Introduction

Overview

Because of their inductive nature, neural networks have the ability to infer complex non-linear relationships between an asset price and its determinants. Although this approach can potentially lead to better non-parametric estimators, neural networks are not always easily accepted in the financial economics community, mainly because there do not exist established procedures for testing the statistical significance of the various aspects of the estimated model. The primary aim of this book is to provide a coherent set of methodologies for developing and assessing neural models, with a strong emphasis on their practical use in the capital markets. Partly a tutorial, partly a review, this chapter gives an introduction to investment management, positions neural networks and finally gives an introductory exposure to a novel neural model identification procedure, which is synergetic rather than competitive to theory formulation.