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    Prediction of Bankruptcy and Financial Risks:

    Using the Z-Score Model for

    AEX- and AMX- Investment Decisions

    Bachelor Thesis Economics and Business, Finance Track

    Finance group

    Faculty of Economics and Business

    University of Amsterdam

    Date: July 2010

    Name student: Maur its Kruithof

    UvA student number: 5603404

    Supervisor: Dr. Zacharias Sautner

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    Contents

    1. Introduction ..32. Literature Study.. ..4

    2.1. First Studies Related to Prediction of Corporate Bankruptcy... ..4

    2.2. Discriminant Analysis and Financial Ratios Theory...42.2.1. Beavers Univariate Discriminant Analysis (1966)....42.2.2. Altmans Multivariate Discriminant Analysis (1968)... ..52.2.3. Developments After Beavers and Altmans Work....62.2.4. Ohlsons Probabilistic Approach (1980)... ..72.2.5. Financial Ratios versus Structural Approach Theory.... ..8

    2.3. The Z-Score Model ..82.3.1. Component Ratios of the Model..8

    2.4. Practical Use of the Z-Score Model.. ..92.4.1. Z-Score Model Accuracy and Type I and II Errors... ..92.4.2. Linking Z-Scores with Bond Ratings 10

    3. Model & Data.113.1. Z-Score Model and Research Sample 113.2. Dataset AEX- and AMX-listed Firms123.3. Z-Score Trends of AEX- and AMX-indices..12

    4. Analysis..154.1. AEX- and AMX-sectors Analysis... 154.2. Investment Decisions.. 17

    4.2.1. High Z-Scores174.2.2. Low Z-Scores18

    4.2.3. Z-Scores and S&P Bond Ratings...195. Conclusion..206. References.. 21

    Appendices

    Appendix 1 AEX-index Dataset. 23Appendix 2 AMX-index Dataset... 27Appendix 3 Sector-overview AEX- and AMX-index (Z-Scores) 31

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    1. I ntroduction

    The bankruptcy of Lehman Brothers in September 2008 was for many people the beginning of

    uncertain times. The world was facing a financial crisis, which was followed by a recession.

    Bankruptcies and financial distress lie in wait for many businesses. What impact does the

    recession have on the financial health of companies? Are the reserves big enough to deal with

    liquidity problems and decreasing sales and profits?

    In this thesis, all the non-financial companies of the AEX- and AMX-indices will be checked on

    their financial health by using the Z-Score Model. This model was developed by Edward

    Altman in 1968. It was used to predict corporate defaults, but it is also useful as a tool to

    recognize financial difficulties. The financial companies are excluded because this model is not

    suitable for banks, insurance companies and real estate investment companies.

    In chapter two, after the introduction, this model will be explained and compared with other

    models in a literature study. Is it really true that one can predict corporate defaults (Altman,

    1968; Beaver, 1966; Wilcox, 1971)? If so, would it not be a self-fulfilling prophecy, because no

    one trusts a firm that is likely to go bankrupt? What kind of information is valuable to predict

    default? And, are the models and outcomes reliable or accurate?

    During uncertain times like these investors have nothing to go by. Calculating the Z-Score of a

    company may help in the process of investing. Chapter three describes the research sample and

    data that are used for the calculations. The research in this thesis shows that the Z-Scores of

    2008 and 2009 are the lowest of the six-year period 2004-2009. Most ratios used from the

    balance sheets have been decreased substantially.

    The differences between sectors are analyzed in chapter four. The companies are divided in sixmain sectors, namely industrials, materials, IT & telecom, food & beverage, consumer

    discretionary and energy. The Z-Scores are demonstrating similarities between the industrials

    and materials sectors with almost equal movements. These sectors are the only two sectors with

    a recovery in 2009, where the others did not recover at all. This chapter ends with an analysis of

    high and low Z-Scores , and what investors could do with this information.

    As is usual, this thesis will be completed with discussion and concluding remarks in chapter five.

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    2. Literature Study

    In the academic literature many articles and books have been published on the prediction of

    bankruptcy. Most of this work is not solely about the prediction of bankruptcy; it also covers the

    detection of financial difficulties and other financial risks (Altman, 2006). Investors, regulators,

    security analysts, lawyers, managers, consultants and many more need this information for their

    judgments.

    2.1. First Studies Related to Prediction of Corporate Bankruptcy

    Winakor and Smith (1935) concluded that failing firms exhibit significantly different ratio

    measurements than continuing companies. Thirty years later in the late 1960s the knowledge,methods and theories of the prediction of bankruptcy began to develop rapidly.

    According to Altman (1968), literature in that time suggested that a gap has arisen between

    theory (more rigorous statistical techniques with sophisticated models) and practice

    (traditional ratio analysis with data from financial statements) and empirical verification

    would ensure that that gap could be bridged again. The questions which ratios are the most

    important in detecting bankruptcy potential, what weights should be attached to those ratios and

    how the weights should be established, needed to be answered (Altman, 1968).

    2.2. Discriminant Analysis and Financial Ratios Theory

    2.2.1. Beavers Univariate Discriminant Analysis (1966)In 1966 Beaver found that a number of indicators could discriminate between matched samples

    of failed and nonfailed firms for as long as five years prior to failure (Beaver, 1966). These 30

    indicators or financial ratios perform best to predict a bankruptcy if they are tested one ratio at a

    time (univariate). Beaver has categorized these 30 financial ratios in six groups (see Table 1).

    This table shows that Beaver explored a wide range of ratios in his study and that there is some

    overlap. When for example 30% of the Working Capital of the examined companies is in Cash

    or Quick Assets, the ratios 1 and 2 of Group IV and 1 and 4 of Group VI will reveal a similar

    behavior, which is not desirable. The data can be analyzed by a comparison of the mean values

    and the likelihood ratios. Beaver explains that the multiratio analysis should reduce the common

    elements to a minimum to prevent this amount of overlap (Beaver, 1966).

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    Table 1 List of Ratios tested by Beaver (1966)

    Group I (Cash-Flow Ratios) Group V (Liquid Asset to

    1. Cash-Flow to Sales Current Debt Ratios)

    2. Cash-Flow to Total Assets 1. Cash to Current Liabilities3. Cash-Flow to Net Worth 2. Quick Assets to Current Liabilities

    4. Cash-Flow to Total Debt 3. Current Ratio (Current Assets

    Group II (Net-Income Ratios) to Current Liabilities)

    1. Net Income to Sales Group VI (Turnover Ratios)

    2. Net Income to Total Assets 1. Cash to Sales

    3. Net Income to Net Worth 2. Accounts Receivable to Sales

    4. Net Income to Total Debt 3. Inventory to Sales

    Group III (Debt to Total-Asset 4. Quick Assets to Sales

    Ratios) 5. Current Assets to Sales

    1. Current Liabilities to Total Assets 6. Working Capital to Sales

    2. Long-Term Liabilities to Total Assets 7. Net Worth to Sales3. Current plus Long-Term Liabilities 8. Total Assets to Sales

    to Total Assets 9. Cash Interval (Cash to Fund Ex-

    4. Current plus Long-Term Liabilities penditures for Operations)

    plus Preferred Stock to Total Assets 10. Defensive Interval (Defensive

    Group IV (Liquid-Asset to Assets to Fund Expenditures for

    Total-Asset Ratios) Operations

    1. Cash to Total Assets 11. No-credit Interval (Defensive

    2. Quick Assets to Total Assets Assets minus Current Liabilities

    3. Current Assets to Total Assets to Fund Expenditures for Operations)

    4. Working Capital to Total Assets

    Source: Beaver (1966)

    Beaver made use of paired samples of 79 failed and 79 non-failed companies with comparable

    asset-size and operating within the same industry. The ability to predict failure is strongest in

    the cash-flow to total debt ratio. The net income to total assets ratio predicts second best and the

    total debt to total assets next best (Beaver, 1966). The question why these three perform better

    than the other 27 ratios is not answered. Besides that, these three ratios only perform best in this

    sample and may not be significant in other samples. None of these three ratios are used in

    Altmans Z-Score model, which is remarkable. Beaver suggested in his next study (1968) that

    multivariate models may yield better results than his univariate model.

    2.2.2. Altmans Multivariate Discriminant Analysis (1968)It so happened that Altman presented the results of his study in 1968. He selected 33 pairs of

    sample companies (bankrupt and non-bankrupt), all in the manufacturing industry. Altman

    introduced a function according to the Multiple Discriminant Analysis (MDA) and not the

    (popular) regression analysis, although a carefully devised and interpreted multiple regression

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    analysis methodology could have been used (Altman, 1968). Altman advocates for MDA

    because of the successful use in consumer credit evaluation and investment classification

    (Altman, 1968). He collected almost similar data as Beaver and sorted this data into 22 financial

    ratios. MDA then attempts to derive a linear combination of these financial ratios which best

    discriminates between the groups bankrupt and non-bankrupt. So the linear function that

    appears from the computer is the best model for that particular sample. If Altman used

    another sample the outcome would be different.

