Identifying influential variables for corporate credit ratings in the U.S. technology sector

Abstract

Credit Rating Agencies play a fundamental role in providing information to market participants. However, a high number of companies are not rated. Thus, the thesis aims to provide a sensible model in order to forecast corporate ratings for listed companies in the U.S. technology sector. The variables collected are financial and market-based information available to the public. Further, board and gender diversification data are included to investigate if they have some impact on rating assignment. Since the outcome of this research is categorical, a Logistic Regression model was chosen to identify influential variables and how much significantly they modify a rating outcome. Once the prediction accuracy of the model is assessed, the results are compared to theoretical background to draw conclusions on the possibility to provide an initial rating outlook to unrated companies.