Chaos theory is a complicated mathematical theory that seeks to explain the effect of seemingly insignificant factors. Chaos theory is considered by some to explain chaotic or random occurrences, and the theory is often applied to financial markets as well as other complex systems such as predicting the weather. Chaotic systems are predictable for a while and then appear to become random.
The Origins of Chaos Theory
The first real experiment in chaos theory was conducted by a meteorologist, Edward Lorenz. Lorenz worked with a system of equations to predict the weather. In 1961, Lorenz wanted to recreate a past weather sequence using a computer model based on 12 variables including wind speed and temperature. These variables, or values, were graphed with lines that rose and fell over time. Lorenz was repeating an earlier simulation in 1961.
However, on this day, Lorenz rounded his variable values to just three decimal places instead of six. This tiny change drastically transformed the whole pattern of two months of simulated weather. Thus, Lorenz proved that seemingly insignificant factors can have a huge effect on the overall outcome.
Chaos theory explores the effects of small occurrences that can dramatically affect the results of seemingly unrelated events.
Chaos Theory and the Markets
There are two common fallacies about stock markets. One is based on classical economic theory and claims that markets are 100 percent efficient and unpredictable. The other theory is that markets are, at some level, predictable. Otherwise, how do big trading houses and investors consistently make profits?
The truth is that markets are complex and chaotic systems and their behavior has both systemic and random components. Stock market forecasts can be precise only to a certain extent.
As Lorenz proved, complex chaotic systems are vulnerable to minor changes, and these can disrupt a system, pushing it far away from its equilibrium. Market system dynamics can be described as two basic feedback and causal loops that influence various aspects of the stock market. A positive feedback loop is self-reinforcing. For example, a positive effect in one variable increases the other variable, which, in turn, also increases the first variable. This leads to exponential growth in the system, moving it out of its equilibrium and eventually leading to a collapse of the system (a bubble). Conversely, a negative feedback loop has a similar effect, the system responds to a change in the opposite direction.
Periods with high uncertainty may not be caused just by system dynamics. Environmental factors such as natural disasters, earthquakes, or floods can also cause markets to be volatile as can sudden drops in a single stock.
In finance, chaos theory argues that price is the last thing to change for a security. Using chaos theory, a change in price is determined through mathematical predictions of the following factors: a trader's personal motivations (such as doubt, desire, or hope, all of which are nonlinear and complex), changes in volume, the acceleration of changes, and momentum behind the changes.
While some theorists maintain that chaos theory can help investors boost their performance, the application of chaos theory to finance remains controversial.