scholarly journals POSSIBILITIES TO APPLY CLASSICAL BANKRUPTCY PREDICTION MODELS IN THE CONSTRUCTION SECTOR IN LITHUANIA

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Rasa Kanapickiene ◽  
Rosvydas Marcinkevičius
Keyword(s):  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Construction Sector

scholarly journals A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Economies ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 82
Author(s):  
Gintare Giriūniene ◽  
Lukas Giriūnas ◽  
Mangirdas Morkunas ◽  
Laura Brucaite
Keyword(s):  
Statistical Data ◽  
Prediction Models ◽  
Scientific Literature ◽  
Data Representation ◽  
Bankruptcy Prediction ◽  
Construction Sector ◽  
Macroeconomic Variables ◽  
Graphic Data ◽  
Macroeconomic Indicators ◽  
Business Conditions

Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

scholarly journals Modelling bankruptcy prediction models in Slovak companies

2017 ◽  
Vol 39 ◽  
pp. 01013 ◽  
Author(s):  
Maria Kovacova ◽  
Jana Kliestikova
Keyword(s):  
Prediction Models ◽  
Bankruptcy Prediction

scholarly journals Family Businesses in Global Economy and Evaluating their Financial Health Using Bankruptcy Models

2021 ◽  
Vol 129 ◽  
pp. 03031
Author(s):  
Maria Truchlikova
Keyword(s):  
Financial Distress ◽  
Global Economy ◽  
Agricultural Sector ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Value Added ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Family Businesses ◽  
Financial Health

Research background: Predicting and assessing financial health should be one of the most important activities for each business especially in context of turbulent business environment and global economy. The financial sustainability of family businesses has a direct and significant influence on the development and growth of the economy because they still represent the backbone of the economy and play an important role in national economies worldwide accounting. Purpose of the article: We used in this article the financial distress and bankruptcy prediction models for assessing financial status of family businesses in agricultural sector. The aim of the paper is to compare models developed by using three different methods to identify a model with the highest predictive accuracy of financial distress and assess financial health. Methods: The data was obtained from Finstat database. For assessing the financial health of selected family businesses bankruptcy models were used: Chrastinova’s CH-Index, Gurcik’s G-Index (defined for Slovak agricultural enterprises) and Altman Z-score. Findings & Value added: This article summarizes existing models and compares results of assessing financial health of family businesses using three different models.

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

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