FINANCIAL RATIOS AS THE PREDICTOR OF CORPORATE DISTRESS IN

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					FINANCIAL RATIOS AS THE PREDICTOR OF CORPORATE
              DISTRESS IN MALAYSIA




             NAZRINN FARISS IDRIS




    FACULTY OF BUSINESS AND ACCOUNTANCY
            UNIVERSITY OF MALAYA


                  JUNE 2008
FINANCIAL RATIOS AS THE PREDICTOR OF CORPORATE
              DISTRESS IN MALAYSIA




             NAZRINN FARISS IDRIS




    FACULTY OF BUSINESS AND ACCOUNTANCY
            UNIVERSITY OF MALAYA



                  JUNE 2008




                                                 2
FINANCIAL RATIOS AS THE PREDICTOR OF CORPORATE
              DISTRESS IN MALAYSIA




               NAZRINN FARISS IDRIS




        Bachelor of Business Administration
                     (Finance)
           Wichita State University, USA
                    1995 / 1998




    Submitted to the Graduate School of Business
       Faculty of Business and Accountancy
      University of Malaya, in partial fulfillment
             Of the requirement for the
        Masters of Business Administration


                     JUNE 2008




                                                     3
                                 ABSTRACT




The 1997 Asian Financial Crisis severely impacted Malaysia’s domestic
economy. In the space of just 3 short weeks, almost RM300 billion in market
capitalization was wiped off the local bourse. While some argue the situation
had been predicted, clearly the depth and suddenness of the event had caught
many by surprise. To many Malaysians, the occurrence of the financial crisis
highlighted the need for firm action and activities to be constantly monitored
and regulated. As the value of listed firms are often supported by the use of
funds supplied by the general public, the potential loss in equity value as a
direct or indirect result of firm distress, bankruptcy, and reorganization could
have wide ranging effects. Clearly, a pre-emptive tool to identify potential
problems had to be developed. One tool often cited was through the use of
financial ratios. First developed around the turn of the 20th century, research
argued that it could predict firm health with a 78% accuracy rate up to five years
before distress or failure.


In order to evaluate the usefulness of financial ratios to predict firm status in
Malaysia, data for firms listed on the Industrial sector of Bursa Malaysia’s Main
Board were collected. By compiling the data in the form of financial ratios, we
hoped that differences between those from healthy firms and those from
distressed firms could be evaluated. Through the use of a ratio analysis, tests of
significance, and logit analysis, we found that financial ratios did indeed
discriminate the two groups. For almost all ratios tested, the difference between
the two groups was found to be statistically significant. Furthermore, by
comparing the two groups based on their liquidity and profitability, results
indicate that firm distress or failure could be predicted based on these
characteristics.




                                                                                 4
                         ACKNOWLEDGEMENT


First and foremost, I would like to express my deepest appreciation to my

project paper supervisor, Dr. Rubi Ahmad from the Faculty of Business and

Accountancy, University of Malaya, for her invaluable time, patience, and ideas

in guiding me to complete this project. Dr. Rubi, given your already heavy

workload, I truly appreciate all the help you had given me.



In addition, I would also like to take this opportunity to thank all MBA City

Campus staff, the staff of University of Malaya’s library, and those attached with

KLSE’s Resource Centre. You have all aided me in ways that I can hardly

express, and for thank I thank you. Without your kind help, advice, and

encouragement, I truly believe that this paper would not have been possible.




                                                                                 5
                           LIST OF TABLES




TABLE 1       GROUP STATISTIC                                APPENDIX

TABLE 3:      BURSA MALAYSIA PN4 RECLASSIFICATION CRITERIA   Page 43

TABLE 4.1.1   SUMMARY OF PROFITABILITY RATIO STATISTICS      Page 51

TABLE 4.1.2   SUMMARY OF CASH FLOW RATIO STATISTICS          Page 53

TABLE 4.1.3   SUMMARY OF LIQUIDITY RATIO STATISTICS          Page 54

TABLE 4.1.4   SUMMARY OF LONG-TERM RATIO STATISTICS          Page 57

TABLE 4.2     MULTICOLLINEARITY DIAGNOSTIC                   Page 60

TABLE 4.3     COEFFICIENT CORRELATION MATRIX                 Page 61

TABLE 4.4     LOGIT ANALYSIS MODEL 1                         Page 62

TABLE 4.5     LOGIT ANALYSIS MODEL 1 ODDS RATIO              Page 64

TABLE 4.6     MODEL 1 PREDICTIVE ACCURACY                    Page 67

TABLE 4.7     LOGIT ANALYSIS MODEL 2                         Page 68

TABLE 4.8     MODEL 2 PREDICTIVE ACCURACY                    Page 69




                                                                        6
                           LIST OF APPENDICES




Appendix 1: TABLE 1 – GROUP STATISTICS

Appendix 2: SUMMARY STATISTICS

Appendix 3: Independent Samples Test 1998

Appendix 4: Independent Samples Test 1999

Appendix 5: Independent Samples Test 2000

Appendix 6: Independent Samples Test 2001

Appendix 7: PN4 Criteria

Appendix 8: t-TEST Results

Appendix 9: LOGIT ANALYSIS – ALL VARIABLES

Appendix 10: LOGIT ANALYSIS – MODEL 1

Appendix 11: LOGIT ANALYSIS – STEPWISE REGRESSION

Appendix 12: SAMPLE POPULATION




                                                    7
TABLE OF CONTENTS



ABSTRACT                                                    4

ACKNOWLEDGEMENT                                             5

LIST OF TABLES                                              6

LIST OF APPENDICES                                          7

CHAPTER 1: INTRODUCTION
     1.1 BACKGROUND                                         10
     1.2 SIGNIFICANCE OF THE STUDY                          12
     1.3 PURPOSE OF THE STUDY                               15
     1.4 LIMITATIONS OF THE STUDY                           17
     1.5 ORGANIZATION OF THE STUDY                          18


CHAPTER 2: LITERATURE REVIEW
     2.1 INTRODUCTION                                       19
     2.2 PREDICTING BANKRUPTCY: UNIVARIATE ANALYSIS         20
     2.3 PREDICTING BANKRUPTCY: DISCRIMINANT ANALYSIS       26
     2.4 PREDICTING BANKRUPTCY: MULTIPLE DISCRIMINANT       28
     ANALYSIS
     2.5 PREDICTING BANKRUPTCY: LOGIT, PROBIT & LOGISTIC    34
     ANALYSIS


CHAPTER 3: RESEARCH METHODOLOGY
     3.1 HYPOTHESIS                                         40
     3.2 RESEARCH DESIGN                                    41
     3.3 VARIABLES UNDER STUDY: DEPENDENT VARIABLE          43
     3.4 INDEPENDENT VARIABLES                              44
                 3.4.1 LIQUIDITY RATIOS                     44
                 3.4.2 PROFITABILITY & PRODUCTIVITY RATIO   46
                 3.4.3 CASH FLOW RATIO                      47
                 3.4.4 LONG-TERM SOLVENCY RATIO             48

                                                                 8
     3.5 DATA COLLECTION                                   49
     3.6 DATA ANALYSIS                                     49


CHAPTER 4: RESULTS & ANALYSIS
     4.1.1 RATIO ANALYSIS & T-TEST: Profitability ratios   51
     4.1.2 RATIO ANALYSIS & T-TEST: Cash Flow ratios       53
     4.1.3 RATIO ANALYSIS & T-TEST: Liquidity ratios       54
     4.1.4 RATIO ANALYSIS & T-TEST: Solvency ratios        57
     4.2 LOGIT ANALYSIS                                    58
     4.2.1 LOGIT ANALYSIS: STREAMLINING THE MODEL          59
     4.2.2 LOGIT ANALYSIS: MODEL 1                         62
     4.2.3 LOGIT ANALYSIS: MODEL 2                         68


CHAPTER 5: CONCLUSION
     5.1 SUMMARY                                           71


REFERENCES
APPENDIX




                                                                9
                        CHAPTER 1: INTRODUCTION


1.1 BACKGROUND



In mid to late 1960‘s, William H. Beaver (1966) and Edward I. Altman (1968)

published what financial experts today would refer to as the pioneering work in

the study and prediction of corporate bankruptcy. Although the works of these

authors were developed, written and published independently of one another,

as these works came out at about the same time, the different but

complimentary manner in which each study addressed the concerns at hand

resonated throughout the financial community of 1960‘s America. Today,

almost forty-years after these articles were first published, the works of Beaver

and Altman is continually cited by contemporary researchers as the basis of

their own studies. And with the spurt of interest created and generated,

academicians and contemporary researchers use it as a foundation to develop,

create, and identify newer and more accurate approaches to understanding

and predicting corporate bankruptcy.



Today, while the spurt of interests surrounding the understanding and

prediction of corporate bankruptcy is to be expected given the volatility and

uncertainties of the global economic climate, when Beaver and Altman first

wrote their paper, these reasons were not their motivation. If we were to read

the first page of either author’s work, ironically, their study in the area of

corporate bankruptcy was driven more chance than by design. For both

authors, the initial purpose of their works was driven more by their need to

understand and evaluate the effectiveness of financial ratios. In the 1960’s,

                                                                               10
although the use of ratio analysis was widespread, its significance was doubted

by many.



In the years leading up to these works, academicians questioned the purpose

and value of ratio analysis as an analytical tool through which business

performance could be evaluated. With theorists downgrading the arbitrary rules

of thumbs such as comparisons of one company’s ratio with that of another’s,

these attacks constituted more than an attack on the usefulness of ratio

analysis as an analytical tool. Seen from a different angle, the attacks on the

validity and usefulness of the information presented through ratio analysis

could also be constituted as an attack on the validity and usefulness of

corporate financial statements as a source of financial information.



As financial statements are reports that state a company’s financial condition

and performance sometime during the past, many academicians were

increasingly disillusioned with using historical information to predict the future.

In a sense, the prediction of corporate bankruptcy was seen as an illustrative

example through which an assessment of the value and function of ratio

analysis could established and determined. By proving the usefulness of ratio

analysis, doing so would give credence to the use of past information to predict

future performances.




                                                                                 11
1.2 SIGNIFICANCE OF THE STUDY



In looking back at the reasons cited by Beaver and Altman as to what

constitutes the motivation and purpose for their research, it is amusing that this

was driven more by chance and convenience than by market demand. Given

the volatility and uncertainties prevalent in the global economy, we would have

expected these issues to drive any study on corporate distress and failure.

Perhaps the economic climate between the 1960’s and that of the new

millennium are quite different. But, given the high costs of financial failure and

restructuring, the understanding of financial distress and bankruptcy is as vital

today as it would have been then.



Through the understanding of financial distress and bankruptcy, businesses

could be better managed and evaluated as stakeholders would have at their

disposal the knowledge and tools required to monitor and evaluate firm action.

According to Aharony (1980) corporate failure is an indication of resource

misallocation deemed undesirable from a social standpoint. Given the finite

resources available, it is therefore imperative that firm action maximizes the

returns to stakeholders.



From the point of view of business managers, by understanding of the topic

better, the insights provided exposes them to the challenges that lie ahead.

Through proper planning and resource allocation, courses of action can be put

into place. The incentive here is that “good companies” can differentiate

themselves from the rest, allowing them to capitalize on opportunities to


                                                                                12
maximize their profits whilst minimizing their costs. Owners or shareholders

stand to benefit directly through share value maximization. Investors on the

other hand can also use financial ratios as a tool through which investment

choices can be identified, evaluated, and subsequently monitored. Doing so

ensures that the potential returns from investments reflect the risks borne by

investors.



From a lender’s point of view, by having a better grasp of the factors affecting

corporate distress and bankruptcy, firm specific risks can be determined. By

more accurately identifying the factors that can drive a company to distress and

bankruptcy, lenders can evaluate firm financial positions more confidently.

Chartkou (2005) 1states that while lenders are concerned with the burden of

bad loans and the premium value needed to undertake those risks, borrowers

want to borrow at lowest possible rates. As a result, this benefits the lender as

they are able to “price” their investment to reflect the risks bared. Given the high

financial costs associated with financial default and corporate failure and the

volatility of today’s economic environment, the price of failure is sometimes too

great.



While the reasons mentioned above are the positive reasons why the study is

beneficial to us all, perhaps the more important reasons why this study should

be undertaken is due to the potential adverse effects that comes as a result not

being able to identify the characteristics of financial distress. According to

Beaver (1968), evidence suggests that a large portion of firm value is lost


1
    Working paper
                                                                                 13
during a corporate reorganization and liquidation process. This point is echoed

by Russell (1999) who found that by comparing the pre-bankruptcy equity value

with the value after bankruptcy declaration, firm equity value can on average fall

by approximately 70%. For many of us in Malaysia, the 1997 Asian Financial

Crisis (AFC) illustrates the point.



In 1997, due to the financial crisis that had afflicted most economies within the

region, companies listed on the Bursa Saham Kuala Lumpur (BSKL) lost

hundreds of billions of dollars in market value almost overnight. According to

Isa (2005), as a result of the AFC, approximately 80% or RM300 billion of the

market value of the local bourse was lost in the span of just 3 weeks. While

some may argue that this crisis was predicted in many ways, it was clear that

due to the lack of research and understanding of the factors that trigger

distress, the event had caught almost everyone by surprise. While the effects of

the AFC were unprecedented, the reality is that it clearly adversely affected

public listed firms in Malaysia. Though some were more affected than others,

clearly more had to be done to distinguish and regulate high risk firm activities.



If we look back at past records, for some, even though more than 3 years had

past from the time the crisis initially began, its impact and effects could still be

seen. With mounting debts, huge accumulated losses, and poor cash flows, by

2001, 91 companies from various sectors would need to be reclassified as

financially distressed under BSKL’s newly introduced Practice Note 4/2001

provision2. Under a PN4 status, troubled companies would be given the time


2
    Practice note 4/2001 - Please refer to Appendix.
                                                                                 14
and opportunity to regularize their activities in-line with the requirements and

provisions of the law. Failing which, these companies would be delisted and

removed from the official list of companies on the local bourse.




1.3 PURPOSE OF THE STUDY



In light of the evidence previously stated, it should by now be clear that in order

for investors, companies, lenders, and government regulators to safeguard the

value of the public’s investment, understanding the characteristics of financially

distressed companies is the vital first step to developing a relevant and

accurate distress prediction model. While most past and current literature have

focused their attention on the identification and prediction of corporate

bankruptcy models, this paper will instead will try to look at it from a financial

distress point of view. According to Isa (2005), the lack of work on financial

distress stems from the difficulties in objectively defining distress. In contrast,

bankruptcy is legally defined. Through Bursa Malaysia’s efforts however, we

here in Malaysia are blessed as conditions of distress can be legally defined. By

being able to define the conditions legally, data can be selected objectively.



As just mentioned, while the majority of predictive models reviewed focus their

investigation on the ability to predict bankruptcy, there are a few reasons why

the lessons and implications are also applicable to corporate distress. Firstly,

Ward (1997) suggests that financial distress or economic bankruptcy is often

described by one of the following circumstances: (1) a condition of negative net


                                                                                 15
worth, (2) an inability to pay debts as they are due, and (3) as a legal definition

under which a firm continues to operate or liquidate under court protection.

Given these conditions, it is clear that the occurrence of distress supercedes

bankruptcy.



Secondly, while firms that experience distress are often more likely to fail and

go bankrupt, not all distressed firms actually do. Based on his study, Ward

(1997) found that the amount of time a firm can remain distressed before filing

for bankruptcy can stretch up to 7 years. This finding is verified by Shirata

(1999) who studied the predictors of distress in a Japanese setting. The reason

cited for this is that while financial distress is a critical event, it is seldom a fatal

event given the wide range of preventive and prescriptive options available to

the firm. Gilbert et al (1990) puts it best when she states that “Bankruptcy filing

can be viewed as a strategic and voluntary response by management to

financial problems”. So given the huge window of opportunity available to rectify

the situation and the high costs of bankruptcy, the high cost of failure should

provide reason enough for us to want to identify the situation early. Based on

empirical evidence, it has been suggested that financial ratios can signal

potential bankruptcy up to 5 years before its occurrence.



