“Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, one by one.” ―
Most of us have read a very good book, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective wisdom shapes business, economies, societies and nations.
https://www.amazon.com/dp/B0002P0CHY/ref=cm_sw_r_tw_dp_U_x_DVnKEbD8Q8BWP via @amazon
“In this endlessly fascinating book, New Yorker columnist James Surowiecki explores a deceptively simple idea that has profound implications: large groups of people are smarter than an elite few, no matter how brilliant. Groups are better at solving problems, fostering innovation, coming to wise decisions, even predicting the future.”
Our thesis – Always go “Against the Herd in making simple decisions”!
Ro (Reproduction Number) of a signal in a Herd >1 , without immunity to the effect
In reality like stock markets the Ro = 99. Therefore computed it is
Ro =1-(1/99) = 99% of participants get infected without immunity because when the individuals outside the herd decide to profit from the herd participation with a simple buy or sell decision, greater than 99% of individual all participants underperform”
“The basic reproduction number, R0, is defined as the expected number of secondary cases produced by a single (typical) infection in a completely susceptible population”, It is important to note that R0 is a dimensionless number and not a rate, which would have units of time−1 . Some authors incorrectly call R0 the “basic reproductive rate.”
James Holland Jones, Department of Anthropological Sciences Stanford University May 1, 2007
What is the definition of a herd?
a situation in which people act like everyone else without considering the reason why
But before we begin let us examine some data on the behaviour of herds in markets and the Ro that created the virus of returns that were built on turning assets in a balance sheet such as Cash into liabilities by borrowing debt and compounding debt into equity via stock buybacks! Have you ever imagined the Ro for the entire herd of investors in stock markets?
Why would executives and boards allow each other to convert cash in balance sheets into compounding liabilities (Owners Funds, Current & Long Term Liabilities) creating a massive bubble in value?
Our theory in Calculus of Causality, Cause => Behaviour => Effect.
For most executives and often boards, stock grants are based on RoE. Simple. With these contracts executives work week on week, month on month and quarter on quarter to create value which must be seen in the EPS (earnings per share) which investors want to see to further accelerate the rate of return on equity, creating value for all. the result. Behaviours of Quantum Rationality (Yes AND No or Yes NAND No) to look for opportunities other than the business itself.
What a great opportunity post the 2008/09 crack of TARP and the ability to borrow capital close to zero interest rates. The result $4.6 Trillion by companies from 2013 to 2019 which is greater than the entire FCF (Free Cash Flows) of all companies listed put together for stock buybacks accelerating the velocity of the indices, while compounding Liabilities in the balance sheets without job creation for the core business. This is the greatest asset bubble in the history of capitalism, over leveraged balance sheets without value. What comes next is the effect.
With the current market cap of the DJIA at over $8.33 T, the simple napkin calculation without going into specific ratios of debt serviced, etc. back, et al. the borrowing to create the current stock markets are over 55% of the entire market cap of the index, while corporate debt is at $10T or over 120% of the entire market capitalisation of the Dow Jones!!!
So this really means that the real deleveraging will be over 50% of current values of all assets and you can compute what that means for stock prices in what is about to follow when the Ro>99 unwinds. Real value we will have to go back to 2014 to understand what the Time Value of Money/NPV and apply these numbers to see where the real value lies. This picture is not pretty (we have proprietary algorithms based on mapping value capture in a market).
The real growth in the markets came from herds with a Ro >99 or at =1-(1/99) = 98.99% of all investors affected, albeit with one issue, no herd immunity!
Examples below are three random indices, DJIA, Russell 1000 and SENSEX from 2014 to 2020 January/February. The herd Ro is clear.
The Ro for the herd in the month of April across a random selection of markets is a clear reflection of the herd continuing to miss the point.
An excellent read is an article below which will give you a very good insight into what is really going on.
We’ve Sailed Into The Doldrums: Implications Of The Demise Of Financial Engineering
Which is why the greatest performers in herd markets like stock markets are individuals. To name a few in random order, Ed Thorp (Princeton/Newport Partners, 20% return, beat the market for 30 years), Warren Buffet (Berkshire Hathaway), Jim Simons (Rennaisance), Paul Singer (Elliot), Ray Dalio (Bridgewater), Seth Klarman (Baupost), Larry Fink (Blackrock), Daniel Och (Och-Ziff), David Seigel (Two Sigma), Peter Lynch (Fidelity), George Soros (Quantum Fund), Jim Rogers (Quantum), et al have been individuals that understood the Ro behaviour without immunity in herds and went against the complex problems that herds and made simple decisions and heuristics which by design are;
- Addressing Wicked problems (like stock markets)
- Sub-optimal solution design (around herd behaviour)
- Have Large Search Spaces (Finding possible answers to a problem)
- NP hard ((non-deterministic polynomial-time hardness)
Look at the recent controversy of Bill Ackman’s CNBC interview on March 18, 2020 people claimed he caused the markets to stutter. He did something very simple. Go against the herd and won. He invested $2.7m and resulted in a $2.6B return of a 1000x! Ackamn bet on the Ro of the herd of investors, not the Ro of the SARS CoV2 virus and it paid off. But this is not unique and happens every day. We call this the “foolish herd” theory.
We look at the basis of the thesis. Why against the herd? Let’s us start in what we believe is the best guide to repeatable history or “back to the future “syndrome that we constantly see from highs and lows of stock markets to responses to the current pandemic such as SARSCoV2 and stimulus packages seen as a solution to all challenges of Livelihoods without the life, or lives without the livelihoods in its mechanism design solution to a wicked problem.
“We find that whole communities suddenly fix their minds upon one object, and go mad in its pursuit; that millions of people become simultaneously impressed with one delusion, and run after it, till their attention is caught by some new folly more captivating than the first.”
In very interesting research conducted on how any colonies make decisions it was found that colonies performed well in difficult tasks but individuals when tasks were easy.
The spread of the SARS CoV2 or COVID19 as it is called is a clear reflection of herds and decision making. While everyone knew that isolating and working around the model was a clearer way the reality was different. From heads of state to the WHO to local governments to individuals, the herd did not make simple decisions on whether the SARS CoV2 was a pandemic (WHO), location, mobility, capital infusion, etc. etc.
What really spread the virus was the Ro of human behaviour not the Ro of the virus itself!
So why these models repeat themselves? The key is to understand the “Causality” and its effect. We deal with wicked problems on a continuous basis from how to deal with a sudden job opportunity or losses in investment in equity or a health challenge that is absolutely unexpected.
Social apps are a clear example of herd behaviour. The Ro of people in social networks is what lead to the creation and rapid spread of all social apps.
The work is proprietary and meant to understand how to design mechanism around wicked problems and how can an interdisciplinary approach of behaviour-economics-computer science together create potential unique game-changing solutions with the potential to impact Lives + Livelihoods.
Apply these principles we are looking at products, technologies, platform designs, portfolio stock models, policy design, etc. We have a different take against the wisdom of crowds and that is based on how to look at herds and how to make decisions (behavioural and decision sciences). To start with, look around you, your social interactions via chats or groups or apps that one uses, look at the stock markets and you’ll stop following the herd. But will you?