“An application of Altman Z-score model to analyze the bankruptcy risk: cases of multidisciplinary enterprises in Vietnam”

This study applied Altman Z-score model to assess the bankruptcy risk of a set of multidisciplinary enterprises of various types, mainly small and medium enterprises, with data taken from official financial reports of 180 enterprises in Soc Trang province. The binary logistic regression was employed to assess the impact of non-financial and financial factors on the bankruptcy risk of enterprises. The research findings showed that both the non-financial factors such as business area, types and size of the business, the educational level of managers and executors and other characteristics, and the financial factors (indicators) such as earnings before tax, net profit/equity ratio, earnings before interest and tax/total assets ra- tio, equity/total debt ratio, affect the bankruptcy risk of enterprises. Predicting the bankruptcy risk and measuring its determinants play an important role not only as an effective managing tool of the business, but also as evidence for policymakers to support the sustainable development of business.


INTRODUCTION
Vietnam's economy is increasingly integrated and closely related to the regional and global economy once engaging in new generation free trade agreements such as the Comprehensive and Progressive Agreement for Trans-Pacific Partnership, the European Union Vietnam Free Trade Agreement. Such engagement not only opens up many business opportunities for Vietnam, but also entails numerous challenges. According the 2018 Provincial Competitiveness Index Report, 8.3% of businesses planned to downsize or close down as a result of not being able to adapt to market changes or increasingly intense competition. It was worth noticing that though the rate slightly increased from 8.2% in 2017, it was the third highest since the Provincial Competitiveness Index (PCI) survey was introduced in 2005. This even became an alarming issue for local governments in its great efforts to achieve the target of 1 million operational businesses by 2020 set by the Government's Resolution number 35 in 2016 (VCCI and USAD, 2018).
A company's "survival" opportunity can be predicted by using financial report analysis (Vikash, 2018) to have early warnings about financial hardship or bankruptcy risk prediction. From the 60s of the last century, the bankruptcy risk was one of the issues that received great interest. Many studies worldwide have strongly focused on verifying the bankruptcy risk analysis. Among proactive authors, Beaver (1966) and Altman (1968) were seen as pioneers in the applications of financial ratios to predict the bankruptcy risk in empirical studies. Beaver (1966) used the univariate analysis method through evaluating each individual financial index. However, when indicators were conflicting, it was difficult to conclude. Therefore, Altman (1968) recommended a method of multivariate analysis to overcome this problem and the model was first published on September 1968. Subsequently, Altman, Haldeman, and Narayanan (1977) introduced the bankruptcy prediction model, the Z-score model, to overcome the shortcomings of the original model which was followed by Z ′ and Z ′′ models. After over fifty years from introducing the initial version of Z-score bankruptcy models, this model has become the most well-known method to provide an early warning sign of financial hardship in many studies of scholars and practitioners around the world .
Various versions of Z-score are calculated to match different characteristics of enterprises. In this study the authors used the Z-score ( ) Z ′′ index to assess the bankruptcy risk of multidisciplinary enterprises and by types of business. Through the assessment, the study estimated the factors affecting the bankruptcy risk of the enterprises. The research findings were expected to identify the determinants of bankruptcy risk, to serve the decision-making of the enterprises and to suggest an evidence base for governmental agencies to introduce business support policies.

Z-score
The Z-score model for predicting the probability of bankruptcy was first introduced in 1968 by Edward I. Altman. This model was proved to be accurate in predicting the bankruptcy at a rate of 94%. The initial sample consisted of 33 bankrupt enterprises and 33 enterprises with stable finance in the manufacturing industry (Altman, 1968). The model forecasted relatively accurately up to 2 years before bankruptcy occurred by considering the Z-score value of enterprises.
Z-score is an index combining 5 different financial ratios with different weights and is applied to equitized enterprises. The original Z-score formula is:  X -sales/total assets.
Where Z-score of greater than 2.99 clearly falls into the "non-bankrupt" sector, those enterprises having a Z below 1.81 are all bankrupt risk. The area between 1.81 and 2.99 is defined as the "zone of ignorance" or "gray area" because of the susceptibility to error classification (Altman, 1968

