Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions
نویسندگان
چکیده
This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using daily price indices with 2026 observations from October 2011 December 2019. employs a nonlinear spectral maximum overlapping discrete wavelet transform (MODWT) five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), Coiflet (C6) in conjunction adaptive network-based fuzzy inference system (ANFIS). We have selected oil (Loil) repo rate (Repo) as input values according correlation, Engle Granger Causality test, multiple regressions. The variables this been collected Authority for Statistics Central Bank. output variable is obtained Tadawul. performance proposed (MODWT-LA8-ANFIS) evaluated terms mean error (ME), root square (RMSE), absolute percentage (MAPE). Also, we compared MODWT-LA8-ANFIS traditional models, which are autoregressive integrated moving average (ARIMA) ANFIS model. results show that better than models. Therefore, capable decomposing markets.
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ژورنال
عنوان ژورنال: Journal of Mathematics
سال: 2021
ISSN: ['2314-4785', '2314-4629']
DOI: https://doi.org/10.1155/2021/9954341