Fractal Markets Hypothesis (FMH)

What Is Fractal Markets Hypothesis (FMH)?

The fractal markets hypothesis (FMH) asserts that time series data of stock market prices exhibits properties similar to fractals and attributes these properties to varying time horizons and information among investors.

Understanding Fractal Markets Hypothesis

According to the fractal markets hypothesis (FMH), convergence of time horizons and information sets toward the short-term during times of heightened market uncertainty can be modeled as a collapse of the fractal structure of market prices. This can produce sudden spikes in market volatility and lack of market liquidity witnessed during crashes and crises. The FMH is an extension of the widely utilized efficient market hypothesis (EMH).

The FMH was developed by Ed Peters in his 1994 book, Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. Fractal patterns in general display the property that the appear to be similar or self-repeating when viewed at different scales. Peters argued that time series of stock market prices do not simple resemble a random walk (as described by the EMH), but actually exhibit fractal properties in that they have a similar structure when sampled at different time intervals.

This fractal pattern in financial markets creates a distinction between long-term investors who may focus on market fundamentals and short-term investors who may focus more on technical analysis. Because the different groups of investors operate over different investment horizons with different sets of information they can help to provide market liquidity to one another which can help to stabilize the long run despite day-to-day volatility. Trades by long-term investors balance the trades of short-term investors — ensuring securities can easily be traded without dramatically impacting valuations.

However, that changes in bearish markets. Problems can occur when a sudden shock leads to heightened uncertainty among long-term investors causing them to shift their focus on to a short time horizon and information relevant to short-term fluctuations. This results in a market where all or most investors are short-term investors, with few long-term counterparts to supply liquidity for short-term trades.

Suddenly, all investors behave like short-term investors, reacting to short-term price movements and information. This shift causes markets to become less liquid and more inefficient. The withdrawal of liquidity from the market can produce extreme short-term swings in market prices that characterize sudden market crashes.

2008 financial crisis led many observers to question dominant economic theories and perspectives on markets. EMH posits that investors act rationally and markets are efficient, meaning prices should always reflect an asset’s true value. That way of thinking was questioned once again in the wake of the Great Recession.

Alternative theories, such as noisy market hypothesis, adaptive market hypothesis, and FMH, that examine investor behavior throughout a market cycle, including booms and busts, gained prominence.

Important

Fractal market hypothesis seeks to explain investor behaviors in all market conditions, something the popular efficient market hypothesis fails to do.

Chaos Theory, Fractals, and Markets

Falling into the framework of chaos theory, the FMH explains markets using the concept of fractals — fragmented geometric shapes that can be broken down into parts that replicate the shape of the whole.

With respect to markets, advocates of this theory claim that stock prices move in fractals. They use this as the basis for a form of technical analysis; in the same way that the patterns of fractals repeat themselves along all time frames, stock prices also appear to move in replicating geometric patterns through time.

That analysis focuses on the price movements of assets based on the belief that the history of stock prices repeats itself at different scales. Following this framework, the FMH studies investor horizons, the role of liquidity, and the impact of information through the business cycle.

Limitations of Fractal Market Hypothesis

Perhaps the most glaring problem with quantifying and utilizing the FMH is deciding the length of time that the “fractal” pattern should be repeated in trying to project market direction. A pattern could be repeated on a daily, weekly, monthly, or even longer basis. But since fractals are inherently recursive in an infinite cycle, a trader may not know when to start or at which scale to operate.

It is, therefore, extremely difficult to accurately project the time period of repetition, despite it likely being closely related to the investment horizon. It is also worth noting that the pattern would likely not be identically repeated.