scholarly journals Predictors of Financial Distress and Bankruptcy Model Construction

2014 ◽  
Vol 2 (6) ◽  
pp. 34-42 ◽  
Author(s):  
Dana Kubíčková Kubíčková ◽  
Vladimír Nulíček
Keyword(s):  
Discriminant Analysis ◽  
Predictive Models ◽  
Financial Distress ◽  
Model Construction ◽  
Multiple Discriminant Analysis ◽  
Financial Situation ◽  
Transition Economics ◽  
New Model ◽  
Special Regard ◽  
Multivariate Discriminant

The aim of this paper is to prepare the bankruptcy model construction. In the first part, multivariate discriminant analysis and its possibilities in deriving predictive models are characterized. The second part defines the possible indicators/predictors of financial distress of companies, which could be included in the new bankruptcy model. The model itself compares different views of factors that affect the company’s financial situation and contrasts the indicators that were constructed in the model in previous works (with special regard to the models in the transition economics). The result is the collection of 39 indicators to be verified in the next stage of the research project employing the multiple discriminant analysis methods to specify which of them to be included in the new model.

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.

scholarly journals Analysis of Altman Model and Ohlson Model in Predicting Financial Distress of Manufacturing Companies in the Indonesia Stock Exchange

2017 ◽  
Vol 1 (1) ◽  
pp. 51-63
Author(s):  
Elsa Imelda ◽  
Ignacia Alodia
Keyword(s):  
Discriminant Analysis ◽  
Financial Distress ◽  
Stock Exchange ◽  
Logit Analysis ◽  
Net Income ◽  
Manufacturing Companies ◽  
Multiple Discriminant Analysis ◽  
Retained Earnings ◽  
Ohlson Model ◽  
Altman Model

The purpose of this research is to examine the accuracy of the Altman Model and the Ohlson Model in Bankruptcy Prediction.The research population is all companies who are listed on the Indonesian Stock Exchange. The sample of the research is 40 manufacturing companies listed on the Indonesian Stock Exchange in the period of 2010-2014 that are divided into companies with financial distress and those without financial distress.The data analysis technique is the Multiple Discriminant Analysis and Logit Analysis. The Multiple Discriminant Analysis is derived from the Altman Model while the Logit Analysis is derived from theOhlson Model. The results show that the Ohlson Model and the Logit Analysis are more accurate than the Altman Model and the Multiple Discriminant Analysis in predicting bankruptcy of manufacturing firms in the Indonesian Stock Exchange (BEI) in 2010-2014. Also, the results of the study reveal that the ratio of retained earnings to total assets; earning before interest and taxes to total assets; market value of equity to total liabilities; sales to total assets; and debt ratio, return on assets, working capital to total assets and net income were negative in the last two years. Hence constitutes the benchmark for consideration in determining the financial distress of a company.

scholarly journals A Review of Financial Distress Prediction Models: Logistic Regression and Multivariate Discriminant Analysis

2017 ◽  
Vol 1 (3) ◽  
pp. 13-23
Author(s):  
Ehsan ul Hassan ◽  
Zaemah Zainuddin ◽  
Sabariah Nordin
Keyword(s):  
Discriminant Analysis ◽  
Financial Distress ◽  
Prediction Models ◽  
Early Prediction ◽  
Multivariate Discriminant Analysis ◽  
Advantages And Disadvantages ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Precautionary Measures ◽  
Multivariate Discriminant

In corporate finance, the early prediction of financial distress is considered more important as another occurrence of business risks. The study presents a review of literature for early prediction of financial bankruptcy. It contributes to the formation of a systematic review of the literature regarding previous studies done in the field of bankruptcy. It addresses two most commonly used financial distress prediction models, i.e. multivariate discriminant analysis and logit. Models are discussed with their advantages and disadvantages. After methodological review, it seems that logit regression model (LRM) is more advantageous than multivariate discriminant analysis (MDA) for better prediction of financial bankruptcy. However, accurate prediction of bankruptcy is beneficial to improve the regulation of companies, to form policies for companies and to take any precautionary measures if any crisis is about to come in future.

scholarly journals Predicting biological parameters of estuarine benthic communities using models based on environmental data

2004 ◽  
Vol 47 (4) ◽  
pp. 613-627 ◽  
Author(s):  
José Souto Rosa-Filho ◽  
Carlos Emílio Bemvenuti ◽  
Michael Elliott
Keyword(s):  
Discriminant Analysis ◽  
Linear Regression ◽  
Predictive Models ◽  
Benthic Communities ◽  
Environmental Data ◽  
Biological Parameters ◽  
Southern Brazil ◽  
Multiple Discriminant Analysis ◽  
Sediment Characteristics ◽  
Benthic Assemblages

This study aimed to predict the biological parameters (species composition, abundance, richness, diversity and evenness) of benthic assemblages in southern Brazil estuaries using models based on environmental data (sediment characteristics, salinity, air and water temperature and depth). Samples were collected seasonally from five estuaries between the winter of 1996 and the summer of 1998. At each estuary, samples were taken in unpolluted areas with similar characteristics related to presence or absence of vegetation, depth and distance from the mouth. In order to obtain predictive models, two methods were used, the first one based on Multiple Discriminant Analysis (MDA), and the second based on Multiple Linear Regression (MLR). Models using MDA had better results than those based on linear regression. The best results using MLR were obtained for diversity and richness. It could be concluded that the use predictions models based on environmental data would be very useful in environmental monitoring studies in estuaries.

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