Abstract This paper analyses whether Altman Z-score models, can predict correctly company failures. The empirical analysis examines all listed in the Athens Exchange companies, during the period 2002-2008 and discontinuations of operation for these companies during the same period. It is investigated whether Z-score models can predict bankruptcies for a period up to three years earlier. Our study shows that Altman model performs well in predicting failures. This is in line with other findings.
The empirical results are interesting since they can be used by company management for financing decisions, by regulatory authorities and by portfolio managers in stock selection. Keywords: Valuation, Altman, Regulation, Share price, Capital markets, bankruptcy JEL Classification Codes: G33:G14 1. Introduction 1. 1. Examined Issues In the study it is examined whether z-score alone can predict business failure for the examined companies in the examined period. To examine this it is investigated whether z-scores one up to four years before bankruptcy can predict business failures or financial problems.
The study is interesting for financial analysts and portfolio managers given that in the case that discriminant analysis is useful, they can use it for stock picking and asset allocation. Discriminant analysis can also be a valuable tool for investors. Companies with high probabilities to bankrupt should trade at a discount to their value. If this is correct, then this paper provides an interesting contribution to bankruptcy literature, by linking z-score with business failure prediction and asset allocation in Greece. 1. 2.
Previous Research Multivariate prediction of bankruptcy as established by using univariate analysis of bankruptcy predictors was initially developed by Beaver (1967, 1968) who found that a number of indicators could © Research Journal of Internat? onal Stud? es – Issue 12 (October. , 2009) 21 discriminate between matched samples of failed and non-failed firms for as long as five years prior to failure. Altman (1968) defines five predicted factors and sets the base for other researchers to examine the validity of multivariate models.
Following to the Beaver and Altman research seems to verify the validity of Altman models, but their prediction ability is found gradually lower. Begley et. al (1996) examines the Altman z-model and concludes that the model performs better in US data during the 1980s than during the period 1990-1995. Similar are the findings of Grice and Ingram (2001), who also find better performance for manufacturing companies. 2. Data Issues and Methodology 2. 1. Data
In order to test the credit risk of the construction companies in Greece, two Z–score models are examined, in particular the z-score models developed in Altman (1993). The financial data used are annual and cover the period of 1999-2006. To compute the market value used, we take the market value of the company on 31 December of each year. The prices were taken by EFFECT S. A. database and we use the Athens stock exchange daily report to identify when the company share price was suspended. From our sample we exclude companies that were listed for less than three years, and companies that merged.
On total, the examined sample consists of 373 companies, listed on the Athens Stock exchange in the examined period, 45 of them bankrupted or their shares were suspended permanently and 328 companies did not bankrupt or had their shares permanently suspended. The examined cases of companies that bankrupted or their shares were suspended permanently are the following (Table 1). Table 1: Companies that their shares were suspended permanently, or get bankrupt Company Name Sector 1 Alfa Alfa Diversified 2 Connection Apparel 3 Datamedia Information Technology 4 Elephant Retail 5 Ipirotiki Publishing 6 Microland Retail 7 Rainbow.
Information Technology 8 Sea Farm Ionian Fisheries 9 Stabilton Real Estate 10 Alte Metal Products 11 Alisida Footwear 12 Mesochoritis Construction 13 Betanet Construction 14 Leventakis Construction 15 Gener Construction 16 Galis Leisure 17 Daios Plastics Chemicals 18 Daring Metal Products 19 Diekat Construction 20 Dinamiki Zois Fitness 21 Hellenic Fisheries Fisheries © Research Journal of Internat? onal Stud? es – Issue 12 (October, 2009) 22 22 Empedos Construction 23 Ergas Construction 24 Etma Chemicals 25 European Technical Construction 26 Themeliodomi Construction 27 Ideal Information Technology 28 Intersat Construction.
29 Kerameia Allatini Building Materials 30 Korasidis Retail 31 Korfil Textiles 32 Lannet Communications 33 Multirama Retail 34 Balafas Real Estate 35 NEL Ferries 36 Plias Information Technology 37 Pouliades Wholesale 38 Promota Promotion 39 Saos Ferries 40 Space Information Technology 41 Corinth Pipeworks Metal Products 42 Tasoglou Wholesale 43 Texnodomi Construction 44 Steel Sheet Co Metal Products The number of companies that get bankrupted and/or their shares were suspended permanently are the following (Table 2). Table 2: Number of Companies that get bankrupt and/or their shares were suspended permanently Year.
