Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting

Alenezy, Abdullah H.; Ismail, Mohd Tahir; Jaber, Jamil J.; Wadi, S. Al; Alkhawaldeh, Rami S.

doi:10.1371/journal.pone.0278835

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…However, external factors such as political events and changes in market trends were not considered in this study. Alenezy et al (2022) proposed a hybrid model using the FIR.DM and MODWT-LA8 models for predicting Tadawul closing price data. The proposed models were evaluated and found to outperform standard models such as ARIMA and FIR.DM.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Alenezy

Ismail

Wadi

et al. 2023

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We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the error function gradient, while the HyFIS approach combines the advantages of neural networks and fuzzy logic systems to create a more robust and accurate learning model. The MODWT uses five mathematical functions to form a discrete wavelet basis. The dataset used includes the daily closing prices of the Tadawul stock market from August 2011 to December 2019. Inputs were selected based on multiple regression, tolerance, and variance inflation factor tests, and the oil price (Loil) and repo rate (Repo) were identified as input variables. The output variable is represented by the logarithm of the Tadawul stock market price (LSCS). MODWT-LA8 (ARIMA(1,1,0) with drift) outperforms other WT functions on the 80% dataset, with an ME of (0.00000532), MAE of (0.003214182), and MAPE of (0.06449683). The addition of WT functions to the FS.HGD and HyFIS models increases their forecasting ability. Based on the reduced RMSE (0.048), MAE (0.038), and MAPE (0.538), the MODWT-LA8-FS.HGD outperforms traditional models in predicting the remaining 20% of datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Alenezy

Ismail

Wadi

et al. 2023

Risks

Self Cite

View full text Add to dashboard Cite

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A predictive modeling for health expenditure using neural networks strategies

Saleh,

Alkhawaldeh,

Jaber

2023

Journal of Open Innovation: Technology, Market, and Complexity

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Estimating Volatility of Saudi Stock Market Using Hybrid Dynamic Evolving Neural Fuzzy Inference System Models

Hamadneh,

Jaber,

Sathasivam

2024

JRFM

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This paper examines the volatility risk in the KSA stock market (Tadawul), with a specific focus on predicting volatility using the logarithm of the standard deviation of stock market prices (LSCP) as the output variable. To enhance volatility prediction, it proposes the combined use of the dynamic evolving neural fuzzy inference system (DENFIS) and the nonlinear spectral model, maximum overlapping discrete wavelet transform (MODWT). This study utilizes a dataset comprising 4609 observations and investigates the inputs of lag 1 of the close stock price (LCP), the natural logarithm of oil price (Loil), the natural logarithm of cost of living (LCL), and the interbank rate (IB), determined through autocorrelation (AC), partial autocorrelation (PAC), correlation, and Granger causality tests. Regression analysis reveals significant effects of variables on LSCP: LCP has a negative effect, and Loil has a positive effect in the ordinary least square (OLS) model, while LCL and IB have positive effects in the fixed effect model and negative effects in the random effect model. The MODWT-Haar-DENFIS model was developed as we found that the model has the potential to be an effective model for stock market forecasting. The results provide valuable insights for investors and policymakers, aiding in risk management, investment decisions, and the development of measures to mitigate stock market volatility.

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Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting

Cited by 3 publications

References 32 publications

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

A predictive modeling for health expenditure using neural networks strategies

Estimating Volatility of Saudi Stock Market Using Hybrid Dynamic Evolving Neural Fuzzy Inference System Models

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