Je continue un peu dans la foulée d’analyse des rapports courants des sociétés de ma liste « technologies nouvelles en énergie ». Je fais de mon mieux pour développer sur les premières observations que j’ai déjà présentées dans « Mes lampes rouges » ainsi que dans « Different paths ». Comme je résume partiellement ce que j’ai lu, ma première conclusion est une confirmation de mes intuitions initiales. Ce que nous appelons l’industrie de l’hydrogène est en fait une combinaison des technologies de pointe (piles à combustible à la base d’hydrogène, par exemple) avec des technologies bien établies – quoi que sujettes à l’innovation incrémentale – comme l’électrolyse ou le stockage des gaz volatiles. Il semble y avoir d’importants effets d’échelle, probablement en raison de la complexité technologique. Les sociétés relativement plus grandes, comme Plug Power ou Fuel Cell Energy, capables d’acquérir d’autres sociétés et leurs technologies, semblent être mieux placées dans la course technologique que des indépendants qui développent des technologies propriétaires de façon indépendante. C’est un truc que j’ai déjà remarqué dans le photovoltaïque et dans l’industrie de véhicules électriques : oui, il y a des petits indépendants prometteurs mais la bonne vieille intégration industrielle, surtout en verticale, semble revenir comme stratégie de choix après de décennies de bannissement.
J’ai remarqué aussi que la catégorie générique « technologies d’hydrogène » semble attirer du capital de façon un peu inconsidérée. Je veux dire qu’il semble suffisant de dire « Eh, les gars, on invente dans l’hydrogène » pour que les investisseurs se précipitent, peu importe si le modèle d’entreprise est viable et transparent, ou pas-tout-à-fait-vous-comprenez-c’est-confidentiel. Je vois dans l’industrie d’hydrogène le même phénomène que j’observais, il y a encore 4 ou 5 ans, dans le photovoltaïque ou bien chez Tesla : lorsqu’une technologie nouvelle commence à prendre son envol en termes de ventes, les organisations qui s’y greffent et développent sont un peu démesurées ainsi qu’exagérément dépensières et il faut du temps pour qu’elles se fassent vraiment rationnelles.
Pour gagner un peu de distance vis-à-vis le business d’hydrogène, je commence à piocher dans les rapports courants d’autres sociétés sur ma liste. Tesla vient en tête. Pas sorcier, ça. C’est la plus grosse position dans mon portefeuille boursier. Je lis donc le rapport courant du 4 août 2022 qui rend compte de 13 propositions soumises à l’assemblée générale d’actionnaires de Tesla le même 4 août, ainsi que de l’opération de fractionnement d’actions prévue pour la seconde moitié d’août. Ce dernier truc, ça m’intéresse peut-être le plus. La version officielle qui, bien entendu, sera mise à l’épreuve par le marché boursier, est que le Conseil D’Administration souhaite rendre les actions de Tesla plus accessibles aux investisseurs et employés et procédera donc, le 17 août 2022, à un fractionnement d’actions en proportion trois-pour-une en forme d’une dividende-actions. Chaque actionnaire enregistré ce 17 août 2022 verra le nombre de ses actions multiplié par trois. Les nouvelles actions fractionnées entreront en circulation boursière normale le 24 août 2022.
Il est vrai que les actions de Tesla sont plutôt chères en ce moment : presque $900 la pièce et ceci après la forte dépréciation dans la première moitié de l’année. Formellement, le fractionnement en proportion trois-pour-une devrait diviser cette cotation par trois, seulement le marché, ça suit les règles d’économie, pas d’arithmétique pure. Je pense que par la fin de 2022 on aura trois fois plus d’actions de Tesla flottantes et cotées à plus qu’un troisième du prix d’aujourd’hui encore qu’entre temps, il y aura des turbulences, je vous le dis. J’attache donc ma ceinture de sécurité – en l’occurrence c’est une position en Apple Inc., bien plus stable et respectable que Tesla – et j’attends de voir la valse boursière autour de ces actions fractionnées.
A part cette histoire de fractionnement, les autres 12 propositions couvrent 4 qui ont été acceptées – dont une relative au fractionnement déjà signalé – ainsi que 8 propositions non-acceptées. Les 4 acceptées sont relatives à, respectivement :
>> la nomination de deux personnes au Conseil D’Administration
>> l’accès par procuration, proposition sans engagement présentée par actionnaires en minorité
>> la ratification du choix de PricewaterhouseCoopers LLP comme auditeur financier de Tesla pour l’année comptable 2022
>> l’accroissement du nombre d’actions ordinaires de Tesla par 4 000 000
Les 8 propositions rejetées se groupent en deux catégories distinctes d’une façon intéressante. Il y en a donc deux qui viennent des cadres gestionnaires de Tesla et qui postulaient de modifier l’acte d’incorporation de Tesla de façon à éliminer la règle de majorité qualifiée de 66 et 2/3% dans les votes, ainsi qu’à réduire à 2 ans le mandat des directeurs du Conseil d’Administration. Ces deux propositions-là ont perdu car elles n’avaient pas… de majorité qualifiée de 66 et 2/3%. Les 6 propositions restantes parmi les non-acceptées étaient toutes des propositions sans engagement de la part d’actionnaires minoritaires et toutes les 6 demandaient des rapports additionnels ou bien des changements afférents à, respectivement : la qualité de l’eau, travail forcé d’enfants, le lobbying, la liberté d’association, arbitrage dans les affaires d’emploi, la diversité au sein du Conseil d’Administration, les politiques internes contre le harassement et la discrimination.
Dans le vocabulaire politique de mon pays, la Pologne, nous avons l’expression « compter les sabres ». Elle désigne des votes qui sont perdus d’avance mais qui servent à compter la taille de la coalition possible que le proposant donné pourrait rallier pour quelque chose de plus sérieux. Je bien l’impression que quelqu’un chez Tesla commence à compter les sabres.
Je passe au rapport courant de Tesla du 20 juillet 2022 qui, en fait, annonce leur rapport financier du 2ème trimestre 2022. Ça a l’air bien. Le bénéfice net pour la première moitié de 2022 a triplé par rapport à la même période de 2021, le flux de trésorerie se fait plus robuste. Rien à dire.
Je tourne vers un modèle d’entreprise beaucoup plus fluide, donc celui de Nuscale Power ( https://ir.nuscalepower.com/overview/default.aspx ). Lorsqu’on lit la présentation générale de ce business (https://ir.nuscalepower.com/overview/default.aspx ), tout colle à merveille : NuScale Power fournit des petits réacteurs nucléaires innovatifs, où un module peut fournir 77 mégawatts de puissance. Seulement, lorsque je commence à lire leur rapport annuel 2021, ça se corse, parce que le rapport est publié par l’entité nommée Spring Valley Acquisition Corporation, qui se présente comme une société coquille incorporée dans les îles Cayman, sous la forme légale de société exonérée. Le management déclarait, dans le rapport annuel 2021, que le but de Spring Valley Acquisition Corporation est de conduire une fusion ou bien une acquisition, un échange d’actions ou bien leur achat, une acquisition d’actifs, une réorganisation ou bien une autre forme de regroupement d’entreprises. Au mois de mars 2021 ; Spring Valley Acquisition Corporation est entrée en un accord tripartite, accompagné d’un plan de fusion, avec sa filiale en propriété exclusive, Spring Valley Merger Sub, Inc., incorporée dans l’état de Delaware, ainsi qu’avec Dream Holdings Inc., une autre société incorporée dans le Delaware, celle-ci sous la forme de société d’utilité publique. C’est une nouveauté dans la loi des sociétés dans le Delaware, introduite en 2013. Un article intéressant à ce sujet est accessible sur « Harward Law School Forum on Corporate Governance ».
Ainsi donc, en mars 2021, Spring Valley Acquisition Corporation, Spring Valley Merger Sub, Inc. et Dream Holdings Inc. avaient convenu de conduire un regroupement d’entreprises avec AeroFarms. Dream Holdings fusionne avec Spring Valley Merger Sub. En octobre 2021, l’accord en question a été résilié. En décembre 2021, Spring Valley Acquisition Corporation entre en un nouvel accord tripartite, encore une fois avec la participation de Spring Valley Merger Sub. Cette fois, Spring Valley Merger Sub est introduite comme une LLC (société à responsabilité limitée) incorporée dans l’état d’Oregon. La troisième partie de l’accord est NuScale Power LLC, aussi incorporée en Oregon. Poursuivant cet accord, Spring Valley Acquisition Corporation change de lieu d’incorporation des îles Cayman pour l’état de Delaware, pendant que Spring Valley Merger Sub LLC fusionne avec et en NuScale Power LLC. Après la fusion, Spring Valley change de nom et devient NuScale Power Corporation.
Comment a marché la combine ? Eh bien, voici une annonce courante de NuScale Power, datant d’hier (10 août), où NuScale donne un aperçu de leurs résultats pour le 2nd trimestre 2022. La perte d’exploitation pour cette première moitié de l’année 2022 était de 44,75 millions de dollars, un peu moins que dans la première moitié de 2021. Leurs actifs ont presque triplé en 12 mois, de $121,2 millions à $407,3 millions. Côté exploitation, une nouvelle entité opérationnelle est créé sous le nom de « VOYGR™ Services and Delivery (VSD) » avec la mission d’organiser les services, les fournitures et la gestion clients pour la technologie VOYGR™. Cette dernière est la technologie pour bâtir et exploiter des centrales nucléaires à puissance moyenne sur la base de « NuScale Power Module™ », soit avec 4 modules dedans et une puissance de 924 mégawatts de puissance électrique (VOYGR-4) soit avec 6 modules (VOYGR-6).
Cette comparaison rapide d’évènements relativement récents chez Tesla et NuScale Power me conduit à la conclusion que si je veux comprendre à fond un modèle d’entreprise, il faut que je m’intéresse plus (que je l’avais fait jusqu’à présent) à ce qui se passe dans les passifs du bilan. Je vois que des différentes phases d’avancement dans le développement d’une technologie s’accompagnent des stratégies financières très différentes et le succès technologique dépend largement du succès de ces mêmes stratégies.
I am studying their current reports. This is the type of report which listed companies publish when something special happens, which goes beyond the normal course of everyday business, and can affect shareholders. I have already started with Fuel Cell Energy and their current report from July 12th, 2022 (https://d18rn0p25nwr6d.cloudfront.net/CIK-0000886128/b866ae77-6f4a-421e-bedd-906cb92850d7.pdf ), where they disclose a deal with a group of financial institutions: Jefferies LLC, B. Riley Securities, Inc., Barclays Capital Inc., BMO Capital Markets Corp., BofA Securities, Inc., Canaccord Genuity LLC, Citigroup Global
Markets Inc., J.P. Morgan Securities LLC and Loop Capital Markets LLC. Strange kind of deal, I should add. Those 10 financial firms are supposed to either buy or intermediate in selling to third parties parcels of 95 000 000 shares in the equity of Fuel Cell Energy. The tricky part is that the face value of those shares is supposed to be $0,0001 per share, just as it is the case with the ordinary 837 000 000 shares outstanding, whilst the market value of Fuel Cell Energy’s shares is currently above $4,00 per share, thus carrying an addition of thousands of percentage points of capital to pay.
>> Joint Development Agreement with ExxonMobil, related to carbon capture and generation, which includes the 7,4 MW LIPA Yaphank fuel cell project
>> a carbon capture project with Canadian National Resources Limited
>> a program with U.S. Department of Energy regarding solid oxide. I suppose that ‘solid oxide’ stands for solid oxide fuel cells, which use a solid, ceramic core of fuel, which is being oxidized and produces energy in the process.