Applications of Z-scores
There are various studies on Z-scores and their applications in business types or industries to measure and predict the bankruptcy risk of enterprises in many nations.
Leonardo and Jaime (2003) concluded that the Z-score index had good predictability of bankruptcy risk of Italian manufacturing enterprises. Meanwhile, this index predicted accurately for the textile industry in Pakistan within four years, although this predictable rate gradually decreased over the years when an experimental study on 12 enterprises with financial stability and 9 of these went bankrupt in 2000-2010 period (Hussain, Ali, Ullah, & Ali, 2014).
In Sri Lanka, the Z-score was also used to predict the bankruptcy risk of seven commercial companies, data taken during the five-year period from 2010 to 2014 (Niresh & Pratheepan, 2015). In India, Apoorva, Curpod and Namratha (2019) showed a predictable ability of Z-score three years in advance when companies went bankrupt.
The reliability and accuracy of the Z-score model are still a matter of debate. Odibi, Basit, and Massan (2015) have shown that this model was very suitable for the significance level of 0.000 and the accuracy of Z-score between 86% and 99.6% when applied to 34 enterprises including both bankrupt enterprises and enterprises with financial stability in Malaysia during 2010-2014. The accurate prediction rate was 93.8% before the bankruptcy occurred for Jordanian enterprises operating in non-financial services and industrial services in the period 1990-2006 (Alkhatib & Bzour, 2011). However, this rate dropped to 81% when applied to Indian emerging market companies and decreased to 72% two years before bankruptcy occurred (Bhatt, 2012).
The Z-scores are considered as a good tool in "financial health check" and helps financial manag-ers, analysts, investors, to predict the bankruptcy risk in business or the financial sustainability of the enterprises to make rational and prudent decisions (Vikash, 2018; Sulphey & Nisa, 2013). The Z-score index was also used as an internal criterion to assess the reputation of borrowers or to help investors consider making the decisions (Pradhan, 2014).
The Z-scores were not only applied to the evaluation of listed enterprises but also used in evaluating small and medium enterprises. Umiaty and Dinarjad (2018) used Altman's Z-score as a tool to measure the bankruptcy risk between small and medium enterprises in business incubators. In the study of Selassie, Tarekegn, and Ufo (2016), Z-score was employed to analyze the financial issues of small and medium enterprises with a sample of 30 enterprises in the Wolaita Region of Ethiopia operating in three areas of production, service, and trade. Jan and Marimuthu (2015) also used Altman's Z-score model to assess the insolvency of small and medium enterprises in Malaysia. In another study of Milka and Celia (2018), the Z-score was applied as the evaluation standard to analyze and predict the financial status of small and medium enterprises from Tuxtepec, Oaxaca in Mexico. Similarly, in the study of Galinoma and Paul (2016), the Z-score was also used as a tool to analyze the financial difficulties in the data from financial reports of 102 small and medium enterprises in a developing economy like Tanzania. Altman's Z-score model was also one of the three models selected for web-applications to assess the financial health of small and medium enterprises (David & Lenka, 2012).
In Vietnam, Cuong and Anh (2010) used the Z-score model to assess the bankruptcy risk of seafood processing enterprises listed on Vietnam's stock market. The study came out with some recommendations to reduce the bankruptcy risk of seafood processing enterprises and contribute to banks' policies to improve the efficiency of credit risk management in banking operations.
Regarding the financial activities of Vietnamese banks, Duong (2013) applied the Z-score to predict the bankruptcy risk of 36 commercial banks and considered this score as a standard to assess the financial health of businesses and support the banks to make timely responses to changes of the market.
The Z-score can assess the risks of businesses that are indirectly accompanied by the risks of commercial banks when lending (Canh, 2014). The study by Hieu (2015) proposed to use Z-score models as a tool to re-evaluate credit rankings and estimate the probability of the bankruptcy risk of borrowers. The Z-score again confirmed in Vietnam that both academic and practical circles were the most widely recognized and used in the world (Hanh & Hoa, 2016).
The prediction accuracy of the Z-score model on the bankruptcy risk was examined in Vietnam. Anh and Hang (2012) examined the reliability of this model with a sample of 293 listed enterprises in Ho Chi Minh City Stock Exchange. Test results showed that Altman's model achieved an accurate forecasting rate of 91% at one year before the financial exhaustion. This rate dropped to 72% within two years.
Therefore, through the Z-score model which has been studied and widely applied for a long time in many countries around the world, and the results of applied experimental analysis in Vietnam showed that the Z-score model still proves superiority in analyzing and evaluating business activities, classifying risk areas, credit ratings, assessing the bankruptcy level of enterprises and being able to apply for an enterprise or a larger number of enterprises in the scale of analysis, for enterprises by many industries, sectors, and by different sizes of capital sources (Van, 2016).

