International Journal of Innovation and Economic Development
Volume 6, Issue 1, April 2020, Pages 7-18
Insolvency Risk. Application of Altman Z-Score to the Auto Parts Sector in Romania
1 Rodica Baciu (Boanta), 2 Brezeanu Petre, 3 Adrian Simon
1,2 The Bucharest University of Economic Studies Department of Finance School, Bucharest, Romania
3 George Emil Palade” University of Medicine, Pharmacy, Science and Tech. of Târgu Mureș
Abstract: This paper intends to apply the Altman Z-score model to all the companies active in the wholesale of motor vehicle parts and accessories (NACE 4531), with extended financial statements. Using the panel data model over the time series for 2008-2016 on the companies of this sector, we conclude that 99% of the Z-score is explained by the independent variables (working capital, capital structure, turnover, earnings before interest and tax), with estimated parameters very close to the model`s classical values. The sample description of the paper and the corresponding results highlight the Z-score evolution by turnover clusters and principal components, with the largest companies performing the best (the only cluster with Z-score median above 3). We notice a tendency for decreasing high-risk companies and increase in the medium risk companies, whereas the low-risk companies are relatively stable. This improvement is mostly due to the increasing capitalization rate and less external debt, despite the deteriorating working capital and operating margin. We believe that future research to evaluate Z-score sensitivity under stress test scenarios would be very useful to provide an insight into companies’ insolvency risk amid increasing interest rates and different fiscal tax on dividends.
Keywords: Insolvency risk, Altman Z-score, Third keyword, Corporate finance, Econometrics
1.1 Literature Review
New York University Finance Professor Edward Altman, looking to develop a tool to be used in predicting the bankruptcy, he developed a new model using ratios, named the Altman Z-score formula in 1967, later published in 1968. This model is already available, even in modern economy to predict the bankruptcy with two or three years in advance, this can prevent the bankruptcy risk, if the financial managers test the level of this indicator and then take the necessary measures to improve the health of the company. According to the Sanobar Anjun, Altman’s revised Z-score model is one of the most effective Multiple Discriminant Analysis, which has been researched throughout the last 40 years. Beside this model to evaluate the risk of bankruptcy we know KWM Model, developed by KMV Corporation based on Merton’s (1973) analytical model of firm’s value , Credit Metrics is a statistical model developed by J.P Morgan, the investment bank, in the year 1995, Value at risk (VAR) is a statistical risk measure used by some Indian banks, but the most relevant remain Altman Z score model, that has been consistently reported to have a 95 % accuracy of prediction of bankruptcy up to two years prior to failure on nonmanufacturing firms as well.( A.K. Saini 2011). The model, presented by Altman, is a quantitative balance-sheet and income statement method of determining a company’s financial health. Altman start to build his model with calculation of 22 common financial ratios for different non-financial companies, and then used multiple discriminant analysis to choose a small number of those ratios that could best distinguish between a bankrupt firm and a healthy one (Altman, 2000). When apply the model of Z-score for all non-financial companies, the result should be interpreted, how the lower the score, the greater the risk of the company falling into financial distress. We based the original research on data from publicly held manufacturers (66 firms, half of which had filed for bankruptcy). The Altman Z-score model is a multiple regression analysis with the following variables:
Z= 1.2* X1 + 1.4*X2 + 3.3* X3 + 0.6*X4 + 1.0*X5
|X1||Working Capital/ Assets||This indicator reflects liquidity of the company. A very small proportion of the working capital (especially negative values for more than three consecutive years) in total assets may raise funding problems for the company (Chouhan et al., 2014). Nevertheless, positive working capital doesn’t always reflect strong internal financing capacity, if receivables and inventories are not monetized (Iqbal and Zhuquan, 2015). For example, a very lengthy collection period of receivables exposes the subject company to clients` default and systemic risk. According to Singhania and Mehta, 2017, the more inventories the company is holding, the higher the exposure to market value volatility risk, short term liquidities pressures, and operational risks (depositing conditions and insurance costs).|
