The hopefully crazy semester

Another handful of educational material, for the apparently (hopefully) crazy semester at the university. Crazy because of the virus, stands to reason. Things are never crazy because we make them so, stands to reason, once again.

I am making a big, fat bottom line at my investment portfolio in the stock market, and I am using this opportunity to make some educational material. The point of using my experience in education. It is personal experience, important to enrich theory. It is a story of personal limitations in business decisions, and understanding those limitations is important for understanding microeconomics as the substance of decisions, macroeconomics as their context, and management as their execution.

I have successful experience, together with hindsight on the mistakes I made. I can utilize it as valuable material to share and to build some teaching on. Since January 2020, I have invested  $7 924,76 in the stock market, and today (August 25th, 2020), my investment portfolio is worth  $11 719,91. I have 47,89% of return on the cash invested, over a period of 7 months. Not bad for a theoretician, isn’t it? I am deeply convinced that personal experience is impossible to bypass in any true teaching. Whatever kind of story I am telling on the moment, I always tell the story of my own existence. I can make it genuine and truthful just as well. Here is the link to the first, introductory video in this path: ‘My investment experience, my teaching and my science #1’  [Invest 1 2020-08-25 11-54-58 ; ]

In the second video of the same series [Invest 2 2020-08-26 07-37-08; ], I focus on the presentation of my investment portfolio. I stress two points. Firstly, the portfolio which I hold now is the cumulative outcome of past trials and errors. Secondly, my portfolio shows many alternative scenarios of what could possibly have happened to my money, had I invested in just one among the 27 positions, thus if I had not diversified. I could have made +313% or -49%, instead of the 48% I had made as of August 25th 2020. I study more fundamentally the case of General Electric, which is one of my financial failures as for now. Turns out they have stakes in aviation, and that sucks in the times of pandemic.

In the third video of the series ‘Business Models in the Media Industry’ [Media BM 3 2020-08-26 08-24-42; ] I focus more in depth on studying the case of Netflix. You can have a glimpse of their transition from a streamer of externally made content to a business based on in-house made content. You can also see how strongly their business model is grounded in the assumption of constant growth in size.

In my second video devoted to Political Systems [PolitSys 2 2020-08-26 09-02-47; ] I use two cases, i.e. the constitutions of France and Finland, to give my readers, followers and students a first glimpse on forms of political power. You can see that general concept in the context of distinction between a presidential system (France) vs a Parliamentary one (Finland).  

Business models and the nature of truth – back to school #2

I am introducing another handful of educational content in the form of video tutorials.

The video recorded on August 23rd, noon sharp, is the first in a separate path of teaching devoted to Political Systems. The link to You Tube is here: PolitSys 2020-08-23 11-16-44 ( ). The video introduces two case studies: the constitution of Uganda ( ) and the constitution of India ( ). In terms of theory, two articles are hinted at: Almond, G. A. (1956). Comparative Political Systems. The Journal of Politics, 18(3), 391-409 ( ), and Easton, D. (1957). An approach to the analysis of political systems. World Politics: A Quarterly Journal of International Relations, 383-400 ( ). General concepts which you will find developed in this video are:

>> Constitution as a double-function tool: the set of rules for the political game, and the foundation of the national legal system

>> Constitutional systems as paradoxes: rules of the very brutal political game put together, in the same document, with ambitious ethical principles for the entire nation.

>> The principle of national sovereignty

>> The method of studying constitutions by simulated removal and negation of rules

Material recorded on Monday, August 24th, 2020 (Econ Basics 1 2020-08-24 08-02-06 ; ) contains the first, more or less formalized lecture in the fundamentals of economics. I use five essential readings – Netflix Annual Report 2019, Discovery Annual Report 2019, Adam Smith’s ‘Wealth of Nations’, David Ricardo’s ‘Principles of Political Economy and Taxation’, and Carl Menger’s ‘Principles of Economics’ – in order to show the basis axes of approach to economic sciences. Firstly, it is the special social tension between the diversity of skills and social roles, on the one hand, and the fact of them all summing up to one big body of labour (Smith). Secondly, I introduce the distinction between capital and labour, and the importance of capital resources (Ricardo, example Netflix). Thirdly, and finally, I present the concept of economic good (Carl Menger) and the importance of translating technology into products.