2003 2004 2005 2006 2007 Total No of Companies 12 9 9 4 10 44 Companies belonging in the financial sector (banks, investment companies) were excluded. 2. 2. Examined model The most well-known quantitative model for predicting bankruptcy is Altman’s Z-score, which was developed in 1968 by Edward I. Altman, professor at Stern School of Business. The Z-score is a set of financial ratios in a multivariate context, based on a multiple discriminated model, for the firms where a single measure is unlikely to predict the complexity of their decision making or the scope of their entire activities.
Altman examined a list of twenty two possible ratios, and finally has chosen five that had the best results when they were applied together were selected after numerous tests for the discriminant © Research Journal of Internat? onal Stud? es – Issue 12 (October, 2009) 23 function. This model was later modified to the Altman (1993) model that uses the same variables multiplied by different, however, factors. The discriminant function is: Z = 1. 2X1 + 1. 4X2 + 3. 3X3 + 0. 6X4 + 1. 0X5 Given that X1 = Working Capital / Total Assets, (WC/TA) X2 = Retained Earnings / Total Assets, (RE/TA)
X3 = Earnings Before Interest and Taxes / Total Assets, (EBIT/TA) X4 =Market Value Equity / Book Value of Total Liabilities, (MVE/TL) and X5 = Sales / Total Assets, (S/TA) Altman defined a “grey area” which is between 1. 81 and 2. 99. Firms, with z-scores within this range, are considered uncertain about credit risk and considered marginal cases to be watched with attention. Firms with Z scores below 1. 81 indicate failed firms. Although, the cut-off point was set at 2. 675, Altman advocates using the lower bound of the zone-of-ignorance (1.81) as a more realistic cutoff Z-Score.
So if Z < 1. 81, then the company has a high probability of default. On the other hand, the company is solvent, meaning that it is financial healthy. Some credit analysts, private underwriting agents, financial analysts, auditors and firms themselves were concerned that since the original model requires stock price data (X5), it was only applicable to publicly traded entities. 3. Results The validity of the model is tested by examining the percentage of cases that fall within the predictable range of companies.
For example, bankrupted companies must fall within the price range that is expected to be for these companies (e. g. smaller than 1. 8), while non-bankrupted companies must fall within the price range that is expected to be for those companies. Cases not predicted correctly are defined as Type 2 error cases. Our results show some support for Altman model. Altman model findings Examining the Altman (1993) model we find out that it can predict the majority of companies that will get bankrupt, even when the z-scores of these companies are computed up to six (2) years earlier. Table 3:
Altman z score prediction statistics, Bankrupted Companies Year -4 -3 -2 -1 All 44 44 44 44 Bankrupted Companies Correctly Classified 9 17 23 29 Type 2 error 35 27 21 15 % correct 20% 39% 52% 66% Success rate for failed companies varies from 66% (year -1) and gradually diminishes to 52%, 39% and 20% for year -2, -3 and -4 respectively. Therefore, z-score gives a good indication of problems, at least one year before the company will exhibit financial problems. However, the model performs poorly when prediction time horizon increases. Data for non-bankrupted companies are illustrated on the following table.
© Research Journal of Internat? onal Stud? es – Issue 12 (October, 2009) 24 Table 4: Altman z score prediction statistics, Non Bankrupted Companies Year -4 -3 -2 -1 All 244 215 193 168 Non Bankrupted Companies Correctly Classified 190 149 121 91 Type 2 error 54 66 72 77 % correct 78% 69% 63% 54% The model gives a good indication for companies that will not face problems even in longer span time-horizons. The model has been successful in classifying the majority of non-bankrupted companies in all the examined periods (-1, -2, -3 and –4 years).
In particular, 78% of companies are correctly classified for the long times span (4 years) a percentage that gradually diminishes to 54% for a year time span. Overall the model succeeds to identify bankrupted and non-bankrupted companies. Aggregated data are illustrated on the following table. Table 5: Altman z score prediction statistics, All Companies Year -4 -3 -2 -1 All 288 259 237 212 All Cases Correctly Classified 199 166 144 120 Type 2 error 89 93 93 92 % correct 69% 64% 61% 57% The model succeeds to identify bankrupted and non-bankrupted companies by 57%-69% depending on the time horizon examined (1-4 years).
It is noted that the model takes account of market values. The ability of the model to take account of market values, together with redefined beta factors, seems to give it an additional strength. However, the model also has weaknesses, main being its volatile results. Volatility of z-scores The estimated Z-factors vary over time. Z-scores appear higher during bullish markets, while they appear lower during bearish years. As seen on the following graph, average z-scores of both failed and non-failed companies are high in 1999, and then they gradually decrease, to reach by 2005 the lowest levels. ©
Research Journal of Internat? onal Stud? es – Issue 12 (October, 2009) 25 Graph 1: Development of z-score for failed and non-failed companies Z-score Quartile Statistics (Q1, Median, Q3), 1999-2006 100 median q1 q3 10 1 1999 2000 2001 2002 2003 2004 2005 2006 This development is universal among the examined companies. The examined companies zscore gradually increases or decreases at similar pace. This affects the percentage of companies that fall within illustrated on the following table. Graph 2: Development of z-score for failed companies Percentage of Companies Below Cut-Off Point Companies having a z-score below the cut-off point 45% 40% 35% 30% 25% 20% 15% 10% 5% 0%.
