The revised Altman Z -score Model: Verifying its Validity as a Predictor of Corporate Failure in the Case of UK Private Companies.

Transcription

1 The revised Altman Z -score Model: Verifying its Validity as a Predictor of Corporate Failure in the Case of UK Private Companies. Student Number: A dissertation submitted to the University of Leicester In partial fulfilment of the requirements for the degree of MSc Banking and Finance Word counts: 14,440 September 2015

2 Contents List 1 Acknowledgement... i 2 Abstract... ii 3 Introduction The purpose of this research Research questions Key question Sub-questions Organisation of the research Background Introduction The concept of corporate failure The reasons for corporate failure Failure phases Why is disclosure about the prospects for the corporate failures crucial? The most prominent models for predicting corporate failure Univariate Analysis (UA) Multiple Discriminate Analyses (MDA) Corporate failure prediction models in the UK Altman s Z-score (1968) model Financial Ratios 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, Equity/Book Value of Total Liabilities (MVE/TL) X5, Sales/Total Assets (S/TA) Altman s revised Z -score (1968) model Altman s revised Z score (1993) model Criticism Alternative Models Logit regression analysis Artificial neural networks Model (ANN) Empirical studies Methodology Model and Methodology Research Variables Dependent Variable Independent Variables Proposed methods for collecting data... 26

3 5.4 Sample Statistical analysis Results and data analysis Data analysis of the failed firms (12 private furniture manufacturing companies) Ahmarra Limited Traesko 12 Limited ABS Realisations Limited Trafalgar Office Furniture Limited Adeptias Limited FFC (UK) Limited Inter Alia Limited Black Arrow (2009) Limited Full Circle Future Limited Sngl Realisations (2011) Limited Vitrathene Ltd Sector Holdings Limited Data analysis of the non-failed firms (12 private furniture manufacturing companies) Impey Showers Ltd Ultra Furniture Limited Nestledown Beds Limited Buoyant Upholstery Limited Dura Beds Ltd Khyber Upholstery Limited European Door Concepts Limited Moffett And Sons Limited Flair Flooring Supplies Limited F.C. Brown (Steel Equipment) Limited Niko (INT) Limited Mobili Office Limited Discussion Conclusions Limitations and recommendations Bibliography... 44

4 11 Appendix Appendix1: The five financial ratios average of each of 15 failed companies in one year and two years before bankrupt Appendix2: The five financial ratios average of each of 12 non-failed companies in one year and two years before bankrupt Appendix3: Revised Altman Z -Score for the 12 Failed Companies Appendix4: Revised Altman Z -Score for the 12 Non-failed Companies Appendix5: First year results classification Appendix6: Second year results classification Figures List Figure (1): Financial ratios of the revised Altman Z -score model Figure (2): Value of revised Altman Z -score model (1983) Tables List Table1: Inactive Firms Table2: Active Firms Table3: Failed companies data Table4: Non-failed companies data... 35

5 1. Acknowledgement Before everything, I would like to thank Almighty God for giving me the chance to study this course and finished successfully. I would like to express my gratitude to my supervisor Mr. Jim O'Hare for the useful comments, remarks and engagement through the learning process of this master thesis. I would like to thank my family: my parents, my wife, my siblings and my daughters for supporting me emotionally and morally throughout writing this thesis and my life in general. I also would like to thank all of my friends who supported me in writing, and incentive me to strive towards my goal. I would like to thank my all colleagues for their support. I would like to thanks all the banking and finance teaching staff for their help and support. Last but not the least, I would like to thankful Kurdistan Region Government (KRG) who providing the financial support which enabled me to complete this master degree within Human Capacity Development Program (HCDP). i

6 2. Abstract During the past three decades failure prediction has become a considerable concern for stakeholders in firms. Therefore, predicting the financial bankruptcy of corporations by utilising financial ratios is a subject that has been explored in different ways over the last few decades and the present economic environment ensures that these models may be more beneficial than ever before. Therefore, in order to find the most accurate model for prediction, a variety of financial ratios and failure prediction models have been applied. Moreover, different techniques and models have been applied for bankruptcy prediction in the UK, such as the multiple discriminate analysis (MDA) approach, the logit regression analysis (LRA) and the artificial neural networks (ANN) model. It is clear that the most accurate and most reliable model for predicting corporate failure is the Altman Z-score model, which is based on the MDA approach, due to its wide use among researchers, academics and practitioners in various countries. The primary objective of this research was to examine the accuracy and validity of the revised Altman Z -score model in predicting corporate bankruptcy through analysing 24 private furniture manufacturing companies in the UK. These companies were divided into 12 failed and 12 active companies. Furthermore, in this study, the FAME database was used in order to obtain the data available in the financial reports of each active and inactive company. The results of this thesis illustrate that the accuracy of the revised Altman Z -score model for inactive companies was found to be 83.3% and 66.7% in years one and two before bankruptcy, respectively. However, the Z -score accuracy for non-failed companies was found to be 91.7% at one year prior to failure and 81.3% at two years prior to bankruptcy. It was shown in this research that the predictive ability of the revised Altman Z -score model was accurate in predicting bankruptcy in the UK. Therefore, concerned authorities can use this model to take corrective or preventive action. ii

7 3. Introduction It is obvious that profits and growth are the main objectives for a business. As a consequence, the evaluation of if a company is progressing well or a prediction that a corporation will go bankrupt has been researched widely among researchers and academics in different countries. According to Neophytou and Molinero (2004), over the past four decades studies on the ability to predict the failure of corporations have been broadly produced by academics, researchers and practitioners. However, corporate failure is considered to be the most significant challenge faced by numerous businesses in various industries around the world. As a result, the problem of corporate failure continues in contemporary economies. A rigorous and reliable method for predicting bankruptcy status has not yet been discovered and so research attention is most likely to continue. It is clear that the failure of corporations does not happen suddenly and that there are many factors that lead businesses to fail. The majority of economists agree that the high proportion of interest rates, high debt burdens, the nature of businesses operations, government regulations and substandard economic times, such as a recession, might contribute to the failure of businesses. Moreover, studies conducted in the field of corporate failure prediction in the UK, Australia, the US and Canada have considered that newly emerging companies and private and small companies with ineffective supervisory and regulatory policies, as well as a weakness in cash flow, are significantly exposed to financial distress compared to public, large and well-established companies (Charalambous et al., 2000). The accuracy and validity of the failure prediction models are useful to numerous economic agents, such as prospective investors, managers, customers, suppliers, lenders, creditors and others. Consequently, there has been a continuous interest paid to failure prediction modelling in financial and accounting studies ever since the pioneering work first published by William Beaver in The number of corporate failure prediction 1

8 models has rose significantly, especially in the 1980s and 1990s, due to increased data availability and the improvement and development of econometric methods. Thus, the majority of this work has been heavily influenced by a number of early studies, such as Altman (1968), Ohlson (1980), Zavgren (1985) and Dewaelheyns et al. (2006). According to Wang and Campbell (2010), US corporations data have been used by many researchers who have provided different techniques to help identify bankruptcy. It is reported that the Altman Z-score model (1968) and Ohlson s model (1980) are two models that are well accepted and commonly used at present. After the spread of the Altman Z-score model, studies on this model increased widely. Examples of studies include: Deakin (1972); Edmister (1972); Taffler (1982, 1983); Goudie (1987); Grice and Ingram (2001); Agarwal and Taffler (2007); Boritz, Kennedy and Sun (2007); and Sandin and Porporato (2007). In general, after the financial crisis in 2008 the need for developing bankruptcy prediction models became more vital than ever. Researchers have examined many of the models in order to identify their ability to predict corporate failure. Examples of studies include: Beaver (1966), Altman (1968), Deakin (1972), Kida (1980), Ohlson (1980), Taffler (1983) and Shirata (1998). In addition, the accuracy of these models is questionable. Therefore, advanced economies, such as those of the US, the UK, Canada, Malaysia and China, have been used as case studies (Mohammed et al., 2012). It is reported that the Altman Z-score model (1968) was the first study that identified companies as failed and non-failed companies. Altman s study (1968) analysed 33 inactive and 33 active companies using MDA. Ultimately, Altman's Z-score results found that the accuracy of the first and the second year prior to failure was 95% and 72%, respectively. Public manufacturing firms were utilised in the original Altman Z-score model (1968) for predicting bankruptcy. Later, private manufacturing firms were employed in the revised Altman Z -score model (1983). The accuracy of this last model was demonstrated by the 95% and 73% accuracy at year one and year two prior to failure, respectively. 2

