The moment of reassessment


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

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

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

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

Graph 1

Graph 2

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

Graph 3

Graph 4

Graph 5

Graph 6

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Table 1 – 11 BIT Studios, selected financial data

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

Table 2 – Asseco Business Solutions, selected financial data

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

Table 3 – Talex, selected financial data

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

Table 4 – Bioton

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

Table 5 – PBKM, selected financial data

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

Table 6 – First Solar

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

Table 7 – Medtronic

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

Table 8 – SMA Solar

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

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

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

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

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

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

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

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

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

My editorial on You Tube

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

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

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

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

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

Table 1

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

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

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

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

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

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

Long story short, it goes like…

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

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

… etc.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Partial outcomes from individual tables

My editorial on You Tube

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The kind of puzzle that Karl Friedrich was after

My editorial on You Tube

Over the last few updates, I have been indulging in the mathematical logic of Gaussian process, eating it with the spoon of mean-reversion. My so-far experience with using the logic of Gaussian process is that of my personal strategy as regards investment in the stock market, and especially as regards those short, periodical episodes of reshuffling in my investment portfolio, when I am exposed to, and I frequently yield to the gambling-like temptation of short trade (see Acceptably dumb proof. The method of mean-reversion , Fast + slower = compound rhythm, the rhythm of life, and We really don’t see small change ). Gambling-like is the key concept here. I engage into quick trade, and I feel that special flow, peculiar to gambling behaviour, and yet I want that flow to weave around a rational strategy, very much in the spirit of Abraham de Moivre’s ‘The doctrine of chances: or, A method of calculating the probabilities of events in play’, published in 1756. A bit of gambling, yes, but informed gambling.  

I am trying to understand why a neural network based on mean-reversed prices as input consistently underestimates the real price, and why the whole method of mean-reversion fails with super-stable prices, such as those of cobalt or uranium (see We really don’t see small change).

I like understanding things. I like understanding the deep logic of the things I do and the methods I use. Here comes the object of my deep intellectual dive, the normal distribution. In the two pictures below, you can see the initial outline of the problem.

How does a function, namely that of normal distribution, assist my process of decision making? Of course, the first-order answer is simple: ‘it gives you numbers, bro’, and when you see those numbers you essentially know what to do’. Good, great, but I want to understand HOW EXACTLY those numbers, thus the function I use, match with my thinking and my action.

Good. I have a function, i.e. that of normal distribution, and for some reason that function works. It works geometrically. The whole mathematical expression serves to create a fraction. If you look carefully at the equation, you will understand that with given mean value μ and standard deviation σ, there is no way this function can go above 1. It is always a fraction. A fraction can be seen from different angles. Firstly, it is a portion of something, like a / b, where a < b. There is a bigger something, the denominator of the fraction, σ[(2π)0,5] = σ* 2,506628275. (elevation to power 0,5 replaces the sign of square root, which I cannot reproduce exactly from the keyboard, as a font).  Secondly, as we talk about denominators, a fraction is a change in units of measurement. Instead of measuring reality in units of 1 – the smallest prime number – we measure reality in units of whatever we put in the denominator of the fraction. Thirdly, a fraction is a proportion between two sides of a rectangle, namely the proportion between the shorter side and the longer side.

Good, so what this function of normal distribution represents is a portion cut of a bigger something equal to σ[(2π)0,5], and that something is my unit of measurement, and, in the same time, it is the longer side of a rectangle. The expression σ[(2π)0,5] is something like one dimension of my world, whilst the whole equation of normal distribution, i.e. the value of that function, makes the other dimension. Is the Gaussian world a rectangular world? I need to know. I start talking to dead people. Usually helps. This time, my interlocutor is Karl Friedrich Gauss, in his General Investigations of Curved Surfaces, presented to the Royal Society, October 8th, 1827.

What many people ignore today is that what we call a Gaussian curve is the outcome of a mathematical problem, which, initially, had virtually nothing to do with probability. What Karl Friedrich Gauss (almost) solved was the problem of geodetic measurements, i.e. the distinction between the bird’s flight distance, and the actual length of the same distance on the rugged and uneven surface of the Earth. I know, when we go through mountains, it is sometimes uphill, sometimes downhill, and, on average, it is flat. Still, when you have to build a railroad through the same mountains, the actual length (spell: cost) of rails to put on the ground is much greater than what would be needed for building the same railroad in the plain. That’s the type of puzzle that Karl Friedrich was after.

Someone could say there is no puzzle. You want to know how long a rail do you need to go over a mountain, you send surveyors and they measure it. Splendid. Yet, civil engineering involves some kind of interference with the landscape. I can come up with the idea of putting my railroad alongside like the half-height of the mountain (instead of going right over its top), or maybe we could sort of shave off the top, couldn’t we, civilised people whom we are? Yes, those ideas are all valid, and I can have a lot of them. Sending surveyors each time I come up with a new concept can become terribly time- and money-consuming. What I could do with is a method of approximating each of those alternative distances on a curved surface, a method which finds good compromise between exactitude and simplicity.

