Bassam A. Ibrahim’s research while affiliated with Mansoura University and other places

In this study, we examine how managers in firms that have practiced tax avoidance strategically use sustainability activities together with their tax avoidance practices. More specifically, we investigate the moderating impact of ESG on the association between tax avoidance and firm value. Using a sample of French listed companies during years 2012-2021, we hypothesized and find that ESG rating negatively and significantly moderates the relationship between corporate tax avoidance and firm market valuation. Overall, our results suggest that investors reward firms for good ESG performance, perceiving such companies as more valuable. However, when these firms engage in higher tax liabilities, the positive effect of ESG on firm value is slightly reduced. This nuanced insight highlights the importance of considering how tax strategies interact with ESG initiatives in shaping overall firm value. This study, thus, provides theoretical and practical consequences that will encourage businesses and politicians to promote sustainable development. Our findings remain robust to an array of tests, including a number of different tax avoidance measures and potential endogeneity problems.

This study investigates the predictability power of oil prices and six international stock markets, namely, China, France, the UK, Germany, Japan, and the USA, on the Saudi stock market using five Machine Learning (ML) techniques and the Generalized Method of Moments (GMM). Our analysis reveals that prior to the 2006 collapse, oil exerted the least influence on the Saudi market, while the UK and Japan were the most influential stock markets. However, after the collapse, oil became the most influential factor, highlighting the strong dependence of Saudi Arabia's economic structure on oil production. This finding is particularly noteworthy given Saudi Arabia's efforts to reduce its reliance on oil through Vision 2030. We further demonstrate that China's influence on the Saudi market increased significantly after the 2006 collapse, surpassing that of the UK. This is attributable to the substantial trade between China, Japan, and Saudi Arabia, as well as the rise in Saudi foreign direct investment in China, and the decline in such investment in the UK post-collapse. Our results carry important implications for stock market investors and policymakers alike. We suggest that policymakers in Saudi Arabia should continue to diversify their economy away from oil and strengthen economic ties with emerging markets, particularly China, to reduce their vulnerability to oil price fluctuations and ensure sustainable economic growth.

The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets, particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic. This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets, focusing on gold and six major stock markets (Japan, USA, UK, China, Germany, and France) using daily data from January 2, 2011, to June 2, 2022, with 2,958 daily observations. We employ DCC-GARCH, wavelet coherence, and cascade-correlation network models to analyze the relationship between Bitcoin and those markets. Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty. We also find evidence of long-term contagion between Bitcoin and the six stock markets, with short-term contagion observed in Chinese and Japanese markets during COVID-19. These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets. Hedge funds, mutual funds, and individual and institutional investors can benefit from using our findings in their risk management strategies. Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions. It holds significant implications for stock market investors and policymakers, providing evidence for potentially using cryptocurrencies for hedging, for diversification, or as a safe haven.

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

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 machine learning 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.

Despite the burgeoning interest in environmental, social and governance (ESG) ratings, current results regarding ESG rating‐performance relationship are inconclusive. Since what affects this disagreement is ambiguous, we examine how internal control weaknesses (ICW) may affect the relationship between ESG rating and a firm's performance. In fact, employing a sample of French listed firms during the period between 2012 and 2018, we predicted and found that both ICW and ESG ratings have a positive and significant influence on a firm's performance. In addition, the results indicate that ICW negatively and significantly moderates the relationship between ESG ratings and corporate performance. Moreover, the robustness of the results is checked through the generalized method of moments regression. We also offer theoretical and practical implications to drive policymakers and businesses to assure sustainable development. We expect that our study can help managers to strengthen their internal resources, such as the internal control (IC) and ESG ratings to improve a firm's performance.