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In the domestic credit market creditor and debtor rights are clearly defined. In contrast, sovereign debt repayment is largely contingent on the debtor government’s willingness to repay as enforcement of contracts at the international level is limited. In this chapter we explore different sources of sovereign debt crises as opportunistic and myopic behavior by debtor nations, over-consumption of imported goods, credit temptation by lenders eager to allocate savings surpluses, and unexpected consequences of initially seen appropriate policies. We explore how these factors have played out in the Euro-debt crisis and outline a framework for creditor responsibility to complement debtor self-restraint.

The importance of obtaining a sovereign credit rating from an agency is still underrated in Africa. Literature on the determinants of sovereign credit ratings in Africa is scarce. The purpose of this research is to determine what the determinants are for sovereign credit ratings in Africa and whether these determinants differ between regions and income groups.

A sample of 19 African countries' determinants of sovereign credit ratings are compared between 2007 and 2014 using a panel-ordered probit approach.

The findings indicated that the determinants of sovereign credit ratings differ between African regions and income groups. The developmental indicators were the most significant determinants across all income groups and regions. The results affirm that the identified determinants in the literature are not as applicable to African sovereigns, and that developmental variables and different income groups and regions are important determinants to consider for sovereign credit ratings in Africa.

The results affirm that the identified determinants in the literature are not as applicable to African sovereigns, and that developmental variables and different income groups and regions are important determinants to consider for sovereign credit ratings in Africa. Rating agencies follow the same rating assignment process for developed and developing countries, which means investors will have to supplement the allocated credit rating with additional information. Africa can attract more investment if African countries obtain formal, accurate sovereign credit ratings, which take the characteristics of the continent into consideration.

The study attempts to examine the effect of the COVID-19 pandemic on the economic growth and public debt of the Indian economy. The authors also attempt to make quarterly projections of economic growth and external debt (ED) for the next five years. The objective is to understand how much time the economy takes to recover and at what pace. Consequently, this study elucidates the composition of debt after the crisis in the next five years.

To predict India's gross domestic product (GDP) and ED for the next five years, the authors used an auto-regressive integrated moving average (ARIMA) model. This model was built under a Box–Jenkins methodology (Box and Jenkins, 1976) and was subjected to an augmented Dickey–Fuller (ADF) test to check the stationarity of the data. The methodology includes three main steps to estimate and forecast the model: identification, estimation, and diagnostic and forecasting.

The study finds that the outbreak of the COVID-19 pandemic has significant implications for economic growth and public debt. The economy faced contraction in the first quarter of the year 2020 due to the suspension of economic activities and still struggling with the negative values of GDP. The forecasting results reveal that ED will continue to grow to meet the increasing health expenditure needs, and GDP will also bounce back slowly after the end of the year 2021. It has been noticed that the recurrent crisis derails the developing economies from the path of sustainable development to a prolonged economic slump with mounting public debt.

The study examines the impact of the COVID-19 pandemic on economic growth and public debt with particular reference to India. To the best of the authors’ knowledge, this is the first time the quarterly projections for GDP and ED have been made after the COVID-19 crisis.

The authorship authentication (AA) problem is concerned with correctly attributing a text document to its corresponding author. Historically, this problem has been the focus of various studies focusing on the intuitive idea that each author has a unique style that can be captured using stylometric features (SF). Another approach to this problem, known as the bag-of-words (BOW) approach, uses keywords occurrences/frequencies in each document to identify its author. Unlike the first one, this approach is more language-independent. This paper aims to study and compare both approaches focusing on the Arabic language which is still largely understudied despite its importance.

Being a supervised learning problem, the authors start by collecting a very large data set of Arabic documents to be used for training and testing purposes. For the SF approach, they compute hundreds of SF, whereas, for the BOW approach, the popular term frequency-inverse document frequency technique is used. Both approaches are compared under various settings.

The results show that the SF approach, which is much cheaper to train, can generate more accurate results under most settings.

Numerous advantages of efficiently solving the AA problem are obtained in different fields of academia as well as the industry including literature, security, forensics, electronic markets and trading, etc. Another practical implication of this work is the public release of its sources. Specifically, some of the SF can be very useful for other problems such as sentiment analysis.

This is the first study of its kind to compare the SF and BOW approaches for authorship analysis of Arabic articles. Moreover, many of the computed SF are novel, while other features are inspired by the literature. As SF are language-dependent and most existing papers focus on English, extra effort must be invested to adapt such features to Arabic text.