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| Mean reversion cycle |
| Mukul Pal / Apr 21, 2011, 00:36 IST |
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We have read about management professor Douglas McGregor’s X and Y theory of human behaviour at work. The X theory suggests employees need to be taken to task and Y is about dealing with self-motivated employees. How could you rebuke people and get results out of them? The psychologist Daniel Kahneman, winner of the 2002 Nobel Prize in economics, pointed that regression to the mean might explain why rebukes seem to improve performance, while praise backfires. This explained why the X theory leaders delivered.
TENDING TO THE MEAN
Findings of the behavioural finance theory that losers outperformed winners over longer periods, is just another extension of the mean reversion theory. If it was not for mean reversion, behavioural finance may never have become mainstream. It was mean reversion which actually allowed the subject to crack open the 250-year old classical economics armour.
How? Historically, economists and statisticians have seen randomness and patterns together. Very few have attempted to explain this phenomenon. Jacob Bernoulli’s work on the Law of Large Numbers talked about probabilistic fate (order) in random events. Abraham de Moivre’s probability became ‘The doctrine of chances’. Pierre-Simon Laplace who created the method of least squares gave a pattern to a set of probabilistic observations. Francis Galton built on the idea and wrote about mean reversion.
MARKETS EITHER PREDICTIVE OR RANDOM?
Random observations could not only be fitted in a pattern, but the pattern itself showed an oscillation around an average value. Random roll of dice has statistical patterns. The world was random and patterned at the same time. Gauss’ normal distribution curve was a pattern encompassing random observations. The problem started when Louis Bachelier extended this idea to create financial mathematics. His theory of speculation used random Brownian motion to evaluate stock options.
The bell-shaped pattern got engraved in academic conscience as new theories emerged. Efficient market hypothesis claimed that though markets were random and unpredictable, they had a mean reverting pattern which was predictive. Are they predictive and unpredictive at the same time? It was the gap that behavioural finance exploited to challenge rationality. It was not rational for classical finance and economics to claim the market was predictive (mean reverting) and unpredictive (random) at the same time. On the random side, past had no connection with the future, while on the mean reversion side, past pattern was repeating in future. The classical theories were not consistent.
TIME PATTERN OF RETURNS
Behavioural finance rightfully brought out the weakness in classical theories of economics and finance by proving that human preferences not only varied as the waiting time changed, but also worked against the classical economic utility model, which assumes more stable preferences. It also highlighted that time-varying risk was a reality. Mean reversion worked across short and long-time frames. It was a distinct pattern of time-based returns at work. About 25-40 per cent of the three to five-year returns are predictable from the past returns. The more extreme the winners and losers, the greater are the subsequent reversal.
Beta, as a measure of risk, was misleading because both winners and losers had very a peculiar time pattern of returns. According to behavioural finance, the strategy of selling extreme winners and buying extreme losers was extraordinarily successful. Though ‘time’ has been given a critical importance in the theory, behaviourologists preferred explaining mean reversion through sentiment but not time. Time patterns create mean reversion cycles, explain the order in randomness and also create human behaviour.
The author is CMT and Co-Founder, Orpheus CAPITALS, a global alternative research firm
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