Intraday return volatility in Saudi Stock Market: An evidence from Tadawul All Share Index

Shaik, Abdul Rahman; Syed, Abdul Malik

doi:10.5267/j.msl.2019.3.012

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…It resulted in a significant impact over sentiments on the returns within the financial market which cannot be neglected. To measure the investor sentiments, various studies are developed in financial markets which had used the psychological line index (Ryu et al ., 2017; He et al ., 2019; Kim et al ., 2019; Shaik and Syed, 2019). This indicator helps investors who trade on a daily basis to make any transaction decision.…”

Section: Methodsmentioning

confidence: 99%

The asymmetric effect of COVID-19 on investor sentiment: evidence from NARDL model

Mili

Amoodi

Bawazir

2023

RBF

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PurposeThis study aims to investigate the asymmetric impact of daily announcements regarding COVID-19 on investor sentiment in the stock market.Design/methodology/approachThis study uses a Non-Linear Autoregressive Distribution Lag (NARDL) model that relies on positive and negative partial sum decompositions of the Coronavirus indicators. Five investor sentiments had been used and the analysis is conducted on the full sample period from 24th February 2020 to 25th March 2021.FindingsThe results show that new cases have a greater impact on investor sentiment compared to daily announcements of new deaths related to COVID-19. In addition to revealing a significant impact of new COVID-19 new cases and new death announcements on a daily basis on investor sentiment over the short- and long-term, this paper also highlights the nonlinearity and asymmetry of this relationship in the short and long run. Investors' sentiments are more affected by negative news regarding Covid 19 than positive news.Originality/valueFinancial markets have been severely affected by COVID-19 pandemic. This study is the first to measure the extent of reaction of investors to positive and negative announcements of COVID-19. Interestingly, this study examines the asymmetric effect of daily announcements on new cases and new deaths by COVID-19 on investor sentiments and derive many implications for portfolio managers.

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Section: Methodsmentioning

confidence: 99%

The asymmetric effect of COVID-19 on investor sentiment: evidence from NARDL model

Mili

Amoodi

Bawazir

2023

RBF

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“…As a result, the effects of financial crises in other economies are transferred to the domestic economy. Consequently, the global financial crisis in 2008 hit Tadawul heavily [39].…”

Section: Plos Onementioning

confidence: 99%

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

et al. 2022

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This research employs the gradient descent learning (FIR.DM) approach as a learning process in a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) to improve volatility prediction of daily stock market prices using Saudi Arabia’s stock exchange (Tadawul) data. The MODWT comprises five mathematical functions and fuzzy inference rules. The inputs are the oil price (Loil) and repo rate (Repo) according to multiple regression correlation, and the Engle and Granger Causality test Engle RF, (1987). The logarithm of the stock market price (LSCS) in Tadawul reflects the output variable. The correlation matrix reveals that there is no collinearity between the input variables, and the causality test demonstrates that the input variables significantly influence the outcome variable. According to the multiple regression, there is a substantial negative influence between Loil and LSCS but a significant positive effect between Repo and output. For the 80% dataset under ME (0.000005), MAE (0.003214), and MAPE (0.064497), the MODWT-LA8 (ARIMA(1,1,0) with drift) for the LSCS variable performs better than other WT functions. In the novel hybrid model MODWT-FIR.DM, each function’s approximation coefficient (LSCS) is applied with input variables (Loil and Repo). We evaluate the performance of the proposed model (MODWT-LA8-FIR.DM) using different statistical measures (ME, RMSE, MAE, MPE) and compare it to two established models: the original FIR.DM and other MODWT-FIR.DM functions for forecasting 20% of datasets. The outcomes show that the MODWT-LA8-FIR.DM performs better than the traditional models based on lower ME (3.167586), RMSE (3.167638), MAE (3.167586), and MPE (80.860849). The proposed hybrid model may be a potential stock market forecasting model.

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“…External financial crises are transferred to the domestic markets. One such incident happened when the global financial crisis hit the Saudi stock market in 2008 [44].…”

Section: Outlier Detectionmentioning

confidence: 99%

Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns

Rashedi

Ismail

Hamadneh

et al. 2021

Journal of Mathematics

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Artificial intelligence (AI) based business process optimization has a significant impact on a country’s economic development. We argue that the use of artificial neural networks in business processes will help optimize these processes ensuring the necessary level in the functioning and compliance with the foundations of sustainable development. In this paper, we proposed a mathematical model using AI to detect outliers in the daily return of Saudi stock market (Tadawul). An outlier is defined as a data point that deviates too much from the rest of the observations in a data sample. Based on the Engle and Granger Causality test, we selected inflation rate, repo rate, and oil prices as input variables. In order to build the mathematical model, we first used the Tukey method to detect outliers in the stock return data from Tadawul that are collected during the period from October 2011 to December 2019. In this way, we categorized the stock return data into two classes, namely, outliers and nonoutliers. These data are further used to train artificial neural network in conjunction with particle swarm optimization algorithm. In order to assess the performance of the proposed model, we employed the mean squared error function. Our proposed model is signified by the mean squared error value of 0.05. The proposed model is capable of detecting outlier values directly from the inflation rate, repo rate, and oil prices. The proposed model can be helpful in developing and applying intelligent optimization techniques to solve problems in business processes.

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Intraday return volatility in Saudi Stock Market: An evidence from Tadawul All Share Index

Cited by 7 publications

References 30 publications

The asymmetric effect of COVID-19 on investor sentiment: evidence from NARDL model

The asymmetric effect of COVID-19 on investor sentiment: evidence from NARDL model

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

Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns

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