    The multivariate model can reach greater statistical significance than the univariate model and

    the MDA creates an entire profile of financial ratios relevant to the selected companies, as well

    as the interaction of these ratios. Analyzing these ratios together could remove possible

    ambiguities (the early mentioned overlap) and misclassifications.

    The result of Altmans study is the following model (Altman, 1968):

    Z = .012X1 + .014X2 + .033X3 + .006X4 + .999X5 (1)

    where: X1= working capital total assets

    X2= retained earnings total assets

    X3= (earnings before interest and taxes) total assets

    X4= (stock price * outstanding shares) total liabilities

    X5= Sales total assets

    The gap between practice and theory is bridged by presenting an application for the real

    credit world: a firm with a Z-Score greater than 2.675 is classified as a nonbankrupt firm.

    Firms with a deteriorating financial future have a Z-Score between 1.81 and 2.675, also called

    the grey area. The grey area is not really safe, but also not in serious trouble. Potentially

    insolvent companies have a Z-Score below 1.81. Chapter 2.3 provides a full explanation of the

    Z-Score model.

    2.2.3. Developments After Beavers and Altmans WorkBeaver and Altman have set the mainstream theoretical framework for decades to follow.

    Altman explains in his book that additional research was undertaken by various researchers, like

    Blum, Deakin and Libby (Altman, 2006), addressing several methodological issues. But the

    most notable contribution from the 1970s comes from Wilcox (Hillegeist et al, 2004).

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    According to Hillegeist, most of the research on the predictive power of financial ratios has

    been focused on the statistical methodology (Hillegeist et al, 2004). Altman himself was the first

    one to recognize that the MDA was not as widely used as Regression Analysis (1968). It was

    more difficult to do valid regression analysis (considering assumptions of normality etc.) than to

    calculate a linear formula using MDA with the technology of 1968. The only thing that has to be

    done is to discriminate between the two groups (bankrupt and non-bankrupt) and MDA does the

    job well. Later, some major statistical developments took place in the early 1980s. An

    alternative statistical methodology called the probabilistic or logit methodology was introduced

    by Ohlson (Altman, 2006).

    2.2.4. OhlsonsProbabilistic Approach (1980)In his study Ohlson (1980) addresses a number of modelling issues and summarizes the many

    statistical problems in using the MDA. Until then, MDA was the most popular methodology in

    the financial ratio theory and calculations. Ohlsons main issues were (1) the small company

    samples; (2) the source of the data (comes from one database Moodys); and (3) not knowing if

    the financial statements were published before or after the bankruptcy.

    Ohlson (1980) summarizes the statistical problems of MDA as follows:

    (i) Looking at the distributional properties of the coefficients, e.g. the variance-covariance matrices should be the same for bankrupt and non-bankrupt firms. A

    violation of this condition may be unimportant or irrelevant if the model only has to

    discriminate. Ohlson wants to go further than the traditional econometric analysis

    and finds it urgent to test variables for statistical significance.

    (ii) The score that an MDA model gives has little intuitive interpretation. It may helpfor decision problems, but it does not take away the chance of a misclassification.

    (iii) As mentioned before, if a different sample is used it may not lead to the sameconclusions. Classified as an MDA problem by Ohlson the matching procedures toget a set of paired samples are arbitrary. This is in fact a data collection issue.

    In contrast with Beaver and Altman the sample used in Ohlsons study was over 2,000 firms, of

    which 105 companies failed. Beaver and Altman research after the bankruptcy has taken place

    (ex-post), while Ohlson researches ex-ante. Ohlson also estimates a probability of default.

    The Probability of Bankruptcy is known as the O-Score (Ohlson, 1980). Together with the Z-

    Score they are widely used by banks and investors as credit scoring models to analyze credit

    risk.

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    2.2.5. Financial Ratios versus Structural Approach TheorySince 1980 more statistical developments were made and Financial Ratios Theory was replaced

    by Structural Approach Theory. Formulas like Black and Scholes, Merton, KMV, Jones and

    Trussel became more popular and accurate. According to Hillegeist et al (2001), a combination

    of the two theories is the most powerful. Apparently, the information captured by the accounting

    based theory holds more information than the marked-based theory, and vice versa.

    2.3. Z-Score model

    The Z-Score model is doing more than just calculating a number which predicts bankruptcy.

    The model can also be used as an analytical technique/tool to discover financial risks faced by

    the corporation. Critical (financial) problems could be: (1) liquidity problems; (2) operational

    problems; (3) shareholders confidence; and (4) leverage problems. The Z-Score is a summary

    statistic of these (potential) problems (Arnold and Earl, 2006).

    2.3.1. Component Ratios of the Model

    The Z-Score model consists of five ratios mentioned in paragraph 2.2.2.; where X1 (Working

    Capital/Total Assets) is covering liquidity issues. This ratio is a measure of the net liquid assets

    of the firm relative to the total capitalization (Altman, 2006). The working capital ratio is the

    most instable ratio of all. For example, banks do not have a clear working capital ratio which

    can be used for Z-Score calculations. Therefore they are excluded from the sample used in this

    thesis.

    X2 (Retained Earnings/Total Assets) covers shareholder claims against assets. When a firm has

    high retained earnings relative to their total assets it is financed with profits from previous years

    and though have not utilized much debt. This ratio also implicitly shows the age of the firm and

    the cumulative profitability over the life of the company (Altman, 2006).

    Profitability is measured in X3 (EBIT/Total Assets). This ratio measures the pure productivity of

    the firm, independent of tax, leverage or interest factors. It is an important ratio for credit risk

    analysis, because it shows the ability of making profits given the total assets. The X3 ratio is the

    most significant ratio of the Z-Score model. Thus, a small change of this ratio results in a bigger

    change of the Z-Score compared to the other four ratios.

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    The variable with the smallest coefficient, but still an important ratio, is X4 (Market Value of

    Equity/Total Liabilities). The measure shows how quick the firm can be insolvent and what the

    shareholders level of confidence is. If total equity plus debt exceeds the firms assets, the firm

    will be insolvent and will go bankrupt. The shareholders confidence is measure d by market

    stock prices. Thus, share prices should be positively related with Z-Scores.

    The fifth ratio of the Z-Score model is X5 (Sales/Total Assets). This capital turnover ratio ranks

    high in its contribution to the overall discriminating ability if the Z-Score Model, but is the least

    significant. This is because its relationship to other variables. Although there is a wide variation

    among industries, the importance of MDA is its ability to separate groups using multivariate

    measures. In the end, the Z-Score Model is an indicator whether firms are financially healthy,

    risky or completely insolvent.

    The coefficients of the Z-Score Model are determined by the computer algorithm and not by

    Altman himself. A computer algorithm is a finite and well-defined sequence of instructions that

    describe computations and eventually terminating in a final ending state. If Altman had changed

    his sample, other variables and/or different coefficients would be the outcome of the Z-Score

    Model. This is also the case for the boundaries of the critical areas. Now, below 1,81 is very

    critical, but with a different sample it could have been lower or higher.

    2.4.Practical Use of the Z-Score Model

    2.4.1. Z-Score Model Accuracy and Type I and II ErrorsSince the introduction of the Z-Score Model in 1968, many tests of the Z-Score Models

    accuracy were performed. In his book, Altman (2006) provides outcomes of three important

    tests. Again, the research was done after firms went bankrupt or were already distressed (ex-

    post). The Z-Score Model, using a cut off score of 2.675, was between 82 percent and 94

    percent accurate, one financial reporting period prior to the Chapter 11 (Altman, 2006). On the

    other hand, the Type II error (classifying firms as distressed when they do not go bankrupt or

    default in their obligations) increased to 25 percent having Z-Scores below 1.81.

    The Type I error (firms that go bankrupt or default in their obligations, and are missed by the

    Z-Score Model) seems to be quite acceptable (less than 20 percent). Altman concludes that in

    almost four decades of research U.S. firms are far more risky than in the past (Altman, 2006).

    Firms are more leveraged (debt-equity ratio) and have less retained earnings relative to the total

    assets.

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    Other tests (Al-Rawi, 2008, Altman 1984, 2002, Gerantonis, 2009 and Moyen, 2005) have

    shown similar results. In each test the Z-Score Model is accurate and reliable. But authors also

    mention the fact that the Z-Score Model is not the only tool for checking a firms financial

    status. One should always consider other tools, facts, calculations and information to determine

    distress or default probabilities.

    2.4.2. Linking Z-Scores with Bond RatingsInvestors, security analysts, regulators and other parties also look at Bond Ratings, such as the

    S&P ratings, Moodys and Fitch. Since there has been a large database and number of defaults

    that had ratings attached to their securities, Altman (2006) can link his Z-Score Model to these

    ratings and compare them. The results are as follows:

    Table 2 Average Z-Scores by S&P Bond Rating, 1996-2001

    Average

    AnnualNumberof firms

    AverageZ-Score

    StandardDeviation

    AAA 66 6,2 2,06

    AA 194 4,73 2,36

    A 519 3,74 2,29

    BBB 530 2,81 1,48

    BB 538 2,38 1,85B 390 1,8 1,91

    CCC 10 0,33 1,16

    Da

    244 -0,2 n.a.aMedian, based on data from 2000 to 2004.

