FINANCE DISTRESS PREDICTION: A CASE STUDY OF

UCHUMI SUPERMARKETS LTD "

BY

WANJIKU PAUL KAMAU -—

A Project Report Submitted to the Chandaria School of Business in

Partial Fulfillment of the Requirement for the Degree of Masters in

Business Administration (MBA)

U S I U - A

400000019959

UNITED STATES INTERNATIONAL UNIVERSITY

United States International universi,.

Africa - Librarv

SUMMER 2013

D E C L A R A T I O N

I , the undersigned, declare that this is my original work and has not been submitted to any

other college, institution or university other than the United States International

University in Nairobi for academic credit.

Signed: Date:

WanjikuPaul Kamau ̂ 2^85)

This project has been submitted as a course requirement with my full consent and

approval as the appointed supervisor

Signed: Q Date

Professor Scott Bellows

Signed: (jR^^ Date: U ) ^ i 1

Dean, Chandaria School of Business

Signed: Date:

Deputy Vice Chancellor, Academic Affairs

ii

C O P Y R I G H T

All rights reserved; no part of this work may be reproduced, stored in a retrieval system

or transmitted in any form or by any means, electronic, mechanical, photocopying,

recording or otherwise without the express written authorization from the writer.

Wanjiku Paul Kamau © 2013

iii

A B S T R A C T

The purpose of this study was to investigate the financial distress prediction with Uchumi

supermarkets as a case-study. The study was hinged on three research questions which

were; what is the predictive ability of Altman's Z-score while using published financial

Information? What is the relevance of capital structure in finance distress prediction?

What is relevance of liquidity in finance distress prediction?

A descriptive design was the research methodology that was adopted. A biased sampling

technique was adopted by selecting the financial statements of the period running from

2003 to 2013, from where the data was collected from. The extracted information was

analyzed using SPPS. Analysis was done to establish the relationships between variables

that had been used for the finance distress prediction. In analysis, trends were drawn,

ANOVAs and regression being conducted to establish causal relationships with the Z

score.

The research was able to demonstrate that Altman Z score had predictive capability in

relation to Uchumi supermarkets. The Z score was able to predict accurately as compared

to the auditors opinion on the going concern of an organization.

The relevance of capital structure on the finance distress prediction was noted as

significant as established through Anova and regression. Equity part of the capital

stmcture was noted to be significant whereas the long term debt was noted as

insignificant to the finance distress prediction.

The relevance of the liquidity to the finance distress prediction was noted as significant.

The working capital component was noted significant though the current liabilities were

insignificant in the relationship with the Z score, the distress predictor.

The conclusion on the research was that the Altman Z score had the predictive ability, and

the capital structure and liquidity were significantly relevant to the Z score which was the

financial distress predictor.

There were gaps that were noted during the study that formed the recommendations for

further study on the effect of non- financial factors in the distress as well as the

significance of the auditors' opinion was the areas recommended for further studies.

iv

A C K N O W L E D G E M E N T

This project wouldn't have been complete without the valuable assistance from several

sources. Most of all I would like to thank my lecturer Prof Scott Bellows for the

consistent knowledge he has imparted towards the completion.

Florence Kinyua, Investment analyst at Genesis Investment, Pauline Kimotho, Company

Secretary at Uchumi Supermarkets, and Librarians at Nairobi Securities Exchange and

Capital Markets Authority has all had an impact on this project.

Last but not the least, Grace my wife and Shirley my 9 month old daughter, kept me

company as I worked through the nights to finish the study.

v

A C K N O W L E D G E M E N T

This project wouldn't have been complete without the valuable assistance from several

sources. Most of all I would like to thank my lecturer Prof. Scott Bellows for the

consistent knowledge he has imparted towards the completion.

Florence Kinyua, Investment analyst at Genesis Investment, Pauline Kimotho, Company

Secretary at Uchumi Supermarkets, and Librarians at Nairobi Securities Exchange and

Capital Markets Authority has all had an impact on this project.

Last but not the least, Grace my wife and Shirley my 9 month old daughter, kept me

company as I worked through the nights to finish the study.

v

To my daughter Shirley

D E D I C A T I O N

vi

A B B R E V I A T I O N S AND ACRONYMS

ICDC Industrial Commercial and development

KWAL Kenya Wines Agencies Ltd

KNTC Kenya National trading corporation

PTA Preferential Trade Area

FDF Financially Distressed Firm

MDA Multi Discriminate Analysis

vii

T A B L E OF CONTENTS

D E C L A R A T I O N ii

COPYRIGHT iii

A B S T R A C T iv

A C K N O W L E D G E M E N T v

DEDICATION vi

A B B R E V I A T I O N S AND A C R O N Y M S vii

L I S T OF T A B L E S xi

C H A P T E R ONE 1

1.0 INTRODUCTION 1

L I Background of the Problem 1

1.2 Statement of the Problem 3

1.3 Purpose of the Study 4

1.4 Research Questions 4

1.5 Significance of the Study 4

1.6 Scope of the Study 6

1.7 Definition of Terms 6

1.8 Chapter Summary 7

C H A P T E R T W O 8

2.0 L I T E R A T U R E R E V I E W 8

2.1 Introduction 8

2.2 Application of Altman Z Score in Predicting Distress Using Published Information... 8

2.3 Relevance of Capital Structure in financial Distress prediction 13

2.4 Relevance of Liquidity in Finance Distress Prediction 17

2.5 Chapter Summary 20

vii i

CHAPTER T H R E E 21

3.0 R E S E A R C H M E T H O D O L O G Y 21

3.1 Introduction 21

3.2 Research Design 21

3.3 Population and Sampling Design 22

3.4 Data Collection method 23

3.5 Research Procedure 23

3.6 Data Analysis Methods 23

3.7 Chapter Summary 23

C H A P T E R FOUR 24

4.0 R E S U L T S AND FINDINGS 24

4.1 Introduction 24

4.2 Variables Predicting Financial Distress 24

4.3 Predictive Ability of Altman's Z-Score Using Published Financial Information 25

4.4 Relevance of Capital Structure in Financial Distress Prediction 26

4.5 Relevance of Liquidity in Financial Distress Prediction 28

4.6 Chapter Summary 31

C H A P T E R F I V E 32

5.0 DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS 32

5.1 Introduction 32

5.2 Summary 32

5.3 Discussions 33

5.4 Conclusions 36

5.5 Recommendations 37

ix

R E F E R E N C E S 39

APPENDIX 1: Research Data 43

X

L I S T OF T A B L E S

Table 2.1: Bankmptcy Prediction Models and Authors 9

Table 2.2: Altman's Z-Score Models 11

Table 2.3: Relationship between Disturbance and Information 13

Table 4.1: Variables Predicting Financial Distress 24

Table 4.2: Computed Altman Z score 25

Table 4.3: Comparison between Altman Score and Auditors Opinion 26

Table 4.4: Correlation of Equity, Long-Term Debts and Capital Structure 26

Table 4.5: Equity, Long-Term Debts, Capital Structure and Z-Score 27

Table 4.6: Correlation of Current Liabilities, Current Assets and Working Capital 29

Table 4.7: Current Liabilities, Current Assets, Working Capital, Z-Score 29

Table 4.8: Working Capital, Capital Structure and Z-Score 30

Table 4.9: Model Summary 30

Table 4.10: ANOVA 31

Table 4.11: Coefficients 31

xi

C H A P T E R ONE

1.0 INTRODUCTION

1.1 Background of the Problem

Financial crisis has thrown many financially strong companies out of business all over the

world (Ray and Mahavidyalaya, 2011). The companies were affected because they

couldn't face the challenges and the economy had unexpected changes. Before the global

financial disaster of 2007, the period from 1999 to 2002, over 400 firms in United States

of America with assets in excess of $ 100 million and as much as $400 billion in debt and

claims, filed for bankruptcy (Hayes, Hodge and Hughes, 2010). Economic liberalizafion

has been increasingly adopted in Africa, Kenya included, and amongst the tenets of

liberalization is allowing institution to go bankrupt (Mugarura, 2011).

Ray and Mahavidyalaya (2011) describe financial distress as the ultimate declaration by

an organization of its inability to sustain current operations given its current debt

obligations. The importance of the debt is because all firms must have some debt levels to

expand operation or for survival. Ray and Mahavidyalaya (2011) state that good

economic planning often requires a firm to finance some of its operation with debt and

the degree to which a firm has debt in excess of assets or is unable to pay its debt as it

comes due, are the two most common factors in corporate financial distress.

