Cautiously bon-vivant

I keep developing on a few topics in parallel, with a special focus on two of them. Lessons in economics and management which I can derive for my students, out of my personal experience as a small investor in the stock market, for one, and a broader, scientific work on the civilizational role of cities and our human collective intelligence, for two.

I like starting with the observation of real life, and I like ending with it as well. What I see around gives me the initial incentive to do research and makes the last pitch for testing my findings and intuitions. In my personal experience as investor, I have simply confirmed an initial intuition that giving a written, consistent and public account thereof helps me nailing down efficient strategies as an investor. As regards cities and collective intelligence, the first part of that topic comes from observing changes in urban life since COVID-19 broke out, and the second part is just a generalized, though mild an intellectual obsession, which I started developing once I observed the way artificial neural networks work.

In this update, I want to develop on two specific points, connected to those two paths of research and writing. As far as my investment is concerned, I am seriously entertaining the idea of broadening my investment portfolio in the sector of renewable energies, more specifically in the photovoltaic. I can notice a rush on the solar business in the U.S. I am thinking about investing in some of those shares. I already have, and have made a nice profit on the stock of First Solar ( ) as well as on that of SMA Solar ( ). Currently, I am observing three other companies: Vivint Solar ( ),  Canadian Solar ( ), and SolarEdge Technologies ( ). Below, I am placing the graphs of stock price over the last year, as regards those solar businesses. There is something like a common trend in those stock prices. March and April 2020 were a moment of brief jump upwards, which subsequently turned into a shy lie-down, and since the beginning of August 2020 another journey into the realm of investors’ keen interest seems to be on the way.

Before you have a look at the graphs, here is a summary table with selected financials, approached as relative gradients of change, or d(x).

 Change from 01/01/2020 to 31/08/2020
Companyd(market cap)d(assets)d(operational cash-flow)
First Solar+23,9%-6%Deeper negative: – $80 million
SMA Solar+27,5%-10%Deeper negative: -€40 million
Vivint Solar+362%+11%Deeper negative: – $9 million
SolarEdge+98%0+ $50 million
Canadian Solar+41%+4%+ $90 million

There are two fundamental traits of business models which I am having a close look at. Firstly, it is the correlation between changes in market capitalization, and changes in assets. I am checking if the solar businesses I want to invest in have their capital base functionally connected to the financial market. Looks a bit wobbly, as for now. Secondly, I look at current operational efficiency, measured with operational cash flow. Here, I can see there is still a lot to do. Here is the link to You Tube video with all that topic developed: Business models in renewable energies #3 Solar business and investment opportunities [Renew BM 3 2020-09-06 09-20-30 ; ].

Those business models seem to be in a phase of slow stabilization. The industry as a whole seems to be slowly figuring out the right way of running that PV show, however the truly efficient scheme is still to be nailed down. Investment in those companies is based on reasonable trust in the growth of their market, and in the positive impact of technological innovation. Question: is it a good move to invest now? Answer: it is risky, but acceptably rational; once those business models become really efficient, the industry will be in or close to the phase of maturity, which, in turn, does not really allow expecting abnormally high return on investment.  

This is a very ‘financial’, hands-off approach to business models. In this case, business models of those photovoltaic businesses matter to me just to the extent of being fundamentally predictable. I don’t want to run a solar business, I just want to have elementary understanding of what’s going on, business-wise, to make my investment better grounded. Looking from inside a business, such an approach is informative about the way that a business model should ‘speak’ to investors.

At the end of the day, I think I am most likely to invest in SolarEdge. It seems to have all the LEGO blocks in place for a good opening. Good cash flow, although a bit sluggish when it comes to real investment.

As regards COVID-19 and cities, I am formulating the following hypothesis: COVID-19 has awakened some deeply rooted cultural patterns, which date back to the times of high epidemic risk, long before vaccines, sanitation and widespread basic healthcare. Those patterns involve less spatial mobility in the population, and social interactions within relatively steady social circles of knowingly healthy people. As a result, the overall frequency of social interactions in cities is likely to decrease, and, as a contingent result, the formation of new social roles is likely to slow down. Then, either digital technologies take over the function of direct social interactions and new social roles will be shaping themselves via your average smartphone, with all the apps it is blessed (haunted?) with, or the formation of new social roles will slow down in general. In that last case, we could have hard times with keeping up our pace of technological change. Here is the link to You Tube video which summarizes what is written below: Urban Economics and City Management #4 COVID and social mobility in cities [ Cities 4 2020-09-06 09-43-06 ;  ].