9 3.1 The purpose of this research The main objective of this research was to verify the accuracy of the revised Altman Z -score model (1983) in order to determine whether it is an optimal model for predicting corporate failure using recent data of UK furniture manufacturing companies in the period through examining 12 inactive and 12 active companies. 3.2 Research questions For this purpose four questions have been generated. 3.3 Key question a) This research is based on a key question: can the revised Altman Z -score failure prediction model (1983) be applied to detect the possibility of the failure of companies? 3.4 Sub-questions a) How accurate is the revised Altman Z -score failure prediction model (1983) in predicting bankruptcy using previous financial statements? b) What are the differences between active and inactive companies according to the revised Altman Z -score model (1983) when applied to private furniture manufacturing companies in the UK? c) To what extent is it possible to employ the revised Altman Z -score failure prediction model (1983) to predict the corporate failure of private furniture manufacturing corporations in the UK? 3.5 Organisation of the research The study proceeds as follows. Section four provides a review of the literature, which consists of the concept, reasons and phases for failure and non-failure corporate. At the end of this section, the Altman Z-score failure prediction approach is clarified and a discussion about predicting corporate bankruptcy, as well alternative models for doing so, 3

10 is outlined. Section five describes the methodology, which is composed of the samples and data that are utilised in this thesis. Section six gives the results, which are analysed and interpreted. Section seven discusses the results. Section eight gives the conclusion and an assessment of the limitations of this paper and a discussion of future work. Finally, section nine supplies the references and appendixes for this study. 4

11 4. Background 4.1 Introduction In the late 1960s, several studies were performed to develop the failure prediction and financial distress model which has lasted until this day. It could be argued that the Beaver (1966) and Altman (1968) models were the two most influential for predicting bankruptcy and financial distress. These two mechanisms have been improved and replicated by numerous researchers for a variety of companies in different countries. It was Beaver s study (1966) that used a single financial ratio analysis (Cash Flow/Total Debt). He used univariate analysis approach in his study in order to classify corporations as solvent or bankrupt. Altman (1968) expanded on the work of Beaver (1966) and he established his own model called (Altman Z-score). The Altman Z-score model consists of five financial rations based on the multivariate approach, Multiple Discriminate Analysis (MDA) instead of Univariate Analysis (UA) (Galvão et al., 2004). Moreover, the prediction of corporate bankruptcy has been well-researched by other researchers using the MDA. For instance, Deakin (1972) has used the MDA technique in order to predict bankruptcy. He developed the failure prediction model by randomly choosing 23 non-failed firms and 11 failed firms; therefore this choose has led some to be vague about Altman's 1968 model. In addition, Kida (1980) and Taffler (1983) have used MDA approach to predict corporate bankruptcies (Wang and Campbell, 2010). On the other hand, in terms of forecasting corporate bankruptcies, there are some other studies which have used logistic regression model as a standard to predict firm's failure. For example, logistic regression analysis has been utilized by Ohlson (1980) to predict company bankruptcy. His study has been adapted to United States companies to estimate and determine the probability of failure for each firm separately. He believes that the logistic regression model faces less criticism than the MDA approach. Lennox (1999) has studied the reasons of corporate failure prediction using the logistic regression approach on a sample of UK listed firms in the period 1987 to He shows that well specified logit and probit techniques may be best for distinguishing the 5

12 corporate failure prediction than the MDA approach. Furthermore, several research studies have recently shown that artificial intelligence - for instance, Neural Networks (NN) - could be used as an alternative technique in order to predict corporate failure (Sun and Li, 2008). Altman et al. (1977) take discriminate analysis and logit analysis to be the two techniques widely used as a model for predicting corporate bankruptcy. Meanwhile, there is a continuing increase in the utilisation of a discriminant analysis approach. In addition, Calandro (2007) believes that Altman s Z-score mostly used the discriminate analysis approach, as well as depending upon fundamental financial ratios analysis as an input. Despite a significant number of failure prediction models being available in the business field, researchers and the business community together predominantly depend on the models that were expanded by Altman (1968) and Ohlson (1980) (Boritz et al., 2007). Therefore, in the field of accounting and finance research, the failure prediction model developed by Altman (1968) has been commonly utilised. Moreover, academics and practitioners have both employed Altman's model widely as a measure of differentiation for later bankruptcy classification (Charitou et al., 2004). This section is organised as follows. Initially, it provides an overview of the meaning of business failure and non-failure. Thus, the reasons and the stages of the bankruptcy prediction approach will be discussed. In addition, the concept of Altman's Z-score failure prediction model will be described in this section. Thereafter, different problems will be discussed that related to the Altman Z-score failure prediction model. Here, the criticism faced by business failure prediction will be explained, as well as the alternative approaches to develop corporate failure prediction models. Eventually, this section will illustrate the existing empirical studies and previous research studies. 4.2 The concept of corporate failure Failure is a situation in which a business has to close down because of the inability to continue its work successfully. According to Argenti (1986), there are two types of failure. Firstly, economic failure, defined as firm's failure to achieve the return on capital 6

13 invested. Secondly, financial failure is a situation when a company faces financial insolvency. In these cases, a firm may be liquidated and this leads the firm to bankruptcy (MEEKS and MEEKS, 2009). However, the concept of business failure c00an be defined in different ways. Some examples of business failure are bankruptcy, bond defaults, bank loan defaults, insolvency, the delisting of a firm, liquidation and government interference through special financing (Altman and Narayanan, 2007). Under a broad definition, Wu (2010) has defined business failure as the circumstances in which a company cannot fulfill its obligations to lenders, preferred stock shareholders, suppliers or where a firm is bankrupt according to law. On the other hand, financial distress is a term that is utilised excessively in the financial studies available. Levratto (2013) defines it as whenever a company's liabilities exceed its book value of assets, predominantly it leads to financial distress. It is clear that an increase in fixed expenditures in a company, it might leads to a raise in the risk of financial distress (Johnsen and Melicher, 1994). Thus, the bankruptcy and insolvency are the two terms, which are used commonly in the literature. The bankruptcy process begins when the firms are incapable of paying back their obligations to banks, suppliers, tax authorities and employees. When aggregate liabilities of firm override the face value of the company's assets, this leads to bankruptcy, whereupon the assets are utilised to repay a portion of outstanding debt (McKee, 2003). In contrast, insolvency is a case in which the company is no longer able to meet its financial obligations when debts become payable. Insolvency happens when current assets are less than current liabilities (Ahn, 2001). 4.3 The reasons for corporate failure There are a significant number of reasons that lead to business failure. As a consequence, most of the studies have attempted to identify the causes of business failure. Levratto (2013) suggests that the conditions of the business in internal and external situations have a significant impact upon business failure. The internal factors include administrative 7

14 mistakes, loss of customers, location and difficulties faced with commercial credit. However, the external factors are comprised of increases in competition amongst companies, in the overheads the doing business and insurance. Moreover, there are some other factors that affect business failure. For instance, financial factors include a high proportion of debt, loss of capital and the difficulty of ensuring new capital. In addition, taxes can be the other factor that impacts on business failure, including problems with the tax authorities. Last but not least, natural disasters and accidents may also be amongst the reasons that lead to business failure (Bradley and Rubach, 2002). Many economists attribute the reason for the failure of the company as financial distress, which predominantly occur as a result of the inability or the inefficiency of administration and lack of experience. Furthermore, the phenomenon of shrinking profits and substantial debt burdens can be observed in the case of economic recession and high interest rates. In addition, the nature of operations and state rules as determinants of industry might contribute to a company's financial distress (Mbat and Eyo, 2013). It can be argued that there are many factors inside and outside to the company that could be responsible for corporate failure. 4.4 Failure phases There are several stages to be undergone by a company before announcing the failure of its commercial activity. According to Ooghe and De Prijcker (2008), the oldest and most prominent failure processes were explored by Argenti (1976). In terms of failure phases, Argenti indicates that there are three different failure processes experienced by the company, which begins with successful processes and ends with a case of insolvency. A defect is considered to be the first indication of failing firms, which may include skills shortages or personal mistakes, for example administrative weakness, such as an authoritarian executive director, and failures in accounting skills, such as budgetary monitors. Mistakes are the second trajectory of company failure explained by Argenti (1976). They happen with the passage of time as a consequence of the defects of the first phase of 8