I pass to the current reports of Plug Power (https://www.ir.plugpower.com/financials/sec-filings/default.aspx ). Interesting things start when I go back to the current report from June 23rd, 2022 (https://d18rn0p25nwr6d.cloudfront.net/CIK-0001093691/36efa8c2-a675-451b-a41f-308221f5e612.pdf ). This is a summary presentation of something which looks like the company’s strategy. Apparently, Plug Power plans to have 13 plants with Green Hydrogen running in the United States by 2025, with a total expected yield of 500 tons per day. In a more immediate perspective, the company plans to locate 5 new plants in the U.S. over 2022 (total capacity of 70 tons per day) and 2023 (200 tons per day). Further, I read that what I thought was a hydrogen-focused company, has, in fact, a broader spectrum of operations: eFuel and methanol, ammonia, vehicle refueling, blending and heating, refining of natural oil, and the storage of renewable energy.
As part of its strategy, Plug Power announces the acquisitions of companies supposed to bring additional technological competences: Frames Group (https://www.frames-group.com/ ) with power transmission systems and technology for building electrolyzers, ACT (Applied Cryo Technologies: https://www.appliedcryotech.com/ ) for cryogenics, and Joule (https://www.jouleprocess.com/about ) for the liquefaction of hydrogen. My immediate remark as regards those acquisitions, sort of intellectually straight-from-the-oven-still-warm-sorry-but-I-told-you-still-warm, is that Plug Power is acquiring a broad technological base rather than a specialized one. Officially, those acquisitions serve to enhance the Plug Power’s capacity as regards the deployment of hydrogen-focused technologies. Yet, as I am rummaging through the websites of those acquired companies, their technological competences go far beyond hydrogen.
A little bit earlier this year, on my birthday, May 9th, Plug Power published a current report (https://d18rn0p25nwr6d.cloudfront.net/CIK-0001093691/203fd9c3-5302-4fa1-9edd-32fe4905689c.pdf ) coupled with a quarterly financial report (https://d18rn0p25nwr6d.cloudfront.net/CIK-0001093691/c7ad880f-71ff-4b58-8265-bd9791d98740.pdf ). Apparently, in the 1st quarter 2022, they had revenues 96% higher than 1Q 2021. Nice. There are interesting operational goals signaled in that current report. Plug Power plans to reduce services costs on a per unit basis by 30% in the 12 months following the report, thus until the end of the 1st quarter 2023. The exact quote is: ‘Plug remains focused on delivering on our previously announced target to reduce services costs on a per unit basis by 30% in the next 12 months, and 45% by the end of 2023. We are pleased to report that we have begun to see meaningful improvement in service margins on fuel cell systems and related infrastructure with a positive 30% increase in first quarter of 2022 versus the fourth quarter of 2021. The service margin improvement is a direct result of the enhanced technology GenDrive units that were delivered in 2021 which reduce service costs by 50%. The performance of these enhanced units demonstrates that the products are robust, and we expect these products will help support our long-term business needs. We believe service margins are tracking in the right direction with potential to break even by year end’.
When a business purposefully and effectively works on optimizing margins of profit, and the corresponding costs, it is a step forward in the lifecycle of the technologies used. This is a passage from the phase of early development towards late development, or, in other words, it is the phase when the company starts getting in control of small economic details in its technology.
I switch to the next company on my list, namely to Green Hydrogen Systems (Denmark, https://investor.greenhydrogen.dk/ ). They do not follow the SEC classification of reports, and, in order to get an update on their current developments, I go to their ‘Announcements & News’ section (https://investor.greenhydrogen.dk/announcements-and-news/default.aspx ). On July 18th, 2022, Green Hydrogen Systems held an extraordinary General Meeting of shareholders. They amended their Articles of Association, as regards the Board of Directors, and the new version is: ‘The board of directors consists of no less than four and no more than nine members, all of whom must be elected by the general meeting. Members of the board of directors must resign at the next annual general meeting, but members of the board of directors may be eligible for re-election’. At the same extraordinary General Meeting, three new directors have been elected to the Board, on the top of the six already there.
Still earlier this year, on April 12th, Green Hydrogen Systems announced ‘design complications in its HyProvide® A-Series platform’, and said complications are supposed to affect adversely the financial performance in 2022 (https://investor.greenhydrogen.dk/announcements-and-news/news-details/2022/Green-Hydrogen-Systems-announces-technical-design-complications-in-its-HyProvide-A-Series-platform/default.aspx ). When I think about it, design normally comes before its implementation, and therefore before any financial performance based thereon. When ‘design complications’ are serious enough for the company to disclose them and announce a possible negative impact on the financial side of the house, it means some serious mistakes years earlier, when that design was being conceptualized. I say ‘years’ because I notice the trademark symbol ‘®’ by the name of the technology. That means there had been time to: a) figure out the design b) register it as a trademark. That suggests at least 2 years, maybe more.
I quickly sum up my provisional conclusions from browsing current reports at Fuel Cell Energy, Plug Power, and Green Hydrogen Systems. I can see three different courses of events as regards the business models of those companies. At Fuel Cell Energy, broadly spoken marketing, including financial marketing, seems to be the name of the game. Both the technology and the equity of Fuel Cell Energy seems to be merchandise for trading. My educated guess is that the management of Fuel Cell Energy is trying to attract more financial investors to the game, and to close more technological deals, of the joint-venture type, at the operational level. It further suggests an attempt at broadening the business network of the company, whilst keeping the strategic ownership in the hands of the initial founders. As for Plug Power, the development I see is largely quantitative. They are broadening their technological base, including the acquisitions of strategically important assets, expanding their revenues, and ramping up their operational margins. This a textbook type of industrial development. Finally, at Green Hydrogen Systems, this still seems to be the phase of early development, with serious adjustments needed to both the technology owned and the team that runs it.
Those hydrogen-oriented companies seem to be following different paths and to be at different stages in the lifecycle of their technological base.
Me revoilà, je me suis remis à blogguer après plusieurs mois de pause. Il fallait que je prenne soin de ma santé et entretemps, je repensais mes priorités existentielles et cette réflexion pouvait très bien avoir quelque chose à faire avec les opioïdes que je prenais à l’hôpital après mon opération.
Redémarrer après un temps aussi long est un peu dur et enrichissant en même temps. C’est comme si j’enlevais de la rouille d’une vieille machine. J’adore des vieilles machines que je peux dérouiller et réparer. J’ai besoin de quelques vers d’écriture pour m’orienter. Lorsque j’écris, c’est comme si je libérais une forme d’énergie : il faut que j’y donne une direction et une forme. Je suis en train de travailler sur deux trucs principaux. L’un est mon concept d’Energy Ponds : une solution complexe qui combine l’utilisation des béliers hydrauliques pour accumuler et retenir l’eau dans des structures marécageuses ainsi que pour générer de l’électricité dans des turbines hydroélectriques. L’autre truc c’est ma recherche sur les modèles d’entreprise dans le secteur amplement défini comme nouvelles sources d’énergie. Là, je m’intéresse aux entreprises dans lesquelles je peux investir via la Bourse, donc les véhicules électriques (comme investisseur, je suis in fidèle de Tesla), les systèmes de stockage d’énergie, la production d’hydrogène et son utilisation dans des piles à combustible, le photovoltaïque, l’éolien et enfin le nucléaire.
Intuitivement, je concentre mon écriture sur le blog sur ce deuxième sujet, donc les modèles d’entreprise. Raison ? Je pense que c’est à cause de la complexité et le caractère un peu vaseux du sujet. Le concept d’Energy Ponds, quant à lui, ça se structure peu à peu comme j’essaie – et parfois je réussis – à y attirer l’intérêt des gens aux compétences complémentaires aux miennes. En revanche, les modèles d’entreprise, c’est vaseux en tant que tel, je veux dire au niveau théorique, ça me touche à plusieurs niveaux parce que c’est non seulement de la science pour moi mais aussi une stratégie d’investissement en Bourse. Par ailleurs, je sais que lorsque je blogue, ça marche le mieux avec de tels sujets, précisément : importants et vaseux en même temps.
Voici donc une liste de sociétés que j’observe plus ou moins régulièrement :
La première différentiation sur cette liste c’est ma propre position comme investisseur. Je tiens des positions ouvertes sur Tesla, Nuscale Power, Energa, PGE, Tauron et ZPUE. J’en ai eu dans le passé sur Lucid Group, First Solar et SolarEdge. En revanche, Rivian, Fuel Cell Energy, Plug Power, Green Hydrogen Systems, Nel Hydrogen ainsi que Next Hydrogen – ceux-là, je regarde et j’observe sans y toucher.
La deuxième différentiation est relative aux flux opérationnels de trésorerie : il y en a des profitables (Tesla, First Solar, SolarEdge, Energa, PGE, Tauron et ZPUE) et des pas-tout-à-fait-et-ça-va-venir-mais-pas-encore profitables (Rivian, Lucid Group, Nuscale Power, Fuel Cell Energy, Plug Power, Green Hydrogen Systems, Nel Hydrogen, Next Hydrogen).
Comme je viens de faire ces deux classifications, il me vient à l’esprit que j’évalue les modèles d’entreprise selon le critère de revenu propriétaire tel que défini par Warren Buffett. A ce propos, vous pouvez consulter soit le site relations investisseurs de son fonds d’investissement Berkshire Hathaway (https://www.berkshirehathaway.com/ ) soit un très bon livre de Robert G.Hagstrom « The Warren Buffett Way » (John Wiley & Sons, 2013, ISBN 1118793994, 9781118793992). Les entreprises qui dégagent un surplus positif de bénéfice net et amortissement sur les dépenses capitalisées en actifs productifs sont celles qui sont déjà mûres et stables, donc financièrement capables de lancer quelque chose comme une nouvelle vague de changement technologique. En revanche, celles où cette valeur résiduelle « bénéfice net plus amortissement moins dépenses capitalisées en actifs productifs » est négative ou proche de zéro sont celles qui ont toujours besoin de venir à termes avec la façon dont ils conduisent leur business et sont donc incapables de lancer un nouveau cycle de changement technologique sans assistance financière externe.
Je me concentre sur les sociétés spécialisées dans l’hydrogène : Fuel Cell Energy, Plug Power, Green Hydrogen Systems, Nel Hydrogen, Next Hydrogen. Les technologies de production et d’utilisation d’hydrogène semblent être le matériel pour la prochaine vague de changement technologique en ce qui concerne l’énergie. Encore, il y a hydrogène et hydrogène. Le business de production d’hydrogène et de sa fourniture à travers des stations de ravitaillement c’est la technologie d’électrolyse et de stockage des gaz volatiles, donc quelque chose de pas vraiment révolutionnaire. Il y a espace pour innovation, certes, mais c’est de l’innovation incrémentale, rien qui brise les murs de l’ignorance pour ainsi dire. En revanche, l’utilisation d’hydrogène dans les piles à combustible, ça, c’est une technologie de pointe.
Dans ces deux cas de développement de technologie des piles à combustible (donc piles à hydrogène), soit Fuel Cell Energy et Plug Power, je passe en revue leur bilans et je les compare avec les autres trois sociétés, orientées plus spécifiquement sur l’électrolyse et le ravitaillement en hydrogène. Je m’arrête à leurs passifs. Quatre trucs m’intéressent plus particulièrement : est-ce qu’ils ont un capital social positif, la proportion « dette – capital social », la structure dudit capital social et les pertes accumulées dans le bilan.
Comme ces 5 sociétés n’ont pas toutes publié leurs rapports du 2nd trimestre 2022, je compare leurs rapports annuels 2021.
>> Fuel Cell Energy https://investor.fce.com/Investors/default.aspx : capital social de $642,4 millions, fait 79% des passifs du bilan ; la source principale du capital social est la prime d’émission de $1,9 milliards, ce qui permet de compenser un déficit accumulé de $1,266 milliards.
>> Plug Power https://www.ir.plugpower.com/overview/default.aspx : capital social de $4,6 milliards, soit à peu de choses près 78% des passifs et alimenté par une prime d’émission de $7,07 milliards qui compense un déficit accumulé de $2,4 milliards.
>> Green Hydrogen Systems https://investor.greenhydrogen.dk/ ; avec ceux-là, je commence à convertir les monnaies ; Green Hydrogen Systems est une société danoise et ils rapportent en couronnes danoises, soit 1 DKK = 0,14 USD ; le capital social ici est de $164,06 millions, fait 90% des passifs, vient surtout d’une prime d’émission de $243,71 millions et contient un déficit accumulé de $96,87 millions.
