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General bankruptcy prediction models for the Visegrád Group. The stability over time

100%
TOMCZAK S. K.
Operations Research and Decisions
|
2023
|
vol. 33
|
issue 4
171-187
EN
Managers of enterprises must constantly face the continual changes on the market and fight for survival in a world of high competition. Therefore, it is important to systematically monitor the company’s financial condition. This will help to identify problems and give specific time to take corrective action. Bankruptcy prediction models are usually constructed for local goals. The purpose of the article is to build not only regional but also general discriminant and logit models for the SMEs operating in the construction industry in Visegrád Group countries. A total of 32 unique models were built and verified along with the Altman model for emerging markets. The paper also contributes to the literature by assessing the stability of the constructed models over time, which the models’ authors do not usually measure. The results showed that regional models are characterized by higher accuracy than general ones. However, general models can be adapted to the analyzed Visegrád Group with an accuracy of approximately 90%. The G1 LR model can be considered the best model, as it has relatively high accuracy and over-time stability.
2
Publication available in full text mode
Content available

General bankruptcy prediction models for the Visegrád Group : the stability over time

100%
Tomczak S. K.
Operations Research and Decisions
|
2023
|
vol. 33
|
issue 4
EN
Managers of enterprises must constantly face the continual changes on the market and fight for survival in a world of high competition. Therefore, it is important to systematically monitor the company’s financial condition. This will help to identify problems and give specific time to take corrective action. Bankruptcy prediction models are usually constructed for local goals. The purpose of the article is to build not only regional but also general discriminant and logit models for the SMEs operating in the construction industry in Visegrád Group countries. A total of 32 unique models were built and verified along with the Altman model for emerging markets. The paper also contributes to the literature by assessing the stability of the constructed models over time, which the models’ authors do not usually measure. The results showed that regional models are characterized by higher accuracy than general ones. However, general models can be adapted to the analyzed Visegrád Group with an accuracy of approximately 90%. The G1 LR model can be considered the best model, as it has relatively high accuracy and over-time stability.
3
Publication available in full text mode
Content available

On the quick estimation of probability of recovery from COVID-19 during first wave of epidemic in India: a logistic regression approach

86%
Joshi H., Azarudheen S., Nagaraja M. S., Chandraketu S.
Statistics in Transition new series
|
2022
|
vol. 23
|
issue 2
197-208
EN
The COVID-19 pandemic has recently become a threat all across the globe with the rising cases every day and many countries experiencing its outbreak. According to the WHO, the virus is capable of spreading at an exponential rate across countries, and India is now one of the worst-affected country in the world. Researchers all around the world are racing to come up with a cure or treatment for COVID-19, and this is creating extreme pressure on the policy makers and epidemiologists. However, in India the recovery rate has been far better than in other countries, and is steadily improving. Still in such a difficult situation with no effective medicine, it is essential to know if a patient with the COVID-19 is going to recover or die. To meet this end, a model has been developed in this article to estimate the probability of a recovery of a patient based on the demographic characteristics. The study used data published by the Ministry of Health and Family Welfare of India for the empirical analysis.
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