scholarly journals The Effect of Financial Ratio (Altman Z-Score) on Financial Distress Prediction in Manufacturing Sector in Indonesia 2016-2018

2020 ◽  
Author(s):  
Agus Arianto Toly ◽  
Ratna Permatasari ◽  
Elva Wiranata
Keyword(s):  
Financial Distress ◽  
Manufacturing Sector ◽  
Z Score ◽  
Financial Ratio ◽  
Financial Distress Prediction ◽  
Distress Prediction

scholarly journals Financial distress prediction of listed companies – empirical evidence on the Vietnamese stock market

2020 ◽  
Vol 17 (2) ◽  
pp. 377-388
Author(s):  
Tran Quoc Thinh ◽  
Dang Anh Tuan ◽  
Nguyen Thanh Huy ◽  
Tran Ngoc Anh Thu
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
Financial Statements ◽  
Listed Companies ◽  
Research Model ◽  
Z Score ◽  
Economic Decisions ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
High Level

Financial distress is a matter of concern in the recent period as Vietnam gradually enters global markets. This paper aims to examine the factors of Altman Z-score to detect the financial distress of Vietnamese listed companies. The authors use a sample of 30 delisted companies due to financial problems and 30 listed companies on the Vietnamese stock market from 2015 to 2018. They employ Independence Samples T-test to test the research model. It is found that there are significant differences in the factors of Altman Z-score between the group of listed companies and the group of delisted companies. Further analyses using subsamples of delisted companies show that the factors of Altman Z-score are also statistically different between companies with a low level of financial distress and those with a high level of financial distress. Based on the results, there are some suggestions to assist practitioners and the State Securities Commission in detecting, preventing, and strictly controlling financially distressed businesses. These results also enable users of financial statements to make more rational economic decisions accordingly.

scholarly journals PREDIKSI FINANCIAL DISTRESS BERDASARKAN VARIABEL-VARIABEL KEUANGAN PADA INDUSTRI TEKSTIL

2018 ◽  
Vol 4 (1) ◽  
pp. 54-60
Author(s):  
REFNI SUKMADEWI
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Z Score ◽  
Financial Distress Prediction ◽  
Distress Prediction

Penelitian ini bertujuan untuk memberikan bukti empiris mengenai faktor-faktor yang mempengaruhi financial distress perusahaan. Penelitian ini menguji peran rasio keuangan dalam memprediksi terjadinya financial distress pada perusahaan industri tekstil yang tercatat di Bursa Efek Jakarta. Analisis diskriminan digunakan untuk menguji kemampuan rasio keuangan untuk memprediksi financial distress dan membangun model prediksi distress financial dengan menggunakan prosedur stepwise. Variabel indikator adalah rasio keuangan. Hasil penelitian menunjukkan bahwa ada empat rasio yang berbeda dan secara signifikan mempengaruhi model prediksi distress keuangan. Rasio tersebut adalah Rasio Aktiva Lancar / Kewajiban Lancar, Modal Kerja / Jumlah Aktiva, Pendapatan Bersih / Total Aktiva, Kewajiban / Total Aset. Hasil klasifikasi berdasarkan nilai cut-off-Z Score mampu memprediksi kesulitan finansial perusahaan pada industri tekstil dengan tingkat akurasi 0f 100%. Tingkat akurasi model menunjukkan bahwa model diskriminan akurat dalam mengukur tekanan keuangan pada perusahaan industri tekstil. Kata kunci: Financial distress, Prediction Model, Rasio Keuangan

FINANCIAL DISTRESS PREDICTION AND SEEING THE INFLUENCE OF THE ALTMAN RATIO ON COMPANY FINANCIAL DISTRESS

2018 ◽  
Vol 23 (3) ◽  
pp. 236-243
Author(s):  
Hadhi Dharmaputra Juliyan ◽  
Bertilia Lina Kusrina
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Large Role ◽  
Corporate Bankruptcy ◽  
Mining Companies ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Score Model

This research aims to determine the level of the bankruptcy of the company and to see if the Altman ratio can predict the condition of corporate bankruptcy in mining companies on the Indonesia Stock Exchange because mining companies have a large role in the Indonesian economy. This study uses the Altman Z-Score model analysis to see how much the company's bankruptcy prediction and uses logistic regression to see how much the influence of the Altman ratio in predicting corporate bankruptcy. Keywords: financial distress, the Altman z–score, bankruptcy prediction

scholarly journals Does audit report information improve financial distress prediction over Altman's traditional Z ‐Score model?

2019 ◽  
Vol 31 (1) ◽  
pp. 65-97
Author(s):  
Nora Muñoz‐Izquierdo ◽  
Erkki K. Laitinen ◽  
María‐del‐Mar Camacho‐Miñano ◽  
David Pascual‐Ezama
Keyword(s):  
Financial Distress ◽  
Z Score ◽  
Audit Report ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Score Model

The Impact of Auditors' Opinions, Macroeconomic and Industry Factors on Financial Distress Prediction: An Empirical Investigation

2009 ◽  
Vol 12 (03) ◽  
pp. 417-454 ◽  
Author(s):  
Bi-Huei Tsai ◽  
Cheng-Few Lee ◽  
Lili Sun
Keyword(s):  
Financial Distress ◽  
Financial Statements ◽  
Macroeconomic Factors ◽  
Financial Ratio ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Market Factors ◽  
The Impact ◽  
Better Than

