Predicting Unlisted SMEs' Default: Incorporating Market Information on Accounting-based Models for Improved Accuracy

Panos Andrikopoulos; Amir Khorasgani

doi:10.1016/j.bar.2018.02.003

Predicting Unlisted SMEs' Default: Incorporating Market Information on Accounting-based Models for Improved Accuracy

Panos Andrikopoulos, Amir Khorasgani

School of Economics, Finance and Accounting

Research output: Contribution to journal › Article › peer-review

20 Citations (Scopus)

601 Downloads (Pure)

Abstract

The risk associated with lending to small businesses has become more important since regulations started obliging banks to use separate procedures in assessing SMEs' credit worthiness. However, current accounting-based models for SMEs do not account for the impact of market information on default prediction. We fill this gap in the literature by introducing a hybrid default prediction model for unlisted SMEs that uses market information of listed SMEs (comparable approach) alongside existing accounting information of unlisted SMEs. Our results suggest that the accuracy of this default prediction modelling approach in the hold-out sample, during the period of the financial crisis 2007-09 and for the entire sample-period, improves considerably. We conclude that the proposed hybrid model is a good replacement for existing standard accounting-based methods on SMEs' default prediction.

Original language	English
Pages (from-to)	559-573
Number of pages	15
Journal	The British Accounting Review
Volume	50
Issue number	5
Early online date	3 Mar 2018
DOIs	https://doi.org/10.1016/j.bar.2018.02.003
Publication status	Published - Sept 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in The British Accounting Review. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in The British Accounting Review, [(in press), (2018)] DOI: 10.1016/j.bar.2018.02.003

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

SMEs finance
Merton-KMV model
default prediction
market-based factors
accounting-based factors

ASJC Scopus subject areas

Finance

Access to Document

10.1016/j.bar.2018.02.003

Post-PrintAccepted author manuscript, 1.39 MBLicence: CC BY-NC-ND

Panagiotis Andrikopoulos
- Research Centre for Financial & Corporate Integrity - Centre Director
Person: Professional Services

Cite this

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title = "Predicting Unlisted SMEs' Default: Incorporating Market Information on Accounting-based Models for Improved Accuracy",

abstract = "The risk associated with lending to small businesses has become more important since regulations started obliging banks to use separate procedures in assessing SMEs' credit worthiness. However, current accounting-based models for SMEs do not account for the impact of market information on default prediction. We fill this gap in the literature by introducing a hybrid default prediction model for unlisted SMEs that uses market information of listed SMEs (comparable approach) alongside existing accounting information of unlisted SMEs. Our results suggest that the accuracy of this default prediction modelling approach in the hold-out sample, during the period of the financial crisis 2007-09 and for the entire sample-period, improves considerably. We conclude that the proposed hybrid model is a good replacement for existing standard accounting-based methods on SMEs' default prediction.",

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author = "Panos Andrikopoulos and Amir Khorasgani",

note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in The British Accounting Review. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in The British Accounting Review, [(in press), (2018)] DOI: 10.1016/j.bar.2018.02.003 {\textcopyright} 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/",

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T1 - Predicting Unlisted SMEs' Default: Incorporating Market Information on Accounting-based Models for Improved Accuracy

AU - Andrikopoulos, Panos

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N1 - NOTICE: this is the author’s version of a work that was accepted for publication in The British Accounting Review. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in The British Accounting Review, [(in press), (2018)] DOI: 10.1016/j.bar.2018.02.003 © 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

PY - 2018/9

Y1 - 2018/9

N2 - The risk associated with lending to small businesses has become more important since regulations started obliging banks to use separate procedures in assessing SMEs' credit worthiness. However, current accounting-based models for SMEs do not account for the impact of market information on default prediction. We fill this gap in the literature by introducing a hybrid default prediction model for unlisted SMEs that uses market information of listed SMEs (comparable approach) alongside existing accounting information of unlisted SMEs. Our results suggest that the accuracy of this default prediction modelling approach in the hold-out sample, during the period of the financial crisis 2007-09 and for the entire sample-period, improves considerably. We conclude that the proposed hybrid model is a good replacement for existing standard accounting-based methods on SMEs' default prediction.

AB - The risk associated with lending to small businesses has become more important since regulations started obliging banks to use separate procedures in assessing SMEs' credit worthiness. However, current accounting-based models for SMEs do not account for the impact of market information on default prediction. We fill this gap in the literature by introducing a hybrid default prediction model for unlisted SMEs that uses market information of listed SMEs (comparable approach) alongside existing accounting information of unlisted SMEs. Our results suggest that the accuracy of this default prediction modelling approach in the hold-out sample, during the period of the financial crisis 2007-09 and for the entire sample-period, improves considerably. We conclude that the proposed hybrid model is a good replacement for existing standard accounting-based methods on SMEs' default prediction.

KW - SMEs finance

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KW - market-based factors

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ER -

Predicting Unlisted SMEs' Default: Incorporating Market Information on Accounting-based Models for Improved Accuracy

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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Panagiotis Andrikopoulos

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