Real Option Valuation with Neural Networks

TL;DRAbstract

We propose to use neural networks to value options when analytical solutions do not exist. The basic idea of this approach is to approximate the value function of a dynamic program by a neural net, where the selection of the networks weights is done via Simulated Annealing. The main benefits of this method as compared to traditional approximation techniques are that there are no restrictions on the type of the underlying stochastic process and no limitations on the set of possible actions. This makes our approach especially attractive for valuing real options in flexible investments. We, therefore, demonstrate this method by valuing flexibility to costly switch production between several products under various conditions.

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