In financial markets, speculators bet that the current price of a security is above or below its future value, and buy or sell according to the current price.
In an efficient market, prices on traded assets, e.g., stocks, bonds, or property, already reflect all known information. No investor can have an advantage in predicting a return on a stock price - since no one has access to information not already available to everyone.
Empirical market analyses have consistently found problems with the efficient market model, the most consistent being that stocks with low price-to-earnings outperform other stocks.
What are Market Returns?
A stock market graph is shown in figure 1. Stock market fluctuations as measured over a designated time interval are termed “market returns”. Some of the fluctuations are negative, some are zero and some are positive. It makes sense that most fluctuations will be relatively small and that a few will be large. In theory, this gives rise to a bell curve distribution as in figure 2 (Singh & Prabakaran). Actual market returns are those recorded by the bar graph. Note, the actual data do not exactly match the bell distribution - the data is slightly skewed. This result is typically found by most researchers and has profound implications as discussed below.
The Efficient Market Hypothesis (EHM)
The traditional school of thought assumes that markets are “efficient”. That is, prices already reflect all current information that could anticipate future events. Investors absorb information linearly, that is, as it comes out. Therefore, only the speculative, stochastic (random) component could be modeled and this distribution is simply a bell curve. The value component, change in price due to changes in value, cannot be modeled.
Inherent in the efficient model is the notion that investors are completely rational. Chief investment strategist, Edgar Peters, believes that EHM originally gained popularity with academics because the mathematics was nicely tractable and easily modeled capital markets.
Challenges to Efficiency
If one considers for a moment that market returns are normally distributed with “white noise”. Then returns must be the same at all investment horizons. Forecasting changes in economic value would not be useful to speculators; nor would planned approaches to investment. Fundamental or technical analysis could not be used to achieve superior gains.
Of course, the implications of EHM present obvious anomalies to real life experiences. There are investors who indeed have beaten the market. One of the greatest speculators, W.G. Gann used his own brand of technical analysis to beat the markets. Warren Buffett’s investment strategy focuses on undervalued stocks, setting an example for numerous followers.
The Fractal Market Hypothesis (FMH)
Fractal Market Hypothesis was originally proposed by Edgar Peters, and explores the application of chaos theory and fractals to finance.
The main idea of the fractal model is that investors may not directly react to information, according to the information being received. Instead, investors may react with delay, as in the case of information confirming a recent trend change. This type of behavior is a non-linear reaction, as opposed to a linear reaction assumed by the efficient market model.
The fractal model accounts for markets that consist of many irrational investors trading at different investment horizons. As long as the market maintains a fractal structure with no characteristic time scale, the market should remain stable. If all investor's time horizon were to become unique, the market would become unstable because everyone would respond (trade) according to the same information at the same time.
The reader may be interested in a related article - Investors' Crowd Behavior in Financial Markets.
References:
- "Chaos and Order in the Capital Markets". Edgar E. Peters. John Wiley & Sons, NY. 1996.
- “On the Distribution of Returns & Memory Effects in Indian Capital Markets”. J.P. Singh and S. Prabakaran. International Research Journal of Finance and Economics. Issue 14 (2008) pdf.
Harry P. Schlanger - Hello, I started out as a physicist working for research organisations. Mostly in the area of heat transfer in solids and porous media. ...
