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.

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.

Financial soundness of Islamic banks: does the structure of the board of directors matter?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Afef Khalil ◽  
Imen Ben Slimene
Keyword(s):  
Corporate Governance ◽  
Board Of Directors ◽  
Islamic Banks ◽  
Z Score ◽  
Data Set ◽  
Content Type ◽  
Additional Hypothesis ◽  
The Board Of Directors ◽  
Financial Soundness ◽  
The Impact

Purpose The purpose of this paper is to examine the Board of Directors’ characteristics and their impact on the financial soundness of Islamic banks. Design/methodology/approach Regression analysis is applied to test the impact of the Board of Directors’ characteristics on the financial soundness of Islamic banks, using a panel data set of 67 Islamic banks covering 20 countries from 2005 to 2018. The Z-score indicator is used to evaluate the Islamic banks’ soundness. To check the robustness of the results, this paper uses other dependent variables (CAMEL) than the Z-score. Findings The main results show that the presence of an independent non-executive director negatively impacts the financial soundness of Islamic banks, while the chief executive officer duality practice has a positive effect on it. Other characteristics of the Board of Directors do not significantly impact the financial soundness of Islamic banks (foreign director, institutional director, chairman with a Shari’ah degree, interlocked chairman and the Board of Directors’ size). Practical implications This study aims to fill the gaps in the literature that discuss the Board of Directors’ role in corporate governance and its impact on the financial soundness of Islamic banks. In other words, it shows the role played by the Board of Directors and improves the knowledge of the corporate governance-financial soundness relationship. Plus, managers, investors and regulators may gain evocative insights, particularly those looking to improve their Islamic banks’ soundness by restructuring their boards’ composition. Originality/value This study sheds new light on the literature on Islamic banking by clarifying the relationship between the Board of Directors and the financial soundness of Islamic banks. Contrary to previous research, this paper uses an additional hypothesis stating that a chairman with a Shari’ah degree (Fiqh Muamalt) has a positive impact on the financial soundness of Islamic banks.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

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

The impact of the board of directors and the Shariah board on the financial soundness of Islamic banks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Afef Khalil
Keyword(s):  
Board Of Directors ◽  
Capital Adequacy ◽  
Islamic Banks ◽  
Z Score ◽  
Data Set ◽  
Content Type ◽  
The Board Of Directors ◽  
Financial Soundness ◽  
The Impact ◽  
The Relationship

Purpose The purpose of the study is to examine the relationship between the board of directors (BODs) and the Shariah board (SB) and assess its impact on the financial soundness of Islamic banks (IBs). Design/methodology/approach The authors use a regression model to test the effects of the relationship between the BOD and the SB on the financial soundness of IBs by applying a panel data set of 61 IBs, covering 18 countries from 2008 to 2014. The dependent variable is the Z-score indicator. To test the robustness of the results, the authors use dependent variables other than the Z-score [A rating of Capital adequacy (C), Asset quality (A), Management (M), Earnings (E), Liquidity (L), and Sensitivity (S) (CAMELS)] for 2018. Findings The results show that meetings between directors and SB members significantly reduce the financial soundness of IBs. The relationship between the BOD and the SB increases conflicts of interest and agency costs. However, a representation of the SB at the BOD meetings and vice versa does not affect financial soundness. The Accounting and Auditing Organization for Islamic Financial Institutions and the Islamic Financial Services Board corporate governance standards do not require the presence of the SB representative at the BOD meetings or vice versa, which justifies the results. Practical implications This study attempts to fill gaps in the literature by investigating the impact of meetings between the SB and the BOD on the financial soundness of IBs across the world. The results suggest that the BOD’s frequent interference in the affairs of the SB can have adverse effects on IBs and should be avoided. Originality/value The authors depart from the previous literature by using three new characteristics that link the BOD to the SB. Methodologically, the authors use three new measures to evaluate this relationship and its effect on the financial soundness of IBs. This study is unique because it explores the comparative impacts of the presence of a SB representative at the BOD meetings and a director at the SB meetings and meetings between the two governing boards of IBs.

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
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