The Power of Numerical Indicators in Predicting Bankruptcy: A Systematic Review

Abstract

This paper systematically reviews the behavior of numerical indicators in predicting future bankruptcy of companies through statistical analysis models. Following the PRISMA standard, ten primary studies were included in the review. The obtained results underline (1) the ability of numerical indicators, through simple statistical analysis models, to forecast the bankruptcy of businesses and companies and (2) the reliability of cash flows in predicting financial distress through statistical analysis, and (3) models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country’s economic conditions can potentially impact their predictive accuracy.

1. Introduction

The most well-known method of economic analysis takes place with the use of numerical indicators (Sfakianakis 2022). The significance of financial ratios lies in their ability to uncover the financial stability of a corporation, thereby ensuring its competitive standing through consistent growth and mitigating any potential financial hazards (Kliestik et al. 2020). Relative ratios offer essential information about a company’s operations, helping investors and analysts make decisions (Kliestik et al. 2020; Czerwińska-Kayzer et al. 2021; Dahiyat et al. 2021). Cash flow ratios (cash to sales, cash to assets, and cash to equity) present a dynamic view of the company’s financial performance by capturing the statement changes, while the traditional ratios (current ratio, quick ratio, debt-to-equity ratio) provide a static view of the financial performance by measuring a single point in time (Atieh 2014). Bankruptcy is the legal status of a company that is unable to repay its debts, leading to court proceedings for the settlement of obligations, often resulting in liquidation or reorganization. Default risk, on the other hand, refers to the likelihood that a company will be unable to meet its debt obligations as they become due. It is a key indicator of financial distress and can serve as a precursor to bankruptcy (Valaskova et al. 2023; Shahrour et al. 2021).

Predicting financial distress continues to be a research concern because of its impact on business. Moreover, improving the accuracy of default models is crucial for their role as early warning systems for corporations regarding their financial health, ultimately helping to prevent default (Shahrour et al. 2021). Thus, economic distress forecasting models and their information are valuable tools for decision-makers. The absence of use and correct evaluation of the results of these models can eventually lead to the bankruptcy of a company (Voda et al. 2021).

Financial performance models are based on statistical analysis methods. Characteristic examples are linear, multivariate, discrete, probit, and logit analyses (Bellovary et al. 2007). More specifically, linear analysis involves simple linear regression models that predict financial distress based on a linear relationship between the dependent variable (e.g., probability of bankruptcy) and one or more independent variables (e.g., financial ratios). The process of multivariate analysis entails the simultaneous examination of multiple variables in order to comprehend their combined influence on the likelihood of financial distress. Furthermore, multivariate techniques can provide more nuanced insights than univariate methods. Moreover, discrete analysis deals with variables that can take on discrete values, often used in the context of classification problems where firms are categorized as either distressed or not. Moreover, probit models are used for binary dependent variables and assume a normal distribution of the error term. This method estimates the probability that a firm will experience financial distress based on predictor variables. Logistic regression models, akin to probit models, are employed to analyze binary outcomes while assuming a logistic distribution for the error term (Bellovary et al. 2007).

Based on the literature, logistic regression is highlighted as the most frequently used technique for predicting financial distress and bankruptcy. Logistic regression’s popularity can be attributed to its robustness, interpretability, and the ability to handle various types of predictor variables, making it a preferred choice among researchers and practitioners in the field (Bateni and Asghari 2020). On the other hand, partial least squares discriminant analysis (PLS-DA) successfully addresses the problem of multicollinearity compared to other methods by incorporating data from principal component analysis (PCA) and multiple linear regressions (Barker and Rayens 2003).

With the primary goal of optimizing the models and increasing the prediction accuracy, the models have undergone many adjustments. Using only traditional indicators can lead to wrong estimates to the company’s detriment (Bragoli et al. 2022). For this reason, financial distress prediction models that incorporated indicators that yielded more accurate predictions were developed (Jandaghi et al. 2021). However, these models should be used cautiously since a financially troubled company does not imply a bankrupt company (Grice and Dugan 2001; Nicolescu and Tudorache 2016). The financial difficulty of a company means bankruptcy or non-payment of loans (Bruynseels and Willekens 2012).

