By John Edwards

The butterfly effect concept has become important in the finance world as globalization continues to increase and capital markets connect. Volatility in one small area of the international markets can grow rapidly and bleed into other markets, and a hiccup in one corner of the international markets can have global consequences. Improvements in technology and wider access to the Internet has increased the degree to which international markets influence each other. This has led to more episodes of extreme market volatility.

The butterfly effect has become well-known in popular culture, and the concept has clear applications to finance. It and chaos theory may provide a partial explanation for the unpredictability of capital markets.

Origin and Meaning of Butterfly Effect

The phrase “the butterfly effect” was first coined during a scientific meeting in 1972. Scientist Edward Lorenz gave a talk on his work regarding weather prediction models. The phrase suggests that the flap of a butterfly’s wings in Japan could create a small change in the atmosphere that might eventually lead to a tornado in Texas.

Lorenz studied how small differences in initial values led to large differences in weather models at the Massachusetts Institute of Technology. In 1961, he had entered an initial condition in a weather model as 0.506, rather than the precise number of 0.506127, which resulted in a completely different and unexpected weather pattern. In 1963, he wrote a paper on this concept, titled “Deterministic Nonperiodic Flow.” The butterfly effect concept shows how difficult it is to predict dynamic systems, such as weather and financial markets. Study of the butterfly effect has led to advances in chaos theory.

Application of Chaos Theory to Markets

Capital markets go through alternating periods of calm and storminess. However, they are not always chaotic, and the shift between calm and chaos is often sudden and unpredictable. Some believe that these concepts of chaos theory can be used to understand how financial markets operate.

Markets tend to grow bubbles that eventually pop with drastic consequences. Financial bubbles often grow because of positive feedback. When investors make money during a rise in the financial markets, other observers think the investors must have made a smart decision, which leads the observers to invest their own money in the markets. The result is more buying and stock prices going higher. The positive feedback loop leads to prices beyond any logical or justifiable level. The loop eventually ends, and the last investors in are left hanging with the worst positions.

The same concept can explain volatile bear markets. The markets can suddenly shift due to outside factors, which causes investors to pay attention only to negative news. Initial selling leads to more selling as market participants liquidate their positions. The negative feedback loop tends to accelerate quickly, often resulting in a market full of undervalued stocks.

Fractals and the Markets

Prominent scientist Benoit Mandelbrot applied his work in fractals in nature to financial markets. He found that examples of chaos in nature, such as the shape of shorelines or clouds, often have a high degree of order. These fractal shapes can also explain chaotic systems, including financial markets. Mandelbrot noted that asset prices can jump suddenly with no apparent cause.

Many in the markets tend to dismiss the extreme events that occur less than 5% of the time. Mandelbrot argued that these outliers are important and play a significant role in financial market movements. Traditional portfolio theory tends to underestimate how often these high-volatility events occur. While his fractals cannot predict price movements, he argued that they could create a more realistic picture of market risks.

Examples of the Butterfly Effect in Markets

Although technology has increased the impact of the butterfly effect in global markets, there is a long history of financial bubbles going back to the tulip market bubble in Holland during the 17th century. Tulips were a status symbol among the elite. They were traded on exchanges in Dutch towns and cities. People sold their belongings to begin speculating on tulips. However, prices began to drop and panic selling ensued.

There are more recent examples of bubbles. On October 1987, known as Black Monday, the Dow Jones Industrial Average (DJIA) lost around 22% in one trading day, the largest percentage drop ever for that market. There was no apparent cause for the drop, though the DJIA had some large down days the week before, and there were international issues in the Persian Gulf. In retrospect, issues with panic selling and perhaps program trading might be partly to blame.

In 2015, the Chinese stock market encountered significant volatility, dropping over 8% in one day. Similar to Black Monday, there was no single event or cause for the drop. This volatility quickly spread to other markets, with the S&P500 and the Nikkei losing around 4%. Also like Black Monday, there had been weakness in the Chinese markets in prior months.

Chinese officials had begun devaluing the renminbi. However, the main cause was likely the high degree of margin used by Chinese retail investors. When prices began to drop, investors received margin calls from their brokers. Retail investors were forced to liquidate their positions quickly to meet the margin calls, leading to a negative feedback loop of selling. In years prior, the Chinese government encouraged people to put their money in the market. Markets will only become more interconnected as technology continues to improve, and the butterfly effect will continue to be a factor in global markets.