an assessment of the effect of financial distress on
TRANSCRIPT
AN ASSESSMENT OF THE EFFECT OF FINANCIAL DISTRESS ON FINANCIAL
PERFORMANCE OF MANUFACTURING FIRMS LISTED IN NAIROBI
SECURITIES EXCHANGE
HILLARY KOMBO OSORO
BCOM (EGERTON UNIVERSITY)
A RESEARCH PROJECT SUBMITTED TO THE SCHOOL OF POSTGRADUATE
STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE
CONFERMENT OF MASTER OF BUSINESS ADMINISTRATION (FINANCE
OPTION) DEGREE, KISII UNIVERSITY
NOVEMEBR 2018
ii
DECLARATION AND RECOMMENDATION
DECLARATION BY THE STUDENT
I, the undersigned declare that this research project is my original work and it has never been
presented to any university for academic credit. All information from other sources are duly
cited and acknowledged.
Signature.................................. Date...............................
Hillary Kombo Osoro
CBM12/10460/14
RECOMMENDATION BY THE SUPERVISORS
This research project has been submitted for examination with our approval as the University
Supervisors.
Signature................................. Date....................................
Dr. Andrew Nyang‟au, PhD
Lecturer
School of Business and economics
Department of finance and accounting
Kisii University
Signature................................. Date....................................
Prof. Christopher Ngacho, PhD
Associate professor
School of Business and economics
Department of finance and accounting
Kisii University
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PLAGIARISM DECLARATION
DECLARATION BY STUDENT
i. I declare I have read and understood Kisii University rules and regulations, and other documents concerning academic dishonesty
ii. I do understand that ignorance of these rules and regulations is not an excuse for a violation of the said rules. iii. If I have any questions or doubts, I realize that it is my responsibility to keep seeking an answer until I understand.
iv. I understand I must do my own work.
v. I also understand that if I commit any act of academic dishonesty like plagiarism, my thesis/project can be assigned a fail grade (“F”)
vi. I further understand I may be suspended or expelled from the University for Academic Dishonesty.
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DECLARATION BY SUPERVISOR (S)
I/we declare that this thesis/project has been submitted to plagiarism detection service.
The thesis/project contains less than 20% of plagiarized work .
I/wehereby give consent for marking.
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DECLARATION OF NUMBER OF WORDS
Please note at Kisii University Masters and PhD thesis shall comprise a piece of scholarly writing
of not less than 20,000 words for the Masters degree and 50 000 words for the PhD degree. In
both cases this length includes references, but excludes the bibliography and any appendices.
Where a candidate wishes to exceed or reduce the word limit for a thesis specified in the
regulations, the candidate must enquire with the Director of Postgraduate about the procedures
to be followed. Any such enquiries must be made at least 2 months before the submission of the
thesis.
Please note in cases where students exceed/reduce the prescribed word limit set out, Director of
Postgraduate may refer the thesis for resubmission requiring it to be shortened or lengthened.
Name of Candidate: HILLARY K. OSORO ADM NO: CBM12/10460/14
School of business and economics Department of accounting and finance
Thesis Title AN ASSESSMENT OF THE EFFECT OF FINANCIAL DISTRESS ON
FINANCIAL PERFORMANCE OF MANUFACTURING FIRMS LISTED IN
NAIROBI SECURITIES EXCHANGE
I confirm that the word length of:
1) The thesis, including footnotes, is …………… 2) the bibliography is ………………
And, if applicable, 3) the appendices are ……………………………………………..
I also declare the electronic version is identical to the final, hard bound copy of the thesis and corresponds with those on which the examiners based their recommendation for the award of the degree. Signed: …………………………………… Date:…………………… … (Candidate)
I confirm that the thesis submitted by the above-named candidate complies with the relevant word length specified in the School of Postgraduate and Commission of University Education regulations for the Masters and PhD Degrees.
Signed:........................ Email……..……………….Tel…………………..Date:…………… (Supervisor 1)
Signed:........................ Email……..……………….Tel…………………..Date:…………… (Supervisor 2)
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COPYRIGHT
All rights are reserved. No part of this project or information herein can be reproduced, be
deposited in a retrieval system or be transmitted in any form or by recording, electronic,
photocopying, mechanical, or other ways, devoid of prior written permission of the author
or Kisii University on that behalf.
© 2018, Osoro Hillary Kombo
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ACKNOWLEDGEMENT
I would like to present my utmost gratitude to my supervisors Dr. Andrew Nyang‟au and
Prof. Christopher Ngacho, who supported me in the whole writing process, who always gave
me invaluable guidance and suggestions throughout the making of this project and all faculty
staff especially those of Accounting and Finance Department who made learning and
working together pleasant and bearable. I would like to also thank my friends, who were
selfless to share their ideas and knowledge, and who encouraged, advised and accompanied
me during the time.
