COVID-19, mobility, leisure, and travel around the world

The paper uses daily data from Google to analyze the impact of the COVID-19 pandemic on people's mobility around the world regarding three types of leisure and travel activity: retail shopping and recreation;participation in open spaces, such as parks, beaches, gardens, and marinas;and food and medicine. These types of activity reflect important leisure and travel choices of people under conditions of epidemiological risk. We use the daily mortality rate as a key predictor. The results show that higher rates of mortality are associated with a lower mobility in shopping and recreation activities, higher mobility in open space activities, and rather insignificant changes in mobility regarding necessities. The effect depends on the stringency of the lockdown policies. The results remain robust after applying endogeneity analysis and country-level controls.

The return volatility and shock transmission patterns of chosen S&P and Dow Jones sustainability indices and their conventional counterpart

Purpose>The purpose of this study is to investigate the dynamic return volatility connectedness among S&P, Dow Jones (DJ) sustainability indices and their conventional counterparts.Design/methodology/approach>This study uses time-series daily data for 10 S&P and DJ indices over the period of December 1, 2012 to December 8, 2021. The authors divide the data into three periods;over the whole sample, pre and during the Covid-19 pandemic. The study adopts the connectedness approach developed by Diebold and Yilmaz (2014).Findings>The results reveal a high degree of connectedness between S&P and DJ indices and their relative sustainability indices over the whole sample, pre and during the Covid-19 pandemic, indicating that the sustainability indices converge toward their conventional peers. The results further show that the conventional S&P500, S&P Euro 50 and DJWI are the main transmitters of shocks, whereas the S&P400, S&P500 and S&P50 sustainability indices are the main receivers of shocks.Originality/value>The study provides novel insights in terms of shock transmission of S&P and DJ sustainability indices and their conventional counterparts, where there is a lack of investigation of the connectedness between indices in this field.Practical implications>The study has significant implications for investors and portfolio managers to devise portfolio strategies to minimize risk and trace the cause, the direction and the magnitude of risk transmission among different indices. Also, the results help policymakers to manage diverse types of risks associated with S&P and DJ indices. Finally, faith-based and ethical investors would be able to predict the pairwise spillover connectedness between these indices.

Early warning system to predict energy prices: the role of artificial intelligence and machine learning.

The COVID-19 pandemic has inflicted the global economy and caused substantial financial losses. The energy sector was heavily affected and resulted in energy prices massively tumbling. The Russian invasion of Ukraine has fueled the energy maker more volatile. In such uncertain contexts, an Early Warning System (EWS) would efficiently contribute to stabilizing market swings. It will leverage the ability to control operating costs and pave the way for smooth economic recovery. Within this framework, we deploy Machine Learning (ML) models to forecast energy equity prices by employing uncertainty indices as a proxy for predicting energy market volatility. We empirically examine the comparative effectiveness of prevalent ML models and conventional approaches (regression) to forecast the energy equity prices by utilizing the daily data from 1/6/2011 to 18/1/2022 for four US uncertainty and eight energy equity indices. Results show that the Nonlinear Autoregressive with External (Exogenous) parameters (NARX) of Neural Networks (NN) scored significantly better accuracy than all other (25) ML models and conventional approaches. The study outcomes are beneficial for policymakers, governments, market regulators, investors, hedge and mutual funds, and corporations. They improve stakeholders' resilience to exogenous shocks, blaze the recovery path, and provide evidence-based for assets allocation strategies.

The role of project’s initiatives focused on the reduction of environmental footprints during COVID-19: evidence from the United States firms

In this paper, we examine whether the projects of the United States (U.S.) corporations have implemented initiatives to reduce the environmental footprint of their supply chains during coronavirus (COVID-19). Environmental footprint reductions could be achieved by reducing waste, reducing resource use, and reducing ecological emissions by introducing environmental management systems in the supply chains. For this aim, the project’s initiatives play a crucial role. This study has the primary purpose of examining the impact of ecological footprints on financial performance achieved by US corporations’ initiatives implemented through projects during the COVID-19 period. The final sample comprises 9997 company-year observations over the investigation period between 2010 and 2020. The results suggest that firms implementing the initiatives to reduce environmental footprint have shown a significant positive financial performance during the COVID-19 period. The results are robust to alternative specifications of informativeness and sensitivity tests controlling for time-invariant firm characteristics and alternative firms' performance measures. Our results corroborate with stakeholder theory, which implies implementing green policies will alleviate the agency issue and safeguard the shareholders' interest. Moreover, it clearly demonstrates the positive impact of environmental projects-focused organizations on the financial and environmental performance even while challenging and disrupting situations such as this unprecedented pandemic.