|X2||Retained Earnings / Assets||This report reflects reserves and investment strategy of the company. Lower or decreasing values of reported results (figure on balance, the equity component) may indicate a decreasing trend of the profits, reserves erosion due to recent year’s losses or increasing dividend distribution (Koussis et al., 2017). Under these situations, the subject company exposure to external debt is increasing, making it more vulnerable to fluctuations of external financing conditions (Royer, 2017).|
|X3||Earnings Before Interest and Tax / Assets||Reflects the operating result and efficiency of using the assets to generate operating income. Lower values for several consecutive years may indicate a deterioration of the result from ordinary activities (basic) of the company. According to Khajavi and Ghoohestani, 2015, this might be caused by lower gross margin (due to pressure from suppliers or clients) or higher operational costs (due to average salary increase or higher costs with fuel and energy). The higher the operating margin, the more value-added is generated by the company, reducing, thus, the risk of insolvency (Tian and Yu, 2017).|
|X4||Shareholders’ equity/ Debt||This indicator shows the structure of financing and the self-financing ability of the company (Turner, 2016). Lower values indicate high dependence on external funding and reduced perspectives of additional funding. Indeed, increasing debt in the first stage will lower the weighted average cost of capital (due to tax savings provided by interest-bearing debt). Nevertheless, if the company becomes over-indebted, that will increase the financial burden, jeopardize business partnerships with suppliers or clients and increase the risk of insolvency (Wu et al., 2017).|
|X5||Turnover/ Assets||How effectively the company uses its assets to generate income. The reference value is the industry average. Low levels may indicate that too much capital is locked in assets. High values may indicate that the company has too few assets for the potential sales level.|
Interpretation of results:
Z < 1,80 =>correspond for companies with a high probability of insolvency.
1,80 < Z < 3 => correspond for companies with a medium probability of insolvency.
Z > 3 =>correspond for companies with a low probability of insolvency.
1.2 Problem Statement
We applied The Altman Z-score model to all the companies active in the wholesale of motor vehicle parts and accessories, NACE 4531, with extended financial statements submitted for the entire appraised period were taken into consideration (to eliminate the survivorship effect). Since we needed extended format of the financial statements, we included only companies with a turnover above one mil EUR, resulting in a total number of 168 companies. As illustrated in the next two tables, we observe a large concentration of revenues among the companies with a turnover above five mil EUR, the latter weighting almost 80% from value (revenues perspective) in the total sample of companies.
Table 1: Number of distribution companies
|Year / Number||1. 1-2 mil EUR||2. 2-3 mil EUR||3. 3-5 mil EUR||4. +5 mil EUR||Total
Table 2: Turnover distribution of companies
|Year / Turnover (bn RON)||1. 1-2 mil EUR||2. 2-3 mil EUR||3. 3-5 mil EUR||4. +5 mil EUR||Total|
Source: Ministry of finance, data processed by the author
The sample is reflecting a general challenge of the business environment in Romania, related to the increasing polarization of revenues. As observed in the right table, the largest 1.000 companies active in Romania weight almost 50% of the total revenues generated by all companies during 2016, as compared to a 35% concentration during 2008. This phenomenon is narrowing the middle layer business and lowering the overall competitiveness of companies.
Table 3: Revenues of top 1.000 companies in Romania
|Year||Turnover (bn RON)||Weight in total|
Source: Ministry of finance, data processed by the author
Given the overall concentration of revenues among the largest companies in the global business environment and the selected list of companies, we divided the sample into four different clusters by turnover level, which will further represent the cross-sectional series in the panel data model described in the methodology. The following table and graph illustrate the results of Altman Z-score applied to the sample of companies active in the wholesale of motor vehicle parts and accessories (NACE 4531). We computed the Z-score for the 168 companies, for the entire appraised period (2008-2016), and the evolution illustrates a tendency for decreasing high-risk companies (from 25 in 2008 down to 15 in 2016) and increase in the medium risk companies (from 43 in 2008 to 56 in 2016), whereas the low-risk companies are relatively stable.
Table 4: Altman Z-score distribution
|Year||High Risk||Medium Risk||Low Risk|
We calculated the Z-score median for all the selected companies by different clusters. As illustrated in the next table and graph, the overall Z-score has marginally decreased for most of the groups, with the largest companies (+5 mil EUR turnover) performing the best according to the Altman Z-score (median value below 3).