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DEDICATION
I take this opportunity to thank God for making this study possible. I also dedicate this study
to my wife Maureen Okari, my parents Andrew Osoro and Florence Oyieko who supported
me morally throughout my study period.
.
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TABLE OF CONTENTS
DECLARATION AND RECOMMENDATION ....................................................................... ii
RECOMMENDATION BY THE SUPERVISORS ................................................................... ii
PLAGIARISM DECLARATION............................................................................................... iii
DECLARATION OF NUMBER OF WORDS.......................................................................... iv
COPYRIGHT ................................................................................................................................ v
ACKNOWLEDGEMENT ........................................................................................................... vi
DEDICATION............................................................................................................................. vii
TABLE OF CONTENTS .......................................................................................................... viii
LIST OF TABLES ...................................................................................................................... xii
LIST OF FIGURES ................................................................................................................... xiii
LIST OF APPENDICES ........................................................................................................... xiv
LIST OF ABBREVIATIONS AND ACRONYM .................................................................... xv
ABSTRACT ................................................................................................................................ xvi
CHAPTER ONE
INTRODUCTION .......................................................................................................................... 1
1.1 Background of the Study .......................................................................................................... 1
1.1.1 Financial Distress in Kenya ................................................................................................... 4
1.1.2. Financial Performance of Manufacturing Firms ................................................................... 6
1.2. Statement of the Problem ......................................................................................................... 8
1.3 Overall objective of the Study .................................................................................................. 9
1.3.1 Specific objectives ................................................................................................................. 9
1.4 Research hypotheses ............................................................................................................ 9
1.5 Significance of the study ......................................................................................................... 10
1.6 Scope and justification of the Study ....................................................................................... 10
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1.7 Limitations of the Study.......................................................................................................... 10
1.8 Assumptions of the Study ....................................................................................................... 10
1.9 Operational Definitions of key Terms .................................................................................... 10
CHAPTER TWO
LITERATURE REVIEW ............................................................................................................. 13
2.1 Theoretical Literature Review ................................................................................................ 13
2.1.1. Credit Risk Theory .............................................................................................................. 13
2.1.2 Pecking order theory of financing........................................................................................ 14
2.1.3 Shiftability Theory ............................................................................................................... 16
2.1.4 Gambler‟s Ruin Theory ....................................................................................................... 17
2.2 Empirical Literature Review ................................................................................................... 18
2.2.1 Liquidity and Financial Performance ................................................................................... 18
2.2.2 Solvency and Financial Performance ................................................................................... 20
2.2.3 Financial Health and Financial Performance ....................................................................... 21
2.2.4 Financial Performance ......................................................................................................... 23
2.3 Research Gap .......................................................................................................................... 23
2.4 Conceptual Framework ........................................................................................................... 25
CHAPTER THREE
RESEARCH METHODOLOGY.................................................................................................. 27
3.1 Research Design...................................................................................................................... 27
3.2 Target Population .................................................................................................................... 27
3.3 Sample Size ............................................................................................................................. 28
3.4 Data Collection Instruments and Procedure ........................................................................... 28
3.5 Data Analysis and presentation ............................................................................................... 28
3.5.1 Analytical Model ................................................................................................................. 30
3.5.2 Test of Significance ............................................................................................................. 30
3.6 Ethical consideration ............................................................................................................... 30
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CHAPTER FOUR
DATA ANALYSIS AND DISCUSSION OF RESEARCH FINDINGS ..................................... 32
4.1 Descriptive Statistics ............................................................................................................... 32
4.2 Liquidity .................................................................................................................................. 32
4.2.1 Current Ratio ........................................................................................................................ 33
4.2.2 Liquidity and Return on Asset ............................................................................................. 34
4.2.3 Liquidity and Return on Equity ........................................................................................... 34
4.3 Solvency .................................................................................................................................. 35
4.3.1 Debt to Equity Ratio ............................................................................................................ 35
4.3.2 Solvency and Return on Asset ............................................................................................. 36
4.3.3 Solvency and Return on Equity ........................................................................................... 36
4.4 Financial health using the Z-score model and financial performance .................................... 37