Thirdly, Taffler (1984) argues that the results of bankruptcy prediction studies

should be interpreted as a description of distress rather than of bankruptcy. The

reason is that prior research has succeeded in identifying differences between

healthy and distressed companies. Gilbert et al (1990) found it difficult to

differentiate between distressed and bankrupt firms. To date, as very little


                                                                                      16
research on the topic has been written within the context of Malaysia, we hope

doing so will spur further interest on the topic. With the exception of Mohd Isa

(2005) who chose to apply Altman’s bankruptcy predictive model to Malaysian

based companies, little else is written on the topic.




1.4 LIMITATIONS OF THE STUDY



In order to discover the variables at play and their significance, we hope to do

this by analyzing and comparing the characteristics of healthy and distressed

firms as given by their financial ratios. Given the availability of a relatively large

sample of distressed or PN4 companies, we hope to use this to identify specific

characteristics that act as pre-cursors to financial distress.



According to Needles (1995) the use of ratios is not without its limitations.

Firstly, the use of financial ratios by themselves is meaningless as the values

themselves do not mean much. In order for it to be useful, it should be

compared with past values or against other companies as a point of reference.

As our study focuses on firms listed on the Industrial Sector of Bursa Malaysia’s

Main Board, the second limitation is that inferences are limited to other similar

type firms. Thirdly, Altman (1968) and Beaver (1966) argue that the use of ratio

comparisons should be limited to companies of comparative sizes. Thus, using

the results to form judgments on smaller non-listed entities may be misleading

and inappropriate as companies of different size and industry operate and

behave differently. Lastly, according to Needles (1995), factors like accounting


                                                                                   17
methodologies employed and the date when the analysis is conducted can also

dictate the results from ratio analysis. As Balance Sheet items are often

snapshots of values at a particular point time, variations stemming from

different dates can significantly affect the conclusions observed. As data

collected is grouped by the fiscal year, certain biases are unavoidable and must

therefore be interpreted carefully.




1.5 ORGANIZATION OF THE STUDY



The rest of the paper will be organized as follows. Chapter 2 discusses the

literature review related to financial distress and bankruptcy prediction models.

Chapter 3 elaborates on the manner in which research methodology is

designed and conducted. Chapter 4 presents and discusses the results of the

research. And finally, Chapter 5 concludes the study.




                                                                               18
                        CHAPTER 2: LITERATURE REVIEW



2.1 INTRODUCTION



In this section we will be reviewing past literature to identify the current body of

knowledge on the subject matter. As we are interested in identifying and

understanding the characteristics of distressed firms, let us first begin by

reviewing the manner in which previous works were conducted. By

understanding the differing methodologies used in the past to explore the

relationship between distressed and bankrupt firms, this should allow us to

understand the context and limitations of the studies themselves. This is

extremely important as researchers often complain that the most difficult issue

afflicting any investigation on topic is its lack of theoretical underpinning. As

Ohlson (1980) states, in the absence of any theory on bankruptcy, the selection

of appropriate functions to study is a huge problem for researchers. As a

practical solution, all a researcher can do is to choose based on computational

and interpretive simplicity. This view is also shared by Zmijewski (1984) and

Gilbert et al (1990).



For most researchers, while determining the variables of interest and their

implied relationship to financial distress or bankruptcy is very important, the

methodologies used to asses the relationships are often viewed more critically

than the variables themselves. As shall be seen, given the variables under

study, a large degree of the differences between differing works is driven more

by differing methodologies than by differences in the variables of interest. For


                                                                                  19
this reason, the review of literature will be partitioned according to the

approaches used. Hopefully, by understanding the context and limitations of

the studies, perhaps this will give us a clue on why certain variables were

chosen, what are their significance, and the relationship these variables have

with financial distress3.




2.2 PREDICTING BANKRUPTCY: UNIVARIATE ANALYSIS



Beginning with the pioneering works of Beaver (1966) who sought to determine

the usefulness and effectiveness of financial ratios as an analytical tool through

which corporate bankruptcy could be predicted, the primary concern Beaver

had with the use of ratios was not with so much related to use of ratios as a

method or form through which financial-statement data could be presented, but

rather with the underlying predictive ability of the financial statements

themselves. In effect, Beaver wanted to obtain empirical verification of the

usefulness of accounting data. To do this, he took the predictive ability of ratios

to signal business failure as a proxy on the predictive powers of the ratios

themselves.



In order to do this, he began by defining “failure” as the inability of the firm to

meet its maturing financial obligations. Operationally, a firm is said to have

failed when any of the following events occur; bankruptcy, bond default, an

overdrawn bank account, or the non-payment of a preferred stock dividend.

3
 While a large percentage of the previous works reviewed center their discussions on the topic of bankruptcy, as
most firms will undergo a period of distress before filing for bankruptcy, understanding the knowledge and factors
                                                                                                                20
This definition of failure is shared by Altman (1968), Charitou (2000), and a host

of other researchers. Having defined the dependent variable of interest, he then

went about selecting the independent variables.



In looking at the independent variables under his study, what made Beaver’s

study a pioneering look into corporate bankruptcy was his use of multiple ratios

as the independent variable. By looking at 30 financial ratios from several

distinct groups, his study differed from past research as he used the variables

to identify ratios that could best explain and predict bankruptcy. Before then,

studies focused their efforts on testing one explanatory variable against the

dependent variable. By pitting a singular explanatory variable against the

dependent variable, comparisons of predictive power and relative significance

often proved to be a futile exercise. By combining the use of multiple variables,

their relative effect and significance could be ranked and identified.



Out of all possible combinations, the criteria used to identify the variables were

driven by several factors. The first criterion was popularity as given by its

frequent appearance in financial literature. The second criterion was dictated by

the past performances of the ratios themselves. The third and final selection

criterion was dictated by its ability to be defined as a “cash-flow” concept. Any

ratio which met these criteria was included in the study. Although the manner

used seems void of any clear and objective rules, the lack of theoretical

underpinning necessitates the approach. Similar approaches were also used

by researchers like Deakin (1972), Ohlson (1980), Altman (1968), and Dichev



afflicting bankruptcy should be a good proxy to advancing our knowledge of distressed companies.
                                                                                                   21
(1998).



Having identified the variables of interest, they were then tested for their ability

to predict bankruptcy. As a result, the number of independent variables chosen

was narrowed down to seven which exhibited the best performance. Amongst

them were six accounting ratios and one accounting measure. The reason he

did this was that for his multi-ratio analysis to convey as much additional

information possible, common elements had to be eliminated4. The selected

ratios were now confined to; Cash Flow to Total Debt, Net Income to Total

Asset, Total Debt to Total Asset, Working Capital to Total Asset, Current Ratio,

No Credit Interval5, and Total Assets.



To analyze the ratios, their predictive power was tested through their ability to

correctly classify firms. Based on the accuracy of ratios to predict group

classification6, ratios were differentiated and ranked. The higher the accuracy

(or the lower the misclassification rate), the better it was as a predictor of failure.

The ability to predict failure was best given by the ratio of Cash Flow to Total

Debt. Used by itself, it could correctly predict and classify firm status 87% of the

time in the year preceding failure, and 78% of the time five years prior to failure.

The next best indicator is given by the ratio of Net Income to Total Assets. This

ratio correctly classified firms 87% of the time the year prior to failure and 72%

of the time five years prior to failure. As both these ratios were based on a cash

flow concept and were highly correlated to one another, he deemed the results

to be as expected.

4
    ratios with common numerators and or denominators were dropped
5
    No Credit Interval = (Defensive Assets - Current Liabilities)/ Operational Expenditures
                                                                                              22
In analyzing the comparisons of means between failed and non-failed

companies, Beaver found that given the mean values for failed firms, a trend

could be seen. With the mean ratios for failed firms being significantly lower up

to five years before failure, the decline in value grew increasingly worse as

bankruptcy loomed. As the mean values for non-failed firms exhibited a more

stable value throughout the period under study, no such trend or pattern could

be seen. As this pattern is evident in all ratios under the study, he suggests that

the use of ratios to predict failure is not without its merits.



In a follow-up to his initial paper released just several years after the first,

Beaver (1968) advanced his look into the prediction of bankruptcy to include the

use of market returns. By doing so, he sought to investigate the extent to which

changes in the market value of stocks could be used to predict failure. By using

the same sample population to conduct his study, the annual rates of returns for

both failed and non-failed firms were computed for up to five years prior to

failure.



Through a cross sectional analysis which sought to investigate the variables’

value at a particular point in time, Beaver had instinctively expected failed firms

to carry a higher expected rate of return compared to their healthier

counterparts. The reason for this was simple. As failing firms would be

expected to carry a higher probability of failure, the relatively higher inherent

risks should compensate risk averse investors with a higher return on their


6
    Firms are classified into two groups; fail or not failed.
                                                                                 23
investment. Based on the evidence presented however, it was clear that at no

point in time did ex post returns differ between failed and non-failed firms. This

to him was extremely surprising. As he had expected investors to behave in a

logical and rational manner, it was assumed that at the start each period, the

solvency and inherent risk of the firm will be evaluated to ensure that expected

returns would commensurate the risks of the firm. If at any time the solvency of

the firm deteriorated, stock prices would fall as investors adjusted their

positions, giving a rate of return lower than would have been initially expected.



A longitudinal analysis however found that the mean distribution of firm returns

did indeed vary with time. Although non-failed firms exhibited no significant

upward or downward trend, the median returns from failed firms were lower in

the years approaching failure. As failure approached, these differences grew

increasingly larger. This indicated that on average, an unexpected decline in

solvency position does indeed impact the ex post returns of failed firms.

Furthermore, as data also showed a larger return dispersion, failed firms

carried a greater risk of variability7. These findings were the same whether the

returns were adjusted or unadjusted for market wide economic effects.



In comparing the predictive powers of his ratio model to that of the market

model, Beaver used a dichotomous classification test to measure the rate of

misclassification errors8. By measuring the percentage misclassification error

rates, the lower misclassification error rate given by ratios than market returns


7
  Both the Avg. and Marginal rates of returns were lower for failed firms. The dispersion of returns were also much
greater
8
  Misclassification rates were given by how accurately each model would correctly predict group membership.
Barniv (2000) gives the following example: if a cutoff probability value of 0.5 is used, firms with a failure
                                                                                                                24
clearly suggests that it is a superior predictor. Through the use financial ratios,

78% of his sample could accurately classified five years before failure.



Continuing on with the research conducted by Beaver, Deakin (1972) sought to

advance the knowledge on bankruptcy prediction by first verifying Beaver’s

results. This was done by replicating Beaver’s original study. Although the

general procedure used by Deakin to evaluate and estimate the probability of

bankruptcy is somewhat similar to that employed by Beaver, the differences

amongst these approaches mainly centered on how the authors chose to define

failure or bankruptcy. Unlike Beaver who had chosen to define “failed” firms to

include those that missed-out on fulfilling their preferred dividend payments and

those which defaulted on their loan obligations, Deakin was more stringent on

his definition. Only firms that were bankrupt, insolvent, or liquated by creditors

would be deemed as failed in his study.



The independent variables used by Deakin were taken directly from those

highlighted by Beaver. With a sample consisting of 32 failed and non-failed

firms, like Beaver before him, the firms were matched on the basis of industry

classification, asset size, and of course, year of financial data. To test his

findings, percentage error rates were calculated. Although some slight

differences could be seen between the two, Deakin admits that his small

sample size prohibits him from determining if these differences were indeed

significant.




probability > 0.5 would be classified as a bankrupt firm.
                                                                                 25
To work around this, he uses a Spearman rank-order correlation coefficient to

allow a comparison of the predictive power of ratios in the two studies. As the

rank-order correlation coefficient was very high in four of the five years under

study, this confirmed Beaver’s findings. Interestingly, the low correlation

coefficient in year 3 before failure was attributed to the rapid expansion failed

firms undertook three to four years before failure. This was financed primarily

through the use of increased debt and preferred stock instead of common

equity or retained earnings. As these firms would later be unable to generate

the income required to support higher debts, these “assets” would be quickly

lost. Soon after, asset and debt values would fall back to expected levels.



Based on the error classification rates obtained through this methodology,

although the 20% 9 misclassification rate the year preceding failure would

indicate the appearance of a fairly accurate model, Deakin felt that through the

use of an alternative model to predict bankruptcy, a lower misclassification rate

could be obtained. The methodology he chose to accomplish this was through

the use of a Discriminant Analysis (DA).




2.3 PREDICTING BANKRUPTCY: DISCRIMINANT ANALYSIS



The purpose of Discriminant Analysis (DA) is to find a linear combination of

ratios which would best discriminate between groups under study. A major

assumption to DA is that samples must be drawn randomly. To avoid bias,

9
    As given by the ratio of Cash Flow to Total Debt.

                                                                               26
Deakin chose a second sample consisting of 32 non-failed firms chosen

randomly.



By inputting the ratios used by Beaver into the DA model, a scaled vector would

indicate the relative contribution of each variable. This would then be used

produce a score that maximizes the distinction between the two groups. The

significance of each of the discriminant functions is then measured by Wilks

lambda. This is used to test the hypothesis that the means of the ratio vector

from both groups are similar. By converting it into an F value, the ratio value

indicates the probability of a significant separation between the scores of a

failed and non-failed firm. Based on the results generated, the variables that

carry a high value would indicate a significant contribution to the predictive

power of the function. Those with a low value indicate the opposite.



From Deakin’s results, he finds that of his original variables, the ratios deemed

as significant predictors of failure varied depending on the year of study. In year

five before failure for example, the three most significant predictors of failure

were given by Working Capital to Total Asset, Total Debt to Total Asset, and

Quick Asset to Sales. In the year prior to failure, they were; Working Capital to

Total Asset10, the Current Ratio, and the ratio of Current Asset to Total Asset11.

Interestingly, when Deakin tried to omit variables with little predictive power to

create a more concise model, misclassification errors increased significantly.



In light of the evidence presented by Deakin, the question that is often asked


10
     Scaled vector score for years 2-4 prior to failure indicate low predictive abilities.
                                                                                             27
now is how DA compares to Univariate Analysis. Like before, model accuracy is

measured           by      comparing           their      misclassification            error       rates.       With

misclassification error rates of 14%12 one year prior to failure and 10% two

years prior to failure, the use of the DA model appears to produce better results.




2.4 PREDICTING BANKRUPTCY: MULTIPLE DISCRIMINANT ANALYSIS



Like Beaver before him, Altman’s (1968) study was initially motivated by the

need to asses the quality of ratio analysis as an analytical tool. By using

corporate bankruptcy as an illustrative example, he hoped to determine the

value of financial ratios through the use of a Multiple Discriminant Analysis

(MDA) technique. MDA is a statistical technique used to classify observations

into several a priori (formal) groupings dependent upon the observations of

individual characteristics. MDA is primarily used when the dependent variable

appears in qualitative form, which in this case it would appear as either

bankrupt or non-bankrupt.



For Altman, the univariate approach originally employed by Beaver suffered

from too many deficiencies. Firstly, as the use of a univariate approach centers

on the identification of a singular signal of impending problem, information

presented in this form is confusing and easily misinterpreted13. Secondly, as


11
  While the ratio of Working Capital to Total Asset is significant one year and five years before
bankruptcy, this is an anomaly. Again, the predictors deemed significant varied every year studied.
12
   Recall earlier that Deakin’s replication of Beaver’s original study produce 20% misclassification error rate the
year prior to failure.
13
   Example: Imagine a company with poor profitability. Taken solely, we would conclude that it is a higher risk than
another firm with superior profitability levels. But, given that it also has better liquidity, does it still remain the
                                                                                                                    28
there are many ratios available, the selection and combination of differing ratios

would each result its own unique implications. Due to this, ranking and selecting

the appropriate combination to use is both tricky and nearly impossible.



As such, Altman argued that for financial ratios to be relevant it has to be an

extension of past findings, building upon and combining the factors deemed

significant in predicting failure. Now, the focus shifts from identifying a ratio that

can explain or predict failure to identifying the combination of ratios and their

weights (as a measure of factoring their overall impact) that would best explain

the situation.



Looking at the variables under study, initially, 22 ratios were chosen based

upon their popularity and potential relevancy. They were classified into 5

standard ratio categories; liquidity, profitability, leverage, solvency, and activity

ratios. Of the original 22, 5 were chosen as doing the best overall job. This was

done through; (i) observation of their statistical significance, (ii) evaluation of

inter-correlations amongst independent variables, (iii) observation of the

predictive accuracy of the various profiles, and, (iv) the judgment of the analyst.

The final set included; (i) Working Capital to Total Asset, ii) Retained Earnings

to Total Asset, iii) EBIT to Total Asset, iv) Market Value of Equity to Book Value

of Debt, and (v) Sales to Total Asset.



