We believe that markets are inefficient because time and again market participants behave illogically, basing their decisions on emotion rather than rational thinking. Emotionalism is generally agreed to be the largest contributing factor to failure in the market place. When traders become emotional they become irrational, and when they become irrational they make bad investment decisions. In fact, some of the most epic failures in business as a whole can be linked to psychology. Emotions like fear, greed, regret, remorse, confidence and pride often result in flawed decision making. It's these bad decisions, decisions driven by emotion, that create temporary mispricing's in the stock market that can be exploited for profit by the astute trader.
If markets were efficient, in other words market participants behaved rationally and logically, trading would offer few opportunities for consistent profit. However, as we shall illustrate below, markets are not efficient. Some of the most compelling evidence of market inefficiency caused by trader irrationality has been put forth by proponents of behavioural finance. Behavioural finance, when market participants base decisions on emotion, is diametrically opposed to theories of random market behaviour and efficient market hypothesis. In 2002 Daniel Kahneman was awarded the Nobel Prize in economics for his work on behavioural finance which resulted in broad acceptance of the theory by the academic community.
We are avid supporters of behavioural finance and focus all our research efforts within this field.
Evidence of Market Inefficiency
Analysing the daily returns of the JSE Top 40 index since 1995 we find that they do not follow a normal distribution - one which is expected of a random series of data. Our study in fact generated a kurtosis value of 5.6, which implies that price follows a leptokurtic distribution in the short-term. That is, prices tends to cluster around the mean more than suggested by a random distribution. Furthermore, the distribution exhibits amplified tails, or an increased probability of extreme events. In layman terms the market has a tendency to revert to the mean (clustering around the mean) as well as display powerful and sustainable trends (amplified tails) from time to time.
Our study validates what technical analysts have known for well over a hundred years: the market can be successfully traded for profit by employing either a trend following or mean reversion approach.
Mean reversion: attempts to capitalise on price reversions as seen in the clustering of price around the average price. Technicians employ tools such as Wilder's Relative Strength Index or Stochastics.
Trend following: attempts to capitalise on price trends as seen in the amplified tails by buying highs and selling even higher. Technicians employ tools such as moving averages and price breakouts.
The market tends to be range bound around 70% to 80% of the time - as seen in the clustering around the average price - resulting in mean reversion being a more prevalent phenomenon than price trends. Therefore, strategies designed to exploit mean reversion typically enjoy high win rates, low drawdowns, quick recoveries and smooth equity curves. These are very desirable strategy attributes that led to our intensive research within this approach. However, there is one caveat: Mean reversion requires traders to fade mass psychology - buying when the majority are selling and selling when the majority are buying - and therefore poses considerable implementation risk. Without a well-tested mechanical approach traders can easily become victim to the very emotion they're attempting to exploit. In order to protect from emotional bias, strategies need to be automated to the largest extent possible. Furthermore, tail risk, or the risk that price matures into a powerful trend without the expected reversion, needs to be carefully controlled.
We've been researching mean reversion strategies for over a decade. Our research has led us down many interesting rabbit holes, most of which were fruitless, but valuable nonetheless. Our success came when we began to control tail risk and automate the trade process. We believe we've uncovered a tremendously robust approach to exploiting mean reversion. QuantLab is the fruit of our labour.