Gauss assumed that when we convert the observation of anything curved – rugged land, or the orbit of a planet – into linear equations, we lose information. The challenge is to lose as little an amount thereof as possible. And here the story starts. Below, you will find a short quote from Gauss: the first paragraph of the introduction.   


Investigations, in which the directions of various straight lines in space are to be considered, attain a high degree of clearness and simplicity if we employ, as an auxiliary, a sphere of unit radius described about an arbitrary centre, and suppose the different points of the sphere to represent the directions of straight lines parallel to the radii ending at these points. As the position of every point in space is determined by three coordinates, that is to say, the distances of the point from three mutually perpendicular fixed planes, it is necessary to consider, first of all, the directions of the axes perpendicular to these planes. The points on the sphere, which represent these directions, we shall denote by (1), (2), (3). The distance of any one of these points from either of the other two will be a quadrant; and we shall suppose that the directions of the axes are those in which the corresponding coordinates increase.’

Before I go further, a disclaimer is due. What follows is my own development on Karl Friedrich Gauss’s ideas, not an exact summary on his thoughts. If you want to go to the source, go to the source, i.e. to Gauss’s original writings.

In this introductory paragraph, reality is a sphere. Question: what geometrical shape does my perception of reality have? Do I perceive reality as a flat surface, as a sphere (as it is the case with Karl Friedrich Gauss), or maybe is it a cone, or a cube? How can I know what is the geometrical shape of my perception? Good. I feel my synapses firing a bit faster. There is nothing like an apparently absurd, mindf**king question to kick my brain into higher gear. If I want to know what shape of reality I am perceiving, it is essentially about distance.

I approach the thing scientifically, and I start by positing hypotheses. My perceived reality is just a point, i.e. everything could be happening together, without any perceived dimension to it. Sort of a super small and stationary life. I could stretch into a segment, and thus giving my existence at least one dimension to move along, and yet within some limits. If I allow the unknown and the unpredictable into my reality, I can perceive it in the form of a continuous, endless, straight line. Sometimes, my existence can be like a bundle of separate paths, each endowed with its own indefiniteness and its own expanse: this is reality made of a few straight lines in front of me, crossing or parallel to each other. Of course, I can stop messing around with discontinuities and I can generalise those few straight lines into a continuous plane. This could make me ambitious, and I could I come to the conclusion that flat is boring. Then I bend the plane into a sphere, and, finally things get really interesting and I assume that what I initially thought is a sphere is actually a space, i.e. a Russian doll made of a lot of spheres with different radiuses, packed one into the other.

I am pretty sure that anything else can be made out of those seven cases. If, for example, my perceived reality is a tetrahedron (i.e. any of the Egyptian pyramids after having taken flight, as any spaceship should, from time to time; just kidding), it is a reality made of semi-planes delimited by segments, thus the offspring of a really tumultuous relationship between a segment and a plane etc.

Let’s take any two points in my universe. Why two and not just one? ‘Cause it’s more fun, in the first place, and then, because of an old, almost forgotten technique called triangulation. I did it in the boy scout times, long before Internet and commercial use of Global Positioning System. You are in the middle of nowhere, and you have just a very faint idea of where exactly that nowhere is, and yet you have a map of it. On the map of nowhere, you find points which you are sort of spotting in the vicinity. That mountain on your 11:00 o’clock looks almost exactly like the mountain (i.e. the dense congregation of concentric contour lines) on the map. That radio tower on your 01:00 o’clock looks like the one marked on the map etc. Having just two points, i.e. the mountain and the radio tower, you can already find your position. You need a flat surface to put your map on, a compass (or elementary orientation by the position of the sun), a pencil and a ruler (or anything with a straight, smooth, hard edge). You position your map conformingly to the geographical directions, i.e. the top edge of the map should be perpendicular to the East-West axis (or, in other words, the top edge of the map should be facing North). You position the ruler on the map so as it marks an imaginary line from the mountain in the real landscape to the mountain on the map. You draw that straight line with the pencil. I do the same for the radio tower, i.e. I draw, on the map, a line connecting the real radio tower I can see to the radio tower on the map. Those lines cross on the map, and the crossing point is my most likely position.

Most likely is different from exact. By my own experience of having applied triangulation in real outdoors (back in the day, before Google Maps, and almost right after Gutenberg printed his first Bible), I know that triangulating with two points is sort of tricky. If my map is really precise (low scale, like military grade), and if it is my lucky day, two points yield a reliable positioning. Still, what used to happen more frequently, were doubtful situations. Is the mountain I can see on the horizon the mountain I think it is on the map? Sometimes it is, sometimes not quite. The more points I triangulate my position on, the closer I come to my exact location. If I have like 5 points or more, triangulating on them can even compensate slight inexactitude in the North-positioning of my map.   