Figure 1: Risk distribution by Z-score
Table 5: Z-score distribution by turnover
|Year /Z-Median Value||1-2 mil EUR||2-3 mil EUR||3-5 mil EUR||+5 mil EUR||Total|
Figure 2: Z-score median values
If we compare 2016 with the previous year, we observe a marginal improvement of the median Z-score value for all the companies in the appraised sector, from 2,45 (2015) to 2,89 (2016). This improvement is mostly due to increasing capitalization rate and less external debt, despite the deteriorating working capital (the current rate lowering from 1,76 in 2015 down to 1,43 in 2016) and operating margin (from 3% in 2015 down to 1,2% in 2016). If we consider the evolution for the entire appraised period and compare 2016 with 2008, we observe a gradual deteriorating of the Z-score, mainly caused by a lower operating margin, the latter decreasing from 7,8% in 2008 down to only 1,2% in 2016. The Altman Z-score components are computed for each year and illustrated for all 168 appraised companies in the next two tables.
Table 6: Altman Z-score variables for all the appraised companies
Source: Ministry of finance, data processed by the author
Table 7: Financial ratios for all appraised companies
|Year||Current Rate (Current Assets / Short Term Debt||Equity Rate (Equity / Assets)||Debt Rate (Total Debt / Assets)||Turnover (bn RON)||EBIT : Sales||EAT : Sales|
Source: Ministry of finance, data processed by the author
2. Research Methodology
We applied the Altman Z-score over the selected sample of data, taking the next steps:
- We considered all companies from the appraised sector (wholesale of motor vehicle parts and accessories, NACE 4531) with extended financial statements submitted for the entire appraised period (to eliminate the survivorship effect). Since we need extended format of the financial statements, we included only companies with a turnover above 1 mil EUR, resulting a total number of 168 companies;
- the resulting companies were divided into four cross-sectional groups, depending on the turnover level: 1-2 mil EUR, 2-3 mil EUR, 3-5 mil EUR and +5 mil EUR;
- The 5 variables of the Altman Z-score model have been computed for each cross-sectional group and year during the appraised period. Since the regression equation contains both time series and cross-sectional data, a model with panel data was used, with 36 observations in total (9-time series and 4 cross-section data).
The model used is a multifactorial regression equation with fixed effects, to highlight the different profile of companies according to their turnover range. According to Gujarati (page 637), dummy variables that do not vary in time, but are different for each cross-sectional category (the 4 classes of companies according to the registered business figure) are to be used. Since the estimated values for the dummy variables are incidental (the difference from the cross-sectional series used as a reference), we will use a number of three dummy variables, the constant of the multifactor regression equation being the first class of companies. Thus, the multifactorial regression equation becomes:
Applying the multifactor regression equation previously described on the data panel in E-Views, we obtain the following result:
The same result we found by entering the cross-sectional observations and time series directly into the panel data model, namely:
This way the equation becomes:
if X1 (working capital /assets) raises with 100bp, then the Z score raises with 125bp
if X2 (retained earnings /assets) raises with 100bp, then the Z score raises with 142bp
if X3 (earnings before interest and tax /assets) raises with 100bp, then the Z score raises with 349bp
if X4 (equity /debt) raises with 100bp, then the Z score raises with 60bp
if X5 (turnover /assets) raises with 100bp, then the Z score raises with 98bp
A main indicator that shows if the model is well-specified is R-squared, which indicates how many percent of the total dependent variable variance is explained by the independent variables using the following formula:
R-squared = (TSS-SSE)/TSS = RSS/TSS = 0,9994
The higher the value of this indicator aims to 1, the better the model. In our case, 99.94% of the Z-score is explained by the five independent variables of the model.
The R-squared indicator increases if we added new independent variables to the multifactorial regression equation, but it also causes loss of degrees of freedom. Therefore, an adjusted measure of R-squared is better because it takes into account the number of independent variables included in the regression (Codarlasu and Ghidesciuc, pp. 44). The latter we calculated it using the following formula:
n is the number of observations
k – the number of independent variables included in the regression
The E-Views results indicate a value of 99.93% for the adjusted R-squared coefficient, very close to that of the R-squared coefficient.
