Forecasting the Performance of Tadawul All Share Index (Tasi) Using Geometric Brownian Motion and Geometric Fractional Brownian Motion

Alhagyan, Mohammed; Al-Duais, Fuad S.

doi:10.17654/as062010055

Cited by 6 publications

(5 citation statements)

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“…In general, the empirical results of this study agree with earlier empirical works such as Abbas & Alhagyan (2022;), Mansour & Alhagyan (2022, Alhagyan & Yassen (2023), Alhagyan (2022), Willinger et al (1999), Painter (1998), Rejichi & Aloui (2012) and Alhagyan & Alduais (2020) to name just a few. Subsequently, we are strongly recommend investors and trader to invest in the Energy Sector in Saudi Arabia because of its predictability and stability.…”

Section: Discussionsupporting

confidence: 91%

“…Moreover, one can observe that the values of MSE of SVGFBM, GFBM, and SVGBM are close together while GBM is relatively far. These outcomes run in the same direction as many experimental researches for instance Willinger et al (1999), Rejichi and Aloui (2012), Alhagyan (2022), Painter (1998), Alhagyan and Yassen (2023), Alhagyan and Alduais (2020), and Abbas and Alhagyan (2022;. Figure 3 illustrates the comparison between the actual close prices versus forecasted close prices.…”

Section: Forecasting and Evaluationsupporting

confidence: 54%

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Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Alhagyan

2024

Preprint

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The future index prices are viewed as a critical issue for any trader and investor. For this craving, various models have shown up in the literature. The Geometric Brownian motion (GBM) is one of the popular models. This work examines four types of GBM as per the presence of memory or kind of volatility. These models include classical GBM with memoryless and constant volatility assumptions, SVGBM with memoryless and stochastic volatility assumption, GFBM with memory and constant volatility assumption, and SVGFBM with memory and stochastic volatility assumption. These models are utilized in an empirical study to forecast the future index price of Energy Sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, mean square error (MSE) and mean absolute percentage error (MAPE). The outcomes showed that the SVGFBM occupies the highest level of accuracy according to smallest values of MSE and MAPE. While the accuracy of GBM come in the tail of the list models under study. These results have affirmed the positive affection of combining memory and stochastic volatility assumption into the GBM model, which agreed findings of numerous earlier works. Furthermore, the findings showed the GFBM models are more accurate than GBM models regardless of the type of volatility. While, under the same type of memory, the models with stochastic volatility assumption are more accurate than the corresponding models of constant volatility assumption. In general, all models considered in this work uncovered high accuracy through the value of MAPE ≤ 10%. This indicates of the ability of applying these models in a real financial environment. Through this empirical study, we can say that the Energy Sector in Saudi Arabia is predictable and stable and afterward we urge financial investors and stockholder to trade and invest in this sector.

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Section: Discussionsupporting

confidence: 91%

Section: Forecasting and Evaluationsupporting

confidence: 54%

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Alhagyan

2024

Preprint

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“…However, our findings partially contradict previously reported findings for market behavior, such as those obtained by Alhagyan and Alduais [60], who discovered that both GBM and GFBM had good accuracy with minimal differences, implying that both models can be used to forecast the performance of the selected index (TASI).…”

Section: Discussioncontrasting

confidence: 76%

“…Geometric fractional Brownian motion has been studied extensively, with many studies identifying potential applications in the context of neuronal models [58]. It was validated by a rigorous statistical test with added white Gaussian noise based on the autocovariance function [59] and has been applied in studies both for stock indexes [60] and for the price of financial securities [61], in the case of cryptocurrencies [62], in the case of modeling epidemic diseases, such as coronavirus [63], and for the price of goods [50]. Furthermore, the GFBM has been utilized in a great deal of literature research to evaluate derivative titles (options) [64][65][66][67].…”

Section: Introductionmentioning

confidence: 99%

Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion

et al. 2021

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In this article, we propose a test of the dynamics of stock market indexes typical of the US and EU capital markets in order to determine which of the two fundamental hypotheses, efficient market hypothesis (EMH) or fractal market hypothesis (FMH), best describes market behavior. The article’s major goal is to show how to appropriately model return distributions for financial market indexes, specifically which geometric Brownian motion (GBM) and geometric fractional Brownian motion (GFBM) dynamic equations best define the evolution of the S&P 500 and Stoxx Europe 600 stock indexes. Daily stock index data were acquired from the Thomson Reuters Eikon database during a ten-year period, from January 2011 to December 2020. The main contribution of this work is determining whether these markets are efficient (as defined by the EMH), in which case the appropriate stock indexes dynamic equation is the GBM, or fractal (as described by the FMH), in which case the appropriate stock indexes dynamic equation is the GFBM. In this paper, we consider two methods for calculating the Hurst exponent: the rescaled range method (RS) and the periodogram method (PE). To determine which of the dynamics (GBM, GFBM) is more appropriate, we employed the mean absolute percentage error (MAPE) method. The simulation results demonstrate that the GFBM is better suited for forecasting stock market indexes than the GBM when the analyzed markets display fractality. However, while these findings cannot be generalized, they are verisimilar.

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“…The methods they used were Vector Auto‐Regression (VAR), Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX), multivariate Generalised AutoRegressive Conditional Heteroskedasticity (GARCH), and Long Short‐Term Memory (LSTM). They found that LSTM is the best method for forecasting. Alhagyan and Alduais (2020)They forecasted the TASI index using Geometric Brownian Motion and Geometric Fractional Brownian Motion. They forecasted the daily prices of TASI and found that both these models can predict TASI with trivial differences between the models. Alshammari, Ismail, Al‐Wadi, Saleh, and Jaber (2020)They investigated increasing the forecasting accuracy of the volatility of TADAWUL from October 2011 to December 2019.…”

Section: Literature Reviewmentioning

confidence: 99%

A case study of Gulf Securities Market in the last 20 years: A Long Short‐Term Memory approach

Sen

Choudhury

2023

Statistica Neerlandica

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Various researches have been conducted on forecasting stock prices. Several tools ranging from statistical techniques to quantitative methods have been used by researchers to forecast the market. But so far, very little research has been done on forecasting the stock markets of the Gulf countries such as Saudi Arabia, United Arab Emirates, Oman, Kuwait, Bahrain, and Qatar. Our approach is to predict the market indices of the Gulf countries using Long Short‐Term Memory (LSTM) techniques. Thereafter, we optimized the hyperparameters of the LSTM technique using various optimization methods such as Grid Search and Bayesian Optimization with Gaussian Process and found out the best‐suited hyperparameter for the LSTM model. We tried the LSTM method for predicting the indices using data from the last twenty years.

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Forecasting the Performance of Tadawul All Share Index (Tasi) Using Geometric Brownian Motion and Geometric Fractional Brownian Motion

Cited by 6 publications

References 0 publications

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM

Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion

A case study of Gulf Securities Market in the last 20 years: A Long Short‐Term Memory approach

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