The video recorded slightly later on August 24th, 2020 (Renew BM 2 2020-08-24 09-35-20; ) is the second educational piece in the stream devoted to Business Models in the industry of Renewable Energies. I stay with the two business cases from the first video, i.e. First Solar Inc. and SMA Solar Technology AG, and I focus on connecting their capital accounts – their respective BALANCE SHEETS – to their business models. In terms of the capital base, First Solar is six times bigger than SMA. First Solar’s business model is based, capital-wise, on using retained earnings and additional paid-in capital to finance property, plant, equipment and a large reserve of cash. As regards SMA Solar, they mostly use retained earnings and long-term, complex contractual debt in order to finance factories and large inventories. What emerges as a common denominator between the two is the stream of capital from retained earnings to the financing of fixed productive assets.

In the second video focused on business models in the media industry (Media BM 2 2020-08-24 13-42-46;  ), I keep working with two business cases: Netflix, and Discovery Communications. This time, I focus on deconstructing a business model out of the capital account, i.e. from the balance sheet of a company. I present it in the form of a game, which I frequently practice in class with my students: I ask them to identify the biggest numbers (financial aggregates) on both the active and the passive side of the balance sheet. I demonstrate this exercise in this video and explain how you can use the balance sheet to guess the fundamental traits of a business model.  

I am also putting online a second video in the educational path devoted to the philosophy of science (Phil Science 2 2020-08-24 14-17-58;  ). I am investigating the nature of truth, with three basic readings: Philosophical Essay on Probabilities’ by Pierre Simon, marquis de Laplace, ‘Truth and Method’ by Hans Georg Gadamer, and an article entitled ‘Conscious agent networks: Formal analysis and application to cognition’, by Chris Fields, Donald D. Hoffman, Chetan Prakash, and Manish Singh. I briefly discuss the limitations we, humans, encounter when trying to discover truth about reality.

Back to school

More than an entire month has passed since I placed my last update on this blog. I took some time strictly off – some human tribes call it ‘vacation’ – and I have been assiduously doing science. Back from vacation, and imbibed with new science, I am blogging again. Actually, this new science is so fresh that I need to blog about it just to put some order in my findings and my ideas.

I have been doing science, and, in the same time, I have been preparing my teaching content for the new academic year.  As for science, I have been focusing on two things: the general theory of collective intelligence, on the one hand, and the puzzling data on urban density, on the other hand. I am going to develop on that second issue more exhaustively, as these are facts, and facts have disquieting a tendency to bring new insights into the comfortably established theory.

As regards teaching, I have three big curriculums to prepare for the winter semester: economics, management, and economic policy together with political systems. In this update, I am bringing, as sort of test missiles, my first three educational videos for the next semester. In other words, this update on my blog is actually a long, articulated link to those videos on You Tube. Below, I give the links and a short explanation for each of those three.

The video which I recorded around 2 p.m., on August 22nd, 2020, is pertinent to Business Models in the Film and TV production business. In my teaching of management, I have that special path, addressed to students in the Major ‘Film and TV Production’. I am teaching them the basics of management, with a special edge on show business. In this specific video you will see the beginning of two case studies: Netflix and Discovery Inc. You will see the basic tips for finding and retrieving financial reports of those businesses, as well as the first steps into analysing their business in depth. Here is the link to the You Tube video: Media BM 2020-08-22 13-41-17 (–1KQ ).  

The video recorded around 2:30 p.m., August 22nd, 2020, regards the Philosophy of Science. It is both extra-curricular content for all those among my students who want to develop their scientific edge, and my auto-reflection on the general issue of collective intelligence, and the possibility to use artificial neural networks for the study thereof. I dive into three readings: ‘Civilisation and Capitalism’ by Fernand Braudel, ‘Philosophical Essay on Probabilities’ by Pierre Simon, marquis de Laplace, and finally ‘Truth and Method’ by Hans Georg Gadamer. I focus on fundamental distinctions between reality such as it is, on the one hand, our perception, and our understanding thereof. The link is here: Phil Science 2020-08-22 14-30-16 ( ).