I want to gain some insight into the epidemiological angle of that claim, and I am passing in review some recent literature. I start with: Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., & Rinaldo, A. (2020). Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proceedings of the National Academy of Sciences, 117(19), 10484-10491 ( ). As it is usually the case, my internal curious ape starts paying attention to details which could come as secondary for other people, and my internal happy bulldog follows along and bites deep into those details. The little detail in this specific paper is a parameter: the number of people quarantined as a percentage of those positively diagnosed with Sars-Cov-2. In the model developed by Gatto et al., that parameter is kept constant at 40%, which is, apparently, the average level empirically observed in Italy during the Spring 2020 outbreak. Quarantine is strict isolation between carriers and (supposedly) non-carriers of the virus. Quarantine can be placed on the same scale as basic social distancing. It is just stricter, and, in quantitative terms, it drives much lower the likelihood of infectious social interaction. Gatto el al. insist that testing effort and quarantining are essential components of collective defence against the epidemic. I generalize: testing and quarantine are patterns of collective behaviour. I check whether people around me are carriers or not, and then I split them into two categories: those whom I strongly suspect to host and transmit Sars-Cov-2, and all the rest. I define two patterns of social interaction with those two groups: very restrictive with the former, and cautiously bon vivant with the others (still, no hugging). As the technologies of testing will be inevitably diffusing across the social landscape, that structured pattern is likely to spread as well.    

Now, I pay a short intellectual visit to Jiang, P., Fu, X., Van Fan, Y., Klemeš, J. J., Chen, P., Ma, S., & Zhang, W. (2020). Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour. Journal of Cleaner Production, 123673 . Their methodology is based on correlating spatial mobility of cars in residential areas of Singapore with the risk of infection with COVID-19. A 44,3% ÷ 55,4% decrease in the spatial mobility of cars is correlated with a 72% decrease in the risk of social transmission of the virus. I intuitively translate it into geometrical patterns. Lower mobility in cars means a shorter average radius of travel by the means of available urban transportation. In the presence of epidemic risk, people move across a smaller average territory.

In another paper (or rather in a commented dataset), namely in Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific data, 7(1), 1-7. , I find an enlarged catalogue of metrics pertinent to spatial mobility. That paper, in turn, lead me to the functionality run by Google: . I went through all of it a bit cursorily, and I noticed two things. First of all, countries are strongly idiosyncratic in their social response to the pandemic. Still, and second of all, there are common denominators across idiosyncrasies and the most visible one is cyclicality. Each society seems to have been experimenting with the spatial mobility they can afford and sustain in the presence of epidemic risk. There is a cycle experimentation, around 3 – 4 weeks. Experimentation means learning and learning usually leads to durable behavioural change. In other words, we (I mean, homo sapiens) are currently learning, with the pandemic, new ways of being together, and those ways are likely to incrust themselves into our social structures.    

The article by Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., … & Brownstein, J. S. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497 ( ) shows that without any restrictions in place, the spatial distribution of COVID-19 cases is strongly correlated with spatial mobility of people. With restrictions in place, that correlation can be curbed, however it is impossible to drive down to zero. In plain human, it means that even as stringent lockdowns as we could see in China cannot reduce spatial mobility to a level which would completely prevent the spread of the virus. 

By the way, in Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special, 12(1), 16-26 ( ), I read that the whole idea of tracking spatial mobility with people’s personal smartphones largely backfired because the GDS transponders, installed in the average phone, have around 20 metres of horizontal error, on average, and are easily blurred when people gather in one place. Still, whilst the idea went down the drain as regards individual tracking of mobility, smartphone data seems to provide reliable data for observing entire clusters of people, and the way those clusters flow across space. You can consult Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020). Population flow drives spatio-temporal distribution of COVID-19 in China. Nature, 1-5.  ( .