The increasing complexity of the global business environment has made corporate bankruptcy a critical concern for stakeholders, from investors to company executives. Accurate prediction of bankruptcy is not only essential for risk management but also for strategic planning, product development, and market positioning. By fully understanding the behavior of indicators, stakeholders are now equipped with a powerful tool that enhances their decision-making ability. This knowledge empowers members of organizations, enabling them to make informed choices about product development, marketing strategies, and investments. Consequently, this review serves as a cornerstone, closing the divide between theoretical understanding and real-world implementation within the field of business strategy.

The scope of this review includes studies that have applied numerical indicators and statistical models to predict bankruptcy across various industries and geographies. This paper follows the PRISMA framework, ensuring a rigorous and structured analysis of the available literature. The findings of this review are particularly relevant for business leaders, investors, and policymakers seeking to improve risk management practices and anticipate financial distress in companies. In addition, the review provides a foundation for future research, encouraging the development of more sophisticated models and approaches for predicting bankruptcy in an increasingly complex economic landscape. The research question is posed as follows:

What numerical indicators and statistical analysis methods have been used in the literature to predict companies’ future bankruptcy?

2. Methodology

Primary studies from the literature are sought, which use numerical indicators (either traditional or cash flow) in predicting the future financial state/distress of companies, with the help of statistical analysis in the online databases of Scopus and Scholar, following the PRISMA standard (Figure 1) (Page et al. 2021). The time frame is from 2013 to 2023, while the choice of English language is the most prevalent among researchers worldwide. Previous studies from the specified period are excluded since an increased volume of reviews was observed until 2015. This study does not consider models employing machine learning methods, such as neural networks. The focus is comparing traditional statistical analysis methods for bankruptcy prediction, excluding machine learning methodologies from our search and analysis.

More specifically, for the Scopus database, the advanced search engine is used, adopting the following algorithm:

TITLE (bankruptcy OR default OR “early warning” OR “failure prediction” OR “financial distress” OR “financial difficulty” OR insolvency) AND (model OR prediction OR forecast OR ratio OR indicator) AND PUBYEAR > 2012 AND PUBYEAR < 2025 AND (LIMIT-TO (OA, “all”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (EXACTKEYWORD, “Article”)).

For the Scholar database, the same descriptors are used.

3. Results

Most scholars used logistic regression to predict corporate bankruptcy (Abdullah et al. 2019; Waqas and Md-Rus 2018; Cultrera and Brédart 2016; Fawzi et al. 2015; Brédart 2014). More specifically, according to Abdullah et al. (2019), certain financial ratios such as the direct liquidity ratio, short-term liabilities to total liabilities, return on assets, sales to total assets, and net income to equity capital play a crucial role in predicting bankruptcy. They also found that larger companies are more susceptible to this risk. This finding was further supported by Waqas and Md-Rus (2018), who emphasized the importance of considering the size of a firm when developing bankruptcy prediction models by highlighting that the ratio of cash flows from operations to sales was an important predictor of financial distress, while the ratios of market showed no predictive ability for bankruptcy. Specifically, their model achieved an impressive 92% accuracy rate.

Cultrera and Brédart (2016) developed a bankruptcy prediction model with satisfactory prediction accuracy (79.23%), emphasizing that profitability and liquidity ratios predict excellent bankruptcy for Belgian SMEs. Furthermore, companies with lower liquidity, profitability, debt structure, and value-added ratios were found to have a higher likelihood of bankruptcy, whereas the solvency ratio (cash flow/total debt) did not demonstrate effective predictive capabilities. Accordingly, with the logistic regression method, Fawzi et al. (2015), indicated that the solvency and profitability ratios ((CFFO + I)/I, CFFO/TL, CFFI/TL, and CFFO/TR) based on cash flow variables are excellent predictors of financial bankruptcy. With an impressive accuracy rate of 82.1%, the model successfully classified the majority of sample cases, indicating its accuracy in discriminating between healthy and unhealthy firms. Brédart (2014) also indicated that the model using three simple financial ratios, and more specific (a) net income/total assets, (b) current ratio, and (c) equity/total assets, as explanatory variables, shows a prediction accuracy of more than 80%.

