Financial forecasting based on artificial neural networks: Promising directions for modeling

Abstract

Financial forecasting plays a critical role in present economic context where neural networks have become a good alternative technique over traditional methods. Vast ranges of neural models are developed to achieve better accuracy in forecasting. In addition, the ways to find out a good neural architecture is being explored by the research community. In the literature, main problems are figured out within the area of data preparing and neural network design. In this paper, the reasons that affect the performance of the models are discussed based on empirical and mathematical evidence. Finally, this paper presents the directions towards a more suitable neural model for financial forecasting by combining data preprocessing techniques, clustering techniques and support vector machine.