    Source: Altman (2006), Compustat data tapes, 1996-2001

    Altman uses a three-step process for the assignment of appropriate default probabilities on

    corporate credit assets:

    1. Credit Scoring Models (Z-Score, O-Score Models)2. Capital Market Risk Equivalentsusually bond ratings.

    3. Assignment of Probability of Default and possibly Loss Given Default (LGD) on portfolio.

    Some argue step 3 is a combination of step 1 and 2, but Altman uses different methods in order

    to calculate PDs and LGDs (Altman 2006). This bachelor thesis focuses on step 1 (Z-Scores)

    and step 2 (S&P Bond Ratings), and gives an analysis of the financial status of AEX- and

    AMX-listed firms after the financial crisis of 2008 and the recession of 2009.

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    3. Model and Data

    The Z-Score Model is introduced and explained in the previous chapter. The third chapter

    provides the data and Z-Score calculations of all non-financial AEX- and AMX-listed

    companies over the period 2004-2009.

    3.1. Z-Score Model & Research Sample

    As mentioned before in chapter 2, the Z-Score Model provides scores which determine if

    companies are potentially in distress or not. The total amount of AEX- and AMX-listed

    companies is 50, but in this bachelor thesis only 39 firms will be part of the sample. This is

    because some errors aroused with DataStream and the working capital ratio. It seems thatfinancial companies such as banks, insurance and (real estate-) investment companies do not

    have a clear working capital ratio to work with, at least DataStream did not provide the ratio

    properly. The companies that are excluded from this sample are AEGON, Corio and ING from

    the AEX-index. And from AMX-index: BinckBank, Delta Lloyd, EUROCOMMERCIAL, SNS

    Reaal and VastNed Retail.

    Other firms are recently added to the stock exchange and therefore listed too short (AEX:

    TomTom and AMX: Mediq). The AMX firm AMG accessed the stock exchange on 11th July

    2007, data for the year 2004 werent available. DataStream did not provide any useful figures of

    Reed Elsevier (too many errors), therefore this company is also not included.

    Although the Dutch stock exchange has a strong and large financial sector (12,6 percent), the

    industry and materials sectors (28 percent) are represented even more in the weighted index.

    Furthermore, some of the (world) market leaders have a stock exchange quotation in the

    Netherlands and are originally Dutch firms. For example Royal Dutch Shell (15,83 percent),

    Philips (8,0 percent), Ahold (4,1 percent), Heineken (3,4 percent) and Akzo Nobel (3,3 percent).

    An analysis of the developments and trends of the Z-Scores per sector will be given in chapter

    four.

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    3.2. Dataset Z-Scores AEX- and AMX-listed Firms

    Balance sheets from the six-year period 2004-2009 are used for this dataset. The figures are

    imported with DataStream, directly into Excel. The ratios Working Capital, Total Assets, Sales,

    EBIT, Equity, Debt and Retained Earnings are extracted from these balance sheets and the

    following formula calculated the final Z-Score: Z = .012X1 + .014X2 + .033X3 + .006X4 +

    .999X5. The full AEX-overview can be found in Appendix 1 from page 23 to 26 and for AMX-

    listed firms from page 27 to 30 in Appendix 2.

    The outcomes of the Z-Score calculations are summarized in an overview (Table 3, page 13).

    This table is the most important table of this bachelor thesis, because it contains all relevant

    information in one overview. Again, Z-Scores below 1,81 means that firms are in the danger

    zone, scores between 1,81 and 2,675 are in the grey area and scores above 2,675 are in the

    safe zone.

    3.3. Z-Score Trends of AEX- and AMX-indices

    The average Z-Score over this six-year period is 3,07 for the AEX-index and 2,94 for the AMX-

    index (Figure 1, page 14). The lowest score in this research sample is achieved in the year 2008,for both stock exchanges with 2,50 and 2,60 as average Z-Scores. This is within Altmans grey

    area, and therefore demonstrates the danger and strength of the 2008 financial crisis. The

    retained earnings declined because firms immediately made depreciations. Also the debt/equity

    ratio changed due to losses on stock exchanges and increasing amounts of debt.

    Most figures and ratios are very volatile over the period 2004-2009, where 2004 is the best year

    (expressed in Z-scores) and 2008 is the worst. The decline of Z-Scores starts from 2005 until

    2008 for the AMX-index. The AEX on the other hand goes up and down each year and shows

    no clear pattern. The recovery after the 2008 financial crisis already starts in 2009. Although,

    the 2009 average AEX Z-Score is still below the average score over the six-year period. The

    2009 AMX Z-Score is slightly above the six-year average score (3,12 vs. 2.94), which indicates

    a strong recovery.

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    Table 3 Summary overview of test results: Z-Scores of 39 AEX and AMX firms

    AEX-Index

    2004 2005 2006 2007 2008 20092004-2009

    S&P Credit

    Rating

    Ahold 2,64 2,56 3,03 3,50 3,95 2,87 3,09BBB-

    Air France KLM 1,55 1,04 1,50 1,63 1,78 0,95 1,41Akzo Nobel 2,55 2,62 2,85 4,08 1,80 2,31 2,70BBB+

    Arcelor Mittal 3,96 2,06 1,82 2,19 2,35 2,82 2,53BBB+

    ASML Holding 2,86 2,96 6,03 4,54 4,44 3,24 4,01BBB-

    BAM 3,26 1,91 1,71 1,82 2,01 1,83 2,09

    Boskalis 17,21 7,28 6,64 6,58 2,93 11,20 8,64

    DSM 3,03 3,78 4,32 3,85 3,29 3,45 3,62A-

    Fugro 1,46 2,44 2,29 2,49 2,56 2,78 2,34

    Heineken 2,11 2,21 2,64 2,93 1,30 1,65 2,14

    KPN 1,44 0,53 0,61 0,58 0,61 0,87 0,77BBB+

    Philips 4,30 4,47 5,51 6,27 4,20 3,96 4,79A-

    Randstad 5,27 5,08 8,70 4,61 2,02 3,70 4,90

    Royal Dutch Shell 6,08 6,49 6,65 6,87 5,77 4,83 6,12AA+SBM Offshore 1,65 1,73 1,77 1,99 1,51 1,88 1,76

    TNT 3,29 3,73 3,15 2,59 2,42 2,37 2,93BBB+

    Unilever 1,97 2,22 2,67 2,84 2,90 2,97 2,60A+

    Unibail-Rodamco 1,42 2,34 2,65 1,62 0,93 0,78 1,62

    Wereldhave 1,66 1,49 2,85 2,47 2,01 1,87 2,06

    Wolters Kluwer 1,17 1,25 1,19 1,56 1,26 1,21 1,27BBB+

    Average 3,44 2,91 3,43 3,25 2,50 2,88 3,07

    AMX-Index

    2004 2005 2006 2007 2008 20092004-2009

    S&P Credit

    Rating

    Aalberts Indust. 1,87 1,83 1,81 2,15 1,88 1,99 1,92

    AMG #NA 1,72 1,77 2,72 1,84 2,31 2,07

    Arcadis 4,83 3,04 2,89 2,43 2,52 2,76 3,08

    ASM International 2,06 1,94 2,67 3,22 3,45 1,76 2,52BB-

    Crucell 6,73 7,23 5,86 4,49 4,54 9,13 6,33

    CSM 2,73 3,94 3,07 3,55 3,31 3,96 3,43

    Draka 1,81 1,80 1,92 2,30 2,70 2,92 2,24

    Heijmans 2,92 2,50 2,41 2,47 2,19 2,91 2,57

    Imtech 5,08 5,26 3,80 3,10 2,18 2,65 3,68

    Logica 2,26 2,50 2,08 2,49 3,11 3,80 2,71

    Nutreco 3,54 3,86 4,38 3,16 3,06 4,24 3,71Ordina 3,20 3,77 2,98 3,21 1,56 2,87 2,93

    Oc 3,05 1,74 2,11 2,42 2,22 2,09 2,27

    Smit International 3,16 2,54 2,87 2,54 1,99 3,32 2,74

    Ten Cate 3,46 2,86 4,21 2,88 2,62 3,46 3,25

    USG People 3,65 1,06 1,72 2,68 2,94 3,50 2,59

    Vopak 1,66 1,66 1,77 1,81 1,53 1,73 1,69

    Wavin 1,45 1,01 1,73 1,91 1,96 2,68 1,79

    Wessanen 5,27 5,62 5,05 4,34 3,78 1,28 4,22

    Average 3,26 2,94 2,90 2,84 2,60 3,12 2,94

    Source: DataStream and author calculations using the Z-Score Model, June 2010

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    Figure 1 Average Z-Scores AEX- and AMX-index (2004-2009 period)

    Source: DataStream and author calculations using the Z-Score Model, June 2010

    More operational ratios such as EBIT and Sales achieved their lowest values in 2009. The

    liquidity and banking crisis took place in 2008 and therefore also the financial difficulties and

    risks, but the real recession and economic depression affected the balance sheets in 2009, which

    were operational difficulties.