>> Nel Hydrogen https://nelhydrogen.com/investor-relations/ ; cette fois, c’est la Norvège et les couronnes norvégiennes à 1 NOK = 0,1 USD ; le capital social monte à $503,87 millions ce qui donne 84% des passifs et se base sur une prime d’émission de $559,62 millions et compense avec surplus un déficit accumulé de $97,16 millions.
>> Next Hydrogen (précédemment BioHEP Technologies Ltd.) https://nexthydrogen.com/investor-relations/why-invest/ ; cette fois, ce sont les dollars canadiens – à 1 CAD = 0,77 USD – et les dollars canadiens propriétaires de Next Hydrogen font un capital social de $29,1 millions qui, à son tour, fait 78,6% des passifs et – surprise – vient surtout du capital-actions pur et simple de $58,82 millions et contient un déficit accumulé de $32,24 millions.
Je commence à voir un schéma commun. Toutes les 5 sociétés ont un modèle d’entreprise très propriétaire, basé sur le capital social beaucoup plus que sur la dette. Cela veut dire Peu de levier financier et beaucoup de souveraineté stratégique. Dans les quatre cas sur cinq, donc avec l’exception de Next Hydrogen, cette structure propriétaire est basée sur une combine avec les prix d’émission des actions. On émet les actions à un prix d’appel follement élevé par rapport au prix comptable basé sur les actifs. Seuls les initiés savent pourquoi c’est tellement cher et ils payent, pendant que le commun des mortels est découragé par cette prime d’émission gigantesque. Tout en entrant en Bourse, les fondateurs de la société restent maîtres du bilan et donnent à leurs participations une liquidité élégante, propre au marché financier public.
Dans le cinquième cas, donc avec Next Hydrogen, c’est plus transparent et moins tordu : c’est le capital-actions qui pompe le capital social et ça semble donc plus ouvert aux actionnaires autres que les fondateurs.
Dans tous les cas, le capital social sert à compenser un déficit accumulé de taille très importante et en même temps sert à créer un coussin de liquide sur le côté actif du bilan. Les actifs autres que l’argent liquide et ses équivalents sont donc largement financés avec de la dette.
Prendre contrôle propriétaire d’une entreprise profondément déficitaire indique une détermination stratégique. La question se pose donc, c’est une détermination à faire quoi au juste ? Je rétrécis mon champ d’analyse à Fuel Cell Energy et je commence à passer en revue leurs rapports courants. Le Rapport courant du 12 Juillet 2022 informe que Fuel Cell Energy est entrée en contrat de vente sur marché ouvert (anglais : « Open Market Sales Agreement ») avec Jefferies LLC, B. Riley Securities, Inc., Barclays Capital Inc., BMO Capital Markets Corp., BofA Securities, Inc., Canaccord Genuity LLC, Citigroup Global Markets Inc., J.P. Morgan Securities LLC and Loop Capital Markets LLC dont chacun est designé comme Agent et tous ensemble sont des « Agents ». Le contrat donne à Fuel Cell Energy la possibilité d’offrir et de vendre, de temps en temps, un paquet de 95 000 000 actions (contre les 837 500 000 actions déjà actives) à valeur nominale de $0,0001 par action (soit la même valeur nominale que les actions déjà en place). Ces offres occasionnelles de 95 000 000 actions peuvent se faire aussi bien à travers les Agents qu’aux Agents eux-mêmes. Cette dualité « à travers ou bien à » se traduit en une procédure de préemption, ou Fuel Cell Energy offre les actions à chaque Agent et celui-ci a le choix de d’accepter et donc d’acheter les actions, ou bien de décliner l’offre d’achat et d’agir comme intermédiaire dans leur vente aux tierces personnes. Fuel Cell Energy paiera à l’Agent une commission de 2% sur la valeur brute de chaque transaction, que ce soit l’achat direct par l’Agent ou bien son intermédiaire dans la transaction. Par le même contrat, Jefferies LLC et Barclays Capital Inc. ont convenu avec Fuel Cell Energy de mettre fin à un contrat similaire, signé entre les trois parties en juin 2021.
Intéressant. Fuel Cell Energy entreprend d’utiliser son capital social comme plateforme de coopération avec une sorte de club d’institutions financières. Ces paquets de 95 000 000 actions à valeur nominale de $0,0001 par action font nominalement $9500 chacun, soit à peu près les dépenses voyage demi-mensuelles d’un PDG dans les organisations signataires du contrat. Pas vraiment de quoi déstabiliser un business. La commission de 2% sur un tel paquet fait $190. Seulement, l’émission publique de 837 000 000 actions existantes de Fuel Cell Energy s’était soldée par une prime d’émission de 5 157 930%. Oui, une prime d’émission de plus de 5 millions de pourcent. Ça fait beaucoup de points de pourcentage. Le moment quand j’écris ces mots, le prix boursier d’une action de Fuel Cell Energy est de $4,15 (soit 4149900% de plus que la valeur comptable). Par ailleurs, le volume d’actions en circulation est de 19 722 305, qui fait un free float d’à peine 19 722 305 / 837 500 000 = 2,35%. Chacun de ces paquets de 95 000 000 actions convenus par le contrat en question fait plus que ça et il peut donner occasion à une prime d’émission de plus de $394 millions et une commission de presque $8 millions.
Je n’aime pas ça. Comme investisseur, j’ai toutes me lampes rouges qui clignotent lorsque je pense à investir dans Fuel Cell Energy. Ce contrat du 12 juillet 2022, c’est carrément du poker financier. Je sais par expérience que le poker, c’est divertissant, mais ça ne va pas de pair avec une stratégie d’investissement rationnelle. Il me vient à l’esprit ce principe de gestion qui dit que lorsque les gestionnaires d’une société ont trop de liquide inutilisé à leur disposition, ils commencent à faire des trucs vraiment bêtes.
I am working on my long-term investment strategy, and I keep using the Warren Buffet’s tenets of investment (Hagstrom, Robert G.. The Warren Buffett Way (p. 98). Wiley. Kindle Edition.).
At the same time, one of my strategic goals is coming true, progressively: other people reach out to me and ask whether I would agree to advise them on their investment in the stock market. People see my results, sometimes I talk to them about my investment philosophy, and it seems to catch on.
This is both a blessing and a challenge. My dream, 2 years ago, when I was coming back to the business of regular investing in the stock market, was to create, with time, something like a small investment fund specialized in funding highly innovative, promising start-ups. It looks like that dream is progressively becoming reality. Reality requires realistic and intelligible strategies. I need to phrase out my own experience as regards investment in a manner, which is both understandable and convincing to other people.
As I am thinking about it, I want to articulate my strategy along three logical paths. Firstly, what is the logic in my current portfolio? Why am I holding the investment positions I am holding? Why in these proportions? How have I come to have that particular portfolio? If I can verbally explain the process of my so-far investment, I will know what kind of strategy I have been following up to now. This is the first step, and the next one is to formulate a strategy for the future. In one of my recent updates (Tesla first in line), I briefly introduced my portfolio, such as it was on December 2nd, 2021. Since then, I did some thinking, most of all in reference to the investment philosophy of Warren Buffett, and I made some moves. I came to the conclusion that my portfolio was astride a bit too many stocks, and the whole was somehow baroque. By ‘baroque’ I mean that type of structure, where we can have a horribly ugly little cherub, accompanied by just as ugly a little shepherd, but the whole looks nice due to the presence of a massive golden rim, woven around ugliness.
I made myself an idea of what are the ugly cherubs in my portfolio from December 2nd, and I kicked them out of the picture. In the list below, these entities are marked in slashed bold italic:
Why did I put those specific investment positions into the bag labelled ‘ugly little cherubs in the picture’? Here comes a cognitive clash between the investment philosophy I used to have before I started studying in depth that of Warren Buffet and of Berkshire Hathaway. Before, I was using the purely probabilistic approach, according to which the stock market is so unpredictable that my likelihood of failure, on any individual investment, is greater than the likelihood of success, and, therefore, the more I spread my portfolio between different stocks, the less exposed I am to the risk of a complete fuck-up. As I studied the investment philosophy of Warren Buffet, I had great behavioural insights as regards my decisions. Diversifying one’s portfolio is cool, yet it can lead to careless individual choices. If my portfolio is really diversified, each individual position weighs so little that I am tempted to overlook its important features. At the end of the day, I might land with a bag full of potatoes instead of a chest full of gems.
I decided to kick out the superfluous. What did I put in this category? The superfluous investment positions which I kicked out shared some common characteristics, which I reconstructed from the history of the corresponding ‘buy’ orders. Firstly, these were comparatively small positions, hundreds of euros at best. This is one of the lessons by Warren Buffet. Small investments matter little, and they are probably going to stay this way. There is no point in collecting stocks which don’t matter to me. They give is a false sense of security, which is detrimental to the focus on capital gains.
Secondly, I realized that I bought those ugly little cherubs by affinity to something else, not for their own sake. Two of them, FedEx and Allegro, are in the busines of express delivery. I made a ton of money of their stock, just as on the stock of Deutsche Post, during the trough of the pandemic, where retail distribution went mostly into the ‘online order >> express delivery’ pipeline. It was back then, and then I sold out, and then I thought ‘why not trying the same hack again?’. The ‘why not…?’ question was easy to answer, actually: because times change, and the commodity markets have adapted to the pandemic. FedEx and Allegro has returned to what it used to be: a solid business without much charm to me.
Four others – Soligenix, Altimmune, CureVac and Oncolytics Biotech – are biotechnological companies. Once again: I made a ton of money in 2020 on biotech companies, because of the pandemic. Now, emotions in the market have settled, and biotech companies are back what they used to be, namely interesting investments endowed with high risk, high potential reward, and a bottomless capacity for burning cash. Those companies are what Tesla used to be a decade ago. I kept a serious position on a few other biotech businesses: Intellia Therapeutics, Biogened, Biomaxima, and Selvita. I want to keep a few of such undug gems in my portfolio, yet too much would be too much.
Thirdly, I had a loss on all of those ugly little cherubs I have just kicked out of my portfolio. Summing up, these were small positions, casually opened without much strategic thinking, and they were bringing me a loss. I could have waited to have a profit, but I preferred to sell them out and to concentrate my capital on the really promising stocks, which I nailed down using the method of intrinsic value. I realized that my portfolio was what it was, one week ago, before I started strategizing consciously, because I had hard times finding balance between two different motivations: running away from the danger of massive loss, on the one hand, and focusing on investments with a true potential for bringing long-term gains.
I focus more specifically on the concept of intrinsic value. Such as Warren Buffet used it, intrinsic value was based on what he called ‘owner’s earnings’ from a business. Owner’s earnings are spread over a window in time corresponding to the risk-free yield on sovereign bonds. The financial statement used for calculating intrinsic value is the cash-flow of the company in question, plus external data as regards average annual yield on sovereign bonds. The basic formula to calculate owner’s earnings goes like: net income after tax + amortization charges – capital expenditures). Once that nailed down, I divide those owner’s earnings by the interest rate on long-term sovereign bonds. For my positions in the US stock market, I use the long-term yield on the US federal bonds, i.e. 1,35% a year. As regards my portfolio in the Polish stock market, I use the yield 3,42% for Polish sovereign bonds on long-term.
I have calculated that intrinsic value for a few of my investments (I mean those I kept in my portfolio), on the basis of their financial results for 2020 and compared it to their market capitalisation. Then, additionally, I did the same calculation based on their published (yet unaudited) cash-flow for Q3 2021. Here are the results I had for Tesla. Net income 2020 $862,00 mln plus amortization charges 2020 $2 322,00 mln minus capital expenditures 2020 $3 132,00 mln equals owner’s earnings 2020 $52,00 mln. Divided by 1,35%, that gives an intrinsic value of $3 851,85 mln. Market capitalization on December 6th, 2021: $1 019 000,00 mln. The intrinsic value looks like several orders of magnitude smaller than market capitalisation. Looks risky.