The video recorded early in the morning of August 23rd, 2020, is devoted to Business Models in the industry of Renewable Energies. The general concept of business models is the overarching common denominator in my teaching of economics and management in the coming academic year (2020/2021). Here, I start with a quick glance on two business cases: FIRST SOLAR and SMA SOLAR. You can see there two different business models, one oriented on big scale in manufacturing, the other one focused on building complex networks and platforms of exchange. Here comes the link to You Tube: Renew BM 2020-08-23 07-52-34 ( ).

Chitchatting about kings, wars and medical ventilators: project tutorial in Finance

My editorial on You Tube

I continue with educational stuff, so as to help my students with their graduation projects. This time, I take on finance, and on the projects that my students are to prepare in the curriculum of ‘Foundations of Finance’. The general substance of those projects consists in designing a financial instrument. I know that many students struggle already at the stage of reading that sentence with understanding: they don’t really grasp the concept of designing a financial instrument. Thus, I want to sort of briefly retake it from the beginning.

The first step in this cursory revision is to explain what I mean by ‘financial instrument’. Within the framework of that basic course of finance, I want my students to develop intellectual distinction between 5 essential types of financial instruments: equity-based securities, debt-based securities, bank-based currencies, virtual currencies (inclusive of cryptocurrencies), and insurance contracts. I am going to (re)explain the meaning of those terms. I focus on those basic types because they are what we, humans, simply do, and have been doing for centuries. Those types of financial instruments have been present in our culture for a long time, and, according to my own scientific views, they manifest collective intelligence in human societies: they are standardized parcels of information, able to provoke certain types of behaviour in some categories of recipients. In other words, those financial instruments work similarly to a hormone. Someone drops them in the middle of the (social) ocean. Someone else, completely unknown and unrelated picks them up, and their content changes the acquirer’s behaviour. 

When we talk about securities, both equity-based and debt-based, the general idea is that of securing claims, and then making those secured claims tradable. Look up the general definition of security, e.g. on Investopedia. If you want, in your project, to design a security, the starting point is to define the assets it gives claim on. Equity-based securities give direct, unconditional claims on the assets held by a business (or by any other type of social entity incorporated in a business-like way, with an explicit balance sheet), as well as conditional, indirect claims on the dividend paid out of future net income generated with those assets. Debt-based securities give direct, unconditional claim on the future cash flows, generated by the assets of the given business. The basic idea of tradable securities is that all those types of claims come with a risk, and the providers of capital can reduce their overall risk by slicing the capital they give into small tradable portions, each accompanied by a small portion of adjacent risk. Partitioning big risks and big claims into small parcels is the first mechanism of reducing risk. The possibility to trade those small parcels freely, i.e. to buy them, hold them for however long pleases, and then sell them, is the second risk-reducing device.

The entire concept of securities aims, precisely, at reducing financial risks connected to investing big amounts of capital into business structures, and thus at making that investment more attractive and easier. Historically, it literally has been working like that. Over centuries, whenever people with money were somehow reluctant to connect with people having bold ideas, securities usually solved the problem. You were a rich merchant, like in the 17th century-France, and your king asked you to lend him money for the next war he wanted to fight. You would answer: ‘Of course, my Lord, I would gladly provide you with the necessary financial means, yet I have a tiny little doubt. What if you lose that war, my Lord? Who’s going to pay me back?’. Such an answer could lead into two separate avenues: decapitation or securitization of debt. The former was somehow less interesting financially, but the latter was a real solution: you lend to the King, in exchange he hands you his royal bonds (debt-based securities), and you can further sell those bonds to whoever is interested in betting on the results of war.      

Thus, start with a simple business concept, e.g. something of current interest, such as a factory of medical ventilators. You have a capital base, i.e. some assets, and you finance them with equity and liabilities. Classical. You can skip the business planning part by going to the investors relations site of any company you know, taking their last financial report and simply simulating a situation when those guys want to increase their capital base, i.e. add to their assets. I mentioned medical ventilators, so you could go and check Medtronic’s investors relations site ( ), and pick their latest quarterly financials. They have assets worth $92 822 mln, financed with $51 953 mln in equity and $40 869 in debt. Imagine they see big business looming on the horizon, and they want to accumulate $10 000 mln more in assets. They can do it either through additional borrowing, or through the issuance of new shares in the stock market.