The partial moral of the fairy tale is that representing my reality as a sphere around me comes with some advantages: I can find my place in that reality (the landscape) by using just an imperfect representation thereof (the map), and some thinking (the pencil, the ruler, and the compass).  I perceive my reality as a sphere, and I assume, following the intuitions of William James, expressed in his ‘Essays in Radical Empiricism’ that “there is only one primal stuff or material in the world, a stuff of which everything is composed, and if we call that stuff ‘pure experience,’ then knowing can easily be explained as a particular sort of relation towards one another into which portions of pure experience may enter. The relation itself is a part of pure experience; one of its ‘terms’ becomes the subject or bearer of the knowledge, the knower,[…] the other becomes the object known.” (Excerpt From: William James. “Essays in Radical Empiricism”. Apple Books).

Good. I’m lost. I can have two alternative shapes of my perceptual world: it can be a flat rectangle, or a sphere, and I keep in mind that both shapes are essentially my representations, i.e. my relations with the primal stuff of what’s really going on. The rectangle serves me to measure the likelihood of something happening, and the unit of likelihood is σ[(2π)0,5]. The sphere, on the other hand, has an interesting property: being in the centre of the sphere is radically different from being anywhere else. When I am in the centre, all points on the sphere are equidistant from me. Whatever happens is always at the same distance from my position: everything is equiprobable. On the other hand, when my current position is somewhere else than the centre of the sphere, points on the sphere are at different distances from me.

Now, things become a bit complicated geometrically, yet they remain logical. Imagine that your world is essentially spherical, and that you have two complementary, perceptual representations thereof, thus two types of maps, and they are both spherical as well. One of those maps locates you in its centre: it is a map of all the phenomena which you perceive as equidistant from you, thus equiprobable as for their possible occurrence. C’mon, you know, we all have that thing: anything can happen, and we don’t even bother which exact thing happens in the first place. This is a state of mind which can be a bit disquieting – it is essentially chaos acknowledged – yet, once you get the hang of it, it becomes interesting. The second spherical map locates you away from its centre, and automatically makes real phenomena different in their distance from you, i.e. in their likelihood of happening. That second map is more structured than the first one. Whilst the first is chaos, the second is order.

The next step is to assume that I can have many imperfectly overlapping chaoses in an otherwise ordered reality. I can squeeze, into an overarching, ordered representation of reality, many local, chaotic representations thereof. Then, I can just slice through the big and ordered representation of reality, following one of its secant planes. I can obtain something that I try to represent graphically in the picture below. Each point under the curve of normal distribution can correspond to the centre of a local sphere, with points on that sphere being equidistant from the centre. This is a local chaos. I can fit indefinitely many local chaoses of different size under the curve of normal distribution. The sphere in the middle, the one that touches the very belly of the Gaussian curve, roughly corresponds to what is called ‘standard normal distribution’, with mean μ = 0, and standard deviation σ =1. This is my central chaos, if you want, and it can have indefinitely many siblings, i.e. other local chaoses, located further towards the tails of the Gaussian curve.

An interesting proportion emerges between the sphere in the middle (my central chaos), and all the other spheres I can squeeze under the curve of normal distribution. That central chaos groups all the phenomena, which are one standard deviation away from me; remember: σ =1. All the points on the curve correspond to indefinitely many intersections between indefinitely many smaller spheres (smaller local chaoses), and the likelihood of each of those intersections happening is always a fraction of σ[(2π)0,5] = σ* 2,506628275. The normal curve, with its inherent proportions, represents the combination of all the possible local chaoses in my complex representation of reality.    

Good, so when I use the logic of mean-reversion to study stock prices and elaborating a strategy of investment, thus when I denominate the differences between those prices and their moving averages in units of standard deviation, it is as if I assumed that standard deviation makes σ =1. In other words, I am in the sphere of central chaos, and I discriminate stock prices into three categories, depending on the mean-reversed price. Those in the interval -1 ≤ mean-reversed price ≤ 1 are in my central chaos, which is essentially the ‘hold stock’ chaos. Those, which bear a mean-reversed price < -1, are in the peripheral chaos of the ‘buy’ strategy. Conversely, those with mean-reversed price > 1 are in another peripheral chaos, that of ‘sell’ strategy.

Now, I am trying to understand why a neural network based on mean-reversed prices as input consistently underestimates the real price, and why the whole method of mean-reversion fails with super-stable prices, such as those of cobalt or uranium (see We really don’t see small change). When prices are super-stable, thus when the moving standard deviation is σ = 0, mean-reversion, with its denomination in standard deviations, yields the ‘Division by zero!’ error, which is the mathematical equivalent of ‘WTF?’. When σ = 0, my central chaos (the central sphere under the curve) shrinks a point, devoid of any radius. Interesting. Things that change below the level of my perception deprive me of my central sphere of chaos. I am left just with the possible outliers (peripheral chaoses) without a ruler to measure them.

As regards the estimated output of my neural network (I mean, not the one in my head, the one I programmed) being consistently below real prices, I understand it as a proclivity of said network to overestimate the relative importance of peripheral chaoses in the [x < -1] [buy] zone, and, on the other hand, to underestimate peripheral chaoses existing in the [x > 1] [sell] zone. My neural network is sort of myopic to peripheral chaoses located far above (or to the right of, if you prefer) the center of my central chaos. If, as I deeply believe, the logic of mean-reversion represents an important cognitive structure in my mind, said mind tends to sort of leave one gate unguarded. In the case of price estimation, it is the gate of ‘sell’ opportunities, which, in turn, leads me to buy and hold whatever I invest in, rather than exchanging it back into money (which is the exact economic content of what we call ‘selling’).         

