Asma Sghaier

Cette étude se propose de mesurer la performance, l’efficience et la concurrence des banques islamiques et conventionnelles dans la région de MENA et ses déterminants. Nous utilisons à cet effet le ROAA et ROAE pour appréhender la rentabilité globale et la méthode de l’enveloppement des données (DEA) et la méthode de l’analyse de la frontière stochastique (SFA) pour calculer les scores d’efficacité technique. Les résultats révèlent des évolutions semblables des deux types de mesure de performance. L’analyse par catégorie de banques révèle que les banques conventionnelles sont plus performantes que les banques islamiques. Malgré les changements technologiques qu’a connu le système bancaire de la région MENA, l’analyse de l’efficience montre que l’inefficience technique provient de l’inefficience technique pure. Enfin, l’efficacité des banques de la région MENA est sensible aux variables telles que la crise, les dépôts, la capitalisation et notamment les variables liées aux lignes de métiers.

The purpose of technology is not to make finance better, but to make finance serve real life better. Fintech has grown much faster in China than in the United States. In China, this success has come not from an initial technology advantage, but from integration between finance and real-life needs. This experience has important implications for understanding financial innovations, and for the development of inclusive finance. China has experienced a financial technology boom (Fintech), enabling it to be a world leader of the field. China's decision makers and authorities are promoting a digital vision the aim of which is to foster financial inclusion. Using FinTech as a catalyst for financial inclusion can be an effective and less costly solution for China. This study examines the leading role of FinTech in the efforts of the Chinese authorities towards financial accessibility in China. Using time series econometrics over the 2010 to 2020 period, we show that FinTech promotes financial inclusion. More specifically, the results show that only the access dimension can be promoted using FinTech. However, for the usage dimension, FinTech does not have a positive effect. Such findings should further encourage Chinese authorities to invest in FinTech to help remove barriers to financial inclusion.

Governance (which entails transparency, accountability, the rule of law and the presence of effective and legitimate institutions) is considered an essential factor in economic development. A large number of academic studies have attempted to identify and explain the influence of governance quality on economic growth, bearing on different theoretical perspective and using a panoply of estimation methods, including correlation and regression analyses. This study approaches the phenomenon from a predictive analytical perspective using contemporary Machine Learning techniques to uncover the most important predictors of economic growth in the MENA region in sample observed from 1996 to 2020. Random Forest algorithm was used with three machine learning models (Support Vector Machine, Boosted TREE, Linear Regression) to predict economic growth. The empirical results indicated that the predictions obtained using Random Forest were more accurate than those obtained by the other models. The results indicated that Government Effectiveness, Control of Corruption and Rule of Law are the most influential factors explaining economic growth.

Today, cryptocurrencies are rapidly gaining popularity and sweeping all the economies of the world, but the bulk of the literature is devoted to a few cryptocurrencies only. The purpose of this chapter is to analyze of the cryptocurrency market. More than 2000 cryptocurrencies are examined, and a set of 70 cryptocurrencies were recovered for a sample spanning 2015-2018. The degree of relationship between the variables was then investigated. The PCA was performed. This analysis allows the initial variables to be replaced by five factors that retain almost all of the information (91.028% of the total information) and have the advantage of being uncorrelated. Therefore, the authors have concluded that the first factor corresponds to the cci30 index used by the crypto funds while the rest of the factors can be distinguished according to some variants.

The objective of this paper is to measure the degree of Home Bias within the holdings of portfolio and to identify their determining factors. By following an intuitive reasoning, the authors have chosen a number of susceptible factors that have an impact on Home Bias. In fact, they have developed an international CAPM (Capital Asset Pricing Model). This model is estimated for 20 countries, with the use of cross-section econometrics. The authors' results show that all countries have recorded a high level of Home bias in their holdings of portfolio. In order to study whether the Home Bias of the newly emerging markets and that of the developed markets react differently to the determining factors or not the authors have evaluated the model so much jointly for all markets as separately for the developed and the newly emerging ones. In the case of classification of the sample, the results have permitted us to draw an important conclusion and to have cognizance that the volatility of the exchange rate is statistically significant concerning the newly emerging economies at a threshold of 1%, while it is hardly remarkable for the developed countries. This means that this variable prevents the American investors from investing in the former countries. Samely, for both variables of joint- variance and size.

The main goal of this chapter is to originally analyze one of the irreversible factors of the international competitiveness of the companies that are related to the entrepreneurial economic intelligence. The originality of this research lies on the assimilation of the actions related to the identification and exploitation of opportunities and thus, the entrepreneurial orientation, as an integral part of the entrepreneurial economic intelligence which is able to stimulate the international competitiveness of the companies in the specific case of Tunisia.

Since the outbreak of the financial crisis, the major global credit rating agencies have implemented significant changes to their methodologies to assess the sovereign credit risk. Therefore, bond rating prediction has become an interesting potential for investors and financial institutions. Previous research studies in this field have applied traditional statistical methods to develop models which provide prediction accuracy. However, no overall distinguished methods have been used in predicting bond ratings. Moreover, recent studies have suggested ensembles of classifiers that may be used in bond rating prediction. This article proposes an improved machine learning aimed to predict the rating of financial bonds. We empirically compare the classifiers ranging from logistic regression and discriminant analysis to nonparametric classifiers, such as support vector machine, neural networks, the cost-sensitive decision tree algorithm and deep neural networks. Three real-world bond rating data sets were selected to check the effectiveness and the viability of the set of the classifiers. The experimental results confirm that data mining methods can represent an alternative to the traditional prediction models of bond rating.

The term “terrorism” is notoriously difficult to define, but terrorism has become a global concern over the last two decades. The findings of prior research on the social, political, and economic determinants of terrorism are mixed and inconclusive. Accordingly, this study investigates the relationship between socioeconomic indicators and terrorism in Tunisia for the period 1979–2015. The error correction model has been used to determine the stability of any long‐term relationship. Using autoregressive distributed lag bound testing, the findings reveal that the unemployment rate, political instability, the informal sector and higher school enrollment rates positively and significantly affect terrorism. However, gross domestic product per capita and foreign direct investment have a negative and significant impact on terrorism. Interestingly, the poverty rate has no relationship to terrorism. Policy implications and future research are also discussed.