ANN Forecasting Models for ISE National-100 Index
- Resource Type
- Authors
- Atilla Aslanargun; Senay Asma; Ozer Ozdemir
- Source
- Journal of Modern Applied Statistical Methods. 9:579-583
- Subject
- Artificial Neural Network
Statistics and Probability
Index (economics)
Artificial neural network
Ise Stock Market
computer.software_genre
Time series modeling
Time Series Modeling
Financial analysis
Econometrics
Autoregressive integrated moving average
Data mining
Statistics, Probability and Uncertainty
computer
Forecasting
Mathematics
- Language
- ISSN
- 1538-9472
Prediction of the outputs of real world systems with accuracy and high speed is crucial in financial analysis due to its effects on worldwide economics. Because the inputs of the financial systems are timevarying functions, the development of algorithms and methods for modeling such systems cannot be neglected. The most appropriate forecasting model for the ISE national-100 index was investigated. Box-Jenkins autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are considered by using several evaluations. Results showed that the ANN model with linear architecture better fits the candidate data