Hosn el Woujoud Bousselmi

The purpose of this article is to present and test a conceptual framework that describes how the government’s commitment in improving corporate social responsibility (CSR) practices promotes the attraction of foreign direct investment (FDI) in Tunisia. As such, this conceptual framework inspires the existence of an interaction between the improvement of CSR practices by public policies (PP), and the attraction of FDI. In this regard, this study applied structural equation modeling (SEM) to empirically test this proposed model. It finds that the Tunisian government is valuing CSR and considering it as an investment. It presents examples of instruments, PP and tools that encourage to adopt CSR practices, thus, enhancing the attraction of FDI, which will have a positive impact on the growth of the country in terms of wealth creation, jobs and poverty reduction.

Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data’ obtained from Bloomberg are used to obtain forecasts of S&P and Moody’s ratings directly from these data with high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.