Interesting. When I use the normal distribution to study stock prices, one tail of the distribution – the one with abnormally high values – is sort of neglected to the benefit of the other tail, that with low values. It looks like the normal distribution is not really normal, but biased.

Fast + slower = compound rhythm, the rhythm of life

My editorial on You Tube

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Acceptably dumb proof. The method of mean-reversion.

My editorial on You Tube

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mean-reversed price of Biomed Lublin

01.04.2020      3,196722673

02.04.2020      3,590790488

03.04.2020      4,173460856

06.04.2020      3,713915308

07.04.2020      1,870944561

08.04.2020      1,71190807

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

Mean-reversed price of 11Bit

01.04.2020      -0,470302222

02.04.2020      -0,418676901

03.04.2020      -0,308375679

06.04.2020      0,518241443

07.04.2020      -0,230731307

08.04.2020      -0,036589487

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

Mean-reversed price of Asseco Business Solutions

01.04.2020      -0,498949327

02.04.2020      -0,454984411

03.04.2020      -0,467396289

06.04.2020      -0,106632042

07.04.2020      -0,062469251

08.04.2020      -0,006786983

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

Mean-reversed price of Airway Medix

01.04.2020      2,362791403

02.04.2020      1,922976365

03.04.2020      3,70150467

06.04.2020      3,768162474

07.04.2020      1,973153986

08.04.2020      1,441837178

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

Mean-reversed price of Biomaxima

01.04.2020      3,81413095

02.04.2020      3,413908533

03.04.2020      3,001585581

06.04.2020      2,378442856

07.04.2020      1,631660778

08.04.2020      1,668652998

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

Mean-reversed price for Bioton

01.04.2020      1,219809883

02.04.2020      1,50983756

03.04.2020      3,76644111

06.04.2020      3,986920426

07.04.2020      2,434789898

08.04.2020      1,888320575

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

Mean-reversed price for Mercator Medical

01.04.2020      1,664368071

02.04.2020      1,605024371

03.04.2020      2,408595698

06.04.2020      3,673484581

07.04.2020      1,846496909

08.04.2020      2,130831881

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

A day of trade. Learning short positions.

My editorial on You Tube

I am betting on short-term developments in the stock market. Yesterday, the stocks of biotech and medical companies in the Warsaw Stock Market went through a rapid depreciation. I decided to play short-term today. This is something that has NEVER worked for me in the past, yet, this year, I want to learn new things about investing in the stock market.

During the day of yesterday, the profits I described in my last update, entitled ‘Doing things we don’t quite know how to do well is what we, humans, do all the time’, suddenly melted down, to a large extent. The strategy I am going to test today consists in selling, in the morning, three of my positions – Airway Medix, Biomed Lublin, Biomaxima – betting that their price will fall today just as deeply as it had fallen yesterday, and then buy them back at the end of the day, at a lower price. Selling them today in the morning will allow me to consume the profits I have still left on them, and, if I am betting correctly on the today’s developments in the market, their price will fall even more today. By the end of the today’s trading session, I should be able to buy them back at a lower price.

In the table below, you can see a short summary of the situation.

Company (position)Rate of return on April 7th, 2020 (YESTERDAY MORNING)Rate of return on April 8th, 2020 (THIS MORNING)Remarks
Asseco Business Solutions-5,88%-5,88% 
Biomed Lublin410,20%246,94%For sale in the morning, to buy back by the end of the day
Biomaxima21,82%1,82%For sale in the morning, to buy back by the end of the day
Airway Medix116,82%72,73%For sale in the morning, to buy back by the end of the day
Mercator Med.19,33%-5,95% 

Why am I not waiting for the early-morning developments in the stock market? I could wait like 30 minutes, from 9:00 to 9:30 a.m., and then decide whether I sell. If the prices of those three – Airway Medix, Biomed Lublin, Biomaxima – bounce back in the morning, there will be no point in selling. There is one caveat to that: if the day is a downwards revision day, the first 30 minutes of trade, precisely between 9:00 and 9:30, are usually marked with a very sharp drop in price. This is a recurrent pattern I have already noticed. Thus, if wait that first half-an-hour, I can lose some opportunities to make profit.

I need a plan B, in case the daily developments in the market go against my expectations. There are two issues, as regards my expectations. Firstly, it is possible that prices of those three stocks rise during the day and I cannot buy them back at a lower price. Buying them back at a higher price would not be good business. I need a plan what to do with the proceeds from the morning sell-out, if these particular events play out differently from what I expect to happen. I thought I could observe the movements in prices on the remaining 4 positions –  11Bit, Asseco Business Solutions, Bioton, and Mercator Medical – and maybe buy into those. I am particularly interested in Bioton. In the past, that stock gave me a huge gain, and in that whole portfolio of mine, this is the only clearly undervalued position, with a market-to-book ratio of 0,73. There is potential for growth in this company.