The potential of prediction of the Z-score model for predicting UK corporate bankruptcy was investigated by Almamy et al. (2016) through discriminant analysis. The inclusion of cash flows in conjunction with the Z-score variable yielded a remarkably significant outcome in forecasting bankruptcy among companies, as indicated by their research findings. In fact, the model demonstrated a predictive power of 82.9%, surpassing that of the Z-score model.

Aiming to identify significant predictors of bankruptcy, researchers also used multivariate discrete analysis to develop their models (Valaskova et al. 2023; Sfakianakis 2021). More specifically, Valaskova et al. (2023) created models based on 6–14 economic indicators, testing various combinations of predictors and coefficients. They found that the total debt ratio consistently exhibited the highest discriminant power and therefore included it in all models. The resulting models showed an overall discrimination ability of over 88%. Sfakianakis (2021), on the other hand, focused on 28 Greek firms during the turbulent period 2008–2015. The author concluded that certain factors, namely, the liquidity ratio, cash flow interest coverage, and economic value added (EVA) divided by total assets, played a crucial role in predicting bankruptcy in Greece. These variables were incorporated into a model that successfully classified 96.43% of grouped cases one year prior to bankruptcy. Moreover, when predicting bankruptcy two and three years before, the adjusted model maintained the same variables and accurately classified 92.86% and 89.29% of the clustered cases, respectively.

The issue of bankruptcy in the Polish and Slovak transport industry was examined by Brozyna et al. (2016). They focused on identifying potential threat signals and their impact on companies. To assess the risk of bankruptcy, two statistical models were used: linear discriminant analysis and logistic regression. The findings showed that these methods demonstrated a remarkable ability to accurately predict bankruptcy.

Serrano-Cinca and Gutiérrez-Nieto (2013) were the first to predict the 2008 US banking crisis through the use of partial least squares discriminant analysis (PLS-DA). In order to evaluate its effectiveness, this approach was compared with eight commonly used algorithms for bankruptcy prediction. The findings showed that there was no clear superiority of one algorithm over the others when considering precision, repeatability, type I error, and type II error. It is worth noting, however, that PLS-DA allows tolerance of multicollinearity, while linear discriminant analysis (LDA) or linear regression (LR) can yield equally favorable results if the researcher uses an appropriate variable selection procedure.

The review yielded a summary of the results, which can be found in

Table 1. Summary of review studies.