    Since the beginning of the financial crisis in 2008, the AMX-index performs better than the

    AEX-index, expressed in value changes (percentages). In this research sample the average Z-

    Scores are higher since 2008. It is not unusual that mid caps perform better in a bullmarket than

    large caps, because investors are risk-seeking. In a bearmarket, risky assets (mid caps in this

    case) are sold more often.

    Besides the differences of the AEX- and AMX-indices, there are also discrepancies among

    sectors. Some sectors made strong recoveries in 2009, whereas others did not recover at all.

    Chapter four will give an analysis of this phenomenon.

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    4. Analysis

    This chapter analyzes the Z-Scores more detailed per sector. It also covers the patterns, trends,

    rationale and developments of the Z-Scores from the dataset used in this bachelor thesis. Finally,

    this chapter explains where the Z-Score can be used for and which parties may benefit from the

    calculations if investment decisions have to be made.

    4.1. AEX- and AMX-sectors Analysis

    The AEX- and AMX-indices can be divided into six main sectors (seven if the financial sector

    was included). Table 4 provides an overview of how the companies are scattered:

    Table 4 Companies per sector

    IT &Telecom

    Food &Beverage

    ConsumerDiscretionary

    Industrials (1) Industrials (2) Materials Energy

    ASML Ahold Oc Aalberts SBM Akzo Nobel Shell

    ASM Int. CSM Philips Air-France KLM Smit AMG VopakImtech Heineken Wolters Kluwer Arcadis TNT Arcelor MittalKPN Nutreco BAM Unibail Rodamco Crucell

    Logica Unilever Boskalis USG People DrakaOrdina Wessanen Fugro Wavin DSM

    Heijmans Wereldhave Ten CateRandstad

    Source: author compilation, June 2010

    The sectors are the same as Bloomberg uses in its analysis. The industrials are very well

    represented with 15 companies, while the energy and consumer discretionary on the other hand

    only consists of respectively two and five companies. The energy sector has a very high Z-Score,

    but that is because Royal Dutch Shell has a very strong financial structure and solvency. At the

    same time Royal Dutch Shell is the only firm with a AA+ credit rating. Therefore, the image of

    a strong energy sector is distorted.

    Each sector of the stock exchange has different Z-Scores over the period 2004-2009. The

    materials and industrials sector demonstrate similar patterns and trends, with strong recoveries

    in 2009 after a huge drop in 2008. The other four sectors also point out similar movements with

    further decreases in 2009. Figure 2 provides an overview of the Z-Scores per sector.

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    Figure 2 Z-Scores of all AEX- and AMX-sectors

    Source: DataStream and author calculations using the Z-Score Model, June 2010

    The reason why the materials and industrials sectors move together has to do with their business

    cycles. Each sector has a different business cycle within the economic and market cycle. The

    Sector Rotation Model (Figure 3) shows that investors try to anticipate how the market reacts to

    economic changes.

    Figure 3 Sector Rotation Model

    Source:www.stockcharts.com, Legend: Market Cycle and Economic Cycle

    http://www.stockcharts.com/http://www.stockcharts.com/http://www.stockcharts.com/http://www.stockcharts.com/
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    The difficulty for investors is to identify in which cycle the economy is. The very low Z-Scores

    of 2008 and many recoveries in 2009 indicate that the market bottom (Market Cycle) and full

    recession (Economic Cycle) of this Sector Rotation Model are behind us. Therefore, the best

    moment to invest (at the lowest point in the market) is already behind us.

    Appendix 3 provides a full overview of all sectors with the linked Z-Scores. This shows

    together with Figure 2 that the weakest sectors (expressed in Z-Scores) during the financial

    crisis are the Industrials and IT & Telecom sectors and the strongest are the Energy and Food &

    Beverage sectors. It is up to the investor if he or she wants to invest in a strong or weak sector.

    The growth and profitability perspectives of weak sectors are maybe better, but could also be

    more risky because failure or default is nearby. This trade off is the basic principle of most

    investments.

    4.2. Investment Decisions

    According to the Sector Rotation Model and with the recoveries taken into account, the Market

    Cycle is now bull market. This means investors are more risk-seeking than before and thus will

    take more positions in the AMX-index. The performance of this index will be better than the

    larger AEX-index, if we follow the above explanation. But there is also a lot of uncertainty and

    volatility around markets, because investors and other parties expect a second crisis.

    4.2.1. High Z-ScoresGood or wise investment decisions are above all a matter of experience rather than science.

    Tools like the Z-Score model can make decisions more sophisticated, but not comprehensive.

    Firms with high Z-Scores are generally financed very safe and have a solid financial structure.

    They do not have a high leverage ratio (X4) and mostly keep a lot of retained earnings (X2).

    High profits from shareholding cannot be made very quickly in a short time. In most cases,

    these companies are stable in the long run. Companies from the AEX- and AMX-indices with

    average Z-Scores above the value of 2,675 are considered highand safe (Altman, 2006).

    The risk is relatively low, but this also means returns and/or yield are low, according to the

    Capital Asset Pricing Model. Investors should have a long time horizon for these types of

    companies and should not expect high returns or profits in the short-run (Table 5).

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    Table 5 Companies with high Z-Scores for safe and long-term investments

    AEX-index AMX-index

    Ahold Arcadis

    AkzoNobel Crucell

    ASML CSM

    Boskalis ImtechDSM Logica

    Philips Ordina

    Randstad Nutreco

    Royal Dutch Shell Smit

    TNT Ten Cate

    Wessanen

    Source: author compilation, July 2010

    4.2.2. Low Z-ScoresFirms with low Z-Scores have an aggressive type of financing compared to firms with high Z-

    Scores. Short-term profits are easier made and the shares (or securities) are far more risky to

    hold. In some cases firms are shortcoming of sales or working capital. This means weak

    productivity (X5) and a fragile financial structure (X1). The Z-Score tells the investor to be

    careful with this company and he or she should do more analysis and research before investing

    in this particular company. There are 19 companies with an average Z-Score above 2,675 and

    20 with an average Z-Score below 2,675.

    Table 6 Companies with low Z-Scores (within Altmans grey area or below)

    AEX-index AMX-index

    Air France-KLM Aalberts Industries

    Arcelor Mittal AMG

    BAM ASM International

    Fugro Draka

    Heineken Heijmans

    KPN Oc

    SBM Offshore USG People

    Unilever Vopak

    Unibail-Rodamco Wavin

    Wereldhave

    Wolters Kluwer

    Source: author compilation, July 2010

    Companies with red marking have Z-Scores below Altmans 1,81 and are considered dangerous

    and risky. If an investor wants to invest in these companies, he or she should check other

    models.

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    4.2.3. Z-Scores and S&PBond RatingIn the end, the Z-Score is not the only factor or model to check when making investment

    decisions. As mentioned before in chapter 2.4.2., bond ratings are step 2 in the Altman (2006)

    process. These ratings are very important for investors and they rely on it. Not every AEX- and

    AMX-company has a S&P credit rating, therefore not all 39 companies are included in Table 7.

    Table 7 S&P bond ratings compared with Z-Scores

    Company Avg. Z-Score S&P Credit Rating

    Royal Dutch Shell 6,12 AA+

    Philips 4,79 A-

    ASML Holding 4,01 BBB-

    DSM 3,62 A-

    Ahold 3,09 BBB-TNT 2,93 BBB+

    AkzoNobel 2,70 BBB+

    Unilever 2,60 A+

    Arcelor Mittal 2,53 BBB+

    ASM International 2,52 BB-

    Wolters Kluwer 1,27 BBB+

    KPN 0,77 BBB+

    Source: DataStream and author calculations using the Z-Score Model, June 2010

    There are four discrepancies (compared to Table 2) in this table, namely ASML Holding,

    Unilever, Wolters Kluwer and KPN. The imperfection of ASML Holding is a low debt/equity

    ratio (three times more equity than debt) which makes the Z-Scores very high. Unilever has a

    very large and negative working capital ratio, which causes the low Z-Scores. The problem with

    Wolters Kluwer is that the company has two times more debt than equity and also has a

    negative working capital ratio. KPN beats them all with four times more debt than equity,

    negative retained earnings, a small EBIT and a negative working capital ratio.

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    5. Conclusion

    Over the last four decades, many articles have been published on corporate financial distress

    and bankruptcy. In summary, the first methodology tries to predict default based on a number of

    financial ratios. Beaver and Altman (1966 and 1968) both used the Discriminant Analysis

    within the framework of the Financial Ratios Theory. The Z-Score Model became the most

    popular and widely used model until a more structural approach was introduced by Wilcox

    (1971). In 2001, Hillegeist found that a combination of the two theories was the strongest.

    Although the Z-Score Model is linear, the underlying hypotheses of Altmans model may not be

    formulated as linear equations. It is not clear if the interdependence between variance, variables

    and volatility influence the results of the researches. Therefore, further (statistical) methodsneed to be developed in the near future.