Bonaccorsi, G., Pierri, F., Cinelli, M., Flori, A., Galeazzi, A., Porcelli, F., … & Pammolli, F. (2020). Economic and social consequences of human mobility restrictions under COVID-19. Proceedings of the National Academy of Sciences, 117(27), 15530-15535 ( ) show an interesting economic aspect of the pandemic. Restrictions in mobility give the strongest economic blow to the poorest people and to local communities marked by relatively the greatest economic inequalities. Restrictions imposed by governments are one thing, and self-imposed limitations in spatial mobility are another. If my intuition is correct, namely that we will be spontaneously modifying and generally limiting our social interactions, in order to protect ourselves from COVID-19, those changes are likely to be the fastest and the deepest in high-income, low-inequality communities. As income decreases and inequality rises, those adaptive behavioural modifications are likely to weaken.

As I am drawing a provisional bottom line under that handful of scientific papers, my initial hypothesis seems to hold. We do modify, as a species, our social patterns, towards more encapsulated social circles. There is a process of learning taking place, and there is no mistake about it. That process of learning involves a downwards recalibration in the average territory of activity, and smart selection of people whom we hang out with, based on what we know about the epidemic risk they convey. This is a process of learning by trial and error, and it is locally idiosyncratic. Idiosyncrasies seem to be somehow correlated with differences in wealth. Income and accumulated capital visibly give local communities an additional edge in the adaptive learning. On the long run, economic resilience seems to be a key factor in successful adaptation to epidemic risk.

Just to end up with, here you have an educational piece as regards Business models in the Media Industry #4 The gaming business[ Media BM 4 2020-09-02 10-42-44;]. I study the case of CD Projekt ( ), a Polish gaming company, known mostly for ‘The Witcher’ game and currently working on the next one, Cyberpunk, with Keanu Reeves giving his face to the hero. I discover a strange business model, which obviously has hard times to connect with the creative process at the operational level. As strange as it might seem, the main investment activity, for the moment, consists in terminating and initiating cash bank deposits (!), and one of the most important operational activities is to push further in time the moment of officially charging customers with some economically due receivables. On the top of all that, those revenues deferred into the future are officially written in the balance sheet as short-term liabilities, which CD Projekt owes to…whom exactly?   

Healthily dosed meanness

I am connecting the dots, progressively. People tend to, by the way. Essentially, all that stuff called ‘civilisation’ consists in people figuring s**t out, progressively.

I am connecting two paths of my educational content, i.e. the account of my investment experience in the stock market, and urban economics, on the one hand, with a third one, namely the philosophy of science and especially the concept of truth, on the other hand.

My so-far adventure with the philosophy of science allows me to approach truth under different angles. One of the most down-to-earth tests for truth is the capacity to recognize when someone is lying to me. From the perspective of Pierre Simon, marquis de Laplace[1], I can recognize a lie when the things which someone tells me are endowed with very low probability of happening, given the knowledge I already have about the phenomena concerned. Gotcha’, f**ker! You went too far into and under the tail of the curve which sets my distribution of probability. Here, a bit accidentally, Pierre Simon, marquis de Laplace, sort of agrees with Sir George Maynard Keynes, when he wasn’t even a Sir yet, as for the theory of probability[2]. Agreement is reached as regards the claim that in practical life choices, the kind of probability that matters to us is the probability of claims we make about reality, whilst the strictly speaking probability of single phenomena happening in a given place and time is nice to know, yet of little utility in daily life.

If, alternatively, I follow the hermeneutic take by Hans Georg Gadamer[3], and you, my friend tell me things which are ugly, in the first place, and do not match at all the patterns of my historically grounded culture, you are probably telling me lies. If I take still another turn, and follow the recently formulated Interface Theory of Perception (Hoffman et al. 2015[4]; Fields et al. 2018[5]), lies are claims which contradict my empirically grounded knowledge about the way I can have the best possible payoffs from interactions with my environment.

