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IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Article . 2022 . Peer-reviewed
License: CC BY SA
Data sources: Crossref
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Financial Distress Prediction with Stacking Ensemble Learning

Authors: Hadi, Muhammad Fadhlil; Liang, De-Ron; Priyambodo, Tri Kuntoro; SN, Azhari;

Financial Distress Prediction with Stacking Ensemble Learning

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.

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Keywords

altman ratio, Finance; Computer Science, Electronic computers. Computer science, Q300-390, beneish m-score, QA75.5-76.95, Cybernetics, stacking ensemble learning, Altman Ratio; Beneish M-Score; Prediction of Financial Distress; Stacking Ensemble Learning, prediction of financial distress

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
gold