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

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

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

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

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

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

The general formula for calculating any rate of return on investment is: ** RR = [profit] / [investment]. **In the stock market, with a given number of shares held in portfolio, and assumed constant, both profit and investment can be reduced to prices only. Therefore, we modify the equation of return into:

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

In the graph below, I am illustrating this method with the case of Bioton, one of the companies whose stock I currently hold in my portfolio. I chose a complex, line-bar graph, so as to show graphically the distinction between the incidence of loss (i.e. negative return) vs that of gain. My opening price is the one I paid for 600 shares of Bioton on April 6^{th}, 2020, i.e. PLN 5,01 per share. I cover one year of trading history, thus 247 sessions. In that temporal framework, Bioton had 12 days when it went above my opening price, and, sadly enough, 235 sessions closed with a price below my opening. That gives me probabilities that play out as follows: ** P(positive return) = 12/247 = 4,9%** and

**. Brutal and sobering, as I see it. The partial moral of the fairy tale is that should the past project itself perfectly in the future, this if all the stuff that happens is truly cyclical, I should wait patiently, yet vigilantly, to spot that narrow window in the reality of stock trade, when I can sell my Bioton with a positive return on investment.**

*P(negative return) = 235/247 = 95,1%*Now, I am going to tell a different story, the story of First Solar, a company which I used to have an investment position in. As I said, I used to, and I do not have any position anymore in that stock. I sold it in the beginning of April, when I was a bit scared of uncertainty in the U.S. stock market, and I saw a window of opportunity in the swelling speculative bubble on biotech companies in Poland. As I do not have any stock of First Solar, I do not have any real opening price. Still, I can play a game with myself, the game of ‘as if…’. I calculate my return as if I had bought First Solar last Friday, April 24^{th}. I take the closing price from Friday, April 24^{th}, 2020, and I put it in the same calculation as my opening price. The resulting story is being told in the graph below. This is mostly positive a story. In strictly mathematical terms, over the last year, there had been 222 sessions, out of a total of 247, when the price of First Solar went over the closing price of Friday, April 24^{th}, 2020. That gives ** P(positive return) = 222/247 = 89,9%**, whilst

**.**

*P(negative return) = 10,1%*The provisional moral of this specific fairy tale is that with First Solar, I can sort of sleep in all tranquillity. Should the past project itself in the future, most of trading days is likely to close with a positive return on investment, had I opened on First Solar on Friday, April 24^{th}, 2020.

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

Company (investment position) | Probability of negative return | Probability of positive return | Link to the graph of return |

My current portfolio | |||

11Bit | P(negative) = 209/247 = 84,6% | P(positive) = 15,4% | 11Bit: Graph of return |

Airway Medix (243 sessions) | P(negative) = 173/243 = 71,2% | P(positive) = 70/243 = 28,8% | Airway Medix: Graph of return |

Asseco Business Solutions | P(negative) = 221/247 = 89,5% | P(positive) = 10,5% | Asseco Business Solutions: Graph of return |

Bioton | P(negative) = 235/247 = 95,1% | P(positive) = 12/247 = 4,9% | Bioton: Graph of return |

Mercator Medical | P(negative) = 235/247 = 95,1% | P(positive) = 12/247 = 4,9% | Mercator: graph of return |

PBKM | P(negative) = 138/243 = 56,8% | P(positive) = 105/243 = 43,2% | PBKM: Graph of return |

Interesting outliers from the past | |||

Biomaxima (218 sessions) | P(negative) = 215/218 = 98,6% | P(positive) = 3/218 = 1,4% | Biomaxima: Graph of return |

Biomed Lublin | P(negative) = 239/246 = 97,2% | P(positive) = 7/246 = 2,8% | Biomed Lublin: graph of return |

OAT (Onco Arendi Therapeutics) | P(negative) = 205/245 = 83,7% | P(positive) = 40/245 = 16,3% | OAT: Graph of return |

Incyte Corporation | P(negative) = 251/251 = 100% | P(positive) = 0/251 = 0% | Incyte: Graph of return |

First Solar | P(negative) = 10,1% | P(positive) = 222/247 = 89,9% | First Solar: Graph of return |

Completely new interesting outliers | |||

Tesla | P(negative) = 226/251 = 90% | P(positive) = 25/251 = 10% | Tesla: Graph of return |

Medtronic | P(negative) = 50/250 = 20% | P(positive) = 200/250 = 80% | Medtronic: Graph of return |

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

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