Altmetric

Bankruptcy prediction for SMEs using relational data

File Description SizeFormat 
SMEstudyDSS.pdfAccepted version445.18 kBAdobe PDFView/Open
Title: Bankruptcy prediction for SMEs using relational data
Authors: Tobback, E
Bellotti, T
Moeyersoms, J
Stankova, M
Martens, D
Item Type: Journal Article
Abstract: Bankruptcy prediction has been a popular and challenging research area for decades. Most prediction models are built using financial figures, stock market data and firm specific variables. We complement such traditional low-dimensional data with high-dimensional data on the company's directors and managers in the prediction models. This information is used to build a network between small and medium-sized enterprises (SMEs), where two companies are related if they share a director or high-level manager. A smoothed version of the weighted-vote relational neighbour classifier is applied on the network and transforms the relationships between companies into bankruptcy prediction scores, thereby assuming that a company is more likely to file for bankruptcy if one of the related companies in its network has already failed. An ensemble model is built that combines the relational model's output scores with structured data and is applied on two data sets of Belgian and UK SMEs. We find that the relational model gives improved predictions over a simple financial model when detecting the riskiest firms. The largest performance increase is found when the relational and financial data are combined, confirming the complementary nature of both data types.
Issue Date: 1-Oct-2017
Date of Acceptance: 14-Jul-2017
URI: http://hdl.handle.net/10044/1/64798
DOI: https://dx.doi.org/10.1016/j.dss.2017.07.004
ISSN: 0167-9236
Publisher: Elsevier
Start Page: 69
End Page: 81
Journal / Book Title: Decision Support Systems
Volume: 102
Copyright Statement: © 2017 Elsevier B.V. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Operations Research & Management Science
Computer Science
Data mining
Relational data
Network analysis
Bankruptcy prediction
SME
FINANCIAL RATIOS
NEURAL-NETWORKS
FAILURE
CLASSIFICATION
MODELS
MARKET
RISK
01 Mathematical Sciences
08 Information And Computing Sciences
15 Commerce, Management, Tourism And Services
Information Systems
Publication Status: Published
Online Publication Date: 2017-07-18
Appears in Collections:Mathematics
Statistics



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx