How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts

Abstract

This paper contributes to solving the puzzle of assessing the financial performance of private/unlisted companies. The inner characteristics of these companies make the adoption of traditional best practices in estimating risk premia difficult or impossible. Moreover, the lack of market data and comparable information biases the perception of corporate performance and generates the misallocation of credit fundings (both quantities and pricing). Hence, in this paper, we develop an Integrated Rating Methodology (IRM) to estimate a more efficient corporate “return-to-risk” measure. Our IRM is rooted in the seminal “certainty equivalent” model as developed by Lintner in 1965, but we modify it using a shortfall approach, and then compute a “confident equivalent” that is compliant with Fischer Black’s zero-beta model as well as the Basel agreements. An empirical application of the approach is conducted with a sample of 13,583 non-financial SMEs in the north-east regions of Italy, where there is evidence of inefficient bank financing. We back-test our IRM by rating these companies using corporate financial data during the period 2007–2014, which encompasses both the Great Financial Crisis and the European sovereign debt crisis. Our empirical results depict a clear crowding-out effect of credit allocations when we compare our IRM scoring measure with the actual raising ability and the cost of capital relating to these firms. We find that 36% of companies are underfunded, even if they have a superior IRM score, while 27% of them are funded without merit. Interestingly, this last figure is in line with the average non-performing loan ratio provided by official Italian statistics from 2015 to 2020. Therefore, we conclude that our IRM methodology is promising and may be better at estimating risk financing in small private companies (including start-ups) than internal banking models. These initial results will drive our forthcoming research towards creating an IRM 2.0.