In looking at the final ratios chosen, the variable profile did not necessarily

contain the ones with the most significant predictive power as measured



riskier of the two or is it the opposite. This is the flaw to the use of a univariate approach.
                                                                                                  29
independently14. In order to improve upon Beaver’s univariate approach, the

variables had to be chosen based on their overall impact. This was done by

determining the relative contribution each variable made to the total

discriminating power of the model. In fact, although “Sales to Total Asset” is the

least significant of all ratios measured individually, due to its relationship with

other variables in the model, it has the second highest discriminating ability. A

probable reason for this could be due to the high negative correlation observed

between EBIT to Total Asset and Sales to Total Asset.



There are several advantages to using MDA. Firstly, when the coefficients are

applied to the ratio under study, firms can be classified into mutually exclusive

groups. By doing so, this allows us to consider common characteristics in the

entire profile and observe the manner in which they interact. Secondly, because

a two-state MDA model is analyzed in only one dimension15, the classification

process is easily interpreted. The resulting single discriminant score or Z value

can then be used to classify firms. The final discriminant function developed by

Altman is as follows;


Z = .012 (WC/TA) + .014(RE/TA) + .033(EBIT/TA) + .006(Market Value Equity/ Book Value Debt) + .999(Sales/TA)




By inputting data of a particular firm into the model and calculating its Z-score, a

Z-score value below 1.2 would imply a high probability of bankruptcy and a

Z-score above 2.9 would imply a low probability of bankruptcy. For those with


14
    To test the discriminating abilities of these 5 variables, an F-test was employed. For variables (i) to (iv), the high
F ratio at the .001 level indicates that there is significant differences between bankrupt and non-bankrupt group. The
f ratio for variable (v) however does not show significant difference between the two groups.
15
    MDA reduces the analyst’s space dimension by G-1. In a two group model, analysis is reduced to a single
dimension as given by;           Z=v1x1+v2x2+…+vNxN                         -where; vN represents discriminant
                                                                                                                       30
scores between 1.2 and 2.9, results were deemed inconclusive. Much like

before, an evaluation of the model’s predictive ability is measured through the

use of determining its classification error rates. Through the use of the Z-score,

Altman found that this predictor could correctly classify firms 95% of the time

one year prior to failure. Beyond that however, the model’s accuracy

deteriorates significantly16 suggesting that its predictive ability is unreliable.



Other significant findings from the study also confirmed the deterioration of

financial ratios as bankruptcy approached, and that for the majority of firms, the

largest changes occurred two to three years prior to bankruptcy.



Given the simplicity of Altman’s Z model, Isa (2005) argues that its popularity is

to be expected. As it provided a benchmark through which similar firms could

be compared, its indicators of financial strength can and has often been used to

predict distress. However, as many detractors will argue, the model is not

without its flaws. By using Altman’s original model to study predictors of

financial distress in Japan, Shirata (1999) argued that the manner in which

Altman conducted his research was flawed given certain limitations. Firstly, as

Altman never specified the manner in which variables under his study were

identified, it is difficult to state for certain if the variables he chose contained the

best set possible. By using powerful data mining tools like Classification And

Regression Tree model (CART) and Stepwise, these approaches allowed her

uncover explanatory variables more objectively. Unlike Stepwise procedure

which assumes that variables are normally distributed, CART selects variables



coefficient                                          xN represents independent variables
                                                                                           31
without needing such assumptions. Beginning with 61 financial ratios as the

basis of her study, the use of the Stepwise and CART helped identify four

variables deemed significant predictors of distress. This was given by the ratios

of Retained Earnings to Total Assets, Accounts plus Notes Payable to Sales,

Current Gross Payable to Previous Gross Payable, and the ratio of Interest

Expense to Borrowing. Correlation analysis ensured that these variables were

independent of each other. Amazingly, none of the ratios selected included any

profitability or liquidity ratios. Although they were widely believed to be

predictors of distress, her study proved that in the case of Japanese firms, they

were not significant predictors.



Secondly, as Altman and many researchers after him would choose to conduct

their studies through a paired sampling method, Shirata argued that the biases

introduced can lead to misleading conclusions. In Altman’s defense, the paired

sampling approach used was done in order to minimize the effects of firm size.

For Altman, although he acknowledges that size would be a significant factor in

determining distress, in order to easily evaluate the impact of ratios, size would

have to be a controlled variable. The reason he does this is due to his inability to

clearly define its relationship to bankruptcy. This view is also shared by Beaver.

Beaver realized that while a paired design sample selection methodology

mitigates the disruptive influence of asset size and industry, its use would also

virtually eliminate any predictive power these factors might have had. Another

method to control the effect of size on the independent variables as suggested

by Peat (2003) is by simply dividing the variables of interest with total assets. In


16
     Accuracy for years 2 to 5 are 72%, 48%, 29%, and 36% respectively.
                                                                                 32
doing so, total asset will ceases to be an explanatory variable.



 Shirata’s study however proves that given the four variables chosen through

CART and Stepwise, the impact of industry and size does not influence the

discriminant power of the variables. Given the significantly larger sample

population used by Shirata, this finding is unique. But with a 13.84%

misclassification error rate, her model does appear to be universal in its

application.



 As Altman’s model was based on research conducted with financial data from

the 1940s through to 1960s, Begley et al (1996) feared that using this model

well after it was first developed could result in high misclassification errors. The

reason for this is that in the time since it was first developed, economic

conditions have changed significantly. Through changes in bankruptcy laws

and changes in the acceptance of higher leverage, he feared the model was

now obsolete.



By applying the financial data from the 1980s to Altman’s original model, Begley

found that Type I and Type II misclassification rates17 were much higher than

those from the initial study. Interestingly, unlike Shirata, Begley’s study found

size to be a significant predictor of distress18. Furthermore, with the increasing

acceptance of higher leverage, debt level is not as significant a predictor as

before. In line with financial expectations, however, given the higher debt level


17
  Type I error is 18.5% and Type II error is 25.1% giving an overall misclassification rate of 21.8% one
year prior to failure.
18
  The effect identified by Begley is shared by Dichev (1998) who also found that size effect to be weak to
nonexistent for firms in the 1980s and 1990s
                                                                                                       33
carried by firms in the 1980s, the significance of liquidity is greater now than

before as the size of a firm’s interim obligations were now much larger.



In order to update Altman’s original model, Begley re-estimated the values of

Altman’s Z model. As a result, Working Capital to Total Assets is now the most

powerful predictor of probable distress. In Altman’s original model, this variable

was interestingly the variable with the least predictive power. This result

however is in line with the increasing importance of liquidity given higher use of

leverage. Furthermore, while the ratio of Sales to Total Assets was the second

most important predictor in the original model, Begley’s re-estimation found this

to be the least important predictor. As a result of the re-estimation process, the

misclassification rates were now lower, but not by much.




2.5 PREDICTING BANKRUPTCY: LOGIT, PROBIT & LOGISTIC ANALYSIS



Continuing with our review of past research on bankruptcy and bankruptcy

prediction, the next breakthrough for research in this field came through the

efforts of Ohlson (1980). Given the simplicity and accuracy of the methodology

prescribed by Altman’s (1968) MDA Approach, its popularity and usage was

widespread. However, to Ohlson, MDA was not without its drawbacks. Firstly,

the use of MDA imposes certain statistical requirements on the distributional

properties of the predictors. As Altman’s model clearly violated this condition,

Ohlson sees this as a limitation of its predictive abilities. Secondly, as the output




                                                                                  34
from the use of MDA is a score, its output allows for little else than an intuitive

interpretation. And thirdly, as most users of MDA have used a paired sampling

design to choose their sample population, the bias introduced could lead to

misleading results.



In order to avoid the problems mentioned above, Ohlson instead chose to study

the topic through the use of a conditional logit analysis. Through the use of logit,

the fundamental estimation problem can be reduced to finding out the

probability of a firm failing within a specified frame of time given the population it

belongs to. No assumptions regarding prior probability of failure is needed nor

do we need to determine the distribution of predictors. According to Barniv

(2000) logit or probit models can be seen as a limited dependent variable where

only two possible outcomes can occur. It normally takes the following form:



       P = P (y=1)/X = F(X’B)

               Where; P is the probability of bankruptcy,

                       Y = 1 if bankruptcy has occurred, Y = 0 if otherwise

                       X’ = (X1, …, Xk) is the vector of independent variables

                       B’ = (B1,…, Bk) is the coefficient corresponding to values of X

                       And, F(X’B) is the cumulative distribution function given by logit as:

                               P = F(X’B) = (eX’B)/ 1+eX’B         =    1/(1+e-X’B)




Furthermore,     unlike    most     past    research        that       use   paired   sampling

methodologies to select firm sample population, Ohlson’s study relies on

observations from 105 bankrupt firms and 2058 non-bankrupt firms with

financial data from 1970 to 1976.
                                                                                                35
Unlike earlier studies which derive their data through Moody’s Manual,

Ohlson’s data is extracted from the company’s 10-K financial statements. This

is an important difference because through a 10-K filing, the public can check

whether the bankruptcy occurs before or after the filing date. For Ohlson, this

issue of timing matters because if the purpose of the investigation is to forecast

relationships then such details are important. Using predictors with statements

released after the bankruptcy date overstates the predictive power of the

models19. Dichev (1998) points out that as some firms are delisted long after

entering bankruptcy the predictive powers of models which do not address the

issue of bankruptcy timing must be interpreted with caution.



In looking at the manner in which Ohlson chose his ratios or independent

variables, again, simplicity was his most important criteria. These variables

included; Size, Total Liabilities to Total Asset, Working Capital to Total Asset,

Current Ratio, Net Income to Total Asset, comparisons of Total Liabilities to

Total Assets 20 , funds from operations/ Total Liabilities, a measure of Net

Income represented by INTWO21, and a measure of change in net income

represented by CHIN.



By comparing the means of failed firms one and two years prior to failure with

those from non-bankrupt firms, ratios exhibited deterioration as it moves closer

to bankruptcy. Although the data used is not comparable to Beaver (1966), in


19
   Under normal circumstances, it is possible for a company to file for bankruptcy at some point in time after the
fiscal year date but before releasing the financial statements. Neglecting this possibility may lead to back-casting for
many of the failed firms.
20
     a value of 1 if TL>TA, O otherwise
                                                                                                                     36
general, the results are similar. With the exception of Size, the standard

deviations of failed firms were larger one year prior to failure than that of their

non-bankrupt counterparts. Based on the coefficient estimates and t-statistics

calculated for the independent variables under study, Size appears to be an

important predictor. With a t-statistic value exceeding 3.7 in all models used22, it

is a statistically significant predictor of bankruptcy.



Other significant predictors include financial structure, performance and

liquidity given by the ratios of; Total Liabilities to Total Assets, Net Income to

Total Assets, and Current Liabilities to Current Assets. And, given the fact that

financial state variables and performance variables show little correlation to one

another, this would indicate that the contributions of these variables are

significant and independent of one another.



Looking at the results of the logit model used, as it was able to predict

bankruptcy 96.12% of the time within one year and 95.55% within two years of

its application, initial results would indicate a more accurate model than those

previously developed (example given, Altman’s Z model). These findings are

shared by Dichev (1998) and Begley et al (1996).



Through the efforts of Begley (1996), the result from Ohlson’s original model

was retested with the use of more current data. By applying financial data from

the 1980s to Ohlson’s model and cutoffs, surprisingly, Type I error rates


21
 1 if net income positive in last 2 years, and 0 otherwise
22
 Ohlson uses 3 models. Model 1 predicts failure within 1 year. Model 2 predicts failure within 2 years.
Model 3 predicts failure within 1- 2 years.
                                                                                                    37
remained the same. But as Type II error rate has increased, overall

misclassification rates 23 were now higher than before. This indicated that

changes in the operating environment affected the model.



To update Ohlson’s model for changes in the environment, Begley began a

re-estimation process by replicating Ohlson’s original logit analysis with data

from the 1980s. As expected, the increasing acceptance of leverage reduced

the significance of debt level as given by the ratio of Total Debt to Total Assets.

Given the higher debt levels carried by firms, this situation is expected to put

more strain on their liquidity positions. The increase in the coefficient of

Working Capital to Total Assets implies that working capital is now more

important than before.



More significant however was the finding that two variables had switched signs

indicating an opposite relationship with distress than was previously expected.

The ratio of Current Liabilities to Current Assets was now inversely related to

distress, and the ratio of Net Income to Total Assets was now positively related

to distress. Begley admits that these results were clearly against economic

convention.



While the use of the re-estimated model has resulted in a lower Type II error

rate, the Type I and overall error rate is now higher than before.24 This suggests

that the new model does not improve the user’s ability to classify bankrupt and


23
   In Ohlson’s original study, misclassification rate was 14.9%. By using it with data from the 1980s,
misclassification rates were now 18.7%.
24
   Misclassification error rate using Ohlson’s original model is 18.7%. The overall re-estimated model
misclassification error rate is now 22.1%.
                                                                                                     38
non-bankrupt firms. Its use however can still be merited. If users are more

inclined to minimize Type II error classifications, the re-estimated model is by

far superior. But, as the costs of Type I errors are often more costly, then

Begley admits that the use of Ohlson’s original model is empirically justified.




                                                                                  39
CHAPTER 3: RESEARCH METHODOLOGY



3.1 HYPOTHESIS



Based on the evidence deduced from past literature, numerous works have

identified and confirmed the existence of financial differences between failed

and non-failed firms. While most previous works reviewed and cited focus on

the characteristics of failed and non-failed firms, we differ slightly as we will use

the knowledge to identify and investigate firms in financial distress. Gilbert

(1990) demonstrated that when bankrupt and distressed firms were

investigated, the model used was unable to distinguish between the mean from

one group to that from another. According to Ward (1997), previous works were

prevented from such an investigation due to the unavailability of a clear and

objective definition for distress. But as Isa (2005) pointed out, with the

conditions set forth in Bursa Malaysia’s Listing Requirement, distressed listed

firms in Malaysia can be legally defined, identified, and classified.



From the pioneering works of Beaver (1966) and Altman (1968) all the way to

Peat (2003), research has shown that through the use of various analytical

tools, these differences can be identified. Through the use of financial ratios as

an investigative tool, researchers have been able to use these unique ratios to

identify specifically what the differences are. However, it must be noted that

although differences were found to exist, the characteristics or the ratios

deemed predictive were often as unique as the research themselves.




                                                                                  40
In this study, we hope to first identify the characteristics of distressed firms and

to investigate if these characteristics as given by their financial ratios are indeed

different than that of healthy firms. Given past results, we expect it to differ.

Having identified the characteristics of firms in distress, we ultimately hope to

use these explanatory variables to predict financial distress.



As such, the following null hypothesis will be tested:

       H01: There is no significant difference between the ratios of healthy

              and distressed firms.




3.2 RESEARCH DESIGN



The purpose of this study is to (a) determine the characteristics of distressed

firms through the use of financial ratios and (b) to investigate the predictive

powers of the ratios (independent variables) to correctly classify firms as being

healthy or distressed.



The sample period under study will be from 1998 to 2001. This sample period

was chosen for several reasons. Firstly, the occurrence of the Asian Financial

Crisis in 1997 severely impacted most firms in Malaysia and in the region.

Given the unusual occurrence, we begin with data from 1998 to ensure that the

broad ranging effects of the regional financial crisis are minimized. Secondly,

given the small number of firms entering conditions of financial distress post

2002, it is expected that any comparisons between the financial data of healthy


                                                                                  41
and distressed firms beyond this period could lead to misleading and inaccurate

conclusions as the small sample size will introduce new bias.



In determining the population of firms under study, much like the earlier works

of Beaver (1966) and Altman (1968), we focus our investigative look into

Malaysian public listed firms classified on the Industrial Products sector of

Bursa Malaysia’s Main Board. While all firms listed on the Industrial Sector of

the Main Board will be eligible, inclusion will only occur if it meets the following

selection criterion;



       a) The firm must be classified under Bursa Malaysia’s Industrial

           Products sector as at 31st December 2001.

       b) Healthy firms must have complete financial data for the periods

           between     1998    and    2002     accessible    through    Bloomberg

           Professional’s online financial database.

       c) As    PN4    companies      are    often   excluded   from    Bloomberg

           Professional’s coverage, distressed firms must have been classified

           as a PN4/2001 company by Bursa Malaysia and have their financial

           records stored within Bursa Malaysia’s Resource Centre.



