INSIGHTS

FRACTAL MARKET HYPOTHESIS

Ed Peters
|
June 5, 2020

The world – and markets – look very different than they did at the start of 2020. In my 1994 book, I outlined the Fractal Market Hypothesis (FMH), which addresses periods of instability like the one we’re going through. Briefly, here is a summary of the underlying logic:

  1. The market consists of many investors with different investment horizons.
  2. The information set that is important to each investment horizon is different. The longer-term horizons are based more upon fundamental information, and shorter-term investors base their views on more technical information. As long as the market maintains this fractal structure, with no characteristic time scale, the market remains stable because each investment horizon provides liquidity to the others.
  3. When long-term investors begin to question the validity of their information, their investment horizon shrinks, making the overall investment horizon of the market more uniform.
  4. When the market’s investment horizon becomes uniform, the market becomes unstable because trading becomes based upon the same information set, which is interpreted in a more uniform way. So good news causes increased buying while bad news results in increased selling.
  5. Liquidity dries up, causing high volatility in the markets, because most of the trading is on one side of the market.
  6. Eventually the long term becomes more certain and stability returns to the market as investment horizons broaden and become more diverse.
  7. During periods of low uncertainty, markets will exhibit well-behaved, finite variance statistics. In high uncertainty environments, markets will exhibit fat-tailed risks and unstable variance more associated with the stable Paretian distribution as described by Mandelbrot (1962).

Since I introduced the hypothesis, there have been a number of empirical studies which support the FMH in events like the tech bubble and the Great Recession (both of which occurred after publication). In this paper, for example, Kristoufek uses wavelet analysis and shows that during a crisis the shorter frequencies dominate, while during the low vol periods there’s no characteristic frequency, just as the theory predicts. There have since been other researchers who have used Kristoufek’s technique on other markets, including emerging markets and even bitcoin, and found that the FMH applies. Since this most recent crash looks to be another example of the conditions outlined in the hypothesis, it may be worth considering how this type of behavior could impact your portfolio.

https://www.nature.com/articles/srep02857?proof=true1


Past results are not indicative of future investment results. Commodities trading involves substantial risk of loss.
© First Quadrant, L.P. 2020. Intended for Institutional and Qualified Eligible Persons Use Only. The views expressed are the views of First Quadrant, L.P. only as of the date shown and are subject to change without notice based on market and other conditions. Forecasts, estimates, and certain information contained herein are based upon proprietary research and should not be considered as investment advice, recommendation, or solicitation of any particular security, strategy or investment product. This publication has been prepared without taking into account your objectives, financial situation or needs. Before acting on this information, you should consider its appropriateness having regard to your objectives, financial situation or needs. It is your responsibility to be aware of and observe the applicable laws and regulations of your country of domicile. All material has been obtained from sources believed to be reliable, but its accuracy is not guaranteed. No part of this material may be reproduced in any form, or referred to in any other publication, without express written permission.

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