Polemis and Gounopoulos (2012) identify corporate finance distress prediction as among

the most researched topics in the finance and strategic management literature. This is due

to the fast-moving business envirormient and intense international competition. Studies

have shown that finance distress prediction models consistently outperform auditors

going on concern in discriminating companies in financial distress (Kurruppu, Laswad

and Oyelere, 2003).

Reliable finance distress model with consistent predictive power has become essential in

today's business environment, especially to suppliers and other stakeholders that rely on

firms' solvency for their own success (Hayes, Hodge and Hughes, 2010). Smith and

Graves (2005) note that finance distress models have turned to be accurate in their

classification of corporate failure and alludes that few companies fail without having been

identified as financially distressed.

1

There are a variety of failure prediction models that have been developed using

techniques such as multiple discriminant analysis (MDA), logit, probit, recursive

partitioning, hazard models and neural networks. However, despite the variety of models

available, both the business community and researchers often rely on the models

developed by Altman (1968) and Ohlson (1980). A survey of the literature shows that the

majority of international failure prediction studies employ MDA (Wang and Campbell,

2010).

Despite several attempts on bankruptcy prediction, decades after the initial Altman

seminal study, financial distress prediction research is yet to reach to an unambiguous

conclusion. Lack of harmony in the various studies of financial distress prediction can be

partially attributable to the nature of the explanatory variables. There has been attempt to

discriminate between financial characteristics of successfiil firms and the one facing

failure. The objective has always been to develop a model that uses financial ratios to

predict which firms have greatest likelihood of becoming insolvent in the near future.

Altman is perhaps the best known of these researchers who uses multiple discriminate

analyses (Ray and Mahavidyalaya, 2011).

The MDA model has been questioned by Ohlson especially on the restrictive statistical

requirements imposed by the model. To overcome the limitations, Ohlson model

employed logistic regression to predict company failure. Logit model and US firms were

used to develop an estimate of the probability of failure for each firm. The argument was

that the model overcomes some of the criticisms of MDA, which requires an assumption

of a normal distribution of predictors, and suffers from the arbitrary nature of identifying

non-failed "matching" firms. However, the nine independent variables selected by Ohlson

that were thought to be helpful in predicting bankruptcy, were not based on a theoretical

justification for the selection (Wang and Campbell, 2010).

Hayes, Hodge and Hughes (2010) suggest that a fiirther exploration on the Altman Z

score should be conducted, to determine its effectiveness in a variety of contexts and

cultural settings. However, Ray and Mahavidyalaya (2011) noted that the explorations

should be devoted to the emerging economies as since the seminal work of Altman in

1968 on Z score, least attention has been devoted to the emerging economies.

2

Although corporate failures are perceived to be a problem of developed economic

environments, firms operating in emerging economies are no exception. There have been

numerous researchers who have attempted to improve upon and replicate such studies in

capital markets worldwide. However, in the emerging economies, the topic has been of

less attention mainly due to the short history of financial markets in emerging economies

(Ray and Mahavidyalaya, 2011).

Uchumi Supermarkets was established in 1976 with ICDC, K W A L and KNTC being the

initial shareholders (Uchumi, 2013).On 1st June 2006, Uchumi supermarket was placed

imder receivership due to financial difficulties in repaying debt amounting to Kenya

Shilling 957 million to PTA Bank and Kenya Commercial Bank. The closure came just

after a rights issue of 1.2 billion and 1.3b loss recorded in the year 2005. 17 branches

were closed and 1200 workers were rendered jobless. The rights issue in 2005 was

snubbed by the leading investors such as ICDCI, Kenya wines agencies Ltd and Sameer

Group and it is the new shareholders who seemed not to have predicted the financial

difficulties that pumped money to the sinking organization (Wahome, 2006).

Uchumi' s placement on receivership lead to suspension of the shares from trading at

Nairobi Securities Exchange then operating as Nairobi Stocks Exchange. As Wang and

Campbell (2010) observed, once a firm is delisted, its stock becomes worthless since

there is no platform for exchange of the stock any more. Stock as in case of Uchumi

supermarkets are near worthless as although there has been continuous demand for

establishing platforms for exchanges of stocks of delisted firms, no such kind of platform

has been created (Wang and Campbell, 2010).

Wang and Campbell (2010) note that the delisted firm in general continues operating for a

period of time, but shareholders have essentially lost their investment. This is the period

that Uchumi Supermarkets was in between 2006, when it was placed in receivership and

2011, when the receivership was eventually lifted.

1.2 Statement of the Problem

Researchers have used Altman's Z score models in the service, manufacturing

industry, publically listed companies, and banks alike to predict i f the business will be

distressed (Anjum, 2012).

3

Corporate failure studies undertaken in United Kingdom for American companies, while

applying the Altman Z-model, produced different results, resulting to a conclusion that

finance distress models derived in one country are not necessarily applicable to another.

This alludes that models are affected by differences in market structures, socio-economic

factors, provision and implementation of law, political environment, and accounting

standards especially when it is a comparison of findings from developed and developing

economies (Shuk-Wem, et al, 2011).

Hayes, Hodge and Hughes (2010) suggest that a further exploration on the Altman Z

score should be conducted, to determine its effectiveness in a variety of contexts and

cultural settings. However, Ray and Mahavidyalaya (2011) noted that the explorations

should be devoted to the emerging economies since the seminal work of Altman in 1968

on Z score, least attention has been devoted to the emerging economies.

This study sought to explore the area of finance distress prediction through the Altman Z

score fiarther by varying the cultural settings and context through consideration of

Uchumi Supermarkets, an organization operating in Kenya, an emerging economy.

1.3 Purpose of the Study

The project aimed at evaluating finance distress prediction of Uchumi Supermarkets Ltd.

1.4 Research Questions

1.4.1 What is the predictive ability of Altman's Z-score while using published financial

Information?

1.4.2 What is the relevance of capital structure in finance distress prediction?

1.4.3 What is relevance of liquidity in finance distress prediction?

1.5 Significance of the Study

The project was aimed at evaluating finance distress prediction capability in relation to

Uchumi Supermarkets. The study is helpful to the following parties;

1.5.1 Corporate Managers

The study was important to corporate managers operating in the emerging economies on

the finance distress prediction. More so, the predictive ability is essential in tracking any

4

recovery strategies. The management are also informed in the capital structure ownership

and liquidity that affect organization during the distress and recovery.

1.5.2 Auditors

A predictive model is helpful to auditors as it may warn of company's vulnerability. This

in turn helps in their assignments which may even help to shield them against

professional negligence, as they employ a reliable tool.

The study is therefore critical to determine whether the auditors can place such reliance

on the model.

1.5.3 Lenders

The study was critical to lenders as they wil l have an opportunity to determine whether

the model is sufficiently predictive in assessing a company defaulting on its loan.

Financial institutions utilize distress prediction models to evaluate customers'

creditworthiness when processing loans. When the financial institutions identify

companies that have the potential of falling into financial distress, relevant preventive or

corrective action are recommended. Actions range from rejecting loan applications or

assist borrowing companies to identify organizational weaknesses and take essential steps

to rectify them in order to avoid loan defaults. Capital injection is also a recommendation

(Shuk-Wem,Yap, Yap, and Khong, 2011)

1.5.4 Regulatory Agencies

Regulatory agencies such as capital markets Authority are concerned whether a

monitored company is in danger of failure. This is usually to protect shareholders from

losses arising from mismanagement.

The study is therefore crucial as the agencies determine whether the model can be relied

upon to detect distress and monitor the recovery strategies. The role of the capital

structure and liquidity in distress is also essential in the policy formulation by the

agencies.

5

1.5.5 Researchers

Researchers find the study relevant as it assesses the Altman Z model relevance in

Kenyan context. The model on being tested in an emerging economy provides areas for

further interest. Researchers is also keen on the role of capital structure and liquidity on

finance distress and this is key on whether it is consistent with the earlier research works

on capital structure.

1.6 Scope of the Study

Uchumi Supermarkets was established in 1976 and was listed in the then Nairobi Stocks

exchange, (currently Nairobi Securities Exchange) in 1992. It was placed under

receivership in 2006 and the receivership was lifted in 2011.

The study looked at the financial statements from 2003 to 2012. The placement on

receivership and subsequent lifting, falls within the period covered and the period wil l be

adequate to assess the accuracy of the prediction models.

1.7 Definition of Terms

1.7.1 Financial distress prediction

Ray and Mahavidyalaya (2011) define financial distress prediction of a company as the

declaration of an organization inability to sustain future operations given its current

financial conditions.

1.7.2 Liquidity

It is a measure of excess of current assets over the current liabilities and it is used as a

measure of the firm's ability to meet its maturing short-term obligations (Ray and

Mahavidyalaya, 2011).