You can go through the reports of Medtronic as well as through their corporate governance rules, and start by taking your own stance at the basic question: if Medtronic intends to accrue their assets by $10 000 mln, would you advise them to collect that capital by equity, or by debt, or maybe to split it somehow between the two. Try to justify your answer in a meaningful way.

If you go for equity-based securities (shares in equity), keep asking questions such as: what should be the nominal value (AKA face value) of those shares? How does it compare with the nominal value of shares already outstanding with this company? What dividend can shareholders expect, based on past experience? How are those new shares expected to behave in the stock market, once again based on the past experience?

If your choice is to bring capital through the issuance of debt-based securities, go for answering the following: what should be the interest rate on those corporate bonds? What should be their maturity time (i.e. for how long should they stay in the market of debt before Medtronic buys them back)? Should they be convertible into something else, like in the shares in equity, or in some next generation of bonds? Once again, try to answer those questions as if I were just a moderately educated hominid, i.e. as if I needed to have things explained simply, step by step.

See? Chitchatting, talking about kings, wars and medical ventilators, we have already covered the basics of preparing a project on equity-based securities, as well as on the debt-based ones.

If you want to go somehow further down those two avenues, you can check two of my blog updates from the last academic year: Finding the right spot in that flow: educational about equity-based securities , and  Unconditional claim, remember? Educational about debt-based securities.

Now, we talk about money, i.e. about a hypothetical situation when my students design a new currency in the framework of their project. Money is strange, to the extent that technically it should not have any intrinsic value of itself, as a pure means of exchange, and yet any currency can be deemed mature and established once its users start hoarding it a little bit, thus when they start associating with it some sort of intrinsic value. Presently, with the development of cryptocurrencies, we distinguish them from bank-based or central-unit-based currencies. In what follows immediately, I am focusing on the latter category, before passing to the former.

So, what is a bank-based currency, AKA central-unit-based currency? A financial institution, e.g. a bank, issues a certain number of monetary units (AKA monetary titles), which are basically used just as a means of exchange. The bank guarantees the nominal value of that currency, which, in itself, does not embody any claim on anything. This is an important difference between money and securities: securities secure claims, money doesn’t. Money just assures liquidity, understood as the capacity to enter into exchange transactions.   

When designing a new currency, step #1 consists in identifying a market with liquidity problems, e.g. we have 5 developing countries, which do business with each other: they trade goods and services, business entities from each of those countries invest in the remaining four etc. Those 5 countries have closed or semi-closed monetary systems, i.e. their national currencies either are not exchangeable at all against any other currency, or there are severe limitations on such exchange (e.g. you need a special authorization from some government agency). Why do those countries have closed monetary systems? Because their governments are afraid that if they make it open, thus when they allow free exchange against foreign currencies, the actual exchange rate will be so volatile, and so prone to speculative attacks (yes, there are bloody big sharks in those international financial waters) that the domestic financial system will be direly destabilized. Why any national currency should be so drastically volatile? It happens when this currency is not really exchanged a lot against other currencies, i.e. when exchange is sort of occasional and happens in really big bundles. There is not enough accumulated transactional experience. Long story short, we have national currencies which are closed because of the possible volatility and are so prone to volatility because they are closed systems. Yes, I know it sounds stupid. Yet, once you see that mechanism at work, you immediately understand. In the communist Poland, we had a closed monetary system, with our national currency, the zloty, technically being not exchangeable at all against anything else. As a result, whenever such exchange actually took place, e.g. against the US dollar, you needed to be a wizard, or a prime minister, to predict more or less accurately the applicable exchange rate.