This study investigates the usefulness of auditors' opinions, market factors, macroeconomic factors, and industry factors in predicting financial distress of Taiwanese firms. Specifically, two non-traditional auditors' opinions are evaluated: "long-term investment audited by other auditors" ("other auditor"), and "realized investment income based on non-audited financial statements" ("no auditor").The results of the 22 discrete-time hazard models show that "other auditor" opinions have incremental contribution in predicting financial distress, in addition to "going concern" opinions. This suggests that "other auditor" opinions possess higher risk of overstating earnings and firms with such income items are more likely to fail. Besides, we find that the macroeconomic factors studied significantly explain financial distress. Particularly, the survivals of electronic firms are more sensitive to earnings due to higher earnings fluctuations in such firms. Finally, models with auditors' opinions, market factors, macroeconomic factors, and industry factors perform better than the financial ratio-only model in financial distress prediction.

scholarly journals ANALISIS PERBANDINGAN METODE ALTMAN Z-SCORE DAN METODE SPRINGATE SCORE DALAM MENENTUKAN FINANCIAL DISTRESS PADA PERUSAHAAN ASURANSI JIWA SYARIAH TAHUN 2014-2018

2020 ◽  
Vol 7 (12) ◽  
pp. 2494
Author(s):  
Iif Maulidya ◽  
Dian Filianti
Keyword(s):  
Life Insurance ◽  
Financial Distress ◽  
Financial Statements ◽  
Insurance Companies ◽  
Z Score ◽  
Marine Life ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Score Method ◽  
Method Show

ABSTRAKTujuan penelitian ini adalah untuk mengetahui dan menganalisis prediksi financial distress pada perusahaan asuransi jiwa syariah dari tahun 2014 hingga 2018 dengan menggunakan metode Altman Z-Score dan Springate S-Score. Penelitian ini menggunakan data sekunder yaitu laporan keuangan tahunan perusahaan asuransi jiwa syariah periode 2014-2018. Metode pengumpulan data yang digunakan adalah metode dokumentasi dengan menggunakan studi pustaka. Studi literatur ini dilakukan dengan mengambil informasi secara tidak langsung terkait dengan laporan keuangan dan profil lengkap perusahaan asuransi yaitu melalui situs resmi perusahaan asuransi jiwa syariah dan Otoritas Jasa Keuangan (OJK). Hasil akhir penelitian ini menunjukkan bahwa kinerja keuangan yang dianalisis dengan metode Altman Z-Score dan Springate S-Score pada perusahaan asuransi jiwa syariah periode 2014-2018 tergolong dalam keadaan financial distress. Hasil prediksi financial distress dengan menggunakan metode Altman Z-Score menunjukkan tiga perusahaan mengalami financial distress yaitu PT. Bringin Jiwa Sejahtera, Asuransi Jiwa, PT. Tokio Marine Life Insurance Indonesia, PT. Asuransi Jiwa Asia Tengah Raya. Hasil prediksi financial distress dengan metode Springate S-Score menunjukkan lima perusahaan mengalami financial distress yaitu PT. Asuransi Jiwa Tokio Marine, PT. Panin Daichi Life, PT. Bringin Jiwa Sejahtera, Asuransi Jiwa, PT. Asuransi Jiwa Bringin Jiwa Sejahtera, dan PT. Asuransi Jiwa Asia Tengah Raya.Kata kunci: financial distress, metode Altman Z-Score, metode Springate S-Score ABSTRACTThe purpose of this study was to determine and analyze the prediction of financial distress in Islamic life insurance companies from 2014 to 2018 using the Altman Z-Score and Springate S-Score methods. This study uses secondary data, namely the annual financial statements of Islamic life insurance companies for the 2014-2018 period. The data collection method used is the documentation method using literature studies. This literature study was carried out by taking information indirectly related to financial statements and complete profiles of insurance companies, namely through the official website of the Islamic life insurance company and the Financial Services Authority (OJK). The final results of this study indicate that the financial performance analyzed by the Altman Z-Score and Springate S-Score methods in Islamic life insurance companies in the 2014-2018 period was classified as being in a state of financial distress. The results of financial distress prediction using the Altman Z-Score method show that three companies experience financial distress, namely PT. Bringin Jiwa Sejahtera, Life Insurance, PT. Tokio Marine Life Insurance Indonesia, PT. Central Asia Life Insurance Raya. The results of financial distress prediction using the Springate S-Score method show that five companies experience financial distress, namely PT. Tokio Marine Life Insurance, PT. Panin Daichi Life, PT. Bringin Jiwa Sejahtera, Life Insurance, PT. Bringin Jiwa Sejahtera Life Insurance, and PT. Central Asia Life Insurance Raya.Keywords: financial distress, Altman Z-Score method, Springate S-Score method

scholarly journals Financial distress prediction of Islamic banks using tree-based stochastic techniques

2018 ◽  
Vol 44 (6) ◽  
pp. 759-773 ◽  
Author(s):  
Khaled Halteh ◽  
Kuldeep Kumar ◽  
Adrian Gepp
Keyword(s):  
Financial Distress ◽  
Gradient Boosting ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Z Score ◽  
Data Set ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Stochastic Gradient Boosting ◽  
Distress Prediction

PurposeFinancial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues.Design/methodology/approachThis paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) by utilising cutting-edge stochastic models, namely decision trees, stochastic gradient boosting and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables.FindingsThe results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional “Altman Z-Score” and the “Altman Z-Score for Service Firms” methods. However, using the “Standardised Profits” method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking.Originality/valueThese findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken.

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