Through the filtering process mentioned above, the sample size for healthy

firms listed on the Industrial Products sector has been reduced 53 firms. The

sample size for distressed firms given the aforementioned criterion is 13.

Therefore the total number of firms in our sample is 66 firms.




                                                                                 42
3.3 VARIABLES UNDER STUDY: DEPENDENT VARIABLE



For the purpose of this study, the dependent variable is the financial

classification of public listed companies in Malaysia. Firms are either classified

as normal healthy companies or distressed companies. Unless specified,

companies are assumed to be normal and healthy.



To identify distressed firms, we use the conditions set forth in Bursa Malaysia’s

Listing Requirement. Through the conditions stipulated by Practice Note

4/200125 issued in relation to paragraph 8.14 of the listing requirements, this

provision aimed at ensuring the financial condition of listed companies warrants

trading is used to classify firms as distressed. From here on, these firms will

sometimes be referred to as PN4 companies.




         Table 3: Bursa Malaysia PN4 reclassification criteria summary

              –   The shareholders’ equity is equal to or less than 25% of the issued
                  and paid-up capital.
              –   Receivers and/or managers have been appointed
              –   Winding up of subsidiary or associated company which accounts 50%
                  of the total assets.
              –   Auditors have expressed an adverse or disclaimer opinion.
              –   Auditors have expressed a modified opinion with emphasis on the
                  listed issuer’s going concern.
              –   A default in payment.
              –   The listed issuer has suspended or ceased:-
                       (i) all of its business or its major business; or
                       (ii) its entire or major operations,




                                                                                        43
3.4 VARIABLES UNDER STUDY: INDEPENDENT VARIABLES



Independent variables under study within this research will comprise of 13

financial ratios from four specific groups. These 13 ratios comprise of those

representing liquidity ratios, productivity and profitability ratios, cash flow ratios,

and long-term solvency ratios. As the problem of a lack of theoretical

underpinning as a guide to variable selection has been covered earlier, the use

of the independent variables under our study is based on the popularity of the

ratios from past research and their past performance in reviewed literature.



3.4.1 LIQUIDITY RATIOS



Liquidity ratios measure a firm’s ability to meet its short-term obligations.

Through the use of cash and other easily sellable liquid assets, the size and

composition of these assets can be used to cover payables, short-term debt,

and other liabilities. As these ratios basically measure the coverage and

cushion provided by the firm’s more liquid assets, it is expected that higher

liquidity values should provide a better buffer to distress and insolvency. This

suggests an inverse relationship with distress. For this study, the ratios used

will be as follows:



           a) Working Capital to Sales: (Current Asset-Current Liabilities)/ Sales –

               Working Capital measures the “current” position of the company. By

               definition, current liabilities are paid out of current assets. A positive


25
     Refer to Appendix for more on Practice Note 4/2001.
                                                                                      44
          working capital indicates assets can be used to continue operations.

          By dividing against asset size, this allows comparison of working

          capital against firms of differing sizes.

       b) Cash to Total Asset: Cash/ Total Asset - measures the portion of a

          company’s assets held in cash or marketable securities. A high ratio

          acts as a buffer to safety.

       c) Cash to Current Liabilities: Cash/ Current Liabilities – measures a

          company’s ability to meet short-term obligations immediately. A

          higher ratio indicates greater ability.

       d) Current Asset to Current Liabilities: Current Assets/ Current

          Liabilities – also known as Current Ratio, measures the firm’s ability

          to pay near-term obligations. Oftentimes, “Current” refers to periods

          of 1 year or less. The higher the ratio, the larger the firm’s safety

          cushion against default.

       e) Current Asset to Total Asset: Current Asset/ Total Asset – measures

          the proportion of assets that can be easily sold or converted to cash.

          A higher value provides a larger cushion should an unexpected

          obligation surface.

       f) Working     Capital    to     Total   Asset:   (Current   assets-Current

          Liabilities)/Total Assets – measures liquidity.



Given the extensive research coverage liquidity ratios have received in the past,

evidence suggest that liquidity can be a significant predictor of distress. While

its’ overall importance was not particularly significant in Altman’s (1968) original

study, the increasing acceptance of leverage in the 1980s and 1990s has


                                                                                 45
meant an increase in significance given the larger debt maintenance costs. This

view is also shared by Begley (1996) and Deakin (1972). Shirata (1998)

however disagrees with this. To her, liquidity is not significant. Given the results

presented by Begley (1990) and Deakin (1972) the ratio of Working Capital to

Total Asset could be an important explanatory variable.



3.4.2 PROFITABILITY & PRODUCTIVITY RATIOS



According to Fazilah (2000), the firm’s earning capacity and its continued ability

to generate and improve profits is usually its most important objective. Through

profitability ratios, we can investigate how well the firm performs given the

resources available to it. It is normally presented in percentages and the higher

the percentage, the better its performance. Therefore, profitability is expected

to be inversely related with distress. For this study, the ratios used will be as

follows:



       a) Earnings to Total Assets: EBIT/ Total Assets – measures the

           productivity of the company’s assets. The higher the utilization

           percentage, the better its productivity.

       b) Return On Assets: Net Income/ Total Asset – measures the

           effectiveness of the company’s use of assets. A higher value

           indicates better utilization of resources and could indicate effective

           management.




                                                                                 46
       c) Sales to Total Asset: Sales/ Total Assets - measures revenue

          generation. By dividing sales value against asset size, this allows

          comparison of revenue generation against firms of differing sizes.



Based on past research surrounding profitability and productivity ratios,

evidence suggests explanatory significance. The ratio of Net Income to Total

Assets studied by Beaver (1966) for example could be used to correctly classify

firms 87% of the time one year prior to bankruptcy and 72% 5 years before.

While this was also verified by Ohlson (1980), Begley’s (1996) re-estimation of

Ohlson’s study revealed profitability to be positively related to distress. Though

he admitted this to be puzzling, he could offer no explanation for the

relationship.



3.4.3 CASH FLOW RATIOS

According To Needles, because cash flows are needed to pay debts when they

are due, cash flow ratios can indicate solvency and liquidity. Through the efforts

of Beaver (1966), cash flow ratios could accurately classify firms up 78% of the

time 5 years before bankruptcy. This suggests significant predictive potential.

Because a higher ratio will indicate more cash flows will be available to meet

financial obligations, we expect higher ratio values to translate into lower

distress risk. The ratios to be included from this grouping are:



       a) Cash Flows to Sales: Cash Flows/Sales – measures the ability of the

          firm’s sales to generate operating cash flows.




                                                                                47
      b) Cash Flows to Total Debt: Cash Flows/ Total Debt – measures the

          length of time a company will need to pay off its debts with just its

          cash flows. Although not realistic, a high ratio signals a less risky

          company better able to payoff its debt.

      c) Cash Flows to Current Liabilities: Cash Flows/ Current Liabilities –

          measures whether or not the company is earning enough to cover its

          current liabilities. If the ratio is below 1.0, then the company must

          either find an alternative way to finance its commitments or slow

          down the rate it is spending its cash.




3.4.4 SOLVENCY RATIOS



One of the best ways for us to measure a firm’s riskiness is to examine its

solvency ratios. According to Fazilah (2000), unlike liquidity ratios which is

concerned with the company’s ability to meet near-term obligations, solvency

ratios tend to take a long-run point of view. By measuring long-term debt for

degree of financial leverage, this can identify future problems. The ratio to be

used here is:



      a) Total Debt to Total Assets: Total Debt/ Total Assets – measures the

          company’s financial risk by determining the amount of assets

          financed by debt. A higher ratio signals greater financial risks.




                                                                              48
Looking at empirical evidence to support its inclusion, Deakin (1972) and

Ohlson (1980) prove the ratio to be significant. However, these studies were

conducted with pre-1980s data. With the acceptance of higher debt use in the

1980s and 1990s, its power as a descriptive variable has declined. This view is

supported by Begley (1990). Regardless, as the use of debt increases the

amount needed to service it, positive increases in debt should increase the risk

of distress.



3.5 DATA COLLECTION



This study makes use of secondary data available in Bloomberg Professional

online database for the collection of financial data for healthy firms. With a huge

database available, Bloomberg Professional service provides comprehensive

financial data for most public firms listed any of the major indices in the world.

Available data for public listed firms includes; balance sheet data, income

statement data, statement of cash flows, commonly used financial ratios, share

price, and dividend records. As was earlier mentioned, the financial data used

for PN4 companies are either those included in Bloomberg Professional service

or those with hardcopy filings maintained by Bursa Malaysia’s Resource

Center. The financial data found in hardcopy filings maintained by Bursa

Malaysia are taken from their Annual Reports.



3.6 DATA ANALYSIS



In line with the findings of Beaver (1966), Altman (1968), and Ohlson (1980),


                                                                                49
comparisons of the financial ratios used in this study are expected to highlight

differences in the average mean values for healthy firms and those of

distressed firms. To investigate if these earlier findings are merited, the first

step of our analysis will be to conduct a ratio analysis. Having done so, we will

then test these differences for significance. Next, through the use of a logit

regression model to further analyze the relationship between the dependent

and independent firms, this should aid us on discovering the probability of a

healthy firm becoming distressed.



Data will be analyzed through the use of Microsoft’s Excel (Excel) spreadsheet,

Statistical Package for Social Sciences (SPSS) Version 14 for Windows, and

the use of Intercooled Stata Version 8.0. The use of Excel will be primarily to

calculate the values of financial ratios for all firms under study for the period

between 1998 and 2001. Thereafter, SPSS and Stata will be used to perform

the various statistical analyses on the dependent and independent variables.

Again, based on the findings presented by previous researchers, differences

between the two groups could be possible.




                                                                               50
                        CHAPTER 4: RESULTS AND ANALYSIS



4.1 RATIO ANALYSIS & T-TEST



4.1.1 RATIO ANALYSIS & T-TEST: Profitability ratios


Table 4.1.1 : Profitability Ratio - Two sample t-test with equal variances

      Variable          Group      Observations       Mean         Std Dev           t        df
                        Status

        nita               0           219          .0276142       .2623657       5.7604      268
                           1            51          -.2618334     .50961.38

       ebitta              0           219          .0623366      .2177557        5.2390      268
                           1            51          -.1763142     .5033522

       salesta             0           215          9.694038      100.3764        0.4987      261
                           1            48          2.444032      13.27714

Note: Non-failed group distinguished by status 0 and failed group distinguished by status 1


Looking at the mean value for the ratios under study, data from Table 4.1.1

suggest that differences between the two groups do appear to exist. If we

compare the average cumulative mean values for profitability ratios given by

NITA and EBITTA throughout the period under study, the results presented

clearly shows that healthy non-distressed firms are more profitable than their

distressed PN4 counterparts.



With NITA and EBITTA for healthy firms averaging 2.77% and 6.23%, the

positive value of these ratios indicates that profits are generated throughout the

period. For PN4 companies on the other hand, as its NITA and EBITTA ratios

are -26.2% and -17.6% respectively, the negative value of these ratios

suggests that these firms had suffered significant losses during the period. This



                                                                                                    51
corresponds to 29% (NITA) and 24% (EBITTA) difference between the two

groups.



If we observe the mean values each year within our period of study as found in

Table 1 of the Appendix, several observations can be made. Firstly, for each

year under study, the average profitability of the healthy group is significantly

higher than those of the PN4 group. Secondly, while the profitability of healthy

firms is positive every year, as a whole, the PN4 groups suffer losses each and

every year. Thirdly, the mean values for healthy firms do appear to be stable

with little variation over time. For the PN4 group, although the mean values are

consistently negative, its values do appear to be more erratic.



All these factors do suggest that differences between the two groups do exist.

To see if these differences are indeed significant or just a result of random

errors, a t-test will be used to asses if the differences between the two groups

are statistically significant. Based on the results of the t-test presented in the

appendix, the t-values for NITA and EBITTA are 5.76 and 5.24.By comparing

these values with those from the Critical Values of the t Distribution Table

whose tabled t-value obtained is 1.645 given an alpha of 0.05 and 268 degrees

of freedom, the larger value clearly suggests that these differences are

significant.



Given statistical convention, the large disparity between the two t-values

indicates that the probability of the means being similar is small and unlikely

due to the impact of random errors or fluctuations. This result is in-line with the


                                                                                52
findings presented by Beaver (1966) and Ohlson (1980) both of whom found

profitability as given by NITA to be a significant predictor of distress.



4.1.2 RATIO ANALYSIS & T-TEST: Cash Flow Ratios


Table 4.1.2 : Cash Flow Ratio - Two sample t-test with equal variances

      Variable          Group     Observations       Mean         Std Dev           T         df
                        Status

       cfsales            0            214         .0698125      .3363745         1.5917      260
                          1             48         -.0170025     .3639241

        cftd              0            219         .1438238      .3597432         2.9745      268
                          1             51         -.0086326     .1360677

        cfcl              0            219         .2269034      .5013146         3.8070      270
                          1             51         -.0513442     .2977745

Note: Non-failed group distinguished by status 0 and failed group distinguished by status 1


For Cash Flow ratios, again we find that differences in cumulative mean values

between the two groups do appear to be significant. If we look at the average

cumulative mean values for the Cash Flow ratios throughout the period under

study, the largest differences in means is given by the CFCL and CFTD ratios.

For CFCL, the cumulative difference in means between the two groups is a

staggering 27.8%. For CFTD, the average difference is 15.3%.



Looking at the means values in greater detail, the positive Cash Flow ratios of

healthy firms does indicate that cash is generated from its operations. As cash

is often how firms meet expected and unexpected obligations, a positive cash

flow is an asset to the firms. As the mean values for CFCL and CFTD for

distressed PN4 firms are -5.1% and -1%, the negative cumulative means

suggests that should the need for cash arise, other sources of income

(sometimes external sources) will be needed.

                                                                                                    53
If we look at the movement of the mean values over time as given by Table 1 in

the Appendix, the following observations can be made. Firstly, the mean Cash

Flow ratios for PN4 group are lower than those from healthy firms each year

under study. Secondly, the CFCL mean values for PN4 firms are consistently

negative throughout the study period (almost every year for CFTD). As we can

see, for the healthy group, the ratios are positive throughout with the mean

value being stable with no noticeable upward or downward trend. Lastly, for

PN4 group, the means are slightly more erratic with a tendency to get worse as

distressed reclassification date looms. Given these factors, a statistical

difference between groups does appear to exist. To confirm this, we again turn

to the t-test.



By comparing the t-value calculated for CFCL and CFTD with the tabled t-value

found in the Critical Values of the t Distribution Table, the comparisons again

verifies the differences to be significant. With calculated CFCL and CFTD

t-values 3.80 and 2.97, the large disparity with the tabled t-value (t= 1.645)

does suggest the differences are not due a random error. Therefore,

statistically we can conclude that differences do exist, and these ratios can be a

predictor of distress. While this will only be verified later through the use of a

logit analysis, the findings are in accordance with those of Beaver (1966).



4.1.3 RATIO ANALYSIS & T-TEST: Liquidity ratios


Table 4.1.3 : Liquidity Ratio - Two sample t-test with equal variances

      Variable          Group     Observations       Mean         Std Dev   t   df
                        Status

                                                                                     54
wcsales                0         214              .0242214      1.078106      5.3526          260
                       1         48               -2.008213     5.099299

wcta                   0         219              -.0067682     .6024754      5.3926          268
                       1         51               -.7115569     1.484854

cashta                 0         219              .2222634      .509383       4.5434          270
                       1         51               -.1165082     .3185023

cata                   0         219              .4343766      .231741       -.15960         268
                       1         51               .4941126      .2764944

cacl                   0         219              1.978614      2.739189      2.0643          270
                       1         51               1.079808      3.052731

Note: Non-failed group distinguished by status 0 and failed group distinguished by status 1


Looking at the differences in cumulative liquidity ratios for both healthy and

distressed firms, differences between the two groups does appear large and

significant. If we observe the difference in cumulative means for WCTA,

WCSALES, and CASHTA, the approximate difference in mean values are

70.5%, 203%, and 33.9%. Given the values of Working Capital either as a

percentage of Total Assets or as a percentage of Sales, the differences

between the two groups suggests that unlike most healthy firms, distressed

firms suffer significantly from a lack of liquidity.