15 failing companies; for instance, high leverage, the company's inability to continue or failure in large projects, and over-trading. Mistakes and other symptoms of dysfunction are considered to be the last stage that leads to fully visible causes of failure, such as creative accounting or deteriorating ratios. According to Laitinen (1993), generally the path of failure may vary from company to company according to its age (Bercovitz and Mitchell, 2007), or in reference to failure to its industry, (Thornill and Amit, 2003; Ooghe and De Prijcker, 2006) or to its size. Eventually, it can be seen that failure does not occur suddenly. On the contrary, it begins when the company is going through a bad situation and therefore getting worse even up to the conditions of failure. 4.5 Why is disclosure about the prospects for the corporate failures crucial? A disclosure probability study of the company s failure is important to both external and internal entities such as managers, investors, creditors, employers, government, customers and others. Business failure may cause considerable damages and enormous costs to the whole economy and society (Ahn, Cho & Kim, 2000). Ropega (2011) suggests that it is important to address the financial and non-financial symptoms that lead to the deteriorating financial situation of a company. The deteriorating conditions of the firm may lead to the following: a reduction in sales, profit and a decrease in liquidity (Ooghe & De Prijcker, 2006, Koksal & Arditi, 2004, Korol & Prusak, 2005; Bednarski, 2001; Argenti 1976; Sharma & Mahajan, 1980); a high level of debt (Koksal and Arditi, 2004, Korol & Prusak, 2005; Argenti, 1976); a decrease in market share (Crutzen & Van Caillie, 2007; Zelek, 2003); and excessive energy that exceeds the company s capacities (Ooghe & De Prijcker, 2006; Zelek, 2003; Bednarski, 2001). Moreover, there are two main reasons for detecting business failure. Firstly, access to the roots of failure through a study of it. It is obvious that, to prevent the collapse of a firm, it is important to correct and address the fundamental reasons that lead to business failure. Secondly, the combination of causes, consequences and symptoms may help us reach the origins of failure and address them opportunely (Ropega, 2011). 9

16 4.6 The most prominent models for predicting corporate failure Univariate Analysis (UA). Fitzpatrick s (1932) was possibly the oldest study to predict corporate failure. Thus, he is the first person to have analysed the financial ratio in order to distinguish between active and inactive companies. The Univariate Analysis (UA) model has been used in his study, which includes 13 financial ratios to identify failure. However, Patrick's model has not demonstrated a considerable association with failure (Bellovary et al., 2007). Fitzpatrick s work was subsequently followed by studies that carried out by William Beaver. Beaver (1966) was a pioneer of corporate failure prediction models, applying a univariate model on 30 financial ratios in order to classify corporations as solvent or bankrupt at that time. In the period , Beaver chose a sample of 79 listed failed firms, which tried to match every non-failed company with failed companies from the same industry and of the same size. Eventually, he illustrated the particular financial ratios that were crucial in predicting failure. Financial ratios can correctly recognise failure with a proportion of 78% for five years before bankruptcy ( 2015). In addition, Balcaen and Ooghe (2006) suggest that the main point of either criterion is contrasted in this predicting model. Thus, a company's value is to be compared at the cut-off point through each measure, and this is what based upon this model. Furthermore, this model is straightforward, being that it is easy to employ and does not demand statistical experience. On the other hand, this model also has some drawbacks because it mainly depends on the assumption of a linear correlation between all measures and the case of bankruptcy Multiple Discriminate Analyses (MDA) Altman (1968) extended Beaver s work in his study of corporate failure prediction models by employing the MDA model to the failure classification model ( 2015). Thus, in the 1970s and 1980s (Altman, 1968; Altman & Lavallee, 1981; 1982; Izan, 1984), it was stated that the discriminant analysis MDA technique was extensively used for corporate bankruptcy studies. As well, according to 10

17 Altman (2000), the MDA approach is considered to be a more familiar statistical mechanism, which was utilised to classify and to forecast corporate failure. In a study of Jo and Han (1996), Laitinen and Kankaanpaa (1999) have found that there are three phases to the MDA approach. The first phase is to predict the coefficient of the variations. The second stage is measure the discriminant of every situation in regard to the sample score and in the final phase the cases have be classified that rely on pieces in the result. Altman (1968) has drawn attention to the fact that the variables in the MDA approach provide considerable information. In contrast, the variables in the univariate method do not give much information. Moreover, it is clear that in the MDA model, whenever the discriminant score of the company decreases the probability of company's fail will increase, in contrast to the companies that have a high percentage of discriminant score; thus, its failure rate reduces (Balcaen and Ooghe, 2006). 4.7 Corporate failure prediction models in the UK The work of each of a number of researchers (Beaver, 1966; Altman, 1968; Wilcox, 1972; Ohlson, 1973; Deakin, 1980; Taffler, 1983; and Boritz et al, 2007), reflects significant utilisation of a developed country in their studies for predicting corporate failure; thus, the prediction of corporate failure has been well-researched as a case study in the UK by a variety of researchers. For example, Agrawal and Taffler (2009) have compared the performance between the Taffler Z-score model and the market-based model. Eventually, they found that the information that has been obtained from both models was unique information for predicting company bankruptcy. However, the forecasts about the Taffler s Z-score approach in the UK have been questioned by Charalambakis et al. (2009). Taffler's Z-score and the univariate hazard model was then compared with the hazard model by Shumway (2001), who points out that the first model was less precise than the second for predicting corporate failure. Moreover, Lennox (1999) used a logistic regression on a sample of listed firms in the UK for the period between 1987 and His study suggests that the logit and probit models 11

18 could be more accurate in identifying and predicting corporate bankruptcy than a discriminant analysis technique. It must therefore be recognised that the UK is one of the countries which has been widely used as a case study for company failure prediction. 4.8 Altman s Z-score (1968) model A study by Wu (2010) shows that Altman s Z-score model (1968) is the first, pioneering approach to use financial ratios to identify or predict company bankruptcy. Since that time, it has been considered that the evaluation and apply of financial ratios has become a vital component for failure prediction techniques. In addition, Edward Altman s Z-score model (1968) is commonly utilised to assess company insolvency. His model composed of five linear combinations of business ratios, which used a multivariate approach, MDA, in order to measure the business performance or competence of a firm. For instance, financial ratios can be calculated as a criterion of company performance; those involving profitability, liquidity, capital structure and efficiency (Altman, 1968). Moreover, Altman (1968) has drawn attention to the fact that the MDA approach has a marked preference compared to the traditional univariate ratio analysis. The first advantage is that the statistical MDA approach has the possibility of analysing an entire set of explanatory variables with their interaction in the same instant. The second advantage is that the MDA technique decreases the number of explanatory variables that are being considered. The Altman analysis is concerned with two categories of companies which active and inactive companies - and thus converts this analysis to its simplest form. Altman's study consists of 66 manufacturing companies with 33 bankrupt and 33 non bankrupt. Thus, his study consists of a list of 22 financial variables (ratios) which have been compiled for evaluation. However, only five financial variables (ratios) have been chosen from this list based on their capacity predictive for company bankrupt such as liquidity, profitability, leverage, solvency and activity. Altman s original Z-score model (1968) equation was: 12

19 Z= 0.012X X X X X5 Z= Cumulative Values Based upon Altman's formula, the firms were classified into three categories according to the company's sustainability. For instance, if the firm is in the distress area then there is a strong probability of failures when the Z-score index of the company is below 1.8. On the other hand, when the Z-score index exceeds 2.99, it is considered that the enterprise is in the safe zone, with a low percentage of company failure. Moreover, when the value of the Z-score index is greater than 1.80 and less than 2.99, there is no strong evidence to specify the financial condition of the company; that is, the results cannot precisely ascertain whether the company is in the safe or distressed zone (Altman, 1968). Z < 1.80 Distress Zone Z > 2.99 Safe Zone 1.8 < Z < 2.99 Grey Zone 4.9 Financial Ratios X1, Working Capital/Total Assets (WC/TA) The working capital/total assets ratio is one of the commonly found ratios in the research of firm issues. It is a measure of the net liquid assets of the corporate in comparison to the overall capitalisation. The differences between current liabilities and current assets are considered as working capital. Obviously, size and liquidity features should be taken into consideration. Generally speaking, current assets are found to be low in comparison to total assets, when a company undergoing consistent operations fails. This one is found to be the most valuable ratio amongst the evaluated three liquidity rations because the quick ratio and the current ratio were observed to be less hopeful (Altman, 1968) X2, Retained Earnings/Total Assets (RE/TA) The overall amount of reinvested losses or/and earning of a corporate during its whole life can be obtained by retaining earnings. This is also called earned surplus. It is worth 13

