An overhead of individuals

I think I have found out, when writing my last update (‘Cultural classes’) another piece of the puzzle which I need to assemble in order to finish writing my book on collective intelligence. I think I have nailed down the general scientific interest of the book, i.e. the reason why my fellow scientists should even bother to have a look at it. That reason is the possibility to have deep insight into various quantitative models used in social sciences, with a particular emphasis on the predictive power of those models in the presence of exogenous stressors, and, digging further, the representativeness of those models as simulators of social reality.

Let’s have a look at one quantitative model, just one picked at random (well, almost at random): autoregressive conditional heteroscedasticity AKA ARCH (https://en.wikipedia.org/wiki/Autoregressive_conditional_heteroskedasticity ). It goes as follows. I have a process, i.e. a time-series of a quantitative variable. I compute the mean expected value in that time series, which, in plain human, means arithmetical average of all the observations in that series. In even plainer human, the one we speak after having watched a lot of You Tube, it means that we sum up the values of all the consecutive observations in that time series and we divide the so-obtained total by the number of observations.

Mean expected values have that timid charm of not existing, i.e. when I compute the mean expected value in my time series, none of the observations will be exactly equal to it. Each observation t will return a residual error εt. The ARCH approach assumes that εtis the product of two factors, namely of the time-dependentstandard deviation σt, and a factor of white noise zt. Long story short, we have εttzt.

The time-dependent standard deviation shares the common characteristics of all the standard deviations, namely it is the square root of time-dependent variance: σt = [(σt)2]1/2. That time-dependent variance is computed as:

Against that general methodological background, many variations arise, especially as regards the mean expected value which everything else is wrapped around. It can be a constant value, i.e. computed for the entire time-series once and for all. We can allow the time series to extend, and then each extension leads to the recalculation of the mean expected value, including the new observation(s). We can make the mean expected value a moving average over a specific window in time.

Before I dig further into the underlying assumptions of ARCH, one reminder begs for being reminded: I am talking about social sciences, and about the application of ARCH to all kinds of crazy stuff that we, humans, do collectively. All the equations and conditions phrased out above apply to collective human behaviour. The next step in understanding of ARCH, in the specific context of social sciences, is that ARCH has any point when the measurable attributes of our collective human behaviour really oscillate and change. When I have, for example, a trend in the price of something, and that trend is essentially smooth, without much of a dentition jumping to the eye, ARCH is pretty much pointless. On the other hand, that analytical approach – where each observation in the real measurable process which I observe is par excellence a deviation from the expected state – gains in cognitive value as the process in question becomes increasingly dented and bumpy.

A brief commentary on the very name of the method might be interesting. The term ‘heteroskedasticity’ means that real observations tend to be grouped on one side of the mean expected value rather than on the other. There is a slant, which, over time, translates into a drift. Let’s simulate the way it happens. Before I even start going down this rabbit hole, another assumption is worth deconstructing. If I deem a phenomenon to be describable as white noise, AKA zt, I assume there is no pattern in the occurrence thereof. Any state of that phenomenon can happen with equal probability. It is the ‘Who knows?’ state of reality in its purest form.

White noise is at the very basis of the way we experience reality. This is pure chaos. We make distinctions in this chaos; we group phenomena, and we assess the probability of each newly observed phenomenon falling into one of the groups. Our essential cognition of reality assumes that in any given pound of chaos, there are a few ounces of order, and a few residual ounces of chaos. Then we have the ‘Wait a minute!’ moment and we further decompose the residual ounces of chaos into some order and even more residual a chaos. From there, we can go ad infinitum, sequestrating streams of regularity and order out of the essentially chaotic flow of reality. I would argue that the book of Genesis in the Old Testament is a poetic, metaphorical account of the way that human mind cuts layers of intelligible order out of the primordial chaos.

Seen from a slightly different angle, it means that white noise zt can be interpreted as an error in itself, because it is essentially a departure from the nicely predictable process εt = σt, i.e. where residual departure from the mean expected value is equal to the mean expected departure from the mean expected value. Being a residual error, zt can be factorized into zt = σ’t*z’t , and, once again, that factorization can go all the way down to the limits of observability as regards the phenomena studied.     