1.7.3 Capital Structure

Antoniou, Guney and Paudyal (2008) define capital structure as a mixture of equity and

debt employed in an organization.

6

1.8 Chapter Summary

This chapter highlights a background into finance distress prediction. It analyzes the work

already done globally by the researchers in the same field then carefially narrows it down

on the problem faced in Uchumi Supermarkets. The main purpose of this study was to

investigate finance distress prediction and the role played by capital structure and

Liquidity.

In the chapter two, literature on finance distress prediction models and the various

components used in the prediction will be analyzed and the existing gaps will also be

covered. Chapter three will address the research methodology which will include the

research design, the population, sample size and sampling technique. Data collection and

research procedure will also be discussed in this chapter. Chapter four will focus on the

analysis of data collected. Chapter five wil l give a summary of the findings and

discussion on the findings.

7

CHAPTER TWO

2.0 L I T E R A T U R E R E V I E W

2.1 Introduction

This chapter notes that finance distress prediction is one of the issues that has remained

relevant in corporate finance for decades and this is reflected in the large number of

books and articles devoted to this area .Various works on various aspects of ffinance

distress prediction has been discussed in this chapter. The research questions shall be

reviewed in detail to establish the inter-relationship with the finance distress prediction

and the different related aspects as covered in other works.

2.2 Application of Altman Z Score in Predicting Distress Using Published

Information

2.2.1 Financial Distress

Financial distress is defined by Reddy and Reddy (2013) as a situation where a firm's

operating cash flows are insufficient to satisfy current obligations and the firm is forced to

take corrective actions. Reddy and Reddy (2013) allude that financial distress is a stage

before bankruptcy where a company's creditors are paid with significant difficulty or not

being paid. Often financial distress is indicated by additional costs, such as fees paid to

lawyers or the costs of extra interest for late payments.

Ray and Mahavidyalaya (2011) notes financial distress as the ultimate declaration by an

organization of its inability to sustain current operations given its current debt obligations.

The importance of the debt is because all firms must have some debt levels to expand

operation or for survival. Ray and Mahavidyalaya (2011) state that good economic

planning often requires a firm to finance some of its operation with debt and the degree to

which a firm has debt in excess of assets or is unable to pay its debt as it comes due, are

the two most common factors in corporate financial distress.

When a firm is under financial distress, the main effects as noted by Reddy and Reddy

(2013) are reduction in firm market value, cancellation of credit terms by suppliers and

customers cancelling orders in anticipation of non-timely deliveries. Financial distress

(

8

prediction has been a critical accounting and financial research area since 1960s and there

have been various models that have been developed (Ray and Mahavidyalaya, 2011).

2.2.2 Altman Financial Distress Prediction Model

Polemis and Gounopoulos (2012) identify corporate failure as among the most researched

topics in the finance and strategic management literature. This is due to the fast-moving

business environment and intense international competition.

Different authors have used various kind of models in different years. The table below

shows the models that have been formulated to predict corporate failure over different

spans of period.

Table 2.1: Bankruptcy Prediction Models and Authors

Type of Model Author Date Fitzpatrick 19.'̂ 2

Univariate Merwin 1942

Univariate Walter 19-'>7 Beaver 1966 Altman 1968 Edniister 1972 Deakin 1972 Blum 1974 Moyer 1977 Altman, Haldcrman, & 1977

Multiple Discriminant Naarayanan Analysis Altman 1983

Booth 1983 Rose & Giroux 1984 Casey & Bartczak 1985 Lawrence & Bear 1986 Po.ston, Harmon, & Gramlich 1994 Grice & Ingram 2001

Source : Anjum (2012)

Multiple discriminant analysis has been noted to have the highest prediction accuracy for

predicting "failed" companies (Wang and Campbell, 2010). This is viewed as the most

important type of accuracy, since the cost of misclassifying failed firms, is much higher

for the investor than misclassifying firms that actually do not fail (Wang and Campbell,

2010)

9

Altman's Z-Score formula is a multivariate formula used to measure the financial health

of a company and to diagnose the probability that a company will go bankrupt within a

two-year period (Hayes, et al, 2010). Anjum (2012) describes the Altman's revised Z-

score model as one of the most effective Muhiple Discriminant Analysis model,

which has been researched throughout the last 40 years.

Edward Altman, a financial economist and professor at New York's Stem School of

Business, developed Altman's Z in 1968 and Hayes, Hodge and Hughes (2010) note that

the score has gained acceptance to auditors and management accountants. The Altman's Z

formula works well provided the scores fall within the "in the tails," meaning that low

and high scores may more accurately predict financial distress than scores that fall in the

gray area (Hayes, et al, 2010).

Hayes, Hodge and Hughes (2010) outline the original Z-Score as:

Z=l.2X\ \ + 3.3X3 + .6X4 = 1.0X5,

Where;

XI = working capital / total assets

X2 = retained eamings / total assets

X3 = eamings before Interest and taxes / total assets

X4 = market value of equity / book value of debt

X5 = sales / total assets.

X I measures the liquidity in relation to firm's size. It is a measure of net liquid asset of a

concem to the total capitalization which measures the firm's ability to meet its maturing

short-term obligations (Ray and Mahavidyalaya, 2011).

X2 is an indicator of the cumulative profitability of over time which indicates the

efficiency of the management in manufacturing, sales, administration and other activities

(Ray and Mahavidyalaya 2011). The ratio is also used to measure the Leverage of an

enterprise, which is the portion of assets financed through retained eamings compared to

debt financing (Diakomihalis, 2012).

X3 is a measure of the managements overall effectiveness as shown by the returns

generated on sales and investment (Ray and Mahavidyalaya 2011). Diakomihalis (2012)

describes this ratio as exceptionally suitable for business failure prediction.

10

X4 as a measure shows how much of an asset can decline in values before liabilities

exceed the assets and the concerns become insolvent. It measures the extent to which the

firm has been financed by debt. Creditors look to the equity to provide the margin of

safety, but by raising fund through debt, owners gain the benefit of marinating control of

the firm with limited investment (Ray and Mahavidyalaya, 2011).

X5 is the capital turnover ratio which is a standard financial measure for illustrating the

sales generating capacity of the assets (Ray and Mahavidyalaya, 2011).

The original model focused on the manufacturing firms and there was need for an

alternative model to cover the non-manufacturing industrials (Hayes, Hodge and Hughes,

2010). There have been revisions to the model to evolve from the initial coverage of the

publicly listed manufacturing firms to incorporate privately held firms and to distinguish

between manufacturing and non-manufacturing firms (Anjum, 2012).

The table below illustrates the evolution of Altman Z score and the coefficient variables

under each model, and classification criteria have been outlined. The accuracy in the

prediction of each model has also been shown under the classification results where false

bankruptcy has been eliminated in the most recent model that was revised in 1993.

Table 2.2: Altman's Z-Score Models

Cix:f( ictenls Var iab les

Or ig inal Model ( 1 9 6 8 )

Revised Mtxlel ( 1 9 8 3 )

Rev ised Fou r Mode l ( 1 9 9 3 )

X Z 1.21 0 .717 6.56

xz 1.41 0 .847 3.26

xz . \ 3 0 .3.107 6.62

xz 0.60 0 .42 1.05

xz 0 .999 0.998 N7.A. Cutof f sc-ores < l . « l < 1.23 > 1 . 1 0

Bankrupt firms >2.67 >2 .90 > 2 . 6 0

Non Bankrupt Pirnis

Grey A r e a 1.81-2.67 1 .2.V2.90 I . H > - i 6 0

Classi f icat ion Results

Actua l Bankrupt 9 4 % 90.9% 90.99^. False Bankrupt 6 % 9 . 1 % 9.i%

Actual Bankrupt 9 7 % 9 7 % 9 7 . 0 % Fal.se Bankrupt 3 % 3Cf

Source: Anjum (2012)

11

The new Z- model for bankruptcy prediction appears accurate up to 5 years prior to

failure with successful classification to above 90%. The inclusion of the retailing firms in

the same model as manufacturers does not affect the results negatively. The linear

structure of the model outperforms the quadratic tests in model validity especially on long

term validity (Altman, Haldeman and Narayanan, 1977).

2.2.3 Published Financial Information on Prediction Models

Laitinen and Laitinen (1998) notes that use of accounting information solely may lead to

misclassification of firms. The probability of misclassification may be greater, the greater

the inconsistency between the firm-specific information and the actual status of the firm

(Laitinen and Laitinen, 1998).