20 noting that an earned surplus account is subject to manipulation by stock dividend announcements. This measure, which is the cumulative earning over time, was earlier considered to be a new ratio. This ratio is found to be implicitly affected by the age of a company and an old company might have higher retained earnings/total assets ration than a young company. This is because the younger company has not had enough time to increase its cumulative profits. Therefore, this analysis is argued not to be appropriate for young companies because their chance of being classified as a failed company is higher compared to the chance of older company. It is reported that about 50% of the bankrupted companies in 1993 did so in their earlier years of existence. Moreover, the leverage of a company is also measured by this ratio. The companies with low TA compared to RE are reported to have not used as much debt and have depended on the retention of profits to finance their assets (Altman, 2000) X3, Earnings Before Interest and Taxes/Total Assets (EBIT/TA) The true productivity of a company s assets is measured by the EBIT/TA ratio without taking into consideration leverage or tax factors. This ratio is believed to be extremely appropriate for investigating firm bankruptcy because the ultimate existence of the company depends on earning power (Altman, 1968) X4, Equity/Book Value of Total Liabilities (MVE/TL) Liabilities is the measuring of both the long and current term, while equity is found to be the market value of all the shares of common, preferred and stock. This measure demonstrates how much the firm s assets might decline in value before the assets become lower than liabilities and the company becomes bankrupt. In other words, a firm with a market value of its debt of $500 and its equity of $1000 might experience a two third decrease in asset value prior to bankruptcy. Nevertheless, if assets decrease one third in value, the same company with $250 equity will failed. A market value, which is not considered in most of the insolvency investigations, is included in this ratio (Altman, 2000). 14

21 4.9.5 X5, Sales/Total Assets (S/TA) Ratio is the well-known ratio showing the sales generating efficiency of the company s assets. It is widely used for dealing with competitive situations. This ratio is considered to be the least considerable ratio on an individual basis. Consequently, it is found to be quite an important ratio. It should be noted that, depending on the univariate statistical significance test, this ratio would have disappeared. Nevertheless, it is ranked as the second most important ratio for contributing to the total discriminate ability of the model. This is because it has a unique and quite significant association to other variables in the model (Altman, 2000) Altman s revised Z -score (1968) model It is obvious that the original Altman Z-score (1968) model was utilized discriminant analysis as a first phase and depends upon on data for publicly held manufacturers companies. Subsequently, Z-score technique was extended by its author (Altman, 1983) to be used for other industrial sectors such as private manufacturing companies. Thus, revised Altman Z'-score (1983) was published as an exceptional model for those sectors. As a result of that, original Z-score formula was changed by Altman to replace book value of equity for market value in X4 in order to match them with different parameters. This leads to change in the classification standards and Z-score results. Finally, the revised Altman Z'-score formula is shown as follows: Z = 0.717X X X X X5 Where: X1= Working Capital/ Total Assets X2= Retained Earnings/ Total Assets X3= Earnings Before Interest and Taxes/ Total Assets X4= Market Value of Equity/ Book Value of Total Liabilities X5= Sales/ Total Assets, Altman (1983). 15

22 Z < 1.23 Distress Zone (High Risk of Bankrupt) 1.23 < Z < 2.9 Grey Zone (Uncertain Results) Z > 2.9 Safe Zone (Low Risk Area (Healthy) 4.11 Altman s revised Z score (1993) model After original Altman Z-score model was extended and access the revised Altman Z'- score model in In that year Altman was continued research and produced a further revised model that employed for predicting corporate failure. This model called Z''-score, which is utilised for other industrial sectors such as non-manufacturing companies and for emerging market companies. Moreover, in the Altman Z''-score model the variables X5 was excluded, sales/total assets, thus solely four ratios kept in this new model. Ultimately, the revised Altman Z''-score formula was presented as follows (Altman, 1993). Z= 6.56 (X1) (X2) (X3) (X4) The new Z-score model ratios are listed such as: X1= Working capital/total assets X2= Retained earnings/total assets X3= Earnings before interest and taxes /total assets and X4= Book value/total liabilities. Therefore, the cut-off scores are also adjusted so that index scores of Z''< 1.10 indicate bankrupt companies. However, index scores of Z''>2.60 are indicators of healthy companies. Moreover, companies with Z''-score index between 1.10 and 2.60 are determined to exist in the grey zone, Altman (1993). 16

23 4.12 Criticism According to Li (2012) there are significant number of studies that documented as an evidence of the effectiveness of Altman s Z-score in forecasting company bankruptcy and financial distress, for instance (Gerantonis, et. al (2009), Xu & Zhang (2009), Wang & Campbell (2010), Lugovskaya (2010) & Janakiram (2011), Al Zaabi (2011), Gutzeit & Yozzo (2011), Wang & Li & Rahgozar (2012)). However, Altman s model is not free from criticisms; there are numerous studies that have received criticism to this model. For example, Shumway (2001) develops a hazard technique and draws criticism against Altman' Z-score technique. Moreover, another study demonstrated by Campbell, Hilscher, and Siglagyi (2011) follows the same approach of reasoning as Shumway. However, Shumway is outperforming them as the first model that evaluated the weak performance of distressed shares. At the end, they are agreed unanimously to orientate blame to the Altman s paper with respect to the modeling and the ratios applied (Li, 2012). Some other criticism is provided against the ratios that employed by Altman, for instance according to the Hillegeist et al. (2004) and Gharghori et al. (2006), Altman Z-score model includes numerous measures of accounting variables which drawn from the financial and income statements. It might be the relied upon the financial statements do not provide predictive value for firm's future. Also it depends solely on one of the five variables as X4 = Market value of equity / Total liabilities, as an assumption to identify the company s failure. Furthermore, another drawback of Altman's Z-Score is its inability to include a measure of asset volatility. This volatility is one of the significant matters that measure the value of the company's assets to meet its obligations Hillegeist et al. (2004). In addition, Ingram and Grice (2001) believe that the Altman Z-score has the best performance in manufacturing firms than firms in other industries. Likewise, Begley et al. (1996) consider that Altman s Z-score model applies in more accurate for US firms for predicting corporate failure in certain periods than others. 17

24 4.13 Alternative Models Logit regression analysis Logit Analysis technique has recently been widely used in many areas of the social science for the modeling of discrete outcomes. It is reported that discrete choice theory was used for the developing of this technique. The theory of discrete choice describes the discrete behavioral responses of persons to the governments and business market actions when there are two or more potential incomes. Thereby, the theoretical foundations of this model are found to be based with microeconomic theory of customer character. After Lo (1986) had conducted study to recognize the superior technique between discriminant and logit analysis, he found that the two techniques are significantly related (Balcaen and Ooghe, 2006). As Balcaen and Ooghe (2006) indicate that failing corporates and nonfailing corporates are categorized in the logist analysis depended on their logit score as well a certain cutoff score for the technique. Then, the cutoff point and the logist score are compared; and the company will more likely fail, if the cutoff point is lower than the logit score. However, if the cutoff is higher than the score, the corporate is more likely to be non-fail Artificial neural networks Model (ANN) Artificial Neural Networks The idea behind the artificial neural networks is based on the newly understanding of the physiology of the nervous system. There are billions neuron cells in the human brain which interact to for processing information in humans. It is known that each neuron sends inhibitory or excitatory signals to other neurons. This technique is used to emulate the way human neurons work. Artificial Neural Networks solved many problems and is widely used in expert system, modeling, signal processing and forecasting. Generalization is the predicting method which is used by neural networks. This technique has been used in different fields and for solving complex issues. ANN has reported to be better than MDA analysis in the business environment especially in cases like stock price and bond price performance. Artificial neural networks have been used to many different fields and have illustrated its capacities in solving complex 18

25 problems (Yoon, Swales and Margavio, 1993; Yoa and Lui, 1997; Dutta, Shekhar and Wong, 1994) Empirical studies Altman and Narayanan (1997) considered that the studies about corporate failure prediction models are embedded not only in the US, because of it contains a number of large international companies, but also in several countries outside the US the companies were faced bankruptcy and knowing the global knowledge might assists obviate the repercussions or decrease the number of these failures. Likewise, according to Laitinen and Kankaanpaa (1999), there are a wide of studies on corporate failure prediction models on the world. For example, after financial crisis of 2008 many companies have entered the distress zone in different countries. In Greece there were many companies that have entered the distress area. As a result of that, the Altman Z-score model was examined by many researchers such as Gerantonis et al. (2009) examined the ability of Altman's Z-score model to predict failure before it occurs. The data have used on companies that registered on Athens Stock Exchange in the period between 2002 and The results of their study have proved the Altman Z-score accuracy to predict corporate failure at the following way, at the first year there was 66%, and this percentage decreased in to 52% at second year, 39% at third year and down to the 20% at fourth year prior bankruptcy. Furthermore, another study in Greece has been done by Diakomihalis (2012) in different kinds of hotels in order to investigate the accuracy of the Altman Z-score model. Each of Altman's Z-score models were used in his study in order evaluates the corporate bankruptcy. Eventually, the results demonstrated that accuracy of the revised Altman Z'-score was 88.2% percentage at first year prior failure. In addition, Alareeni and Branson (2012) investigated the failure prediction for Jordanian industrial companies in order to define the accuracy of Altman Z-score model before it occurs. The rate of identification accuracy of the Z-score was 73.40% at first year, at the second year 74.46% and at the third year 70.21%. Also the accuracy of Altman Z-score model was examined in China by Wang et al. (2010). utilising data from public 19