At this point, I am going to put the whole reasoning on its head, as regards white noise. It is because I know and use a lot the same concept, just under a different name, namely that of mean-reverted value. I use mean-reversion a lot in my investment decisions in the stock market, with a very simple logic: when I am deciding to buy or sell a given stock, my purely technical concern is to know how far away the current price from its moving average is. When I do this calculation for many different stocks, priced differently, I need a common denominator, and I use standard deviation in price for that purpose. In other words, I compute as follows: mean-reverted price = (current price – mean expected price)/ standard deviation in price.

If you have a closer look at this coefficient of mean-reverted price, its nominator is error, because it is the deviation from mean expected value. I divide that error by standard deviation, and, logically, what I get is error divided by standard deviation, therefore the white noise component zt of the equation εt = σtzt. This is perfectly fine mathematically, only my experience with that coefficient tells me it is anything but white noise. When I want to grasp very sharply and accurately the way which the price of a given stock reacts to its economic environment, I use precisely the mean-reverted coefficient of price. As soon as I recalculate the time series of a price into its mean-reverted form, patterns emerge, sharp and distinct. In other words, the allegedly white-noise-based factor in the stock price is much more patterned than the original price used for its calculation.

The same procedure which I call ‘mean-reversion’ is, by the way, a valid procedure to standardize empirical data. You take each empirical observation, you subtract from it the mean expected value of the corresponding variable, you divide the residual difference by its standard deviation, and Bob’s your uncle. You have your data standardized.

Summing up that little rant of mine, I understand the spirit of the ARCH method. If I want to extract some kind of autoregression in time-series, I can test the hypothesis that standard deviation is time-dependent. Do I need, for that purpose, to assume the existence of strong white noise in the time series? I would say cautiously: maybe, although I do not see the immediate necessity for it. Is the equation εt = σtzt the right way to grasp the distinction into the stochastic component and the random one, in the time series? Honestly: I don’t think so. Where is the catch? I think it is in the definition and utilization of error, which, further, leads to the definition and utilization of the expected state.

In order to make my point clearer, I am going to quote two short passages from pages xxviii-xxix in Nicolas Nassim Taleb’s book ‘The Black Swan’. Here it goes. ‘There are two possible ways to approach phenomena. The first is to rule out the extraordinary and focus on the “normal.” The examiner leaves aside “outliers” and studies ordinary cases. The second approach is to consider that in order to understand a phenomenon, one needs first to consider the extremes—particularly if, like the Black Swan, they carry an extraordinary cumulative effect. […] Almost everything in social life is produced by rare but consequential shocks and jumps; all the while almost everything studied about social life focuses on the “normal,” particularly with “bell curve” methods of inference that tell you close to nothing’.

When I use mean-reversion to study stock prices, for my investment decisions, I go very much in the spirit of Nicolas Taleb. I am most of all interested in the outlying values of the metric (current price – mean expected price)/ standard deviation in price, which, once again, the proponents of the ARCH method interpret as white noise. When that metric spikes up, it is a good moment to sell, whilst when it is in a deep trough, it might be the right moment to buy. I have one more interesting observation about those mean-reverted prices of stock: when they change their direction from ascending to descending and vice versa, it is always a sharp change, like a spike, never a gentle recurving. Outliers always produce sharp change. Exactly, as Nicolas Taleb claims. In order to understand better what I am talking about, you can have a look at one of the analytical graphs I used for my investment decisions, precisely with mean-reverted prices and transactional volumes, as regards Ethereum: https://discoversocialsciences.com/wp-content/uploads/2020/04/Slide5-Ethereum-MR.png .

In a manuscript that I wrote and which I am still struggling to polish enough for making it publishable (https://discoversocialsciences.com/wp-content/uploads/2021/01/Black-Swans-article.pdf ), I have identified three different modes of collective learning. In most of the cases I studied empirically, societies learn cyclically, i.e. first they produce big errors in adjustment, then they narrow their error down, which means they figure s**t out, and in a next phase the error increases again, just to decrease once again in the next cycle of learning. This is cyclical adjustment. In some cases, societies (national economies, to be exact) adjust in a pretty continuous process of diminishing error. They make big errors initially, and they reduce their error of adjustment in a visible trend of nailing down workable patterns. Finally, in some cases, national economies can go haywire and increase their error continuously instead of decreasing it or cycling on it.