The truth is that truth is complex and requires experience, judgment and healthily dosed meanness. That being said, let’s tackle the two problems at hand: my investment in the stock market, and the civilizational role of cities as demographic anomalies. As regards the former, here is the deal. My next instalment of investment comes. Every month, I invest in the stock market the rent which I collect from an apartment in town (i.e. in Krakow, Poland), roughly $670. Every month, I reconsider my investment portfolio and I decide what to buy, and what to sell. I am going to use the theories of truth which I tentatively outlined in the preceding paragraphs, in order to approach my next investment decision in strict scientific terms. Theories of truth will serve me to assess the well-founded of my decisions. Roughly speaking, when I choose between a limited number of alternative options, I can claim, about each of them, that this specific way to do things is the best one. If that claim is true, I can assume that it is truly the best option. Theories of truth are used here to assess the veracity of situation-specific claims. As I think about it, things are going to turn really funny if I come to the conclusion that I can label more than one option as truthfully the best. We’ll live, we’ll see. Anyway, here comes the video content: Invest 5 2020-09-02 07-55-26 ; .

As I have been doing my research on the civilizational role of cities, I have kept repeating and I still maintain that cities are demographic anomalies with a purpose. I am going to use those theories of truth as an intellectual toolbox for nailing down precisely the phenomenon of demographic anomaly. In other words, I want to determine which specific spatial distribution of human population can be truthfully labelled as anomalous, and, on the top of that, I want to assess, just as truthfully, what is the most likely scenario of change in urban life, urban economics and city management under the impact of COVID-19. In this case, theories of truth serve me to assess the veracity of general, theoretical claims. Here is the video on You Tube: Cities 3 2020-09-02 08-38-47 ;  .

I am using theories of truth in two different contexts, namely one situationally specific, and another one theoretically general, and, in my next step, I take on describing those contexts more abundantly. The context of investment decision comes with an important trait, as the philosophy of science comes, i.e. with an apparently clear, yet a bit blurry a distinction between assumptions and hypotheses.

[1] Laplace, Pierre Simon, marquis de, 1795 – 1902, A Philosophical Essay on Probabilities, Project Gutenberg EBook, #58881

[2] Keynes, John Maynard, 1921, A Treatise On Probability, McMillan and Co., Project Gutenberg Ebook #32625

[3] Gadamer, Hans Georg, 2004, Truth And Method, 2nd, revised edition, Continuum Books, ISBN 08264 7697X

[4] Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic bulletin & review, 22(6), 1480-1506.

[5] Fields, C., Hoffman, D. D., Prakash, C., & Singh, M. (2018). Conscious agent networks: Formal analysis and application to cognition. Cognitive Systems Research, 47, 186-213.

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.

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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

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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?).   

We really don’t see small change

My editorial on You Tube

Whatever kind of story I am telling, it is, at the end of the day, my own story, the story of my existence: this is hermeneutic philosophy, which I fully espouse intellectually. What’s my story, then? My essential story, I mean, the one which I weave, barely perceptibly, into the fabric of my narration about anything?

I think this is a story of change and learning. I change, my life changes, and I learn. Yes, I think that change is the most general denominator in my existence. You would say that it is the story of us all. Yes, indeed it is. We change, things change, and we learn. I think I was nine, when I got scarlet fever, AKA scarlatina. Nasty stuff: I spent almost two months with a fever around 39 degrees Celsius (= 102 Fahrenheit), on huge doses of Erythromycin (which is nasty stuff in itself). I remember doctors just sighing and alluding, in conversations with my parents, that we are sailing further and further into the hardly charted at all seas of maybe-it-is-going-to-work medicine. I had cardiac damage, and most probably some brain damage. I am not quite sure of that last one: in 1977, in the communist Poland, it was not like you can go and have your kid’s brain CT scanned just like that. Still, after that scarlet fever, I started to stutter (which had been haunting me until quite recently) and I started having learning problems at school. School fixed itself after like 3 years, stuttering took another 38 years or so (still have some echo of that in me), and here I am, having consumed and hopefully owned that particular avenue of change.