Let’s see the Q3 2021 unaudited cash-flows. Here, I extrapolate the numbers for 9 months of 2021 over the whole year 2021: I multiply them by 4/3. Extrapolated net income for Q3 2021 $4 401,33 mln plus extrapolated amortization charges for Q3 2021 $2 750,67 minus extrapolated capital expendituresfor Q3 2021 $7 936,00 equals extrapolated owner’s earnings amounting to $4 401,33 mln. Divided by 1,35%, it gives an extrapolated intrinsic value of $326 024,69 mln. It is much closer to market capitalization, yet much below it as for now. A lot of risk in that biggest investment position of mine. We live and we learn, as they say.
Another stock: Apple. With the economic size of a medium-sized country, Apple seems solid. Let’s walk it through the computational path of intrinsic value. There is an important methodological remark to formulate as for this cat. In the cash-flow statement of Apple for 2020-2021 (Apple Inc. ends its fiscal year by the end of September in the calendar year), under the category of ‘Investing activities’, most of the business pertains to buying and selling financial assets. It goes, ike:
Investing activities, in millions of USD:
>> Purchases of marketable securities (109 558)
>> Proceeds from maturities of marketable securities: 59 023
>> Proceeds from sales of marketable securities: 47 460
>> Payments for acquisition of property, plant and equipment (11 085)
>> Payments made in connection with business acquisitions, net (33)
Now, when I look at the thing through the lens of Warren Buffett’s investment tenets, anything that happens with and through financial securities, is retention of cash in the business. It just depends on what exact form we want to keep that cash under. Transactions grouped under the heading of ‘Purchases of marketable securities (109 558)’, for example, are not capital expenditures. They do not lead to exchanging cash money against productive technology. In all that list of investment activities, only two categories, namely: ‘Payments for acquisition of property, plant and equipment (11 085)’, and ‘Payments made in connection with business acquisitions, net (33)’ are capital expenditures sensu stricto. All the other categories, although placed in the account of investing activities, are labelled as such just because they pertain to transactions on assets. From the Warren Buffet’s point of view they all mean retained cash.
>> Net Income $94 680 mln + Depreciation and Amortization $11 284 mln + Purchases of marketable securities $109 558 mln + Proceeds from maturities of marketable securities $59 023 mln + Proceeds from sales of marketable securities $47 460 mln – Payments for acquisition of property, plant and equipment $11 085 mln – Payments made in connection with business acquisitions, net $33 mln + Purchases of non-marketable securities $131 mln + Proceeds from non-marketable securities $387 mln = Owner’s earnings $311 405 mln.
I divide that number by the 1,35% annual yield of the long-term Treasury bonds in the US, and I get an intrinsic value of $23 067 037 mln, against a market capitalisation floating around $2 600 000 mln, which gives a huge overhead in the former over the latter. Good investment.
>> Depreciation, amortization and accretion $232,93 mln
>> Impairments and net losses on disposal of long-lived assets $35,81 mln
… and then come the Cash flows from investing activities:
>> Purchases of property, plant and equipment ($416,64 mln)
>> Purchases of marketable securities and restricted marketable securities ($901,92 mln)
>> Proceeds from sales and maturities of marketable securities and restricted marketable securities $1 192,83 mln
>> Other investing activities ($5,5 mln)
… and therefore, from the perspective of owner’s earnings, the net cash used in investing activities is not, as stated officially, minus $131,23 mln. Net capital expenses, I mean net of transactions on financial assets, are: – $416,64 mln + $901,92 mln + $1 192,83 mln – $5,5 mln = $1 672,61 mln. Combined with the aforementioned net income, amortization and fiscally compensated impairments on long-lived assets, it makes owner’s earnings of $2 339,7 mln. And an intrinsic value of $173 311,11 mln, against some $10 450 000 mln in market capitalization. Once again, good and solid in terms of Warren Buffet’s margin of security.
I start using the method of intrinsic value for my investments, and it gives interesting results. It allows me to distinguish, with a precise gauge, between high-risk investments and the low-risk ones.
Me revoilà sur mon blog et je me concentre sur un truc : ma stratégie d’investissements boursiers. Je veux optimiser ma stratégie et à cette fin je me réfère à Warren Buffett et à sa philosophie d’investissement telle que vous et moi pouvons la trouver dans les rapports annuels de Berkshire Hathaway Inc. (https://www.berkshirehathaway.com/reports.html ). Je prends donc les principes de Warren Buffett et je les applique comparativement à deux compagnies : Tesla (https://ir.tesla.com/#tab-quarterly-disclosure ), la plus grande position dans mon portefeuille d’investissement, d’une part, et Selvita (https://selvita.com/investors-media/ ), une société polonaise de biotechnologie, sur les actions de laquelle je commence à développer un investissement sérieux.
Avec Tesla, j’ai déjà entamé une analyse façon Warren Buffett (Tesla first in line) et maintenant je continue de manière comparative. C’est un truc qui marche : lorsque je veux comprendre quelque chose de complexe, je peux comparer cette chose complexe avec une autre chose complexe. Comparaison est une stratégie cognitive fondamentale. Elle me permet d’apercevoir les différences et les similarités entre des phénomènes complexes (tout est complexe, en fait) et de comprendre ainsi ce que la science cognitive désigne comme « saillance ».
>> l’entreprise en tant que telle : le modèle d’entreprise est-il simple et compréhensible ? l’entreprise a-t-elle une histoire cohérente d’exploitation ainsi que des perspectives favorables à long terme ?
>> la gestion : est-ce que la gestion de l’entreprise semble rationnelle ? Les gestionnaires semblent-ils agir dans le meilleur intérêt des actionnaires ? Les gestionnaires résistent-ils les
modes et les pressions institutionnelles externes ?
>> la finance : quel est le retour sur capitaux propres ? quels sont les bénéfices agrégés pour les actionnaires ? Quelle est la marge de bénéfice dans les produits de l’entreprise ? Quelle est la concordance entre rétention de trésorerie d’une part et l’accroissement de valeur boursière ?
>> le marché boursier : quelle est la valeur économique de la société en question ? comment cela concorde-t-il avec sa valeur boursière ?
Je me concentre sur la concordance entre la valeur économique de, respectivement, Tesla et Selvita, d’une part et leur valeur boursière d’autre part. Dans la stratégie modèle de Warren Buffett, la valeur économique d’une entreprise est égale au flux prévisible de trésorerie d’activités d’exploitation, escompté avec un taux de retour sur investissement sans risque. Pour donner un exemple pratiqe de cette méthode de base, je cite et traduis un passage du livre « The Warren Buffett Way » par Robert G. Hagstrom, plus précisément le fragment des pages 136 – 137 de l’édition Kindle, où la méthode de Buffett est démontrée dans son achat de Washington Post en 1973. Alors : « Nous commençons par calculer les revenus propriétaires pour l’année fiscale : bénéfice net de $13,3 millions plus dépréciation et amortissement de $3,7 millions moins les investissements capitalisables de $6,6 millions, ça donne un revenu propriétaire de $10,4 millions. Si nous divisons ce revenu par le taux de rendement des obligations souveraines long-terme de la Trésorerie Fédérale des États-Unis (6,81%), la valeur de Washington Post atteint $150 millions […]. Buffett dit qu’avec le temps, les investissements capitalisables d’un journal vont être égales au flux d’amortissement et de ce fait le bénéfice net devrait être une bonne estimation du revenu propriétaire. »
Le bénéfice net de Tesla pour 2020 était de $862 millions, la charge d’amortissement montait à $2 322 millions, et le solde d’investissements capitalisables fût $3 132. J’obtiens un revenu propriétaire de $52 millions. J’utilise deux taux de rendement comme référence : celui des obligations souveraines du Trésor Polonais, soit 3,242% (puisque j’investis à partir de Pologne), ainsi que celui des obligations souveraines long-terme de la Trésorerie Fédérale des États-Unis (1,35%), puisque mon résultat sur Tesla est déterminé par la valeur intrinsèque de Tesla telle qu’estimée par le marché financier pour Tesla se trouve aux États-Unis.
Après avoir divisé le revenu propriétaire de Tesla pour 2020 par ces deux taux alternatifs, j’obtiens une fourchette de valeur intrinsèque entre $1 603,95 milliards et $3 851,85 milliards. La capitalisation boursière de Tesla est couramment de $1 019 milliards, mais tout récemment, le 4 novembre, elle atteignait $1 248,43 milliards.
En ce qui concerne les d’investissements capitalisables, ça se corse. Je trouve un investissement en actifs matériels et immatériels de valeur totale de PLN15 003 636, ainsi que « l’acquisition d’autres actifs financiers » qui monte à PLN10 152 560. Selon la logique de Warren Buffett, l’investissement strictement dit, donc celui qui est déductible du revenu propriétaire, est celui en actifs productifs afférents à l’exploitation. L’acquisition d’actifs financiers est un placement, pas un investissement en actifs d’exploitation.
Je pense donc que je peux calculer le revenu propriétaire de Selvita de deux façons différentes. La première variante c’est « bénéfice net plus amortissement moins l’investissement en actifs matériels et immatériels » et dans la deuxième variante je considère l’acquisition d’actifs financiers comme un flux additionnel de trésorerie et je l’ajoute au solde du premier calcul. J’obtiens ainsi un revenu propriétaire façon Warren Buffett dans la fourchette entre PLN18 444 005 et PLN28 596 565. Je divise par le taux de rendement comme des obligations souveraines du Trésor Polonais (3,242%) et j’obtiens une fourchette correspondante de valeur intrinsèque entre PLN568 908 235 et PLN882 065 545. La dernière capitalisation boursière de Selvita est de PLN1 478 millions, avec un maximum sur les 12 mois derniers noté le 5 juillet 2021, égal à PLN2 894,7 millions.
Ma conclusion provisoire est que, sur la base des résultats financiers audités pour 2020, Tesla reste sous-valorisée par le marché boursier et ça donne des opportunités intéressantes. En revanche, Selvita semble être un peu gonflée en Bourse et je dois être sur mes gardes. Maintenant je passe à l’extension de l’exercice précèdent avec la méthode de MRQ ou « Most Recent Quarter », soit avec les résultats financiers non-audités de deux sociétés pour le troisième quart de 2021. Je fais un truc très primitif, qui est néanmoins utilisé fréquemment en analyse financière, donc j’extrapole les résultats de trois quarts de l’année fiscale en les multipliant par « 4/3 ». Oui, c’est simpliste et ça donne juste une estimation très provisoire de ce que les résultats annuels audités pour 2021 peuvent bien être. Néanmoins, cette méthode permet de simuler l’état d’esprit d’autres investisseurs qui – tout comme moi – utilisent la méthode de valeur intrinsèque façon Warren Buffett.
Je commence par Tesla, encore une fois (https://www.sec.gov/Archives/edgar/data/1318605/000095017021002253/tsla-20210930.htm#consolidated_statements_of_cash_flows . Bénéfice net $3 301 millions plus charge d’amortissement $2 063 moins investissements capitalisables de $5 952, ça donne… merde… – $588. Embarrassant, n’est-ce pas ? Revenu propriétaire négatif veut dire valeur intrinsèque négative. Esquive élégante : « Buffett dit qu’avec le temps, les investissements capitalisables d’un journal vont être égales au flux d’amortissement et de ce fait le bénéfice net devrait être une bonne estimation du revenu propriétaire. » Bon, Tesla, c’est presque comme un journal, quoi. Sauf que ça n’a rien à voir. Ce n’est même pas le même type fondamental de bien économique. Enfin, essayons avec l’équivalence « bénéfice net = revenu propriétaire ». J’extrapole le bénéfice net pour les 9 mois de 2021 sur les 12 mois de l’année fiscale et ça donne $4 401,33 millions. Je divise par le taux de rendement des obligations souveraines long-terme de la Trésorerie Fédérale des États-Unis (1,35%) et j’ai $326 024 milliards.
Je commence à comprendre la danse folle autour des actions de Tesla. Vous regardez le bénéfice net et ça a l’air de décoiffer (positivement). Vous jetez un coup d’œil sur les dépenses capitalisables d’investissement et vous commencer à vous poser des questions. Si le calcul très simple de Warren Buffett donne autant de doute, pas étonnant que plusieurs petits investisseurs se laissent prendre au jeu des grands fonds d’investissement futés.
