The
theoretical basis for the work

Price curves in the markets are a classic example of an
auto-correlated time series. The market can be seen as a
set of parallel brains integrating prices and financial
information which alter buying/selling which in turn alters
the price levels and generates new financial information.
The "waves" of herding behavior amongst traders result in
non-linear statistical signatures in the price-time series
which can be exploited for fun and profit, because on the
whole, the parallel market-brain system generates the data
upon which it acts - a classic recursive function.

Because fear and greed are the relatively constant human
emotions which drive trading, some patterns tend to repeat
themselves. The time series is analyzed as
a
multifractal
in both time and price dimensions based on the work of
Benoit Mandelbrot. Proprietary routines are used to find
matches in patterns of price/time by comparing the
non-linear parameters obtained from the scaling
analyses. A comparison of the non-linear parameters to the
original time series is analyzed for statistical
validity and the valid patterns are used to
reconstruct and project time series behavior into the
future. The current VIX models are implemented in a Support
Vector Machine (SVM)/neural network.

In answer to the question of "If you're so good why do you
need to sell information?" the answer is simple. Monetizing
the information we use for our own investments is an
efficient way of gleaning additional capital for
investment.