4.5 Financial performance ............................................................................................................ 40
4.6 Inferential Statistics ................................................................................................................ 41
4.6.1 Correlation Analysis ............................................................................................................ 42
4.6.2 Regression Analysis ............................................................................................................. 43
4.7 Financial distress and Return on Asset ................................................................................... 43
4.7.1 Model Summary................................................................................................................... 43
4.7.2 Analysis of Variance ............................................................................................................ 44
4.7.3 Regression Coefficients ....................................................................................................... 44
4.8 Financial distress and Return on Equity ................................................................................. 46
4.8.1 Model summary ................................................................................................................... 46
4.8.2 Analysis of Variance ............................................................................................................ 46
4.8.3 Regression Coefficients ....................................................................................................... 47
4.9. Hypothesis Testing................................................................................................................. 48
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS ................................................... 49
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5.1 Summary of the findings ......................................................................................................... 49
5.1.1 Liquidity level and financial performance ........................................................................... 49
5.1.2 Solvency level and financial performance ........................................................................... 50
5.1.3 Financial Health and financial performance ........................................................................ 50
5.2 Conclusion of the Study .......................................................................................................... 51
5.2.1 Liquidity and financial performance .................................................................................... 51
5.2.2 Solvency level and financial performance ........................................................................... 51
5.2.3 Financial Health and financial performance ........................................................................ 52
5.2.4 Financial distress and financial performance ....................................................................... 52
5.3 Recommendations of the Study .............................................................................................. 52
5.3.1 Liquidity and financial performance .................................................................................... 52
5.3.2 Solvency and financial performance .................................................................................... 53
5.3.3 Financial Health and financial performance ........................................................................ 53
5.4 Suggestions for Further Study ................................................................................................ 53
REFERENCES ............................................................................................................................. 55
APPENDICES .............................................................................................................................. 62
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LIST OF TABLES
Table 3. 1 Target Population 28
Table 4. 1 Current Ratio 33
Table 4. 2. Liquidity and Return on Asset 34
Table 4. 3 Liquidity and Return on Equity 34
Table 4. 4 Solvency 35
Table 4. 5 Asset liability ratio and Return on Asset 36
Table 4. 6 Asset liability ratio and Return on Equity 37
Table 4. 7 Z-Score 37
Table 4. 8 Z -score values using original model 38
Table 4. 9 ROA values using original model 40
Table 4. 10 ROE values using original model 41
Table 4. 11 Correlations Matrix 42
Table 4. 12 Model Summary 43
Table 4.13 ANOVA for financial distress and ROA 44
Table 4. 14 Regression coefficient for financial distress and ROA 45
Table 4. 15 Model Summary 46
Table 4. 16 ANOVA for financial distress and ROE 46
Table 4. 17 Regression coefficient for financial distress and ROE 47
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LIST OF FIGURES
Figure 2. 1: Conceptual Framework 25
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LIST OF APPENDICES
APPENDIX A: APPLICATION FOR RESEARCH PERMIT 62
APPENDIX B: NACOSTI RESEARCH AUTHORIZATION 63
APPENDIX C: NACOSTI RESEARCH CLEARANCE PERMIT 64
APPENDIX D: MANUFACTURING COMPANIES FINANCIAL STATEMENTS 65
APPENDIXE: COMPANIES LISTED IN THE NSE 81
APPENDIX F: PUBLICATION 82
APPENDIX G : PLAGARISM REPORT 83
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LIST OF ACRONYM
AMEX - American Stock Exchange
CMA- Capital Market Authority
CSR - Corporate Social Responsibility
NSE- Nairobi Securities Exchange
NYSE- New York Stock Exchange
ROA - Return on Asset
SME- Small Medium Enterprises
TES - Tehran Stock Exchange
USA - United States of America
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ABSTRACT
Financial distress has been on the rise all over the world and more rampant in Africa
continent. Financial distress leads to firms being bankrupt and finally collapse. This
problem has been increasing and it has to be curbed since manufacturing firms contribute
to the growth of the economy. The essense of this study was to assess the effects of
financial distress on the financial performance of manufacturing firms listed in Nairobi
security exchange. The study specific objective were; to determine the effect of liquidity
on financial performance of listed manufacturing firms, to establish the effect of solvency
on financial performance of listed manufacturing firms and to analyse the effect of
financial health on financial performance of the listed manufacturing firms. The study used
credit risk theory, pecking order theory of financing,Gambler‟s Ruin Theory and
shiftability theory. Descriptive research design was used and census approach method
was used in the study where all eleven companies were selected without sampling. The
sample size included all the nine active listed manufacturing companies at the NSE. Data
for all the variables in the study was extracted from audited published reports and financial
statements of the listed manufacturing firms in the NSE covering the years 2011 to 2015
where quarterly reports were used. Data collected was analysed using SPSS version 22 and
microsoft excel spread sheet. Descriptive statistic such as mean, average, weighted mean
and standard deviation was also used in analysing the data. The Return on Assets and
Return on Equity ratios were used to measure the firm‟s financial performance with the
formulation of multiple regression analysis to establish the effect of financial distress on
financial performance of listed manufacturing companies on the NSE. The study
established AR2 of 0.983 which implied that 98.3% of the changes in financial
performance (ROA) of the firms listed at NSE was attributed to the changes in
independent variables considered in the model while for ROE the findings indicated that
AR2 was .885 which implied that ROE explained 88.5% of performance in the SACCOs.
The findings showed that liquidity negatively impacts on the ROA of the firms listed at
NSE. The effect of liquidity on ROA and ROE is not statistically significant at 5% level of
significance. Solvency negatively affects ROA and ROE of firms listed at NSE. Financial
health was found to positively influence ROA and ROE though the effect is not
statistically significant.