26 companies during in the period between In accordance with their results the accuracy of the revised Altman Z'-Score model was considerably high percentage compared with original Altman Z-Score model. As shown above, it can be argued that there are a variety of approaches that have been utilised for predicting corporate failure, because the business failure is very helpful for financial directors, investors, and other users of the financial statements and have a significant economic cost as well. Generally speaking, among all of these approaches the Altman's three versions (1968, 1983, and 1993) have been applied widely in various countries around the world in order to predict corporate bankruptcy. However, it must be admitted that these three models do not devoid of the criticism that faced by many researchers. 20

27 5. Methodology The aim of this chapter is to describe the revised Altman Z'-score model as a methodology for predicting corporate failure and discuss the issues following. At the beginning, a revised Altman Z'-score model will be described. Secondly, the data sources that are used in this research will be explained. Moreover, in this section the revised Atman Z'-score model's variables will also be classified. At the end of this section there will be an explanation of the methods of measurement of the selected samples. 5.1 Model and Methodology This research relies upon the most prominent models adopted in the field of detecting the probabilities of corporate bankruptcy in the future. Quantitative data methodology is utilised in this study; that is, applying the Altman Z -score models in the field of corporate failure prediction of the UK s private manufacturing firms. It has recently been argued that the majority of studies that have worked on predicting corporate failure have used US data in their thesis. However, in this thesis, the data of UK private manufacture of furniture is used for forecasting corporate failure because the UK is considered one of the most developed countries and the London Stock Exchange Market one of the main global financial markets. Moreover, the UK has an accounting policy that is different from US accounting policy. As a consequence, in this research the revised Altman Z'-score (1983) model has been utilised in an UK private manufacturing firm to verify its validity as a predictor of corporate failures. It is clear that the Altman's Z-score model has been used considerably in the US and an impressive number of research studies have proved the accuracy and reliability of this model in predicting corporate failure there. In addition, this model has been widely used in developed countries such as Malaysia, Japan, China and also several developed European countries. Altman (1983) states that the original Altman Z-Score model depends upon the Market Value of Equity (MVE) of the company as well as being applied solely to publicly traded 21

28 firms. However, the Altman Z-score has been revised repeatedly by its author (Altman, 1983; 2002), so that it can be applied not only to public companies, but it applies to private companies and service sector companies as well. The revised Altman Z -score (1983) model has been employed by significant number of researchers for predicting corporate failure, for instance (Pitrouva, 2012; Diakomihalis, 2012; Kumar et al, 2013; Chouhan, Chandra and Goswami, 2014) and other. In this research the revised Altman Z'- score (1983) approach was utilized as a methodology, which depends on the Multivariate Discriminant Analysis models with the use of a set of financial ratios. These financial ratios are statistically helpful to determine the corporates failure in the future. It is clear that the Altman is the first one who used the MDA approach to distinguish between the corporate failure issues. Therefore, several variables are prepared and collected in this analysis to determine the company's failure. This technique has been called Z'-score model, which is utilised especially for private manufacturing firms. Eventually, the Altman Z'-score (1983) formula is illustrated as follows: Z = 0.717X X X X X5 Z = Overall Index 5.2 Research Variables This research is based on two types of variables, given as follows: Dependent Variable The formula of the revised Altman Z'-score (1983), utilised as a criterion of company's failure, is considered to be the dependent variable Independent Variables The independent variables consist of a number of the financial ratios which applied by the revised Altman Z'-score (1983) model. This model includes five financial ratios illustrated in the figure below. 22

29 Figure (1): Financial ratios of Altman Z-score model The first ratio proclaimed by Altman (1968) is X1 which measures the liquidity ratio of the company. X2 is the second ratio that measures the cumulative profitability of the company. The third ratio is X3, which measures the productivity of the company while ignoring tax effects and interest. X4 is the fourth ratio identified by Altman (1968). This ratio fundamentally illustrates a company's insolvency. It indicates how much the company's assets can decrease before the company s liabilities exceed its assets. Finally, X5 is an activity ratio. This ratio is considered as a standard that shows the sales generating capability of the company's assets (Altman, 1968). 23

30 It is clear from figure (1) that each variable has a numerator and a denominator. For example, X1 is working capital divided by total assets. The numerator is working capital which is commonly calculated as current assets minus current liabilities. The working capital of the company is indicated as a measure of whether a company has sufficient short-run assets to meet its short-run debts. However, the denominator is total assets, and it is the common denominator of the most variables. It is defined as the total amount of current and long-run assets that held by a company. Generally, total assets are significantly important to identify the extent of the company's ability to use its assets efficiently (Chouhan, Chandra & Goswami, 2014). Secondly, X2 includes the retained earnings divided by total assets. Retained earnings are defined as the percentage of firm's net earnings that not paid as a dividend to shareholders. However, these net earnings are retained by the company to be utilised in payment of debts or to be re-invested in its essential businesses (Altman et al., 1995). Elliott et al. (2014) believe that, as a consequence of low or negative in retained earnings of the company, the accumulated losses appear each year in the company's balance sheet, which is called the accumulated losses of the company. Thus, the retained earnings can be calculated according to the following equation: Retained Earnings = Beginning Retained Earnings + Net Income Dividends Earnings before interest taxes divided by total assets is the X3 in the revised Altman Z -score formula. Earnings Before Interest Taxes (EBIT) is here the measure of a company's profitability. As well as EBIT, the operating profit and operating income of the company are indicated. It is usually measured as the difference between sales (revenue) and expenditures, after it is excluded from tax and interest. EBIT is usually rearranged and calculated as follows (Chouhan, Chandra & Goswami, 2014). Earnings Before Interest Taxes = Operating Income + Other Income 24

31 The Book Value of Equity (BVE) divided by the Book Value Total Liabilities (BVTL) is considered as an X4 in the revised Altman Z -score (1983) formula. The term book value of equity indicates the difference between total assets minus total liabilities. It is usually called shareholder funds or equity, which refers to the net worth of a business and can be found on a balance sheet in annual reports. It can also be calculated as a preferred and common share capital, adding to it retained earnings and reducing from it treasury shares (Altman et al, 1995). However, the denominator of the X4 is Book Value of Total Liabilities (BVTL). Altman et al. (1995) state that the total liabilities are the aggregate of overall debts for which a company is responsible. Thus, it can be calculated as Total Liabilities = short term Liabilities + long term liabilities. Moreover, they mentioned that the total assets on the balance sheet in the annual report must be equal to the sum of all the total liabilities and equity. The last ratio of the Altman Z -score is X5, which consists of sales divided by the total assets (S/TA). Hayes et al. (2010) indicate that sales are an aggregate amount of money that a company actually earns during a particular period. According to the revised Altman Z'-score (1983) model, we can identify its various zones as follows: Figure (2): Value of the revised Altman Z -score model (1983). 25

32 It is clear that Figure (2) illustrates the various zones according to the company's sustainability. Based upon the revised Altman Z'-score (1983) model, the company has been classified into three categories. For example, if the firm is in the distress area there is a high probability of failure when the Z'-score index of the firm is below the proportion of In contrast, if the firm is in the low risk area, which is called the safe zone, then it is considered that the enterprise is in the safe zone when the Z -score index exceed the proportion of However, when the Z'-score is greater than 1.23 and less than 2.99 this leads to uncertain results being received and it s difficult to know exactly whether the company is in the safe or distress zone. 5.3 Proposed methods for collecting data It is clear that the data collection term is used to describe a method of arranging and collecting information. Generally, there are several methods of classifying data. However the most common ways to collect data is through a division between primary and secondary data. The term primary data refers to the data that collected by the researcher himself or herself for a particular purpose. On the other hand, secondary data can be defined as information collected from another party and it is used by the researchers for different purposes (Long-Sutehall et al. 2010). However, the secondary data of private manufacturing furniture companies in the U K is used in this research, which was prepared and collected from the company s financial statement in the annual report. Thus, it can be argued that the secondary data is sufficient for this thesis. As it is mentioned above that secondary data was used in this study. This data was prepared and collected through the FAME database, which is accessible in the library of Leicester University. In general, FAME is a financial information database that contains inclusive accounting and non-accounting data. It also contains a financial information database of more than 7 million firms in the UK and Ireland with up to 10 years of data. The FAME database helps users to search through a wide range of standards. For example, name, industry, size, geographical area, code and many others. As a consequence of the advantages provided by the FAME database, it is considered to be the 26