I am (mildly) obsessed about the connection between the collective intelligence of human societies. Not just human, as a matter of fact; viruses become kind of trendy, recently. I am going to develop on the concept of mean-reversed price precisely in that spirit, i.e. the link between us, humans, being collectively smart, and the ways to use artificial intelligence so as to discover how exactly collectively smart we are. In my previous two updates, I outlined the logic of mean-reversed price as analytical tool for nailing down a workable strategy of investment in the stock market. See ‘Acceptably dumb proof. The method of mean-reversion’ (earlier, April 9th, 2020), and ‘Fast + slower = compound rhythm, the rhythm of life’ (later, April 11th, 2020). Now, I go out of the stock market, and about into commodities. I want to check my intuitions in a different transactional context, and I want my writing to be useful for students in the courses of International Trade, International Management, and Macroeconomics.

Here is a perfectly normal world, where the entire social activity is centred on making (mining, growing) and trading 4 commodities: pork meat (lean hogs), uranium, coffee, and cobalt. Perfectly normal, I say. We raise pigs, and eat them, we make a lot of nuclear bombs, and a lot of electronics, and, obviously, doing all those things requires big amounts of coffee. In that perfectly normal world, the logic of ‘Price * Quantity’ still holds (see: ): we, humans, do all kinds of crazy and wonderful things, doing those things makes us generate an aggregate amount Q of economic utility, we go about that utility in recurrently patterned deals of exchange (AKA transactions), and observing transactional prices in, respectively, pork meat (lean hogs), uranium, coffee, and cobalt, can be possibly informative about how’s life going for us. Here is the link to download the Excel file with prices: .

I learn by accumulating knowledge, which allows, in the first place, distinguishing the normal from the alarming. I go Gaussian about it, and thus I build my expectations as moving average of past prices, and I denominate my perception in units of just as moving a standard deviation. Once again, I am in the world of mean-reversion.

I allow different temporal perspectives in my learning, and I introduce one more fundamental distinction, namely between learning with full memory, and learning with imperfect recall. The ways of calculating mean-reverted prices, which I showed in ‘Acceptably dumb proof. The method of mean-reversion’, and ‘Fast + slower = compound rhythm, the rhythm of life’, are marked with imperfect recall. I remember over a limited window in time: 30 days, 7 days etc. If my window is 30 days, on the 32nd day I forget whatever I remembered from day 1; on day 33, it is day 2 that I forget etc. Economic sciences convey substantial evidence that most markets, and most societies, as a matter of fact, shake off their memories every now and then. Yes, it seems that we like forgetting collectively.

Still, I want to have an alternative of not forgetting, and I introduce slightly different a method of calculating mean-reverted price: my temporal window stretches as far into the past as my data reaches back. My ‘lag’ in the equation grows every day. On day 15, I mean-revert the actually observed price with an average of prices 14 days back, and a standard deviation with the same window. On day 20, I reach back 19 days; on day 300, it is 299 days into the past etc. I call it mean-reversed cumulative.   

Once again, what mathematically is called mean-reversion is a typical pattern of our human cognition. We learn in order to slow down learning. Now, let’s see if it really works in all cases. I encourage you to go and retrieve the Excel file with those prices of : pork meat (lean hogs), uranium, coffee, and cobalt (link HERE), and practice calculating the mean-reversed prices. You will notice something interesting: sometimes it does not work at all. If you do the operation in Excel, it will yield the ‘DIV/0!’ error, which means that you are trying to divide by zero, which just doesn’t do in decent mathematics. The denominator we are dividing by is standard deviation, and when the phenomena observed are rrreaallly stationary, their standard deviation is equal to zero. In human cognition, it corresponds to a situation when the observable gradient of change is too subtle to be perceived and processed. We need perceivable change in order to learn. No change, no experience to put in your belt, sorry bro’. In this perfectly normal world, where we focus our activity on lean hogs, uranium, cobalt and coffee, such impossible situation happens a lot with uranium and cobalt, whilst taking place much less frequently with pork meat and coffee. In the reality we are currently experiencing, there are phenomena variable enough to offer our brain some material for trying to look clever, and there are others, like undertows of what’s happening, too stationary to be noticed.

The capacity to perceive change depends on the time frame of change. Those ‘WTF!? Division by zero!’ situations happen more frequently with shorter temporal windows. When I compute my moving average and moving standard deviation over a period of 7 days, and I observe the prices of cobalt, ‘DIV/0!’ happens like half of the time. When I stretch my temporal reference up to 30 days, many of those embarrassing absences of judgement disappear, and when I just go for cumulative moving average (and standard deviation), it happens just once, on day one, and then Bob’s my uncle: I always have some change to learn from.