Those 5 countries have two options. For one, they can use a third-country, strong currency as a local means of exchange, i.e. their governments, and their national business entities can agree that whenever they do business transnationally, they use a reference currency to settle their mutual obligations. The second option consists in creating an international currency, specifically designed for settling business accounts between those countries. This is how the ECU, the grandpa of the euro, was born, back in the day. The ECU was a business currency – you couldn’t have it in your wallet, you just could settle your international accounts with it – and then, as banks got used to it, the ECU progressively morphed into the euro. What you need for such a currency is a financial institution, or a contractually established network thereof, who guarantee the nominal value of that business currency.

If our 5 countries go for the second option, the financial institution(s) who step in as guarantors if the newly established currency need to bring to the table something more than just mutual trust. They need to assign, in their balance sheets, specific financial assets which back the aggregate nominal value of the new currency put in circulation. Those assets can consist of, for example, a reserve basket of other currencies. Once again, it sounds crazy, i.e. money being guaranteed with money, but this is how it works.

Therefore, step #2 in designing a new, bank-based currency, requires giving some aggregate numbers. What is the aggregate value of transactions served by the new currency? Let’s go, just as an example, for $100 billion a year. How long will each unit of the new currency spend on an individual bank account? In a perfectly liquid market, each unit of currency is used as soon as it has been received, thus it just has one night to sleep on a bank account, and back to work, bro’. In such a situation, that average time on one account is 1 day. Therefore, in order to cover $100 billion in transactions, we need [$100 / 365 days in the year] = $0,2739726 billion = $274 million in currency. If people tend to build speculative positions in that currency, i.e. they tend to save some of it for later, the average time spent on an individual account by the average unit of that new money could stretch up to 2 weeks = 14 days. In such case, the amount of currency we need to finance $100 billion in transactions is calculated as [$100 / (365/14)] = [$100 * 14 / 365] = $3,8356 billion.

There is a catch. I talk about introducing a new currency, but I keep denominating in US dollars, whence the next question and the next step, step #3, in a project devoted to this topic. The real economic value of our money depends on what we do with that money, and not really on what we call it. One of the things we do with an international currency is to exchange it against national currencies. In this case, we are talking about 5 essentially closed national currencies. For the sake of convenience, let’s call them: Ducat A, Ducat B, Ducat C, Ducat D, and Ducat E. Once again for sheer convenience we label the new currency ‘Wanderer’. So far, our 5 countries have been using the US dollar for international settlements, whence my calculations denominated therein. The issue of exchange rate of the Wanderer against the US dollar, as well as against our 5 national Ducats, is a behavioural one. Yes, behavioural: it is about human behaviour.

We have businesspeople doing international business in USD, and we want to convince them to switch to the Wanderer. What arguments can we use? There are two: exchange rate per se, and exchange rate risk. Whoever is a national of our 5 countries, needs to exchange their national Ducat against the US dollar and the other way around. As neither of the Ducats is freely convertible, exchange with the dollar takes place, most probably, in the form of big, bulk transactions, like once a month, mediated by the central banks of our 5 countries. Those bulk transactions yield an average exchange rate, and an average variance around that average.

We want to put in place an alternative scheme, where the national Ducats (A, B, C, D, E) are exchanged in real time against the Wanderer, and then the Wanderer gets exchanged against the US dollar. The purpose is to make the exchange {Ducat Wanderer USD} more attractive, average-rate-wise or variance-in-rate-wise, than the incumbent {Ducat Individual, National Central Bank USD} one. Some of you might think it is not realistically possible, yet it really is. If 5 central banks of developing countries gang up together to buy and sell US dollars, they can probably achieve a better price, and less volatile a price, as compared to what each of them separately could have. There is even an additional trick, and this is like really a trick: central banks of our 5 countries could hold some of their financial reserves in US dollars, more specifically the part devoted to backing the Wanderer. That’s the trick that our central bank in Poland, the National Central Bank of Poland, uses all the time. We are in the European Union, but we do not belong to the European Monetary Union, and yet we do a lot of business with partners in the eurozone. The National Bank of Poland holds important financial reserves in euros, and thus gives itself a better grip on the exchange rate between the Polish zloty and the euro.

