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Nonlinear Relationships and Their Effect on the Bankruptcy Prediction

Author

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  • Christian Lohmann

    (Schumpeter School of Business and Economics)

  • Thorsten Ohliger

    (parcIT GmbH)

Abstract

This study uses a generalized additive model to analyze nonlinear relationships and their effect on the bankruptcy prediction in terms of a company’s probability of default. Specifically, this study examines which of the performance indicators that are contained in a company’s annual financial statements affect nonlinearly that company’s probability of bankruptcy. Furthermore, this study examines the specific form that such nonlinear relationships take and interprets their economic relevance to predicting bankruptcy. On the basis of comprehensive data on German companies, this study shows empirically that there are statistically and economically relevant nonlinear relationships that influence the bankruptcy prediction. These nonlinear relationships are observed both below and above specific thresholds with respect to a company’s adjusted equity ratio, asset structure ratio based on tangible assets, adjusted return on assets, sales, and age. Our findings show that, to increase the accuracy of bankruptcy forecasts, it is necessary to take into account nonlinear relationships in models of bankruptcy prediction.

Suggested Citation

  • Christian Lohmann & Thorsten Ohliger, 2017. "Nonlinear Relationships and Their Effect on the Bankruptcy Prediction," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 18(3), pages 261-287, August.
    • Handle: RePEc:spr:schmbr:v:18:y:2017:i:3:d:10.1007_s41464-017-0034-y
    DOI: 10.1007/s41464-017-0034-y
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    Cited by:

    1. Christian Lohmann & Thorsten Ohliger, 2020. "Bankruptcy prediction and the discriminatory power of annual reports: empirical evidence from financially distressed German companies," Journal of Business Economics, Springer, vol. 90(1), pages 137-172, February.
    2. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.

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