A quick review of statistics is justified. A normal distribution bell curve provides a way to estimate and shape the probability of an occurrence in a data set. Statistics suggests that increasing the number of random observations in a data set increases tendency to form a normal distribution curve. KOTM took about 252 daily net changes in the SPY and calculated the annual 1, 2, and 3 sigma ranges for 2003 to 2012. Observations outside these ranges were the trading days of interest and the frequency of them is displayed in the chart below.
From 2011 to 2012, the increase in the number of net changes outside 2 and 3 standard deviations, either way, was astounding. The frequency of net changes outside the two sigma range were up over 50% and, more importantly, the frequency of net changes outside the three sigma range jumped up a massive 300%…while the number of trading days stayed constant.
The ‘why’ is still to be determined…Obama, Bernanke, QE, HFT, Europe, or China could all be possible answers to ‘why’ because macro events drive volatility and uncertainty around these subjects does not help either. Either way however, the data is interesting.
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