Bankruptcy Of Companies Research Articles

Bankruptcy Risk and Market Reaction to Capital Expenditure

This study aims to examine whether the condition of the bankruptcy risk of a company will influence the market response to capital expenditure. The main hypothesis of this research is the positive market reaction to the level of capital expenditure issued will be different in companies with a high level of bankruptcy risk and companies with low bankruptcy risk. The study was conducted on 56 companies with large capitalization on the Indonesia Stock Exchange for the period 2018-2021. The results of hypothesis testing indicate that the market responds positively to capital expenditures and the company's bankruptcy risk conditions. In addition, it is proven that in companies that are at risk of bankruptcy, the market reacts positively to capital expenditures made by companies, while in companies that are not in a state of bankruptcy, the market does not respond to capital expenditures made by companies. The results of this study are expected to be used by market participants when they analyze the information on capital expenditures made by the company. Keywords: Capital Expenditure, Bankruptcy Risk, Market Response

Accounting information transparency and business performance: A case of G7 construction companies

A high level of company bankruptcy in certain countries and a low level of profitability actualizes the need to find additional mechanisms for increasing the efficiency of their activities. One of such mechanisms is the growth of information transparency. The study deals with examining the effects of accounting information transparency on business performance on the example of construction companies in G7 countries. The transparency index was used as a parameter characterizing the level of accounting information transparency. The level of business performance was analyzed using the following indicators: value added of the construction industry, investment in the construction industry, number of construction firms, profitability of the construction industry, annual all-work construction output index, and total employees in construction firms. The dependence between the indicators was analyzed using the multiple regression analysis, Dickey-Fuller, Philips Perron, and Johansen tests. According to the results, the most vital link was between the level of accounting information transparency and the volume of investments (increased information transparency by 1 point leads to an increase in the volume of investments from 1.7% to 4.6%). At the same time, the level of accounting information transparency practically does not affect the number of employees (change by 0.1-0.2%) and added value (change by 0.1-0.3%). It was concluded that the policy of accounting information transparency should be an essential element of company strategy aimed to increase the level of its investment attractiveness and confidence of investors and consumers in its activities.

"PREDICTING BANKRUPTCY IN ROMANIA USING ARTIFICIAL NEURAL NETWORK "

In this paper we will present the results achieved from our experiments which predict the bankruptcy of limited liability companies in Romania, using artificial neural networks. All information and data used were received from the Romanian Ministry of Public Finance and National Trade Register, the data being reported by companies in 2018 and 2019. The data set is mixed, consisting of both public and private data. The private data could be used following an agreement with the two institution mentioned above. The sample consists of both healthy companies and bankrupt companies, each company comprising a total of 17 variables to analyze. The result obtained was good, more precisely following the experiments performed, it resulted in an accuracy of 97.67% for training, respectively 96.27% for testing.

Comparative Analysis of Financial Distress Models in Predicting Bankruptcy during Covid-19 Pandemic

This study aims to determine whether there is a difference between the financial distress models in predicting bankruptcy, and the most accurate financial distress models bankruptcy in construction and building companies listed on Indonesia’s Stock Exchange for the 2020-2021 period. The financial distress models used in this research are Springate, Taffler, and Zmijewski. The research population consisted of 18 companies and the sample was taken as many as 16 companies using purposive sampling method. The data research was collected from the company’s financial statement. This research uses a descriptive comparative method by conducting the Kruskal Wallis test to determine the difference between the Springate, Taffler, and Zmijewski. The results showed that there is a significant difference between the financial distress models in predicting bankruptcy predictions for the construction and building companies listed on Indonesia’s Stock Exchange for the 2020-2021 period. The most accurate model in bankruptcy prediction is the Taffler model with an accuracy rate of 100% and the error rate of 0%. Zmijewski has 81,25% of accuracy rate and 18,75% of error. Then the lowest accurate model in bankruptcy prediction is the Springate model with 12,50% of accuracy and 87,50% of error rate.

COMPARATIVE ANALYSIS OF ALTMAN, SPRINGATE AND ZMIJEWSKI MODELS IN PREDICTING BANKRUPTCY

This study aims to determine the potential for bankruptcy in timber sub-sector companies and their management that are listed on the Indonesia Stock Exchange for the 2017-2020 period using the Altman, Springate, and Zmijewski models and to find out the comparison of the three models. This type of research is quantitative research. The research population is the company's sub-sector of wood and its processing. The research sample is PT. SLJ Global Tbk (SULI) and PT. Tirta Mahakam Resources Tbk (TIRT). Data analysis techniques use the Altman, Springate, and Zmijewski models. The results of the study show that SULI and TIRT are predicted to experience potential bankruptcy in 2017-2021 using the Altman and Zmijewski model. 2018-2021 for SULI and 2020-2021 for TIRT using the Springate model. The results of the Mann Whitney test show that there is no difference in predicting the potential for bankruptcy of SULI and TIRT using the three models.

