What Is Fractal Markets Hypothesis (FMH)?

The fractal markets hypothesis (FMH) is an alternative investment theory to the widely utilized efficient market hypothesis (EMH). It analyzes the daily randomness of the market and the turbulence witnessed during crashes and crises.

Key Takeaways

  • Fractal markets hypothesis analyzes the daily randomness of the market—a glaring absence in the widely utilized efficient market hypothesis.
  • It examines investor horizons, the role of liquidity, and the impact of information through a full business cycle.
  • The market is considered stable when it is comprised of investors of different investment horizons given the same information.
  • Crashes and crisis happen when investment strategies converge to shorter time horizons.

Understanding Fractal Markets Hypothesis

The 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 fractal market hypothesis (FMH), that examine investor behavior throughout a market cycle, including booms and busts, gained prominence. Formalized in 1991 by Edgar Peters, the fractal market hypothesis (FMH) was introduced as a way of creating a foundation for the technical analysis of the pricing adjustment of assets under the central premise that history repeats itself. 


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

The fractal markets hypothesis (FMH) dictates that financial markets, and particularly the stock market, follow a cyclical and repeatable pattern. One thing it has in common with EMH is that both theories rely heavily on the prevalence of information with investors. From there, they take different paths.

According to the fractal markets hypothesis (FMH), during stable economic times, information does not dictate investment horizons and market prices. There are various numbers of long-term investors who balance the numbers of short-term investors—ensuring securities can easily be traded without dramatically impacting valuations.

That changes in bearish markets. Suddenly, all investors trend towards short-term horizons, reacting to price movements and information. This shift causes markets to become less liquid and more inefficient, triggering crashes and crises.

Fractal Market Hypothesis Method

Falling into the framework of chaos theory, the fractal markets hypothesis (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, one can see that stock prices move in fractals. Due to this characteristic, technical analysis is possible: 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 history repeats itself. Following this framework, the fractal markets hypothesis (FMH) studies investor horizons, the role of liquidity, and the impact of information through a full business cycle.

Limitations of Fractal Market Hypothesis

Perhaps the most glaring problem with quantifying and utilizing fractal markets hypothesis (FMH) is deciding the length of time that the “fractal” pattern should be repeated in a market leading projection. 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.