Je passe à Selvita : https://selvita.com/wp-content/uploads/2021/11/Selvita-Group-Consolidated-Financial-Statements-Q3-2021.pdf . Bénéfice net sur les 9 mois de 2021 fait PLN8 844 005, les charges d’amortissement sur la même période montent à PLN17 764 894, acquisition d’actifs productifs est de PLN9 655 884 et celle d’actifs financiers c’est PLN3 172 566. Je somme des deux façons alternatives décrites plus tôt, j’extrapole sur les 12 mois, je divise par le taux d’intérêt sur les obligations de Trésor Polonais (3,242%) et j’obtiens une valeur intrinsèque entre PLN660 936 257 et PLN784 623 041. C’est toujours très en-dessous de la capitalisation boursière de Selvita. Je dois faire gaffe.
Once again, a big gap in my blogging. What do you want – it happens when the academic year kicks in. As it kicks in, I need to divide my attention between scientific research and writing, on the one hand, and my teaching on the other hand.
I feel like taking a few steps back, namely back to the roots of my observation. I observe two essential types of phenomena, as a scientist: technological change, and, contiguously to that, the emergence of previously unexpected states of reality. Well, I guess we all observe the latter, we just sometimes don’t pay attention. I narrow it down a bit. When it comes to technological change, I am just bewildered with the amounts of cash that businesses have started holding, across the board, amidst an accelerating technological race. Twenty years ago, any teacher of economics would tell their students: ‘Guys, cash is the least productive asset of all. Keep just the sufficient cash to face the most immediate expenses. All the rest, invest it in something that makes sense’. Today, when I talk to my students, I tell them: ‘Guys, with the crazy speed of technological change we are observing, cash is king, like really. The greater reserves of cash you hold, the more flexible you stay in your strategy’.
Those abnormally big amounts of cash that businesses tend to hold, those last years, it has two dimensions in terms of research. On the one hand, it is economics and finance, and yet, on the other hand, it is management. For quite some time, digital transformation has been about the only thing worth writing about in management science, but that, namely the crazy accumulation of cash balances in corporate balance sheets, is definitely something worth writing about. Still, there is amazingly little published research on the general topic of cash flow and cash management in business, just as there is very little on financial liquidity in business. The latter topic is developed almost exclusively in the context of banks, mostly the central ones. Maybe it is all that craze about the abominable capitalism and the general claim that money is evil. I don’t know.
Anyway, it is interesting. Money, when handled at the microeconomic level, tells the hell of a story about our behaviour, our values, our mutual trust, and our emotions. Money held in corporate balance sheets tells the hell of a story about decision making. I explain. Please, consider the amount of money you carry around with you, like the contents of your wallet (credit cards included) plus whatever you have available instantly on your phone. Done? Visualised? Good. Now, ask yourself what percentage of all those immediately available monetary balances you use during your one average day. Done? Analysed? Good. In my case, it would be like 0,5%. Yes, 0,5%. I did that intellectual exercise with my students, many time. They usually hit no more than 10%, and they are gobsmacked. Their first reaction is WOKEish: ‘So I don’t really need all that money, right. Money is pointless, right?’. Not quite, my dear students. You need all that money; you just need it in a way which you don’t immediately notice.
There is a model in the theory of complex systems, called the ants’ colony (see for example: (Chaouch, Driss & Ghedira 2017; Asghari & Azadi 2017; Emdadi et al. 2019; Gupta & Srivastava 2020; Di Caprio et al. 2021). Yes, Di Caprio. Not the Di Caprio you intuitively think about, though. Ants communicate with pheromones. They drop pheromones somewhere they sort of know (how?) it is going to be a signal for other ants. Each ant drops sort of a standard parcel of pheromones. Nothing to write home about, really, and yet enough to attract the attention of another ant which could drop its individual pheromonal parcel in the same location. With any luck, other ants will discover those chemical traces and validate them with their individual dumps of pheromones, and this is how the colony of ants maps its territories, mostly to find and exploit sources of food. This is interesting to find out that in order for all that chemical dance to work, there needs to be a minimum number of ants on the job. In there are not enough ants per square meter of territory, they just don’t find each other’s chemical imprints and have no chance to grab hold of the resources available. Yes, they all die prematurely. Money in human societies could be the equivalent of a pheromone. We need to spread it in order to carry out complex systemic changes. Interestingly, each of us, humans, is essentially blind to those complex changes: we just cannot wrap our mind around quickly around the technical details of something apparently as simple as the manufacturing chain of a gardening rake (do you know where exactly and in what specific amounts all the ingredients of steel come from? I don’t).
All that talk about money made me think about my investments in the stock market. I feel like doing things the Warren Buffet’s way: going to the periodical financial reports of each company in my portfolio, and just passing in review what they do and what they are up to. By the way, talking about Warren Buffet’s way, I recommend my readers to go to the source: go to https://www.berkshirehathaway.com/ first, and then to https://www.berkshirehathaway.com/2020ar/2020ar.pdf as well as to https://www.berkshirehathaway.com/qtrly/3rdqtr21.pdf . For now, I focus on studying my own portfolio according to the so called “12 immutable tenets by Warren Buffet”, such as I allow myself to quote them:
>> Business Tenets: Is the business simple and understandable? Does the business have a consistent operating history? Does the business have favourable long-term prospects?
>> Management Tenets: Is management rational? Is management candid with its shareholders? Does management resist the institutional imperative?
>> Financial Tenets Focus on return on equity, not earnings per share. Calculate “owner earnings.” Look for companies with high profit margins. For every dollar retained, make sure the company has created at least one dollar of market value.
>> Market Tenets: What is the value of the business? Can the business be purchased at a significant discount to its value?
(Hagstrom, Robert G.. The Warren Buffett Way (p. 98). Wiley. Kindle Edition.)
Studying that whole portfolio of mine through the lens of Warren Buffet’s tenets looks like a piece of work, really. Good. I like working. Besides, as I have been reading Warren Buffett’s annual reports at https://www.berkshirehathaway.com/ , I realized that I need a real strategy for investment. So far, I have developed a few efficient hacks, such as, for example, the habit of keeping my s**t together when other people panic or when they get euphoric. Still, hacks are not the same as strategy.
I feel like adding my own general principles to Warren Buffet’s tenets. Principle #1: whatever I think I do my essential strategy consists in running away from what I perceive as danger. Thus, what am I afraid of, in my investment? What subjective fears and objective risks factors shape my actions as investor? Once I understand that, I will know more about my own actions and decisions. Principle #2: the best strategy I can think of is a game with nature, where each move serves to learn something new about the rules of the game, and each move should be both decisive and leaving me with a margin of safety. What am I learning as I make my moves? What my typical moves actually are?
Now, I assume that if I can understand why and how numbers change in the financial statements of a business, I can understand the business itself. The first change I can spot in that balance sheet is property, plant and equipment, net passing from $12 747 million to $17 298 million in 12 months. What exactly has happened? Here comes Note 7 – Property, Plant and Equipment, Net, in that quarterly report, and it starts with a specification of fixed assets comprised in that category. Good. What really increased in this category of assets is construction in progress, and here comes the descriptive explanation pertinent thereto: “Construction in progress is primarily comprised of construction of Gigafactory Berlin and Gigafactory Texas, expansion of Gigafactory Shanghai and equipment and tooling related to the manufacturing of our products. We are currently constructing Gigafactory Berlin under conditional permits in anticipation of being granted final permits. Completed assets are transferred to their respective asset classes, and depreciation begins when an asset is ready for its intended use. Interest on outstanding debt is capitalized during periods of significant capital asset construction and amortized over the useful lives of the related assets. During the three and nine months ended September 30, 2021, we capitalized $14 million and $52 million, respectively, of interest. During the three and nine months ended September 30, 2020, we capitalized $13 million and $33 million, respectively, of interest.
Depreciation expense during the three and nine months ended September 30, 2021 was $495 million and $1.38 billion, respectively. Depreciation expense during the three and nine months ended September 30, 2020 was $403 million and $1.13 billion, respectively. Gross property, plant and equipment under finance leases as of September 30, 2021 and December 31, 2020 was $2.60 billion and $2.28 billion, respectively, with accumulated depreciation of $1.11 billion and $816 million, respectively.
Panasonic has partnered with us on Gigafactory Nevada with investments in the production equipment that it uses to manufacture and supply us with battery cells. Under our arrangement with Panasonic, we plan to purchase the full output from their production equipment at negotiated prices. As the terms of the arrangement convey a finance lease under ASC 842, Leases, we account for their production equipment as leased assets when production commences. We account for each lease and any non-lease components associated with that lease as a single lease component for all asset classes, except production equipment classes embedded in supply agreements. This results in us recording the cost of their production equipment within Property, plant and equipment, net, on the consolidated balance sheets with a corresponding liability recorded to debt and finance leases. Depreciation on Panasonic production equipment is computed using the units-of-production method whereby capitalized costs are amortized over the total estimated productive life of the respective assets. As of September 30, 2021 and December 31, 2020, we had cumulatively capitalized costs of $1.89 billion and $1.77 billion, respectively, on the consolidated balance sheets in relation to the production equipment under our Panasonic arrangement.”
Good. I can try to wrap my mind around the contents of Note 7. Tesla is expanding its manufacturing base, including a Gigafactory in my beloved Europe. Expansion of the manufacturing capacity means significant, quantitative growth of the business. According to Warren Buffett’s philosophy: “The question of where to allocate earnings is linked to where that company is in its life cycle. As a company moves through its economic life cycle, its growth rates, sales, earnings, and cash flows change dramatically. In the development stage, a company loses money as it develops products and establishes markets. During the next stage, rapid growth, the company is profitable but growing so fast that it cannot support the growth; often it must not only retain all of its earnings but also borrow money or issue equity to finance growth” (Hagstrom, Robert G.. The Warren Buffett Way (p. 104). Wiley. Kindle Edition). Tesla looks like they are in the phase of rapid growth. They have finally nailed down how to generate profits (yes, they have!), and they are expanding capacity-wise. They are likely to retain earnings and to be in need of cash, and that attracts my attention to another passage in Note 7: “Interest on outstanding debt is capitalized during periods of significant capital asset construction and amortized over the useful lives of the related assets”. If I understand correctly, the financial strategy consists in not servicing (i.e. not paying the interest due on) outstanding debt when that borrowed money is really being used to finance the construction of productive assets, and starting to service that debt only after the corresponding asset starts working and paying its bills. That means, in turn, that lenders are being patient and confident with Tesla. They assume their unconditional claims on Tesla’s future cash flows (this is one of the possible ways to define outstanding debt) are secure.
Good. Now, I am having a look at Tesla’s Consolidated Statements of Operations (in millions, except per share data, unaudited: https://www.sec.gov/Archives/edgar/data/1318605/000095017021002253/tsla-20210930.htm#consolidated_statements_of_operations ). It is time to have a look at Warren Buffett’s Business Tenets as regards Tesla. Is the business simple and understandable? Yes, I think I can understand it. Does the business have a consistent operating history? No, operational results changed in 2020 and they keep changing. Tesla is passing from the stage of development (which took them a decade) to the stage of rapid growth. Does the business have favourable long-term prospects? Yes, they seem to have good prospects. The market of electric vehicles is booming (EV-Volumes; IEA).
Is Tesla’s management rational? Well, that’s another ball game. To develop in my next update.