    Nevertheless, the information and accuracy of the Z-Score Model should not be underestimate.

    Until today, many tests have shown that the Z-Score Model is a good indicator for financial

    difficulties. In this thesis, Z-Scores of all the AEX- and AMX-index companies were calculated,

    excluding the financials. The influence of the 2008 financial crisis is reflected by the Z-Scores

    calculated in Table 3, with the lowest scores in that particular year as a first result of the test.

    There are some recoveries in 2009, but in most cases the Z-Scores are still not above theaverage score of the period 2004-2009. The recoveries demonstrate that the worst part of the

    crisis is behind us.

    The sector analysis shows the similarities of the industrials and materials sectors. These sectors

    are the only two sectors with a higher Z-Score in 2009 compared to 2008. The Sector Rotation

    Model explains why the sectors have different patterns. This is because sectors have their own

    business cycle within the Economic and Market Cycles. This is important information for

    investors, given the fact that they want to invest at the lowest point of the market. Because the

    industrials and materials sectors showed recoveries, other sectors will show these in 2010.

    Investors should take advantage of this information and analysis.

    The Z-Scores also separate chaff from wheat. Companies with high Z-Scores in combination

    with high credit ratings are considered as safe investments. Low Z-Scores tell the investor to

    be cautious. This thesis showed that, by using the Z-Score Model, investments decisions can be

    well thought-out and that the model is a practical and useful tool to support in that process.

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    6. References

    Al-Rawi, K. et al. (2008) The Use Of Altman Equation For Bankruptcy Prediction In An

    Industrial Firm,International Business & Economics Research Journal, Vol. 7, pp. 115-128.

    Altman, Edward I. (1968) Financial Ratios, Discriminant Analysis and the Prediction of

    Corporate Bankruptcy,Journal of Finance, Vol. 23, pp. 589-609.

    Altman, Edward I. (1984) The Succes Of Business Failure Prediction Models, Journal of

    Banking and Finance, Vol. 8, pp 171-198.

    Altman, Edward I. (2000) Predicting Financial Distress of Companies: Revisiting the Z-Score

    and Zeta Models, Working Paper.

    Altman, Edward I (2002) Corporate Distress Prediction Models In A Turbulent Economic And

    Basel II Environment, Working Paper, Stern School of Business, NY University.

    Altman, Edward I. and Hotchkiss, E. (2006) Corporate Financial Distress and Bankruptcy, 3rd

    edition, John Wiley and Sons Ltd.

    Arnold, T. and Earl Jr., J.H. (2006) Applying Altmans Z-Score in the Classroom,Journal of

    Financial Education, Vol. 32, pp. 98-103.

    Beaver, W. (1966) Financial Ratios as Predictors of Failure, Journal of Accounting Research,

    Vol. 4, pp. 71-111.

    Berk, J. and DeMarzo, P (2007) Corporate Finance, Pearson Addison Wesley.

    Eisdorfer, A. (2008) Empirical Evidence of Risk Shifting in Financially Distressed Firms,

    Journal of Finance, Vol. 63 no. 2, pp. 609-637.

    Hillegeist, S., Keating, E., Cram, D., Lundstedt, K. (2004) Assessing the Probability of

    Bankruptcy,Review of Accounting Studies, Vol. 9, pp. 5-34.

    Gerantonis, N. et al. (2009) Can Altman Z-Score Models Predict Business Failures in Greece,

    Research Journal of International Studies, Vol. 12, pp. 21-28.

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    Moyen, N and Bhagat, S (2005) Investment and Internal Funds of Distressed Firms, Journal of

    Corporate Finance, Vol. 11, pp. 449-472.

    Ohlson, J. (1980) Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of

    Accounting Research, Vol.19, pp. 109-131.

    Ramaswami, M. and Moeller, S. E. (1990)Investing in Financially Distressed Firms, Quorum

    Books

    Winakor, A., and Smith, R. (1935). Changes in the Financial Structure of Unsuccessful

    Industrial Corporations.Bulletin No. 51, University of Illinois, Bureau of Business

    Websites:

    Euronext, Index Announcement 2009,http://www.euronext.com/fic/000/055/017/550171.pdf

    Standard and Poors Credit Ratings,www.standardandpoors.com

    StockCharts, investors website,www.stockcharts.com

    http://www.euronext.com/fic/000/055/017/550171.pdfhttp://www.euronext.com/fic/000/055/017/550171.pdfhttp://www.euronext.com/fic/000/055/017/550171.pdfhttp://www.standardandpoors.com/http://www.standardandpoors.com/http://www.standardandpoors.com/http://www.stockcharts.com/http://www.stockcharts.com/http://www.stockcharts.com/http://www.stockcharts.com/http://www.standardandpoors.com/http://www.euronext.com/fic/000/055/017/550171.pdf
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    Appendix 1 Z-Score Calculations of 20 AEX-listed Firms (DataStream/Excel)

    Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML

    Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS

    2004 20096000 12844000 11700000 13485630 3042666

    2005 19367010 21390000 11530000 25597420 3549139

    2006 17914000 26472000 11832000 83755970 3750657

    2007 13574000 26644000 18613010 90417260 3926720

    2008 13234000 30661010 17844000 95017970 37912612009 13504000 27962000 18087010 85755580 3594234

    Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML

    Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL

    2004 1311000 -221000 3438000 2502115 1868871

    2005 516000 -910000 2951000 5158062 1785836

    2006 835000 402000 3393000 11219920 2244625

    2007 894000 274000 10540000 8986515 2309176

    2008 1158000 957000 660000 9803572 1964906

    2009 1080000 -3337000 1681000 6475346 1704714

    Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML

    Code EBIT EBIT EBIT EBIT EBIT

    2004 284000 177000 1319000 5120831 406911

    2005 280000 758000 1479000 4072676 487546

    2006 1486000 1585000 1661000 6569930 9202862007 1265000 1521000 829000 12124400 903552

    2008 1328000 1515000 -457000 9241871 359542

    2009 1330000 -883000 714000 -1965105 -122247

    Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML

    Code SALES SALES SALES SALES SALES

    2004 56068000 12325000 13051000 4766153 1542737

    2005 54107010 12337000 12902000 4956951 1678180

    2006 52000000 19078000 12705000 21355890 2696572

    2007 45643010 21448000 13351000 24599060 2473677

    2008 44014000 23073010 12846000 58084420 3927957

    2009 25722000 24660000 15415000 85018940 2218095

    Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML

    Code EQUITY EQUITY EQUITY EQUITY EQUITY

    2004 4508000 4043000 3035981 4308502 13916012005 4651000 5134000 3414981 8586900 1711836

    2006 5030000 7734000 4144000 31932270 2156455

    2007 3810000 8299000 11032000 38829230 1907617

    2008 4676000 10536000 7463000 39632160 1988769

    2009 5440000 5622000 7775000 42609410 1774768

    Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML

    Code TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT

    2004 9270000 4337000 2952000 1459260 825056

    2005 7082000 8189000 3059000 7028568 878911

    2006 5983000 9081000 2961000 20137790 388839

    2007 4882000 8555000 3589000 20979490 603222

    2008 3744000 7920000 3679000 24466580 647050

    2009 3203000 9419000 3872000 17318780 663102

    Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASMLCode RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR.

    2004 -8110000 93000 770000 3492643 351570

    2005 -9756000 359000 860000 6675786 663034

    2006 -8993000 913000 1067000 11350290 1239689

    2007 -6923000 891000 9225000 16133120 1526201

    2008 -5260000 748000 -1179000 21829360 1698929

    2009 -4598000 -814000 215000 20757120 1450156

    Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES

    2004 2,638930214 1,552924902 2,548201702 3,96291202 2,858689137

    2005 2,559460828 1,041738789 2,622554844 2,058458846 2,95964833

    2006 3,031145751 1,49506045 2,846054632 1,815466528 6,036369973

    2007 3,499963329 1,6337665 4,08125076 2,19385916 4,535919359

    2008 3,951564354 1,784614978 1,803677346 2,349016155 4,441420332

    2009 2,86620682 0,950983209 2,314660185 2,820459917 3,244150964

    AVG. 3,09 1,41 2,70 2,53 4,01

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    Name BAM BOSKALIS DSM FUGRO HEINEKEN

    Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS

    2004 3228950 1070434 8576000 958723 10401000

    2005 4953802 1325495 9508000 1117148 11543000

    2006 6393628 1580829 9595000 1382167 12602000

    2007 6968671 2198047 9482000 1682093 12632000

    2008 6691222 2544813 9261000 2097017 203040002009 6700800 2544813 9292000 2340640 19619010