1
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Financial distress is a burning problem to almost all the markets in the world. The term financial
distress or failure is a situation when a company is unable to meet its financial obligations
especially to its creditors leading to bankruptcy and even liquidation (Hellen, 2013). According
to Platt and Platt (2006), a firm is said to be in financial distress when it gets into a demanding
situation whether financially, operationally or legally such that it cannot honour its obligations
when they fall due. He also provided a multidimensional approach of determining whether an
entity is financially distressed by checking whether it has reported negative earnings before
special items such interest, depreciation, amortization and tax thus implying that entities, which
were financially distressed often, reported a loss from their key operational activities.
Financial distress falls in tight cash situations when the firm is not able to pay the owed amount
within the due date. This is in line with the leverage position of a firm. If no interventions are
injected, this condition can force a firm into bankruptcy or liquidation (Hu, 2011). This condition
arises from wrong financial decisions made by firm managers in the long run operations OFA
firm (Filberk and Krueger, 2005). Financial distress has affected many investors and huge cash
outflow has been lost as a result of this problem, Baker (2011). Business failure is problematic to
both developed and developing economies. There is therefore the need to investigate the main
determinants of financial distress specifically for developing markets specifically Kenyan
economy Kemboi (2012).
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In United States of America (USA), business failure received exposure during 1970s more so the
recession years of 1980 to1982 heightened attention during explosion of default and large firms‟
bankruptcies in the year 1989-1991 and an unprecedented interest in the 2001-2003 corporate
debacle and distressed years. Between 1989-1991, 34 companies under Chapter 11 of USA
bankruptcy code with liabilities greater than $1billion filed for protection while in 2001-2003
100 billion dollar babies filed for the same (Altman et.al. 2006). According to the Federal
Deposit Insurance Corporation, 2015, the United States alone from the year 2009 to 2015 about
486 banks are said to have collapsed and these had cost the country close to $74,777.8 billion in
asset worth.
In Malaysia which was started as an economic deregulation in 1991 (Kotane, 2013). It was
reported in Malaysia the economic turbulences and political meltdown hogged the country since
the year 2000 creating a new and challenging financial distress (Outecheva, 2007). Financial
health of Banks and manufacturing firms was declared between 1998 and 2003, however the
super profits was attributed to non-core operations which were declared illegal by monetary
authorities (Raheman, 2007).
Financial services sector in Zimbabwe, witnessed phenomenal growth since economic
deregulation in 1991. It was reported that economic turbulences and political meltdown hogged
the country since the year 2000 creating a new and challenging environment. Between 1998 and
2003, Banks and manufacturing firms declared super profits attributed to non-core operations
which were declared illegal at a later stage by monetary authorities. The core type of illegitimate
transaction that was common among the banks was the foreign currency black market which was
a consequence of deregulation that has been a dramatic increase in competition of financial
services sector. The phenomenal growth in the sector has raised the question of market saturation
(Zororo, 2006).
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In Nigerian, most firms suffer had from inadequate liquidity to handle their day-to-day
operational activities (Olugbenga, 2010). However, the low profitability and return on
investment become the natural consequence (Okafor, 2011). Most of the pharmaceutical firms in
Nigeria achieved negative sales growth in 2009. Ndukauba et al., (2011) attributed this result to
funding the gap necessary for the capacity upgrading and challenging operating conditions
giving rise to poor working capital management. The manufacturing sector of the Nigerian
economy has really experienced great shocks and distresses in recent years. Though the distress
syndrome appears to be more prominent and far-reaching in the banking sector, the truth is that
more manufacturing companies have failed than banking institutions of late. Besides outright
failure, few manufacturing organizations utilize over fifty per cent of their installed capacities.
The reasons for this ugly development range from exchange rate problems, inflation, government
unstable policies and other disequilibria in the macro economy.
Ghana is not an exception to this phenomenon, as it has also witnessed many corporate failures
over the years. From 2004 to 2005 alone Ghana Airways Limited, Juapong Textiles Ltd and
Divine Gold Mines all failed with assets worth $38.2 million (Addo and Nipah, 2006).In the
banking sector, Bank for housing and construction, Meridian BIAO bank, Bank for Credit and
Commerce International, Tana Rural Bank, Ghana Co-operative Bank, TanoAgya Rural Bank,
the National Savings and Credit Bank, City Savings and Loans have all collapsed. Other
financial institutions like Multi Credit Microfinance, MfaMicrofinance, EquipSusu
Microfinance, Busy Fingers, Unity Trust Microfinance, Devine Microfinance and Emends
Microfinance and recently DKM Microfinance have been shut down due to inadequate capital,
fraud and regulatory laxity. The effects of these failures have very serious consequences on the
institutions and the general public as a whole as depositors undeservedly sometimes lose their
money.
