Is It Worth the While? The Relevance of Qualitative Information in Credit Rating

Lehmann, Bina

doi:10.2139/ssrn.410186

Cited by 55 publications

(43 citation statements)

References 24 publications

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“…We acknowledge that our analysis could still be improved using qualitative variables as predictors in the failure prediction model to better discriminate between SMEs (as recent literature, e.g. Lehmann (2003) and Grunet et al (2004), demonstrate).…”

Section: Introductionmentioning

confidence: 93%

“…Furthermore, recent literature (e.g. Lehmann (2003) and Grunet et al (2004)) concludes that quantitative variables are not sufficient to predict SME default and that including qualitative variables (such as the number of employees, the legal form of the business, the region where the main business is carried out, the industry type, etc.) improves the models' prediction power.…”

Section: Selection Of the Variablesmentioning

confidence: 99%

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Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

124

217

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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 behaviour 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 panel data 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 percent 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. JEL classification: G21; G28

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Section: Introductionmentioning

confidence: 93%

Section: Selection Of the Variablesmentioning

confidence: 99%

Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

124

217

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“…Indeed, credit scoring model can capture the behaviour of loan officer -even if it is a bias-and improve classification accuracy through learning. For example, Cornée et al (2009) followed the methodology of previous studies that have investigated the role of loan officer's subjective judgment in credit prevision accuracy for traditional banks (Grunert, Norden, & Weber, 2005;Lehmann, 2003) and concluded that loan officer's subjective assessment in MFI usually increases credit classification accuracy and cannot be neglected. This result leads us to investigate the relation between the quality of loan officers' subjective judgment and the learning through lenders-borrowers relationship.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the predictive accuracy of microloan officers’ subjective judgment

Baklouti¹,

Baccar²

2013

IJRSM

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The peculiarity in lending methodology in Microfinance institution characterized by unchallenged dominance of the loan officers in the decision-making prompted us to investigate the predictive accuracy of their subjective judgement. In addition, we investigate if the accuracy of this information depends on the strength of lenders-borrowers relationship.The objective of this paper is to understand the loan officer behaviour in default prediction task. Using credit file data from Tunisian Microfinance bank, we have found evidence that subjective judgment of loan officers has statistically significant predictive capability of default risk. In addition, our results suggest that novice loan officers have incorrect prediction of future default events because they are prone to different behavioral biases. Novice loan officers may overweight any information that is consistent with their existing beliefs and overestimate the precision of their own private information. However, through learning experienced loan officers have less behavior bias and more accurate feeling allowing them to make an accurate default prediction. To make correct decision in relationship lending approach, credit committee can rely on subjective judgment of experienced loan officers mainly when the latter judged the creditworthiness of frequent borrowers. As soft information is not easily and accurately transferable, Microfinance institutions have more interest to retain their experienced loan officers than their borrowers.

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“…Chen and Shimerda (1981) show that out of more than 100 financial ratios, almost 50% were found useful in at least one empirical study. Some scholars have argued that quantitative variables are not sufficient to predict SME default and that including qualitative variables (such as the legal form of the business, the region where the main business is carried out, and industry type) improves the models' predictive power (for example, Lehmann 2003;Grunert, Norden, and Weber 2005). However, the data used here are based on firms' financial statements, which do not contain such qualitative variables.…”

Section: Selection Of the Variablesmentioning

confidence: 99%

Hometown Investment Trust Funds: An Analysis of Credit Risk

Yoshino

Taghizadeh–Hesary

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Asian Development Bank InstituteThe Working Paper series is a continuation of the formerly named Discussion Paper series; the numbering of the papers continued without interruption or change. ADBI's working papers reflect initial ideas on a topic and are posted online for discussion. ADBI encourages readers to post their comments on the main page for each working paper (given in the citation below). Some working papers may develop into other forms of publication. Farhad Taghizadeh-Hesary is an adjunct researcher at the School of Economics, Keio University, and a visiting scholar at the Institute of Energy Economics of Japan. Currently he is also assisting the ADBI Dean in his research.The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms.Working papers are subject to formal revision and correction before they are finalized and considered published.Asian Development Bank Institute Kasumigaseki Building 8F 3-2-5 Kasumigaseki, Chiyoda-ku Tokyo 100-6008, JapanTel:+81-3-3593-5500 Fax:+81-3-3593-5571 URL:www.adbi.org E-mail: info@adbi.org AbstractIn Asia, small and medium-sized enterprises (SMEs) account for a major share of employment and dominate the economy. Asian economies are often characterized as having bank-dominated financial systems and underdeveloped capital markets, in particular venture capital markets. Hence, looking for new methods of financing for SMEs is crucial. Hometown investment trust funds (HIT) are a new form of financial intermediation that has now been adopted as a national strategy in Japan. In this paper, we explain the importance of SMEs in Asia and describe about HITs. We then provide a scheme for the credit rating of SMEs by employing two statistical analysis techniques, principal components analysis and cluster analysis, and applying various financial variables to 1,363 SMEs in Asia. Adoption of this comprehensive and efficient method would enable banks to group SME customers based on financial health, adjust interest rates on loans, and set lending ceilings for each group. Moreover, this method is applicable to HITs around the world. JEL Classification: G21, G24...

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Is It Worth the While? The Relevance of Qualitative Information in Credit Rating

Cited by 55 publications

References 24 publications

Modeling Credit Risk for SMEs: Evidence from the US Market

Modeling Credit Risk for SMEs: Evidence from the US Market

Evaluating the predictive accuracy of microloan officers’ subjective judgment

Hometown Investment Trust Funds: An Analysis of Credit Risk

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