Bankruptcy prediction for SMEs using relational data

Ellen Tobback, Tony Bellotti, Julie Moeyersoms, Marija Stankova, David Martens

Research output: Journal PublicationArticlepeer-review

34 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)69-81
Number of pages13
JournalDecision Support Systems
Volume102
DOIs
Publication statusPublished - Oct 2017
Externally publishedYes

Keywords

  • Bankruptcy prediction
  • Data mining
  • Network analysis
  • Relational data
  • SME

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'Bankruptcy prediction for SMEs using relational data'. Together they form a unique fingerprint.

Cite this