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1.1.1 Financial Distress in Kenya
In Kenya, Capital Markets Authority was established in 1989 through the Capital Markets
Authority Act, Cap 485 where capital market authority has the objective to control and manage
the orderly development of Kenya's capital markets. Capital Market Authority (CMA) has a
responsibility to keep scrutiny of firms listed in NSE with regards to capital, liquidity and other
aspects with the aim of ensuring financial stability of these firms. Nairobi Stock Exchange (NSE)
has been operating for 50 years and it has 58 listed firms categorized into industrial and allied
(manufacturing) companies, finance and investment, commercial and services, agriculture and
alternative investment market segment which are less than what the country inherited at
independence (Ngugi et al., 2009). The study further noted that NSE has a responsibility for
growth and regulation of the market operations to ensure efficient exchange. For an efficient
stock exchange, the companies listed in NSE are anticipated to be financially healthy so as to
ensure economic growth of the country.
Financial system in Kenya has grown significantly over the last years and has become the
largest in East Africa ( Hellen, 2013). In comparison with other East African economies,
Kenyan manufacturing sector is credited for its size and diversification. However, there have
been constraints in the growth of the sector due to financial distresses.
The financial health of manufacturing industry is an important prerequisite for economic
stability and growth. As a consequence, the assessment of manufacturing firms financial
conditions is a fundamental goal for many stakeholders. The cost of manufacturing firms
failure is colossal and hence ailing firms require quick action by supervisory authority to
salvage them before they collapse (Cheserek, 2007). Due to this, many financially distressed
firms never file for bankruptcy, due to privatization, whereas healthy firms often file for
5
bankruptcy to avoid taxes and expensive lawsuits (Theodossiou et al., 1996). Financial distress
can be subdivided into four sub-intervals i.e. decline in performance, failure, insolvency, and
default. Whereas decline and failure affect the profitability of the company, insolvency and
default are rooted on liquidity (Outecheva, 2007). He also noted that, a company can be
distressed without defaulting as default and bankruptcy cannot occur without the preceding
period of financial distress.
Incentives of employees work decreases due to financial distress and stimulates them to
renegotiate their compensation packages or end up resigning. Managers, stockholders, lenders
and employees are concerned about their firm‟s financial strength. The job security of managers
and employees is not assured should their firms struggle financially. The government, as a
regulator in a competitive market, has concerns about the cost of financial distress for firms, and
it controls capital adequacy through the regulatory capital requirement (Ming, 2000). Financial
distressed firms usually falls in a tight cash situation in which it is difficult to pay the owed
amounts on the due date leading to bankruptcy or forced liquidation.
Non-lending from financial institution makes investors to fear supplying capital to the distressed
firm or make funds available but at high costs and rigid terms and conditions. Unavailability of
funds on acceptable terms adversely affect the operating performance of the distressed firm,
while shareholders may be tempted to undertake risky projects using whatever cash the firm is
left with so that they can litigate liquidation of the firm. Competitors tend to pursue an
aggressive marketing and price strategy in order to attract customers of the vulnerable company
as a consequence, the distressed company suffers losses in sales leading to loss of the market
share (Natalia, 2007). Thus this study is motivated to understand how financial performance
6
of manufacturing firms is affected by financial distress. This will enable manufacturing
firms to take corrective measures in due time to avoid the devastating results.
1.1.2. Financial Performance of Manufacturing Firms
Financial performance is a measure of how well a firm can use resources from its main mode of
business and create revenues. The term is used as a general measure of a firm's overall financial
health over a given period of time by ability to meet its financial and operational goals. The
financial performance is often measured using key traditional accounting performance indicators
such as Return on Assets, Operating Profit margin, Earnings before Interest and Tax, Economic
Value Added or Sales growth (Crabtree & DeBusk, 2008). However, measurement issues can
arise when there is lack of uniformity in maintaining of the financial data. In this regard, balance
sheet manipulations and choices of accounting methods may also lead to values that allow only
limited comparability of the financial strength of companies. Return on Asset and Return on
Equity ratios are used to associate against another reference, such as an industry standard
(Mudida & Ngene, 2010). This type of comparison helps to establish financial goals and identify
problem areas.
Financial performance measures are intended to avail operations, analyse their activities from a
financial standpoint and provide subsidiary information needed to make good management
decisions (Crane, 2008). There are many variables which affects a company‟s performance, such
as tax rate and debt maturity that influences company‟s option in investing. In this study, the
researcher examined the impact of accounting ratios base on reviewing company‟s financial
performance (Tian, 2007). All businesses need capital to operate, to enter into incipient ventures
or to expand the operation (Crane, 2008).
7
The facility to magnetize investors is an asset that deserves to be apperceived as well as the
capacity to acquire additional cash sanctions of a business to undertake emerging or expanded
activities. Assessing an operation‟s competency to magnetize supplemental capital may be
arduous without someone who is inclined to lend or invest. The financial ratios, and how they
compare to kindred operations, provide some designation of the business' credit reserve.
Conducting conventional check-ups on financial condition and performance, the organizations or
companies are more liable to treat causes rather than address only symptoms of quandaries.
