Neural Networks in Finance

5 Estimating and Forecasting with Artificial Data

5.1 Introduction

This chapter applies the models and methods presented in the previous chapters to artificially generated data. This is done to show the power of the neural network approach, relative to autoregressive linear models, for forecasting relatively complex, though artificial, statistical processes.

The primary motive for using artificial data is that there are no limits to the size of the sample! We can estimate the parameters from a training set with sufficiently large degrees of freedom, and then forecast with a relatively ample test set. Similarly, we can see how well the fit and forecasting performance of a given training and test set from an initial sample or realization of the ...