1. 998 1. 999 2. 000 2. 001 2. 002 2. 003 2. 004 2. 005 2. 006 2. 007 Year Our results may indicate that bullish markets give even to financially distressed company the ability to survive. It is evidently correct, according to our opinion, since during bullish markets, financially distressed companies can raise capital easier, something that gives them the ability to cover their need in capital for the next 1-2 years.
However, as these companies do not generate positive operating cash flows, the capital raised is not a panacea, so financial problems appear again whenever the macroeconomic conditions are bad and the companies can not raise capital. © Research Journal of Internat? onal Stud? es – Issue 12 (October, 2009) 26 It seems, therefore, that the ability of companies to raise capital does not improve significantly their financial prospects.
If companies have the ability to improve their financial position during good years in capital markets, while being unable to improve them in the long run, then Altman z-scores are useful tool for the company management, either to proceed in company restructuring, or to proceed to a merger with other companies. At the same time, if Altman z-scores tend to return to historically low levels over a business cycle (6-10 years), then good financial and market years should provide the opportunity to portfolio managers to decrease their position in companies they identify to have low Altman z-scores.
4. Conclusion Nearly 15% of the listed companies bankrupt or the trading of their shares was suspended during the examined period. This high percentage of failure makes the investigation and prediction of company failures a useful tool for both financial managers and analysts, since the ability to predict these failures is valuable. For this scope in this paper it is examined whether z-score model, developed by Altman 1993 can predict bankruptcies. We find evidence that this model is useful in identifying financially troubled companies that may fail up to 2 years before the bankruptcy.
The model is useful, probably because it matches both accounting data and market value, having so information content as identified in Dichev 1998. Overall model success rate is not statistical significant, but when it comes to failure cases it can predict 54% of them one year before failure. The predictive ability of Altman model is in line with findings by other researchers in Greece and in the United States. Christopoulos et. Al. (2007) finds that Altman is useful in predicting Greek telecom company failures, while Altman (2002) find supportive evidence in the US market.
Vergos et al (2006) and Christopoulos et al (2006) also show that analysts’ predictions and company announcements may affect considerably market prices up to 18 months before the announcement of negative financial results, something that leads to incorporation of probability of failure in company prices, and respective company Altman z-score that are affected by market price of shares, well before the company will declare bankruptcy. In other words, analysts’ recommendations, as well as market rumours, that affect the company price may explain why Altman model is more useful than mere traditional financial analysis.
The empirical results are interesting for both portfolio managers and company management. If companies have the ability to improve their financial position during good years in capital markets, while being unable to improve them in the long run, then Altman z-scores are useful indication to the company management to proceed to a merger with other companies, so as to preserve the company value. Besides, if Altman z-scores tend to return to historically low levels over a business cycle, then bull markets should provide the opportunity to portfolio managers to decrease their position in companies they identify to have low Altman z-scores.
© Research Journal of Internat? onal Stud? es – Issue 12 (October, 2009) 27 References             Altman, E. , “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance, September 1968. Altman, E. , 1993, Corporate Financial Distress and Bankruptcy, Second Edition, John Wiley & Sons, New York. Altman, E. , J. Hartzell, and M. Peck, “Emerging Markets Corporate Bonds: A Scoring System,” Salomon Brothers Inc, New York, 1995. Beaver, W., 1967, Financial Ratios as Predictors of Failure, Empirical Research in Accounting: Selected Studies, Supplement, Journal of Accounting Research 5, 71-127.
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Prediction Model,” Journal of Business Research, 53-61, 10, 2001 Vergos, K. , Christopoulos A. , and J. Mylonakis, “The Impact of Publicity and Press Announcements on Share Prices: An Empirical Study”, The Icfaian Journal of Management Research, vol. VII No 3, March 2008, pg 35-55. Vergos, K. , Christopoulos, A. , “The effects of acquisitions on the market value of the banking sector: An empirical analysis from Greece”, European Journal of Scientific Research (EJSR), Vol. 24, Issue 3, 2008. © Research Journal of Internat? onal Stud? es – Issue 12 (October, 2009) 28.
University/College: University of Arkansas System
Type of paper: Thesis/Dissertation Chapter
Date: 3 November 2016
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