33 main source for the preparation and collection of information for companies in the UK and Ireland. After preparing and collecting information through the FAME database, five financial ratios were selected to analyse the companies failure, which is considered as a criterion to identify the failure for two years prior the distressed period. These five financial ratios each include a liquidity ratio, a profitability ratio, a solvency ratio, a leverage ratio and an activity ratio, which are prepared and collected from the balance sheet and income statement in the company's annual report. Moreover, using the FAME database, the companies can be classified into two categories such as active and non-active companies. In addition, the non-active companies are divided into two parts: a dissolved firm and an in-liquidation firm. Additionally, the companies that are registered as in-liquidation amongst the UK companies are considered as unsuccessful companies. Finally, it should be state that, in this research, the failed and non-failed companies have been selected are those during the period Sample This study focuses on 12 financially active private manufacturing companies and 12 financially inactive private manufacturing companies in the UK. The entire samples have been chosen and tested in the period of the 12 last years from After studying and analysing these companies through the FAME database, information was collected about these companies and the desired financial ratios were selected. This procedure has facilitated the process of classifying and identifying the UK private manufacturing companies in terms of being either a failed and healthy company. In this research study, the active and inactive companies were matched by the size and industry. From the period 2002 to 2014 all of the inactive companies selected and analysed was furniture manufacturing companies. Furniture manufacturing companies have been chosen because of their importance to society and the economy. In addition, this sector was selected because of its high proportion of assets held by the companies. 27

34 It is clear that the Altman was the first to use this model in the In his study he tried to select companies the size of whose assets was more than one million dollars. For this reason, all companies identified in this research were had assets exceeding one million pounds. Therefore, once the company's names had been identified, the data was collected about these companies from the balance sheet and income statement in the annual report of each. Finally, all the successful and unsuccessful companies in this study are represented in table one and two, respectively. Table1. Inactive Firms 28

35 Table2. Active Firms 5.5 Statistical analysis In this study, the data was collated and analysed with the help of the Microsoft Excel computer package. Thus, each of the variables in the revised Altman Z -score formula was calculated by this software. Moreover, Microsoft Excel was utilised to acquire the financial distress levels of private furniture manufacturing companies in the UK. There are various distress zones: the distress zone, the gray zone and the safe zone. Finally, the average of the revised Altman Z -score variables was calculated by Microsoft Excel. 29

36 6. Results and data analysis The purpose of this chapter is to interpret the results of the financial data obtained from 12 failed and 12 non-failed private furniture manufacturing companies in the UK, which were analysed using the revised Altman Z -score technique. The average ratios from two years were calculated for each failed and successful company, which are given in Appendixes 1 and 2, respectively. Furthermore, the Altman Z -scores for both years for the failed companies were measured and are presented in Appendix 4. The Altmans Z -scores for both years for the healthy companies were calculated and are illustrated in Appendix 5. In addition, the Altman Z -score of each successful and unsuccessful company was analysed and compared with the literature, thus all Altman Z'-score variables X1, X2, X3, X4 and X5 were compared and the results of these five variables will be discussed below. As a consequence of numerous factors that influence a company s financial situation and performance, each company was analysed separately. Table3: Failed companies data 30

37 6.1 Data analysis of the failed firms (12 private furniture manufacturing companies) Ahmarra Limited The registration of this firm was announced on 24/03/1992 in the UK and after eleven years it was dissolved in Its last financial statement on 30/06/2003 shows that working capital; turnover and total assets and liabilities were slightly decreased in 2002 relative to However, there was an increase in retained earnings, shareholders funds and operating profit: -59,469, -264,475and 5,525 in 2002 to 1,271, 66,265 and 224,211 in 2003, respectively. This led to an increase in the company's Altman Z -score index from in 2002 to in 2003, which indicates that it was in the distress zone in the second year and the gray zone in the first year. Thus, it is difficult to know exactly whether the company will go bankrupt or not Traesko 12 Limited This company was registered in the UK on 15/08/1997 and after six years it was dissolved. In the last financial report, working capital, turnover and shareholders funds decreased from 405, 10,592 and 698 in 2002 to 112, 8,821 and 419 in 2003, respectively. On the other hand, there was an increase in total assets and liabilities in 2003 compared to Moreover, there was a negative value in operating profit and profit before interest paid, which led to a decrease in the Altman Z -score index from in 2002 to in 2003, as shown in Appendix 3. Thus, the firm was in the distress area in these two years and the likelihood of its failure is very high ABS Realisations Limited This firm was incorporated on 24/10/1930 and dissolved in Records show that there was a considerable decrease in total assets and turnover in 2005 compared to 2004: 3,835,151 and 2,914,032 in 2005 and 4,042,079 and 4,040,908 in 2004, respectively. Moreover, there were extremely negative percentages for net current assets, retaining of earnings and profit before interest paid. As a result, the Altman Z -score index value was in the distress area in the first and second years: in 2004 and in 2005, as 31

38 shown in Appendix 3. Thus there is a very high possibility of the occurrence of financial catastrophe for this company in the next year Trafalgar Office Furniture Limited This company was incorporated on 13/02/2003 in the UK but it announced its liquidation after three years. Its last accounts date was 31/12/2006. Although there was a slight rise in sales from 756,232 in 2005 to 794,162 in 2006, the profit loss account and shareholders funds decreased considerably in 2006 compared to Therefore, as shown in Appendix 3, the Altman Z -score decreased from in 2005 to in Thus, the possibility of the company s failure is expected to be high in the next year, due to it having been in the distress zone in both years Adeptias Limited The liquidation of this company was announced after three years of its registration. It was registered on 4/1/2004 and was successful until Despite increases in the firm s turnover sales and total assets in 2007 relative to 2006, from 521,000 to 105,779 and 201,672 to 130,756, respectively, its working capital declined significantly in 2007 compared to The company became highly negative in its shareholders equity, retaining of earnings and profit loss before interest paid. As a result, the value of the Altman Z -score index declined from in 2006 to in 2007, thus there is a very high possibility of the occurrence of bankruptcy for this company FFC (UK) Limited Reports indicate that this company was dissolved eight years after its incorporation. It was registered in the UK on 6/7/1999 and was effective until 30/6/2007. According to the company s financial statements, its total assets and turnover sales increased slightly from 5,354,003 and 3,896,583 in 2006 to 5,568,474 and 4,218,555 in 2007, respectively. On the other hand, there was a noticeable decline in working capital, shareholders funds and operating profit between 2006 and 2007, from -232,526, -62,616 and 74,189 to -382,350, -176,513 and -6,379, respectively. Thus, the Altman Z -score was reduced 32

39 from in 2006 to in 2007, demonstrating that the firm is in the distress zone and bankruptcy is predicted Inter Alia Limited The Inter Alia Limited firm was established in the UK on 16/4/1991. However, after 17 years it was dissolved and its latest account date was 31/3/2008. The company s turnover sales, total assets and liabilities decreased from 736,476, 607,999 and 397,262 in 2007 to 608,634, 482,729 and 253,323 in 2008, respectively. Conversely, it was shown that the net current assets, retained earnings and shareholders funds increased from 126,544, 101,223 and 210,737 in 2007 to 134,948, 119,729 and 229,497 in 2008, respectively. However, contrary to expectations, the Altman Z -score index was in the gray zone in 2007 and 2008, thus it is difficult to know exactly whether the company will go bankrupt or not Black Arrow (2009) Limited The insolvency of this company was announced 27 years after its registration in the UK. It was founded on 16/11/1983 and its last account date was According to financial reports there were dramatic reductions in turnover, from 6,874,555 in 2008 to 2,016,504 in 2009, and total assets, from 1,321,377 in 2008 to 310,040 in In addition, there was a slight reduction in total liabilities in 2009 compared to Furthermore, there was a high negative value of working capital, shareholders funds and operating profit in the first and second years, leading to a decrease in the company's Altman Z -score index, which was in 2008 and in Thus, the company was in the distress area and there is a high probability of its bankruptcy Full Circle Future Limited The insolvency of this firm occurred five years after its registration in Its last account date was in The company s sales turnover, total assets and liabilities decreased from 594,900, 486,100 and 473,300 in 2008 to 577,800, 464,900 and 443,800 in 2009, respectively. On the other hand, there were rises in earnings before interest paid and operating profit in 2009 compared to The Altman Z -score also 33

