scholarly journals Predicting bankruptcy based on the full population of Croatian companies

2021 ◽  
Vol 72 (5) ◽  
pp. 643-669
Author(s):  
Siniša Bogdan ◽  
Luka Šikić ◽  
Suzana Bareša
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Logit Model ◽  
Bankruptcy Prediction ◽  
Business Sector ◽  
Multiple Discriminant Analysis ◽  
High Classification Accuracy ◽  
Logit Estimation ◽  
Full Population

This paper analyses the bankruptcy prediction based on the population of companies representative of the total business sector in Croatia. The representativity of the sample is achieved through the propensity score matching of the full population of bankrupt and similar non-bankrupt companies. The robust estimation of bankruptcy prediction is carried out through the multiple discriminant analysis (MDA) and logistic regression (logit). The results indicate high classification accuracy of both models, but more favourable performance of the logit estimation. Overall accuracy of the MDA model was 73.7%, while the overall accuracy of the logit model was 76.3%. The results serve as a bankruptcy estimation benchmark for the business sector in Croatia.

scholarly journals Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

2005 ◽  
Vol 55 (4) ◽  
pp. 403-426 ◽  
Author(s):  
Miklós Virág ◽  
Tamás Kristóf
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Bankruptcy Prediction ◽  
Simulation Experiments ◽  
Prediction Techniques ◽  
Further Development

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical-statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.

scholarly journals Comparing the Accuracy of Multiple Discriminant Analyisis, Logistic Regression, and Neural Network to estimate pay and not to pay Dividend

2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Triasesiarta Nur
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Stock Price ◽  
Dividend Policy ◽  
Multiple Discriminant Analysis ◽  
Book Value ◽  
Accuracy Of Prediction ◽  
Research Period ◽  
Accuracy Performance

This study compares the accuracy of prediction to estimate the companies dividend policy; in this case, the company will pay or not pay dividends. The models used in this research are Multiple Discriminant Analysis, Logistic Regression, and Neural Network. The samples are divided into two groups, namely companies that always pay and not pay dividends during the 2015-2018 research period, resulting in 256 samples not paying dividends and 128 samples paying dividends. The results showed that the average Neural Network accuracy performance exceeded the other two models. The best predictor of the company's Dividend Policy in this study is Price to Book Value, Stock Price, Firm Cycle, current ratio, ROA and Exchange Rate. Keywords: Multiple Discriminant Analysis, Logistic Regression, Neural Network, Dividend Policy

Extracting Knowledge from Neural Networks

2011 ◽  
pp. 188-196 ◽  
Author(s):  
Christie M. Fuller ◽  
Rick L. Wilson
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Empirical Studies ◽  
Inductive Learning ◽  
Learning Systems ◽  
Great Promise ◽  
Classifier Systems ◽  
Case Classification

Neural networks (NN) as classifier systems have shown great promise in many problem domains in empirical studies over the past two decades. Using case classification accuracy as the criteria, neural networks have typically outperformed traditional parametric techniques (e.g., discriminant analysis, logistic regression) as well as other non-parametric approaches (e.g., various inductive learning systems such as ID3, C4.5, CART, etc.).

Bankruptcy Prediction Using Quarterly Financial Statement Data

1992 ◽  
Vol 7 (3) ◽  
pp. 269-285 ◽  
Author(s):  
Jane Baldwin ◽  
G. William Glezen
Keyword(s):  
Classification Accuracy ◽  
Prediction Models ◽  
Statistical Significance ◽  
Bankruptcy Prediction ◽  
Securities And Exchange Commission ◽  
Fiscal Year ◽  
Annual Data ◽  
Multiple Discriminant Analysis ◽  
Quarterly Data ◽  
Annual Period

The purposes of this study were to assess the usefulness of quarterly data for predicting bankruptcy and to determine if the earlier prediction by quarterly bankruptcy models can be obtained without the sacrifice of accuracy achieved by annual bankruptcy models. A sample of 40 public firms entering bankruptcy from 1977 to 1983 was matched on the basis of fiscal year, industry, and asset size with 40 nonbankrupt firms. Quarterly financial data were obtained from the firms' 10-Q reports filed with the Securities and Exchange Commission (SEC), whereas annual data were obtained from the 10-K reports. Multiple discriminant analysis was used to derive quarterly bankruptcy prediction models for each of the three quarters before and after the last annual period preceding bankruptcy and for the last annual period preceding bankruptcy. Twenty-four financial ratios that were identified in previous studies as being useful for bankruptcy prediction were selected as the independent variables in the stepwise discriminant process. The classification accuracy, using alternative assumptions regarding prior probability of bankruptcy and cost of misclassification and the statistical significance of the quarterly models for each of the six quarters tested, indicated that quarterly data are useful for predicting bankruptcy. There was no statistical evidence to suggest that the classification accuracy of the annual model was superior to that of the quarterly model. This finding suggests that more timely bankruptcy predictions can be provided to investors, creditors, and auditors by quarterly models without the loss of accuracy provided by annual models.

scholarly journals Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis

Equilibrium ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. 569-593 ◽  
Author(s):  
Tomas Kliestik ◽  
Jaromir Vrbka ◽  
Zuzana Rowland
Keyword(s):  
Discriminant Analysis ◽  
Prediction Models ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Net Income ◽  
Multiple Discriminant Analysis ◽  
Financial Situation ◽  
The Future ◽  
A Company ◽  
Visegrad Group