DATA AND METHODS
For over fifty years since the first introduction of the Z-score model, the model has continuously improved. Despite the huge growth in the size and complexity of the global debt market and accounting balance sheet but the Z-score models have shown that not only longevity, but is also a predictor accurate on the company's predicament, and also successfully revised for some applications .
Besides, the Z model has become simplicity in the calculation, transparency and accuracy consistently over the years, the Z model has been verified and compared to a large number of studies in terms of accuracy of classification and prediction and practice in finance and accounting for over fifty years . The Z ′′ model has advantages over other models due to its use for all industrial, manufacturing and non-manufacturers. In addition, the Z ′′ model also uses bankruptcy risk prediction for enterprises that are not publicly listed firms and there is no big difference between firms operating in different industries due to no consideration to sales/total assets .
This study applied the Z-score to assess the bankruptcy risk of a collection of 180 small and medium non-financial enterprises. Unlike the studies which were based on public financial reporting sources of listed companies, it is very challenging to access official data of unlisted enterprises, mainly small and medium enterprises. Within this empirical study, we, therefore, extracted the official data of enterprises from the financial statements of the enterprises submitted to the Tax Authority of Soc Trang Province after ensuring the anonymity principle of enterprises.
The data in this study allowed the empirical experimentation of various scales, multi-types (private enterprises, limited liability companies, jointstock companies), multi-industries (production, trade -service, construction, other industries) of enterprises. Empirically, many of the abovementioned studies applied the Z ′′ model of Altman for this kind of enterprise data, which was applied again in this study.
As mentioned, the Z ′′ model classifies bankruptcy risks by three categories: Z-score of greater than 2.99 clearly fall into the "non-bankrupt" sector, while those enterprises having a Z below 1.81 are all bankrupt. The area between 1.81 and 2.99 is defined as the "zone of ignorance" or "gray area" because of the susceptibility to error classification (Altman, 1968 as because of potential risks. Then, a binary logistic regression model was applied to measure the factors affecting the bankruptcy risk, which was specified as the following equation: • Firm_age: number of years of operation of the enterprise from the establishment to current. There are two opposing views, according to Omondi and Muturi (2013), the longer the business operated, the better it understood its strengths and promoted more to achieve max-imum efficiency. However, the research of Park, Shin, and Kim (2009) showed that the age of the enterprise had a negative impact on enterprise growth and when the enterprise was "getting older," profit would decrease (Elif, 2016).
• Field: business field, including sub-variables: • SME: small and medium enterprise, 1 if true, 0 if not true; • EBT: earnings before tax.
Profit plays an important role in the business and production activities of the enterprise, both as a goal, as well as a motivation and a condition of existence and development of the enterprise.
• EBIT/TA: earnings before interest and tax (EBIT) to total assets (TA) ratio.
• TE/TD: total equity to total debt ratio. These two factors were inherited from Altman Z-score model.
Altman's Z-model, like most previous studies, often revolved around analyzing the financial factors of groups: financial leverage, profitability, solvency ..., which were determinants of bankruptcy risk of a business. In this study, the authors put into the model of non-financial factors to assess the impact of these factors on corporate bankruptcy risk.