With mean values of -.6% (WCTA), 2.4% (WCSALES), and 22.2% (CASHTA)

for healthy firms, the amount of liquidity carried by these healthy firms are

significantly greater than those of the PN4 group. Liquidity is important as it is

the ability to meet unexpected needs for cash and to pay bills when they are

due. Given the poor profitability and cash flow ratios of distressed firms,

distressed firms are expected to have poor liquidity values.




                                                                                                    55
As the value of these ratios for distressed firms is negative, distressed firms do

not carry the enough liquid assets (as given by Current Assets) to cover

short-term obligations (as given by Current Liabilities). Should an unexpected

obligation or situation suddenly arise, the need for cash will require external

sources of capital as the level of cash and near cash assets held will not be

enough.



If we look at the mean values over time as given by Table 1 in the Appendix, the

following observations can be made. Firstly, while Working Capital (either as a

percentage of Sales or of Total Assets) for both distressed and healthy firms

were poor in 1998, the mean for the healthy group did improve with time. For

distressed firms, Working Capital continually decreased over time. Secondly,

looking at the percentage of cash held, those from the healthy group had

positive cash holdings. As the mean value for thePN4 group is negative, this

again suggests poor liquidity and a need for external credit in order for the firm

to operate on a day to day basis.



Finally, while the level of cash held by healthy firms as a proportion of total

assets grew each year, the decline in cash position for PN4 companies does

suggest a higher probability of default or distress. Given that the cash flow

generated from operations is negative, this situation was to be expected.



Given all these factors, we would therefore expect that the differences between

the two groups are statistically significant. Based on the calculated t-value




                                                                                56
presented in Table 4.1.3, comparison of the calculated value with those from

the Critical Values of the t Distribution Table appears to confirm our suspicions.



As the calculated t-values for WCTA, WCSALES, and CASHTA are 5.4, 5.36,

and 4.54, comparing this with the tabled value of 1.645 proves that the

probability of the means from the two groups being similar is small. Statistically

speaking, the differences do appear significant and could be a good predictor of

possible distress. Let us confirm this later through the use of a logit regression

analysis.



4.1.4 RATIO ANALYSIS & T-TEST: Solvency Ratio



Table 4.1.4 : Long-term Solvency Ratio - Two sample t-test with equal variances

  Variable         Group      Observations       Mean          Std Dev            t                 df
                   Status

tdta           0              219             .7246381       1.157173       -3.7863           268
               1              51              1.518509       1.977196

Note: Non-failed group distinguished by status 0 and failed group distinguished by status 1


While the liquidity ratios highlight the problems faced by distressed companies

in the near future, its long term riskiness is often measured by examining its

solvency ratios. Looking at the cumulative TDTA ratio throughout the period

under study, we can see that PN4 firms are highly leveraged. With a cumulative

TDTA ratio of 1.52 times, the significant use of long term debt does appear to

be a strain for PN4 companies.



As the use of Total Debt to Total Assets indicates the percentage of assets

funded by debt, a ratio greater than 1 suggests that distressed firms have
                                                                                                         57
overextended itself through its use of debt. As the larger use of long term debt is

positively correlated to increasing interest and principal obligations, we do

expect the firms to carry significantly higher financial risks. While this increases

the possibility of interest or principal default, as our earlier analysis had

highlighted negative cash flows and poor profitability records, perhaps the only

way these firms can continue to operate is through increasing their borrowing

activities.



If we look at the behavior of the mean value over time as given by Table 1, while

the ratio of Total Debt to Total Assets for the PN4 group in 1998 is 1.19, by 2001,

this ratio had grown to 1.9 times. Unlike the mean for the PN4 group which grew

increasingly worse as distress looms, the mean for healthy firms was relatively

stable over the period. While the results do suggest that the differences

between the two groups are significant, verifying the statement will require the

use of a t-test. With a calculated t-value of -3.79 as seen in TABLE 4.1.4, the big

difference with the tabled t-value of 1.645 indicates that the differences are

statistically significant.




4.2 LOGIT ANALYSIS



Based on the analysis done thus far, the results presented clearly suggest that

given the different measures available, differences between the mean values of

healthy and distressed firms can be significant. Much like the conclusions

presented by Altman (1968) and Beaver (1966), this point proves that financial

ratios can in fact be used to distinguish between healthy and potentially

                                                                                 58
unhealthy firms.



While this can clearly be done through the use of a simple ratio and t-test

analysis, perhaps we have gotten a little ahead of ourselves as we have yet to

determine if these ratios do infact influence financial distress. The analytical

tools presented earlier measures the significance of the differences amongst

the healthy and distressed means. It however fails to evaluate the relationship

these factors or ratios have with the dependent variable; group status.



In order for us to evaluate the factors that can influence group status and

understand the nature of their connection, the perfect tool available is through

the use of a logit regression analysis. A logit regression analysis is a form of

regression used when the dependent is a dichotomy and therefore can take

one of two possible outcomes; distressed or non-distressed. Its goal therefore

is to estimate or predict the outcome based on the value of variables of interest.



Before we can conduct a logit analysis however, we must first begin the

process by evaluating the variables of interest. By determining which factors

should be included and which factors should be omitted from our logit models,

doing so ensures that our model is robust.



4.2.1 LOGIT ANALYSIS: STREAMLINING THE MODEL



In determining the variables of interest that should be included in our model, we

will discriminate the variables on the basis of their correlation and collinearity.


                                                                                 59
Based on the data presented in Table 4.2, the Variance Inflation Factor (VIF)

generated by Stata shows us how much the variance of the coefficient estimate

is inflated by collinearity. Collinearity is the situation that arises when there is

close to a near perfect relationship between some or all of the variables of

interest. In practical terms, high collinearity indicates that variable redundancy

or overlap does exist. Although this flaw is not fatal to our model, it can cause a

loss in power as the analysis will suffer from the difficulties associated with

disentangling the effects of the variables. This in turn makes model

interpretation more difficult.



                          Table 4.2: Multicollinearity Diagnostic

                        MULTICOLLINEARITY DIAGNOSTICS


                                          SQRT                  R-
                          Variable VIF VIF Tolerance Squared
                        ------------------------------------------------------------
                             wcta       9.05 3.01 0.1105              0.8895
                          wcsales       4.58 2.14 0.2181              0.7819
                           cashta       3.61 1.90 0.2768              0.7232
                             nita     25.51 5.05 0.0392               0.9608
                           ebitta     24.38 4.94 0.0410               0.9590
                             cfcl       9.32 3.05 0.1073              0.8927
                             cftd       7.27 2.70 0.1376              0.8624
                             tdta     11.11 3.33 0.0900               0.9100
                             cacl      1.60 1.26         0.6258       0.3742
                          salesta       1.02 1.01 0.9852              0.0148
                             cata      2.23 1.49         0.4481       0.5519
                           cashcl       1.81 1.35 0.5515               0.4485
                          cfsales       1.93 1.39 0.5175              0.4825
                        ------------------------------------------------------------
                          Mean VIF        7.96




In assessing the collinearity amongst variables, this can be measured by the

square root of VIF. This in essence tells us how much larger the standard error

is compared to what it would be if the variables were uncorrelated to one

another. For statisticians, though VIFs of 10 or more is a definite reason for

                                                                                       60
concern, values greater than 2.5 should also be noted and reviewed. Based on

the data from Table 4.2, the following variables required further investigation:

NITA and EBITTA, CFCL and CFTD, and CASHCL and CACL.



                                Table 4.3: Coefficient Correlation Matrix



                               COEFFICIENT CORRELATION MATRIX


                | wcta wcsales cashta nita                     ebitta      cfcl        cftd
     -------------+---------------------------------------------------------------------------------------------
       wcta | 1.0000
      wcsales | -0.2324 1.0000
       cashta | 0.4768 0.1003 1.0000
          nita | 0.1306 0.0650 0.4504 1.0000
        ebitta | -0.0534 -0.2794 -0.4604 -0.8483 1.0000
          cfcl | -0.0203 -0.0185 0.0212 0.2182 -0.1451 1.0000
          cftd | 0.0281 0.0571 -0.0316 -0.2732 0.1032 -0.8367 1.0000
          tdta | 0.3079 -0.1388 -0.5926 -0.3817 0.2658 -0.0578 0.1320
          cacl | -0.6023 -0.1377 -0.6284 -0.1570 0.0879 0.1235 -0.1240
       salesta | -0.0041 -0.0218 -0.1148 -0.1491 0.1204 -0.0797 0.1970
          cata | -0.2417 -0.1199 0.0805 -0.0137 0.0885 0.0271 -0.0008
        cashcl | -0.6127 -0.0958 -0.7578 -0.2528 0.1900 0.0858 -0.0836
       cfsales | 0.0359 -0.0219 -0.0187 -0.0687 0.0767 -0.6479 0.2221
        _cons | 0.4745 0.2526 0.8232 0.4894 -0.3500 0.0069 -0.0919

                | tdta          cacl      salesta cata          cashcl cfsales _cons
     -------------+---------------------------------------------------------------------------------------------
           tdta | 1.0000
           cacl | 0.2540 1.0000
        salesta | 0.1181 -0.0259 1.0000
           cata | -0.4779 -0.3178 0.0312 1.0000
         cashcl | 0.3491 0.9568 0.0060 -0.2679 1.0000
        cfsales | 0.0334 -0.0047 -0.0936 -0.1452 0.0041 1.0000
          _cons | -0.5445 -0.7568 -0.1121 -0.0233 -0.8141 0.0482 1.0000




To investigate the collinearity of the variables, let us now look at the correlation

matrix of the estimated coefficient (not the variables) as presented in Table 4.3.

As high correlation coefficients between pairs indicate potential collinearity

problems, those with high correlation amongst their coefficients will be taken

out of the model. On the basis of this, the high correlation between EBITTA and

NITA, and CFCL with CFTD results in us removing EBITTA and CFTD from our

                                                                                                                   61
logit Model. The low correlation between CASHCL and CACL on the other hand

for now justifies their inclusion.



4.2.2 LOGIT ANALYSIS: MODEL 1



While the complete results of the logit analysis can be found in the appendix,

the following table highlights the results of the logit analysis.

                                    Table 4.4: Logit Analysis of Model 1


                               MODEL 1 – ALL VARIABLES INCLUDED

   Logit estimates                                                    Number of obs       =       262
                                                                      LR chi2(11)         =     149.91
                                                                      Prob > chi2         =     0.0000
   Log likelihood = -49.813862                                        Pseudo R2           =     0.6008

   ------------------------------------------------------------------------------------------------------------------
          var1 |      Coef.       Std. Err.         z      P>|z| [95% Conf. Interval]
   -------------+---------------------------------------------------------------------------------------------------
          wcta     -3.757966 2.167542 -1.73                 0.083 -8.00627 .4903389
       wcsales       .1517615 .229463             0.66      0.508 -.2979777 .6015007
        cashta     -13.14618 5.826237 -2.26                 0.024 -24.56539 -1.726966
          nita     -4.493003 2.075364 -2.16                 0.030 -8.560642 -.4253632
          cfcl     -2.501012 1.944906 -1.29                 0.198 -6.312958 1.310934
          tdta        1.580787 1.472999 1.07                0.283 -1.306237 4.467812
          cacl        -.945844 1.266378 -0.75 0.455 -3.427899 1.536211
       salesta      -.0006405 .003489             -0.18 0.854 -.0074788 .0061979
          cata        4.181737 2.2948              1.82      0.068 -.3159889 8.679463
        cashcl -.2390359 .9935026                 -0.24 0.810 -2.186265 1.708193
       cfsales      1.846088 1.506245              1.23 0.220 -1.106097 4.798274
         _cons       -3.59651 1.516353             -2.37 0.018 -6.568507 -.6245127




As we can see from the data presented in the table above, the result of the logit

analysis on Model 1 takes into account all 11 variables initially identified from

popular literature. Based on the result of 262 complete observations, the

goodness-of-fit of the model as measured by Pseudo R2 of 0.6008 does

indicate a well fitting model. 4 financial ratios were deemed to be significant

predictors of corporate distress as given by their z-statistic. With a z- statistic
                                                                                                                        62
greater than 1.64 (signifying .05 acceptance level) the variables deemed

statistically significant were; WCTA, CASHTA, NITA, CATA. So based on the

variables or ratios deemed significant, we can therefore argue that liquidity (as

given by WCTA, CASHTA and CATA), and profitability (as given by NITA) are

important characteristics that can be used to differentiate and identify

problematic firms.



In analyzing the results found in the Table 4.4, we can either interpret the logit

through coefficient value or through the odds ratio. For the logit model, the

coefficients are calculated through the use of Maximum Likelihood Estimation

(MLE) method as opposed the Original least Squares (OLS) method employed

by linear regression. While OLS seeks to minimize the sum of squared

distances between the data points and the regression line, MME seeks to

maximize the log likelihood. This reflects how likely the observed values of the

dependent variable can be predicted from the observable values of the

independent variables.



Furthermore, in OLS, the value of the coefficient refers to the change in the

dependent variable as the independent variable changes. In MLE, the slope of

the coefficient is the change in “log odds” of the dependent as the independent

changes. So, from the coefficients presented above, we can see that for one

unit decrease in WCTA, CASHTA and NITA, the log odds of the firm being

reclassified from healthy to distressed or PN4 increases by 3.76, 13.15, and

4.5. For a unit decrease in CATA, the results state that the log odds of a firm

being reclassified decreases by 4.18.


                                                                                63
                              Table 4.5: Logit Analysis Model 1 - Odds



         ------------------------------------------------------------------------------------------------
               var1 | Odds Ratio Std. Err.               z       P>|z| [95% Conf. Interval]
         -------------+---------------------------------------------------------------------------------
               wcta .0233312 .0505713 -1.73                      0.083 .0003334 1.63287
             wcsales 1.163883           .267068        0.66      0.508 .7423179 1.824855
              cashta 1.95e-06           .0000114 -2.26 0.024 2.14e-11 .1778232
               nita       .011187       .0232171 -2.16 0.030 .0001915 .6535324
               cfcl       .082002       .1594861 -1.29 0.198 .0018127 3.709638
               tdta       4.85878 7.156976              1.07 0.283 .2708373 87.16577
               cacl     .3883517 .4917999 -0.75 0.455 .0324551 4.646948
             salesta .9993597 .0034868 -0.18 0.854 .9925491 1.006217
               cata     65.47949 150.2624              1.82      0.068 .7290675 5880.887
              cashcl .7873866 .7822706 -0.24 0.810 .1123355 5.518982
             cfsales     6.334991 9.542047             1.23      0.220 .3308477 121.3009




Since the measure and interpretation of coefficients can sometimes be

confusing, a more natural interpretation of the logit results can be achieved

through the use of Stata’s Odds Ratio. This can be found in Table 4.5 above.

Odds ratio is the probability of the event occurring divided by the probability of

the non-event. In this case, the odds for WCTA, CASHTA, and NITA are 0.023,

1.95e-06, 0.011 respectively. What this means is that for every unit decrease in

the variable, the odds of distress occurring increases by the value for the odds

ratio. For CATA, the odds ratio is approximately 65.48. So, for every unit

increase in CATA, our logit analysis argues that the odds of distress occurring

is 64 times more likely.



In looking at comparisons between the variables this study found to be

significant with those previously conducted, the predictive abilities of liquidity

and profitability ratios is inline with our expectations. Deakin (1972) found that


                                                                                                            64
liquidity as proxied by WCTA was the best predictor of potential distress

reclassification both in the near-term (1 year prior to event) and in the long-term

(5 years prior to event). Although Altman (1968) found WCTA to be the least

predictive of the variables under his study, Deakin’s conclusion is verified by

Begley (1996). In his study, Begley reestimated Altman’s original Z model

through the use of more current data.



For profitability ratios, NITA was deemed the second best predictor by Beaver

(1966). Able to accurately predict potential distress 87% of the time 1 year prior

to the event and 72% of the time 5 years before, the ability to generate profits

appears to be significant in ensuring a firms’ continued survival. Both these

results appear to refute Shirata’s (1998) findings that liquidity and profitability

ratios have no significant importance in determining firm health.



As we look at the analysis just presented, the negative coefficient values

observed for WCTA, CASHTA, and NITA indicates an inverse relationship with

the dependent variable. The value of CATA coefficient indicates a positive

relationship with distress. While the negative relationship is to be expected

given the greater likelihood of distress as the value of these ratios deteriorates,

the positive relationship between CATA and distress is troubling. The reason is

that a positive coefficient indicates that as the proportion of current assets as a

percentage of total assets increase so does the likelihood of a firm being

reclassified as a PN4 company. Although this finding does indicate an error in

either theory or in application, no reason for this finding will be offered.




