock market brokers: their time frames differ.

What if I apply the same logic, i.e. the logic of mean-reversion, to volumes traded, instead of prices? What the mean-reversed volume is informative about? Let’s see. Here below, you can see comparative graphs of Airway Medix with, respectively, stock price and volumes traded daily, both mean-reversed over a window of the last 30 days of trade. You can see that volumes swing much more frequently than prices. It is as if they were two musical tunes: volumes modulated at a faster pace, and prices going at a slower one. Familiar? No? It is rock’n roll. Fast + slower = compound rhythm. The rhythm of life.

How can I generalize into any market? You can go and watch my tutorial in economics, the one about prices and quantities. It connects interestingly: .

Acceptably dumb proof. The method of mean-reversion.

My editorial on You Tube

It is 5:43 a.m. (yes, forty-three minutes after five o’clock in the morning, and I am completely sane), and I am starting another day of fascinating life. I know I could say: another day of this horrible epidemic, or another day of that limiting lockdown. I know I could, yet I am not. I say: fascinating life. This is how I feel. This whole situation, i.e. the pandemic and the resulting lockdown, it all makes my blood flow faster. There is a danger, out there, and there are us, who can face this danger. Us, not just me. There is the collective ‘us’ who adapts, organizes, and collectively says: ‘There is no f**king way we surrender’. This is the beauty of life.

Would I say the same to someone who has just lost their job, due to the lockdown, and has a family to take care of? In spite of all the apparent ridicule of such a claim in such a situation, yes, I would say the same, and you know why? Because there is no viable alternative. Should I say to this person: ‘Yeah, they’re completely right, those people who say you are f**ked. There is nothing you can do, just sink into despair and complain occasionally’.

I am drawing a bottom line under my yesterday’s quick trade, in the Polish stock market. You can read about the details in A day of trade. Learning short positions. I am progressively wrapping my mind around the day of yesterday. Conclusions start floating on the surface of my mind. When I go into a quick, daily trade on short positions, the best moment for making decisions seems to be around 11 – 12 o’clock CET, in the middle of the day. Deciding early in the morning, e.g. starting to trade with a morning sell-out, is not really a good idea. Deciding by the end of the day is tricky, too: the end of the trading day frequently pushes me to selling or buying just out of sheer rush, under the hot breath of time rather than the cool breath of reason.

Recently, a student of mine asked me what I think about short trade. I answered that it is interesting, but it generally sucks for me. It is true that never before have I done any short trade successfully. I remember feeling the pump of adrenaline, peculiar to gambling, yet the financial results were never good. I said it generally sucks for me, and then I tried again, yesterday. This is something I discovered lately: facing my fears and apprehensions can be a fascinating experience. At my age, 52, fears and apprehensions come out of accumulated learning, and the big thing about it is that we accumulate learning in order to stop learning. Facing the things which I am wary or afraid of means questioning my acquired knowledge and habits. It is like digging into one of those cellars, full of objects from the past: I discover new kinds of beauty.

And so, I did it again: I tried short trade, and I meant confronting my acquired wariness. I can see that trading on short, daily positions is a useful skill, and I can develop that skill, to a reasonable level, quite quickly. It is most of all about being aware what I am doing, i.e. cognitively stepping back from action, for a moment, and correct it slightly, so as to make it coherent and purposeful. The key is to own my own story. When I have both cards in my hand: that little gambling nerve, and the intellectual discipline in self-questioning the gambling reflexes, I can thrive on that mix. I love it, actually. My action leads me to forming new ideas about myself. I have just realized that I thrive, as an investor, on two types of action. I can be like a gardener, for one, watching my long-term positions grow and bring fruit in due time. For two, I can be like a hunter, going out for an informed, wise kill.

Wise kill means predating, i.e. violently harvesting from the ecosystem, not killing for the sake of it. There comes an important question I ask myself: how to practice short trade, every now and then, and stay sort of constructive in my investment? I have already learnt, after the day of yesterday, that short trade is a powerful method of quickly adapting my investment to just as quick a change in external conditions.  On the other hand, I want to join, in an informed way, a big stream of investment in positive technological change. Can I reconcile these two: short-term, wisely predatory strategies of adaptation with long-term, positive orientation?

There comes an afterthought, which has just popped up in my mind: wise hunters wait for their prey, instead of running after it. My experience of short trade tells me that it is wise to have a strategy prepared for those days of short positions. To me, short trade is adaptation, and, logically, I should do it in the presence of quickly changing conditions. Just as logically, I should tool myself with some kind of early detection mechanism for violent outbursts in the stock market, when a local speculative bubble is about to swell, or about to implode. Detection in place, I should have strategies for riding a mounting wave, as well as surfing down a collapsing one.