There has been amendment in the Kenyan manufacturing sector which is reflected in the
liquidity ratios which have been above minimum statutory requisite and the earnings
measures which have amended steadily (Becks et al., 2010). Financial distress is very
authentic in Kenya and albeit most manufacturing firms in Kenya are reporting profits
while there are a couple of firms declaring losses (Hellen, 2013). In Kenya, the
manufacturing firms dominate the financial sector and any failure in the sector would have
an immense repercussion on the economic magnification of the country because it has a
contagion effect that could lead to crises and overall financial crisis and economic tribulations
(Ongore and Kusa,2013).
There are various ways to measure the profitability but according to Nyabwanga (2013),
profitability is measured using three major ratios i.e. Return on equity ratio which is the
amount earned compared to the total amount of shareholders equity invested. A high ROE
is favourable as it shows its ability to generate cash internally. It reflects how effectively
manufacturing firms are using shareholders‟ funds (Khrawish, 2011). The return on asset ratio
which is the second ratio of income to its total asset and measures the ability to generate
income by utilizing the assets at its disposal. A high ROA shows the firm is efficient in
8
using its resources. Net interest margin ratio which measures the interest income generated and
the amount of interest paid out to the lenders (Khrawish, 2011). The advantage of these
measurements is their general availability, since every profit oriented organization produces
these figures for the yearly financial reporting (Chenhall & Langfield-Smith, 2007). However,
measurement issues can arise when there is lack of uniformity in maintaining of the financial
data. In this regard, balance sheet manipulations and choices of accounting methods may also
lead to values that allow only limited comparability of the financial strength of companies.
1.2. Statement of the Problem
Manufacturing industry is among the sectors that Kenya expects to facilitate in realization of
vision 2020, by ensuring that there Is provision of efficient financial services and investment
opportunities which will create a vibrant and competitive advantage in Kenya. Firms were
presumed to be a going concern hence having perpetual life. In reality, this may not be the case
as many firms fall under foreseen circumstances. Despite good management restructure and
strategies, manufacturing firms still encounter financial distress leading to their closure.
However, several firms in Kenya have been delisted from NSE due to liquidity, liquidity and
financial health. The delisted firms include Mumias Sugar Company, Eveready Company, Kenya
Airways, East Africa Packaging and Uchumi Supermarket. Mumias Sugar Company has been
facing financial distress leading to their managers and directors hauled in court Kakah (2015)
and Mbaru (2014). Uchumi supermarket having operated for more than 30 years was declared
bankrupt in 2006 and was placed under receivership. With government intervention, the
company had turmoil in 2010 and was relisted in NSE (NSE, 2010). Although it was relisted the
company still faces financial distress leading to closure of its branches because of not able to pay
their creditors when due John, 2016).
9
In response to the above, it is for this reason that i proposed to study this research project titled
an assessment the effects of financial distress n performance of manufacturing firms listed in
Nairobi securities exchange.
1.3 Overall objective of the Study
The main research objective was to assess the effects of financial distress on the financial
performance of manufacturing firms listed in Nairobi securities exchange.
1.3.1 Specific objectives
The study addressed the following specific objectives
i. To determine the effect of liquidity on financial performance of listed manufacturing
firms.
ii. To establish the effect of liquidity on financial performance of listed manufacturing
firms.
iii. To analyse the effect of financial health on financial performance listed manufacturing
firms.
1.4 Research hypotheses
H01 Liquidity does not significantly affect financial distress on financial performance of
manufacturing firms listed on the Nairobi Stock Exchange
H02 Solvency does not significantly affect financial distress on financial performance of
manufacturing firms listed on the Nairobi Stock Exchange
H03 Financial health does not significantly affect financial performance of manufacturing firms
10
1.5 Significance of the study
The research study is important for education purposes and government used in implementation
of laws and regulation of financially distressed firms. This is done by addressing issues that
affect firms quoted in NSE of which most of them help the government attain its economic
stability. The study also helped to sensitize management of companies on early detection of
financially distressed firm and help in saving investors funds, directors and employees of the
firm.
1.6 Scope and justification of the Study
This study sought to establish the existence of financial distress in manufacturing firms listed in
the Nairobi Stock Exchange during the period 2011-2015. It also focused on financial distress
with emphasis on liquidity, solvency and financial health.
1.7 Limitations of the Study
The study was limited to manufacturing companies listed in Nairobi securities exchange hence
the findings may not be generalized to all firms. Data collection was done using secondary data
which may be limited in terms of its reliability.
1.8 Assumptions of the Study
The researcher assumed that the secondary data obtained from Audited financial statements show
a true and fair view of the manufacturing firms listed in NSE. Financial distress was assumed to
be the major contributor in financial performance while holding other factors constant.
1.9 Operational Definitions of key Terms
Financial distress
Financial distress is where a firm's operating cash flows are not enough to fulfil existing bond i.e.
such a trade credit or interest expense and the firm is forced to take corrective action.
