The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning.

This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

Correction to: The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning.

[This corrects the article DOI: 10.1007/s10479-022-05024-4.].

The Impact of COVID-19 on the Relationship between Non-Renewable Energy and Saudi Stock Market Sectors Using Wavelet Coherence Approach and Neural Networks

In this study, we examine the impact of COVID-19 on the relationship between non-renewable energy and Saudi stock market sectors for the period 11 January 2017–22 January 2022. We apply wavelet coherence and Radial Basis Function Neural Network (RBFNN) models. Our results provide evidence that COVID-19 led to an increase in the strength of the relationship between oil as a main non-renewable energy source and Saudi stock market sectors and affected the nature and direction of this relationship. The relationships between oil and commercial and professional services, materials, banks, energy, and transportation sectors are the most affected. Our results will help hedge funds, mutual funds, and individual investors, forecast the direction of Saudi stock market sectors and the use of oil for hedging or diversification during periods of uncertainty and crisis. It will also help decision and policymakers in Saudi Arabia to make the necessary decisions and actions to maintain the stability of the stock market sectors during these periods.

The future of non-financial businesses reporting: Learning from the Covid-19 pandemic.

In this paper we conceptually identify the gap in the literature about lack of business's awareness in non -financial activities, especially biodiversity, which can be responsible for crisis like Covid-19 which can adversely affect the global economy. We recommend approaches to existing business about how to enhance the quality of reporting by considering non-human element in reporting and making it more comprehensive for the stakeholders. We adopt Actor Network Theory (ANT) and the Natural Inventory Model to support our argument that nature consists of both human and non-human. From our observation about the Covid-19 crisis and by consulting the existing relevant literature on CSR, Covid-19, non-financial reporting and integrated reports (IR), we propose the implication of non-financial reporting by companies based on a theoretical framework. We recommend that companies should implement/adopt Circular Economy concept for sustainable business model and report on biodiversity and extinction accounting in more structured and mandatory way via producing IR to create value on short, medium and long terms. This is the first paper to tackle the Covid-19 crisis and offer solution for future reporting. The findings will add value in the academia and society.

Corporate Accountability Towards Species Extinction Protection: Insights from Ecologically Forward-Thinking Companies.

This paper contributes to biodiversity and species extinction literature by examining the relationship between corporate accountability in terms of species protection and factors affecting such accountability from forward-thinking companies. We use triangulation of theories, namely deep ecology, legitimacy, and we introduce a new perspective to the stakeholder theory that considers species as a 'stakeholder'. Using Poisson pseudo-maximum likelihood (PPML) regression, we examine a sample of 200 Fortune Global companies over 3 years. Our results indicate significant positive relations between ecologically conscious companies that are accountable for the protection of biodiversity and species extinction and external assurance, environmental performance, partnerships with socially responsible organizations and awards for sustainable activities. Our empirical results appear to be robust in controlling for possible endogeneities. Our findings contribute to the discussion on the concern of species loss and habitat destruction in the context of corporate accountability, especially in responding to the sixth mass extinction event and COVID-19 crisis. Our results can also guide the policymakers and stakeholders of the financial market in better decision making.