40 increased slightly from in 2008 to in According to its Altman Z -score index, the company is in the gray area, which indicates that it is not clear whether failure is impending Sngl Realisations (2011) Limited This company was established on 4/2/1970 in the UK. The liquidation of this company was announced in Its financial statement shows that working capital, total assets and turnover decreased between 2009 and Conversely, retained earnings and profit before interest paid increased from 9,052 and -4,021 in 2009 to 9,811 and 486 in As shown in Appendix 3, despite an increase in the Altman Z -score index from in 2009 to in 2010, there is still a high probability of bankruptcy for this company Vitrathene Ltd The insolvency of this company was announced in its financial report seven years after its founding. It was founded on 24/03/2003 and was in operation until The firm s turnover sales and net current assets were found to have significantly increased in 2010 compared to 2009: 1,876,799 and 572,192 in 2010 from 1,384,733 and 170,569 in 2009, respectively. However, return earning, book value of equity and operating profits were highly negative. Therefore, the company s Altman Z -score index in 2009 and 2010 was in the distress area, which indicates that there is a high likelihood that the company will fail soon Sector Holdings Limited This firm was incorporated on 15/4/2004 and after eight years of business it announced its liquidation in The company s financial report shows that its turnover, total assets and total liabilities decreased significantly from 6,208,485, 4,255,046 and 2,474,550 in 2011 to 3,991,168, 3,232,684 and 1,656,983 in 2012, respectively. Moreover, shareholders funds and operation profit were also reduced in 2012 compared to As consequence, the Altman Z -score index of the company was in the risk area, which indicates a high probability of bankruptcy. 34

41 Table4: Non-failed companies data 6.2 Data analysis of the non-failed firms (12 private furniture manufacturing companies) Impey Showers Ltd The registration of this company was on 29/3/1999 in the UK. From its latest financial report, although there were slight decreases in total assets and turnover in 2013 compared to 2012, both remained high. Furthermore, there was a reduction in working capital and retained earnings from 3,801 and 5,718 in 2012 to 1,687 and 3,558 in 2013, respectively. In addition, profits before interest paid declined from 1,554 in 2012 to 1,446 in As a consequence, the company s Altman Z -score index was reduced from in 2012 to in However, this firm is still in the low risk area, which indicates that there is a low probability of bankruptcy. 35

42 6.2.2 Ultra Furniture Limited The incorporation of this company was on 04/09/1986 in the UK. The working capital, retained earnings and shareholders funds increased from 346, 752 and 752 in 2012 to 475, 861 and 862 in 2013, respectively. Moreover, there was growth in total assets and liabilities from 4,382 and 3,630 in 2012 to 4,567 and 3,705 in However, there were a reduction in turnover and operating profits between 2012 and 2013, from 20,006 and 586 to 19,402 and 194, respectively. Consequently, the company s Altman Z -score index decreased from in 2012 to in However, the company is still in the safe zone and the company will remain active Nestledown Beds Limited The company s latest financial statement shows that annual turnover, working capital, total assets and liabilities remained stable in 2012 and There was a slight decrease in retained earnings, shareholders equity and profit before interest paid: -3,664, 1,336 and 125 in 2012 and -4,042, 957 and -365 in 2013, respectively. Similarly, the Altman Z -score index changed slightly from in 2012 to in These results show that the company was in the safe zone in the first year, however in the gray zone in the second year, which indicates that it is difficult to know exactly whether the company will fail or not Buoyant Upholstery Limited This firm was incorporated in the UK in Its financial reports show that there were considerable increases in turnover and working capital, from 44,211 and 10,705 in 2013 to 50,230 and 13,028 in 2014, respectively. This led to increases in retained earnings and profit before interest paid, from 9,235 and 4,218 in 2013 to 12,620 and 4,666 in 2014, respectively. Therefore, its Altman Z -score index increased from in 2013 to in 2014, which shows that the company is in the safe zone and the possibility of bankruptcy is very low Dura Beds Ltd This company s performance was shown to be very encouraging over the last two years. According to its financial report, there have been marked increases in the company s 36

43 turnover sales, working capital and total assets, from 7,872,852, 314,160 and 2,235,231 in 2013 to 8,130,225, 548,156 and 2,590,025 in 2014, respectively. Moreover, shareholders funds and returned earnings increased from 712,632 in 2013 to 857,250 in 2014 and 712,532 in 2013 to 857,241 in 2014, respectively. Thus, as shown in Appendix 3, the Altman Z -score was in high proportion, which shows that that the company will be active in the next year Khyber Upholstery Limited It is clear from the company s financial statement that there has been a decrease in turnover, working capital and shareholders equity, from 638,818, -40,974 and 4,377 in 2013 to 571,450, -60,845 and -878 in 2014, respectively. However, there were slight increases in total assets and operating profit in 2014 compared to As a consequence, the Altman Z -score decreased from in 2013 to in 2014, however the company is still in the safe zone and the probability of failure is very low European Door Concepts Limited The incorporation of this company was on 25/5/1990 in the UK. Although its turnover, working capital and total assets decreased slightly from 1,486,863, 1,674,572 and 2,270,437 in 2013 to 1,480,147, 1,520,286 and 1,965,664 in 2014, respectively, they remained significantly high. Additionally, there was a small reduction in shareholders equity, retained earnings and operating profit in 2014 compared to Consequently, there was a small change in the Altman Z -score index from in 2013 to in 2014, which means the company remains in the safety zone and the chance of failure is very low Moffett And Sons Limited The company was founded on 20/03/1946 in the UK. Its latest financial statement shows that its turnover sales, working capital and total assets decreased slightly in 2013 compared to Furthermore, shareholders funds and retained earnings remained almost the same, with a slight decline in 2014: 3,645,403 and 3,595,403 in 2013 and 3,553,020 and 3,503,020 in 2014, respectively. As shown in Appendix 4, there was a 37

44 small decrease in the Altman Z -score index from 2013 relative to However, the possibility of the company s failure is considerably low because of its position in the safety zone Flair Flooring Supplies Limited The company s financial statement shows that despite a slight decline in working capital, shareholders equity and operating profit from 3,092, 3,943 and 502 in 2013 to 3,047, 3,792 and 136 in 2014, respectively, its turnover, total assets and total assets increased considerably in 2014 compared to The company s Altman Z -score slightly declined from in 2013 to in However, the company s likelihood of bankruptcy is mostly low due to its remaining in the safety zone F.C. Brown (Steel Equipment) Limited The company s financial statement shows that despite increases in turnover and total assets in 2014 from 2013, its working capital, shareholders funds and retained earnings decreased slightly from 9,947, 35,032 and 33,435 in 2013 to 8,778, 33,629 and 32,092 in 2014, respectively. Consequently, as shown in Appendix 4, the Altman Z - score decreased in 2014 from Nevertheless, the company is in an active area and its probability of future failure is low Niko (INT) Limited The company s turnover sales, total assets and retained earnings significantly reduced from 22,683,000, 5,881,000 and 998,000 in 2013 to 8,969,963, 5,045,899 and 209,976 in 2014, respectively. In addition, there was a decrease in working capital, operating profit and shareholders equity from 473,000, 678,000 and 998,000 in 2013 to -1,429,729, -1,541,393 and -209,826 in 2014, respectively. As a result, the company's Altman Z -score dramatically declined from in 2013 to in 2013, which indicates that the company is in the distress zone in the second year and there is a possibility of bankruptcy in the near future. 38

45 Mobili Office Limited This firm was established on 23/8/1996 in the UK. Its turnover decreased from 9,895,830 in 2013 to 9,144,874 in However, there were significant increases in working capital, total assets and retained earnings, from 1,311,691, 4,312,673 and 2,356,418 in 2013 to 1,767,946, 4,528,641 and 2,781,448 in 2014, respectively. Moreover, shareholders equity increased from 2,388,421 in 2013 to 2,813,451 in As shown in Appendix 4, the company s Altman Z -score changed slightly from to in 2014, which indicates that the company is financially stable and its possibility of failure is extremely low. 39

46 7. Discussion It is clear that the main purpose of the majority of the studies that are concerned with failure prediction models is to identify the accuracy and validity of the models in order to discover failure (for example: Beaver (1966); Altman (1968, 1983, 1993); Deakin (1972); Ohlson (1980); Lennox (1999); and others). All researchers used different models in their analyses. Altman (2013) states that, as a consequence of the enormous financial and nonfinancial burdens faced by companies every year, it is crucial to create an accurate model for predicting bankruptcy before it occurs. Moreover, identifying an accurate model for predicting corporate failure is helpful for directors, shareholders, investors and employers. In addition, corporate failure leads to financial catastrophe not only for a company s owners but also for the economy and community as a whole. As a result, there have been several studies conducted by a variety of researchers, academics and professionals concerned with corporate failure that have utilised different types of techniques in order to identify corporate failure (Ahn et al., 2000). Generally, it can be argued that the Altman Z-score model is one of the most accurate models for predicting corporate failure, due to its widespread use. Although the Altman Z-score model (1968) was solely utilised for public and manufacturing firms, it has been applied by numerous researchers to many types of industries. Later, the Altman Z-score model was modified so that it could be applied to other types of companies. For instance, the revised Altman Z -score model (1983) specialises in private manufacturing firms, while the revised Altman Z -score model (1993) can be used for non-manufacturing firms. This study has considered that the best way for determining correct and incorrect classification is the method that evaluates the Altman Z -score model and its ability to predict and classify. There are two types of misses: type 1 errors and type 2 errors in correct classification. First, when a firm goes bankrupt or is expected to fail but does not fail, this is called a type 1 error. On the other hand, when a company that is successful is expected to fail, this is considered a type 2 error (Alareeni and Branson, 2012). In this 40

