After every crash or major geopolitical event that roils the market we are exposed to graphics like this one containing sigma numbers: The message associated with these charts is usually, “We should be very worried because the events that just occurred were really unlikely.” The reader, on the other hand, should be thinking: the person that wrote this really doesn’t understand statistics or Black Swans. …
While it’s certain that short-term volatility exchange traded products (ETPs) like VXX, TVIX, and UVXY are doomed to a contango driven march towards zero, their decay rates are not consistent. Things like bear markets and big corrections can cause big upward swings.
Over the years I have developed a lot of respect for the condition of breadth divergence, when an index such as the S&P 500 is rising and the NYSE daily cumulative advance-decline line is not. In January 2016 I listed the performance of my method on the short side as a function of the number of consecutive weeks of divergence at the time of the …
Everyone agrees the normal distribution isn’t a great statistical model for stock market returns, but no generally accepted alternative has emerged. A bottom-up simulation points to the Laplace distribution as a much better choice. A well-known problem in financial risk assessment is the failure of the normal distribution (also known as the Gaussian distribution) to correctly predict big up or down days on the stock …
In September 2013, I published a post written by Frank Roellinger on his stock market trading system—a modification of the Ned Davis system first published in the 80s. Since Frank’s work was first published here he has shifted his Russell 2000 positions 11 times, each move reported here and on my twitter account. His hypothetical portfolio value has increased from 1658 to 1936 (+16.7%)—impressive given …