Author(s)	Numerical Indicators	Method	Findings
Valaskova et al. (2023)	Total debt ratio Debt to equity ratio Interest coverage ratio Debt to cash flow ratio Reason for financial independence Insolvency rate Gross profit margin ratio Operating profit margin ratio Net profit margin ratio Return on assets ratio Return on equity ratio Cash flow ratio Quick liquidity indicator Current liquidity ratio Current liquidity ratio Collection period ratio Credit period ratio Asset turnover ratio Inventory turnover ratio	Discrete multivariate analysis	(1) The total debt ratio, which exhibited the highest level of discriminatory capability, was incorporated into every model that was developed. (2) The models that were created demonstrated a discrimination ability exceeding 88% in total.
Sfakianakis (2021)	Instant liquidity ratio Cash flow interest coverage Economic value added (EVA) divided by total assets	Discrete multivariate analysis	(1) In Greece, the prediction of bankruptcy can be determined by analyzing key indicators such as the immediate liquidity ratio, cash flow interest coverage, and economic value added (EVA) to total assets. (2) Before the occurrence of bankruptcy, the model accurately classified 96.43% of cases that were grouped together. (3) By employing the identical variables, the adapted prediction model for 2 and 3 years leading up to insolvency attained a precise classification rate of 92.86% and 89.29% for the grouped instances, correspondingly.
Abdullah et al. (2019)	Equity Total liabilities to total assets Short-term liabilities to total liabilities Liquidity (current assets to current liabilities) Total asset sales Earnings before interest and taxes on total assets Net income on equity	Logistic regression	(1) The likelihood of failure is higher for new companies compared to established ones, and as the size of a company increases, so does the risk it faces. (2) Important predictors include the liquidity ratio, which measures - current liabilities to total liabilities - return on assets, sales to total assets, and - net income to equity. (3) The models that predict financial difficulties in small and medium enterprises (SMEs) before a crisis occurs, specifically the one and two year models, exhibit the most precise level of accuracy.
Waqas and Md-Rus (2018)	Net income on total assets Retained earnings on total assets EBIT on total assets Quick assets to current assets Circulating assets in total liabilities Current assets are current liabilities Working capital in total assets Total liabilities to total assets EBIT to interest expense Total equity to total liabilities Working capital in long-term debt Cash flow from operations to total liabilities Cash flow from operations to sales Cash flow from operations to total assets	Logistic regression	(1) Predicting financial distress can be achieved by analyzing key profitability indicators such as net income to total assets, retained earnings to total assets, and earnings before interest and taxes to total assets. (2) The significance of liquidity ratios is underscored by the findings, which showcase the predictive value of ratios such as current assets to total liabilities, working capital to total assets, and current assets to current liabilities in anticipating financial turmoil. (3) Leverage ratios such as total liabilities to total assets and interest coverage ratio play a crucial role in forecasting financial distress. (4) Of the three cash flow ratios, it is only the ratio of cash flow from operations to sales that holds true as a meaningful indicator of potential financial difficulties. (5) The accuracy level stands at 92%.
Almamy et al. (2016)	Working capital/total assets Retained earnings/total assets Earnings before interest and taxes (EBIT)/Total assets Market value equity/total liabilities Sales/total assets Operating cash flow/total liabilities	Discrete multivariate analysis (extension of the Altman Z-score model)	(1) The combination of the original variable Z-score and cash flow is crucial in accurately predicting the likelihood of bankruptcy. (2) The J-UK model exhibited a predictive power of 82.9%, surpassing the Z-score model in comparison. (3) After evaluating the predictive abilities of the J-UK model and the Z-score UK model, it was concluded that the J-UK model exhibited superior performance in accurately predicting outcomes before, during, and after the financial crisis.
Brozyna et al. (2016)	5 liquidity ratios 7 profitability indicators 9 debt ratios and financial leverage effect 4 indicators of operational efficiency 3 financial indicators characterizing the capital and material structure of companies	Linear discriminant analysis and logistic regression	High quality bankruptcy prediction
Cultrera and Brédart (2016)	General circulating liquidity index Return on operating assets before depreciation Global degree of economic independence Percentage of gross value added allocated to tax expenditure Cash flow/total debt	Logistic regression	(1) Firms with lower liquidity, profitability, debt structure, and value added ratios exhibited a higher likelihood of bankruptcy. (2) The model did not indicate any significance in the solvency ratio (total debt/cash flow). (3) It is a higher probability for smaller and recently established SMEs to experience bankruptcy. (4) The level of accuracy achieved is 79.23%.
Fawzi et al. (2015)	Liquidity Indicators Cash flow from operating activities (CFFO)/current liabilities (CL) Solvency ratios Cash flow interest coverage − cash flow from operating activities (CFFO) + interest expense (I)/interest expense (I) Cash flow from operating activities (CFFO)/total liabilities (TL) Cash flow from operating activities (CFFO)/long-term liabilities (LTL) Cash flow from investing activities (CFFI)/total liabilities (TL) Cash flow from financing activities (CFFF)/total liabilities (TL) Cash flow from operating activities (CFFO)/equity (SHE) Efficiency indicators Cash flow from operating activities (CFFO)/total assets (TA) Cash flow from operating activities (CFFO)/fixed assets (FA) Profitability indicators Cash flow from operating activities (CFFO)/net income (NI) Cash flow from operating activities (CFFO)/total revenue (TR)	Logistic regression	(1) With 82.1% accuracy, the model effectively differentiates between healthy and unhealthy firms, as indicated by the rate of correctly classified sample cases. (2) By examining solvency and profitability ratios that rely on cash flow variables, one can effectively forecast financial distress. These ratios, including CFFO+I/I, CFFO/TL, CFFI/TL, and CFFO/TR, have the ability to meet their financial obligations
Brédart (2014)	Profitability ratio, as the value of the ratio net income/total assets during the last accounting year before the filing of the reorganization process. Liquidity ratio, as the value of the current ratio in the last accounting year before the filing of resolution proceedings Solvency ratio, as the value of the ratio total equity/total assets during the last accounting year before the filing of the reorganization process.	Logistic regression	(1) The indicators of profitability, liquidity, and solvency serve as reliable predictors for identifying potential financial distress. (2) The level of accuracy achieved is 83.82%.
Serrano-Cinca and Gutiérrez-Nieto (2013)	Return on assets earnings (INTINCY) Cost of financing asset earnings (INTEXPY) Net interest margin (NIMY) Interest free income to profitable assets (NONIIY) Interest free expenses on speculative assets (NONIXY) Net operating income on assets (NOIJY) Return on assets (ROA) pre-tax return on assets (ROAPTX) Return on equity (ROE) retained earnings on average equity (ROEINJR) The efficiency index, EEFFR, is designed to indicate that as the index value increases, the bank’s efficiency decreases. To measure delinquency, the NPERFV ratio is used, which compares assets that are 90 days or more delinquent to total assets. Net loans and leases on deposits (LNLSDEPR), Total equity capital as a percent of total assets (EQV) 3 asset weighted indices (RBC1AAJ, RBC1RWAJ and RBCRWAJ)	Partial least squares discriminant analysis (PLS-DA)	(1) The level of accuracy achieved is 95.02% (2) Tolerance to multicollinearity