Secondly, for the moment, I assume there will be no big quake on my other positions. What of the entire market goes amok about downwards revision, today? That is a good question. There are two opposite logics to that. Logic A says ‘cut your losses short, don’t keep positions with negative returns’, whilst logic B protests: ‘it is a first principle of business that you sell at a higher price than the one you bought at, unless you really need cash or unless that thing is just never going to go up in price’. From my own experience, which I developed more broadly in the update entitled ‘Which table do I want to play my game on?’, I know a third logic, a cognitive pattern in myself: I need to have at least the impression I understand the rules of the game. Sometimes, I feel that my investment decisions – at least some of them – become so uninformed that what I am doing is actually pure gambling for the gambling’s sake. My experience is that in such moments I should just pull out of the game.

That third logic, which is strictly my own, is certainly an impediment when it comes to quick trade on short positions. I intuitively pull out of situations when I feel forced to make very quick moves, or, if I stay in such a situation, I start making haphazard moves. I have hard times to step back emotionally, and to figure out a quick, on-the-spot strategy. By doing what I am doing today, I expect, precisely, to develop my skills for such situations. I want to force myself to understand quickly the rules of a short-term game in daily trade.  

That’s an interesting thing from the scientific point of view. What is my personal, cognitive distinction between a game I know the rules of – the Abraham de Moivre’s game – on the one hand, and the Bayesian game, where I have to figure out the very space of the game and my essential bearings inside of it?

I have just realized another thing. I can update myself on how the daily trade is going on, at the Warsaw Stock Exchange, with a 15-minute-lag (in practice, it is 20 minutes of lag), and this sets something like a pace of observation for playing out my today’s strategy. Oookay, here comes a report from the battlefield: my selling orders have been executed. Let’s see. Biomed Lublin sold at PLN 3,13 per share, 219,39% of return on this one. Right now, I mean at 9:28, it was at PLN 3,03. For this one, my plan seems to be unfolding nicely, as for now. You know, that’s the thing about big plans: they seem to be unfolding nicely, up to a point. Airway Medix sold at PL 0,718 per share, 63,18% of return in this position. Keeps falling, was at 0,68 on 9:28. Biomaxima sold at PLN 21,60 per share, and, unfortunately, this one comes with a loss of – 1,82%. In this case, the follow up is less clear. It plunged in the morning, but now (9:35) it is climbing back and is at PLN 20,8 per share.

OK, step A done, let’s outline the strategy for step B, to be carried out during the today’s trading day. From the sales of 1000 shares of Biomed Lublin, I have proceeds amounting to PLN 3 130,00  PLN, and I had to pay a brokerage fee of 0,35%, which I will have to pay once again should I be buying back into this stock. Thus, my break-even price for the daily trade would be: PLN 3,13 * (1 – 2*0,35%) < PLN 3,108. That’s the condition I need to strike.

As I have sold my position of 2300 shares in Airway Medix, I have PLN 1 645,62  PLN of proceeds net of brokerage fee. I do the same calculation as for Biomed Lublin, and it goes: PLN 0,718 * (1 – 2*0,35%) < PLN 0,713. In the case of Biomaxima, I can reuse, to buy back in, the PLN 430,49 of proceeds net of brokerage fee, provided the condition PLN 21,60 * (1 – 2*0,35%) < PLN 21,449 is met. Ve vill zee…

Now, a quick look at the remaining sheep in the herd: 11Bit, Asseco Business Solutions, Bioton, and Mercator Medical. 11Bit jumped nicely right after the opening of trade, up to PLN 375, which reduces slightly my loss on this position. Asseco Business Solutions remains stationary, no movement at all. Bioton, my tacit preferee, is at PLN 4,77. Good. It is climbing nicely. If my intuition is correct, investors are moving capital inside the biotech-medical sector, from the clearly overvalued positions (see ‘Doing things we don’t quite know how to do well is what we, humans, do all the time’) to the undervalued ones. It is good financially, as my so-far loss on Bioton is getting shallower each minute, yet it is bad cognitively. If Biomed Lublin, Airway Medix, and Biomaxima go down, as I expect, and Bioton goes up, as I expect as well, which way to go: buy back into those three sold ones, or buy further into Bioton? Oh dear, life is complicated…

Mercator Medical is going nicely up, it was PLN 27,10 on 9:57, which puts me back in saddle, so to say. I start making profit on this one, just 0,74% for the moment, barely to pay for the brokerage fee, yet it looks promising. As I look at the volumes traded, there is a lot of reshuffling. Big volumes get purchased. Looks as if people were buying into this one.