Altman Z-Score Dan Springate: Metode Komparasi Dalam Memprediksi Kemungkinan Kebangkrutan Suatu Perusahaan

Bankruptcy can be experienced by every company that has a decline in the value of profits from year to year. Because Indonesia was hit by COVID-19 in the past, this study will predict company bankruptcy using the Altman Z-Score and Springate methods. In addition, this study was conducted to determine the most accurate bankruptcy prediction method in predicting infrastructure, utility and transportation sector companies listed on the Indonesia Stock Exchange. The sampling technique used was purposive sampling technique with a total sample of 23 companies. The data were analyzed using the calculation of the level of accuracy and type of error from each bankruptcy prediction model, to predict companies experiencing potential bankruptcy. The results of this study indicate that the Z-Score method is the most accurate method. Therefore, it can be concluded that the Altman Z-Score method is more accurate in calculating bankruptcy predictions than the Springate method.

Analisis Perbedaan Kebangkrutan dengan Metode Altman Z-Score, Grover dan Springate

This research was conducted on private cement companies and government cement companies listed on the Indonesia Stock Exchange during the period 2016-2021. The objectives of this study are (1) to find out and explain the calculation of bankruptcy analysis using the Altman Z-score, Springate and Grover (2) To find out and explain the ranking of financial conditions and categories in bankruptcy in cement companies using the Altman, Springate, and Grover methods (3) To find out and explain the analysis of differences in bankruptcy using the Altman Z-score, Springate and Grover. The test carried out in this study is the ANOVA difference test. Different tests to be able to see the calculation of bankruptcy with the Altman z- score, grover and springate methods. that the mean value of the Altman model is 4,2223, which is higher than the Springate model, which is 1.2090 and the Grover model is 0.8582. Tests on both financial distress prediction models show that the significance value is smaller than (0.0000 < 0.05). And shows a sig value of 0.000 and a statistical value of 6,719. The value of sig P value is 0.000 < 0.05. If the P value < the critical limit of 0.05, then there is a significant difference between the two group.

Predicting the Bankruptcy of Cement Companies in Bangladesh: A Study on Dhaka Stock Exchange

The financial status of a company can be measured and predicted using a variety of tools, techniques, and models like ratio analysis, vertical and horizontal analysis, etc. The objective of this study is to identify the bankruptcy position of Bangladeshi-listed cement companies. The Altman Z-Score model has been used here to assess the intensity of insolvency of Bangladeshi cement businesses with listings on Dhaka Stock Exchange (DSE) considering financial information from 2018 to 2021 using Altman Z-Score model. The data has been analyzed using Statistical Package for Social Sciences (version 28.0.1.1) and also Microsoft excel. DSE currently lists seven (07) cement companies. According to the research, Heidelberg Cement Limited and Lafarge Holcim Bangladesh Limited are in the secure region having the maximum Z value of 33.47 and the minimum Z value is 3.17. Confidence Cement Limited, Meghna Cement Limited and Crown Cement Limited in 2021 are in the grey region having the maximum Z value of 2.71 and the minimum Z value of 1.82. Aramit Cement Limited, Crown Cement Limited, and Premier Cement Mills Limited are in the bankrupt region having the Z value of less than 1.80. These three companies must outperform in sales with the improvement in operational costs to overcome bankruptcy. We found a positive correlation between all of the five independent variables and the dependent variable (Z Score).

ANALISIS IMPLEMENTASI ALTMAN Z-SCORE PADA PERUSAHAAN SEKTOR PROPERTI DAN REAL ESTATE YANG TERDAFTAR DI BURSA EFEK INDONESIA SELAMA MASA PANDEMI COVID-19 TAHUN 2019-2021

The property and real estate sector is one of the sectors that has been quite severely affected by the Covid-19 pandemic. Almost all companies in this sector experienced a significant decline in sales due to reduced consumer demand. Therefore, this research aims to analyse the potential bankruptcy of property and real estate sector companies during the Covid-19 pandemic using the Altman Z-Score method. Samples were taken using the purposive sampling method, with 31 companies as the selected sample. Analysis of the data used is descriptive quantitative analysis with data sources in the form of literature and documentation. The results of the study show that in 2019, 5 companies were categorized as bankrupt, then five companies were categorized as bankrupt in 2020, and in 2021, four companies were categorized as bankrupt based on the criteria from the Altman Z-Score. However, companies categorized as bankrupt can survive and continue their business because of the various creative and innovative strategies implemented by each company.