 Chaouch, I., Driss, O. B., & Ghedira, K. (2017). A modified ant colony optimization algorithm for the distributed job shop scheduling problem. Procedia computer science, 112, 296-305. https://doi.org/10.1016/j.procs.2017.08.267
 Asghari, S., & Azadi, K. (2017). A reliable path between target users and clients in social networks using an inverted ant colony optimization algorithm. Karbala International Journal of Modern Science, 3(3), 143-152. http://dx.doi.org/10.1016/j.kijoms.2017.05.004
 Emdadi, A., Moughari, F. A., Meybodi, F. Y., & Eslahchi, C. (2019). A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization. Heliyon, 5(3), e01299. https://doi.org/10.1016/j.heliyon.2019.e01299
 Di Caprio, D., Ebrahimnejad, A., Alrezaamiri, H., & Santos-Arteaga, F. J. (2021). A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights. Alexandria Engineering Journal. https://doi.org/10.1016/j.aej.2021.08.058
I feel like using my hypothesis of collectively intelligent social structures in other fields than just energy and urbanisation, which I have been largely doing so far. This time, I want to make a case for individual freedom as both a factor and a manifestation of collective intelligence. There is a population of humans. Each human has m possible states of being. As soon as two humans interact, one m states of being in the first human interacts with the other m states of being in the other human. It is like an existential geometrical square: those two humans together have m*m = m2 collective states of being. Generally, n humans, with m possible states of being in each of them, can produce mn different states of being together. When n gets substantial, like 38 million people in my home country, Poland, you can hardly expect all of us 38 million Poles having the repertoire of freedom in our behavioural patterns. Some of us will have 3m actually happening states of being, some other will soar into 6m alternative ways of being in the world, whilst still some other others will modestly stick to 0,3m. In that large population, the standard m ways of existing will be an expected state, thus an arithmetical average or an expected interval around it.
Collectively intelligent structures learn by experimenting with many alternative states of themselves. Up to a point, the more such alternative states, the more and better we can learn. There is probably a point where ‘the more’ becomes ‘too much to process’, and then, we face a fork on the road: either we simply ignore some alternative versions of ourselves and we truly learn just from those which we can cover inside our cognitive span, or we try to experiment with everything we can possibly be, and chaos develops. I understand freedom, at the collective level, as the flexibility in shifting between those different states of being. Organized, collective freedom is the ability to explore the sweet spot of transition between order and chaos, and the ability to experiment with as many alternative versions of ourselves as we possibly can. Those collectively defined alternative realities always follow the basic logic of mn. At the end of the day, there are as many versions of us being together as there are us, for one, namely the ‘n’ exponent, and as many as there are possible states of being in the average individual among n, and this is the ‘m’ base.
Degrees of freedom in the average member of society are the foundation of collectively intelligent learning. I guess this is a mathematical argument for individual freedom in legal and political systems. As I think about my whole hypothesis of collectively intelligent social structures, I inevitably ask the question which any social scientist needs to ask: what is the practical usefulness of all that stuff? Social sciences are applied sciences, at the end of the day. However abstract I go in my intellectual peregrinations, my findings and methods need to serve in real life, for designing policies, business strategies, business plans etc. The empirical method I have developed around that whole thing of collective intelligence opens on two practical applications. Firstly, it allows non-arbitrary testing of various empirical observables as actual social outcomes. In policies and business strategies, and, by the way, in the whole realm of social sciences, there is that curse of arbitrary orientations. ‘People strive to maximize profit’. ‘No, they want to optimize dynamic equilibriums in their social games’. ‘Well, maybe, but we can and should educate people towards social justice and environmentally rational behaviour’ etc. etc. All that chatter abounds in literature which deems itself ‘scientific’, and yet it is 100% metaphysics, with no scientific grounds at all. I think my method allows working around that metaphysical part and testing human populations for the actual outcomes they collectively, objectively pursue. Here comes an interesting question: are our goals collective or individual? The more I think about it, the more I am convinced they are collective. When I ask myself about my own goals, at least those which I phrase out explicitly in my mind, they are all sort of categorical rather than idiosyncratically my own. I pursue the types of goals which many other people pursue in their existence. I just hop on those specific wagons, with my own backpack.
Secondly, my method allows exploring the issue of Black Swans, i.e. outlier events, which suddenly become key drivers of social change. The method I have developed allows simulating something like a social chain reaction. An unexpected triggering event happens, and it is unexpected because from our point of view it is random. That triggers a collection of events which we could otherwise fathom, but they have been in the refrigerator of history so far. Now, they are triggered into existence, and, at the same time, the overall cohesion of the social structure weakens, at least temporarily. New things start happening, and old things happen sort of more loosely and chaotically than they used to. I have discovered that depending on the exact orientation assigned a priori to the social structure I study, those social chain reactions can we essentially predictable, completely unpredictable, or, in still another case, we can calm them down exaggeratedly quickly, without really learning from them.
All in all, the method of using a simple neural network as social simulator, which I developed in connection with my hypothesis of collectively intelligent social structures, allows what I perceive as very empiricist a study of social change, much freer of metaphysics than many other methods. Of course, a bit of metaphysics is unavoidable. What we use to call ‘quantitative variables’ in social sciences are always the mathematics of something we think that happens, and we think in terms of our language and culture.
Ooops, pardon my manners, I have gone into philosophy again. Philosophy is nice, but when I stay in this realm longer than what is strictly necessary for feeling like an intellectual, I start feeling as too much of an intellectual and my apish side calls for more ground under my feet. I use this blog for providing a current account of my intellectual journey, and of the actual projects which I am working on. I hope that the paragraphs above are (provisionally) sufficient as regards the intellectual journey, and I can pass to debriefing on my projects.
One of the projects I start working on is a platform for debt-based crowdfunding. This is some sort of comeback to the interest I had in financial schemes for the implementation of small installations in renewable energies. For the less initiated readers, I am quickly going through the basics. You probably know that if your cousin asks you to invest in his or her business, you can do it, on the basis of a private contract of partnership, and, in most countries, you don’t even go to jail afterwards. This is the market of private equity. You can also lend money to your cousin, you can agree as for the exact terms of the loan, and this is financing through private debt. The opposite of private is public, and therefore we have public capital markets on the opposite end of the spectrum. Stock markets are the most visible ones, and sort of next to them are the markets of publicly traded debt, where you can buy and sell bonds of all kinds: corporate, municipal, and sovereign.
Between the strictly private and the regulated public, a transitional zone, of many shades and colours, is to be found. Crowdfunding, sometimes called ‘societal funding’ or ‘communitarian funding’ dwells in this zone, precisely. The basic difference between crowdfunding and private finance strictly spoken is the largely aleatory, social-media-type creation of relations between investors and entrepreneurs. Crowdfunding happens essentially via digital platforms, where entrepreneurs auction their ventures and try to attract whoever is interested in them. Those digital platforms in themselves are marketing engines, essentially. On the other hand, the basic difference between public financial markets and crowdfunding is that the latter does not really allow tradability in financial positions. When I invest my money through crowdfunding, it is much more of a long-term commitment than investment via stock market. Less liquidity in my financial assets means more exposure to long-term risks, and yet less exposure to short-term volatility in market value.
In my own big picture of social reality, I put the emergence of crowdfunding in the same phenomenological bag as I put cryptocurrencies, progressively increasing supply of money in relation to real output in the economy (thus decreasing velocity of money), and increasingly cash-furnished corporate balance sheets. As a civilisation, we are building up a growing base of financial liquidity, and that means we are facing a quickening pace of depreciation in technological assets, and thus we are in the middle of accelerated technological change. Now, a little word is due about the way I understand accelerated technological change. I have encountered quite well-articulated views that technological change is currently disappointingly slow as compared to what we need. Well, maybe, but in strictly spoken business terms, when a piece of technology which I purchased last year ages morally twice as fast as those which I purchased 5 years ago, because new generations of the same equipment pop up faster and faster, this is accelerated technological change, and, as a businessperson, I need to figure out a strategy to cope with that change.
Here, my own point of view of that phenomenon called ‘financialization’ differs significantly from a lot of other researchers. The mainstream doctrine says that increased financialization is a bad thing, it destabilizes the economic system, and it contributes to social inequalities. I think that financialization is the by-product of something else. It is an otherwise rational coping mechanism to smooth and amortize quick social change which, without financialization, could take very nasty forms, like global wars, massive disappearance of human settlements and much greater damage to natural environment than what we use to bitch and moan about today. Just imagine that somewhere in Europe, 5 million people in a post-industrial spot cannot afford to pay for electricity anymore and they start burning wood and coal in stoves instead. This is what could happen in the presence of quick technological change and in the absence of that horrible financialization.
Crowdfunding is essentially attached to new ideas and new business structures. It is seed capital or early development capital. When I invest my money through crowdfunding, I am opening a long-term position in something essentially young, burgeoning and full of uncertainty. One hundred years ago, mustering capital for such a venture would take an entrepreneur years of patient contacts with potential investors. Now, it can take months or even weeks, and this is the tangible gain of time through the use of digital platforms.
That introduction kept in mind, I get closer to the main thread of that project in crowdfunding, namely to the new regulations thereof, likely to enter into force in Poland this autumn, based on recent regulations of the European Union as a whole. I am passing in review the REGULATION (EU) 2020/1503 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 7 October 2020 on European crowdfunding service providers for business, and amending Regulation (EU) 2017/1129 and Directive (EU) 2019/1937, to find at https://eur-lex.europa.eu/legal-content/PL/TXT/?uri=CELEX:32020R1503 . As I usually do, I start from the end, more specifically from Annex II, titled SOPHISTICATED INVESTORS FOR THE PURPOSE OF THIS REGULATION.
A sophisticated investor is an investor who possesses the awareness of the risks associated with investing in capital markets and adequate resources to undertake those risks without exposing itself to excessive financial consequences. Sophisticated investors may be categorised as such if they meet identification criteria, which, in turn, differ according to the legal personality of the entity. Legal persons (like a bunch of folks in a business partnership), are assumed to be sophisticated in their investments if they meet at least one of the following criteria: (a) own funds of at least EUR 100 000 (b) net turnover of at least EUR 2 000 000 (c) balance sheet of at least EUR 1 000 000.
On the other hand, natural persons can call themselves sophisticated investors when the meet at least two of the following criteria:
>> (a) personal gross income of at least EUR 60 000 per fiscal year, or a financial instrument portfolio, defined as including cash deposits and financial assets, that exceeds EUR 100 000;
>> (b) the investor works or has worked in the financial sector for at least one year in a professional position which requires knowledge of the transactions or services envisaged, or the investor has held an executive position for at least 12 months in a legal person considered as sophisticated investor;
>> (c) the investor has carried out transactions of a significant size on the capital markets at an average frequency of 10 per quarter, over the previous four quarters.
The whole distinction between ordinary investors and the sophisticated ones is in the degree of legal protection they are provided with. That distinction essentially taps into an older one, contained in the DIRECTIVE 2014/65/EU OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 15 May 2014 on markets in financial instruments and amending Directive 2002/92/EC and Directive 2011/61/EU (https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32014L0065 ). As it happens sometimes, protection turns out to be a limitation actually. Non-sophisticated investors are generally limited in the amounts of money they can invest, and the repertoire of financial instruments which they can invest in. If one wants not to be treated like a child, they have to make a special, written request to be treated as sophisticated investor, and whatever operator of financial platform is that request addressed to can accept or reject said request.
The Polish prospective regulations on crowdfunding approach things from a different angle. By the way, they are just prospective regulations, and the only official version of that will which I could get my hands on is in Polish. For those who speak the beautiful language of my home country – distinctive, among others, by a record-level density of consonants in one word – I placed the current bill of this regulation in the archives of my blog, just here: https://discoversocialsciences.com/wp-content/uploads/2021/05/Projekt-crowdfunding-.docx . Polish regulators focus mostly on the concept of ‘key investment information sheet’, which I will allow myself to call KIIS in what follows, is present in the European regulations as well. The KIIS should warn prospective investors that the investing environment they have entered into entails risks that are covered neither by deposit guarantee schemes, nor by investor compensation schemes. The KIIS should reflect the specific features of lending-based and investment-based crowdfunding. To that end, specific and relevant indicators should be required. The KIIS should also take into account, where available, the specific features and risks associated with project owners, and should focus on material information about the project owners, the investors’ rights and fees, and the type of transferable securities, admitted instruments for crowdfunding purposes and loans offered. The KIIS should be drawn up by the project owners, because the project owners are in the best position to provide the information required to be included therein. However, since it is the crowdfunding service providers that are responsible for providing the KIIS to prospective investors, it is the crowdfunding service providers that should ensure that the KIIS is clear, correct and complete.