I am reconnecting to my method of quantitative analysis, based on simulating with a simple neural network. As I did that little excursion into the realm of autoregressive conditional heteroscedasticity, I realized that most of the quantitative methods used today start from studying one single variable, and then increase the scope of analysis by including many variables in the dataset, whilst each variable keeps being the essential monad of observation. For me, the complex local state of the society studied is that monad of observation and empirical study. By default, I group all the variables together, as distinct, and yet fundamentally correlated manifestations of the same existential stuff happening here and now. What I study is a chain of here-and-now states of reality rather than a bundle of different variables.    

I realize that whilst it is almost axiomatic, in typical quantitative analysis, to phrase out the null hypothesis as the absence of correlation between variables, I don’t even think about it. For me, all the empirical variables which we, humans, measure and report in our statistical data, are mutually correlated one way or another, because they all talk about us doing things together. In phenomenological terms, is it reasonable to assume that we do in order to produce real output, i.e. our Gross Domestic Product, is uncorrelated with what we do with the prices of productive assets? Probably not.

There is a fundamental difference between discovering and studying individual properties of a social system, such as heteroskedastic autoregression in a variable, on the one hand, and studying the way this social system changes and learns as a collective. It means two different definitions of expected state. In most quantitative methods, the expected state is the mean value of one single variable. In my approach, it is always a vector of expected values.

I think I start nailing down, at last, the core scientific idea I want to convey in my book about collective intelligence. Studying human societies as instances of collective intelligence, or, if you want, as collectively intelligent structure, means studying chains of complex states. The Markov chain of states, and the concept of state space, are the key mathematical notions here.

I have used that method, so far, to study four distinct fields of empirical research: a) the way we collectively approach energy management in our societies b) the orientation of national economies on the optimization of specific macroeconomic variables c) the way we collectively manage the balance between urban land, urban density of population, and agricultural production, and d) the way we collectively learn in the presence of random disturbances. The main findings I can phrase out start with the general observation that in a chain of complex social states, we collectively tend to lean towards some specific aspects of our social reality. Fault of a better word, I equate those aspects to the quantitative variables I find them represented by, although it is something to dig in. We tend to optimize the way we work, in the first place, and the way we sell our work. Concerns such as return on investment or real output come as secondary. That makes sense. At the large scale, the way we work is important for the way we use energy, and collectively learn. Surprisingly, variables commonly associated with energy management, such as energy efficiency, or the exact composition of energy sources, are secondary.

The second big finding is related to a manuscript t which I am still struggling to polish enough for making it publishable (https://discoversocialsciences.com/wp-content/uploads/2021/01/Black-Swans-article.pdf ), I have identified three different modes of collective learning. In most of the cases I studied empirically, societies learn cyclically, i.e. first they produce big errors in adjustment, then they narrow their error down, which means they figure s**t out, and in a next phase the error increases again, just to decrease once again in the next cycle of learning. This is cyclical adjustment. In some cases, societies (national economies, to be exact) adjust in a pretty continuous process of diminishing error. They make big errors initially, and they reduce their error of adjustment in a visible trend of nailing down workable patterns. Finally, in some cases, national economies can go haywire and increase their error continuously instead of decreasing it or cycling on it.

The third big finding is about the fundamental logic of social change, or so I perceive it. We seem to be balancing, over decades, the proportions between urban land and agricultural land so as to balance the production of food with the production of new social roles for new humans. The countryside is the factory of food, and cities are factories of new social roles. I think I can make a strong, counterintuitive claim that social unrest, such as what is currently going on in United States, for example, erupts when the capacity to produce food in the countryside grows much faster than the capacity to produce new social roles in the cities. When our food systems can sustain more people than our collective learning can provide social roles for, we have an overhead of individuals whose most essential physical subsistence is provided for, and yet they have nothing sensible to do, in the collective intelligent structure of the society.