Scientific Research and Recent Findings in Financial Monitoring

The article discusses the existing studies on the issue of financial monitoring and the suggestions that their authors have. The discussion of these studies begins with the American scientist Edward Altman's Z-model, which he proposed to predict the risk of bankruptcy for companies. The article presents both an earlier and a modified version of the Z-model developed by Edward Altman. The article also discusses and evaluates the 9-factor model developed by V. Fulmer and R. Saifulin and G. Kadikov's four-factor models. Among the more recent studies in the field of monitoring is the study of the Portuguese scientist Ana Paula Lopez, who found that the companies served by the audiences of the "big four" no longer cheat in the financial part. Malaysian scholars Noor Aisha Nizan and Zuhaikha Zailan found in 2019 that it is best to combine financial audits with technological and human audits. Also, in 2019, Vilnius scientist P. Poshinaus argued that monitoring should be carried out only by economic-mathematical methods, because they provide great opportunities for finding financial irregularities. And in 2020, Czech scientist Aneta Zemankova proposed to the public to use artificial intelligence in monitoring. With its implementation, smart audit procedures and smart contracts can be performed in an automated manner. The authors of the article conclude that the auditors of Georgia, due to the low level of mathematical education, cannot use blockchains and artificial intelligence, but also economic-mathematical models in the audit, and raise the issue of the need to retrain them in this direction. The article discusses the issues of dividing diversified companies into responsibility centers and their financial monitoring. Keywords: Financial monitoring; probability of bankruptcy; Z - model; Fulmer's model; mathematical models; Responsible centers.

Company Bankruptcy Prediction with SMOTE

Abstract: Effective bankruptcy forecasting is essential for financial firms to make sound loan decisions. In general, the two most essential aspects influencing prediction performance are input variables (or features) such as financial ratios and prediction approaches such as statistical and machine learning techniques. While numerous relevant publications have presented innovative prediction algorithms, relatively few have examined the discriminating potential of bankruptcy prediction variables. In addition to financial ratios (FRs), corporate governance indicators (CGIs) have been identified as an important form of input variable in the literature. The prediction performance produced by merging CGIs with FRs, however, has not been thoroughly investigated. Only a subset of CGIs and FRs were employed in related investigations, and the characteristics used may vary from study to study. As a result, the goal of this work is to evaluate the prediction performance obtained by combining seven distinct FR categories and five different CGI categories. Based on a real-world dataset from Taiwan, the experimental results reveal that the FR categories of solvency and profitability, as well as the CGI categories of board structure and ownership structure, are the most critical variables in bankruptcy prediction. The best prediction model performance is determined by combining prediction accuracy, Type I/II errors, ROC curve, and misclassification cost. However, these findings may not be applicable in certain countries, such as China, where the definition of distressed enterprises is vague and the features of corporate governance measures are not explicit.

Prediction of Financial Distress Model Altman Z-Score (Study on Shipping Companies Listed on the Indonesia Stock Exchange for the 2019-2021 Period)

The transportation and logistics industry plays an important and influential role for Indonesia's economic growth. This industry is a strategic sector with promising business prospects in the country. This study aims to analyze the potential for bankruptcy in transportation and logistics companies listed on the Indonesia Stock Exchange using the Altman Z-Score model. The sample in this study is a transportation and logistics company listed on the Indonesia Stock Exchange in 2019-2021 consisting of 9 companies. The data source used is secondary data in the form of annual financial statements of transportation and logistics companies. The year period of fnancal statemen used by the researcher is from 2018-2020. The results showed that there are 3 companies that have the potential to experience bankruptcy, namely PT. Pelayaran Nasional Bina Buana Raya Tbk. (BBRM), PT. Berlian Laju Tanker Tbk. (BLTA) and PT. Humpuss Intermoda Transportasi Tbk. (HITS). In addition, there are 3 companies that are in a gray area, and 3 companies that are in a healthy financial condition.

