Neural network applications: A critique

Michael de la Maza and Deniz Yuret (1995) ( PS )
Neural network applications: A critique. The Magazine of Artificial Intelligence in Finance, 2(1).


Neural networks are one of the most widely used artificial intelligence methods for financial time series analysis. In this paper we describe the standard application of neural networks and suggest that it has two shortcomings. First, backpropagation search takes place in sum of squared errors space instead of risk-adjusted return space. Second, the standard neural network has difficulty ignoring noise and focusing in on discoverable regularities. Both problems are illustrated with simple examples. We suggest ways of overcoming these problems.