Summing up the case of graduation projects focused on designing a new bank-based currency, here are, rephrased once again, the basic logical steps. Start with identifying a market with liquidity problems, such as closed monetary systems or very volatile national currencies. This is usually an international market made of developing countries. Imagine a situation, when the central banks of the countries in question place some of their financial reserves in a strong currency, e.g. the US dollar, or the Euro, and then the same central banks introduce a currency for international settlements in that closed group of countries. Keep in mind that the whole group of countries will need an amount of currency calculated as: [Aggregate value of international transactions done in a year * [Average number of days that one user holds one unit of currency / 365].  

The whole scheme consists, at the end of the day, in obtaining a better and less volatile exchange rate of individual national currencies against the BIG ONES (e.g. the US dollar) through aggregating their exchange transactions in the financial market.       

That would be all in this tutorial. I have covered three types of financial instruments that my students can possibly design for their graduation: equity-based securities, debt-based securities, and bank-based currencies. In the coming weeks I will try to write something smart on designing cryptocurrencies and insurance contracts. Till then, you can additionally read entry March, 26th, 2019 – More and more money just in case. Educational about money and monetary systems – and entry March 31st, 2019 – The painful occurrence of sometimes. Educational about insurance and financial risk.

Cut some slack. Project tutorial for International Management at the Frycz University.

My editorial on You Tube

I am focusing, for a few days starting from today, on delivering educational content. In the framework of 4 courses I teach, this semester, at the Andrzej Frycz Modrzewski Krakow University, three – Foundations of Finance, International Management, and International Trade – require preparing graduation projects. I am presenting guidelines for those projects, and I start with the way I advise for preparing a project in International Management, Summer-Spring 2020.

When my students prepare a project in management, I keep repeating the truth: neither I, the teacher, nor you, my students, are professional managers. We are looking at the world of management from outside. This is a harsh truth to swallow: I teach something I have almost no practical experience with. What kind of skills can I, the teacher, bring to the table, in such case? I have skills in patterning and modelling social structures. That could be the reason why I do social sciences, and this is the bacon I can feed my students in any kind of management course. Thus, when you do management with me, in class, we are all throwing our limited knowledge at real situations and try to understand our own cognitive limitations. From that angle, the course of management aims at learning how much you don’t know, and what you need to learn about the situations we are talking about.   

In the course of International Management, the general frame for your graduation projects is to figure out an organisational solution to problems, which manifest themselves as officially acknowledged risk factors, explicitly discussed in annual reports of the companies, whose cases we discuss in class. That general approach unfolds in a few distinct steps. You read the annual report of, for example, General Electric, which you have already worked with in the first online class this semester. You take any risk factor named in that report. That risk factor means that something specific can happen, which will harm GE’s business. What exactly is that specific, adverse event? Try to imagine very realistically what kind of real situation can it be. When you do that, you will probably figure out 4 types of situations.

Firstly, someone recurrently makes small mistakes, over and over again. Those small mistakes pile up, and they sort of capitalize on each other. If today I neglect checking something important, tomorrow that negligence is likely to bring some adverse effects, and when I repeat it, i.e. when I skip that important check once again, adverse effects combine. When I neglect to check, whether the salary system for salespeople in my business is working well, those people get more and more pissed every month. Their frustration accumulates, and they react more and more nervously to even small imperfections in the wage system.

Secondly, someone can make one, big, catastrophic mistake, e.g. signing a big, really bad contract, which, in turn, will expose our business to a whole series of adverse outcomes, or, for example, a person will take revenge on the top management by transmitting the details of some in-house technology to a competitor. Please, note that mistakes can fluidly transform, or coexist with opportunistic behaviour. What is seen as a mistake from outside can be the manifestation of wrongful intentions on the part of the person who makes that mistake.

A big, catastrophic event can take place as ‘force majeure’, e.g. a hurricane, or a pandemic such as the present COVID-19 one, and this is the third type of risk factor. Finally, the external structure of our market can change in an unfavourable direction, and this usually takes place on an adverse change in prices, e.g. the present slump in the prices of crude oil, which is a good thing for some businesses, and a very bad one for others. That fourth type of risk is usually called ‘financial risk’.