A Back Propagation Neural Network Model with the Synthetic Minority Over-Sampling Technique for Construction Company Bankruptcy Prediction

Improving model accuracy is one of the most frequently addressed issues in bankruptcy prediction. Several previous studies employed artificial neural networks (ANNs) to enhancethe accuracy at which construction company bankruptcy can be predicted. However, most of these studies use the sample-matching technique and available company quarters or company years in the dataset, resulting in sample selection biases and between-class imbalances. This study integrates a back propagation neural network (BPNN) withthe synthetic minority over-sampling technique (SMOTE) and the use of all of the available company-year samples during the sample period to enhancethe accuracy at which bankruptcy in construction companies can be predicted. In addition to eliminating sample selection biases during the sample matching and between-class imbalance, these methods also achieve the high accuracy rates. Furthermore, the approach used in this study shows optimal over-sampling times, neurons of the hidden layer, and learning rate,all of which are major parameters in the BPNN and SMOTE-BPNN models. The traditional BPNN model isbroughtas a benchmark for evaluating the predictive abilities of the SMOTE-BPNN model. The experientialresults of this paper indicatethat the SMOTE-BPNN model outperforms the traditional BPNN.

Analisis Potensi Kebangkrutan Perusahaan Sub Sektor Transportasi yang Terdaftar di BEI Sebelum dan Selama Pandemi Covid-19

The PSBB policy implemented by the government to deal with the COVID-19 pandemic resulted in a decline in company performance in the transportation sub-sector, resulting in a very drastic decrease in profits during the COVID-19 pandemic. This situation will make it difficult for the company to experience financial distress and even bankruptcy. This study objective is to analyze the bankruptcy of transportation companies listed on the IDX before and during the COVID-19 pandemic. The sample used in this study consisted of 40 companies listed on the Stock Exchange with a purposive sampling method. Annual financial report data is obtained from the Indonesia Stock Exchange. The data analysis technique used is the Altman Z-Score analysis using the third model formula for non-manufacturing companies. Based on this research, it can be concluded that there is a significant difference between the potential of bankruptcy of the transportation sub-sector companies before and during the covid-19 pandemic, this is proven by the Wilcoxon test results show a probability value < 0.05 or 0.043 < 0.05. Transportation sub-sector companies have the potential to go bankrupt during the covid-19 pandemic, this is proven by the average Z-Score of transportation companies which has decreased from -0.3458 to -1.2840 during the covid-19 pandemic. Keyword: altman z-score, bankruptcy analysis, transportation

ANALISIS TINGKAT KEBANGKRUTAN DENGAN MENGGUNAKAN METODE ALTMAN Z – SCORE PADA PERUSAHAAN ROKOK YANG TERDAFTAR DI BEI

This study aims to determine the level of bankruptcy in Cigarette Companies Listed on the Indonesia Stock Exchange (IDX) using the Altman Z-Score Method. The data used is secondary data obtained by using documentation techniques. The population in this study were all cigarette companies listed on the Indonesia Stock Exchange for the 2016-2020 period. So that the samples used in this study were PT Gudang Garam Tbk, PT HM Sampoerna Tbk, PT Wismilak Inti Makmur Tbk, and PT Bentoel Internasional Investama Tbk. The data analysis technique used is descriptive analysis which provides an overview of the financial performance condition of the Cigarette Company based on the resulting financial statements. The results of this study indicate that there are three companies that are predicted to be in the category of not bankrupt or healthy within a period of five years, namely PT. Gudang Garam Tbk, PT. HM Sampoerna Tbk, and PT. Wismilak Inti Makmur Tbk. While PT. Bentoel Internasional Investama Tbk, only in 2016 was categorized in a healthy condition. Meanwhile, in 2017-2020 the company is predicted to be in a gray area condition.

Corporate bankruptcy: Evidence from the plastics and packaging industry in Indonesia

This study analyzes the bankruptcies of companies in the plastic and packaging industry listed on the Indonesian stock exchange. This analysis is essential to know the condition of the company's performance and in what position. The hope is to assist stakeholders in making the best decisions for sustainability. The positive contribution of the plastics and packaging industry in supporting economic growth in Indonesia, as well as the challenges of global issues regarding the negative impact of use, requires companies to avoid financial distress or bankruptcy. However, recent research argues that very few company managers try hard to avoid the risk of bankruptcy. This quantitative descriptive research takes a sample of 10 companies selected purposively with predetermined criteria. Following the arguments of Altman (1983), the research analysis uses the Altman Z-Score for a sample of firms in one homogeneous industry. The results showed that, in general, the plastic and packaging industry was potentially vulnerable and had financial distress or went bankrupt, and there was only one healthy company. The results of this study also have an essential contribution for stakeholders to detect early on the company's financial condition and help make sound managerial decisions.

