The resulting DAI architecture comprises a hierarchy of loosely coupled problem solvers, all operating under the supervision of a top-level control mechanism. Each problem solver works as a classifier system. A learning and refining subsystem is attached to the system for refining the existing knowledge to improve the system performance. Bharati Recent studies have shown that there is predictable variation in returns of financial assets over time. We investigate whether the predictive power of the economic and financial variables employed in the above studies can be enhanced if the statistical method of linear regression is replaced by feedforward neural networks with backpropagation of error.
Overfitted networks have poor generalization capabilities.
We explore two methods that attempt to overcome this shortcoming by reducing the complexity of the network. We show that one cannot say that the linear regression forecasts are conditionally efficient with respect to the neural networks forecasts with any degree of confidence. However, one can say that the neural networks forecasts are conditionally efficient with respect to the linear regression forecasts with some confidence. In particular, we focus on the effect of the weight decay method on data sets with missing values. Three data sets with real missing values and three data sets with missing values created by randomly deleting attribute values are adopted as the test bank in this study.