FINANCIAL APPLICATIONS OF LEARNING FROM HINTS

The basic paradigm for learning in neural networks is'learning from examples' where a training set of input-output examples is used to teach the network the target function. Learning from hints is a gen(cid:173) eralization of learning from examples where additional information about the target function can be incorporated in the same learning process. Such information can come from common sense rules or special expertise. In financial market applications where the train(cid:173) ing data is very noisy, the use of such hints can have a decisive advantage. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months.