Webinar On Business Sustainability During The Covid-19 Pandemic For University Students and Public

The pandemic has posed many challenges for companies to survive. As a result, many layoffs of workers. Layoffs and company bankruptcies have been common during the pandemic. Many businesses face business sustainability. The main objective of this activity is to provide knowledge about the issues of international business sustainability facing every part of the world today, namely about the impact of Covid-19 and its impact on business. This activity focuses on one of the industries most affected, namely aviation. Flights were directly impacted by Covid-19 starting in early 2020 when many airlines had their flights suspended due to social restrictions and quarantine policies, making it difficult for airlines to maintain cash flow with the decline in the number of flights both domestically and internationally. One of the cases where international companies need to maintain the sustainability of their business. In this activity, international business continuity management students will gain knowledge about how airlines are impacted by Covid-19, and how they deal with these issues to sustain the business themselves. This training method is in the form of a webinar method, where speakers share experiences.

Changes in the perception of business risks to the threat of bankruptcy due to COVID-19

The aim of the article is to compare the impact of selected business risks on the threat of bankruptcy of small or medium-sized enterprises (SMEs) before COVID-19 and during the first wave of the COVID-19 pandemic. 688 SMEs from the business environment of the Slovak Republic participated in the research. Business risks, such as market, financial, personnel, operational and strategic risk were examined. Correlation and regression analysis were used to evaluate formulated hypotheses. The results yielded several significant findings. The three most important business risks before COVID-19 include market, financial and personnel risk according to entrepreneurs. Financial risk is the most significant business risk affecting the threat of bankruptcy in the SME segment in the Slovak Republic after the outbreak of COVID-19. The company's financial performance indicators and the ability of respondents to manage financial risk influence the threat of the company's bankruptcy more strongly during the pandemic. The findings are important for state organizations in mitigating the impacts of the COVID-19 pandemic on the business environment of SMEs in the Slovak Republic, as well as for entrepreneurs themselves and organizations that help SMEs.
 

Problemy opieki nad dziećmi w okresie edukacji zdalnej w opinii rodziców. Analiza zjawiska w świetle badań własnych

The impact of the COVID-19 pandemic on the situation of the societies of many countries, including Poland, is very large and concerns, in particular, the increase in the number of deaths, deteriorating health of citizens, bankruptcies of many companies, but also the change from the form of residential to remote education. The new situation, unprecedented so far, forced parents/guardians of children to quickly adapt home conditions to the requirements of remote learning. The existing forms of childcare also required reorganization. Unfortunately, the necessity to combine the professional duties of parents/guardians with the new reality of remote learning revealed many problems and difficult situations. In connection with the above, research was done to diagnose the problems of parents/guardians that they had to deal with during remote education and to learn about their expectations. The research covered parents/guardians of early school age children living in the poviats of Biała Podlaska, Włodawa and Parczew. The results of the research showed that some parents had difficulties in organizing care for their children during remote education, which prompts not only reflection but also searching for new solutions in this field.

Economic Motives and Willingness of Young People to Participate in Fraud

Abstract Fraud is linked to economic and financial pressures that force people to commit it. Any fraud, regardless of the gender and age of the fraud perpetrators, inevitably leaves a short-term or long-term negative mark on society’s economy. Many financial frauds in the past (Enron, WorldCom, Parmalat) have left profound negative consequences on global financial markets. Such “financial strikes” on the financial markets led to financial losses of many companies (not only those in which frauds were committed), the dismissal of many workers, and even the bankruptcy of companies. Successful detection and prevention of fraud imply the harmonious and efficient operation of several factors. One of them understands people’s attitudes towards fraud. It is necessary to understand what motivates an individual to commit fraud and the conditions under which someone would commit fraud. Plenty of resources and time have been invested in understanding the motives for adult fraud, i.e., mature people. Experience has shown that it is much more challenging to educate people of that era about the harmful effects of fraud and thus convince them not to commit fraud. For these reasons, the subject of this paper is the consideration of the attitudes of the younger population towards fraud in Serbia. More precisely, the paper discusses the attitudes of young people aged 18 to 30 regarding motives, pressures, and opportunities to commit fraud, participate in corruption, or offer bribes. The main objective is to indicate the tendency of young people to participate in fraud and examine whether respondents’ gender influences their willingness to participate in fraud. The research results suggest that, according to young people, the main motive why someone would participate in fraud is financial gain and that young people would participate in fraud only if they could get medical intervention sooner. They also believe that fraud is most prevalent in public administration and that men are more prone to fraud than women, but that the influence of gender is very small.