Cultural classes

Some of my readers asked me to explain how to get in control of one’s own emotions when starting their adventure as small investors in the stock market. The purely psychological side of self-control is something I leave to people smarter than me in that respect. What I do to have more control is the Wim Hof method (https://www.wimhofmethod.com/ ) and it works. You are welcome to try. I described my experience in that matter in the update titled ‘Something even more basic’. Still, there is another thing, namely, to start with a strategy of investment clever enough to allow emotional self-control. The strongest emotion I have been experiencing on my otherwise quite successful path of investment is the fear of loss. Yes, there are occasional bubbles of greed, but they are more like childish expectations to get the biggest toy in the neighbourhood. They are bubbles, which burst quickly and inconsequentially. The fear of loss is there to stay, on the other hand.    

This is what I advise to do. I mean this is what I didn’t do at the very beginning, and fault of doing it I made some big mistakes in my decisions. Only after some time (around 2 months), I figured out the mental framework I am going to present. Start by picking up a market. I started with a dual portfolio, like 50% in the Polish stock market, and 50% in the big foreign ones, such as US, Germany, France etc. Define the industries you want to invest in, like biotech, IT, renewable energies. Whatever: pick something. Study the stock prices in those industries. Pay particular attention to the observed losses, i.e., the observed magnitude of depreciation in those stocks. Figure out the average possible loss, and the maximum one. Now, you have an idea of how much you can lose in percentage. Quantitative techniques such as mean-reversion or extrapolation of the past changes can help. You can consult my update titled ‘What is my take on these four: Bitcoin, Ethereum, Steem, and Golem?’ to see the general drift.

The next step is to accept the occurrence of losses. You need to acknowledge very openly the following: you will lose money on some of your investment positions, inevitably. This is why you build a portfolio of many investment positions. All investors lose money on parts of their portfolio. The trick is to balance losses with even greater gains. You will be experimenting, and some of those experiments will be successful, whilst others will be failures. When you learn investment, you fail a lot. The losses you incur when learning, are the cost of your learning.

My price of learning was around €600, and then I bounced back and compensated it with a large surplus. If I take those €600 and compare it to the cost of taking an investment course online, e.g. with Coursera, I think I made a good deal.

Never invest all your money in the stock market. My method is to take some 30% of my monthly income and invest it, month after month, patiently and rhythmically, by instalments. For you, it can be 10% or 50%, which depends on what exactly your personal budget looks like. Invest just the amount you feel you can afford exposing to losses. Nail down this amount honestly. My experience is that big gains in the stock market are always the outcome of many consecutive steps, with experimentation and the cumulative learning derived therefrom.

General remark: you are much calmer when you know what you’re doing. Look at the fundamental trends and factors. Look beyond stock prices. Try to understand what is happening in the real business you are buying and selling the stock of. That gives perspective and allows more rational decisions.  

That would be it, as regards investment. You are welcome to ask questions. Now, I shift my topic radically. I return to the painful and laborious process of writing my book about collective intelligence. I feel like shaking things off a bit. I feel I need a kick in the ass. The pandemic being around and little social contacts being around, I need to be the one who kicks my own ass.

I am running myself through a series of typical questions asked by a publisher. Those questions fall in two broad categories: interest for me, as compared to interest for readers. I start with the external point of view: why should anyone bother to read what I am going to write? I guess that I will have two groups of readers: social scientists on the one hand, and plain folks on the other hand. The latter might very well have a deeper insight than the former, only the former like being addressed with reverence. I know something about it: I am a scientist.

Now comes the harsh truth: I don’t know why other people should bother about my writing. Honestly. I don’t know. I have been sort of carried away and in the stream of my own blogging and research, and that question comes as alien to the line of logic I have been developing for months. I need to look at my own writing and thinking from outside, so as to adopt something like a fake observer’s perspective. I have to ask myself what is really interesting in my writing.

I think it is going to be a case of assembling a coherent whole out of sparse pieces. I guess I can enumerate, once again, the main points of interest I find in my research on collective intelligence and investigate whether at all and under what conditions the same points are likely to be interesting for other people.