Creative accounting contributes to the inconsistency, where good ratios for failing firms

may arise. Creative accounting is as a result of incentives to the management to

manipulate the accoimting data to improve economic figures of a failing firm (Laitinen

and Laitinen, 1998). Inconsistency can also be contributed by the information used being

outdated. The financial situation of a failing firm can drastically change, thus the longer

the last closing of the accounts and evaluation, the more inconsistent would be the ratios

with the reality (Laitinen and Laitinen, 1998).

When the Financial statements are fraudently prepared, the predicting ratios could be

distorted. Yang and Liou (2008) defines fraudulent financial reporting, as an intentional

misstatements or omissions of amounts and/or disclosures in financial statements with the

intent to deceive financial statement users. Yang and Liou (2008) however notes that

despite the methods applied, liquidity-related ratios appear to be the most important

factors in fraud detection as well as in failure prediction.

Models fail to incorporate qualitative factors that trigger distress. Case example is where

the distress or bankruptcy is contributed by dishonesty. In several cases dishonesty may

not necessarily affect financial ratios but can cause an immediate bankruptcy when

uncovered (Laitinen and Laitinen, 1998). Mardjono (2005) concurs by noting that Enron

Company collapsed due to failure to implement good corporate governance as a

prevailing framework.

12

Laitinen and Laitinen (1998) notes that despite having accounting information, the

decision making based on that information may be distorted by factors that are personal to

the decision maker. The factors are such as age, experience or intelligence. The table

below demonstrates the relationship of disturbance and information.

Table 2.3: Relationship between Disturbance and Information

Firm-specific factors

Predicted event; Data characteristics Decision m a k e r ' s Failure or nonfailure

D-m-specific factors erroneous judgment

Failure or nonfailure

D-m-specific factors erroneous judgment

Failure or nonfailure

External factors

erroneous judgment

Failure p r o c e s s

Disturbance factors

Environmental predictability H u m a n utilization

Source: Laitinen and Laitinen (1998)

Studies have shown that finance distress prediction models consistently outperform

auditors' going on concem opinion in discriminating companies in financial distress

(Kurmppu, Laswad and Oyelere, 2003).

2.3 Relevance of Capital Structure in financial Distress prediction

2.3.1 Debt Restructuring on Altering Capital structure

The typical capital structure theory exposition has been known as a tradeoff theory.

Tradeoff theory provides an exposition of the benefits of pmdent debt use and the dangers

of excessive debt use (Stretcher and Johnson, 2011). Although the model has a high level

of mathematical sophistication and complexity, a simplified result can be formed by

observing two of the main features of the model, which are tax shield provided and the

finance distress resulting (Stretcher and Johnson, 2011).

The table below illustrates the point that increase of the debt yields tax shelter as well as

resultant of financial distress cost.

13

Table 2.4 Illustration of trade theory

Source: Stretcher and Johnson (2011)

Dong (2012) findings in his study implied that capital structure decision has ceased being

a solely domestic issue as there has been a variety of cross-border financial products in

the wake of globalization. In small economies, liquidity of holding properties outweighs

the economies of scale in determining long-term debt structure constraining the scale of

debt issuance. Dong (2012) notes also that when there are limited number of investors

increases the potential cost of financial distress.

Al-Najjar and Taylor (2008) indicates that finance distress theory has a negative

relationship between business risk and capital structure. Institutional investors invest in

firms that have a lower business risk, locking them out of the FDFs' which has an impact

on the capital structure. Debt restructuring has a positive effect on performance during the

recovery from the FDF.

Su (2010) noted that there is strong evidence that government-controlled firms use less

debt financing and that government ownership weakens the positive relationship between

unrelated diversification and leverage. Liquidation and renegotiation hypothesis as noted

by Antoniou, Guney and Paudyal (2008) predicts that the renegotiation of public debt is

costly, difficuh and likely to cause the liquidation of financially distressed firms. This

makes companies exposed to such risks are likely to opt for bank debt. Firms with lower

financial distress are theorized to opt for public debt against bank debt since the lower

14

interest cost of public debt outweighs the benefits of fiexible renegotiations in bank debt

(Antoniou, Guney and Paudyal, 2008).

Gurlu and Aksoy (2006) concurs that FDFs are relatively larger in size and use high

leverage than the non-distressed firms. Smith and Graves (2005) argues that FDF with

sufficient free assets which are the tangible assets over the secured loans, are more likely

to escape bankruptcy as it increases the ability to acquire additional funds necessary to

enact a successful turnaround. Antoniou, Guney and Paudyal (2008) noted that firms

which have potential collateral are likely to issue a bank loan. This implies that almost all

bank debts of financially distressed firms are mostly secured while public debts are rarely

secured.

High debt ratio is usually a common characteristic of capital structure of firms prior to

financial distress resulting. FDF with a high debt ratio, though would have an avenue of

raising fimd through the issuance of new shares, as the stock prices are low, they usually

opt for debt restructuring (Chang et al, 2010). Bank borrowing is highest among firms

employing relatively little debt in their capital structure resulting to a negative

relationship between bank debt and leverage (Antoniou, Guney and Paudyal, 2008).

Antoniou, Guney and Paudyal (2008) in a study of companies between German and U K

revealed that there are few similarities in debt-mix structure of companies in the 2

countries. There are also some important differences that are noted. The conclusion on

that study was that debt-mix structure is dependent on the corporate governance and

institutional features of respective countries.

Bank financing is noted to have potential costs such as hold-up problems, monitoring

costs, occurrence of inefficient liquidations and benefits such as low moral hazard and

adverse selection costs and flexible renegotiations (Antoniou, et al, 2008).The

inefficiencies of bond covenants of public debt and the agency cost of hiring a delegated

monitor for bank debt can be traded off to obtain a firm optimal debt ownership structure.

In modeling the determinants of the debt ownership, there is usually an assumption that

the firms have a target bank debt ratio (Antoniou, et al, 2008).

15

2.3.2 Variation of Capital Structure through alteration of capital sources

Capital structure decisions are noted to rely on a complex array of theoretical foundations

and practical considerations. At the managerial level, it is noted that it is impractical to

base decisions purely on theory. While a perception can be developed on an optimal

capital structure, the decision is often obscured by practical limitations to the theoretical

base (Stretcher and Johnson, 2011).

A shortcoming of the distress models is that there is usually no distinction of companies

that have recovery potential. Classifying a FDF as failure without noting the recovery

potential, may invoke a self-ftilfilling prophecy; as such companies may not be able to

attract funds necessary to enact a recovery because lending decisions are based on such

classifications (Smith and Graves, 2005).

Antoniou, Guney and Paudyal (2008) note that a mix of public and private debt allows

FDFs to alter their capital structure through non-court restructurings. The decision on the

source of debt is determined by various arguments. Factors that play a part include

floatation cost that implies that only the large firms that have economies of scale in

issuing a substantial amount of public debt benefit. This is due to the fact that

renegotiation of public debt is costly, difficult and likely to cause the liquidation of FDFs

(Antoniou, et al, 2008).

Capital structure affects firm ownership, as Al-Najjar and Taylor (2008) noted, there

exists a negative relationship between the size of institutional ownership and debt

proportion. Su (2010) stresses the relationship by noting that government ownership of a

firm weakens the positive relationship with leverage.

Al-Najjar and Taylor (2008) state that according to the pecking order theory, and

symmetrical information exist, organization would have first preference on the internal

sources of fund and would issue debt i f they are exhausted. The least attractive would be

issuance of new equity. Chang et al (2010) concurs on the applicability of the pecking

order theory by suggesting that firms follow specific course in raising their funds.

16

2.4 Relevance of Liquidity in Finance Distress Prediction

2.4.1 Effect of Liquidity Levels

Reddy and Reddy (2013) note that liquidity ratios are used to measure short term

solvency and indicates the ability of a firm to meet its debt requirements as when they

become due. Quick ratio (acid-test ratio) as noted by Bhandari and Iyer (2013) is a

traditional but highly popular measure of corporate liquidity. Current assets which are

cash, marketable securities, receivables and pre-paid items minus inventories are divided

by current liabilities. A lower value of this ratio is associated with firm under distress.

FDFs are noted to have lower liquidity level than leverage as compared to healthy firms

(Elloumi and Gueyie, 2001).In a potential acquisition of FDFs, research shows that FDFs

that end up as acquisition targets are usually in better liquidity state than those distressed

and non-acquired (Polemis and Gounopoulos, 2012).FDF are able to boost their liquidity

for a turn around when there are strong economic conditions. During strong economic

conditions, banks have greater liquidity which implies there are sufficient resources for

lending (Smith and Graves, 2005).

