Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

Alenezy, Abdullah H.; Ismail, Mohd Tahir; Wadi, S. Al; Tahir, Muhammad; Hamadneh, Nawaf N.; Jaber, Jamil J.; Khan, Waqar A.

doi:10.1155/2021/9954341

Cited by 14 publications

(12 citation statements)

References 37 publications

(35 reference statements)

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“…Also, there is a difference in means between HyFIS and original CS based on a significant level of less than 5% (reject H0: there is no difference in a mean between HyFIS and original CS). Indeed, the mean of HyFIS is closer to the original CS than ANFIS [48,51].…”

Section: Prediction Resultsmentioning

confidence: 92%

“…e ANFIS consists of both fuzzy logic and an arti cial neural network. For ANFIS training, neural network learning algorithms will use [48]. Two processes, the forward and the backward step, consist of the "ANFIS" learning algorithm.…”

Section: Anfis Modelmentioning

confidence: 99%

“…A standard rule set of base fuzzy if-then rules for the rst order of the Sugeno fuzzy model can be expressed as follows: if x is A 1 and y is B 1 , then f 1 p 1 x + q 1 y + r 1 ; here, p, r, and q are linear output parameters. e "ANFIS" architecture with two inputs and one output is shown in Figure 2 [48,49].…”

Section: Anfis Modelmentioning

confidence: 99%

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Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic

Atawneh

Hamadneh

Jaber

et al. 2022

Journal of Mathematics

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This study explores the impact of electronic payment systems on Saudi Arabia’s customer satisfaction during the COVID-19 pandemic. Descriptive analytical approach of a sample of 1,025 people living in Saudi Arabia was used to answer the study questions and test its hypotheses. Then, a new hybrid fuzzy inference system (HyFIS) is proposed to predict customer satisfaction during COVID-19 pandemic. The proposed system contemplates customer resistance (CR), access to technology (AT), privacy (PV), costs (CT), and speed of efficiency (SE) as the input variables and customer satisfaction (CS) as the output variable. Various statistical tests are utilized to determine the efficiency of input variables in the obtained data. The statistical tests are multicollinearity tests, reliability and validity, ordinal least square (OLS), fixed effect, and random development. As a result, we can determine each input variable’s direct and indirect impact on the CS. Under OLS, fixed effect, and unexpected effect, the SE, CT, PV, AT, and CR considerably impact EP. The EP has been shown to have substantial positive indirect implications. Under OLS, fixed effect, and random effect, the CT, PV, and CR are found to have a significant positive impact on CS. In addition, the AT has a substantial impact on CS in a fixed effect indirect effect. The results of HyFIS were compared to those of the adaptive network-based fuzzy inference system (ANFIS). The results reveal that HyFIS outperforms ANFIS in predicting CS based on the error criterion.

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Section: Prediction Resultsmentioning

confidence: 92%

Section: Anfis Modelmentioning

confidence: 99%

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Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic

Atawneh

Hamadneh

Jaber

et al. 2022

Journal of Mathematics

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“…The MODWT with filters were used to capture the pattern of the k(t) series over time. The MODWT-based decomposition (Alenezy et al [20] and Cornish et al [15]) is an effective approach for revealing variations, magnitudes, and phases of the data. In capturing the pattern of the k(t) series, the BL14 filter has markedly better phase properties than the LA8 and C6 filters.…”

Section: Resultsmentioning

confidence: 99%

Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates

et al. 2021

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This study implements various, maximum overlap, discrete wavelet transform filters to model and forecast the time-dependent mortality index of the Lee-Carter model. The choice of appropriate wavelet filters is essential in effectively capturing the dynamics in a period. This cannot be accomplished by using the ARIMA model alone. In this paper, the ARIMA model is enhanced with the integration of various maximal overlap discrete wavelet transform filters such as the least asymmetric, best-localized, and Coiflet filters. These models are then applied to the mortality data of Australia, England, France, Japan, and USA. The accuracy of the projecting log of death rates of the MODWT-ARIMA model with the aforementioned wavelet filters are assessed using mean absolute error, mean absolute percentage error, and mean absolute scaled error. The MODWT-ARIMA (5,1,0) model with the BL14 filter gives the best fit to the log of death rates data for males, females, and total population, for all five countries studied. Implementing the MODWT leads towards improvement in the performance of the standard framework of the LC model in forecasting mortality rates.

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“…The WT model is a mathematical model that used to convert the original time series data into a timescale domain [27]. The main idea of WT is to analyze time series observations into different series of coefficients through a group of wave-like 23 functions.…”

Section: Mathematical Models 21 Wt Modelmentioning

confidence: 99%

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

Alshammari¹,

Ismail²,

Hamadneh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the most volatile data, and improve forecast accuracy. The results were compared from a number of mathematical models, which are the non-linear spectral model, autoregressive integrated moving average (ARIMA) model and EGARCH model. The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error (MAPE), Mean absolute scaled error (MASE) and Root means squared error (RMSE).

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Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

Cited by 14 publications

References 37 publications

Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic

Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic

Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

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