The role of political risk, uncertainty, and crude oil in predicting stock markets: evidence from the UAE economy

Khalfaoui, Rabeh; Jabeur, Sami Ben; Hammoudeh, Shawkat; Arfi, Wissal Ben

doi:10.1007/s10479-022-04824-y

Cited by 9 publications

(9 citation statements)

References 107 publications

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“…Boosting and bagging (bootstrap aggregation) represent two widely used ensemble learning-based methods in stock market forecasting (Khalfaoui et al, 2023 ; Nti et al, 2020 ). While primarily aimed to reduce bias, boosting comprises an iterative procedure in which the base regressors target difficult-to-predict data samples by adjusting their weights.…”

Section: Methodsmentioning

confidence: 99%

“…The underlying rationale of this method is that combining local explanations from many observations can lead to an understanding of global model structure while maintaining local faithfulness towards its original model. Compared to other AI explanatory approaches, the SHAP method has the advantage of being able to quantify the contribution (positive or negative importance value) of the explanatory features for each point prediction, regardless of the underlying machine learning model (Khalfaoui et al, 2023 ). Moreover, unlike other approaches based on proxy models, Shapley values are derivable for any tree-based model (Jabeur et al, 2021b ).…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, unlike other approaches based on proxy models, Shapley values are derivable for any tree-based model (Jabeur et al, 2021b ). These advantages have been exploited in the context of stock market prediction (Deng et al, 2023 ; Khalfaoui et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

“…The total revenue, that is, the model’s prediction, is fairly distributed to each player (features). The Shapley value considers the influence of the feature of participating or not participating in the game on the model prediction under different circumstances (Khalfaoui et al, 2023 ). Based on this, the Shapley value of each input unit is calculated as follows: where is a set.…”

Section: Methodsmentioning

confidence: 99%

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An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

et al. 2023

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Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

et al. 2023

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“…Another study [ 83 ]) analyzed the spread of COVID-19 in the most affected Brazilian cities using hybrid and single ARIMA models, which integrated EEMD and ARIMA techniques. The results showed that the EEMD performed approximately 27% better than the single model [ 33 , 47 – 49 , 53 – 55 , 71 , 74 , 106 , 110 – 112 , 134 – 143 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

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Combining text analytics and network path extraction to trace CSR in the social sciences: Intellectual structures and diffusion trajectories

Yu,

Xiang,

Pan

2024

Corp Soc Responsibility Env

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Corporate social responsibility (CSR) has evolved over time into a mature interdisciplinary scientific field. However, there is a lack of research to explore the disciplinary association patterns and diffusion trajectories of scientific knowledge within this field. This research proposes a knowledge tracing framework that combines text analysis based on scientific texts and main path extraction based on citation networks to fill this gap. This framework is implemented in a dataset containing 15,949 papers related to CSR in the social sciences published from 1969 to 2022. This study identifies 19 main research directions within this field and their five growth trends. Combined with disciplinary categories of publication venues, both the disciplinary origins and disciplinary association patterns of these knowledge contents are also revealed. Besides that, three knowledge diffusion paths are obtained, which meticulously reveal the knowledge evolution and paradigm shift. Aside from the methodological novelty and result scarcity, this study also identifies research streams and potential opportunities. To begin with, CSR‐related studies have shown the tendency of microscopic and interdisciplinary. Then, related research methods show the tendency to be complicated and focus on causal logic. On this basis, some future agendas of CSR are also proposed in this work.

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The role of political risk, uncertainty, and crude oil in predicting stock markets: evidence from the UAE economy

Cited by 9 publications

References 107 publications

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Combining text analytics and network path extraction to trace CSR in the social sciences: Intellectual structures and diffusion trajectories

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