Here I go. There are two, sort of primary and foundational points. For one, I started my whole research on collective intelligence when I experienced the neophyte’s fascination with Artificial Intelligence, i.e. when I discovered that some specific sequences of equations can really figure stuff out just by experimenting with themselves. I did both some review of literature, and some empirical testing of my own, and I discovered that artificial neural networks can be and are used as more advanced counterparts to classical quantitative models. In social sciences, quantitative models are about the things that human societies do. If an artificial form of intelligence can be representative for what happens in societies, I can hypothesise that said societies are forms of intelligence, too, just collective forms.

I am trying to remember what triggered in me that ‘Aha!’ moment, when I started seriously hypothesising about collective intelligence. I think it was when I was casually listening to an online lecture on AI, streamed from the Massachusetts Institute of Technology. It was about programming AI in robots, in order to make them able to learn. I remember one ‘Aha!’ sentence: ‘With a given set of empirical data supplied for training, robots become more proficient at completing some specific tasks rather than others’. At the time, I was working on an article for the journal ‘Energy’. I was struggling. I had an empirical dataset on energy efficiency in selected countries (i.e. on the average amount of real output per unit of energy consumption), combined with some other variables. After weeks and weeks of data mining, I had a gut feeling that some important meaning is hidden in that data, only I wasn’t able to put my finger precisely on it.

That MIT-coined sentence on robots triggered that crazy question in me. What if I return to the old and apparently obsolete claim of the utilitarian school in social sciences, and assume that all those societies I have empirical data about are something like one big organism, with different variables being just different measurable manifestations of its activity?

Why was that question crazy? Utilitarianism is always contentious, as it is frequently used to claim that small local injustice can be justified by bringing a greater common good for the whole society. Many scholars have advocated for that claim, and probably even more of them have advocated against. I am essentially against. Injustice is injustice, whatever greater good you bring about to justify it. Besides, being born and raised in a communist country, I am viscerally vigilant to people who wield the argument of ‘greater good’.

Yet, the fundamental assumptions of utilitarianism can be used under a different angle. Social systems are essentially collective, and energy systems in a society are just as collective. There is any point at all in talking about the energy efficiency of a society when we are talking about the entire intricate system of using energy. About 30% of the energy that we use is used in transport, and transport is from one person to another. Stands to reason, doesn’t it?

Studying my dataset as a complex manifestation of activity in a big complex organism begs for the basic question: what do organisms do, like in their daily life? They adapt, I thought. They constantly adjust to their environment. I mean, they do if they want to survive. If I settle for studying my dataset as informative about a complex social organism, what does this organism adapt to? It could be adapting to a gazillion of factors, including some invisible cosmic radiation (the visible one is called ‘sunlight’). Still, keeping in mind that sentence about robots, adaptation can be considered as actual optimization of some specific traits. In my dataset, I have a range of variables. Each variable can be hypothetically considered as informative about a task, which the collective social robot strives to excel at.

From there, it was relatively simple. At the time (some 16 months ago), I was already familiar with the logical structure of a perceptron, i.e. a very basic form of artificial neural network. I didn’t know – and I still don’t – how to program effectively the algorithm of a perceptron, but I knew how to make a perceptron in Excel. In a perceptron, I take one variable from my dataset as output, the remaining ones are instrumental as input, and I make my perceptron minimize the error on estimating the output. With that simple strategy in mind, I can make as many alternative perceptrons out of my dataset as I have variables in the latter, and it was exactly what I did with my data on energy efficiency. Out of sheer curiosity, I wanted to check how similar were the datasets transformed by the perceptron to the source empirical data. I computed Euclidean distances between the vectors of expected mean values, in all the datasets I had. I expected something foggy and pretty random, and once again, life went against my expectations. What I found was a clear pattern. The perceptron pegged on optimizing the coefficient of fixed capital assets per one domestic patent application was much more similar to the source dataset than any other transformation.

In other words, I created an intelligent computation, and I made it optimize different variables in my dataset, and it turned out that, when optimizing that specific variable, i.e. the coefficient of fixed capital assets per one domestic patent application, that computation was the most fidel representation of the real empirical data.   

