Looking at Stock Market as A “Complex Adaptive System “

One of my favourite book other than “ Game and Chaos Theory “ is this called “ Complex Adaptive Systems: An Introduction to Computational Models of Social Life, by John H. Miller and Scott E. Page “.

What is “ Complex Adaptive System “  and why is it so important to understand the stocks market? other than “behavioural or psychological “ aspect of the market.

We often look at the things or world as “linear “ and has a very clear “cause and effect “ relationship. The bulk of economics studies is based on equilibrium systems: for example, a balance between supply and demand, risk and reward, price and quantity, this view stems from the idea that economics is a science similar to Newtonian physics, with an identifiable link between cause and effect and implied predictability. When the equilibrium system is hit by an exogenous shock, it absorbs the shock and returns to an equilibrium state.

In most of a complex system, the causation is very weak and doesn’t end up with “intended consequence or relationships”, like “interest rate increase doesn’t lead to price drop among REITs stocks as predicted “, “ Trump’s wining as President of USA doesn’t end up with catastrophic for stocks market as forecast “, “ loose monetary policy doesn’t cause hyperinflation as some economist suggested “ and many more ….

Why ??

Because the stakeholders in the stock markets use their interpretation of the market condition, speculation and information available to them to make their trading decisions. This results in complex interactions between them and eventually the stock markets form a complex system where the stock price of various firms show an emergent behaviour that is difficult to predict.

Normally, the behaviour of the financial market is studied using time-series data and in “ linear “ extrapolation. The analysis of individual time series data becomes very complicated as the number of stocks increases in the market.

Same for analysis being done at the company level, where we use DCF ( Discounted Cash Flow “ model to extrapolate the future cash flow in order to derive the intrinsic value of certain stock.

Also, stock the market keeps experiencing several ups and downs and occasionally faces severe crisis situation that impacts economic growth. Financial crises often arise due to anomalous behaviour of various agents in the financial systems and causing the “ boom and bust “  and stocks market like “pendulum swing “.

So, what exactly is the “ Complex Adaptive System “?

Below from Wikipedia :

A complex adaptive system is a system in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system's behaviour. The study of complex adaptive systems is highly interdisciplinary and blends insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behaviour.

They are complex in that they are dynamic networks of interactions, and their relationships are not aggregations of the individual static entities, i.e., the behaviour of the ensemble is not predicted by the behaviour of the components. They are adaptive in that the individual and collective behaviour mutate and self-organize corresponding to the change-initiating micro-event or collection of events. They are a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to adapt to the changing environment and increase their survivability as a macro-structure

Overview

The term complex adaptive systems, or complexity science, is often used to describe the loosely organized academic field that has grown up around the study of such systems. Complexity science is not a single theory—it encompasses more than one theoretical framework and is highly interdisciplinary, seeking the answers to some fundamental questions about living, adaptable, changeable systems. The study of CAS focuses on complex, emergent and macroscopic properties of the system. John H. Holland said that CAS "are systems that have a large number of components, often called agents that interact and adapt or learn."

Typical examples of complex adaptive systems include: cities; firms; markets; governments; industries; ecosystems; social networks; power grids; animal swarms; traffic flows; social insect (e.g. ant) colonies; the brain and the immune system; and the cell and the developing embryo.

Human social group-based endeavours, such as political parties, communities, geopolitical organizations, war, and terrorist networks are also considered CAS. The internet and cyberspace—composed, collaborated, and managed by a complex mix of human-computer interactions is also regarded as a complex adaptive system. CAS can be hierarchical, but more often exhibit aspects of "self-organization."

Characteristics

Some of the most important characteristics of complex systems are:

·         The number of elements is sufficiently large that conventional descriptions (e.g. a system of differential equations) are not only impractical but cease to assist in understanding the system. Moreover, the elements interact dynamically, and the interactions can be physical or involve the exchange of information

·         Such interactions are rich, i.e. any element or sub-system in the system is affected by and affects several other elements or sub-systems

·         The interactions are non-linear: small changes in inputs, physical interactions or stimuli can cause large effects or very significant changes in outputs

·         Interactions are primarily but not exclusively with immediate neighbours and the nature of the influence is modulated

·         Any interaction can feedback onto itself directly or after a number of intervening stages. Such feedback can vary in quality. This is known as recurrency

·         The overall behaviour of the system of elements is not predicted by the behaviour of the individual elements

·         Such systems may be open and it may be difficult or impossible to define system boundaries

·         Complex systems operate under far from equilibrium conditions. There has to be a constant flow of energy to maintain the organization of the system

·         Complex systems have a history. They evolve and their past is co-responsible for their present behaviour

·         Elements in the system may be ignorant of the behaviour of the system as a whole, responding only to the information or physical stimuli available to them locally

Full text here: https://en.wikipedia.org/wiki/Complex_adaptive_system

A Stock Market is Complex because there are multiple interactions between its agents (retail and institutional buyers and sellers ) which all could happen at a split second.

Another renowned economist “ Michael J. Mauboussin “ the author of the book  “ More Than You Know “ have below observation and some insights for us to take note. 

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Remember the phrase “more is different.” The most prevalent trap is extrapolating the behaviour of individual agents to gain a sense of system behaviour. If you want to understand the stock market, study it at the market level. Consider what you see and read from individuals as entertainment, not as education. Similarly, be aware that the function of an individual agent outside the system may be very different from that function within the system. For instance, mammalian cells have the same metabolic rates in vitro, whether they are from shrews or elephants. But the metabolic rate of cells in small mammals is much higher than the rate of those in large mammals. The same structural cells work at different rates, depending on the animals they find themselves in.

A system is tightly coupled when there is no slack between items, allowing a process to go from one stage to the next without any opportunity to intervene. Aircraft, space missions, and nuclear power plants are classic examples of complex, tightly coupled systems. Engineers try to build in buffers or redundancies to avoid failure, but frequently don’t anticipate all possible contingencies. 

Most complex adaptive systems are loosely coupled, where removing or incapacitating one or a few agents has little impact on the system’s performance. For example, if you randomly remove some investors, the stock market will continue to function fine. But when the agents lose diversity and behave in a coordinated fashion, a complex adaptive system can behave in a tightly coupled fashion. Booms and crashes in financial markets are an illustration.

Dealing with complex systems is inherently tricky because the feedback is equivocal, information is limited, and there is no clear link between cause and effect. A simulation is a tool that can help our learning process.

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My recent purchased of “Comfort DelGro and ThaiBev “ may look great for me at “individual /agent” level, who have received positive feedback from loops ( such as information or news I received, analysts report peers factors etc ). But when things starts to evolve, it may turn out to be good or bad and also the ultimate performance may influence by market’s performance in “aggregate level”.

Which I ultimately call it “ LUCK “ !!

Cheers.

Quote Of The Day :

"Adaptive Social System composes of interacting, thoughtful (but perhaps not brilliant ) agents." from the book "Complex Adaptive System by John H Miller & Scott E Page