Thus, whatever bad happens to a business, the roots of that adverse event usually fall into one of those four categories: repeated, small human mistakes, occasional big mistake, external disaster, or unfavourable external change in the prices of something. Now, think how you can make an organisation resilient to those risks. What kind of people would you need, in order to shield the business against those risks? What kind of jobs should those people do? How can you pay them? What kind of internal control you need? What kind of organisational structure will work better, in terms of resilience? Do you need, for a given business, a solid, relatively slow functional structure with a lot of internal controls, exhaustive documentation etc., or, maybe, what you need is an agile, very horizontal structure, with task-teams focused on projects rather than functional divisions with distinct competences? Which organisational pattern which shield you better against small, bitchy mistakes or frauds? Which is going to play out better when it comes to preventing a disaster-like bad decision?

In that case of General Electric, I asked my students to study the risk of making bad investments or unfavourable dispositions (reminder: disposition, in this context, means selling and entire business or an important portion of strategic assets from a business), thus the risk named as ‘Portfolio strategy execution’. We focused on the healthcare segment of GE’s business (i.e. technologies for healthcare and biotech), and I gave my students (it was in the beginning of March) a task which looks prophetic now. I asked them to imagine that GE wants to sell (i.e. divest from) the entire healthcare segment of their business. Right now, hardly anyone in their right mind would get rid of technological assets in healthcare. Still, in the beginning of march, the s**t we currently have was just outlining itself.

Anyway, we focus on the healthcare segment in the general portfolio of General Electric. In the discussion of ‘Portfolio strategy execution’, in the GE’s annual report for 2019, you can read the following passage: “Our success depends on achieving our strategic and financial objectives, including through dispositions. We are pursuing a variety of dispositions, including the planned sale of our BioPharma business within our Healthcare segment and exiting our remaining equity ownership position in Baker Hughes. The proceeds that we expect to receive from such actions are an important source of cash flow for the Company as part of our strategic and financial planning”. Let’s break it down into adverse events, and then I can take a risk (!) at trying to lead my students from risk factors to organisational solutions that can shield against those risks.

The first sentence of that passage says: “Our success depends on achieving our strategic and financial objectives, including through dispositions”. It roughly means that the top management of GE sees the entire portfolio of businesses, all segments combined, as a hand of cards in a poker game. You probably know that in poker you can ask the croupier to exchange one or more of the cards from your hand against cards from the deck. When you go for such an exchange, you expect that the cards you get from the croupier will make a better match to the remaining ones, which you still keep in hand. You are a top manager with GE, and you decide to sell (i.e. to dispose of) an entire business, in order to generate a cash inflow, which, in turn, will serve you to buy (i.e. invest in) another entire business.

Your basic challenge in such a situation is limited, imperfect information. You know, how the business you intend to sell is playing out with all the rest in your hand, and you have some expectations as for how another business – which you intend to buy – could work with the same rest in your hand. From the cognitive point of view, you are trading actual, hard-facts-based knowledge of a presently owned business, against much foggier expectations as for future possible gains from another business. You are exchanging some known s**t against some unknown s**t, with the unknown being somehow tempting you with potentially higher rewards.

Let’s translate this situation into the four basic types of risk: repeated, small human mistakes, occasional big mistake, external disaster, or unfavourable external change in the prices of something. Someone, further down the corporate hierarchy, could have been making small recurrent mistakes, or could have been perpetrating small recurrent frauds, which could have brought the healthcare business you intend to dispose of to a situation of suboptimal performance. You think the business you want to sell is worth X $ million in terms of expected net income, but in fact it could bring much more, like 2*X $ million, if you eliminate the risk factor of recurrent, small human mistakes. How can an organization shield itself against this type of risk? The most obvious answer is that if you currently control, in a rational way, operational performance in the given business, you can have a pretty good idea of what that business is capable of. If you don’t have such a controlling system, you could be selling a business with a lot of potential, and you would be selling because you cannot see that potential.