                                                                                 65
Another interesting finding that we can observe based on the results of the logit

analysis is the exclusion of the ratio of total debt to total asset (TDTA). As the

study by Deakin (1972) found that long-term solvency ratios as proxied by

TDTA could significantly predict potential distress up to five years prior to

bankruptcy filing, debt ratio was expected to be a significant predictor. In

Deakin’s study, debt ratio could be used to accurately classify PN4 firms 67% of

the time 5 years before the event26.



This was verified by Ohlson (1980). But, as we can see based on our results,

long-term solvency is not a good predictor of distress for Industrial firms in

Malaysia. Perhaps Begley (1996) was right. As investors and firms in the 1980s

and 1990s had grown more comfortable with the increasing use of leverage as

given by debt levels, perhaps this explains the decreasing significance of the

debt ratio.



While the exclusion of long-term solvency measures as significant influencers

of distress were already stumbled upon by earlier researchers, the

insignificance of any cash flow measure is really surprising. While earlier

studies by Altman (1968) argued for the inclusion of these ratios, based on the

results obtained, perhaps the role of cash is not as important as we initially

expected for Industrial firms in Malaysia. In his original study, Altman

determined that CFTD would accurately predict PN4 reclassification with 87%

one year before the event and with 78% accuracy up to 5 years before the

event.


26
     Altman (1968) – TDTA can predict PN4 reclassification 72% of the time 5 years prior to event.
                                                                                                     66
This is significant. As most financial obligation are paid or met through the use

of cash, its exclusion implies that the unavailability of internal cash to meet

financial obligations will require firms to raise cash from external sources. Given

the high degree of short-term and long-term credit carried by firms within our

study, perhaps this explains why financial leverage and cash levels are not

significant factors in influencing distress.



                             Table 4.6: Model 1 Predictive Accuracy

                                         Logistic model for var1

          number of observations = 262
          area under ROC curve = 0.9561

                                            Classification Rate

                     -------- True --------
          Classified |        D          ~D |     Total
          -----------+--------------------------+-----------
              +        |      32          7 |        39
              -        |      16        207 |       223
          -----------+--------------------------+-----------
            Total       |     48        214 |       262

          Classified + if predicted Pr(D) >= .5
          True D defined as var1 != 0
          --------------------------------------------------
          Sensitivity                  Pr( +| D) 66.67%
          Specificity                  Pr( -|~D) 96.73%
          Positive predictive value          Pr( D| +) 82.05%
          Negative predictive value           Pr(~D| -) 92.83%
          --------------------------------------------------
          False + rate for true ~D          Pr( +|~D) 3.27%
          False - rate for true D          Pr( -| D) 33.33%
          False + rate for classified + Pr(~D| +) 17.95%
          False - rate for classified - Pr( D| -) 7.17%
          --------------------------------------------------
          Correctly classified                     91.22%
          --------------------------------------------------




For Model 1, the area under the under Receiver Operating Characteristic

(ROC) curve is .9576. Area under the curve can be used as a measure to test

                                                                                 67
the accuracy of the model in separating and classifying healthy and distressed

firms. With area .9576, statistical convention deems this an excellent

discriminator of group membership. Furthermore, as Model 1 can accurately

classify firm group membership 91.22% of the time, the high classification

accuracy indicates that this model is robust enough to be applied to Industrial

firms listed on the Main Board of Bursa Malaysia.



4.2.3 LOGIT ANALYSIS: MODEL 2



                                     Table 4.7: Logit Analysis Model 2



                                 MODEL 2 – STEPWISE REGRESSION

Logit estimates                                                 Number of obs      =      262
                                                                LR chi2(4)         =    144.25
                                                                Prob > chi2        =    0.0000
Log likelihood = -52.645412                                     Pseudo R2          =    0.5781

------------------------------------------------------------------------------
      var1 |     Coef.         Std. Err.     z        P>|z|      [95% Conf. Interval]
-------------+----------------------------------------------------------------
    wcta      -6.178122 1.109093 -5.57 0.000                    -8.351904 -4.00434
    cata       5.904799 1.72847              3.42 0.001          2.517061 9.292537
    cashta -12.70224 3.422159 -3.71 0.000 -19.40955 -5.994934
    nita      -4.537302 1.658816 -2.74 0.006                    -7.78852 -1.286083
    cons      -4.406335 .777508             -5.67 0.000 -5.930222 -2.882447




Beginning with 11 variables of interest, Model 2 uses Stepwise regression with

a p-value equal to .05 to automatically determine which variables should be

added or dropped from the model. Although the procedure runs the risk of

modeling the noise in the data, it is still considered useful particularly for

exploratory purposes. As our study into the factors influencing financial distress

lack a theoretical underpinning to guide research, stepwise regression allows

us to explore possible relationships.


                                                                                                 68
The results presented indicate that based on the stepwise procedure, factors

deemed significant predictors of distress centers mainly around liquidity (as

given by WCTA, CATA, and CASHTA) and profitability measures (as given by

NITA). As similar findings were found in our analysis of Model 1, reanalyzing

the results appear redundant. Interestingly, positive coefficient value for CATA

is consistent with our earlier finding. As further investigation of this is beyond

the scope of this paper, let us just note that the positive correlation between

current asset level and distress does appear to defy both logic and convention.

The goodness-of-fit of this model as measured by Pseudo R2 of 0.5781

indicates a respectable fitting model.



                             Table 4.8: Model 2 Predictive Accuracy


                                        Logistic model for var1

           number of observations = 262
           area under ROC curve = 0.9479



                                           Classification Rate

           Logistic model for var1

                      -------- True --------
           Classified |        D          ~D |     Total
           -----------+--------------------------+-----------
               +        |      32          6 |        38
               -         |     16        208 |       224
           -----------+--------------------------+-----------
             Total       |     48        214 |       262

           Classified + if predicted Pr(D) >= .5
           True D defined as var1 != 0
           --------------------------------------------------
           Sensitivity                  Pr( +| D) 66.67%
           Specificity                  Pr( -|~D) 97.20%
           Positive predictive value          Pr( D| +) 84.21%
           Negative predictive value           Pr(~D| -) 92.86%
           --------------------------------------------------
           False + rate for true ~D          Pr( +|~D) 2.80%
           False - rate for true D          Pr( -| D) 33.33%
                                                                                69
           False + rate for classified + Pr(~D| +) 15.79%
           False - rate for classified - Pr( D| -) 7.14%
           --------------------------------------------------
           Correctly classified                      91.60%




For Model 2, the area under the under Receiver Operating Characteristic

(ROC) curve is .9479. Area under the curve can be used as a measure to test

the accuracy of the model in separating and classifying healthy and distressed

firms. With area .9479, statistical convention deems this an excellent

discriminator of group membership. Based on the data from the table above, we

see that this model can correctly classify group membership accurately 91.6%

of the time.




                                                                            70
5.0 CONCLUSION



5.1 SUMMARY



Since the pioneering works of Beaver (1966) and Altman (1968), a lot of

research has been conducted to try and understand corporate bankruptcy.

Despite the large volume of research on the topic however, findings from the

studies have proven to be inconsistent and inconclusive. Furthermore, as most

research had focused their studies on understanding and predicting corporate

bankruptcy, little effort has been paid to understanding corporate financial

distress. For researchers, a common reason often cited to explain this is due to

the unavailability of any consistent measure of what defines corporate distress.



For those of us in Malaysia, due to the availability of Bursa Malaysia’s Practice

Note 4/2001 pursuant to paragraph 8.14C(2) of the Listing Requirements, it

addresses the issue of a lack of legal definition for financial distress. By having

a legal definition of distress, our study benefits as we can consistently define

what it is that constitutes financial distress. As past research have found that

financial distress can precede failure by up to 7 years, identifying the problems

early through the help of financial ratios can help firms to avoid costly

bankruptcy and restructuring efforts.



As we look back to the results of the research, financial ratios can help us

identify potential problems. By comparing the mean values of healthy firms with

distressed firms, differences between the two do appear to exist. Through the


                                                                                 71
use of a t-test comparison of group mean values, these differences can be

statistically significant and not just down to random errors. This finding is

valuable because if we can correctly identify the factors or characteristics that

differentiate between healthy and distressed, financial ratios can be used to

predict troubled firms. Therefore we fail to reject the null hypothesis that

differences between the means of healthy and distressed firms are similar.



As we review back the results of the logit analysis, the factors that differentiate

healthy and distressed firms mainly centers on their levels of liquidity and

profitability. Proxied by the ratios of WCTA, CATA, and CASHTA, low liquidity

levels was found to be detrimental to the continued operations of a firm as the

level of liquid assets available to the firm might be insufficient should an

unexpected need arise. As liquidity ratios measure a firms’ ability to meet its

short-term obligations given its level of sellable assets, high liquidity levels

provide the firms with a greater cushion to absorb unexpected events.



Another important characteristic found to differentiate between healthy and

potentially distressed firms is given by their levels of profitability. Firms with

better profitability are often seen as being better managed. By productively

utilizing a firm’s assets and resources, the profits generated by these efforts are

often seen as an indication of firm performance. Based on our results, it is

therefore proven that performance and firm health are positively correlated.



In looking at the factors excluded from our model, it is interesting that cash flow

generation and debt levels are insignificant influencers of firm status. As Beaver


                                                                                 72
(1966) had found cash flow ratios to be a good predictor of potential problems,

we had expected cash flows to be an important characteristic. Perhaps, this

also explains why debt structure is also deemed insignificant. While the studies

conducted in the 1960s and 1970s determined debt level to be positively

related to distress, perhaps the greater acceptance of leverage in the 1980s

and 1990s has allowed firms to reduce their dependence on internal cash.




                                                                              73
APPENDIX 7 – PN4 CRITERIA


Pursuant to paragraph 8.14C(2) of the Listing Requirements, the Exchange prescribes the following
criteria (hereinafter referred to as the “Prescribed Criteria”), the fulfillment of one or more of which will
require a listed issuer to comply with the provisions of paragraph 8.14C and this Practice Note :-
         (a) the shareholders’ equity of the listed issuer on a consolidated basis is equal to or less than
         25% of the issued and paid-up capital of the listed issuer and such shareholders’ equity is less
         than the minimum issued and paid-up capital as required under paragraph 8.16A(1) of the
         Listing Requirements;
         (b) receivers and/or managers have been appointed over the asset of the listed issuer, its
         subsidiary or associated company which asset accounts for at least 50% of the total assets
         employed of the listed issuer on a consolidated basis;
         (c) a winding up of a listed issuer’s subsidiary or associated company which accounts for at least
         50% of the total assets employed of the listed issuer on a consolidated basis;
         (d) the auditors have expressed an adverse or disclaimer opinion in the listed issuer’s latest
         audited accounts;
         (e) the auditors have expressed a modified opinion with emphasis on the listed issuer’s going
         concern in the listed issuer’s latest audited accounts and the shareholders’ equity of the listed
         issuer on a consolidated basis is equal to or less than 50% of the issued and paid-up capital of the
         listed issuer;
         (f) a default in payment by a listed issuer, its major subsidiary or major associated company, as
         the case may be, as announced by a listed issuer pursuant to Practice Note No 1/2001 and the
         listed issuer is unable to provide a solvency declaration to the Exchange.
         (g) the listed issuer has suspended or ceased:-
                   (i) all of its business or its major business; or
                   (ii) its entire or major operations,
                   for any reasons whatsoever including, amongst others, due to or as a result of:-
                   (aa) the cancellation, loss or non-renewal of a licence, concession or such other rights
                   necessary to conduct its business activities;
                   (bb) the disposal of the listed issuer's business or major business; or
                   (cc) a court order or judgment obtained against the listed issuer prohibiting the listed
                   issuer from conducting its major operations on grounds of infringement of copyright of
                   products etc; or
         (h) the listed issuer has an insignificant business or operations.




                                                                                                          74
                    Appendix 3: Independent Samples Test 1998
                                     Levene's Test for
                                    Equality of Variances                         t-test for Equality of Means
                                                                                                        Std.
                                                                                  Sig.      Mean       Error      95% Confidence
                                                                                (2-taile Differe       Differe     Interval of the
                                        F          Sig.       t       df           d)        nce        nce          Difference

                                     Lower         Upper    Lower    Upper      Lower     Upper      Lower       Upper       Lower
          Equal variances assumed
NITA                                    7.288        .009   3.085          66     .003    .46086     .14937       .16263     .75910
          Equal variances not
          assumed                                           1.785    12.676       .098    .46086     .25821       -.09842   1.02014
          Equal variances assumed                                                          -.4482
TDTA                                        .004     .953   -1.093         66     .278               .41000      -1.26687    .37032
                                                                                                7
          Equal variances not                                                              -.4482
          assumed                                           -1.277   22.458       .215               .35096      -1.17526    .27871
                                                                                                7
          Equal variances assumed
WCTA                                        .939     .336   1.258          66     .213    .33940     .26982       -.19931    .87811
          Equal variances not
          assumed                                           1.047    15.275       .311    .33940     .32424       -.35062   1.02942
          Equal variances assumed                                                          -.5607
CACL                                    5.424        .023    -.569         66     .571               .98582      -2.52900   1.40751
                                                                                                5
          Equal variances not                                                              -.5607    1.6819
          assumed                                            -.333   12.732       .744                           -4.20222   3.08073
                                                                                                5         8
          Equal variances assumed
EBITTA                                  8.331        .005   3.061          66     .003    .44673     .14593       .15537     .73810
          Equal variances not
          assumed                                           1.704    12.519       .113    .44673     .26211       -.12174   1.01520
          Equal variances assumed
SALESTA                                 2.255        .138   1.140          65     .258    .32079     .28131       -.24103    .88261
          Equal variances not
          assumed                                           1.813    47.358       .076    .32079     .17693       -.03508    .67666
          Equal variances assumed                                                          -.1037
CATA                                        .589     .446   -1.472         66     .146               .07050       -.24455    .03697
                                                                                                9
          Equal variances not                                                              -.1037
          assumed                                           -1.352   16.596       .194               .07674       -.26600    .05842
                                                                                                9
          Equal variances assumed
CASHTA                                      .048     .828   2.356          66     .021    .28447     .12076       .04336     .52557
          Equal variances not
          assumed                                           2.511    19.602       .021    .28447     .11329       .04785     .52109
          Equal variances assumed                                                         3.0817     1.2850
CASHCL                                 11.073        .001   2.398          66     .019                            .51610    5.64740
                                                                                               5          3
          Equal variances not                                                             3.0817     2.4316
          assumed                                           1.267    12.329       .228                           -2.20070   8.36419
                                                                                               5          3
          Equal variances assumed
WCSALES                                 8.499        .005    .176          65     .861    .07601     .43291       -.78857    .94059
          Equal variances not
          assumed                                            .094    12.352       .927    .07601     .80919      -1.68151   1.83352
          Equal variances assumed
CFTD                                    1.749        .191   1.946          66     .056    .15024     .07720       -.00389    .30436
          Equal variances not
          assumed                                           2.161    20.751       .043    .15024     .06952       .00556     .29492
          Equal variances assumed
CFCL                                        .516     .475   2.237          66     .029    .26863     .12006       .02893     .50834
          Equal variances not
          assumed                                           2.282    18.539       .035    .26863     .11772       .02182     .51545
          Equal variances assumed                                                          -.0360
CFSALES                                     .742     .392    -.238         65     .813               .15124       -.33807    .26603
                                                                                                2
          Equal variances not                                                              -.0360
          assumed                                            -.224   17.089       .825               .16053       -.37457    .30253
                                                                                                2




                                                                                                                  75
                       Appendix 4: Independent Samples Test 1999
                                     Levene's Test for
                                    Equality of Variances                         t-test for Equality of Means
                                                                                                         Std.
                                                                                   Sig.       Mean       Error     95% Confidence
                                                                                 (2-taile Differe       Differe     Interval of the
                                       F           Sig.        t       df           d)         nce       nce          Difference