    Name BAM BOSKALIS DSM FUGRO HEINEKEN

    Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL

    2004 -57923 -132200 2084000 -95348 126000

    2005 223432 -52189 1774000 222485 -474000

    2006 528797 -2063 1478000 150773 229000

    2007 1065194 12161 1627000 171347 -349000

    2008 1035530 -264284 1380000 57542 -309000

    2009 786500 33531 2251000 140301 -1203000

    Name BAM BOSKALIS DSM FUGRO HEINEKEN

    Code EBIT EBIT EBIT EBIT EBIT

    2004 140774 43053 514000 101262 1155000

    2005 267004 84593 754000 149065 1368000

    2006 301748 156389 813000 206748 18950002007 303458 257571 650000 321881 1544000

    2008 304546 321005 842000 416749 1166000

    2009 2500 245333 141000 359599 1934000

    Name BAM BOSKALIS DSM FUGRO HEINEKEN

    Code SALES SALES SALES SALES SALES

    2004 7732023 1045523 6050000 830321 9255000

    2005 6342257 980523 6470000 905821 9474000

    2006 7485975 1136603 7516000 1046008 10345000

    2007 7712231 1170721 8096000 1294815 11392000

    2008 8747131 1555814 8430000 1608219 12218000

    2009 8834766 2093800 9297000 2154474 14319000

    Name BAM BOSKALIS DSM FUGRO HEINEKEN

    Code EQUITY EQUITY EQUITY EQUITY EQUITY

    2004 501913 464979 4745000 223913 33790002005 581677 542851 5408000 465460 3969000

    2006 692594 618636 5718000 562417 5009000

    2007 993530 768050 5244000 699989 5404000

    2008 847400 860118 4567000 929811 4471000

    2009 875000 1295767 4883000 1187731 5351000

    Name BAM BOSKALIS DSM FUGRO HEINEKEN

    Code TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT

    2004 442981 17213 1588000 453173 3571000

    2005 1008959 54525 1710000 337305 3255000

    2006 1849107 71401 1514000 441886 3299000

    2007 2175727 87081 1752000 534860 2656000

    2008 2128527 319160 2293000 616449 10053000

    2009 2106800 81430 2204000 634721 7862000

    Name BAM BOSKALIS DSM FUGRO HEINEKENCode RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR.

    2004 154897 30170 262000 49317 2050000

    2005 199360 386355 4850000 99412 2617000

    2006 284577 448250 5509000 141011 3559000

    2007 580723 576681 5180000 216213 3928000

    2008 623387 742829 4663000 283412 3761000

    2009 587059 907589 5142000 263410 3679000

    Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES

    2004 3,261525107 17,20768252 3,029732306 1,462888425 2,113595816

    2005 1,913238803 7,284031979 3,777069522 2,441883699 2,210771354

    2006 1,711717418 6,638694999 4,316866665 2,287044616 2,644502382

    2007 1,823373653 6,584699491 3,845806462 2,487905594 2,927259383

    2008 2,011156072 2,928022624 3,288146801 2,549102336 1,29857257

    2009 1,831073437 11,20278636 3,444355868 2,778772313 1,651749185

    AVG. 2,09 8,64 3,62 2,33 2,14

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    Name KPN PHILIPS RANDSTAD SHELL SBM

    Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS

    2004 21519010 29260000 1604900 140631400 2005179

    2005 21354000 32338000 1961800 183543100 2081926

    2006 20240000 37353010 2248800 176089500 2220002

    2007 22612000 35372000 3034700 182358600 2483828

    2008 22180000 32488000 7300800 200309800 31104282009 22777010 29284000 5992800 200778300 3242485

    Name KPN PHILIPS RANDSTAD SHELL SBM

    Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL

    2004 -593000 4148000 583000 872608 117612

    2005 -1884000 4731000 669300 10937090 -149230

    2006 -761000 5832000 522200 11473850 109061

    2007 -2517000 8198000 639600 14393910 140410

    2008 -2026000 3938000 1354500 7927438 -242896

    2009 -748000 3859000 500600 8144264 -96197

    Name KPN PHILIPS RANDSTAD SHELL SBM

    Code EBIT EBIT EBIT EBIT EBIT

    2004 2542000 2129000 229100 22212940 101779

    2005 2356000 2176000 297100 36562000 230344

    2006 2211000 1556000 432000 35823060 2128302007 2485000 4744000 551800 37071220 235377

    2008 2541000 338000 12900 34923940 203721

    2009 2811000 745000 98800 15458450 219285

    Name KPN PHILIPS RANDSTAD SHELL SBM

    Code SALES SALES SALES SALES SALES

    2004 11870000 29037010 5257400 176707700 1619367

    2005 11105000 29169010 5294100 172292000 1230419

    2006 11716000 29615010 5919500 221750900 784764

    2007 11896000 31134000 7042800 253676000 1424550

    2008 11936000 25593010 8474800 244556100 1893974

    2009 14427000 26385010 14038400 311914200 2082518

    Name KPN PHILIPS RANDSTAD SHELL SBM

    Code EQUITY EQUITY EQUITY EQUITY EQUITY

    2004 6821000 14860000 341700 62332510 5640642005 5076000 16666000 536200 76917470 757207

    2006 4195000 22997010 790300 80140300 847975

    2007 4490000 21684000 1021600 84912610 913404

    2008 3730000 16243000 2251100 91390620 886535

    2009 3838000 14595000 2325200 95228830 1258227

    Name KPN PHILIPS RANDSTAD SHELL SBM

    Code TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT

    2004 9442000 4513000 220800 10629010 1000981

    2005 9258000 4487000 247800 10926940 808392

    2006 9068000 3869000 96200 11955930 706557

    2007 11755000 3557000 528300 12397820 791431

    2008 12041000 4158000 2472000 16707140 1216500

    2009 13371000 4267000 1284800 24453040 1107078

    Name KPN PHILIPS RANDSTAD SHELL SBMCode RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR.

    2004 1511000 19346000 191000 77594136 385496

    2005 -8771000 21710000 241900 79542610 451702

    2006 -8153000 22085010 375900 78165710 513648

    2007 -6465000 25559010 597900 78851730 610127

    2008 -6103000 20577010 447900 92543740 756614

    2009 -4982000 15947000 509400 91491740 849007

    Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES

    2004 1,439558109 4,302887521 5,27470533 6,075043336 1,651928143

    2005 0,531671376 4,467171249 5,075986755 6,496904578 1,735265004

    2006 0,607272242 5,510974763 8,705378041 6,650822291 1,772476348

    2007 0,583557456 6,269310685 4,607469418 6,870015303 1,989878637

    2008 0,606692914 4,197358717 2,020380208 5,771409672 1,508536661

    2009 0,866628617 3,956841688 3,699715725 4,829296621 1,877682591

    AVG. 0,77 4,78 4,90 6,12 1,76

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    Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER

    Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS

    2004 7887000 32902000 6335000 2049700 4615000

    2005 8208000 38107010 8677200 2440640 5417000

    2006 6097000 36612000 10842900 2650173 5597000

    2007 6892000 36818000 25500800 2802637 5234000

    2008 6980000 35406000 24871810 2823185 63500002009 7462000 36608000 22633810 2595599 5946000

    Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER

    Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL

    2004 584000 -3704000 2854700 0 -169000

    2005 1249000 -3917000 3465400 -910000 -927000

    2006 -119000 -3959000 2289400 402000 -1569000

    2007 -660000 -3214000 2765400 274000 -1521000

    2008 -209000 -2366000 3001800 957000 -1099000

    2009 -27000 -481000 3187400 1135800 -884000

    Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER

    Code EBIT EBIT EBIT EBIT EBIT

    2004 1192000 3521000 379100 194200 330000

    2005 1216000 5414000 1720400 253853 462000

    2006 1392000 5321000 2529400 454939 5270002007 1283000 5594000 1339300 288402 533000

    2008 1008000 7507000 -774700 35298 502000

    2009 632000 5284000 -1443300 -114306 232000

    Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER

    Code SALES SALES SALES SALES SALES

    2004 11785000 42693010 640400 174300 3436000

    2005 11853000 42254000 674700 181400 3422000

    2006 9372000 38771010 657100 167060 3242000

    2007 9412000 39941010 767900 194823 3482000

    2008 9980000 39635010 713600 214258 3406000

    2009 10707000 40523010 1786200 227281 3374000

    Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER

    Code EQUITY EQUITY EQUITY EQUITY EQUITY

    2004 2765000 4032000 1924000 1442400 7750002005 3262000 8361000 4076100 1542162 1098000

    2006 1983000 11230000 6053100 1776209 1194000

    2007 1931000 12387000 14603700 1850065 1178000

    2008 1733000 9948000 12885100 1740283 1414000

    2009 2060000 12065000 11312900 1569554 1334000

    Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER

    Code TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT

    2004 1491000 12266000 2172000 500000 2446000

    2005 1284000 12492000 2869100 630100 2155000

    2006 1566000 8664000 2923500 541039 2175000

    2007 2085000 9525000 7468800 592597 1954000

    2008 2241000 11081000 8445900 739586 2597000

    2009 2016000 9834000 8190500 712814 2421000

    Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWERCode RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR.