My point is that I can stay constructive in my episodes of short trade when I stay strategic, informed, and prepared. Blueprints seldom work perfectly in real life, yet they provide robust structure. I can become destructive in my days of short trade if I go chaotic, and to the extent of chaos in my actions.

A numerical strategy comes to my mind. I target a handful of companies I would like to sort of hang around with, equity wise. Let’s suppose they are Polish companies from the biotech – medical complex, plus some interesting IT ones. I check regularly their prices in the stock market, as well as the volumes traded. I assume that the market can be in two alternative states, from my point of view: either it allows me to be the placid gardener of my investment positions, or it forces me to become the alert and violent hunter. The ‘gardener’ state is when I don’t need to do anything quick, i.e. when I don’t need to adapt through daily short trade. I need to go for a day of short trade hunting when the market somehow goes off the rails. I need to define those rails.

Mathematically, I assume that whatever happens to those stock prices, happens inside a stochastic process, i.e. something slightly crazy, yet crazy in a generally predictable manner. Within that stochastic process, there is the calm and picturesque Gaussian process, where local values go hardly away from their moving average, like no more than one moving standard deviation away either way (i.e. plus or minus). Anything outside that disciplined Gaussian happening triggers the hunter in me and makes me go short trade. This is an approach similar to mean-reversion: the further something drifts away from the expected state, the more alarming it is.   

I assume that cognitively, the still waters of Gaussian process, from my subjective point of view, are set by the behaviour of prices over the last 30 days. I take the moving average price, and the moving standard deviation from that price, from the 30 preceding days. Below, I am exemplifying this logic with historical prices of the company whose shares I sold yesterday – and I regret having been too hasty – namely Biomed Lublin. The curve in the graph shows values calculated as:

Mean_reversed Price (day xi) = {[Price(day xi)] – [Mean(Price xi-30, …, Price xi)]} / Standard deviation (Price xi-30, …, Price xi).

On the graph, I marked with green dashed lines the corridor of ‘calm’ variance, within one moving standard deviation around the moving average ( -1 ≤ x ≤ 1). Inside that corridor, I assume I can just hold whatever stock of Biomed Lublin I have, or, conversely, I should abstain from buying it, unless I really want. The bubble marked with red dashed line shows an example of price wandering way out of that safe corridor. It is an example of alarm zone: it is price rocketing up, and a possibly good occasion for the short trade I planned, and did not complete finally, for yesterday: selling in the morning for a higher price, and buying back, for a lower one, by the end of the day, or next day. If the curve flares in the opposite direction, i.e. below the bottom green line, it is a signal to buy quickly, with an expectation to sell at a higher price.

The graph shows a time window between May 27th, 2019, and yesterday, April 8th, 2020, thus some 10 months with a small change. During that period, should I have been actively trading Biomed Lublin, I should be about half of the time on alert, and going into short trade. As you can see, this otherwise simple strategy of trading involves behavioural assumptions about myself: do I want to go hunting, in the grounds of short trade, as frequently as the graph suggests? It is reasonable not to narrow down the zone of calm, i.e. below one moving standard deviation away from the moving average. On the other hand, I can increase my zone of tolerance (calm) beyond one moving standard deviation.  

Summing up, I have two perspectives on trading a given stock, with this simple model. First of all, in the long view, I can observe how does the curve of mean-reversed closing price behaves generally. Is it rather wild, i.e. does it swing a lot out of the safety zone between -1 and 1, or, conversely, is it rather tame? The more swinging is the curve, the more the given stock is made for a series of short-term trading operations, like buy in and sell out, in a sequence. If, on the other hand, the mean-reversed price tends to stay a lot in the safety zone, that is the type of stock to hold for a long time rather than to prance around a lot. Secondly, I can observe the short-term tendency over the last few days, like the last week of trading, and make myself an idea as for the immediate stance to take.

I use this simple tool to study my own current portfolio of investment positions, plus the two stocks I sold yesterday but I sort of keep them in my crosshairs, as they are biotech, presently dear to my heart sort of generally. Biomed Lublin, to follow, is a wild one, especially those last weeks. Its mean-reversed price has been swinging a lot out of the – 1 ≤ x ≤ 1 zone. This is the type of stock to watch closely, and to be ready to go for a quick kill about it. As for the last days, you can see it gently returning from a ‘quick sell’ zone, and getting into the ‘hold’ one.