11
Financial performance
Firm performance can be observed in two different perspectives either financial performance or
non-financial performance. Financial performance consist of market share productivity and
profitability, whereas, non-financial performance comprises of customer satisfaction, innovation,
workflow improvement and skills development.
Stock market
Stock market is where the price of a firm stocks are determined or established. There are two
types of markets i.e. Physical location Exchange like the NYSE and the American Stock
Exchange (AMEX), NSE and the several other stock exchanges.
Liquidity
Liquidity refers to the available cash for the near future, after taking into account the financial
obligations corresponding to that period.
Debt to Equity Ratio
It is a ratio that measures the extent to which operations are financed by creditors (debt) rather
than owners (equity).
Current ratio
It indicates the extent to which the claims of short term creditors (suppliers, bankers) are covered
by assets that are expected to be converted to cash in the same period.
Quick ratio
It measures the entity‟s ability to meet short term debt obligations with the cash equivalents on
hand.
12
Financial Performance
It is to convey an understanding of some financial aspects of a firm and its analysis identifies that
financial strengths and weaknesses of the firm.
Nairobi Security Exchange:
It is where the trading of Bonus and Share take place.
Return on Asset:
It is a Ratio of the entity‟s ability of the company management to generate income by utilizing
company assets to create profit.
Return on Equity:
This is the ratio of how much profit a company earned compared to the total amount of
shareholder equity invested.
13
CHAPTER TWO
LITERATURE REVIEW
2.1 Theoretical Literature Review
2.1.1. Credit Risk Theory
This theory was first introduced by Merton an American professor in 1974. The theory states
that an attempt must be made in any firm to describe default processes in the credit risk
aspects in terms of structural and reduced form models. Structural model focuses on factors
such as asset and debt amounts to determine the period of default. Structural model provides
a relationship between credit quality and financial conditions of the firm. In Merton‟s
reduced model, a firm defaults at the time of serving the debt and its asset is below its
outstanding debt. This was opposed by Black and Cox (1976) who argued that defaults
occurs as soon as the value of the firms asset fall below a certain threshold contrary to
Merton default which can occur at any time (Elizalde, 2006). The assumption of this theory
rest on the inexistence of transaction costs, bankruptcy costs, taxes or problems with the
value of assets, continuous time of trading, unlimited borrowing and lending at a constant
interest rate if no restrictions on the short selling of assets causes the value of the firm to
change in its capital structure.
The criticism is that it directly applied pricing option developed to make necessary
assumptions to adopt dynamics of assets value process, interest rates, based on numerical
feasible, and solutions to express debt values. The relevance to financial distress is that, it
can be used to analyze the firm‟s value before the maturity of the debts, and if the firm‟s
value falls down to minimal levels before the maturity of debt but is able to improve and
meet debt payment. Delianedis and Geske (2011) adopted this theory to explain the
proportion of the credit spread in corporate bond data set. The small credit risk is attributable
14
to taxes, liquidity and market risk factors. This included financial distress component in
Merton model finding that jumps residual spread to explain the entire financial performance.
Warui (2017) analyzed credit risk management strategies and performance of commercial
banks in Kenya. He adopted the credit risk theory and found that credit risk is amongst
critical factors to think about for any financial institutions involved in any lending activity.
The study adds that financial institutions have to find themselves in making decision on
giving credit to potential borrowers, despite effectively growing their balance sheet and
effectively increasing their returns and cautions to any losses incurred. However, the study
used traditional methods of evaluating credit risks whose problem lays in the extensive
dependence on historical data without quantitative methods. Thus, the findings lie only on
nonpayment throughout the period of financial institution and not only at credit maturity of
the institution, but also financial distress which calls for a study characterized by asset
models where the loss is exogenously short.
2.1.2 Pecking order theory of financing
This pecking order of financing was proposed by Myers in 1984. Myers highlighted that
firms with excess cash reserves are less likely to use external capital providers leaving
financial managers with more discretion over bankruptcy decisions. Thus, identified the
significance of sources of cash stating that there is a difference between using cash as a
method of payment and paying with internal funding. In this pecking order of financing, cash
offered requires external funding which shall have significantly higher returns than debt
offers which uses internally generated funding. Adam (2016) adopted this theory to describe
how debt helps managers using cash holding to explain acquisition returns in the University
of Colorado. The study showed that there is relatively positive returns for cash offers
however, do not make up for negative reactions shareholders have when the bidder is cash
rich. The assumption lies on investors‟ believe that managers make worse investment
15
decisions when they have excess cash reserves, and that these poor investments arise from
agency costs. This theory relies only to the use of cash holdings to explain acquisition of
returns and fails to explain cash holdings affecting financial performance in answering the
question of financial distress.