Now, I face two alternative paths. Path A, I go psycho, I keep staring at the quotes in the stock market, whilst writing a live account in this log. Path B, I step back, into the safe realm of science, I think and write a bit about collective intelligence, and then I come back into the market like early in the afternoon, to decide about the closure of my trade for today. I think I like stepping back more than going psycho, and I choose path B. I am busying myself at phrasing out some intuitions about collective intelligence. What we do is in loop with what we claim we should do, and what we do is the stronger of the two. In other words, behaviour is in loop with consciously formulated cultural content, and, on the long run, behaviour is the spiritus movens of that interaction. In my so-far work, I apply this claim mostly to strategies and ethical values. The latter, i.e. collective ethical orientations, are particularly fascinating for me. I have developed a very simple neural network to uncover the values of a society, out of quantitative socio-economic data. As regards this path of thinking, at the limit of science and philosophy, you can find it developed a bit more in the You Tube video ( ), which accompanies this update.

It is 12:05. A quick update on the market. Biomed Lublin – on 11:50, it was at PLN 3,53, and it is essentially climbing. Much above my break-even threshold. The situation unfolds, as for now, unfavourably for my strategy. With that price, there is no way I should buy it back. Airway Medix – on 11:52, it was at PLN 0,74. Too expensive. Am not going to buy back in. Biomaxima – the price is growing, on 11:50 it was at PLN 23,60. Not buying back.  11Bit –  falling gently, on 11:56 was at PLN 359. Asseco Business Solutions stays firmly stationary, almost no trade at all. Bioton – sort of hesitating, swinging up and down. On the whole, it shows a descending trend. On 11:57, it was at PLN 4,45. Mercator Medical shows a gently ascending trend in price. On 11:58, it was at PLN 27,50.

For the moment, my attempt at short-term trade in the surfing-the-descending-wave style has failed lamentably. Even if I decided to sell the same positions I have actually sold, the later during the day, the better profit I would have. I yielded to panic, and this is something I need to work on. I noticed that I tend to consider profit on my investment positions as something really my own: I develop an emotional attachment to those margins. I need to step back. Still, this is good learning. There is some hope, though. Yesterday, prices really collapsed by the end of the day. Maybe today it will be the same?

As I have feeble prospects to unfold my initial strategy, the plan B, i.e. buying into the positions still held, sounds like a good idea. I am going to wait until around 2 p.m., and then it will be time to go into the fine details of the plan B. For now, I take a partial decision: I am buying further into Mercator Medical. The price is growing steadily over the day, there is no apparent reason why it should fall. I decide to place a ‘buy’ order on this one, 40 shares.

As I am waiting for the right moment to conclude on my biotech positions, I am observing the market. There is that gaming company, Artifex Mundi, sort of a cousin to the 11Bit I already have in my portfolio. I wonder: maybe it would be a good step to diversify, from that overexposure in the biotech sector, into the IT? That Artifex Mundi thing has a nice growth today, and it looks as if they were bouncing back from a temporary trough. Looks like a classical configuration of bull horns. What if I spread the cash I have in hand between Bioton, 11Bit and Artifex Mundi?

I ask myself strange questions. I have set my limits for buying back into Airway Medix, Biomed Lublin, and Biomaxima. Does it mean that I will ever buy their stock again only if their respective prices go below those limits? What if they don’t? Should I refrain like forever from buying those stocks? Funny, I haven’t thought about it before. How long is enough to forget that I could have done a better business?

In the meantime, I notice that I have received the money transfer from my international investment account with the DEGIRO platform. Happened faster than I thought. I am forwarding this money into my Polish investment account.

2:15 p.m. Another market check today. 11Bit keeps swaying gently. It could be interesting to buy in. Biomed Lublin is anchored around PLN 3,50, above my critical threshold. Airway Medix is at PLN 0,71. Just at my threshold, buying those shares back would be stupid now. Artifex Mundi keeps an interesting high, could be worth having a go. Asseco Business Solutions went down slightly. I don’t know what to think about it. Bioton is at PLN 4,40. It does not look good. I prefer to wait with buying into this position. Mercator is around the same position. Biomaxima at PLN 23,80, way above my break-even-threshold.

3 p.m. Decision time. I split my investment between four companies: 11 Bit, Artifex Mundi, Bioton, Airway Medix (at 0,7, below threshold, I buy back 2400 shares; a bit of risk, but workable).

Doing things we don’t quite know how to do well is what we, humans, do all the time

My editorial on You Tube

Here I go again with my investment strategy, and with a live account of what is happening in a tiny little, Central European stock market, namely in Poland. This is a crazy rush on biotech and medical companies, with their market value growing like hell. I joined the fun, like 2 weeks ago, and this is madness, like really. In this update, I am trying to find some method to that madness, and more specifically, I am going to investigate the kind of business, and the kind of assets I have invested in.

Before I develop further, a few words about my stance on the current situation. As I go on any medium, social or general mainstream, everybody is taking a position regarding the COVID-19 pandemic. I am a strong partisan of getting my s**t together in the presence of danger, rather than moaning and complaining. I guess the way I can get my s**t together is to be a good person to the people whom I am close to – my son, my wife, my elderly aunt – then to be a good teacher to my students, and, on the top of that, to be an inspirational scientific blogger for whomever wants to read my blog (  ) and follow my You Tube channel ( ). When I see a lot of people freaking out about things which they don’t have any leverage on or which are really secondary in the present situation, I say to myself: ‘This is the right moment to remain calm and be the glue which holds at least some things together’.