.

4. Discussion

The importance of profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios in forecasting financial distress is emphasized in this review. This finding is corroborated by a previous study conducted by Adnan Aziz and Dar (2006), which analyzed 98 predictions of financial distress and concluded that these indicators are good predictors of financial distress. Additionally, this review demonstrates that statistical analysis supports the reliability of cash flow ratios for forecasting financial distress. The ability of cash flows to predict corporate bankruptcy was also highlighted in a study by Jooste (2007), who identified the cash flow to total debt ratio as the most effective predictor of bankruptcy. When analyzing the cash flow statement, a higher ratio is linked to a reduced likelihood of failure, while a favorable ratio signifies the presence of positive cash flows.

Accordingly, in their literature review, Bellovary et al. (2007) summarize bankruptcy prediction studies for 1965–2004. The authors examine 165 bankruptcy prediction models and conclude that specific characteristics such as model sizes vary dramatically, from 1 to 57 variables, and that the analysis methods of the studies included in their review vary widely. At this point, it is essential to reference the seminal work of Altman (1968), who developed the Z-score model, one of the most influential and widely used tools for bankruptcy prediction. Specifically, Altman demonstrated the importance of factors like company size, profitability, and leverage in assessing bankruptcy.

Although very few studies have used bankruptcy or risk prediction models with variables consisting of cash flow ratio information, they have demonstrated that forecasts using operating cash flows have informative value. In his study, Rodgers (2011) investigated the effectiveness of multivariate discrete analysis (MDA), logistic regression analysis, and operating cash flow analysis as tools in order to predict distress. Specifically, he focused on the 20 most prominent bankruptcy case studies. The cash flow ratios included cash flow to current assets, cash flow before interest and taxes on current assets, and cash flow to current liabilities. The results of this review indicated that the cash flow indicators have an excellent predictive ability of the financial state of the companies by categorizing the healthy from the unhealthy ones with considerable accuracy.

The research of Barua and Saha (2015), agrees with this review’s findings, emphasizing the suitability of cash flow ratios in accurately evaluating companies. These ratios offer insightful data regarding the financial status of a company and serve as an early warning system for potential bankruptcy, helping investors make informed decisions. In their research, Agarwal and Taffler (2008), emphasized the significance of liquidity ratios in accurately predicting instances of financial distress. The same authors underlined that the higher the liquidity levels of a company, the lower the chances of the company facing financial problems. However, the financial soundness and understanding of a company’s financial situation are based on more than just numerical indicators. Instead, a comprehensive analysis that incorporates financial ratios and other financial information is required. Moreover, an index that performs well in one industry may not perform as well in another, as different industries operate in different buying environments, with different economic conditions and buying public characteristics (Atieh 2014).