Banks are the main part of the financial sector in any economy, performing valuable

activities on both sides of the balance sheet. On the asset side, they enhance the flow of

funds by lending to the cash starved users of funds, whereas they provide liquidity to

savers on the liability side (Arif and Anees, 2012). Antoniou, Guney and Paudyal (2008)

note that bank loans are generally aimed at mitigating short-term liquidity problem of

firms resulting to an inverse relation between liquidity and bank loan.

Day's sales in receivables as reflected through the average number of days that a firm

takes to collect revenue after a sale has been made has a positive relation with liquidity

(Shuk-Wem, Yap, and Khong, 2011). Shuk-Wem, Yap and Khong notes that the faster

revenue is collected, the faster it can be used to settle debts resulting to liquidity of the

firm being higher hence lowering the probability of a firm to fall into financial distress.

This leads to a proportion that the lower the day's sales in receivables ratio, the lower the

chances of corporate failure (Shuk-Wem, et al, 2011).

In bailing out banks that has financial distress, the government should first determine the

source of the distress as the political and banks' balance sheet uncertainties may increase

17

financial instability (Yiannaki, 2011). Politico-financial crises may stem from foreign

lenders loss of confidence rather than liquidity issues or lending incapacity at which there

is no need for bailout (Yiannaki, 2011).

In bailing out banks, central banks supply liquidity through issuance of government

securities backed by tax revenue and at the same time paying attention to the main cause

of the financial crisis. However, it can be argued that govermnent can efficiently provide

liquidity except when too much liquidity asks for inflationary pressure (Yiaimaki, 2011).

In the banking and insurance industries, the regulation of capital requirements is

complemented by a regulation of the solvency of companies, requiring a certain amount

of highly liquid assets to be held by these companies in order to avoid liquidity shortfalls

(Krause,2006). This implies that capital requirements as well as solvency are an important

aspect of risk management for companies in the financial services, but the relevance is

extended to all other industries as well (Krause, 2006).

2.4.2 Effect of Asset-write down

Assets of a firm are classified based on the liquidity. Cash and marketable securities form

liquidity assets while long term investments such as plant and machinery, whose main

objective is to produce liquid assets in future, are considered illiquid assets (Hotchkiss,

Eose, Mooradian and Thorbum, 2008).

The long-mn value of any asset, whether tangible or intangible depend on an asset's

ability to provide fiiture benefits and its ability to influence fiiture financial performance

(Sriram, 2008). Investors' evidence of fiiture benefits and performance are indicated by

growth in revenues, profits, and repayment of debts. Currently, investors can find such

performance indicators only by analyzing the data reported in published financial

statements (Sriram, 2008).

Intangible assets are noted not to fit the definition of an "accounting asset" and reporting

their monetary worth is likely to provide unreliable information to investors. Intangible

assets are mainly not published in the financial statements as the measurement of these

assets is subjective and prone to measurement errors (Sriram, 2008). However, Sriram

notes that a firm that owns more intangible assets, reports an improvement in the financial

18

health when the intangible asset values are included. Therefore investors do seem to

consider the strength of the intangible assets when making investment decisions (Sriram,

2008). The conversion of both tangible and intangible assets into other forms of

negotiable value, and to realize greater value forms the value network analysis. Indeed,

the future success of a company or organization as a whole depends on how efficiently a

company can convert one form of value into another (Alice, 2008).

Financial distress arises when liquid assets are not sufficient to meet the existing

requirements of the company contracts. Liquidation of the assets in FDF aims at

rectifying the mismatch. However, the sale results to destruction of going concem and

results to liquidation cost. Liquidation cost, is the difference between the future value the

firm would generate and the current realized value. Capital stmcture is also affected by

the process of liquidating the assets (Hotchkiss, et al, 2008).

Corporate asset write-downs are viewed as official acknowledgment of a firm's impaired

assets and the market interprets it as an asset write-down armouncement by a FDF as a

strong negative signal about the firm's prospects. Write-downs made by FDFs convey

valuable new information to market participants, about increase in leverage and decrease

in debt capacity (Datta and Iskandar-Datta, 2008). Asset reduction is an efficiency

oriented recovery strategy when the financial distress is as a result of inefficient

operations (Smith and Graves, 2005).

Asset sale in FDF appears to benefit the creditors more than the equity holders as

creditors usually force a premature liquidation of the assets. However, firm value declines

when the creditors exert pressure on firms to liquidate assets .However, asset sales

decrease with industry leverage, implying that asset sales are limited by industry

conditions (Hotchkiss, Eose, Mooradian and Thorbum, 2008). However, liquidation of

assets does not show a significant direct effect to a positive total effect during the

recovery process (Laitinen, 2011).

Smith and Graves (2005) argue that distressed companies with sufficient free assets (i.e.

an excess of assets over liabilities, or more specifically of tangible assets over secured

loans) are more likely to avoid bankruptcy. This is because it increases an ability to

acquire the additional funds necessary to enact a successful tumaround. When free assets

19

abound, there is usually an encouragement of continued support from the existing lenders

as the repayment, i f required, there is availability of free assets (Smith and Graves, 2005).

Asset retrenchment and a cost efficiency drive are part of the affect the corporate write

downs. Schoenberg, Collier and Bowman (2013) define an asset retrenchment strategy is

where areas of the firm that are under-performing are appraised to determine i f

efficiencies can be made, or whether it is best to divest the asset completely rather than

allowing it to continue operating at a weaker level than the rest of the firm. The

usefulness of asset retrenchment as a part of the tumaround strategy is dependent on the

firm's ability to generate cash flow from the disposal. The disposal however is affected

by asset specificity, liquidity in the second hand market and exit barriers (Schoenberg, et

al, 2013).

Krause (2006) describe accumulation of the peripheral assets is critical as part of the

pmdent risk management practice. Peripheral assets are whose loss would not harm the

future prospects of the company. The losses that can be covered by peripheral assets do

not have impact beyond the loss of eamings in the current period. Therefore the

operational capital should be invested in the peripheral capital. Examples for suitable

peripheral assets would be excess liquidity in form of cash or cash equivalent assets,

marketable securities, commodities, transferable debentures (factoring), but also

negotiated and unused loan facilities with banks (Krause, 2006).

2.5 Chapter Summary

Various authors have looked at the different aspects of distress prediction and the

relevance of liquidity and capital stmcture. There are some factors that need to be looked

at in the context of distress arising in an emerging economy. The applicability of the

prediction model while considering the relevance of liquidity and capital structure,

addressing emerging economy for retail organization, is what the study aimed at

addressing.

In chapter three, research methodology shall be discussed and also the population,

sampling design and the data collection methods shall be covered.

20

CHAPTER T H R E E

3.0 R E S E A R C H METHODOLOGY

3.1 Introduction

This chapter presents the methodology and procedure that was used for collecting and

analyzing data. There was the description of the research design and detailing of the data

collection procedure. The chapter also reviewed the target population, sampling design

and procedures of data collection methods and data analysis. A summary of the chapter is

then provided at the end of the chapter.

3.2 Research Design

Research design is a framework for the collection and analysis of data. Research design

reflects decisions about the priority being given to a range of dimensions of the research

process (Bryman and Bell, 2007). A descriptive research design was used. Sekaran (2003)

points out that descriptive design is suitable, when a study is describing characteristics of

the variables of interest in a situation. A design is used to structure the research, to show

how all of the major parts of the research project work together to try to address the

central research. This is the best research design for this study as there is an aim of

describing the relevant aspects of finance distress prediction.

Sekaran (2003) confirms the suitability of the descriptive design for this kind of study by

noting that it presents data in a meaningful manner. The presentation enables a researcher

to understand and also helps in thinking systematically about aspects in a given situation.

The presentation through descriptive design offers ideas for further probe and also helps

in making certain simple decisions.

Saunders, Lewis and Thomhill (2003) indicate that surveys form one of the most

frequently utilized methods in business research since it allows the collection of a large

amount of data from a sizeable population in a highly economic way. The survey method

is justified for this research as it contains the most the desirable traits when one has a

sizeable sample size and a short time span for administration. Some of the traits include;

flexibility of data collection, diversity of questions, sample control, control of the data

21

collection environment, quantity of data, response rate, perceived anonymity, speed and

cost.

The dependent variable in this project is the financial distress prediction. The independent

variables are working capital, total assets, retained eamings, eamings before interest and

taxes, market value of equity and book value of debt.

3.3 Population and Sampling Design

3.3.1 Population

Mugenda (2008) defines population as the set of all elements in the universe of interest. It

can further be defined as the entire group of possible respondents to a survey question.