This is when I started wrapping my mind around the idea that artificial neural networks can be more than just tools for optimizing quantitative models; they can be simulators of social reality. If that intuition of mine is true, societies can be studied as forms of intelligence, and, as they are, precisely, societies, we are talking about collective intelligence.

Much to my surprise, I am discovering similar a perspective in Steven Pinker’s book ‘How The Mind Works’ (W. W. Norton & Company, New York London, Copyright 1997 by Steven Pinker, ISBN 0-393-04535-8). Professor Steven Pinker uses a perceptron as a representation of human mind, and it seems to be a bloody accurate representation.

That makes me come back to the interest that readers could have in my book about collective intelligence, and I cannot help referring to still another book of another author: Nassim Nicholas Taleb’s ‘The black swan. The impact of the highly improbable’ (2010, Penguin Books, ISBN 9780812973815). Speaking from an abundant experience of quantitative assessment of risk, Nassim Taleb criticizes most quantitative models used in finance and economics as pretty much useless in making reliable predictions. Those quantitative models are good solvers, and they are good at capturing correlations, but they suck are predicting things, based on those correlations, he says.

My experience of investment in the stock market tells me that those mid-term waves of stock prices, which I so much like riding, are the product of dissonance rather than correlation. When a specific industry or a specific company suddenly starts behaving in an unexpected way, e.g. in the context of the pandemic, investors really pay attention. Correlations are boring. In the stock market, you make good money when you spot a Black Swan, not another white one. Here comes a nuance. I think that black swans happen unexpectedly from the point of view of quantitative predictions, yet they don’t come out of nowhere. There is always a process that leads to the emergence of a Black Swan. The trick is to spot it in time.

F**k, I need to focus. The interest of my book for the readers. Right. I think I can use the concept of collective intelligence as a pretext to discuss the logic of using quantitative models in social sciences in general. More specifically, I want to study the relation between correlations and orientations. I am going to use an example in order to make my point a bit more explicit, hopefully. In my preceding update, titled ‘Cool discovery’, I did my best, using my neophytic and modest skills in programming, the method of negotiation proposed in Chris Voss’s book ‘Never Split the Difference’ into a Python algorithm. Surprisingly for myself, I found two alternative ways of doing it: as a loop, on the one hand, and as a class, on the other hand. They differ greatly.

Now, I simulate a situation when all social life is a collection of negotiations between people who try to settle, over and over again, contentious issues arising from us being human and together. I assume that we are a collective intelligence of people who learn by negotiated interactions, i.e. by civilized management of conflictual issues. We form social games, and each game involves negotiations. It can be represented as a lot of these >>

… and a lot of those >>

In other words, we collectively negotiate by creating cultural classes – logical structures connecting names to facts – and inside those classes we ritualise looping behaviours.

Germany happens too, like all the time

MY EDITORIAL ON YOU TUBE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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The moment of reassessment

MY EDITORIAL ON YOU TUBE

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

30/06/201930/09/201931/12/201930/03/2020
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

30/06/201930/09/201931/12/201930/03/2020
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

2020/3M2019/YE2019/9M2019/6M
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

2020/3M2019/YE2019/9M2019/6M
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

2020/3M2019/YE2019/9M2019/6M
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

2020/3M2019/YE2019/9M2019/6M
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

2020/3M2019/YE2019/9M2019/6M
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

2020/3M2019/YE2019/9M2019/6M
Market cap (€ mln)954,251 199,23902,89887,63
Assets (€ mln)1 031,471 107,321 014,86970,56
Equity (€ mln)415,35416,89411,39406,72
Market cap to assets0,931,080,890,91
DAX Performance Index9 935,8413 249,0112 428,0812 398,80
Elasticity of assets to market cap0,310,312,90
Elasticity of assets to DAX Performance0,020,111,51

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

My editorial on You Tube

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

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

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

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

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

Table 1

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

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

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

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

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

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

Long story short, it goes like…

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

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

… etc.

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

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

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

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

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

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

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

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

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

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

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

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

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

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