Furthermore, the review emphasizes that simple models have the capability to accurately forecast bankruptcy with a high degree of precision through the use of numerical indicators and statistical analysis (Abdullah et al. 2019; Waqas and Md-Rus 2018; Cultrera and Brédart 2016; Fawzi et al. 2015; Brédart 2014; Almamy et al. 2016; Sfakianakis 2021; Brozyna et al. 2016; Serrano-Cinca and Gutiérrez-Nieto 2013), a finding that is consistent with previous studies by Jones et al. (2015, 2017). More specifically, the models developed using multivariate discriminant analysis in this review showed an overall discriminating ability greater than 82.9%, a percentage that is also confirmed in an earlier study by Yap et al. (2010). Even though multivariate discrete analysis is commonly used for bankruptcy prediction, it presents some disadvantages related to statistical assumptions, as, for example, the need for normality, linearity, and independence between variables (Amendola et al. 2017; Marozzi 2016).

The results of this systematic review highlight that most scholars used logistic regression to predict corporate bankruptcy (Abdullah et al. 2019; Waqas and Md-Rus 2018; Cultrera and Brédart 2016; Fawzi et al. 2015; Brédart 2014). Previous studies by Verma and Raju (2021), argue that logistic regression models, compared to multivariate discrete analysis, offer a more robust approach to predicting bankruptcy by examining the probability of bankruptcy without imposing restrictive assumptions. This view is also supported by Ul Hassan et al. (2017). Thus, the studies above agree with the results of this review, further underscoring the superiority of logistic regression models over multivariate discriminant analysis models in predicting corporate bankruptcy.

Also worth mentioning is the pioneering approach to predicting bankruptcy, using partial least squares discriminant analysis (PLS-DA) to address the issue of multicollinearity (Atieh 2014; Serrano-Cinca and Gutiérrez-Nieto 2013). According to pioneers Serrano-Cinca and Gutiérrez-Nieto (2013), while the descriptive statistics findings align with other commonly used bankruptcy models, PLS-DA addresses multicollinearity. However, this does not mean that if a researcher chooses linear discriminant analysis (LDA) or linear regression (LR) and uses an appropriate variable selection procedure, the results will not be as effective in predicting bankruptcy as those obtained with PLS-DA. This conclusion is supported by the literature review of Devi and Radhika (2018).

Nyitrai (2019), aiming to optimize the prediction accuracy of bankruptcy models, conducted a comparative study and found that incorporating past financial ratios of companies as benchmarks can be effective. To conduct a comprehensive assessment of the companies’ financial status, Nyitrai (2019) utilized the minimum and maximum financial ratios from the previous period as a benchmark. The study compared various well-known methods for predicting bankruptcy, including discriminant analysis and logistic regression. The findings indicated that incorporating the minimum and maximum economic indicators into bankruptcy prediction models greatly enhances their predictive accuracy, surpassing models that solely rely on static economic indicators. These results underscore that it is important to analyze the historical financial performance of firms, as it stands as a reliable benchmark for assessing the likelihood of insolvency in the future.

The results of this review could act as a compass in the hands of professionals, protecting the financial stability of businesses and promoting an environment conducive to innovation and growth. With knowledge as an ally, organizations can pivot faster to market dynamics, thus gaining a competitive advantage.