Mean-reversed price of Biomed Lublin

01.04.2020      3,196722673

02.04.2020      3,590790488

03.04.2020      4,173460856

06.04.2020      3,713915308

07.04.2020      1,870944561

08.04.2020      1,71190807

As regards 11Bit, it used to be a wild one, with a high potential for ‘sell’ recommendations. Yet, since the COVID-19 panic erupted in the stock market, and after the Polish stock market started to flirt a lot more with biotech, 11Bit has gone sort of tame. A few weeks ago, there had been a short window for buying, which I missed, unfortunately, like between February 27th and March 27th. The latest developments suggest holding.

Mean-reversed price of 11Bit

01.04.2020      -0,470302222

02.04.2020      -0,418676901

03.04.2020      -0,308375679

06.04.2020      0,518241443

07.04.2020      -0,230731307

08.04.2020      -0,036589487

Asseco Business Solutions is in a different situation. In the past, before the COVID crisis, it would stay a lot above the 1 barrier, thus offering a lot of incentives to sell and consume profits. Yet, over the last month or so, it has nosedived into the alarm zone below -1, just to climb into the -1 ≤ x ≤ 1 safety belt recently. Looks like it morphed from something to kill into something to farm patiently.  

Mean-reversed price of Asseco Business Solutions

01.04.2020      -0,498949327

02.04.2020      -0,454984411

03.04.2020      -0,467396289

06.04.2020      -0,106632042

07.04.2020      -0,062469251

08.04.2020      -0,006786983

Airway Medix is another wild type, with a lot of spikes out of the -1 ≤ x ≤ 1 zone. Still, since May 2019, there was more occasions to buy rather that to sell. Those last weeks, it seems to have really changed its drift, and has rocketed up above 1. I have to be vigilant about this one.

Mean-reversed price of Airway Medix

01.04.2020      2,362791403

02.04.2020      1,922976365

03.04.2020      3,70150467

06.04.2020      3,768162474

07.04.2020      1,973153986

08.04.2020      1,441837178

Biomaxima is a strange case, at least as compared to others. For months, like until the first days of 2020, it had been mostly in the safety zone, with occasional spikes down, below -1, thus with occasional incentives to buy. Since January 2020, it started to sort of punch the ceiling and to burst more and more frequently above 1. Right now, it seems to be in the ‘sell or hold’ zone, with a visible drift down. To watch and react quickly.

Mean-reversed price of Biomaxima

01.04.2020      3,81413095

02.04.2020      3,413908533

03.04.2020      3,001585581

06.04.2020      2,378442856

07.04.2020      1,631660778

08.04.2020      1,668652998

Bioton is a still different story. Over the last 10 months, it had remained like half in the calm zone between – 1 and 1, whilst spending most of the remaining time in the ‘buy’ (x < -1) belt. There was one spike up, in July 2019, when there was some incentive to sell. Yet, now, it is a different story. As it is the case of many Polish biotech companies, the last 2 months have dragged Bioton out of that grey lethargy, into the spotlight of the market. Right now, the mean-reversed price from the last week suggests selling (if I have profit on it) or to hold. Looks like I bought this one on a selling wave: a mistake I could have avoided, had I remembered and applied earlier that method of mean-reversion in price (which I read about regarding the market of electricity).  

Mean-reversed price for Bioton

01.04.2020      1,219809883

02.04.2020      1,50983756

03.04.2020      3,76644111

06.04.2020      3,986920426

07.04.2020      2,434789898

08.04.2020      1,888320575

Mercator Medical is another case where, although I have currently some profit, I should have rather bought earlier (August – September 2019, something like that). That had been a relatively long window of ‘buy’ recommendation. Right now, as I have been investing in it, it is rather the ‘sell or hold’ time.  

Mean-reversed price for Mercator Medical

01.04.2020      1,664368071

02.04.2020      1,605024371

03.04.2020      2,408595698

06.04.2020      3,673484581

07.04.2020      1,846496909

08.04.2020      2,130831881

That cursory, technical analysis of my investment portfolio, together with my immediate targets in the biotech sector, brings me a few interesting insights. First of all, and once again, it pays to do things, and to write about me doing things. The urge I felt to phrase out my feelings after the yesterday’s intense day of short trade pushed me to formalize an acceptably dumb-proof strategy, based on the method of mean-reversion, which I knew theoretically but never thought to apply in practice to my own investment business.