Financial Distress Prediction with Stacking Ensemble Learning

https://doi.org/10.22146/ijccs.76575

Muhammad Fadhlil Hadi(1*), De-Ron Liang(2), Tri Kuntoro Priyambodo(3), Azhari SN(4)

(1) Master Program in Computer Science, FMIPA UGM, Yogyakarta
(2) National Central University, Zhongli
(3) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(4) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of manipulation of financial statements. None of the previous studies combined the five Altman Ratios with the Beneish M-Score. We use Stacking Ensemble Learning to classify crisis companies and perform a comprehensive analysis. This insight helps the investment public make lending decisions by mixing all the financial indicator information and assessing it carefully based on long-term and short-term conditions and possible manipulation of financial statements.


Keywords


Altman Ratio; Beneish M-Score; Prediction of Financial Distress; Stacking Ensemble Learning

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DOI: https://doi.org/10.22146/ijccs.76575

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