ting AI into capital management, and using AI in capital planning. However, information about such systems is generally limited, since disclosure of successful approaches could lead to the loss of competitive advantage, and large sums of money. On activity that appears to be generating the greatest interest on Wall Street is that of data mining, using approaches such as neural networks.Data Mining is the descendant, and to some, the heir and successor of statistics. Statistics and Data Mining pursue the same aim, which is to build compact and understandable models incorporating the relationships (dependencies) between the description of a situation and a result (or a judgement) concerning this description. The underlying assumption is that there is indeed some kind of dependency, i.e. the result, measurement or judgement we are trying to model is derived from some or all of the descriptive variables we have been able to gather. The main difference is that Data Mining techniques build the models automatically while classical statistics tools need to be wielded by a trained statistician with a good idea of what to look for.Data Mining is the process of looking for knowledge and anticipating patterns in data. One of the primary approaches for finding patterns in data is neural networks. Neural networks were named, based on their structural similarity with the process used by the human brain. Although, the methods used by neural nets are beyond the scope of this paper, their applications are generally accessible. For example: a neural network approach can be used to investigate the relationship between a set of financial statement ratios and whether or not the firm goes bankrupt. Another example is for the case where banks must choose whether or not to make a loan, based on a set of input characteristics. In a similar manner, patterns of information are investigated using neural networks to assist in the process of choosing stocks...