The specificity of the Polish regulations as regards the KIIS is largely in the addressees of that information. In the general European regulations, the KIIS is addressed to prospective and actual investors. In Polish regulations, it is strongly stressed that crowdfunding operators should communicate all their KIIS’s to the Financial Supervision Commission (PL: Komisja Nadzoru Finansowego, https://www.knf.gov.pl/en/ ), not later than 7 days before making the same KIIS available to prospective investors. On the other hand, the owner of the project subject to crowdfunding can publish the KIIS on their own platform only after the provider of crowdfunding does in on their own one. We have a sequence of KIISes. The first KIIS goes from the crowdfunding provider to the Financial Supervision Commission, which has at least 7 days to consider (what exactly?). The next KIIS goes from the crowdfunding provider to prospective investors, who also receive the last KIIS from the owner of the crowdfunded project in question.
In a general manner, those Polish regulations give a lot of discretionary prerogatives to the Financial Supervision Commission as regards crowdfunding providers. They can halt a crowdfunding project immediately, and for an essentially indefinite period of time, on the grounds of a simple suspicion. I don’t like it. Someone in charge with the Financial Supervision Commission is the first to know about a crowdfunded project, they can request any information about that project, they can halt the project whenever they want. That smells bad. That smells insider trading. That smells uncontrolled pressure on the owners of crowdfunded projects. Imagine: you start such a project, and then you have a phone call, I mean THE phone call. Someone tells you they know about your crowdfunding campaign, and they would willingly take 60% of your business for 50% of its book value. You refuse, and the next thing you know is your crowdfunding campaign being suspended for an unknown period of time. I know the scheme, I saw it play out, and when it plays out, it looks nasty, believe me. That means people close to the government taking over entire swaths of small business, and the kind of small business, which is particularly exposed to adverse actions, the emerging one.
I need to clear my mind a bit. For the last few weeks, I have been working a lot on revising an article of mine, and I feel I need a little bit of a shake-off. I know by experience that I need a structure to break free from another structure. Yes, I am one of those guys. I like structures. When I feel I lack one, I make one.
The structure which I want to dive into, in order to shake off the thinking about my article, is the thinking about my investment in the stock market. My general strategy in that department is to take the rent, which I collect from an apartment in town, every month, and to invest it in the stock market. Economically, it is a complex process of converting the residential utility of a real asset (apartment) into a flow of cash, thus into a financial asset with quite steady a market value (inflation is still quite low), and then I convert that low-risk financial asset into a differentiated portfolio of other financial assets endowed with higher a risk (stock). I progressively move capital from markets with low risk (residential real estate, money) into a high-risk-high-reward market.
I am playing a game. I make a move (monthly cash investment), and I wait for a change in the stock market. I am wrapping my mind around the observable change, and I make my next move the next month. With each move I make, I gather information. What is that information? Let’s have a look at my portfolio such as it is now. You can see it in the table below:
Value in EUR
Real return in €
Rate of return I have as of April 6ht, 2021, in the morning
CASH & CASH FUND & FTX CASH (EUR)
ALTIMMUNE INC. – COMM
€ 1 147,22
APPLE INC. – COMMON ST
€ 1 065,87
€ 1 712,88
DEEPMATTER GROUP PLC
FEDEX CORPORATION COMM
FIRST SOLAR INC. – CO
GRITSTONE ONCOLOGY INC
MODERNA INC. – COMMON
NOVAVAX INC. – COMMON STOCK
€ 1 200,75
NVIDIA CORPORATION – C
ONCOLYTICS BIOTCH CM
SOLIGENIX INC. COMMON
TESLA MOTORS INC. – C
€ 4 680,34
€ 15 191,41
A few words of explanation are due. Whilst I have been actively investing for 13 months, I made this portfolio in November 2020, when I did some major reshuffling. My overall return on the cash invested, over the entire period of 13 months, is 30,64% as for now (April 6th, 2021), which makes 30,64% * (12/13) = 28,3% on the annual basis.
The 5,53% of return which I have on this specific portfolio makes roughly 1/6th of the total return in have on all the portfolios I had over the past 13 months. It is the outcome of my latest experimental round, and this round is very illustrative of the mistake which I know I can make as an investor: panic.
In August and September 2020, I collected some information, I did some thinking, and I made a portfolio of biotech companies involved in the COVID-vaccine story: Pfizer, Biontech, Curevac, Moderna, Novavax, Soligenix. By mid-October 2020, I was literally swimming in extasy, as I had returns on these ones like +50%. Pure madness. Then, big financial sharks, commonly called ‘investment funds’, went hunting for those stocks, and they did what sharks do: they made their target bleed before eating it. They boxed and shorted those stocks in order to make their prices affordably low for long investment positions. At the time, I lost control of my emotions, and when I saw those prices plummet, I sold out everything I had. Almost as soon as I did it, I realized what an idiot I had been. Two weeks later, the same stocks started to rise again. Sharks had had their meal. In response, I did what I still wonder whether it was wise or stupid: I bought back into those positions, only at a price higher than what I sold them for.
Selling out was stupid, for sure. Was buying back in a wise move? I don’t know, like really. My intuition tells me that biotech companies in general have a bright future ahead, and not only in connection with vaccines. I am deeply convinced that the pandemic has already built up, and will keep building up an interest for biotechnology and medical technologies, especially in highly innovative forms. This is even more probable as we realized that modern biotechnology is very largely digital technology. This is what is called ‘platforms’ in the biotech lingo. These are digital clouds which combine empirical experimental data with artificial intelligence, and the latter is supposed to experiment virtually with that data. Modern biotechnology consists in creating as many alternative combinations of molecules and lifeforms as we possibly can make and study, and then pick those which offer the best combination of biological outcomes with the probability of achieving said outcomes.
My currently achieved rates of return, in the portfolio I have now, are very illustrative of an old principle in capital investment: I will fail most of the times. Most of my investment decisions will be failures, at least in the short and medium term, because I cannot possibly outsmart the incredibly intelligent collective structure of the stock market. My overall gain, those 5,53% in the case of this specific portfolio, is the outcome of 19 experiments, where I fail in 12 of them, for now, and I am more or less successful in the remaining 7.
The very concept of ‘beating the market’, which some wannabe investment gurus present, is ridiculous. The stock market is made of dozens of thousands of human brains, operating in correlated coupling, and leveraged with increasingly powerful artificial neural networks. When I expect to beat that networked collective intelligence with that individual mind of mine, I am pumping smoke up my ass. On the other hand, what I can do is to do as many different experiments as I can possibly spread my capital between.
It is important to understand that any investment strategy, where I assume that from now on, I will not make any mistakes, is delusional. I made mistakes in the past, and I am likely to make mistakes in the future. What I can do is to make myself more predictable to myself. I can narrow down the type of mistakes I tend to make, and to create the corresponding compensatory moves in my own strategy.
Differentiation of risk is a big principle in my investment philosophy, and yet it is not the only one. Generally, with the exception of maybe 2 or 3 days in a year, I don’t really like quick, daily trade in the stock market. I am more of a financial farmer: I sow, and I wait to see plants growing out of those seeds. I invest in industries rather than individual companies. I look for some kind of strong economic undertow for my investments, and the kind of undertow I specifically look for is high potential for deep technological change. Accessorily, I look for industries which sort of logically follow human needs, e.g. the industry of express deliveries in the times of pandemic. I focus on three main fields of technology: biotech, digital, and energy.
Good. I needed to shake off, and I am. Thinking and writing about real business decisions helped me to take some perspective. Now, I am gently returning into the realm of science, without completely leaving the realm of business: I am navigating the somehow troubled and feebly charted waters of money for science. I am currently involved in launching and fundraising for two scientific projects, in two very different fields of science: national security and psychiatry. Yes, I know, they can conjunct in more points than we commonly think they can. Still, in canonical scientific terms, these two diverge.
How come I am involved, as researcher, in both national security and psychiatry? Here is the thing: my method of using a simple artificial neural network to simulate social interactions seems to be catching on. Honestly, I think it is catching on because other researchers, when they hear me talking about ‘you know, simulating alternative realities and assessing which one is the closest to the actual reality’ sense in me that peculiar mental state, close to the edge of insanity, but not quite over that edge, just enough to give some nerve and some fun to science.
In the field of national security, I teamed up with a scientist strongly involved in it, and we take on studying the way our Polish forces of Territorial Defence have been acting in and coping with the pandemic of COVID-19. First, the context. So far, the pandemic has worked as a magnifying glass for all the f**kery in public governance. We could all see a minister saying ‘A,B and C will happen because we said so’, and right after there was just A happening, with a lot of delay, and then a completely unexpected phenomenal D appeared, with B and C bitching and moaning they haven’t the right conditions for happening decently, and therefore they will not happen at all. This is the first piece of the context. The second is the official mission and the reputation of our Territorial Defence Forces AKA TDF. This is a branch of our Polish military, created in 2017 by our right-wing government. From the beginning, these guys had the reputation to be a right-wing militia dressed in uniforms and paid with taxpayers’ money. I honestly admit I used to share that view. TDF is something like the National Guard in US. These are units made of soldiers who serve in the military, and have basic military training, but they have normal civilian lives besides. They have civilian jobs, whilst training regularly and being at the ready should the nation call.
The initial idea of TDF emerged after the Russian invasion of the Crimea, when we became acutely aware that military troops in nondescript uniforms, apparently lost, and yet strangely connected to the Russian government, could massively start looking lost by our Eastern border. The initial idea behind TDF was to significantly increase the capacity of the Polish population for mobilising military resources. Switzerland and Finland largely served as models.
When the pandemic hit, our government could barely pretend they control the situation. Hospitals designated as COVID-specific had frequently no resources to carry out that mission. Our government had the idea of mobilising TDF to help with basic stuff: logistics, triage and support in hospitals etc. Once again, the initial reaction of the general public was to put the label of ‘militarisation’ on that decision, and, once again, I was initially thinking this way. Still, some friends of mine, strongly involved as social workers supporting healthcare professionals, started telling me that working with TDF, in local communities, was nothing short of amazing. TDF had the speed, the diligence, and the capacity to keep their s**t together which many public officials lacked. They were just doing their job and helping tremendously.
I started scratching the surface. I did some research, and I found out that TDF was of invaluable help for many local communities, especially outside of big cities. Recently, I accidentally had a conversation about it with M., the scientist whom I am working with on that project. He just confirmed my initial observations.
M. has strong connections with TDF, including their top command. Our common idea is to collect abundant, interview-based data from TDF soldiers mobilised during the pandemic, as regards the way they carried out their respective missions. The purely empirical edge we want to have here is oriented on defining successes and failures, as well as their context and contributing factors. The first layer of our study is supposed to provide the command of TDF with some sort of case-studies-based manual for future interventions. At the theoretical, more scientific level, we intend to check the following hypotheses:
>> Hypothesis #1: during the pandemic, TDF has changed its role, under the pressure of external events, from the initially assumed, properly spoken territorial defence, to civil defence and assistance to the civilian sector.
>> Hypothesis #2: the actual role played by the TDF during the pandemic was determined by the TDF’s actual capacity of reaction, i.e. speed and diligence in the mobilisation of human and material resources.
>> Hypothesis #3: collectively intelligent human social structures form mechanisms of reaction to external stressors, and the chief orientation of those mechanisms is to assure proper behavioural coupling between the action of external stressors, and the coordinated social reaction. Note: I define behavioural coupling in terms of the games’ theory, i.e. as the objectively existing need for proper pacing in action and reaction.
The basic method of verifying those hypotheses consists, in the first place, in translating the primary empirical material into a matrix of probabilities. There is a finite catalogue of operational procedures that TDF can perform. Some of those procedures are associated with territorial military defence as such, whilst other procedures belong to the realm of civil defence. It is supposed to go like: ‘At the moment T, in the location A, procedure of type Si had a P(T,A, Si) probability of happening’. In that general spirit, Hypothesis #1 can be translated straight into a matrix of probabilities, and phrased out as ‘during the pandemic, the probability of TDF units acting as civil defence was higher than seeing them operate as strict territorial defence’.
