Financial markets present perhaps the greatest challenge to data modeling. Attempts to model financial processes in fact persist only because of the rewards that accrue to the use of even a moderate amount of information.
Recognition of the repeatable patterns in market behavior is facilitated by use of powerful modern statistical techniques. Foremost among these is inductive modeling. With inductive modeling, the best model is extracted from the data, rather than being imposed "from above" by an analyst.
Definition of the "best" model is made possible by the use of a complexity-based modeling criterion such as Minimum Description Length. MDL evaluates a model's perceived accuracy in the context of its complexity (and, incidentally, does not break down in the face of chaotic problems). Whereas traditional statistics only finds the best parameter values for a given model, MDL also allows one to discover which inputs to use and how best to define them.