                                     Lower        Upper      Lower    Upper      Lower      Upper      Lower      Upper      Lower
          Equal variances assumed
NITA                                       .660       .420    2.976         66      .004    .23695     .07962     .07798      .39591
          Equal variances not
          assumed                                             2.657   16.173        .017    .23695     .08917     .04808      .42581
          Equal variances assumed                                                           -.3993                -1.0042
TDTA                                       .699       .406   -1.318         66      .192               .30297                 .20554
                                                                                                 6                      7
          Equal variances not                                                               -.3993
          assumed                                            -2.111   47.424        .040               .18922     -.77994    -.01879
                                                                                                 6
          Equal variances assumed
WCTA                                       .015       .902    2.171         66      .034    .35364     .16287     .02846      .67883
          Equal variances not
          assumed                                             2.559   22.792        .018    .35364     .13819     .06763      .63966
          Equal variances assumed                                                           1.2451
CACL                                    3.865         .054    1.799         66      .077               .69203     -.13650    2.62688
                                                                                                 9
          Equal variances not                                                               1.2451
          assumed                                             3.537   63.338        .001               .35209     .54166     1.94872
                                                                                                 9
          Equal variances assumed
EBITTA                                  1.242         .269    3.216         66      .002    .23799     .07399     .09026      .38572
          Equal variances not
          assumed                                             2.676   15.272        .017    .23799     .08894     .04871      .42727
          Equal variances assumed                                                           10.115     20.946     -31.729
SALESTA                                    .882       .351     .483         64      .631                                    51.95952
                                                                                                05         01          42
          Equal variances not                                                               10.115     9.8156     -9.5723
          assumed                                             1.031   53.023        .307                                    29.80248
                                                                                                05          2           8
          Equal variances assumed                                                           -.0541
CATA                                       .002       .965    -.731         66      .467               .07402     -.20191     .09367
                                                                                                 2
          Equal variances not                                                               -.0541
          assumed                                             -.721   17.840        .480               .07505     -.21190     .10365
                                                                                                 2
          Equal variances assumed
CASHTA                                     .240       .626    2.337         66      .022    .32617     .13956     .04754      .60480
          Equal variances not
          assumed                                             3.145   29.639        .004    .32617     .10373     .11423      .53812
          Equal variances assumed                                                           1.2255
CASHCL                                     .063       .802    1.849         66      .069               .66274     -.09761    2.54878
                                                                                                 8
          Equal variances not                                                               1.2255
          assumed                                             2.516   30.408        .017               .48719     .23116     2.22001
                                                                                                 8
          Equal variances assumed                                                           1.0740
WCSALES                                 2.860         .096    3.797         64      .000               .28289     .50893     1.63921
                                                                                                 7
          Equal variances not                                                               1.0740
          assumed                                             3.095   13.682        .008               .34701     .32818     1.81996
                                                                                                 7
          Equal variances assumed
CFTD                                    1.939         .168    1.415         66      .162    .19248     .13601     -.07908     .46404
          Equal variances not
          assumed                                             2.705   65.673        .009    .19248     .07116     .05038      .33458
          Equal variances assumed
CFCL                                       .776       .382    1.771         66      .081    .30383     .17160     -.03878     .64644
          Equal variances not
          assumed                                             2.463   32.102        .019    .30383     .12335     .05260      .55506
          Equal variances assumed
CFSALES                                 2.000         .162    2.159         64      .035    .19658     .09104     .01470      .37846
          Equal variances not
          assumed                                             1.538   12.660        .149    .19658     .12784     -.08035     .47350



                                                                                                                    76
                    Appendix 5: Independent Samples Test 2000
                                     Levene's Test for
                                    Equality of Variances                         t-test for Equality of Means
                                                                                                         Std.
                                                                                  Sig.       Mean       Error     95% Confidence
                                                                                (2-taile Differe       Differe     Interval of the
                                        F          Sig.       t       df           d)         nce        nce         Difference

                                     Lower         Upper    Lower    Upper      Lower      Upper     Lower       Upper     Lower
          Equal variances assumed
NITA                                        .198     .658   3.347          65     .001     .18492    .05525      .07458      .29525
          Equal variances not
          assumed                                           3.195    17.321       .005     .18492    .05788      .06298      .30685
          Equal variances assumed                                                          -1.182                -2.214
TDTA                                    6.476        .013   -2.291         65     .025               .51640                 -.15152
                                                                                               84                    16
          Equal variances not                                                              -1.182                -3.104
          assumed                                           -1.333   12.689       .206               .88717                  .73856
                                                                                               84                    24
          Equal variances assumed
WCTA                                    9.687        .003   3.045          65     .003     .83479    .27417      .28725    1.38234
          Equal variances not                                                                                    -.2119
          assumed                                           1.729    12.582       .108     .83479    .48288                1.88152
                                                                                                                      3
          Equal variances assumed                                                          1.2548                -.2255
CACL                                    2.470        .121   1.693          65     .095               .74122                2.73515
                                                                                                2                     1
          Equal variances not                                                              1.2548
          assumed                                           3.247    63.827       .002               .38650      .48265    2.02699
                                                                                                2
          Equal variances assumed
EBITTA                                  1.131        .292   2.038          65     .046     .10964    .05380      .00218      .21709
          Equal variances not
          assumed                                           3.343    51.837       .002     .10964    .03279      .04383      .17545
          Equal variances assumed                                                          18.575    54.990      -91.31   128.4646
SALESTA                                     .487     .488    .338          63     .737
                                                                                               73        09         321          7
          Equal variances not                                                              18.575    27.058      -35.56
          assumed                                            .686    59.322       .495                                    72.71385
                                                                                               73        67         239
          Equal variances assumed                                                          -.0738                -.2263
CATA                                        .848     .360    -.966         65     .338               .07640                  .07877
                                                                                                1                     9
          Equal variances not                                                              -.0738                -.2546
          assumed                                            -.864   16.264       .400               .08540                  .10700
                                                                                                1                     2
          Equal variances assumed
CASHTA                                      .603     .440   2.112          65     .039     .35711    .16912      .01937      .69486
          Equal variances not
          assumed                                           3.210    41.568       .003     .35711    .11124      .13256      .58167
          Equal variances assumed                                                          1.1477                -.3085
CASHCL                                      .977     .327   1.574          65     .120               .72918                2.60398
                                                                                                1                     6
          Equal variances not                                                              1.1477
          assumed                                           2.785    62.006       .007               .41215      .32383    1.97159
                                                                                                1
          Equal variances assumed                                                          2.8094                1.0368
WCSALES                                16.043        .000   3.167          63     .002               .88701                4.58199
                                                                                                4                     9
          Equal variances not                                                              2.8094    1.7805      -1.101
          assumed                                           1.578    11.178       .142                                     6.72072
                                                                                                4         1          84
          Equal variances assumed                                                                                -.0808
CFTD                                    4.405        .040   1.129          65     .263     .10508    .09309                  .29100
                                                                                                                      3
          Equal variances not                                                                                    -.0019
          assumed                                           1.964    60.067       .054     .10508    .05351                  .21211
                                                                                                                      4
          Equal variances assumed                                                                                -.0394
CFCL                                    4.894        .030   1.695          65     .095     .22133    .13056                  .48207
                                                                                                                      2
          Equal variances not
          assumed                                           2.650    45.080       .011     .22133    .08351      .05313      .38952
          Equal variances assumed                                                                                -.0393
CFSALES                                     .001     .982   1.417          63     .161     .09588    .06766                  .23110
                                                                                                                      3
          Equal variances not                                                                                    -.0524
          assumed                                           1.372    15.821       .189     .09588    .06990                  .24420
                                                                                                                      4




                                                                                                                  77
                      Appendix 6: Independent Samples Test 2001
                                     Levene's Test for
                                    Equality of Variances                          t-test for Equality of Means
                                                                                                          Std.
                                                                                   Sig.       Mean       Error     95% Confidence
                                                                                 (2-taile Differe       Differe     Interval of the
                                       F            Sig.       t       df           d)         nce        nce         Difference

                                     Lower        Upper      Lower    Upper      Lower      Upper     Lower       Upper     Lower
          Equal variances assumed
NITA                                    2.132         .149   3.121          65     .003     .27940    .08952      .10062      .45818
          Equal variances not
          assumed                                            3.314    17.285       .004     .27940    .08432      .10172      .45707
          Equal variances assumed                                                           -1.174                -2.031
TDTA                                    4.506         .038   -2.734         65     .008               .42936                 -.31658
                                                                                                07                    56
          Equal variances not                                                               -1.174                -2.583
          assumed                                            -1.811   12.243       .095               .64834                  .23543
                                                                                                07                    57
          Equal variances assumed                                                           1.3381
WCTA                                   18.801         .000   4.313          65     .000               .31021      .71856    1.95764
                                                                                                 0
          Equal variances not                                                               1.3381                -.0278
          assumed                                            2.151    11.214       .054               .62205                2.70405
                                                                                                 0                     5
          Equal variances assumed                                                           1.6970    1.0352      -.3705
CACL                                    2.376         .128   1.639          65     .106                                     3.76460
                                                                                                 4         6           3
          Equal variances not                                                               1.6970
          assumed                                            3.357    62.876       .001               .50553      .68678    2.70729
                                                                                                 4
          Equal variances assumed
EBITTA                                     .775       .382   3.157          65     .002     .15694    .04971      .05767      .25621
          Equal variances not
          assumed                                            3.274    16.816       .005     .15694    .04794      .05571      .25817
          Equal variances assumed                                                                                 -.1955
SALESTA                                 2.515         .118   1.223          63     .226     .30867    .25234                  .81292
                                                                                                                       9
          Equal variances not                                                                                     -.0847
          assumed                                            1.630    21.378       .118     .30867    .18940                  .70212
                                                                                                                       8
          Equal variances assumed                                                           -.0015                -.1629
CATA                                    4.634         .035    -.019         65     .985               .08082                  .15986
                                                                                                 6                     7
          Equal variances not                                                               -.0015                -.2226
          assumed                                             -.015   13.357       .988               .10260                  .21949
                                                                                                 6                     1
          Equal variances assumed
CASHTA                                     .053       .819   2.302          65     .025     .38976    .16933      .05158      .72793
          Equal variances not
          assumed                                            2.944    22.915       .007     .38976    .13238      .11586      .66366
          Equal variances assumed                                                           1.9174    1.0330      -.1456
CASHCL                                     .149       .701   1.856          65     .068                                     3.98060
                                                                                                 7         4           6
          Equal variances not                                                               1.9174
          assumed                                            2.518    25.818       .018               .76136      .35193    3.48301
                                                                                                 7
          Equal variances assumed                                                           4.5447    1.0872      2.3713
WCSALES                                22.519         .000   4.180          62     .000                                     6.71801
                                                                                                 0         1           9
          Equal variances not                                                               4.5447    2.2519      -.4618
          assumed                                            2.018    10.165       .071                                     9.55126
                                                                                                 0         3           6
          Equal variances assumed                                                                                 -.0315
CFTD                                    5.225         .026   1.673          65     .099     .16285    .09732                  .35721
                                                                                                                       1
          Equal variances not
          assumed                                            3.159    62.626       .002     .16285    .05154      .05984      .26586
          Equal variances assumed                                                                                 -.0048
CFCL                                    3.944         .051   1.968          65     .053     .32101    .16315                  .64685
                                                                                                                       3
          Equal variances not
          assumed                                            2.960    33.239       .006     .32101    .10844      .10046      .54156
          Equal variances assumed                                                                                 -.1145
CFSALES                                    .560       .457    .923          62     .360     .09818    .10641                  .31088
                                                                                                                       3
          Equal variances not                                                                                     -.0272
          assumed                                            1.580    41.966       .122     .09818    .06213                  .22356
                                                                                                                       0




                                                                                                                   78
                      Appendix 2: SUMMARY STATISTICS




Table 4.1.1: Profitability Ratio - Two sample t-test with equal variances

      Variable          Group     Observations        Mean         Std Dev           t        df
                        Status

        nita               0           219         .0276142        .2623657       5.7604      268
                           1            51         -.2618334      .50961.38

       ebitta              0           219         .0623366       .2177557        5.2390      268
                           1            51         -.1763142      .5033522

       salesta             0           215          9.694038      100.3764        0.4987      261
                           1            48          2.444032      13.27714

Note: Non-failed group distinguished by status 0 and failed group distinguished by status 1




Table 4.1.2: Cash Flow Ratio - Two sample t-test with equal variances

      Variable          Group     Observations        Mean         Std Dev          T         df
                        Status

       cfsales             0           214         .0698125       .3363745        1.5917      260
                           1            48         -.0170025      .3639241

        cftd               0           219         .1438238       .3597432        2.9745      268
                           1            51         -.0086326      .1360677

        cfcl               0           219         .2269034       .5013146        3.8070      270
                           1            51         -.0513442      .2977745

Note: Non-failed group distinguished by status 0 and failed group distinguished by status 1




                                                                                                    79
Table 4.1.3: Liquidity Ratio - Two sample t-test with equal variances

         Variable            Group      Observations     Mean          Std Dev             t          df
                             Status

wcsales                      0        214              .0242214      1.078106      5.3526        260
                             1        48               -2.008213     5.099299

wcta                         0        219              -.0067682     .6024754      5.3926        268
                             1        51               -.7115569     1.484854

cashta                       0        219              .2222634      .509383       4.5434        270
                             1        51               -.1165082     .3185023

cata                         0        219              .4343766      .231741       -.15960       268
                             1        51               .4941126      .2764944

cacl                         0        219              1.978614      2.739189      2.0643        270
                             1        51               1.079808      3.052731

Note: Non-failed group distinguished by status 0 and failed group distinguished by status 1




Table 4.1.4: Long-term Solvency Ratio - Two sample t-test with equal variances

  Variable              Group     Observations         Mean         Std Dev           t              df
                        Status

tdta                0             219             .7246381         1.157173      -3.7863       268
                    1             51              1.518509         1.977196

Note: Non-failed group distinguished by status 0 and failed group distinguished by status 1




                                                                                                           80
         Appendix 12: SAMPLE POPULATION – INDUSTRIAL
           PRODUCTS – MAIN BOARD BURSA MALAYSIA27


 COMPANY - TICKER            Status
 ASB - 1481                           0
 ALCOM - 2674                         0
 AISB - 2682                          0
 ANCOM - 4758                         0
 ANJOO - 6556                         0
 CIHLDG - 2828                        0
 CAMERLN - 3751                       0
 CIMA - 2844                          0
 CCM - 2879                           0
 DELLOYD - 6505                       0
 DIJAENT - 5401                       0
 DRB - 1619                           0
 ESSO - 3042                          0
 FACBIND - 2984                       0
 FCW - 2755                           0
 GBH - 3611                           0
 GOPENG - 2135                        0
 GUH - 3247                           0
 HEXZA - 3298                         0
 HUMEIND - 3328                       0
 ICP - 6829                           0
 JTIASA - 4383                        0
 KSENG - 3476                         0
 KIALIM - 6211                        0
 KRAMAT - 2151                        0
 KYM - 8362                           0
 LEADER - 4529                        0
 LIONCORP - 3581                      1
 MUIIND - 3891                        0
 MAICA - 3743                         0
 MSC - 5916                           0
 MPI - 3867                           0
 MENTIGA - 5223                       1
 METROD - 6149                        0
 MIECO - 5001                         0
 MINHO - 5576                         0
 MUDA - 3883                          0
 NYLEX - 4944                         0
 PMI - 4103                           0
 PETGAS - 6033                        0
 PNEPCB - 6637                        0
 SAPURA - 7811                        0
 SCIENTX - 4731                       0
 SEAL - 4286                          0
 SHELL - 4324                         0

27
     STATUS DENOTES HEALTHY (0) OR DISTRESSES/ PN4 (1)
                                                         81
SINORA - 6262       0
SITATT - 4359       0
SAB - 5134          0
SUBUR - 6904        0
SSTEEL - 5665       0
TASEK - 4448        0
TENGARA - 8257      0
UAC - 4537          0
UNISEM - 5005       0
VS - 6963           0
WEMBLEY - 5428      1
WTK - 4243          0
YTLCMT - 8737       0
JAVA - 2747         1
DOLMITE - 5835      1
AMSTEEL             1
CHG                 1
CASH                1
TONGKAH             1
WING TEIK HLDG      1
RNC CORPORATION     1
FORESWOOD GROUP     1
TRU-TECH HOLDINGS   1




                        82
Appendix 8: t-TEST Results




                         Current Assets to Current Liabilities
                             Two-sample t test with equal variances



