Abstract
This chapter develops two new nonlinear artificial higher order neural network models. They are sine and sine higher order neural networks (SIN-HONN) and cosine and cosine higher order neural networks (COS-HONN). Financial data prediction using SIN-HONN and COS-HONN models are tested. Results show that SIN-HONN and COS-HONN models are good models for some sine feature only or cosine feature only financial data simulation and prediction compared with polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models.
Reference51 articles.
1. Atiya, A.F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929-935.
2. Barron, R., Gilstrap, L., & Shrier, S. (1987). Polynomial and Neural Networks: Analogies and Engineering Applications. In Proceedings of the International Conference on Neural Networks, (Vol. 2, pp. 431-439). New York, NY: Academic Press.
3. Polynomial and standard higher order neural network
4. The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks