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We propose a time value related decision function to treat a classical optionpricing problem raised by Hutchinson-Lo-Poggio. In numerical experiments, thenew decision function significantly improves the original model ofHutchinson-Lo-Poggio with faster convergence and better generalizationperformance. By proving a novel universal approximation theorem, we show thatour decision function rather than Hutchinson-Lo-Poggio's can be approximated onthe entire domain of definition by neural networks. Thus the experimentalresults are partially explained by the representation properties of networks.

The option pricing model based on time values: an application of the universal approximation theory on unbounded domains