    2004 572000 6097000 219000 62036 135000

    2005 559000 10015000 1701400 101350 918000

    2006 561000 12724000 2495200 125443 1227000

    2007 871000 15162000 2005800 146656 2032000

    2008 434000 15812000 -1054000 152961 1564000

    2009 247000 17350000 -1467800 143529 1540000

    Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES

    2004 3,294546856 1,970999218 1,419104909 2,170864437 1,166869893

    2005 3,733771166 2,222734089 2,338121631 1,496691576 1,250137784

    2006 3,154198194 2,672007042 2,648197673 2,847534047 1,189278899

    2007 2,596294016 2,837192286 1,616826107 2,472781174 1,557175797

    2008 2,420038001 2,901699676 0,92673827 2,011533142 1,260541959

    2009 2,368025954 2,966027993 0,775338999 1,865817303 1,210429925

    AVG. 2,93 2,60 1,62 2,14 1,27

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    Appendix 2 Z-Score Calculations of 19 AMX-listed Firms (DataStream/Excel)

    Name AALBERTS AMG ARCADIS ASM int. CRUCELL

    Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL

    2004 3310 #NA 77615 293958 69384

    2005 13274 128403 129167 336264 108290

    2006 65524 74925 131513 381204 226819

    2007 90692 210663 96020 392213 188520

    2008 94568 144212 173834 372029 2040172009 68588 154862 257694 419535 496905

    Name AALBERTS AMG ARCADIS ASM int. CRUCELL

    Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS

    2004 788554 #NA 390465 823054 98891

    2005 971115 447758 636909 811772 169737

    2006 1266640 416072 728179 831245 652907

    2007 1418185 590477 907600 839382 6249202008 1677946 790405 1046138 765513 636297

    2009 1558164 558446 1297194 843155 1010923

    Name AALBERTS AMG ARCADIS ASM int. CRUCELL

    Code SALES SALES SALES SALES SALES

    2004 784589 #NA 848457 581868 7424

    2005 826789 684536 862957 659972 9309

    2006 959911 732396 910205 693101 22943

    2007 1252919 732736 1063943 798179 40852

    2008 1601547 837668 1280279 881094 151313

    2009 1750800 979602 1740239 639370 294861

    Name AALBERTS AMG ARCADIS ASM int. CRUCELL

    Code EBIT EBIT EBIT EBIT EBIT

    2004 91921 #NA 34259 90136 -16597

    2005 120426 43803 58895 23730 -154462006 164804 31002 76587 134132 -96379

    2007 195007 41195 101066 142423 -46706

    2008 165367 44972 120100 72510 8995

    2009 89309 -9611 125699 -55931 40915

    Name AALBERTS AMG ARCADIS ASM int. CRUCELL

    Code EQUITY EQUITY EQUITY EQUITY EQUITY

    2004 226837 #NA 145669 256716 78535

    2005 298440 -23526 176203 238594 137609

    2006 383649 -25854 188881 276458 497300

    2007 530448 168280 187715 318878 4372422008 577010 182872 207585 317682 453492

    2009 615657 148416 351704 241229 738265

    Name AALBERTS AMG ARCADIS ASM int. CRUCELLCode TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT

    2004 350665 #NA 42443 297253 4597

    2005 408730 218392 122801 257400 10278

    2006 512571 209785 143208 228500 46413

    2007 514801 96436 226382 186936 52795

    2008 765298 166600 281491 153682 60751

    2009 630667 142250 374562 243249 52300

    Name AALBERTS AMG ARCADIS ASM int. CRUCELL

    Code RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS

    2004 56344 #NA 20139 24631 -274524

    2005 78767 -129732 124617 -15586 -160559

    2006 100030 -112821 151357 18748 -2478722007 118690 -94146 172361 73965 -293819

    2008 92753 -161589 210366 92111 -2755972009 41471 -138830 271881 16145 -254005

    Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES

    2004 1,871851024 #NA 4,830309746 2,05631149 6,727043635

    2005 1,827810956 1,723966223 3,036916181 1,935020076 7,228991755

    2006 1,808175727 1,76691693 2,894887896 2,673291793 5,862153973

    2007 2,148492192 2,721806896 2,428903827 3,217463488 4,491562521

    2008 1,876140615 1,837830672 2,524834584 3,454335858 4,541455827

    2009 1,987455449 2,306346562 2,755170443 1,757559724 9,132601795

    AVG. 1,92 2,07 3,08 2,52 6,33

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    Name CSM DRAKA HEIJMANS IMTECH LOGICA

    Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL

    2004 249800 346000 349146 309676 317100

    2005 336200 243700 425707 232138 222900

    2006 324400 172500 510292 196801 106600

    2007 275300 294200 488581 138186 -78800

    2008 359200 321600 341839 75663 2042002009 284000 211400 214946 116589 -92300

    Name CSM DRAKA HEIJMANS IMTECH LOGICA

    Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS

    2004 2563100 1599600 1577522 952353 1160800

    2005 2133700 1585100 1901870 1292144 1759300

    2006 2167800 1692300 2124205 1563852 3420200

    2007 2004700 1706200 2199655 1880725 32913002008 2043000 1599600 2355484 2453369 4086500

    2009 1950700 1537400 1853407 2564411 3577300

    Name CSM DRAKA HEIJMANS IMTECH LOGICA

    Code SALES SALES SALES SALES SALES

    2004 3516800 1420200 2509323 2098465 1706600

    2005 3502200 1518600 2549323 2145265 1661500

    2006 2677200 1792300 2742194 2066338 1746000

    2007 2543400 2170400 2916317 2823980 2185500

    2008 2423800 2756100 3278078 3056576 2825900

    2009 2599300 2706800 3630990 3858635 3588000

    Name CSM DRAKA HEIJMANS IMTECH LOGICA

    Code EBIT EBIT EBIT EBIT EBIT

    2004 233900 -5600 84026 70277 61900

    2005 124600 49600 137887 81886 1266002006 104600 90200 126086 112213 159100

    2007 69300 174100 99958 148357 117800

    2008 109000 94100 42697 181817 84200

    2009 141800 9200 -45961 200496 71800

    Name CSM DRAKA HEIJMANS IMTECH LOGICA

    Code EQUITY EQUITY EQUITY EQUITY EQUITY

    2004 726400 479100 456643 323692 385300

    2005 946400 360200 389152 288091 820100

    2006 844900 424400 441843 328920 1525000

    2007 957700 414800 462478 366691 15970002008 941600 440400 426483 395935 2041500

    2009 997800 549500 425825 498053 1897200

    Name CSM DRAKA HEIJMANS IMTECH LOGICACode TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT

    2004 957200 443000 323885 98547 301100

    2005 433600 448900 509865 58669 341400

    2006 522100 535100 624237 105487 734400

    2007 410100 611600 534031 215240 591900

    2008 503900 588700 642673 546420 565000

    2009 392400 368400 366448 529133 429900

    Name CSM DRAKA HEIJMANS IMTECH LOGICA

    Code RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS

    2004 881600 29400 44945 185647 -401100

    2005 920400 81700 266562 212185 -343200

    2006 812400 104400 314874 252560 3146002007 863100 98800 336396 310111 282800

    2008 899000 108800 286447 388691 3474002009 955700 84500 267434 489606 289600

    Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES

    2004 2,725685498 1,809596456 2,916269595 5,078666276 2,256536857

    2005 3,935023069 1,798446494 2,501113028 5,259453059 2,501164394

    2006 3,068178474 1,918482958 2,405998208 3,804756895 2,075586982

    2007 3,550243425 2,302442892 2,474693578 3,101547138 2,491887836

    2008 3,309484283 2,700737564 2,192671893 2,182754702 3,105768126

    2009 3,957338609 2,915526917 2,913695466 2,647796536 3,798468976

    AVG. 3,42 2,24 2,57 3,68 2,70

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    Name NUTRECO ORDINA OCE SMIT INT TEN CATE

    Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL

    2004 398879 50590 828908 41227 124500

    2005 106700 34526 99726 28895 157700

    2006 584300 -4817 401283 42019 149700

    2007 262000 -14238 461968 66290 198800

    2008 317100 -67067 349216 81481 2427002009 272900 -34477 245704 39200 148800

    Name NUTRECO ORDINA OCE SMIT INT TEN CATE

    Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS

    2004 1759100 224376 2126487 407817 378500

    2005 1737300 284429 2720860 516455 483700

    2006 1753600 450891 2521421 577040 477200

    2007 1957200 527198 2404230 756152 7083002008 2161300 453866 2442827 1186781 875000

    2009 2106100 400669 2114444 1183100 728700

    Name NUTRECO ORDINA OCE SMIT INT TEN CATE

    Code SALES SALES SALES SALES SALES

    2004 3674300 345398 2769300 355614 569600

    2005 3787400 351572 2714463 339218 566900

    2006 2944500 418859 2652853 315962 684700

    2007 2874800 485471 2808575 421938 702000

    2008 3296300 583920 3085423 520894 772200

    2009 4943100 696473 2695660 704818 1032600

    Name NUTRECO ORDINA OCE SMIT INT TEN CATE

    Code EBIT EBIT EBIT EBIT EBIT

    2004 115300 22466 114403 33468 33800

    2005 125400 31626 114615 43022 391002006 140700 37974 110847 80135 49300

    2007 161600 46066 135335 99374 69500

    2008 177400 -94310 49783 119562 88000

    2009 161300 -200 -8250 104600 29000

    Name NUTRECO ORDINA OCE SMIT INT TEN CATE

    Code EQUITY EQUITY EQUITY EQUITY EQUITY

    2004 537781 128501 704068 203654 175900

    2005 698200 152947 770830 247749 181800

    2006 744100 194039 674514 288638 238700

    2007 643400 254591 667133 364626 3101002008 655000 163280 635535 566988 366900

    2009 730200 181100 534244 644100 380800

    Name NUTRECO ORDINA OCE SMIT INT TEN CATECode TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT

