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.