Efficient learning of monotone concepts via quadratic optimization
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Improving the classification accuracy of RBF and MLP neural networks trained with imbalanced samples
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S\&P 500 futures options data from 1987 to 1991.