The value of non-financial information in SME risk management

Abstract

Within the commercial client segment, small business lending is gradually becoming a major target for many banks. The new Basel Capital Accord has helped the financial sector to recognize small and medium sized enterprises (SMEs) as a client, distinct from the large corporate. Some argue that this client base should be treated like retail clients from a risk management point of view in order to lower capital requirements and realize efficiency and profitability gains. In this context, it is increasingly important to develop appropriate risk models for this large and potentially even larger portion of bank assets. So far, none of the few studies that have focused on developing credit risk models specifically for SMEs have included non-financial information as predictors of the company credit worthiness. For the first time, in this study we have available non-financial, regulatory compliance and 'event' data to supplement the limited accounting data which are often available for non-listed firms. We employ a sample consisting of over 5.8 million sets of accounts of unlisted firms of which over 66,000 failed during the period 2000-2007. We find that qualitative data relating to such variables as legal action by creditors to recover unpaid debts, company filing histories, comprehensive audit report/opinion data and firm specific characteristics make a significant contribution to increasing the default prediction power of risk models built specifically for SMEs.JEL classification: G33, G32, M13

The Entreprenurial Perception of SME Business Environment Quality in the Czech Republic

“…The results show that, the firms aged within 3-9 years are more vulnerable to failure. (Altman, Sabato, and Wilson, 2010) On the other side for instance Situm (2014) states that age of the firm cannot be good bankruptcy predictor for the companies, and firm size is not statically different for the failed and non-failed firms.…”

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

“…More recently, banks and financial institutions started to realize the importance of distinguishing SMEs from large firms while modelling for credit risk since they require specific risk management tools and methodologies to be developed for them (Altman et al, 2010). In line with this, Dietsch and Petey (2004) argue that German and French SMEs are riskier than large firms but have lower asset correlation with each other.…”

Risk Scoring Models for Trade Credit in Small and Medium Enterprises

“…Moreover, some of the datum required to calculate accounting ratios in studies of the failure of listed companies is not available for SMEs. Other studies using a variety of statistical techniques, have contributed to the knowledge of the insolvency indicators, both financial ( [9], [3]) and non-financial ( [14], [2]) that arise in SMEs. In particular, ( [12]) propose a nonparametric survival approach with a random-forest model, but they also conclude that a simpler logit model outperforms the random-forest model in the out-of-sample validation.…”

“…As pointed out by ( [2]), two of the main factors behind failed SMEs are insufficient capitalization and lack of planning. In the related literature it is common to find terms related to high levels of risk, such as: insolvency, bankruptcy, failure, default, etc.…”

“…The current financial crisis has renewed the interest in research and development of failure prediction models for all of the corporate and retail sectors ( [2]). The literature on the modeling of credit risk for large, listed companies is extensive, and it either uses historical accounting data to predict insolvency or models that rely on market information.…”