Research background: The problem of bankruptcy prediction models has been a current issue for decades, especially in the era of strong competition in markets and a constantly growing number of crises. If a company wants to prosper and compete successfully in a market environment, it should carry out a regular financial analysis of its activities, evaluate successes and failures, and use the results to make strategic decisions about the future development of the business. Purpose of the article: The main aim of the paper is to develop a model to reveal the un-healthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis. Methods: To conduct the research, we use the Amadeus database providing necessary financial and statistical data of almost 450,000 enterprises, covering the year 2015 and 2016, operating in the countries of the Visegrad group. Realizing the multiple discriminant analysis, the most significant predictor and the best discriminants of the corporate prosperity are identified, as well as the prediction models for both individual V4 countries and complex Visegrad model. Findings & Value added: The results of the research reveal that the prediction models use the combination of same financial ratios to predict the future financial development of a company. However, the most significant predictors are current assets to current liabilities ratio, net income to total assets ratio, ratio of non-current liabilities and current liabilities to total assets, cash and cash equivalents to total assets ratio and return of equity. All developed models have more than 80 % classification ability, which indicates that models are formed in accordance with the economic and financial situation of the V4 countries. The research results are important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the un-healthy or unfavorable financial situation of the company.

scholarly journals MODEL PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN MANUFAKTUR SEKTOR INDUSTRI BARANG DAN KONSUMSI

2019 ◽  
Vol 3 (2) ◽  
pp. 77
Author(s):  
Herlina Herlina ◽  
Ahmad Ridho’i ◽  
Anggie Erma Yunita ◽  
Mega Puja Azhari ◽  
Ade Reynaldi Saputra
Keyword(s):  
Logistic Regression ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Financial Distress ◽  
Particle Swarm ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Swarm Optimization ◽  
Vector Machines

Kesulitan keuangan (financial distress) adalah sebuah tahapan yang akan dilalui oleh sebuah perusahaan sebelum mengalami kebangkrutan. Dengan alasan tersebut maka kemampuan untuk memprediksi kesulitan keuangan dapat menjadi informasi yang bermanfaat bagi perusahaan maupun investor. Penelitian mengenai financial distress sudah dimulai dari penelitian Altman pada tahun 1968 menggunakan metode Multiple Discriminant Analysis (MDA). Dimulai dari penelitian Altman, muncul penelitian-penelitian lainnya menggunakan pengembangan metode statistik, seperti Logistic Regression. Dari metode statistik kemudian berkembang dengan munculnya penelitian-penelitian menggunakan metode-metode kecerdasan buatan, serta algoritma evolusi untuk berusaha mendapatkan model prediksi financial distress yang akurat. Tujuan dari penelitian ini adalah untuk membandingkan tingkat akurasi dari model prediksi financial distress perusahaan manufaktur terbuka pada sektor industri barang konsumsi yang terdaftar pada Bursa Efek Indonesia menggunakan metode kecerdasan buatan serta algoritma evolusi. Metode yang digunakan untuk metode kecerdasan buatan adalah metode Support Vector Machines dan untuk model algoritma evolusi menggunakan metode Particle Swarm Optimization-Support Vector Machines. Tingkat akurasi dari masing-masing metode akan diukur dari prosentase misklasifikasi terkecil yang dihasilkan. Dari pengujian model menggunakan metode Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 11,11% dengan menggunakan Kernel Linear dan untuk metode Particle Swarm Optimization-Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 5,56% dengan menggunakan Kernel RBF, ? = 2.

Extracting Knowledge from Neural Networks

2010 ◽  
pp. 320-330
Author(s):  
Christie M. Fuller ◽  
Rick L. Wilson
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Empirical Studies ◽  
Inductive Learning ◽  
Learning Systems ◽  
Great Promise ◽  
Classifier Systems ◽  
Case Classification

Neural networks (NN) as classifier systems have shown great promise in many problem domains in empirical studies over the past two decades. Using case classification accuracy as the criteria, neural networks have typically outperformed traditional parametric techniques (e.g., discriminant analysis, logistic regression) as well as other non-parametric approaches (e.g., various inductive learning systems such as ID3, C4.5, CART, etc.).

scholarly journals Asset Quality as a Predictor of Rural Bank Bankruptcy in Indonesia

2021 ◽  
Vol 4 (1) ◽  
pp. 44-45
Author(s):  
Hesti Budiwati ◽  
Ainun Jariah
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Classification Accuracy ◽  
Prediction Models ◽  
Predictor Variable ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Asset Quality ◽  
Productive Assets

The study aims to form a bankruptcy prediction model of rural bank in Indonesia at a time variation of 1 quarter (MP1), 2 quarters (MP2), 4 quarters (MP4), and 8 quarters (MP8) before bankruptcy. The quality of productive assets as a predictor variable consist of CEA, CEAEA, and NPL. The condition of rural bank bankrupt and non bankrupt as a dependent variable. The analytical method used is logistic regression followed by testing the model accuration. The population of this study is rural bank in Indonesia. The sample used was 241 rural banks that consist of 41 bankrupt rural banks and 200 non bankrupt rural banks. The data used are the quarterly financial statements of 2006 to 2019. The study result showed that of the four prediction models that successfully built, the 1 quarter (MP1) is the most feasible and accurate used as bankruptcy prediction model of rural banks in Indonesia that formed by CEAEA and NPL ratio. The MP1 has a classification accuracy of 93,8% at the level of modelling with cut off point of 0,29 and it has a classification accuracy of 83,93% at the level of validation with cut off point of 0,12. Based on those advantage, the MP1 was chosen as a model that able to predict the bankruptcy of rural bank in Indonesia.

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