The target population of the study was the annual financial statements of Uchumi

Supermarkets Since 1976 when the company was established to the year 2012.

3.3.2 Sampling Design

3.3.2.1 Sampling Frame

According to Bryman (2007) a sampling frame is the listing of all the units in the

population from which the researcher can make a sample. The data was collected from

the financial statements for the period from 2003 to 2012. The financial statements were

collected from Capital Market Authority Library.

3.3.2.2 Sampling Technique

The sampling technique for this study was biased sampling. This is because biased

sampling is suitable when the population of interest is small, identifiable and probability

sampling might eliminate important cases from the study (Bums and Bums, 2008).The

study used the entire set of financial statements from the year 2003 to 2012, one year after

the receivership was lifted.

3.3.2.3 Sampling Size

The data analysed were collected from the financial statements from the year 2003 to

2012, both years inclusive.

22

3.4 Data Collection method

Secondary data was used for this study. From the financial statements of the selected

period the following were collected; working capital, total assets, retained eamings,

eamings before interest and taxes, market value of equity, auditors' opinion and book

value of debt. The values of all the components were collected through an excel

spreadsheet.

3.5 Research Procedure

The financial statements for the period 2003 to 2012 were analysed as source of the

information. Working capital, total assets, retained eamings, eamings before interest and

taxes, market value of equity, auditors' opinion and book value of debt was obtained from

the published and audited financial statements. Data was collected through compilation of

the data and tabulating them in an excel spreadsheet.

3.6 Data Analysis Methods

The raw data collected was transformed into the right format. The analysis on the data

was done in a format that will minimize the errors. Editing was done for any correction of

errors noted. The data was then be tabulated in various ratios for analysis. In analyzing

the data the researcher used both quantitative and qualitative analysis.

The quantitative value collected was used to compute the Z score of which was being

compared with the auditor's opinion in the corresponding year. SPSS was used to help

compute and analyze the data through regression and correlation. The results were then

presented using figures and tables.

3.7 Chapter Summary

This chapter highlighted the key issues to be considered when collecting data. They were

also identification of the best suited research design. The study population was identified

and the process of identifying the sample was also highlighted. The highlighting of the

sample constituted the identification of sampling design and frame. Later the chapter

analyzed the data collection procedure and then came up with how it will be analyzed. In

chapter 4, discussions of the results and findings from the data analyzed are presented.

23

CHAPTER FOUR

4.0 RESULTS AND FINDINGS

4.1 Introduction

The chapter presents the analyzed results and findings of the study on the research

questions concerning the evaluation of finance distress prediction of Uchumi

Supermarket. The first section points out the variables that were tested to ascertain the

prediction of financial distress of the supermarket. The second section determines the

predictive ability of Altman's Z-score while using published financial information. The

third section reveals the relevance of capital structure in financial distress prediction. The

fourth section determines the relevance of liquidity in financial distress prediction. The

last section entails the summary of the whole chapter.

4.2 Variables Predicting Financial Distress

Table 4.1 shows the variables the study used in evaluating the financial distress of

Uchumi supermarket. From the table, it is shown that current liabilities, current assets,

working capital, equity, long-term debts, capital structure and Z-score were the variables

used to evaluate the supermarket's financial distress.

The variables were quantitative and they were as per the published financial statements.

The variables that were used in the distress prediction were as illustrated in the table

below.

Table 4.1: Variables Predicting Financial Distress

Current Liabilities Current Assets Working capital Equity Long term loans Capital Structure Z score

24

4.3 Predictive Ability of Altman's Z-Score Using Published Financial Information

4.3.1 Predictive ability of the Altman Z score

The computation of the Z score was based on the Altman 1993 formula. The calculated Z

score for the period of study were as per Table 4.2.

On applying the Z score where; >1 distress, 1.1- 2.6 Grey Area and <2.6 Safe;

The period before 2006 when the company was placed under receivership the score

shows that the company was in distress and from 2010, 1 year prior to the lifting of

receivership, it showed that the company was in grey zone.

Table 4.2: Computed Altman Z score

Year Z score 2012 1.26 2011 2.13 2010 2.13 2009 (2.63) 2008 (5.38) 2007 (5.57) 2006 (10.95) 2005 (13.07) 2004 (4.39) 2003 (2.11)

The predictive power of the Altman model was compared to the auditor's opinion given

in the published financial statements.

In analyzing the relationship between the predicted Altman score and the auditors'

opinion, there was no relationship, as when the company was in distress zone as per the

Altman Z score, the auditors' opinion was noted as a going concem. In the 8 out of 10

years that the analysis was conducted, the Z score and auditors' opinion were

contradicting to each other.

Table 4.3 presents the comparison of the Z score and the auditors' opinion.

25

Table 4.3: Comparison between Altman Score and Auditors Opinion

Year Z score Altman Status Auditors Opinion

2012 1.26 Grey Zone Going concern

2011 2.13 Grey Zone Going concern

2010 2.13 Grey Zone Going concern

2009 (2.63) Distress Going concern

2008 (5.38) Distress Not going Concem

2007 (5.57) Distress going concem

2006 (10.95) Distress Going concern

2005 (13.07) Distress Going concem

2004 (4.39) Distress Not going concem

2003 (2.11) Distress Going Concem

4.4 Relevance of Capital Structure in Financial Distress Prediction

4.4.1 Equity, Long-Term Debts and Capital Structure

Table 4.4 reveals the relationship between equity, long term debts and capital stmcture.

The relationship between years and capital stmcture was also tested.

From the table 4.4, it is noted that there was a highly significant relationship between

equity and capital stmcture (p<0.01, r=0.773, n=10). On the other hand, there was no

relationship between long term debts and capital stmcture (p>0.05, r=-0.607, n=10). The

study implies that the higher the equity, the higher the significance of capital stmcture.

There was also a high significant relationship between years and capital stmcture (p<0.01,

r= 0.773, n=10).

Table 4.4: Correlation of Equity, Long-Term Debts and Capital Structure

Capital Stmcture Years Pearson Correlation .773" Years

Sig. (2-tailed) .009 Years

N 10

Equity Pearson Correlation .958" Equity Sig. (2-tailed) .000

Equity

N 10

Long-term Debts Pearson Correlation -.607 Long-term Debts Sig. (2-tailed) .063

Long-term Debts

N 10 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

26

4.4.2 Equity, Long-Term Debts, Capital Structure and Z-Score

To test the relationship between equity, long-term debts, capital structure and Z-score,

Table 4.5 was extracted. From the table, there was a highly significant relationship

between equity and Z-score (p<0.01, r=0.848, n=10). There was also a highly significant

relationship between capital structure and Z-score (p<0.01, r=0.879, n-10). On the other

hand, there was no relationship between long-term debts and Z-score (p>0.05, r=-0.549,

n=10). The table 4.5 implies that when equity was high, the Z-score was more positive

and significant.

Table 4.5: Equity, Long-Term Debts, Capital Structure and Z-Score

Z-Score Equity Pearson Correlation .848" Equity

Sig. (2-tailed) .002 Equity

N 10 Long-term Debts Pearson Correlation -.549 Long-term Debts

Sig. (2-tailed) .100 Long-term Debts

N 10 Capital Structure Pearson Correlation .879" Capital Structure

Sig. (2-tailed) .001 Capital Structure

N 10 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

4.4.3 Working Capital, Capital Structure and Z-Score Trend Analysis

Figure 4.6 reveals the trend analysis of working capital, capital structure and Z-score.

From the figure, working capital and capital structure were used to determine the Z-score

which in turn reveals the level of financial distress. It is shown in the figure that working

capital for Uchumi was negative from year 2003 through year 2012. This implies that the

company was unable to pay off its short term debts.

In year 2003 to year 2005, capital structure went down to negative. From year 2006 to

2007 the capital structure went up but in year 2008 it went down. From year 2009

through 2012, the capital structure figure shot up. The analysis reveals that as working

capital and capital structure goes down (negative figure), the Z-score records a negative

figure symbolizing financial distress and as they go up (positive figure), Z-score records a

positive figure symbolizing financial health which is shown as a grey zone.

27

4.5 Relevance of Liquidity in Financial Distress Prediction

4.5.1 Current Liabilities, Current Assets and Working Capital

The Table 4.7 shows the strength of relationship between current liabilities, current assets

and working capital. The table also shows the relationship between working capital and

current assets. From the table, it is well revealed that there was a significant relationship

between working capital and current liabilities. The analysis did not reveal any

relationship between working capital and current assets. The implication of these results

was that the current liabilities were the most sensitive elements in determining the amount

of working capital than the current assets. When current liabilities were reduced, the

working capital increased significantly. The table also shows that there was a significant

relationship between the working capital with the years. As the years increased from 2003

through 2012, the working capital was improving from deep negative towards positive

figure.