To test if the estimated coefficients are relevant from the statistical point of view (different from zero), we can use the t-test, with the following assumptions:
H0 : ß1 = 0
H0 : ß1 ≠ 0
According to the E-Views results, the t-test value for the five exogenous (independent) variables, are presented next, using the previous formula:
Considering that the probability associated with the lower relevant level employed (5%), then the null hypothesis is rejected, and the coefficient is considered to be significant statistically. This can be verified with the fact that the displayed value of the t-test is above the critical value. The latter, can be calculated using the following formula: t_c=t_(α/2;n-m); where α represents the relevance level (5%) divided by 2 (because the test checks if the estimated value is equal or different form 0), n represents the observations number(36), and m represents the number of estimated parameters (9). So the t (0,025;27) value is approximately 2,052. Because t values for all the five independent variables are above the Tc (2,052), the null hypothesis was rejected. Instead, the likelihood associated with the constants for all dummy variables (corresponding to each group of companies according to turnover) is above the relevant level (5%), which implies that the null hypothesis is accepted and the coefficient isn’t statistically significant, (not different from zero value). This is consistent to our expectations, indicating the Z-score formula is valid for all companies, regardless the turnover range.
This test indicates to what extent a set of independent variables explains, as a group, the variation of the dependent variable, and determines the extent to which all coefficients of the regression equation simultaneously have zero values. The hypotheses are:
The value of the F-test calculated by E-views is close to 6588 and we obtained it using the formula:
The F test follows a distribution F, and the critical value is determined as follows:
Fc=Fa; m-1;n-1 = F(0.05;8;27) = 5.28. Since the calculated value (6588) is higher than the critical value (5,28), then the null hypothesis is rejected, which means that at least one of the coefficients of the regression equation is statistically significant. We achieve the same conclusion, if we observe that the associated probability is inferior to the level of relevance to which it is being worked (5%).
4. Conclusion and Recommendations
Altman Z-score model is a multiple regression analysis, becoming very famous due to its simplicity for both understanding and practical use. Based on this model, we calculated the Z-score by using both balance sheet (working capital, capital structure) and income statement elements (turnover, earnings before interest and tax). In this paper, we apply the Altman Z-score for to all the companies active in the wholesale of motor vehicle parts and accessories, NACE 4531, with extended financial statements submitted for the entire appraised period were taken into consideration (to eliminate the survivorship effect). Since we need extended format of the financial statements, we decided to include only companies with a turnover above one mil EUR, in this case we got a total number of 168 companies. Since the regression equation contains both time series and cross-sectional data, a model with panel data was used, with 36 observations in total (9-time series for the period 2008-2016 and 4 cross-section data with different clusters depending on revenues: 1-2 mil EUR, 2-3 mil EUR, 3-5 mil EUR, and +5 mil EUR). The results generated with E-views are very close to the parameters from the original model. According to the R-squared estimated value from the model, 99,94% from the evolution of the Z-score is explained by the independent variables (working capital, retained earnings, earnings before interest and tax, equity and turnover).
Given the high concentration of revenues among the largest companies in the overall business environment and the selected list of companies, the sample is divided into four different clusters by turnover level, representing the cross-sectional series in the panel data model described in the methodology. We computed the Z-score for the selected companies for the entire appraised period (2008-2016), and the evolution illustrates a tendency for decreasing high-risk companies (from 25 in 2008 down to 15 in 2016) and increase in the medium risk companies (from 43 in 2008 to 56 in 2016), whereas the low-risk companies are relatively stable. The overall Z-score has marginally decreased for most of the clusters, with the largest companies (+5 mil EUR turnover) performing the best, according the Altman Z-score (median value above 3). If we compare 2016 with the previous year, we observe a marginal improvement of the median Z-score value for all the companies in the appraised sector, from 2,45 (2015) to 2,89 (2016). This improvement is mostly due to increasing capitalization rate and less external debt, despite the deteriorating working capital (the current rate lowering from 1,76 in 2015 down to 1,43 in 2016) and operating margin (from 3% in 2015 down to 1,2% in 2016). If we consider the evolution for the entire appraised period and compare 2016 with 2008, we observe a gradual deteriorating of the Z-score, mainly caused by reduced operating margin, the latter decreasing from 7,8% in 2008 down to only 1,2% in 2016.