    2004 444400 79874 481251 67814 96200

    2005 377200 60312 896328 103134 157300

    2006 274000 85885 712746 108557 93900

    2007 429500 97362 599869 175134 235200

    2008 540900 110864 611101 334009 336500

    2009 401000 114484 499598 170045 208300

    Name NUTRECO ORDINA OCE SMIT INT TEN CATE

    Code RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS

    2004 297800 14882 339224 27520 45000

    2005 318700 102682 78838 38280 118200

    2006 506900 139585 283482 74969 1817002007 507100 174194 286989 105604 221300

    2008 558900 81283 171710 107808 2659002009 598200 82982 70497 102384 298300

    Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES

    2004 3,538134544 3,196947875 3,047414925 3,15960035 3,456324272

    2005 3,857195486 3,774395085 1,736202596 2,543291904 2,864390808

    2006 4,376157333 2,982114689 2,112345454 2,869874728 4,20907302

    2007 3,162017764 3,207387841 2,417742715 2,541051534 2,879209223

    2008 3,05914616 1,556626759 2,222987811 1,999011627 2,626008252

    2009 4,243134506 2,870711732 2,088457064 3,320508246 3,46197817

    AVG. 3,71 2,93 2,27 2,74 3,25

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    Name USG PEOPLE VOPAK WAVIN WESSANEN

    Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL

    2004 63883 80400 67563 260700

    2005 -116549 65600 213260 230000

    2006 -87703 -27200 158163 269600

    2007 101892 -54500 129370 248400

    2008 23319 -128700 106034 2060002009 -138920 14900 128220 -37700

    Name USG PEOPLE VOPAK WAVIN WESSANEN

    Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS

    2004 514297 1554600 802087 880500

    2005 1972788 1720100 1593766 882600

    2006 1847048 1799600 1452843 857700

    2007 1918384 2116800 1482709 8243002008 1918660 2627700 1365965 806100

    2009 1581980 3130400 1304460 633600

    Name USG PEOPLE VOPAK WAVIN WESSANEN

    Code SALES SALES SALES SALES

    2004 1297800 749600 981621 2431800

    2005 1269879 697400 1017522 2392900

    2006 1317337 652300 1302781 1960300

    2007 2460096 740200 1411141 1690200

    2008 3651695 819000 1580519 1570400

    2009 3815941 923500 1581200 752800

    Name USG PEOPLE VOPAK WAVIN WESSANEN

    Code EBIT EBIT EBIT EBIT

    2004 28405 162400 77508 10900

    2005 60866 181500 99178 418002006 198211 208900 155764 42400

    2007 246492 288700 155065 61200

    2008 101891 294900 79109 56100

    2009 2807 391200 34988 -55900

    Name USG PEOPLE VOPAK WAVIN WESSANEN

    Code EQUITY EQUITY EQUITY EQUITY

    2004 200057 525500 -51355 482800

    2005 472209 584000 5620 484100

    2006 574420 651500 295464 469700

    2007 684684 790300 363196 4097002008 655061 913600 329015 363800

    2009 638812 1231500 551653 149900

    Name USG PEOPLE VOPAK WAVIN WESSANENCode TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT

    2004 157385 617000 568029 137000

    2005 979205 588800 1082046 150100

    2006 650739 543600 614800 163200

    2007 624559 698300 561863 212300

    2008 648311 1046000 509900 259400

    2009 417372 1207100 295400 235300

    Name USG PEOPLE VOPAK WAVIN WESSANEN

    Code RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS

    2004 14784 289500 -77312 1800

    2005 138616 326100 -5903 318300

    2006 236867 418100 64423 3045002007 347708 552600 125954 309900

    2008 332462 710400 133040 2634002009 302319 907500 134464 31400

    Z-SCORES Z-SCORES Z-SCORES Z-SCORES

    2004 3,655162891 1,660223694 1,453389711 5,272542504

    2005 1,061686042 1,65953088 1,00165668 5,617488241

    2006 1,718819071 1,771394156 1,730686961 5,047439494

    2007 2,680359518 1,813029561 1,907380409 4,339267675

    2008 2,940018074 1,525488633 1,963689835 3,781462475

    2009 3,496073227 1,730808442 2,682200304 1,276012591

    AVG. 2,59 1,69 1,79 4,22

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    Appendix 3 Sector-overview AEX- and AMX-index (Z-Scores)

    Sector IT & TELECOM

    2004 2005 2006 2007 2008 2009ASML 2,86 2,96 6,03 4,54 4,44 3,24

    ASM International 2,06 1,94 2,67 3,22 3,45 1,76

    Imtech 5,08 5,26 3,8 3,1 2,18 2,65

    KPN 1,44 0,53 0,61 0,58 0,61 0,87

    Logica 2,26 2,5 2,08 2,49 3,11 3,8

    Ordina 3,2 3,77 2,98 3,21 1,56 2,87

    Z-Scores 2,82 2,83 3,03 2,86 2,56 2,53 avg. 2,77

    Sector FOOD&BEVERAGE

    2004 2005 2006 2007 2008 2009

    Ahold 2,64 2,56 3,03 3,50 3,95 2,87

    CSM 2,73 3,94 3,07 3,55 3,31 3,96

    Heineken 2,11 2,21 2,64 2,93 1,30 1,65

    Nutreco 3,54 3,86 4,38 3,16 3,06 4,24

    Unilever 1,97 2,22 2,67 2,84 2,90 2,97

    Wessanen 5,27 5,62 5,05 4,34 3,78 1,28

    Z-Scores 3,04 3,40 3,47 3,39 3,05 2,83 avg. 3,20

    SectorCONSUMERDISCRETIONARY

    2004 2005 2006 2007 2008 2009

    Oc 3,05 1,74 2,11 2,42 2,22 2,09

    Philips 4,30 4,47 5,51 6,27 4,20 3,96

    Wolters Kluwer 1,17 1,25 1,19 1,56 1,26 1,21

    Z-Scores 2,84 2,49 2,94 3,42 2,56 2,42 avg. 2,78

    Sector INDUSTRIALS

    2004 2005 2006 2007 2008 2009

    Aalberts 1,87 1,83 1,81 2,15 1,88 1,99

    Air-France KLM 1,55 1,04 1,50 1,63 1,78 0,95

    Arcadis 4,83 3,04 2,89 2,43 2,52 2,76

    BAM 3,26 1,91 1,71 1,82 2,01 1,83

    Boskalis 17,21 7,28 6,64 6,58 2,93 11,20

    Fugro 1,46 2,44 2,29 2,49 2,56 2,78Heijmans 2,92 2,5 2,41 2,47 2,19 2,91

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    Randstad 5,27 5,08 8,70 4,61 2,02 3,70

    SBM 1,65 1,73 1,77 1,99 1,51 1,88

    Smit 3,16 2,54 2,87 2,54 1,99 3,32

    TNT 3,29 3,73 3,15 2,59 2,42 2,37

    Unibail-Rodamco 1,42 2,34 2,65 1,62 0,93 0,78

    USG people 3,65 1,06 1,72 2,68 2,94 3,5

    Wavin 1,45 1,01 1,73 1,91 1,96 2,68

    Wereldhave 1,66 1,49 2,85 2,47 2,01 1,87

    Z-Scores 3,64 2,60 2,98 2,67 2,11 2,97 avg. 2,83

    Sector MATERIALS

    2004 2005 2006 2007 2008 2009

    AkzoNobel 2,55 2,62 2,85 4,08 1,80 2,31

    AMG #NA 1,72 1,77 2,72 1,84 2,31

    Arcelor Mittal 3,96 2,06 1,82 2,19 2,35 2,82

    Crucell 6,73 7,23 5,86 4,49 4,54 9,13

    Draka 1,81 1,8 1,92 2,3 2,7 2,92

    DSM 3,03 3,78 4,32 3,85 3,29 3,45

    Ten Cate 3,46 2,86 4,21 2,88 2,62 3,46

    Z-Scores 3,59 3,15 3,25 3,22 2,73 3,77 avg. 3,29

    Sector Energy

    2004 2005 2006 2007 2008 2009

    Royal Dutch Shell 6,08 6,49 6,65 6,87 5,77 4,83

    Vopak 1,66 1,66 1,77 1,81 1,53 1,73

    Z-Scores 3,87 4,08 4,21 4,34 3,65 3,28 avg. 3,90


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