28

Table 4.7: Correlation of Current Liabilities, Current Assets and Working Capital

Working Capital Current Liabilities Pearson Correlation -.732' Current Liabilities

Sig. (2-tailed) .016 Current Liabilities

N 10 Current Assets Pearson Correlation .286 Current Assets

Sig. (2-tailed) .423 Current Assets

N 10 *. Correlation is significant at the 0.05 level (2-tailed).

4.5.2 Current Liabilities, Current Assets, Working Capital, Z-Score

Table 4.8 shows the relationship between current liabilities, current assets, working

capital and Z-score. From the study, there was no relationship between current liabilities

and Z-score, but there was a highly significant relationship between current assets and Z-

score (p<0.01, r=0.863, n=10). There was also a significant relationship between working

capital with Z-score (p<0.05, r=0.689, n=10). The analysis implied that current liabilities

indirectly affected the Z-score through working capital hence the lower the current

liabilities, the higher the significance of Z-score.

Table 4.8: Current Liabilities, Current Assets, Working Capital, Z-Score

Z-Score Current Liabilities Pearson Correlation -.031 Current Liabilities

Sig. (2-tailed) .931 Current Liabilities

N 10 Current Assets Pearson Correlation .863" Current Assets

Sig. (2-tailed) .001 Current Assets

N 10 Working Capital Pearson Correlation .689* Working Capital

Sig. (2-tailed) .028 Working Capital

N 10 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

4.5.3 Working Capital, Capital Structure and Z-Score

Table 4.9 was used to check the strength of relationship between working capital, capital

structure and Z-score. From the table, it was noted that there was high relationship

between capital structure and Z-score (p<0.01, r=0.879, n=10) and a moderate

relationship between working capital and Z-score (p<0.05, r=0.689, n=10).

29

Table 4.9: Working Capital, Capital Structure and Z-Score

Z-Score

Working Capital Pearson Correlation .689"

Working Capital Sig. (2-tailed) 0.028 Working Capital N 10

Capital Structure Pearson Correlation .879**

Capital Structure Sig. (2-tailed) 0.001 Capital Structure N 10

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed)

4.5.4 Regression Analysis 4.5.4.1 Model summary

Table 4.10 reveals a model summary of a regression analysis. The model is used during

prediction of the value of a variable based on the value of another variable. The variable

being used to predict other variable's value is known as an independent variable and a

variable being predicted is called a dependent variable.

Table 4.10: Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate 1 .895' .802 .745 2.62557 a. Predictors: (Constant), Capital Structure, Working Capital

From the table, R and R^ value are shown. The R value is 0.895, which stands for the

simple correlation. This indicates a high degree of correlation. The R^ value specifies how

much of the dependent variable, Z-score, would be explained by the independent

variables; capital structure and working capital. In this case, 80.2% could be explained,

which is very large.

Table 4.10 presents the ANOVA indicating that the regression model forecasted the

resultant variable significantly. From the table, it is indicated that the statistical

significance of the regression model that was applied was significant. P<0.01 indicated

that overall, the model used is significantly good enough in predicting the outcome

variable.

30

Table 4.11: ANOVA

Model Sum of Squares Df Mean Square F Sig.

1 Regression 194.902 2 97.451 14.136 .003" 1 Residual 48.255 7 6.894

1

Total 243.158 9 a. Dependent Variable: Z-Score b. Predictors: (Constant), Capital Structure, Working Capital

From the table 4.12, it is shown that there was a relationship between working capital,

capital structure and Z-score but there was no significant relationship between Z-score

and working capital. There was a significant relationship between Z-score and capital

structure. From the table, the Z-score can be computed as:

Z-score = -5.634 + (3.673E-06) (Capital structure).

Table 4.12: Coefficients

Model

Unstai Coei

idardized ficients

Standardized Coefficients

t Sig. Model B Std. Error Beta t Sig. (Constant) -5.634 2.563 -2.198 0.064

Working Capital 2.26E-06 0 0.219 1.006 0.348 Capital Structure 3.67E-06 0 0.74 3.397 0.011

4.6 Chapter Summary

In this chapter, the study provided the findings with respect to the information extracted

from the Uchumi financial statements. The first section pointed out the variables that

were tested to ascertain the prediction of financial distress of the supermarket. The second

section determined the predictive ability of Altman's Z-score while using published

financial information. The third section revealed the relevance of capital structure in

financial distress prediction. The forth section determined the relevance of liquidity in

financial distress prediction. The next chapter provides the conclusion, summary as well

as the discussions and the recommendations.

31

CHAPTER F I V E

5.0 DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

The main purpose of this study was to investigate the financial distress prediction in using

the Altman Z score and determining the relevance of the liquidity and capital structure in

the distress prediction.

This chapter aims to address two issues. The first is providing a siunmary of the findings

presented in chapter four so as to answer the research questions and then compare the

findings with the previous studies as presented in chapter two. There is then a

presentation of the major conclusions and recommendations for further studies.

5.2 Summary

The study on the financial distress prediction with Uchumi supermarkets was hinged on

three research questions. They were; what is the predictive ability of Altman's Z-score

while using published financial Information? What is the relevance of capital structure in

finance distress prediction? What is relevance of liquidity in finance distress prediction?

A descriptive design was the research methodology that was adopted. Secondary data

from the published financial statements was used. Financial statements used were from

the year 2003 to 2012.

The extracted information was analysed using SPPS. Analysis was done to establish the

relationships between variables that had been used for the finance distress prediction. In

Analysis, trends were drawn, Anova and regression being conducted to establish causal

relationships with the Z score.

In conclusion, the approach of the study being to explore the relationship between the

various variables in distress prediction highlights various levels of significance. The

discussions below highlight more on the findings.

32

5.3 Discussions

A discussion was done on the basis of the research results and findings in comparison to

the literature review as provided in this section.

5.3.1 Predictive ability of Altman's Z-score using published financial Information.

According to the study the Altman Z score was able to predict the financial distress of the

company 3 years before the receivership in year 2006. This is due to the negative score

that the company was scoring from 2003.

Kurruppu, Laswad and Oyelere (2003) indicated that the Z score outperforms the

auditors' opinion in the distress assessment. The study concurred with the observation as

the auditors were indicating the company as a going concem, including the year that it

was placed under receivership. It is only a year later that the company was indicated as

not going concem. This indicates that the auditors' opinion can be taken to be reactive

and cannot be used to predict financial distress whereas the Altman Z score is proactive.

Hayes, Hodge and Hughes, (2010), note Altman Z score to have the capability to predict

the company going bankmpt for a period of two years prior the bankmptcy. The study

confirms the assertion, because as early as 2003, three years before the bankruptcy in

2006, the Z score being negative depicted that the company would go bankmpt within the

period. However, the extension of the period that the company had negative score and

hadn't been placed under receivership, could be attributed to the intervention such as

rights issue of 1.2bn which injected additional capital to the stmggling company.

Laitinen and Laitinen (1998) noted that use of accounting information solely may lead to

misclassification of firms. However, the study relied purely on the accounting

information and the prediction was spot on, contradicting the assertion.

Creative accounting was therefore not noted in the financial statement, as Laitinen and

Laitinen (1998) had noted that in terms of company failing the management falsifies

information, so that it can portray a false picture. By having ratios predict the failure and

it happens, signifies that there was no existence of creative accounting.

Laitinen and Laitinen, (1998) noted that models by failing to incorporate qualitative

factors that trigger distress could make them ineffective. Case example is where the

33

distress or bankruptcy is contributed by dishonesty and may not necessarily affect the

financial ratios. However, the research showed that the model in its purely qualitative

form was sufficient for prediction. This can be used to ascertain that most of the company

actions, always trickle to the financial ratios.

5.3.2 Relevance of capital structure in finance distress prediction

From the study, there is a significant relationship between equity and capital structure

(p<0.01, r=0.773, n=10) and there is no significant relationship between long term debts

and capital structure (p>0.05, r=-0.607, n=10). The study implies that the higher the

equity, the higher the significance of capital structure.

The study on noting that the debt was insignificant to the capital structure, contravened

Gurlu and Aksoy (2006),who had noted that FDFs are relatively use high leverage than

the non-distressed firms.

Antoniou, Guney and Paudyal (2008) had noted that a mix of public and private debt

allows FDFs to alter their capital structure through non-court restructurings. The recovery

activities between 2006 and 2011, supported the assertions as the recovery was

orchestrated by activities that didn't involve courts, but rather conversion of the long and

short term debt into equity.

