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.

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