47 study, Appendixes 5 and 6 shows that the predictive capability of Altman's Z -score was markedly high due to its accuracy rate (66.7%) for failed firms at the first year before failure. However, at two years prior to bankruptcy, its accuracy was 83.3%. Altman s Z -score accuracy for non-failed firms at year one was 83.3% and at year two was 91.7%, as illustrated in Appendixes 5 and 6. It is worth mentioning that a significant number of research have been completed to identify the accuracy and validity of the Altman Z-score model in order to discover the failure one and two year prior it s occurred. For example, Gerantonis et al. (2009) examined the accuracy of Altman s Z-score model and they found that its accuracy at year one before bankruptcy was 66% and at year two before bankruptcy was 52%. Alareeni and Branson (2012) found that the accuracy of Altman s Z-score model was 73.40% at one year and 74.46% at two years prior to failure. In addition, in 1983, Altman applied the approach to private manufacturing firms and he found that the accuracy of this model was 93% one year before failure and 73% two years before to failure. Based upon the revised Altman Z -score model (1983), there are three different zones for a company s sustainability. Firstly, when the Altman Z -score index of the firm is below 1.23, the firm is in the distress area and there is a high possibility of failure. Secondly, when the Altman Z -score index exceeds 2.99, the enterprise is in the safe area and the possibility of failure is very low. When the Altman Z-score is greater than 1.23 and less than 2.99 this leads to uncertain results. Therefore, it is difficult to know exactly whether the company is in the safe or distress zone. In this research the results demonstrated that the Altman Z -score index of 10 failed firms among twelve at the first year was below 1.23, thus the firms were in the distress area and there is a high probability of their failure. Furthermore, there were two companies Inter Alia Limited and Full Circle Future Limited that were found to be in the grey zone. This indicates that the correct classification in the first year was 83.3% and the error was 16.7%. This shows that the Altman Z -score accuracy was 83.3% in the first year, as shown in Appendix 5. In contrast, there were eight firms among 12 that were located within the distress area in the 41

48 second year. This indicates that the correct classification was 66.7% and the error was 33.3%, as presented in Appendix 6. According to these results, it can be argued that the revised Altman Z -score model is considerably accurate for predicting corporate failure a year or two before it occurs. In terms of non-failed firms, the Altman Z -score index of 11 firms among 12 exceeded 2.9 and this indicates that the possibility of failure is very low. However, Buoyant Upholstery Limited was in the grey area. There were 10 firms among 12 that exceeded 2.9 in the second year; Nestledown Beds Limited and Niko (INT) Limited were in the grey and distress zones, respectively. This indicates that there was 91.7% correct classification and 8.3% error; however, in the second year, the correct classification was 83.3% and error was 16.7%. In other words, the Altman Z -score accuracy was 91.7% and 83.3% in the first and second years, respectively. The results show that the revised Altman Z -score model (1983) could be utilised as a measure for predicting UK private furniture manufacturing companies failure and for differentiating between active and inactive firms. 8. Conclusions In conclusion, it is worth mentioning that failure is a common phenomenon that may be encountered by small and large companies in different economies, both developed and developing. Therefore, a country s economy and society as a whole may face considerable damages and enormous costs as a result of the bankruptcy of its companies and financial organisations. As a consequence, predicting business failure is a crucial topic that has gained the attention of many researchers, academics and professionals who 42

49 have long been interested in corporate failure. There are many reasons, both direct and indirect, that lead a company to failure. The direct reasons are under the control of the company s directors and reflect their weaknesses and inefficiencies. However, indirect reasons are outside the control of a company s management and they reflect the surrounding environmental conditions. The primary objective of this research was to examine the accuracy of the revised Altman Z -score model in predicting corporate bankruptcy by applying data from 22 UK private furniture manufacturing firms in the period through using 12 failed and 12 non-failed firms. The results of this research showed that applying the revised Altman Z -score model (1983) was convincing in reporting the probability of failure of the UK private companies. As shown in Appendixes 5 and 6, the accuracy of the Altman Z -score for inactive companies was found to be 83.3% and 66.7% in years one and two, respectively, before failure. However, the accuracy of the Altman Z -score for non-failed firms was found to be 91.7% one year prior to bankruptcy and 81.3% two years prior to failure. Furthermore, the results demonstrated that, despite the value of the Altman Z -score index showing the financial condition of the companies, Altman s five variables have considerable impact on identifying the corporation as a failed or non-failed company. Ultimately, according to the results, it can be argued that the revised Altman Z -score model (1983) can be considered a highly accurate and reliable model and can be utilised for predicting corporate failure. 9. Limitations and recommendations There were a number of restrictions in this study: only one technique for corporate failure prediction was used and the data collected concerning the 12 failed firms was obtainable only from companies financial statements. Moreover, the size of samples utilised in this study was small. In addition, the study only focused on one type of industry and two years before failure to analyse the predictive capability of the revised Altman Z -score model. In terms of recommendations, it is clear that a greater sample size increases the 43

50 accuracy ratio. Furthermore, it can be argued that using more than one model for predicting failure may increase the opportunity to determine the best and most accurate model. Finally, more studies and attempts are proposed that should be carried out to expand the Altman Z-score model and discover a new technique for predicting corporate failure. 10. Bibliography Ahn, B., Cho, S. and Kim, C. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18(2), pp Ahn, C. Y., (2001). Financial and corporate sector restricting in South Korea: accomplishments and unfinished agenda. Japanese Economic Review, 52(4),

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55 MEEKS, G. and MEEKS, J. (2009). Self-Fulfilling Prophecies of Failure: The Endogenous Balance Sheets of Distressed Companies. Abacus, 45(1), pp Mohammed, A. & Kim-Soon, N. (2012). Using Altman's model and current ratio to assess the financial status of companies quoted in the Malaysia Stock Exchange. International Journal of Scientific and Research Publications, 2(7), Neophytou, E. and Molinero, C. (2004). Predicting Corporate Failure in the UK: A Multidimensional Scaling Approach. J Bus Fin & Acc, 31(5-6), pp Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, Ooghe H. & De Prijcker S. (2006). Failure process and causes of company bankruptcy: a typology,working Paper, Universitet Gent. Ooghe, H. and De Prijcker, S. (2008). Failure processes and causes of company bankruptcy: a typology. Management Decision, 46(2), pp Pitrouva, K. (2012). Possibilities of the Altman zeta model application to Czech firms. Ekonomika A Management, 3, Sharma, S. and Mahajan, V. (1980). Early Warning Indicators of Business Failure. Journal of Marketing, 44(4), p.80. Sun, J. and Li, H. (2008). Data mining method for listed companies financial distress prediction.knowledge-based Systems, 21(1), pp.1-5. Taffler, R. (1984). Empirical models for the monitoring of UK corporations. Journal of Banking & Finance, 8(2), pp Wang, Y., & Campbell, M. (2010). Financial ratios and the prediction of bankruptcy: the Ohlson model applied to Chinese publicly traded companies.the Journal of Organizational Leadership and Business, Wu, W. (2010). Beyond business failure prediction. Expert Systems with Applications, 37(3), pp Yao, X. and Liu, Y. (1997). A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Netw., 8(3), pp Yoon, Y., Swales, G. and Margavio, T. (1993). A Comparison of Discriminant Analysis versus Artificial Neural Networks. J Oper Res Soc, 44(1), pp

56 Youn, H. and Gu, Z. (2010). Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model. International Journal of Hospitality Management, 29(1), pp Zelek, A. (2003). Zarządzanie kryzysem w przedsiębiorstwie perspektywa strategiczna. Warszawa: Instytut Organizacji i Zarządzania w Przemyśle ORGMASZ. 11. Appendix Appendix1: The five financial ratios average of each of 15 failed companies in one year and two years before bankrupt. 50

57 51

58 Appendix2: The five financial ratios average of each of 12 non-failed companies in one year and two years before bankrupt. 52

59 Appendix3: Revised Altman Z -Score for the 12 Failed Companies. Appendix4: Revised Altman Z -Score for the 12 Non-failed Companies. 53