Survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning.

Machine Learning: Neural and Statistical Classification

A survey of business failures with an emphasis on prediction methods and industrial applications

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behavior of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on a panel of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out of sample prediction power which is almost 30% higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs.

https://papers.ssrn.com/sol3/Delivery.cfm/abac_234.pdf?abstractid=1007548&mirid=1

Modeling Credit Risk for SMEs: Evidence from the US Market

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyse its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analysed and the most significant variables in predicting the entities’ credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 U.S. firms (with sales less than $65 million) over the period 1994–2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 per cent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs.

/pdf/modelling-credit-risk-for-smes-evidence-from-the-u-s-market-3huffey3xm.pdf

Modelling Credit Risk for SMEs: Evidence from the U.S. Market

Few studies that have focused on developing credit risk models specifically for small and medium-sized enterprises (SMEs) have included non-financial information as a predictor of company creditworthiness. In this study we have available non-financial, regulatory compliance and “event” data to supplement the limited accounting data that is 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 data relating to 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.

http://people.stern.nyu.edu/ealtman/Altman-Sabbato-Wilson-JCR_2010.pdf

The value of non-financial information in small and medium-sized enterprise risk management

This paper reports the main results of an empirical research project dealing with the prognosis and surveillance of corporate credit risks. In the course of the research programme both quantitative (balance sheet analysis, current accounts data analysis) and qualitative (assessment of management) methods were applied. Each of the three parts of the project is independently usable However, the combined application of the three approaches avoids deficiencies of using only one of the components and improves the early detection of credit risks.

The prognosis and surveillance of risks from commercial credit borrowers

Wohin die Branche in den nachsten Jahrzehnten gehen wird, vermag naturlich niemand genau und verlaslich vorherzusagen. Die gemeinsame Wahrung wird viel in Bewegung bringen, sie wirkt uber unterschiedliche Kanale auf alle Bereiche der Banktatigkeit, so das die Prognoseunsicherheit noch groser ist als sonst.

Johann Heinrich von Stein

Papers

The prognosis and surveillance of risks from commercial credit borrowers

Trends im Bankwesen — Wirkungen auf das Bankgeschäft der Zukunft