The Case for a BlackBox Approach to Modeling the Financial Markets
It is an accepted principle that in attempting to build a model of something in the real world, one should have a deep knowledge of the subject that is being modeled. Because domain expertise is crucial for choice of input variables, as well as clear statement of the relationship between the variables, as well as the quantification of the coefficients [parameters] for each equation of the model. However, in recent times, there is some serious study of blackbox approaches to modeling. A blackbox type of model is one where [1] choice of Input need not be restricted to those which are justified to be inputs according to the theoretical principles of the subject to be modeled. [2] A clear statement of the relationship between input variables is not absolutely necessary, instead leaving it to the blackbox to work out the relationship. [3] For [2], non-parametric methods machine-learning methods such as neural nets, fuzzy logic and genetic algorithms are used to classify, detect patterns or ...