A Statistical-Based Approach to Assessing Comparatively the Performance of Non-Banking Financial Institutions in Romania

In this paper we construct a framework that enables us to make class predictions about the performance of non-banking financial institutions (NFIs) in Romania. Our objective is to create a classification model in the form of a logistic regression function that can be used to assess the performance of NFIs based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. Our methodology consists of two phases: a clustering phase, in which we obtain several clusters that contain similar data-vectors in terms of Euclidean distances, and a classification phase, in which we construct a class predictive model in order to place the new row data within the clusters obtained in the first phase as they become available. Our goal is two-fold: to validate the dimensionalities of the map used to represent the performance clusters and the quantisation error associated with it and to use the obtained model to analyze the movements of three largest NFIs during the period 2007–2010. Using our validation procedure that is based on a bootstrap technique, we are now able to find the proper map architecture and training–testing dataset combination for a particular problem. At the same time, using the visualization techniques employed in the study, we understand how different financial factors can and do contribute to the companies’ movements from one group/cluster to another. Furthermore, the classification model is validated based on high training and testing accuracy rates.