Chaos Theory

Reviewed by Annapoorna | Updated on Nov 11, 2021


What is Chaos Theory?

Chaos theory refers to a mathematical theory that justifies for the possibility of getting random results from ordinary equations. The main rule behind this theory is the base understanding that small events can significantly affect the outcomes of apparently unrelated events. This theory is also referred to as ‘non-linear dynamics’. For example, there is a popular theory known as the butterfly effect. It has proved that the cause of a hurricane in China could be due to a butterfly flapping its wings in New Mexico.

Application of Chaos Theory in Stock Markets

Some fields where the theory can see reference today are geology, mathematics, microbiology, biology, computer science, economics, engineering, finance, to mention a few. The chaos theory is an effort to watch and understand the base for complex systems that may appear to be without any order or form, at first glance.

The financial markets are covered in this category with the additional benefit of coming with a rich historical data set. The theory can illustrate one interesting financial phenomenon on how seemingly healthy financial markets can suffer from sudden shocks and crashes. Those mathematicians who believe in the chaos theory have a concept. They state that the price is the very last thing to change for a stock, bond or any other security. This belief suggests that times when there is low price volatility, it necessarily need not reflect the actual health of the market. Looking at the price as the lag indicator leaves investors in the dark to the extent of enabling them to spot crashes before they happen.

Understanding Chaos Theory in Economics

The economic models can be improved through the application of chaos theory. Although it is an extremely complex task to predict the health of an economic system and what factors influence it the most. Economic and financial systems are basically different from those in the classical natural sciences. It is because the former is inherently probable as they result from the interactions of people. Therefore purely deterministic models cannot provide an accurate representation of the data.

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