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A combined approach based on robust PCA to improve bankruptcy forecasting

Author

Listed:
  • Marianna Succurro
  • Giuseppe Arcuri
  • Giuseppina Damiana Costanzo

Abstract

Purpose - Starting from a series of financial ratios analysis, this paper aims to build up two indices which take into account both the firm’s debt level and its sustainability to investigate if and to what extent the proposed indices are able to correctly predict firms’ financial bankruptcy probabilities. Design/methodology/approach - The research implements a statistical approach (tandem analysis) based on both an original use of principal component analysis (PCA) and logit model. Findings - The econometric results are compared with those of the popular Altman Z-score for different lengths of the reference period and with more recent classifiers. The empirical evidence would suggest a good performance of the proposed indices which, therefore, could be used as early warning signals of bankruptcy. Practical implications - The potential application of the model is in the spirit of predicting bankruptcy and aiding companies’ evaluation with respect to going-concern considerations, among others, as the early detection of financial distress facilitates the use of rehabilitation measures. Originality/value - The construction of the indebtedness indices is based on an original use of Robust PCA for skewed data.

Suggested Citation

  • Marianna Succurro & Giuseppe Arcuri & Giuseppina Damiana Costanzo, 2019. "A combined approach based on robust PCA to improve bankruptcy forecasting," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 18(2), pages 296-320, May.
    • Handle: RePEc:eme:rafpps:raf-04-2018-0077
    DOI: 10.1108/RAF-04-2018-0077
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    Citations

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    Cited by:

    1. Bosiljka Srebro & Bojan Mavrenski & Vesna Bogojević Arsić & Snežana Knežević & Marko Milašinović & Jovan Travica, 2021. "Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    2. Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    3. María del Carmen Valls Martínez & Salvador Cruz Rambaud & Isabel María Parra Oller, 2020. "Sustainable and conventional banking in Europe," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-23, February.
    4. Giuseppe Arcuri & Nadine Levratto & Marianna Succurro, 2023. "Does commercial court organisation affect firms’ bankruptcy rate? evidence from the french judicial reform," European Journal of Law and Economics, Springer, vol. 55(3), pages 573-601, June.
    5. Marc König & Manon Enjolras & Christina Ungerer & Mauricio Camargo & Guido Baltes, 2022. "Evaluation of tech ventures' evolving business models: rules for performance-related classification," Post-Print hal-03685241, HAL.
    6. Maren Forier & Nadine Lybaert & Maarten Corten & Niels Appermont & Tensie Steijvers, 2023. "The flip side of the coin: how entrepreneurship-oriented insolvency laws can complicate access to debt financing for growth firms," European Journal of Law and Economics, Springer, vol. 56(3), pages 461-495, December.
    7. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.

    More about this item

    Keywords

    Bankruptcy; Robust PCA; Logit; Z-score; C40; D22; G33; M41;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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