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The goal of this study is to investigate the predictive performance of the machine and deep learning methods in predicting the CAC 40 index and its 40 constituent prices of the French stock market during the COVID-19 pandemic. The study objective in forecasting the CAC 40 index is to analyze if the index and the individual prices will preserve the continuous increase they acquired at the beginning of the administration of vaccination and containment measures or if the negative effect of the pandemic will be reflected in the future.

The authors apply two machine and deep learning methods (KNN and LSTM) and compare their performances to ARIMA time series model. Two scenarios have been considered: optimistic (high values) and pessimistic (low values) and four periods are examined: the period before COVID-19 pandemic, the period during the COVID-19, and the period of vaccination and containment. The last period is divided into two sub-periods: the test period and the prediction period.

The authors found that the KNN method performed better than LSTM and ARIMA in forecasting the CAC 40 index for both scenarios. The authors also identified that the positive effect of vaccination and containment outweighs the negative effect of the pandemic, and the recovery pattern is not even among major companies in the stock market.

The study empirical results have valuable practical implications for companies in the stock market to respond to unexpected events such as COVID-19, improve operational efficiency and enhance long-term competitiveness. Companies in the transportation sector should consider additional investment in R&D on communication and information technology, accelerate their digital capabilities, at least in some parts of their businesses, develop plans for lights out factories and supply chains to keep pace with changing times, and even include big data resources. Additionally, they should also use a mix of financing sources and securities in order to diversify their capital structure, and not rely only on equity financing as their share prices are volatile and below the pre-pandemic level. Considering portfolio allocation, the transportation sector was severely affected by the pandemic. This displays that transportation equities fail to be a candidate as a good diversifier during the health crisis. However, the diversification would be worth it while including assets related to the banking and industrial sectors. On another strand, the instability of this period induced an informational asymmetry among investors. This pessimistic mood affected the assets' value and created a state of disequilibrium opening up more opportunities to benefit from potential arbitrage profits.

The impact of COVID-19 on stock markets is significant and affects investor behavior, who suffered amplified losses in a very short period of time. In this regard, correct and well-informed decision-making by investors and other market participants requires careful analysis and accurate prediction of the stock markets during the pandemic. However, few studies have been conducted in this area, and those studies have either concentrated on some specific stock markets or did not apply the powerful machine learning and deep learning techniques such as LSTM and KNN. To the best of our knowledge, no research has been conducted that used these techniques to assess and forecast the CAC 40 French stock market during the pandemic. This study tries to close this gap in the literature.

The purpose of this paper is to identify efficient governance using a governance efficiency score based on recommendations provided by codes of best practices in order to determine “good governance”.

Based on a sample of 320 US listed firms from 1994‐2001, governance practices were synthesized by an index computed according to a parametric method, the stochastic frontier analysis, which allows taking into account the relation between inputs (governance axes) and outputs (performance).

The use of a latent classis in the specification of the model allowed detecting two groups of firms according to their specific characteristics. The results of affectation equation show that the probability of being in the highest performing group is more important when the firm size, the dividend yield and the return on equity (ROE) are high, while a high leverage level decreases the chance to be in the non‐performing group. Moreover, the model allows establishing a dualist description of the two groups which point out two opposite governance systems. The non‐performing system is characterized by a managerial discretion, an ownership concentration, a dominance of the board by the CEO and a manager entrenchment. However, the highest performing system is characterized by an inside control efficiency and an inside financial control efficiency.

The sample choice presents a selectivity bias. Firms of the sample present some particularities in relation to other US firms, which limits the study generalisation. This study can also be the object of replications in other contexts.

This work is a demarcation in relation to previous works studying corporate governance quality, and particularly the relation between governance and performance. It provides a new econometric approach to develop a synthetic index to evaluate corporate governance firms' practices, wedged on performance level achieved by different firms.

The purpose of this paper is to investigate the impact of discretionary loan loss provisions (DLLPs) and non-performing loans (NPLs) on the liquidity risk of both Islamic banks (IBs) and conventional banks (CBs) before and after the global crisis that hit nations belonging to the Gulf Cooperation Council (GCC).

This empirical study uses balanced panel data on 16 IBs and 58 CBs operating in the six Gulf Cooperation states covering 2000–2014. The data were obtained from the Bankscope database and the banks’ annual reports.

The results indicate that NPLs affect liquidity risk differently across the banks – specifically, there is a significant difference in the funding and managing of liquidity between the two bank types. The authors find that the influence of DLLPs does not vary across the banks in the overall analysis and before the crisis. This finding provides insights into the unique nature of banking risks in dual banking systems. The authors also find that after the crisis, the discretionary LLPs affected liquidity risk differently across the banks.

This study has several practical implications. First, the findings suggest that the Islamic Financial Services Board and other IBs regulators should reassess several regulations, principles and products in order to reduce their credit and liquidity risks. Second, the study emphasizes the need for banks to perform a careful assessment of the effects of their LLP policies. Finally, the findings are also relevant to bankers, as they provide empirical evidence on the effect of loan growth on bank liquidity, suggesting that bankers should improve their loan management.

First, this is the first study to examine discretionary LLPs, NPLs and liquidity risk in IBs; it is also the first comparative study between Islamic and CBs. Second, the study provides evidence on how the global crisis impacted the banking sector and identifies some of the main determinants of liquidity risk for both Islamic and CBs operating in GCC countries.

The purpose of this paper is to investigate the effect of earnings quality on banking stability in Gulf Cooperation Council countries. First, the author isolates the discretionary loan loss provision (DLLP) to investigate the impact of total LLP, DLLP, discretionary accruals and a small positive variation in net income on bank stability. Second, the author investigates differences that may exist between Islamic banks (IBs) and conventional banks (CBs) in terms of the impact of DLLP on bank stability.

This research is based on unbalanced panel data for 39 IBs and 64 CBs in the six Gulf Cooperation Council countries over the 2000–2014 period.

The findings indicate that the extent of stability is negatively associated with the level of DLLP. This study also found significant differences between the two banking sectors in the effect of DLLP on bank stability.

This study has various practical implications. First, it provides insights for governments and regulators about introducing instruments like borrower restrictions and dynamic provisions to reduce LLP, because it negatively affects banking stability. Second, bankers should carefully assess the effects of their LLP strategies to overcome any negative effects. Third, the findings are also relevant to shareholders, investors and bank customers. More specifically, the results will help to improving their understanding of how LLP is not a financial strength and it is subject to managers’ opportunism that can lead to a financial instability. Finally, this study’s results encourage researchers to investigate an unexplored question, namely, the procyclicality of LLP and its determinants and effects.

To the best of the author’s knowledge, this is the first study to investigate differences that may exist between Islamic and CBs in terms of the impact of DLLP on bank stability.