Financial Distress Prediction in Indonesia Stock Exchange's Listed Company Using Case Based Reasoning Concept

Rahayu, Dyah Sulistyowati; Suhartanto, Heru

doi:10.1109/iciea49774.2020.9101948

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Hiring ratio [77]; Number of employees [4,73,94,114,115]; Number of employees representatives on board [88]; Sales per employee (SPE) [104]; Unemployment rate of firm's department [94]; Working capital per employee [94].…”

Section: Employeesmentioning

confidence: 99%

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

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Section: Employeesmentioning

confidence: 99%

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data

2023

Applied Soft Computing

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Systematic Review of Financial Distress Identification using Artificial Intelligence Methods

Kuizinienė

Krilavičius

Damaševičius

et al. 2022

Applied Artificial Intelligence

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Financial Distress Prediction in Indonesia Stock Exchange's Listed Company Using Case Based Reasoning Concept

Cited by 4 publications

References 12 publications

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data

Systematic Review of Financial Distress Identification using Artificial Intelligence Methods

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