Fractal Markets Hypothesis as an alternative to efficient markets

 

We know that the efficient markets hypothesis as originally positioned by Fama is not true. The behavioralists put a stop to the idea that markets are always rational and embed all information in prices, so good science requires an alternative hypothesis or a different way of thinking about how markets use information and generate price dynamics. We have earlier mentioned one alternative "The Discovering Markets Hypothesis - Worth a close look to add to our thinking of market dynamics" which focuses on how competing narratives impact prices. Another alternative has been developed by Edgar Peters who focuses on fractals and the fact that different investors have different time horizons that change with uncertainty. The fractal approach has merit when looking for regime shifts but may be harder to explain the day-to-day movements in price. The work of Peters has been around for some time yet has not taken hold. Neither has the work associated with Discovering markets. We will have to wait while further work is developed. 

The Fractal Markets Hypothesis of Edgar Peters states:

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 (1964).