Financial Distress Classification Analysis of 10 Banks in Indonesia: Z-Score, MLP-ANN & Integrated Model

Wardhani, Sukma Tri Kusuma and Dias Satria,, SE., M.App.EC., Ph.D. (2023) Financial Distress Classification Analysis of 10 Banks in Indonesia: Z-Score, MLP-ANN & Integrated Model. Sarjana thesis, Universitas Brawijaya.

Abstract

Tantangan keuangan dalam lembaga perbankan telah menimbulkan dampak ekonomi yang besar, sehingga memperbesar kemungkinan terjadinya kebangkrutan perusahaan. Perlambatan pertumbuhan ekonomi berdampak buruk pada ekspansi aset korporasi dan operasional perbankan. Adanya tantangan keuangan berdampak langsung pada aksesibilitas kredit, menghambat ekspansi perekonomian, dan meningkatkan kemungkinan terjadinya kebangkrutan perusahaan. Oleh karena itu, antisipasi risiko keuangan sangat penting dalam mengembangkan strategi keuangan, terutama pada saat ketidakpastian muncul. Studi ini memperkenalkan model peringatan krisis kesulitan keuangan suatu perusahaan dengan model integrasi baru yang mengintegrasikan model Z-score dan MLP-ANN. Penelitian ini menyajikan model hybrid yang memanfaatkan sepuluh data perbankan dari Indonesia. Dengan menggabungkan model Altman Z-Score dan MLP-ANN ke dalam kerangka prediksi risiko keuangan, penelitian ini memperoleh hasil yang lebih baik dalam mendeteksi kesulitan keuangan perusahaan dan memberikan peringatan tepat waktu untuk mengurangi potensi kerugian. Temuan empiris menunjukkan bahwa model hibrida yang baru diusulkan mencapai rata-rata tingkat klasifikasi benar tertinggi sebesar 93,90% menurut hasil evaluasi metrik RGEC dan Fitch Ratings. Secara khusus, melampaui akurasi masing-masing sebesar 81,80% dan 87,20% yang dicapai oleh model Altman Z-score dan MLP-ANN. Berdasarkan analisis empiris, diketahui bahwa model Integrasi menunjukkan kinerja yang lebih unggul jika dibandingkan dengan model Altman Z-score dan metode MLP-ANN.

English Abstract

Financial challenges within banking institutions have yielded notable economic repercussions, thereby amplifying the likelihood of corporate insolvency. The deceleration in economic growth has adversely influenced the expansion of corporate assets and banking operations. The presence of financial challenges has a direct impact on the accessibility of credit, acts as an impediment to the expansion of the economy, and elevates the likelihood of corporate insolvency. Therefore, anticipating financial risks is essential in developing a financial strategy, especially during emerging uncertainties. This study introduces a company's financial distress crisis warning model with a new integrated model that integrates the Z-score and multi-layer perceptron neural network models. This study presents a integrated model utilizing ten banking data from Indonesia. By incorporating the Altman Z-Score and MLP-ANN models into a financial risk prediction framework, this study obtains better results in detecting corporate financial distress and providing timely warnings to reduce potential losses. The empirical findings show that the newly proposed integrated model achieves the highest average correct classification rate of 93.90% according to the evaluation results of the RGEC and Fitch Ratings metrics. In particular, surpassing the respective accuracies of 81.80% and 87.20% achieved by the Altman Z-score model and the pure neural network method. Based on empirical analysis, it has been observed that the integrated model shows superior performance when compared to the Altman Z-score model and the pure MLP-ANN method.

Item Type:	Thesis (Sarjana)
Identification Number:	:0523020661
Divisions:	Fakultas Ekonomi dan Bisnis > Ilmu Ekonomi
Depositing User:	Unnamed user with username nova
Date Deposited:	12 Jan 2024 07:44
Last Modified:	12 Jan 2024 07:46
URI:	http://repository.ub.ac.id/id/eprint/209380

Text (DALAM MASA EMBARGO) Sukma Tri Kusuma Wardhani.pdf Restricted to Registered users only until 31 December 2025. Download (2MB)

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