    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 1.978614 .1850973 2.739189 1.613805 2.343423
         1|      51 1.079808 .4274679 3.052731 .2212134 1.938402
    ---------+--------------------------------------------------------------------
    combined | 270 1.80884 .171454 2.817276 1.471277 2.146402
    ---------+--------------------------------------------------------------------
       diff |         .8988063 .4353988                    .0415692 1.756043
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                  Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0          Ha: diff != 0         Ha: diff > 0
       t = 2.0643             t = 2.0643          t = 2.0643
     P < t = 0.9800         P > |t| = 0.0399      P > t = 0.0200




                               Net Income to Total Assets
                             Two-sample t test with equal variances

    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 .0276142 .017729 .2623657 -.007328 .0625565
         1|      51 -.2618334 .0713602 .5096138 -.4051646 -.1185022
    ---------+--------------------------------------------------------------------
    combined | 270 -.0270592 .0208114 .3419668 -.0680332 .0139148
    ---------+--------------------------------------------------------------------
       diff |         .2894476 .0502482                    .1905162 .3883791
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                  Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0          Ha: diff != 0         Ha: diff > 0
       t = 5.7604             t = 5.7604          t = 5.7604
     P < t = 1.0000         P > |t| = 0.0000      P > t = 0.0000



                                                                                     83
Appendix 8: t-TEST Results



                                Total Debt to total Assets
                             Two-sample t test with equal variances

    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 .7246381 .0781945 1.157173 .5705241 .8787521
         1|      51 1.518509 .2768628 1.977196 .962414 2.074604
    ---------+--------------------------------------------------------------------
    combined | 270 .8745916 .0840796 1.381568 .7090539 1.040129
    ---------+--------------------------------------------------------------------
       diff |        -.7938711 .2096719                    -1.206685 -.3810574
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                 Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0         Ha: diff != 0          Ha: diff > 0
       t = -3.7863           t = -3.7863           t = -3.7863
     P < t = 0.0001        P > |t| = 0.0002       P > t = 0.9999




                           Working Capital to Total Assets
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 -.0067682 .0407115 .6024754 -.0870068 .0734703
         1|      51 -.7115569 .2079211 1.484854 -1.129179 -.2939351
    ---------+--------------------------------------------------------------------
    combined | 270 -.139895 .0537607 .8833789 -.2457403 -.0340497
    ---------+--------------------------------------------------------------------
       diff |         .7047887 .1306956                    .4474679 .9621095
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                 Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0         Ha: diff != 0          Ha: diff > 0
       t = 5.3926            t = 5.3926           t = 5.3926
     P < t = 1.0000        P > |t| = 0.0000       P > t = 0.0000




                                                                                     84
Appendix 8: t-TEST Results



                         Current assets to Current Liabilities
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 1.978614 .1850973 2.739189 1.613805 2.343423
         1|      51 1.079808 .4274679 3.052731 .2212134 1.938402
    ---------+--------------------------------------------------------------------
    combined | 270 1.80884 .171454 2.817276 1.471277 2.146402
    ---------+--------------------------------------------------------------------
       diff |         .8988063 .4353988                    .0415692 1.756043
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                  Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0          Ha: diff != 0         Ha: diff > 0
       t = 2.0643             t = 2.0643          t = 2.0643
     P < t = 0.9800         P > |t| = 0.0399      P > t = 0.0200




           Earning Before Interest and Taxes (EBIT) to Total Assets
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 .0623366 .0147146 .2177557 .0333356 .0913377
         1|      51 -.1763142 .070482 .5033422 -.3178814 -.0347469
    ---------+--------------------------------------------------------------------
    combined | 270 .0172581 .0186863 .3070467 -.0195318 .0540481
    ---------+--------------------------------------------------------------------
       diff |         .2386508 .0455528                    .1489639 .3283378
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                  Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0          Ha: diff != 0         Ha: diff > 0
       t = 5.2390             t = 5.2390          t = 5.2390
     P < t = 1.0000         P > |t| = 0.0000      P > t = 0.0000




                                                                                     85
Appendix 8: t-TEST Results



                                    Sales to Total Assets
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 215 9.694038 6.845613 100.3764 -3.799426 23.1875
         1|      48 2.444032 1.91639 13.27714 -1.411248 6.299311
    ---------+--------------------------------------------------------------------
    combined | 263 8.370843 5.607245 90.93424 -2.670158 19.41184
    ---------+--------------------------------------------------------------------
       diff |         7.250006 14.53748                    -21.37566 35.87567
    ------------------------------------------------------------------------------
    Degrees of freedom: 261

                  Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0          Ha: diff != 0          Ha: diff > 0
       t = 0.4987             t = 0.4987           t = 0.4987
     P < t = 0.6908         P > |t| = 0.6184       P > t = 0.3092




                              Current Assets to Total assets
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 .4343766 .0156596 .231741 .403513 .4652402
         1|      51 .4941126 .038717 .2764944 .4163473 .5718779
    ---------+--------------------------------------------------------------------
    combined | 270 .4456601 .014692 .241414 .4167342 .474586
    ---------+--------------------------------------------------------------------
       diff |         -.059736 .0374276                   -.1334254 .0139535
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                  Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0          Ha: diff != 0          Ha: diff > 0
       t = -1.5960            t = -1.5960           t = -1.5960
     P < t = 0.0558         P > |t| = 0.1117       P > t = 0.9442




                                                                                     86
Appendix 8: t-TEST Results




                                   Cash to Total Assets
                                 Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 .2222634 .0344209 .509383 .154423 .2901038
         1|      51 -.1165082 .0445992 .3185023 -.2060884 -.026928
    ---------+--------------------------------------------------------------------
    combined | 270 .1582732 .0302326 .4967716 .0987507 .2177957
    ---------+--------------------------------------------------------------------
       diff |         .3387716 .0745637                    .1919664 .4855768
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                  Ho: mean(0) - mean(1) = diff = 0

       Ha: diff < 0         Ha: diff != 0          Ha: diff > 0
        t = 4.5434            t = 4.5434           t = 4.5434
      P < t = 1.0000        P > |t| = 0.0000       P > t = 0.0000




                               Cash to Current Liabilities
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 .8907428 .178303 2.638644 .5393243 1.242161
         1|      51 -.9574581 .6339817 4.527535 -2.230848 .3159316
    ---------+--------------------------------------------------------------------
    combined | 270 .5416382 .1922369 3.158774 .163158 .9201184
    ---------+--------------------------------------------------------------------
       diff |         1.848201 .4789149                    .9052867 2.791115
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                  Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0          Ha: diff != 0         Ha: diff > 0
       t = 3.8591             t = 3.8591          t = 3.8591
     P < t = 0.9999         P > |t| = 0.0001      P > t = 0.0001




                                                                                     87
Appendix 8: t-TEST Results




                                Working Capital to Sales
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 214 .0242214 .0736978 1.078106 -.121049 .1694919
         1|      48 -2.008213 .7360204 5.099299 -3.488895 -.5275312
    ---------+--------------------------------------------------------------------
    combined | 262 -.348133 .1544699 2.500313 -.6522988 -.0439672
    ---------+--------------------------------------------------------------------
       diff |         2.032435 .3797098                    1.284737 2.780133
    ------------------------------------------------------------------------------
    Degrees of freedom: 260

                 Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0         Ha: diff != 0          Ha: diff > 0
       t = 5.3526            t = 5.3526           t = 5.3526
     P < t = 1.0000        P > |t| = 0.0000       P > t = 0.0000




                                Cash Flow to Total Debt
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 .1438238 .0243092 .3597432 .0959127 .191735
         1|      51 -.0086326 .0189133 .1350677 -.046621 .0293558
    ---------+--------------------------------------------------------------------
    combined | 270 .1150265 .0203529 .3344315 .0749554 .1550976
    ---------+--------------------------------------------------------------------
       diff |         .1524565 .0512552                    .0515424 .2533705
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                 Ho: mean(0) - mean(1) = diff = 0

      Ha: diff < 0         Ha: diff != 0          Ha: diff > 0
       t = 2.9745            t = 2.9745           t = 2.9745
     P < t = 0.9984        P > |t| = 0.0032       P > t = 0.0016




                                                                                     88
Appendix 8: t-TEST Results



                             Cash Flow to Current Liabilities
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 219 .2269034 .0338757 .5013146 .1601376 .2936692
         1|      51 -.0513442 .0416968 .2977745 -.1350946 .0324062
    ---------+--------------------------------------------------------------------
    combined | 270 .1743456 .0293167 .4817221 .1166262 .2320649
    ---------+--------------------------------------------------------------------
       diff |         .2782476 .0730875                    .1343489 .4221463
    ------------------------------------------------------------------------------
    Degrees of freedom: 268

                   Ho: mean(0) - mean(1) = diff = 0

       Ha: diff < 0           Ha: diff != 0           Ha: diff > 0
        t = 3.8070              t = 3.8070            t = 3.8070
      P < t = 0.9999          P > |t| = 0.0002        P > t = 0.0001




                                       Cash Flow to Sales
                                  Two-sample t test with equal variances
    ------------------------------------------------------------------------------
      Group | Obs             Mean Std. Err. Std. Dev. [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
         0 | 214 .0698125 .0229941 .3363745 .0244873 .1151376
         1|      48 -.0170025 .0525279 .3639241 -.1226751                         .08867
    ---------+--------------------------------------------------------------------
    combined | 262 .0539074 .021161 .342521 .0122394 .0955755
    ---------+--------------------------------------------------------------------
       diff |         .086815 .0545429                    -.0205871 .194217
    ------------------------------------------------------------------------------
    Degrees of freedom: 260

                   Ho: mean(0) - mean(1) = diff = 0

       Ha: diff < 0           Ha: diff != 0           Ha: diff > 0
        t = 1.5917              t = 1.5917            t = 1.5917
      P < t = 0.9437          P > |t| = 0.1127        P > t = 0.0563




                                                                                           89
90
                                             Appendix 1: TABLE 1 - GROUP STATISTICS (CONTINUED)

                                                1998                1999                            2000                        2001                          2002
                                    Std.       Std.                Std.       Std.                Std.     Std.                Std.     Std.                Std.      Std.
                                   Deviati    Error               Deviatio    Error              Deviati   Error              Deviati   Error              Deviatio   Error
           STATUS    N    Mean       on       Mean       Mean        n        Mean      Mean       on      Mean      Mean       on      Mean      Mean        n       Mean
               0                              0.0379                                                                                    0.0212
EBITTA              55    0.0789   0.28176          9    0.0653   0.22392    0.03019    0.0781   0.18973   0.02582   0.0273   0.15742         3   0.0345    0.1492    0.02012
                1                             0.2593                                                                 -0.129             0.0429    -0.079
                    13   -0.3679   0.93507          4   -0.1727   0.30164    0.08366   -0.0315    0.0729   0.02022        6    0.1489         8        7          .           .

                0                             0.1345                                                                                    0.1090
SALESTA             54    0.8571   0.98897         8    10.6019   72.1218    9.81454   26.8251   188.939   25.9528   0.8093   0.80118        3    0.7775   0.81477    0.11088
                1                             0.1148                                                                                    0.1548
                    13    0.5363   0.41413         6     0.4869   0.50379    0.14543    8.2494   26.5237   7.65672   0.5006   0.51364        7    0.4497          .           .

                0                             0.1860                                                                                    0.1484
TDTA                55     0.746   1.38005         9     0.7032   1.06577    0.14371    0.7266   1.08349   0.14744   0.7227   1.10102        6     0.739   1.22083    0.16462
                1                             0.2975                                                                                    0.6311
                    13    1.1943   1.07288         6     1.1026   0.44384     0.1231    1.9095   3.15426   0.87483   1.8968   2.18624        1    0.8482          .           .

                0                             0.0346                                                                                    0.0448
CFTD                55    0.1487   0.25708         7     0.1854   0.48526    0.06543    0.1059   0.32994    0.0449   0.1346   0.33276        7    0.1411   0.23456    0.03192
                1                             0.0602                                                                 -0.028             0.0253
                    13   -0.0015   0.21727         6    -0.0071   0.10089    0.02798    0.0008   0.10492    0.0291        3   0.08788        7    0.0428          .           .

                0                             0.0527                                                                                    0.0738
CFCL                55    0.2328   0.39148         9     0.2476   0.59416    0.08012    0.1892    0.4582   0.06235   0.2373   0.54793        8    0.2599   0.50252    0.06838
                1                             0.1052                                                                 -0.083             0.0793
                    13   -0.0358    0.3794         3    -0.0562   0.33817    0.09379   -0.0321    0.2003   0.05555        7   0.27494        7    0.0999          .           .

                0                             0.0653
CFSALES             54    0.0254   0.48025         5     0.1038   0.24568    0.03343    0.0765   0.20971   0.02881   0.0737   0.34581   0.0475    0.0953   0.19722    0.02735
                1                             0.1466                                                                 -0.024             0.0400
                    13    0.0615   0.52866         2    -0.0927   0.42742    0.12339   -0.0194   0.22063   0.06369        5   0.13281        4    0.0808          .           .
  *Healthy firms denoted by STATUS 0, Distress Firms Denoted by STATUS 1.
                                                       Appendix 1: TABLE 1 - GROUP STATISTICS
                                                     1998                  1999                            2000                            2001                            2002
                                                    Std.                            Std.                            Std.                            Std.                            Std.
                                         Std.       Error                Std.       Error                Std.       Error                Std.       Error                Std.       Error
              STATUS    N    Mean      Deviation    Mean     Mean      Deviation    Mean     Mean      Deviation    Mean     Mean      Deviation    Mean     Mean      Deviation    Mean
                   0    55   -0.0412    0.81664    0.11012   -0.0161    0.54917    0.07405    0.0086    0.54378      0.074     0.022    0.45195    0.06094   0.0033     0.60721    0.08188
WCTA
                   1    13   -0.3806    1.09958    0.30497   -0.3698    0.42069    0.11668   -0.8262    1.72048    0.47717   -1.3161    2.14449    0.61906   0.4024           .          .

                    0   55   0.4307      0.2224    0.02999   0.4311     0.23905    0.03223   0.4528     0.23795    0.03238   0.4232     0.23267    0.03137   0.4122     0.23039    0.03107
CATA
                    1   13   0.5345     0.25469    0.07064   0.4852     0.24437    0.06778   0.5266     0.28493    0.07903   0.4248     0.33839    0.09768    0.766           .          .

                    0   55    0.1856    0.39833    0.05371    0.2093      0.481    0.06486   0.2471     0.59176    0.08053    0.2476    0.55764    0.07519    0.2637    0.63103    0.08509
CASHTA
                    1   13   -0.0989    0.35963    0.09974   -0.1169    0.29186    0.08095     -0.11    0.27668    0.07674   -0.1422    0.37741    0.10895   -1.1245          .          .

                    0   55    0.6856    2.09033    0.28186    0.7659    2.28774    0.30848    0.8882    2.58793    0.35217    1.2232    3.42923     0.4624   1.4107     5.09994    0.69401
CASHCL
                    1   13   -2.3962    8.70827    2.41524   -0.4597    1.35963    0.37709   -0.2595    0.77198    0.21411   -0.6942     2.0953    0.60486   -3.092           .          .

                    0   54   0.0779     0.71318    0.09705   -0.0125    0.82479    0.11224    -0.018    1.15925    0.15923    0.0491    1.48275    0.20367   -0.0961    1.83614    0.25463
WCSALES
                    1   13   0.0019     2.89652    0.80335   -1.0866    1.13746    0.32836   -2.8274    6.14315    1.77337   -4.4956    7.43819     2.2427    0.8947          .          .

                    0   55   1.8062     2.13446    0.28781   1.9454     2.47342    0.33352   1.9305     2.64611    0.36009   2.2315     3.55608     0.4795   2.4089     5.08496    0.69198
CACL
                    1   13   2.3669     5.97501    1.65717   0.7002     0.40693    0.11286   0.6756     0.50633    0.14043   0.5345     0.55463    0.16011   2.1064           .          .

                    0   55   0.0444     0.31521     0.0425   0.0491     0.24848    0.03351   0.0506     0.17622    0.02398   -0.0333    0.28504    0.03844    -0.009    0.18825    0.02538
NITA
                    1   13   -0.4165    0.91829    0.25469   -0.1878    0.29794    0.08263   -0.1343    0.18992    0.05268   -0.3127    0.25998    0.07505   -0.1761           .            .
  *Healthy firms denoted by STATUS 0, Distress Firms Denoted by STATUS 1.