Future research to evaluate Z-score sensitivity under stress test scenarios would be very useful to provide an insight of companies’ insolvency risk amid increasing interest rates and different fiscal tax on dividends. This kind of research is very necessary especially given the very unpredictable fiscal environment in Romania. The pro-cyclical fiscal measures cause GDP growth to reach 7% during 2017 in Romania, significantly above the potential level of 3%-3,5%. GDP growth in 2018 only with 4,1% a level under the initial estimation that was at 5,5%, during the medium inflation rate was 4,6% (2018). This is fuelling increasing inflationary pressure, steaming to almost 5% during the first semester of 2018 and 3,2% Central Bank of Romania latest estimate for 2018 full year. That will force the Central Bank to launch a restrictive pace of the monetary policy, already visible with interest rate hike from 1,75% to 2% during January 2018. Under this context, increasing financial burden of companies with translate into a lower operating margin. Moreover, the proposal of dividends tax cut down to zero starting 2019 (according to the latest government plan) would motivate shareholders to distribute profits as dividends and lower the retained earnings. Both factors – reducing dividends tax and interest rate hike- will negatively impact the Z-score results for the companies active in Romania.
Moreover, this companies are now affected by the pandemic situation generated worldwide from COVID-19, the question how long will last the situation and how much will affect the world economy, nobody knows. We think that the bankruptcy risk protection policy will be dramatically changed after recovering from this Covid19 crisis, because managers, banks and governs learned that could face in future with unexpected challenges. Companies will work hard to improve the financial health to can face the next burden, that should appear without somebody to imagine.
- Altman, E. I. (2000). Predicting financial distress of companies:Revisiting the Z-score and ZETA models. Stern School of Business, New York University, 9-12.
- Anjun,S. Business bankruptcy prediction models: A significant study of the Altman’s Z-score model Anjum, (August 13, 2012). Crossref
- Chouhan, V., Chandra, B., and Goswami, S. (2014). Predicting Financial Stability of Select BSE Companies Revisiting Altman Z Score. ILSHS, 26, pp.92-105. Crossref
- Cliffe,M. Pandenomics – how Covid-19 could change the world.April 2020 https://think.ing.com/uploads/reports/Pandenomics_April_2020_FINAL.pdf?utm_campaign=April-07_pandenomics-how-covid-19-could-change-the-world&utm_medium=email&utm_source=emailing_report
- Codarlasu, A. si Ghidesciuc, N, (2008), Applied Econometrics, pp.44-55
- Iqbal, A., and Zhuquan, W. (2015). Working Capital Management and Profitability Evidence from Firms Listed on Karachi Stock Exchange. International Journal of Business and Management, 10(2), pp.25-40. Crossref
- Khajavi, S., and Ghoohestani, S. (2015). The Effects of Cash Flow Uncertainty and Working Capital on Non-Cash Flow Shock Returns. International Business Research, 8(4), pp. 115-121. Crossref
- Koussis, N., Martzoukos S., and Trigeorgis, L. (2017). Corporate liquidity and dividend policy under uncertainty. Journal of Banking & Finance, 81, pp.221-235. Crossref
- Royer, J. (2017). Financing agricultural cooperatives with retained earnings. Agricultural Finance Review, 77(3), pp.393-411. Crossref
- Saini, A.,K, ( 2011). MIS Sustain Credit Riskmetrics Vis a Vis the Future of Indian Banking System, Global Journal of Enterprise Information System http://www.ejournal.co.in/gjeis April – June 2011Volume-3
- Singhania, M., and Mehta, P. (2017). Working capital management and firms’ profitability: evidence from emerging Asian countries. South Asian Journal of Business Studies, 6(1), pp.80-97. Crossref
- Tian, S. and Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics & Finance, 51, pp.510-526. Crossref
- Turner, J. (2016). Net Operating Working Capital, Capital Budgeting, And Cash Budgets: A Teaching Example. American Journal of Business Education (AJBE), 9(1), pp. 31-62. Crossref