The study also disapproves the pecking order theory as noted by (Al-Najjar and Taylor

2008). According to the pecking order theory, organization would have first preference on

the internal sources of fund and would issue debt i f they are exhausted. However, the

study noted that equity was significant in the capital structure, indicating that it was more

relevant in the recovery.

In the years post the 2006, the significance of equity was demonstrated as significance as

the recovery of the firm was geared towards the equity. The debt was converted to equity

and the suppliers also converted claims to equity.

Dong (2012) notes also that when there are limited number of investors , there is an

increase in the potential cost of financial distress. However, this is contradicted in the

study, as despite the increase of number of shareholders in 20005, the company

eventually was placed under receivership in the following year.

34

(Antoniou, et al,2008) noted that firms with lower financial distress are theorized to opt

for public debt against bank debt since the lower interest cost of public debt outweighs

the benefits of flexible renegotiations in bank debt. However the study supported the

assertion since, during the period that the company had negative score, predicting a

finance distress, the company debt was from banks; PTA Bank and Kenya commercial

bank.

Antoniou, Guney and Paudyal (2008) had noted that firms which have potential collateral

are likely to issue a bank loan. This implies that almost all bank debts of financially

distressed firms are mostly secured while public debts are rarely secured. However, this

was also contradicted by the project which showed that debt was insignificant in the

finance distress prediction as well as instead of raising collateral for the banks loans, the

company opted to boost the equity which was the significant variable.

5.3.3 Relevance of Liquidity in Finance Distress Prediction

From the study, there was no significance relationship noted between the current

liabilities and Z-score, but there was a significant relationship between current assets and

Z-score (p<0.01, r=0.863, n=10). There is also a significant relationship between working

capital with Z-score (p<0.05, r=0.689, n=10). The analysis implies that current liabilities

indirectly affect the Z-score through working capital hence the lower the current

liabilities, the higher the significance of Z-score.

The liquidity of the company, which was negative during all the years under study, was

noted not to be improving as the working capital remained negative all through. This

could have been explained by Smith and Graves (2005) who noted that FDF are able to

boost their liquidity for a turn around when there are strong economic conditions.

The current asset though being significantly related wasn't boosted from sale of fixed

assets, as there was no unusual increase in the current assets. The failure to liquidate the

fixed assets to boost the current assets could have been one of the reasons that the

company took a recovery path. This confirms Laitinen (2011) assertion that liquidation of

assets do not have a significant direct effect to a positive total effect during the recovery

process.

35

The recovery having been realized in the years after 2006 and before 2011 when the

receivership was lifted, without any asset reduction, as Smith and Graves (2005), had

depicted, the financial distress wasn't as a result of inefficient operations.

Ar i f and Anees, (2012) noted that banks are the main part of the financial sector in any

economy, performing valuable activities on both sides of the balance sheet. On the asset

side, they enhance the flow of funds by lending to the cash starved users of funds,

whereas they provide liquidity to savers on the liability side. The role of the bank in

supplying the liquidity was confirmed by the research that showed that the PTA bank and

Kenya commercial banks were the main players in the placement on receivership and the

eventual lifting.

Mooradian and Thorbum, (2008) noted that financial distress arises when liquid assets are

not sufficient to meet the existing requirements of the company contracts and the capital

stmcture is affected by the process of liquidating the assets. The research supported the

assertion as the current asset was noted to have a high level of significance with the

prediction score. Though there was no evidence of liquidation of assets, the effect that

such move would have had on the move against financial distress would have been

significant.

Datta and Iskandar-Datta, (2008) noted that the corporate asset write down as an official

acknowledgement of a firm's impaired asset and a strong negative signal about the firm's

prospects. In raising the liquidity levels, the firm didn't write down the assets and this

could have been as a signal that recovery could have been possible.

5.4 Conclusions

5.4.1 Predictive ability of Altman's Z-score using published financial Information

The study had been able to demonstrate that the Altman Z score have predictive ability

that surpasses the auditor's opinion. 3 years before the company was placed under the

receivership, the Z score had shown and this hadn't been picked by the auditors.

36

5.4.2 Relevance of capital structure in finance distress prediction

The study has been able to demonstrate that capital structure is relevant, as it had

significant to the Z score, the predictor of the financial distress. Through the regression,

the relation and significance were denoted.

5.4.3 Relevance of Liquidity in Finance Distress Prediction

The study has also been able to demonstrate that liquidity was relevant in the financial

distress prediction. This was due to the significant relationship between the Z score and

the working capital, which denotes the organization liquidity.

5.5 Recommendations

5.5.1 Recommendations for Improvement

5.5.1.1 Applicability of the Altman's Z-score

The company in its post- receivership period needs to apply the Z score to keep tab on the

financial strategies being employed. The blind use of the auditors opinion, may lead to the

distress going unnoticed.

5.5.1.2 Relevance of capital structure in finance distress prediction

On the realization that equity is a significant part, the company needs to boost its equity

through means such as right issue or issue of additional shares to boost its capital. This

will boost the company against future distress shocks.

5.5.1.3 Relevance of Liquidity in finance distress prediction

The company needs to ensure that there is increase of the current assets and have a

decrease in current liabilities to improve the working capital, which is significant in the

distress prediction.

5.5.2 Recommendations for Further Studies

This study was designed to determine the prediction ability of Altman Z score and the

relevance of the capital structure and liquidity while using the published information in

the prediction.

37

The study on relying on the published financial statements, didn't consider the impact of

non-quantifiable factors of which could have a significant bearing in the placement and

lifting of the receivership. A study is recommended for these factors.

Another area that is recommended for fiirther study is the rationale behind the auditors'

opinion on the going concem aspect of the company. The significance of the opinion

needs to be established, and its relevance on the financial statement.

38

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United States international UnivefSMv

Africa - Library 42 ^

AP

PE

ND

IX 1

: Rese

arch

Data

Ye

ar

Cu

rren

t L

iab

ilities

Cu

rren

t A

ssets

Wo

rking

ca

pita

l E

qu

ity L

on

g term

loans C

ap

ital

Stru

cture

Z score

A

ltman

Sta

tus

Audito

rs O

pin

ion

2003

2,5

44,6

95.0

0

1,441,926.00

(1,1

02

,76

9.0

0)

701,0

00.0

0

701,0

00.0

0

(2.1

1)

Distre

ss G

oin

g C

on

cern

2004

2,6

02,0

70.0

0

1,359,052.00

(1,2

43

,01

8.0

0)

122,0

07.0

0

41

6,3

08

.00

538,3

15.0

0

(4.3

9)

Distre

ss N

ot

going

conce

m

2005

2,1

91,7

65.0

0

551,6

33.0

0

(1,6

40

,13

2.0

0)

(1,1

13

,80

6.0

0)

775,9

78.0

0

(33

7,8

28

.00

) (1

3.0

7)

Distre

ss G

oin

g co

nce

m

2006

1,420,299.00

487,1

17.0

0

(93

3,1

82

.00

) (7

31

,64

7.0

0)

802,4

72.0

0

70,8

25.0

0

(10.9

5)

Distre

ss G

oin

g co

nce

m

2007

1,085,257.00

839,9

38.0

0

(24

5,3

19

.00

) (1

,05

9,1

50

.00

) 1,597,590.00

538,4

40.0

0

(5.5

7)

Distre

ss going

conce

m

2008

1,453,073.00

878,6

24.0

0

(57

4,4

49

.00

) (1

,02

5,1

31

.00

) 1,180,089.00

154,9

58.0

0

(5.3

8)

Distre

ss N

ot

going

Co

nce

m

2009

1,800,824.00

1,041,382.00

(7

59

,44

2.0

0)

(18

0,4

95

.00

) 820,0

89.0

0

639,5

94.0

0

(2.63) D

istress

Go

ing co

nce

m

2010

1,294,438.00

1,193,567.00

(10

0,8

71

.00

) 1,538,933.00

320,1

40.0

0

1,859,073.00

2.13

Gre

y Z

one

G

oin

g co

nce

m

2011

1,542,187.00

1,397,650.00

(1

44

,53

7.0

0)

2,2

79,1

65.0

0

183,3

68.0

0

2,4

62,5

33.0

0

2.13

Gre

y Z

one

G

oin

g co

nce

m

2012

2,2

03,7

69.0

0

1,594,146.00

(60

9,6

23

.00

) 2,6

57,8

10.0

0

80,3

09.0

0

2,7

38,1

19.0

0

1.26

Distre

ss G

oin

g co

nce

m

43