Current Approaches in Neural Network Modeling of Financial Time Series

Abstract

Neural networks are an artificial intelligence method for modeling complex non-linear functions. Artificial Neural Networks (ANNs) have been widely applied to the domain of prediction problems. Considerable research effort has gone into ANNs for modeling financial time series. This paper attempts to provide an overview of recent research in this area, emphasizing the issues that are particularly important with respect to the neural network approach to financial time series prediction task. The purpose of this paper is to provide (1) a survey of research in this area, and, (2) synthesize insights from published research on ANN modeling issues, specifically, those that have a bearing on the design factors of neural network such as variable selection, data preprocessing, and network architecture. The paptir concludes with a discussion of current and emerging research topics related to neural networks in financial time series prediction.