That general probability can be split into local ones, e.g. region-specific. On the other hand, I intuitively associate Hypotheses #2 and #3 with the method which I call ‘study of orientation’. I take the matrix of probabilities defined for the purposes of Hypothesis #1, and I put it back to back with a matrix of quantitative data relative to the speed and diligence in action, as regards TDF on the one hand, and other public services on the other hand. It is about the availability of vehicles, capacity of mobilisation in people etc. In general, it is about the so-called ‘operational readiness’, which you can read more in, for example, the publications of RAND Corporation (https://www.rand.org/topics/operational-readiness.html).
Thus, I take the matrix of variables relative to operational readiness observable in the TDF, and I use that matrix as input for a simple neural network, where the aggregate neural activation based on those metrics, e.g. through a hyperbolic tangent, is supposed to approximate a specific probability relative to TDF people endorsing, in their operational procedures, the role of civil defence, against that of military territorial defence. I hypothesise that operational readiness in TDF manifests a collective intelligence at work and doing its best to endorse specific roles and applying specific operational procedures. I make as many such neural networks as there are operational procedures observed for the purposes of Hypothesis #1. Each of these networks is supposed to represent the collective intelligence of TDF attempting to optimize, through its operational readiness, the endorsement and fulfilment of a specific role. In other words, each network represents an orientation.
Each such network transforms the input data it works with. This is what neural networks do: they experiment with many alternative versions of themselves. Each experimental round, in this case, consists in a vector of metrics informative about the operational readiness TDF, and that vector locally tries to generate an aggregate outcome – its neural activation – as close as possible to the probability of effectively playing a specific role. This is always a failure: the neural activation of operational readiness always falls short of nailing down exactly the probability it attempts to optimize. There is always a local residual error to account for, and the way a neural network (well, my neural network) accounts for errors consists in measuring them and feeding them into the next experimental round. The point is that each such distinct neural network, oriented on optimizing the probability of Territorial Defence Forces endorsing and fulfilling a specific social role, is a transformation of the original, empirical dataset informative about the TDF’s operational readiness.
Thus, in this method, I create as many transformations (AKA alternative versions) of the actual operational readiness in TDF, as there are social roles to endorse and fulfil by TDF. In the next step, I estimate two mathematical attributes of each such transformation: its Euclidean distance from the original empirical dataset, and the distribution of its residual error. The former is informative about similarity between the actual reality of TDF’s operational readiness, on the one hand, and alternative realities, where TDF orient themselves on endorsing and fulfilling just one specific role. The latter shows the process of learning which happens in each such alternative reality.
I make a few methodological hypotheses at this point. Firstly, I expect a few, like 1 ÷ 3 transformations (alternative realities) to fall particularly close from the actual empirical reality, as compared to others. Particularly close means their Euclidean distances from the original dataset will be at least one order of magnitude smaller than those observable in the remaining transformations. Secondly, I expect those transformations to display a specific pattern of learning, where the residual error swings in a predictable cycle, over a relatively wide amplitude, yet inside that amplitude. This is a cycle where the collective intelligence of Territorial Defence Forces goes like: ‘We optimize, we optimize, it goes well, we narrow down the error, f**k!, we failed, our error increased, and yet we keep trying, we optimize, we optimize, we narrow down the error once again…’ etc. Thirdly, I expect the remaining transformations, namely those much less similar to the actual reality in Euclidean terms, to display different patterns of learning, either completely dishevelled, with the residual error bouncing haphazardly all over the place, or exaggeratedly tight, with error being narrowed down very quickly and small ever since.
That’s the outline of research which I am engaging into in the field of national security. My role in this project is that of a methodologist. I am supposed to design the system of interviews with TDF people, the way of formalizing the resulting data, binding it with other sources of information, and finally carrying out the quantitative analysis. I think I can use the experience I already have with using artificial neural networks as simulators of social reality, mostly in defining said reality as a vector of probabilities attached to specific events and behavioural patterns.
As regards psychiatry, I have just started to work with a group of psychiatrists who have abundant professional experience in two specific applications of natural language in the diagnosing and treating psychoses. The first one consists in interpreting patients’ elocutions as informative about their likelihood of being psychotic, relapsing into psychosis after therapy, or getting durably better after such therapy. In psychiatry, the durability of therapeutic outcomes is a big thing, as I have already learnt when preparing for this project. The second application is the analysis of patients’ emails. Those psychiatrists I am starting to work with use a therapeutic method which engages the patient to maintain contact with the therapist by writing emails. Patients describe, quite freely and casually, their mental state together with their general existential context (job, family, relationships, hobbies etc.). They don’t necessarily discuss those emails in subsequent therapeutic sessions; sometimes they do, sometimes they don’t. The most important therapeutic outcome seems to be derived from the very fact of writing and emailing.
In terms of empirical research, the semantic material we are supposed to work with in that project are two big sets of written elocutions: patients’ emails, on the one hand, and transcripts of standardized 5-minute therapeutic interviews, on the other hand. Each elocution is a complex grammatical structure in itself. The semantic material is supposed to be cross-checked with neurological biomarkers in the same patients. The way I intend to use neural networks in this case is slightly different from that national security thing. I am thinking about defining categories, i.e. about networks which guess similarities and classification out of crude empirical data. For now, I make two working hypotheses:
>> Hypothesis #1: the probability of occurrence in specific grammatical structures A, B, C, in the general grammatical structure of a patient’s elocutions, both written and spoken, is informative about the patient’s mental state, including the likelihood of psychosis and its specific form.
>> Hypothesis #2: the action of written self-reporting, e.g. via email, from the part of a psychotic patient, allows post-clinical treatment of psychosis, with results observable as transition from mental state A to mental state B.
I am swivelling my intellectual crosshairs around, as there is a lot going on, in the world. Well, there is usually a lot going on, in the world, and I think it is just the focus of my personal attention that changes its scope. Sometimes, I pay attention just to the stuff immediately in front of me, whilst on other times I go wide and broad in my perspective.
My research on collective intelligence, and on the application of artificial neural networks as simulators thereof has brought me recently to studying outlier cases. I am an economist, and I do business in the stock market, and therefore it comes as sort of logical that I am interested in business outliers. I hold some stock of the two so-far winners of the vaccine race: Moderna (https://investors.modernatx.com/ ) and BionTech (https://investors.biontech.de/investors-media ), the vaccine companies. I am interested in the otherwise classical, Schumpeterian questions: to what extent are their respective business models predictors of their so-far success in the vaccine contest, and, seen from the opposite perspective, to what extent is that whole technological race of vaccines predictive of the business models which its contenders adopt?
I like approaching business models with the attitude of a mean detective. I assume that people usually lie, and it starts with lying to themselves, and that, consequently, those nicely rounded statements in annual reports about ‘efficient strategies’ and ‘ambitious goals’ are always bullshit to some extent. In the same spirit, I assume that I am prone to lying to myself. All in all, I like falling back onto hard numbers, in the first place. When I want to figure out someone’s business model with a minimum of preconceived ideas, I start with their balance sheet, to see their capital base and the way they finance it, just to continue with their cash-flow. The latter helps my understanding on how they make money, at the end of the day, or how they fail to make any.
I take two points in time: the end of 2019, thus the starting blocks of the vaccine race, and then the latest reported period, namely the 3rd quarter of 2020. Landscape #1: end of 2019. BionTech sports $885 388 000 in total assets, whilst Moderna has $1 589 422 000. Here, a pretty amazing detail pops up. I do a routine check of proportion between fixed assets and total assets. It is about to see what percentage of the company’s capital base is immobilized, and thus supposed to bring steady capital returns, as opposed to the current assets, fluid, quick to exchange and made for greasing the current working of the business. When I measure that coefficient ‘fixed assets divided by total assets’, it comes as 29,8% for BionTech, and 29% for Moderna. Coincidence? There is a lot of coincidence in those two companies. When I switch to Landscape #2: end of September 2020, it is pretty much the. You can see it in the two tables below:
As you look at those numbers, they sort of collide with the common image of biotech companies in sci fi movies. In movies, we can see huge labs, like 10 storeys underground, with caged animals inside etc. In real life, biotech is cash, most of all. Biotech companies are like big wallets, camped next to some useful science. Direct investment in biotech means very largely depositing one’s cash on the bank account run by the biotech company.
After studying the active side of those two balance sheets, i.e. in BionTech and in Moderna, I shift my focus to the passive side. I want to know how exactly people put cash in those businesses. I can see that most of it comes in the form of additional paid-in equity, which is an interesting thing for publicly listed companies. In the case of Moderna, the bulk of that addition to equity comes as a mechanism called ‘vesting of restricted common stock’. Although it is not specified in their financial report how exactly that vesting takes place, the generic category corresponds to operations where people close to the company, employees or close collaborators, anyway in a closed private circle, buy stock of the company in a restricted issuance. With Biontech, it is slightly different. Most of the proceeds from public issuance of common stock is considered as reserve capital, distinct from share capital, and on the top of that they seem to be running, similarly to Moderna, transactions of vesting restricted stock. Another important source of financing in both companies are short-term liabilities, mostly deferred transactional payments. Still, I have an intuitive impression of being surrounded by maybies (you know: ‘maybe I am correct, unless I am wrong), and thus I decided to broaden my view. I take all the 7 biotech companies I currently have in my investment portfolio, which are, besides BionTech and Moderna, five others: Soligenix (http://ir.soligenix.com/ ), Altimmune (http://ir.altimmune.com/investors ), Novavax (https://ir.novavax.com/ ) and VBI Vaccines (https://www.vbivaccines.com/investors/ ). In the two tables below, I am trying to summarize my essential observations about those seven business models.
Despite significant differences in the size of their respective capital base, all the seven businesses hold most of their capital in the highly liquid financial form: cash or tradable financial securities. Their main source of financing is definitely the additional paid-in equity. Now, some readers could ask: how the hell is it possible for the additional paid-in equity to make more than the value of assets, like 193%? When a business accumulates a lot of operational losses, they have to be subtracted from the incumbent equity. Additions to equity serve as a compensation of those losses. It seems to be a routine business practice in biotech.
Now, I am going to go slightly conspiracy-theoretical. Not much, just an inch. When I see businesses such as Soligenix, where cumulative losses, and the resulting additions to equity amount to teen times the value of assets, I am suspicious. I believe in the power of science, but I also believe that facing a choice between using my equity to compensate so big a loss, on the one hand, and using it to invest into something less catastrophic financially, I will choose the latter. My point is that cases such as Soligenix smell scam. There must be some non-reported financial interests in that business. Something is going on behind the stage, there.
In my previous update, titled ‘An odd vector in a comfortably Apple world’, I studied the cases of Tesla and Apple in order to understand better the phenomenon of outlier events in technological change. The short glance I had on those COVID-vaccine-involved biotechs gives me some more insight. Biotech companies are heavily scientific. This is scientific research shaped into a business structure. Most of the biotech business looks like an ever-lasting debut, long before breaking even. In textbooks of microeconomics and management, we can read that being able to run the business at a profit is a basic condition of calling it a business. In biotech, it is different. Biotechs are the true outliers, nascent at the very juncture of cutting-edge science, and business strictly spoken. This is how outliers emerge: there is some cool science. I mean, really cool, the one likely to change the face of the world. Those mRNA biotechnologies are likely to do so. The COVID vaccine is the first big attempt to transform those mRNA therapies from experimental ones into massively distributed and highly standardized medicine. If this stuff works on a big scale, it is a new perspective. It allows fixing people, literally, instead of just curing diseases.
Anyway, there is that cool science, and it somehow attracts large amounts of cash. Here, a little digression from the theory of finance is due. Money and other liquid financial instruments can be seen as risk-absorbing bumpers. People accumulate large monetary balances in times and places when and where they perceive a lot of manageable risk, i.e. where they perceive something likely to disrupt the incumbent business, and they want to be on the right side of the disruption.
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.