If you have ever wondered why we have memories of various temporal reach, this might be an interesting avenue to walk down in order to find some answers. When our brain suddenly pulls out into consciousness some old stuff from back when I was twelve, it probably needs to compare data, to find some standard deviation as base of new learning.

Now, I put the same data into a simple neural network, a multi-layer perceptron. My question is: what kind of learning can an intelligent structure make out of observing reality the Newtonian way, with a focus on change?  In layer 1, I put three neurons. Each of them computes a different mean-reversion of the actual price: cumulative (from the beginning of time), the 30-day-based one, and the short one, with just 7 days of reference. In layer 2, another set of 3 neurons standardizes the mean-reversed observations on a scale from 0 to 1. In layer 3, I put one neuron, which assigns random coefficients to standardized observations, each random coefficient ranging between 0 and 1. This neuron experiments. It is the ‘what-happens-if-I-change-my-priorities?’ experimentation. In layer 4, three neurons activate, each based on a different function of neural activation: there is one sigmoid-based, another one working with hyperbolic tangent, and the third one made with ArcSinH, or hyperbolic arcsine. I add that third one because it has the interesting property not to require any standardization of raw data. Sigmoid and hyperbolic tangent are like refined intellectuals, who do not accept any input without a cappuccino as accompaniment. Hyperbolic arcsine is like a child, who just accepts what happens for what it is. In layer 5 of my network, three neurons calculate the error that each of those neural activations make in estimating the output, i.e. the actual price as recorded in the market. Layer 6 contains one neuron, which selects the least error among those coming from layer 5 and feeds it forward to the next round of experimentation.       

If I want my neural network to work, I need to get rid of the ‘DIV/0!’ cases and replace them by some arbitrary value. If at least one observation yields ‘DIV/0!’, the neural network goes on strike and yields the same, i.e. structural error of dividing by zero. Looks like intelligent structures do it all the time: I cannot see change, so I pretend that nothing happened. If I don’t pretend that, I face so strong a cognitive dissonance that I just go to intellectual sleep. Openly admitting that some important information has slipped out of our attention is one of the hardest things to do, cognitively. It is always safer to assume that we know everything we need to know.

Perception of actual empirical values, such as typical neural networks are based on, are maybe more natural and less human. There are less filters. Perception based on mean-reverted values i.e. rooted in change rather than absolute states, is more human-like.

Below, you can see visualisations of prices, respectively in coffee and in cobalt. Each of those markets is shown under two angles. Actual prices, i.e. market closures on each trading day over the last year (blue lines on each graph) are put back to back with prices estimated through the neural network which I have just described (orange line).

Two observations sort of jump to the eye (or maybe it is just my eye?). Prices simulated by the piece of AI are consistently lower that the actual ones, for one. An intelligent structure based on the very human cognitive mechanism of habitual perception and assessment (mean-reversion) consistently underestimates the real magnitude of the phenomenon under scrutiny. Secondly, that underestimation is much more pronounced in the case of cobalt than regarding coffee.

As you might remember from your own calculations, which I encouraged you to perform with those prices, mean-reverted prices of cobalt are much more prone to the ‘DIV/0!’ error, fault of sufficient variance, than the prices of coffee. Cognitively, it means that habitual perception (i.e. based on mean-reversion) tends to underestimate the magnitude of mostly those phenomena, which offer really low variance to our direct perception. We really don’t see small change. This is why we need scales of measurement. We need a scale of temperature, and the corresponding measurements, to assess the local kinetic energy of particles. In our perception, the difference between 35 degrees Celsius and 37 degrees Celsius is not a big deal when it comes to the ambient exterior, but it makes a difference when applied to body temperature.

As you might remember, had you followed ‘Acceptably dumb proof. The method of mean-reversion’ and ‘Fast + slower = compound rhythm, the rhythm of life’, I am developing a strategic tool for investing in the stock market, on the grounds of mean-reversion. What I can already see is that approached from this angle, my strategy could be a shade conservative, consistently downplaying the likelihood of sudden spikes in price, susceptible to offer me big rewards. Have to work on this one.