Bankruptcy risk of the enterprises
According to Table 1 and The ANOVA test results in Table 2 also showed that there was a statistically significant difference in the bankruptcy risk between fields.
In addition, the statistics in Table 3 indicated that joint-stock companies were at a higher level of bankruptcy risk. 21 of 31 joint-stock companies were at risk of bankruptcy, accounting for 67.74%. The bankruptcy risk was also high for limited liability companies with two or more members. Of 49 limited liability companies with two or more members, 27 were at risk of bankruptcy, accounting for 55.10%. Meanwhile, the proportion of single-member limited liability companies and private enterprises being at bankruptcy risk was lower, accounting for 44.44% and 21.05%, respectively.
In order to clarify the differences in bankruptcy risk between types of enterprises, the study continued to conduct ANOVA analysis, and the results showed that there were statistically significant differences between limited liability companies with two or more members and private enterprises between the joint-stock and private enterprises.

Determinants of bankruptcy risk
To employ the binary logistic regression, we re-classified the bankruptcy risk (Risk) by two categories. If 1, Y = the business was at risk of bankruptcy; otherwise, the business was not in danger of bankruptcy. The regression results in Table 3 indicated the correlations between the dependent variable and the independent variables in the general model with statistical significance. Variance inflation factors of independent variables were lower than 2, small enough to conclude that there was no multicollinearity between variables.
As shown in Table 5, the regression results supported the following findings: that the non-financial factors such as fields, types and size of the business, the educational level of the executors, which belong to the characteristics of the enterprises and the operators, and the financial factors such as Earnings before tax, Net profit/Equity ratio, Earnings before interest and tax/Total assets ratio, Equity/Total debt ratio all affected the bankruptcy risk of enterprises.

DISCUSSIONS
The research showed that regarding the characteristics of the executive, the higher educational level of the executive, the lower the bankruptcy risk of the business. The analysis results showed that businesses operating in the manufacturing sector were at a high-er level of bankruptcy risk than those operating in the remaining sectors. In the data set of this study, enterprises operating in the manufacturing sector were mainly processing and aquaculture enterprises. This sector resulted higher profits but also accompanied by higher risks because of large investment in machinery, production lines, as the standards of food safety on the world market are required strictly.
When enterprises were classified by type of establishment, private enterprises were less risky than the others. In contrast, joint-stock companies were at higher risk. Joint-stock companies often have many members, and complex management arrangements require a strict management mechanism. It is also possible that due to disagreements between shareholders and managers, these conflicts will lead to ineffective control (Pi & Timme, 1993). Besides non-financial factors that belong to the characteristics of the enterprises and the operators, financial factors also had direct impacts on bankruptcy risk.
For the financial factor group, the results of the regression analysis confirmed the previous studies. If the earnings before tax was low, the enterprise may have used the profits to pay off debt and interest or the business was inefficient leading to an increased risk to the business. Net profit/equity ratio (ROE) of enterprises had the same impact with the performance of enterprises. The research showed that enterprises with higher ROE had better performance and a lower level of bankruptcy risk. In addition, the higher earnings before interest and tax/total assets ratio (EBIT/TA), the better the profitability of assets of the business or the lower the risk of bankruptcy. The higher ratio of equity/ total debt proved the ability to raise capital by debt of the business as reflected by a lower level of risk.

CONCLUSION AND RECOMMENDATIONS
The study used Altman's Z-score as a tool to assess the bankruptcy risk of enterprises and the binary logistics model to measure the impact of financial and non-financial factors to bankruptcy risk of the business. Basically, these factors affected the bankruptcy risk and were confirmed by many previous empirical studies. However, in this research, two factors, including the age of the enterprise and the gender of the executor, did not have significant influence.
It is useful for managers, investors, and stakeholders to measure and predict the bankruptcy risk, which helps to identify business with potential risks in order to timely formulate the management policies. In