The present review focused on bankruptcy prediction using traditional statistical analysis methodologies, purposely avoiding machine learning techniques. By focusing on such approaches as regression analysis, discriminant analysis, and other standard statistical methods, the present study attempts to clearly present their effectiveness in predicting bankruptcy. At the same time, the importance of machine learning cannot be overlooked in this area. Machine learning techniques, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and deep neural networks, have gained a place in the literature for capturing highly multivariate nature through non-linear relationships in financial data that leads to high precision, especially for volatile environments (Shetty et al. 2022). For example, neural networks do not depend on predefined formulas or any assumptions related to data distributions like the traditional statistical methods do, but rather learn from data based on recognition of patterns to model the relationships among financial variables that are complex and non-linear. Such flexibility places artificial neural networks in a position to handle larger and more intricate datasets, a basic requirement for predicting financial performance in dynamic and uncertain environments characterized by incomplete information. Adapting to new data, exposed neural networks continuously enhance their level of accuracy in predictions. Consequently, ANNs, under these circumstances, can be ideal tools in developing early-warning systems to cater for the high risks associated with bankruptcies for firms as well as financial intermediaries (Shetty et al. 2022; Horak et al. 2020).

5. Limitations

A significant limitation is that different researchers use different methodologies and indicators. This heterogeneity makes conclusion difficult. In addition, the absence of standardized criteria for evaluating the quality of the developed forecast models and the indicators’ appropriateness is a significant limitation (Jahan et al. 2016). Also, the indicators are dynamic and depend on the economic and regulatory conditions and the current market situation. This dynamism implies that indicators that may be relevant in one time frame or under certain conditions might not be as applicable in another (Altman 1968). Consequently, investigations conducted in different time periods are not easily comparable, leading to further challenges in generalizing findings from these studies (Krulicky and Horak 2021; Valaskova et al. 2022). Hence, it is imperative for individuals involved to take into account these restrictions when making decisions. It is crucial to have a comprehension of the specific context and the condition under which a model was developed to appropriately interpret its predictions and apply them effectively.

6. Conclusions

The research problem centers on understanding the effectiveness of traditional statistical analysis methods in predicting business bankruptcy, particularly in a landscape where economic conditions are ever-changing. The results of the present systematic review highlight:

(1)

The ability of numerators, through simple statistical analysis models, to predict the bankruptcy of businesses and companies.

(2)

Statistical analysis indicates that cash flow ratios are a dependable tool for forecasting financial distress.

(3)

Models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country’s economic conditions can potentially impact their predictive accuracy.

Situating the models of bankruptcy prediction in a domain such that firms will be forecast to the period of exact time they risk facing bankruptcy is not the issue; instead, the issue is to state the likelihood that bankruptcy will eventually occur (Srebro et al. 2021). At this stage, these statistical models are used as an alarm system that provides companies with critical signals, prompting actions that could include enhancing earnings or strengthening their financial position. This implies that by identifying impending financial distress, firms are in a position to introduce rectification measures that will prevent them from ultimately becoming insolvent. It is also of great importance to other stakeholders—for example, lenders and investors. For the lenders, estimating companies’ chances of facing bankruptcy reduces credit risks and enables better management decisions on extending or handling credit based on informed judgments. Potential investors use these indicators in gauging a company’s financial health and insolvency risk profile to guide their investment decisions, avoid high-risk ventures, and make rational choices. Thus, models for bankruptcy prediction do not bring benefits only to the company itself but also to other stakeholders making their decisions with view to long-term interests associated with the financial condition of the company (Valaskova et al. 2020). Bankruptcy prediction models play a very crucial role as strategies for sustainability in an organization. This helps firms to take an early advance notice of risks that might lead to their bankruptcy and take necessary precautions in building their financial base to avert the situation (Srebro et al. 2021; Valaskova et al. 2020; Mehreen et al. 2020).

In conclusion, this study underscores the important role numerical indicators play in company bankruptcy forecasting via statistical analysis models. Adhering to the PRISMA standard, our systematic review brings out the key findings from ten primary studies that were included. Importantly, it was shown in the results that even simple statistical methods can effectively predict financial distress, providing stakeholders with valuable insights into the health of organizations. The practical implications of our findings are substantial, as stakeholders, including investors, managers, and policymakers, can leverage these insights to enhance their decision-making processes. Stakeholders can ensure the appraisal of financial stability by focusing on the dependability of cash flow indicators. This also helps them come up with measures to avert crises. Equally, the theoretical implications contribute to the existing body of knowledge on the subject by stressing the role played by quantitative indicators in financial forecasting. Future researchers could further explore advanced analytical techniques and combine qualitative factors for richer predictive models in appraising corporate financial health.