A Structural form Default Prediction Model for SMEs, Evidence from the Dutch Market

Abstract

The objective of this research is to develop a structural form probability of default model for small and medium-sized enterprises, dealing with the methodological issues which arise in the modelling of small commercial loan portfolios. Other motivations are to provide an extensive overview of the characteristics of SMEs, and to provide a list of characteristics for an SME PD model, e.g. time and cost efficiency, broad applicability, limited data requirements, and powerful in predicting default. The structural form model is developed and tested on a unique dataset of private firm's bank loans of a Dutch bank. The results are promising; the model output differs significantly between defaulted and non-defaulted firms. The structural form model can be used on its own, or as an additional variable in a credit risk model. A second PD model is developed using logistic regression with a number of financial ratios, including the structural form measure. This variable is significant in default prediction of SMEs and has some additional predictive power, next to the popular financial ratios. Overall, the results indicate that the structural form model is a good indicator for default of SMEs.

Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises

“…Their inclusion in the model might improve its precision. Second, larger data sets would be insightful, as would other potential methods of modelling credit risk that can confirm the proposed PD model (Fantazzini and Figini, 2009;Rikkers and Thibeault, 2009). Ultimately though, SMEs differ from large corporations from a credit risk standpoint, so this study has wide-ranging implications.…”

“…Their inclusion in the model might improve its precision. Second, larger data sets would be insightful, as would other potential methods of modelling credit risk that can confirm the proposed PD model (Fantazzini and Figini, 2009;Rikkers and Thibeault, 2009).…”

“…upper left corner) on an ROC curve. To calculate initial percentages, we assumed an arbitrary threshold of 50 per cent of the population (Altman and Sabato, 2007;Rikkers and Thibeault, 2009)[7].…”

Revisiting SME default predictors: The Omega Score

“…A second limitation is that we do not use structural models since they require the firm's current market value of assets, which is generally derived from equity market data through an option-based model and other assumptions (volatility of assets over the assessment period, default boundary, expected return on assets, and distribution of assets). Although several ways exist to estimate the market value of a SME (Rikkers & Thibeault, 2009), most of them require many assumptions for their application (that is, the average equity multiple of comparable firms, the present value of expected future dividends, and the discounted free cash flow approach). Accordingly, we invite future researchers to adopt structural models to benchmark the Omega Score.…”

Screening Creditworthiness of SME's: The Case of Small Business Assistance in Turkey

“…They find accurate estimation of credit riskiness of SMEs would help expand the size of the credit markets. Rikkers and Ooghe (2009) 1. SMEs are classified as businesses with annual turnover of less than 50 million euros according to the Basel II Accord.…”