TY  - JOUR
TT  - Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?
AU  - Delavari, Majid
AU  - Alikhani, Nadiya Gandali
AU  - Naderi, Esmaeil
PY  - 2013
DA  - June
JF  - International Journal of Economics and Financial Issues
JO  - IJEFI
PB  - İlhan ÖZTÜRK
WT  - DergiPark
SN  - 2146-4138
SP  - 466
EP  - 475
VL  - 3
IS  - 2
KW  - Stock Return
KW  - Forecasting
KW  - Long Memory
KW  - NNAR
KW  - ARFIMA
N2  - The main purpose of the present study was to investigate the capabilities of two generations of models such as those based on dynamic neural network (e.g., Nonlinear Neural network Auto Regressive or NNAR model) and a regressive (Auto Regressive Fractionally Integrated Moving Average model which is based on Fractional Integration Approach) in forecasting daily data related to the return index of Tehran Stock Exchange (TSE). In order to compare these models under similar conditions, Mean Square Error (MSE) and also Root Mean Square Error (RMSE) were selected as criteria for the models’ simulated out-of-sample forecasting performance. Besides, fractal markets hypothesis was examined and according to the findings, fractal structure was confirmed to exist in the time series under investigation. Another finding of the study was that dynamic artificial neural network model had the best performance in out-of-sample forecasting based on the criteria introduced for calculating forecasting error in comparison with the ARFIMA model.
UR  - https://dergipark.org.tr/tr/pub/ijefi/article/351921
L1  - https://dergipark.org.tr/tr/download/article-file/362782
ER  -