The gum of bubbles (Part 1)

The gum of bubbles (Part 1)

We are all familiar with terms like bubbles, booms, and busts especially in terms of stock & commodity trading and our global financial system. Booms are usually the term used when stock prices move upwards and investors make sound returns. Busts in turn are the opposite, when trades in stocks and commodities fall or move downward causing investors to loose money. Bubbles in turn refer to events when stocks or commodities are priced artificially high, followed by a correction, which always ends up with most investors loosing big, coupled to unexpected systemic consequences. Technically “Bubbles” are instances where information that comprise the backbone of the supposed value of something, are based upon flawed data and a great many implicit assumptions. A good example is last week’s “corruption summit” in London, when the host Head-of-state openly referred to 2 African countries as being the most corrupt. The implicit assumption in this context being Anglo-American countries are not corrupt, only developing & African nations. We will have to wait until the corruption Bubble burst to see such implicit assumptions smack us in the face, but let us focus on our topic, the very real Bubble of derivatives.

Not too long ago, valuing stocks and commodities used fairly simple formulae or algorithms, some more sophisticated than others. A common tool used in the earlier days was typically the “Black & Scholes” model. This trilogy is less about the valuation tools used and /or their accuracy, since all tools in use contain an error value, where parts of the algorithm contain unknown factors. This is the essence of the point, meaning that in order to always pick winning stocks our traders or brokers must have complete or full information, which is a scientific impossibility1. It is thus accepted that all versions of stock and commodity pricing tools inherently contain assumptions (unknowns or error values) about the stock or commodity. Other features that may be included in tools, algorithms or formulae could range from Industry specific indicators (e.g. global rates of oil production; consumption rates, stock-piles; etc.); Performance history of the stock (e.g. blue-chip stocks); Management (e.g. experience of executives running the corporation efficiently); Market size (e.g. growth & potential of consumption patterns); Scarcity (e.g. dwindling mine reserves, or untapped concessions); etc. The point of highlighting these characteristics are to explain that apart from having the error value that allows manipulation of stock pricing (e.g. a trader with a high risk profile may lower this value in his or her algorithm). Add to this the ability to direct news and information about one or more of the parameters noted above (e.g. Announcing a new concession, which would prop-up or drive up the share price of the stock); (Labor patterns influencing pricing & risk)1. All of these elements can, and are being constantly manipulated to varying degrees1. Some of the biggest global scandals were related to activities by traders or firms, trying to artificially propped-up share prices, or alternatively, drive them down, well below their actual value. Ultimately, we have no sound basis for the real value of stocks, other than what the “market tells us”1,2. Considering that “the market” uses some and more of the techniques noted above, suggest the pricing of stocks, shares and futures contracts are technically unknown in real terms. This is further accepted under what is called the Efficient Markets Hypothesis – a flawed theory suggesting: “markets know best” & are able to regulate themselves2, meaning the less oversight, assurance & State interference the better. The theory also denies the possibility of Bubbles and crashes, & still faithfully taught at our “best” economics departments and business schools1, 2. Yet, globally we are in a deepening recessionary position, because we implicitly assume markets to be efficient & able to regulate themselves1-4.

References:

• Udemans, F., 2008, The golden thread: escaping socio-economic subjugation, an experiment in applied complexity science, Authorhouse UK;

• White, M., 2010, Complex Adaptive Systems in Finance and Strategy, https://www.oocities.org/whitemark1/; Mantega, M., & Stanley, H., 2000, An introduction to Econo-physics: acorrelation and complexity in Finance, Cambridge University Press; Crocket, A., 2011, Reforming the global financial architecture: Key note address – Asia and the global financial crises; Asia Economic Policy Conference; Claessens, S., & Kodres. L., 2014, The regulatory responses to the global financial crisis: Some uncomfortable questions: IMF working paper; Research department and institute for capacity development;

• Arthur, W.B. et al, 2013, Economics and the Modern Theories of Cognitive Behavior, SFI working paper;

• Cohan, P., 2010, Big risk: $1.2 Quadrillion derivatives market dwarfs world GDP, Investor center, InvestorCenter;