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This article maps the knowledge structure and evolution of bankruptcy/default prediction research in financial institutions using a bibliometric co-citation approach, which identifies works that are often cited together due to the proximity of their theories, methods, or themes. Bibliographic data were collected from the Web of Science (WoS) for the period 1970-2025, then extracted in a complete bibliographic format and prepared for network analysis. The co-citation procedure is carried out by extracting the reference list, normalizing and unifying citation variations, and preparing a co-citation matrix, which is then visualized and classified using VOSviewer with minimum threshold parameters and normalized association strength. The results of the document ranking based on the number of citations and Total Link Strength (TLS) show the development trajectory of the field from the ratio and Z-Score approach to the logit probabilistic model, the market foundation based on the structural theory of credit risk, the governance perspective supported by agency theory, the dynamic panel methodology/GMM for longitudinal data, to computational waves (neural networks and machine learning) to capture nonlinear patterns of credit scoring. The co-citation map uncovers four main clusters: econometrics & banking risk, structural theory, ratios, and computational, with Merton and Altman acting as the cross-cluster binding nodes. These findings support the design of a more comprehensive early warning system (EWS) by integrating ratio signals, market indicators, governance/agency factors, and ML models to improve accuracy and adaptation.    

The purpose of this study is to examine the differences in the value of the financial distress of manufacturing companies before and after the Covid-19 pandemic, as well as to examine the determinants that affect the financial distress of manufacturing companies before and after the Covid-19 pandemic. This study is a quantitative study with the population in this study namely manufacturing companies in Indonesia. The sample is 1005 Firm Years in manufacturing companies in Indonesia. These Manufacturing Companies were taken from 2016-2021, but we divided the sample into 2 categories, namely 2016-2019, namely in the pre-pandemic period as many as 653 firm years, and during the pandemic as many as 352 firm years. The dependent variable used in this study is financial distress, measured by the O-Score Model. Furthermore, the independent variables are profitability using ROA, liquidity using the current ratio, and leverage using the Debt to Assets Ratio. The test results show no difference between the financial distress of manufacturing companies in Indonesia before and during the pandemic. During the pandemic, financial ratios became more considered in predicting financial distress than before the pandemic. The role of profitability is a factor that impacts financial distress is more impactful in the pre-pandemic period. The role of liquidity being a factor that impacts financial distress is more impactful during the pandemic. Leverage can also be a good indicator in predicting financial distress both in the pre-pandemic and during the pandemic.

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.

Beginning in the mid-1970s, financial distress in local governments became big news when New York City nearly collapsed due to the energy crisis and high inflation. When local governments experience financial distress, the responsibility to provide services to the community both now and in the future cannot be fulfilled. Local governments can only maintain services to the community if they are able to avoid financial distress. If the financial distress condition is maintained, there will be a stagnation in the level of community welfare which will have an impact on regional economic growth because it is not supported by adequate public services. Some local governments like Pennsylvania, have implemented a measure to indicate financial distress as stipulated in the 1987 Financially Distressed City Act (UU 47). However, the local government in Indonesia does not yet have an absolute measure for this financial distress condition. For this reason, financial distress in this study will use a relative measure indicated by the probability value measured using a defined approach. The results of the study provide an overview of how the relevant values in local government financial statements are able to predict financial distress in local governments. The findings show that the variables intergovernmental revenue (IGR), financial independence (FIND), employee expenditure (EMPEX), capital expenditure (CAPEX), and operating position (OPPOS) have a significant effect and can be used as an alternative predictor of financial distress with more comprehensive suitability with the financial characteristics of local government in East Java Province.

This study aims to examine and analyze the effect of financial distress with the Altman and Springate Models on stock returns either directly or indirectly by involving earnings management as a mediation. This study uses secondary data from Textile and Garment Companies listed on the Indonesia Stock Exchange from 2015-2019, with a sample of 20 companies using sampling so that 100 observations are obtained. The data is obtained from the annual financial statements. The data analysis technique used SEM-PLS with the help of WarpPLS 6.0 software. The results of the study provide empirical evidence that financial distress has a positive effect on earnings management, while financial distress and earnings management has a negative effect on stock returns. Earnings management is able to mediate the effect of financial distress on stock returns.

This study aimed to determine financial and non-financial factors influencing future cash flow and their impacts on company's financial distress. This study uses earnings, cash flow from operations, accrual components, working capital (financial), inflation (nonfinancial) as independent variables that are thought to have an effect on future cash flow. In addition, this research also suspects that there is an impact of this influence on company financial distress. The sample used in this study was 30 food and beverage manufacturing companies listed on the Indonesia Stock Exchange from 2013 to 2015. Hypothesis testing, in this research, used Structural Equation Model (SEM) method with AMOS 24 statistic. The result showed that earnings, accrual component, and working capital had a significant positive effect on future cash flow. Furthermore, cash flow from operation had significant negative effect on future cash flow, but inflation did not affect company's future cash flow. These results indicate that financial variables have a significant effect, both positive and negative, on the company's future cash flow. Meanwhile, non-financial variables have no significant effect. Other test resulted that future cash flow had a significant negative effect on financial distress. Based on the results of this study, it can be seen that stakeholders, especially investors, really pay attention to the company's financial performance compared to non-finance. This is due to the significant influence of financial variables on the company's future cash flow and it can have an impact on financial distress. Therefore, future research is expected to explore other financial factors that affect the company's future cash flow, for example, the company's financial risk.