Conclusion #1: if you have in place a rational system of KPIs (Key Performance Indicators), in each business you have in your portfolio, you can make much more informed decisions as for selling (disposing of) each such business. Topic #1, which my students can develop in their projects, and which arises from that partial conclusion, could go as follows: ‘Study the entire portfolio of businesses in General Electric. Look for any piece of information you can find about it. How can you know that each of those businesses is currently working at 100%? What system of performance measurement you would like to see in place, so as to be well informed? At the end of the day, what information would you need to be sure that the decision of selling a business is really well-founded?

Let’s move further. The next sentence, in the same passage says: ‘We are pursuing a variety of dispositions, including the planned sale of our BioPharma business within our Healthcare segment and exiting our remaining equity ownership position in Baker Hughes’. A variety of dispositions means that GE is selling, or, potentially, can be selling at any given moment, many businesses at once. You can lose your balance in the midst of variety. You can do something relatively well, at the expense of doing something else much less efficiently that what you expect from yourself. Let’s try to find ways of preventing it.

When you perform many similar actions in parallel, you would like to carry out each of those actions with a maximum of efficiency. You study, you practice some sport, and you engage in business, and you would like to deliver your A game in each of these fields. There are some basic techniques you can use to assess whether you can find efficient balance at all, and whether your actions are balanced at a given moment. One of those techniques consist in assessing your resources. If, pursuing that existential example, you study, you do sport and you do business, a basic personal resource is time and human energy (i.e. the chemical energy you need in order to generate neurotransmitters, which, in turn, your nervous system needs to have all the major angles covered). Question: do you have enough time to cover studies, sport and business? It is a harsh question. The answer might be no, I haven’t. The even harsher implication of that answer is the necessity to cut something out. I focus on exams, and I give up my performance in an important sports event, or I focus on business and take a sabbatical at the university. Another answer could be yes, I have enough time, but I need to cut some slack. I need to give up on some pleasures (e.g. watching Netflix, or partying), and that will give me 2 extra hours a day for packing all my priorities in it.      

We can translate it back into the context of General Electric. When ‘We are pursuing a variety of dispositions’, we can ask: ‘Do we have enough organisational resources to pursue that entire variety of dispositions efficiently? Do we have enough people, enough computational power in our digital systems, enough good relations (or good enough relations!) with external entities so as to handle all that variety as it is?’. The answer can be yes, we have, or no, we haven’t. In the former case, the immediately following question is: ‘Do we have those resources organized optimally? Does every person involved know what they are supposed to do? Etc.’. In the latter situation, when we conclude that we cannot possibly cover all the angles with the resources we have, we follow up by asking ourselves: ‘What do we do? Do we hire additional human resources, or/and engage additional technology into the process of managing as wide a variety of dispositions as we are currently handling, or, maybe, it is a better idea to reduce variety? Maybe we can postpone some of those dispositions and focus more efficiently on the remaining deals? Does it all have to be carried out right now? Maybe we can make a timeline over the 2 years to come?’. By the way, in unstable market conditions, such as every business is facing now, with the COVID-19 pandemic and its consequences, it might pay off to slow down our decision-making, to observe and learn more before taking strategic decisions.

Conclusion #2: in a given context of external market conditions, the organization we actually have in place has a given capacity to process information and to make strategic change on the grounds of that information. If we want to pursue more operations in parallel than our organizational resources actually allow to, we risk losing our bearings in the midst of variety. There are two alternative ways out of that predicament. On the one hand, we can cut on the variety of our operations and/or our strategic decisions so as to focus on the amount we can really handle. On the other hand, we can expand our organizational resources so as to pursue efficiently the entire variety of actions that presents itself to us.

Thus, a possible topic #2 emerges for my students in International Management. Once again, go over the business of General Electric. Try to understand very practically, what do they mean by ‘pursuing a variety of dispositions’. Variety means what exactly? Now, what organizational resources (people, information, business relations etc.) does GE need so as to carry out efficiently one single disposition? Expand by assuming that you run an investment fund, with participations in many high-tech businesses. Every few months, you need to decide whether each of those businesses is worth holding in your portfolio, or maybe it would be better to sell it. What organizational resources do you need to manage such decisions efficiently? How many people would you need to hire, in such an investment fund? What kind of duties would those people have to carry out, and what skillset you would expect in them?