Yes, things are rough now, and they are going to stay this way for quite some time, and they are likely to drift into even stormier waters. Life is brutal, as we used to say in Poland, back in the times of communism. Yes, it is, and whatever kind of coziness we develop, it is just a soap bubble. Thus, however rough things are going to be, there is always a tomorrow, and it is a good idea to work (work, not moan) to make that tomorrow a better place.

Now, a tough question comes: do I really think that investment in the stock market, even successful, can make tomorrow a better place for anyone else than me? I am honest: there is egoistic pursuit of gain in what I do, yet there is more. I deeply believe that the Beasty (you know, THAT virus) is already changing our civilization. We will have to be tougher and more resilient, and healthcare is one of the fields which the Beasty has really exposed as f**king feeble. We will need better healthcare, and more of it, and we will need a lot of good, new science in the game, properly developed into something workable in real life. I hope that massive investment in biotech stocks, currently taking place in Poland, means a deeper, fundamental drift of resources towards that industry as a whole. I hope that by investing in this sector via the stock market, I am taking part in something socially valuable, which will pay off in the future at many levels of economic utility. I want to find my bearings in that social change.

Moral stance taken on the virus and its corollaries, I pass to the substance of my update: my investment strategy and its scientific development, peppered with some educational content addressed to students of economics and management, my students as well as students in general.

Here are the positions open in my portfolio (hyperlinked names send you to investor-relations’ sites of those companies): 11Bit (IT), Asseco Business Solutions (IT), Bioton (biotech), Biomed Lublin (Biotech), Biomaxima (Biotech), Airway Medix (medical equipment), Mercator Medical (medical equipment). 11Bit and Asseco Business solutions are pre-COVID-crisis acquisitions (beginning of February 2020), and I bought all the rest over the last 2 weeks. As for April 7th, 2020, in the morning, thus ahead of the trading day, my weighted rate of return on investment with that portfolio is 188,4%. Yes, you have seen right. The thing almost tripled in value, and still, I have some positions with negative returns. I start the detailed analysis of that stuff with specifying the individual rates of return I have on each position. Here comes the table with a snapshot of my portfolio.

Company (position)Number of shares heldPrice per shareValue in portfolioRate of return on investment, net of brokerage fee
11Bit4       469,50  PLN    1 878,00  PLN-16,39%
Asseco Business Solutions25         31,40  PLN       785,00  PLN-5,88%
Bioton200           5,30  PLN    1 060,00  PLN-15,87%
Biomed Lublin1000           5,00  PLN    5 000,00  PLN410,20%
Biomaxima20         26,80  PLN       536,00  PLN21,82%
Airway Medix2300           0,95  PLN    2 194,20  PLN116,82%
Mercator Med.20         32,10  PLN       642,00  PLN19,33%

As I am writing these words, I am lurking on how the market is doing, in real time. Bioton falls by 14%. Biomed Lublin falls too. There is visibly a market correction. Do I cut my losses short and sell, or is it just some trading game, and I should hold? Example of emotions vs intellect. As I observe these two, since the beginning of the trading day, i.e. since 9:00 Central European time, there have been a few spikes in volume traded. Some people have either decided to consume their profits from the past days, or to punch the market a bit so as to make the price go down, and they buy back in. Still, both stocks climb back. The morning loss folds onto itself.

Right now, I am experiencing that discrepancy between the long-range view, proper to investment strategy, and the immediate shot of adrenaline on the moment of trading and seeing the market change in front of my eyes (literally, I see it on the screen of my MacBook).

Good. I detach myself a bit from the immediate experience, and I give my mind a kick, so as it takes flight, back into the high registers of prudent, long-seeing strategy. I am going to develop on two points. The method of calculating the weighted rate of return on the whole portfolio, in the first place. That’s educational, skip it if you know it. Then comes the market-to-book analysis, just to see the size of financial bubbles on each of those positions, as well as on the whole thing together.   

I go educational. The first step is to estimate the structure of the portfolio: I calculate the percentage share or the percentage contribution of this specific position to the whole portfolio.

In the next step I take the individual rate of return that comes with any individual investment position, and I multiply it by the share of the corresponding stock in my portfolio. I go like: ‘how important is that thing multiplied by what pay-off that thing brings me’. Once that individual multiplication done, I get the weighted individual rate of return. I do the same for each company, whose shares I hold. Next, I sum up all the thus-calculated, weighted rates of return. The sum total is my WEIGHTED AVERAGE RATE OF RETURN.  

Good, having delivered the parcel of basic teaching, I go into strategizing, i.e. into trying to predict things which are essentially impossible for a human to predict 100% accurately. Doing things we don’t quite know how to do well is what we, humans, do all the time. This is probably how we do so well, at the end of the day.

I walk down a classical financial analysis called ‘market to book’. I take the market capitalization of each company (data from market closure on April 6th, 2020), and I divide it by the book value of its equity. The table below summarizes the results. You see? I like weighted averages. I did it again with the ‘market-to-book’ ratio.    

