Readers reviews of
(MIS)BEHAVIOUR OF MARKETS
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This is an excellent book that explores the core of our understanding of
market behaviour. It is one of the best books of 2005. It challenges the way we
think about markets, and the very foundations of modern portfolio theory. At
heart it is about the standard bell curve. A bell curve describes a normal
distribution. It tells you ‘on average’ how many events will fall within normal
and extreme. This is the core foundation of risk management, portfolio
construction, options trading and modern financial theory. It assumes that
prices are statistically independent – like a coin toss – and that price
variations are normally distributed – a standard deviation.
Mandelbrot claims this is simply wrong because it does not describe the
way markets and prices operate. Markets are characterised by extremes of
volatility that cluster into groups. These extremes cannot be ignored and they
occur on every time scale. Mandelbrot discovered and named fractals. He believes
that the volatility patterns are fractural repetitions.
In explaining how he believes modern financial theory is incorrect he
provides one of the clearest explanations of modern financial theory that I have
come across. This book could be heavy with mathematics. Instead it is clearly
and cogently explained with hardly a number in sight.
Mandelbrot does an excellent job of exploring the weakness of modern
financial theory and in so doing raises some very serious questions about the
adequacy of many of the technical analysis techniques we use that are based on
moving averages - stochastics etc - or on normal distribution – least square
regression, stand deviation, Bollinger bands etc. Mandelbrot does a less
successful job in showing how fractal analysis is used as an analytical tool for
understanding market behaviour. This is acceptable because his intention is to
identify weakness so that we can explore new areas.
I found Mandelbrot’s discussion of what is involved in trading to be less
perceptive. His fascination is with prices as a number series. He believes a
fractal generator can produce a chart that looks like a price chart so therefore
the price chart must look like the one produced by the fractal generator. It is
a flawed flow of logic because it ignores the emotional content of price.
However, the observation that the price pattern is a fractal repetition is
He talks briefly about volatility clustering, but because he is not a
trader, he misses the most significant information inherent in volatility
clustering by focusing on the extreme extremes rather than the minimum
extremes. The Guppy Multiple Moving Average is a tool for tracking volatility
clustering. Markets change at two points. The first is when volatility drops.
This is a minimum extreme shown when the two groups of averages compress and
converge. The second is when volatility expands in a single time frame – the
bubbles in the GMMA analysis. These observations provide a trading solution
based on a better understanding of the fractal behaviour of the market.
Because he treats this as a continuous number series analysis he looks for
long term price data. He starts with cotton because the data stretches back over
100 years. His analysis is based on this long price series and so he ignores the
essence of trading which is the use of stop loss points to select the section of
price activity that the trader finds desirable. His failure to understand the
choice involved in trading leads him to some incorrect conclusions about the
impact of volatility spikes. He assumes trading activity is continuous in the
same way that price data is continuous. He assumes the trader buys and holds
stocks for the same length of time as his long data series.
To be fair, his purpose is not to explain trading behaviour. His purpose
is to understand the behaviour of markets and to suggest ways in which we may
more effectively understand this behaviour. It is brilliant work that contains
the seeds of many trading edges. If you really think about the markets, then
this is the book you must have.
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