Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

Assous, Hamzeh F.; Al-Rousan, Nadia; AL-Najjar, Dania; Al-Najjar, Hazem

doi:10.3390/joitmc6020027

Cited by 15 publications

(21 citation statements)

References 52 publications

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“…Econometrics-based statistical analysis relies mainly on historical trading data, corporate financial data, and macro data to identify and describe patterns of change in stock data over time and predict future stock trends [30,32,33]. Several machine learning algorithms were used to detect patterns in the large amount of financial information, including support vector machines (SVM), artificial neural networks (ANN), Parsimonious Bayes, and Random Forest [24,34].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Daradkeh

2022

Electronics

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Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Daradkeh

2022

Electronics

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“…3. Financial market estimation, COVID-19 and artificial neural network Financial markets estimation known as one of the most challenging estimation as there are many aspects that interfere together on linear and non-linear relations (Qiu et al, 2016;Gurjar et al, 2018;Assous et al, 2020) Nowadays, artificial neural networks (ANN) became a well-known estimation technique in all fields and can be applied to many financial problems such as macroeconomic forecasts (Kemal et al, 2016;Chung and Shin, 2020). Several researchers used ANN to estimate stock market return for many international stock markets (Kemal et al, 2016;Qiu et al, 2016;Sahoo and Mohanty, 2020).…”

Section: Impact Of Covid-19 Pandemic Virusmentioning

confidence: 99%

“…Financial markets estimation known as one of the most challenging estimation as there are many aspects that interfere together on linear and non-linear relations (Qiu et al , 2016; Gurjar et al , 2018; Assous et al , 2020) Nowadays, artificial neural networks (ANN) became a well-known estimation technique in all fields and can be applied to many financial problems such as macroeconomic forecasts (Kemal et al , 2016; Chung and Shin, 2020).…”

Section: Financial Market Estimation Covid-19 and Artificial Neural Networkmentioning

confidence: 99%

“…2. COVID-19 and G8 stock market's indices Financial market's index is one of the vital indictors for any country, and it is important to understand index fluctuation as it is resulted from the interaction between multi factors (economic and non-economic factors) that walk-in line and across each other in challenging way (Assous et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Impact of COVID-19 pandemic virus on G8 countries’ financial indices based on artificial neural network

Al-Najjar

Al-Rousan

AL-Najjar

et al. 2021

JCEFTS

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Purpose The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the COVID-19 pandemic worldwide are causing instability in stock indices every day. These changes resulted in the G8 suffering major losses due to the spread of the pandemic. This paper aims to study the impact of COVID-19 events using country lockdown announcement on the most important stock indices in G8 by using seven lockdown variables. To find the impact of the COVID-19 virus on G8, a correlation analysis and an artificial neural network model are adopted. Design/methodology/approach In this study, a Pearson correlation is used to study the strength of lockdown variables on international indices, where neural network is used to build a prediction model that can estimate the movement of stock markets independently. The neural network used two performance metrics including R2 and mean square error (MSE). Findings The results of stock indices prediction showed that R2 values of all G8 are between 0.979 and 0.990, where MSE values are between 54 and 604. The results showed that the COVID-19 events had a strong negative impact on stock movement, with the lowest point on the March of all G8 indices. Besides, the US lockdown and interest rate changes are the most affected by the G8 stock trading, followed by Germany, France and the UK. Originality/value The study has used artificial intelligent neural network to study the impact of US lockdown, decrease the interest rate in the USA and the announce of lockdown in different G8 countries.

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“…Moreover, to define a job weight in grid system, researchers used mainly two main methods including clustering models [31][32][33] and run-time prediction models. 11,12,[34][35][36][37] In clustering models, jobs are divided into groups by using different discrimination methods such as clustering algorithms, wherein the runtime prediction model is used to predict the job's run time using job variables. The clustering and run-time prediction models were considered as the main modeling approaches for calculating the weight of jobs in a grid system.…”

mentioning

confidence: 99%

Scheduling of workflow jobs based on twostep clustering and lowest job weight

Al-Najjar

Alhady

Mohamad–Saleh

et al. 2021

Concurrency and Computation

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Scheduling is an important technique to improve the productivity of workflow applications in a grid system. Serving workflow applications added overhead on the scheduling system since the scheduler should choose the best zero-dependent job that improves the performance of a grid system. This paper proposes two dependent job scheduling algorithms based on analyzing the effect of categorical and continuous variables of the job, which are used in the calculation of job weight. To find the job weight, twostep clustering is used with 10 groups and the ranking equation. In addition, to verify the ability to apply the proposed algorithms in a real environment, weighted least squares estimation is used. The results showed that the prediction rate is equal to 99.88%, which indicates that the proposed algorithms could be applied in a real grid system with low overhead. Through simulations and after testing the proposed algorithms, the average results showed that the Dependent Job Scheduling (DJS) algorithm outperformed the previous algorithms, in total execution time and an average waiting time with an improvement value of 1.18 and 1.92 times, respectively. While DJS algorithm with weighting factor (DJSJP) outperformed the previous algorithms in total execution time only with an improvement value equal to 1.17 times. The overall results indicated that the proposed algorithms are efficient to be used in a grid system, besides four bits are sufficient to improve the performance of the job scheduling system.

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Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

Cited by 15 publications

References 52 publications

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Impact of COVID-19 pandemic virus on G8 countries’ financial indices based on artificial neural network

Scheduling of workflow jobs based on twostep clustering and lowest job weight

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