Company (position)Total market capitalization (PLN mln)The coefficient ‘market-to-book’ (to book equity)
Asseco Business Solutions1094,333,46
Biomed Lublin311,39,47
Airway Medix55,731,67
Mercator Med.339,912,55
Weighted average4,74

By the look of it, the whole thing looks pretty swollen. Only Bioton keeps a low profile, and there is visibly some potential for long term growth. As I am writing these precise words, it is 1:30 p.m., April 7th, 2020, and I keep lurking at the stock price graphs in real time, and what I see is a downwards revision. Not much, yet prices go down a bit. Minds calm down, gambling yields to strategizing. That’s good, on the whole. Yes, I lose some money from my portfolio, as the day grows older, but I have a comfortable cushion under my ass, anyway, and too much of a speculative bubble is never good for any market. Besides, as I look at those intraday quotes, it was a nosedive in the morning, followed by a gentle growth, yet too gentle to compensate the dive. I will see tomorrow. If it is the same, I will sell, just to keep my gains and see what happens next. By the way, this is a good example of balance between the perceived value of financial stock, and the perceived value of money.   

Now, I want to walk a bit down the avenue I hinted at in my previous update, the one entitled ‘Which table do I want to play my game on?’: to what extent that rush on the stock of biotech companies will reflect in a fundamental change as regards their business? I want to focus on one specific question: ‘What if these companies reacted to that push from the stock market, by   accumulating capital in assets at the same pace as their market capitalization grows?’. From now on, I will focus on the biotech and medical companies in my portfolio. I assume that right now, those from the IT sector follow slightly different a trajectory. I summarize that hypothetical change in the table below.

Company (position)% change in market capitalization over the COVID crisis, since January 1st 2020Assets now (PLN mln)Hypothetical assets, if following the market push (PLN mln)
Bioton33,0%914,181 215,84
Biomed Lublin376,2%81,11386,26
Airway Medix68,6%43,0072,48
Mercator Med.206,3%386,711 184,52
Sum Total1 468,423 182,01

I quickly check the active side of those companies’ balance sheets, so as to nail down the distinction between fixed assets and current assets. I translate the simulation from the table above into a hypothetical investment in fixed assets. Here are the results, i.e. the hypothetical amounts of capital, to be possibly invested in the productive base of those five biotech and medical players: Bioton +PLN 254,39 mln (+ €56,03 mln), Biomed Lublin + PLN 249,62 mln (+ €54,98 mln), Biomaxima + PLN 167,68 mln (+ €36,93), Airway Medix + PLN 24,96 mln (+ €5,50 mln), Mercator Medical + PLN 403,64 mln (+ €88,91).

Now, I have a stupid question. If I were the CEO of Biomaxima (which I am pretty sure I am not likely to be in the predictable future), and I were offered €36,93 of capital to invest in the fixed assets of my business, would I know at all what to invest that money into? I mean, that would mean making my business more than seven times bigger, like in one go. Interesting.     

A civilisation of droplets

I am getting into the groove of a new form of expression: the rubber duck. I explained more specifically the theory of the rubber duck in the update entitled A test pitch of my ‘Energy Ponds’ business concept. I use mostly videos, where, as I am talking to an imaginary audience, I sharpen (hopefully) my own ideas and the way of getting them across. In this update, I am cutting out some slack from my thinking about the phenomenon of collective intelligence, and my use of neural networks to simulate the way that human, collective intelligence works (yes, it works).

The structure of my updates on this blog changes as my form is changing. Instead of placing the link to my video in like the first subheading of the update, I place it further blow, sort of in conclusion. I prepare my updates with an extensive use of Power Point, in order both to practice a different way of formulating my ideas, and in order to have slides for my video presentation. Together with the link to You Tube, you will find another one, to the Power Point document.

Ad rem, i.e. get the hell to the point, man. I am trying to understand better my own thinking about collective intelligence and the bridging towards artificial intelligence. As I meditate about it, I find an essential phenomenological notion: the droplet of information. With the development of digital technologies, we communicate more and more with some sort of pre-packaged, whoever-is-interested-can-pick-it-up information. Videos on you tube, blogging updates, books, articles are excellent examples thereof. When I talk to the camera of my computer, I am both creating a logical structure for myself, and a droplet of information for other people.

Communication by droplets is fundamentally different from other forms, like meetings, conversations, letters etc. Until recently, and by ‘recently’ I mean like 1990, most organized human structures worked with precisely addressed information. Since we started to grow the online part of our civilization, we have been coordinating more and more with droplet information. It is as if information was working like a hormone. As You Tube swells, we have more and more of that logical hormone accumulated in our civilization.

That’s precisely my point of connection with artificial intelligence. When I observe the way a neural network works (yes, I observe them working step by step, iteration by iteration, as strange as it might seem), I see a structure which uses error as food for learning. Residual local error is to a neural network what, once again, a hormone is to a living organism.

 Under the two links below, you will find:

  1. The Power Point Presentation with slides that accompany the YT video

That would be all for now. If you want to contact me directly, you can mail at: .