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Bankruptcy prediction for SMEs using relational data

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  • TOBBACK, Ellen
  • MOEYERSOMS, Julie
  • STANKOVA, Marija
  • MARTENS, David

Abstract

Bankruptcy prediction has been a popular and challenging research area for decades. Most prediction models are built using traditional data such as financial gures, stock market data and firm specific variables. We complement such dense data with ne-grained data by including information on the company's directors and managers in the prediction models. This information is used to build a network between Belgian enterprises, where two companies are related if they share or have shared a director or high-level manager. We start from two possibly related assumptions: (i) if a company is linked to many (or only) bankrupt firms, it will have a higher probability of becoming bankrupt and (ii) the management has an inuence on the performance of the company and incompetent or fraudulent managers can lead a company into bankruptcy. The weighted-vote relational neighbour (wvRN) classier is applied on the created network and transforms the relationships between companies in 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 failed. The more related companies have failed, the higher the predicted probability of bankruptcy. The relational model is then benchmarked against a base model that contains only structured data such as financial ratios. Finally, an ensemble model is built that combines the relational model's output scores with the structured data. We find that this ensemble model outperforms the base model when detecting the riskiest firms, especially when predicting two-years ahead.

Suggested Citation

  • TOBBACK, Ellen & MOEYERSOMS, Julie & STANKOVA, Marija & MARTENS, David, 2016. "Bankruptcy prediction for SMEs using relational data," Working Papers 2016004, University of Antwerp, Faculty of Applied Economics.
  • Handle: RePEc:ant:wpaper:2016004
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    References listed on IDEAS

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    1. H. Ooghe & S. De Prijcker, 2006. "Failure process and causes of company bankruptcy: a typology," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/388, Ghent University, Faculty of Economics and Business Administration.
    2. Chae Woo Nam & Tong Suk Kim & Nam Jung Park & Hoe Kyung Lee, 2008. "Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 493-506.
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    4. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    5. Cielen, Anja & Peeters, Ludo & Vanhoof, Koen, 2004. "Bankruptcy prediction using a data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 526-532, April.
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    8. STANKOVA, Marija & MARTENS, David & PROVOST, Foster, 2015. "Classification over bipartite graphs through projection," Working Papers 2015001, University of Antwerp, Faculty of Applied Economics.
    9. Selwyn Piramuthu & Harish Ragavan & Michael J. Shaw, 1998. "Using Feature Construction to Improve the Performance of Neural Networks," Management Science, INFORMS, vol. 44(3), pages 416-430, March.
    10. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
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