Comparative Analysis Of Machine Learning And Deep Learning Models Integrated With Altman Z-Score For Financial Distress Prediction In Companies Listed On The Indonesia Stock Exchange (IDX)

Abstract

Financial distress is a condition in which a company experiences a decline in its financial situation, which is typically temporary. However, financial distress can worsen if not promptly addressed, leading to bankruptcy. Early identification of potential financial distress in a company is crucial for stakeholders such as investors, creditors, and regulators. In practice, predicting financial distress in a company is not an easy task. One of the methods commonly used to identify early symptoms of financial distress is the method introduced by Altman in 1986. Altman's research model, known as the Z-Score, determines a value based on standard calculations of financial ratios to indicate the likelihood of a company experiencing bankruptcy. The use of artificial intelligence, such as deep learning, can enhance the scope of research on the analysis and prediction of financial distress. This study aims to conduct a comparative analysis of machine learning models, such as the K-Nearest Neighbor Classifier and Random Forest Classifier, and deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This analysis is carried out to evaluate the success rate of the proposed deep learning models in predicting financial distress in companies operating in Indonesia. Based on the conducted research, the K-Nearest Neighbor Classifier algorithm achieved an accuracy of 77.42% during testing and 88.89% during validation, the Random Forest Classifier algorithm achieved an accuracy of 87.09% during testing and 95.24% during validation, the CNN model achieved an accuracy of 95.16% during testing and 96.83% during validation, and the RNN model achieved an accuracy of 93.55% during testing and 96.83% during validation. Based on these results, the deep learning method has a higher average success rate than machine learning models in predicting financial distress.