This theory is relevant to this study, because it states that debt reduces agency costs which are
associated with financial distress by requiring managers to use cash flows to make interests
from principal payment rather than accumulating excess cash. In addition to reducing excess
use of cash, debt provides monitoring of managers through threat of bankruptcy. Michael
(2017) employed the theory to answer the question how debt helps managers. The study adds
that managers make better investments when they cannot exclusively rely on internal
financing and therefore answers this financial distress by studying acquisitions, which are
large observable outcomes of financing process. Finding forms the investigation of
cumulatively abnormal returns in an acquisition stocks at the announcement using cash
support.
Tomu (2012) provides general framework for handling time varying cost of capital, leverage,
and capital values in dynamic free cash of capital structure. This enabled efficient market
analysis of the current theories of capital structure. The costs of financial distress and risk
premium of debt in the cash flow model. The study provides a new focus at cost of tax shield
from the point of view of risk return relations. The cost of tax shield is not constant, but
depends on leverage and is mostly between cost of assets and cost of debt. Moreover, the
model of the firm value and capital structure in presence of risk, taxes, growth and optimal
leverages existing for each combination of financial distress factors, the risk enhanced cash
flow theory can explain both observations of capital structure. It is evidence which resists this
theory‟s highly leveraged low growth firms and moderately leveraged profitable firms.
16
2.1.3 Shiftability Theory
Shiftability Theory was proposed by Dodds in 1982. The theory states that liquidity is
maintained if it holds assets that can be shifted or sold to other investors for cash or lenders
for cash. The arguments indicate that a firm‟s liquidity can be enhanced if it has assets to sell
or stands ready to purchase the asset offered for discount. This identifies and states that
shiftability or transferability of assets is the basis for improving liquidity. Obilor (2013)
adopted the theory to explain the impact of liquidity management on the profitability of banks
in Nigeria. The theory was analyzed using short term funds, cash balances, bank balances
treasury bills and profit after tax. Findings shows that liquidity management is a crucial
problem in profitability hence recommended for optimal level of liquidity to maximize
profitability. The theory has not been adopted to explain financial distress in terms of
financial performance which will be addressed by this study.
The assumption of the theory is that liquidity is always for sell and stands ready to purchase
the asset offered. Theory further assumes that highly marketable security held by a firm is an
excellent source of liquidity. Eljelly (2004) contends that firms ensure convertibility without
delay and appreciable loss that assets must meet liquidity management theory involved in
obtaining funds from depositors and other creditors from market. In determining the value of
financial distress for particular firms, there is need to seek answer on depositors‟ funds,
creditors‟ funds and mix of funds for any firm. Although, the assumption has been made, the
criticism analyzes management examining the activities involved in supplementing the
liquidity need through use of borrowed finance. It only supplements liquidity can be derived
from liabilities of the firm balance sheet.
17
2.1.4 Gambler’s Ruin Theory
Gambler Ruin theory was developed by Feller, W in 1968 who based it on the probability
theory where a gambler wins or loses money by chance. The gambler starts out with a
positive, arbitrary, amount of money where the gambler wins a dollar with probability p and
loses a dollar with a probability (1-p) in each period. The game continues until the gambler
runs out of money (Espen, 1999). The firm can be thought of as a gambler playing repeatedly
with some probability of loss, continuing to operate until its net worth goes to zero
(bankruptcy). In context of the firm‟s financial distress, a firm would take the place of a
gambler. The firm would continue to operate until its net worth goes to zero implying
bankruptcy. The theory assumes that a firm has a given amount of capital in cash, which
would keep entering or exiting the firm on random basis depending on firm‟s operations. In
any given period, the firm would experience either positive or negative cash flow. Over a run
of periods, there is one possible composite probability that cash flow will be always negative.
Such a situation would lead the firm to declare bankruptcy, as it has gone out of cash. Hence,
under this approach, the firm remains solvent as long as its net worth is greater than zero.
This net worth is calculated from the liquidation value of stockholders‟ equity. With an
assumed initial amount of cash, in any given period, there is a net positive that a firm‟s cash
flows will be consistently negative over a run of periods, ultimately leading to bankruptcy
(Aziz & Dar, 2006). The major weakness of this theory is that it assumes that a company
starts with a certain amount of cash. The two main difficulties with this theory when
predicting bankruptcy is that the company has no access to securities markets and the cash
flows are results of independent trials and managerial action cannot affect the results (Espen,
1999).
18
2.2 Empirical Literature Review
2.2.1 Liquidity and Financial Performance
Ibrahim (2015) did a study on the effect of liquidity on the financial performance of
construction and allied companies listed at the Nairobi securities exchange with the main
purpose to determine the effect of liquidity on financial performance of construction and
allied companies listed at the Nairobi Securities Exchange (NSE). The objective of the study
was to establish the effect of liquidity on the financial performance of construction and allied
companies listed at the NSE. The study covered a period of past 10 years from 2005 to 2014.
Secondary data was collected from NSE and multiple regression analysis was used in the data
analysis. The study revealed that liquidity positively affects the financial performance of
construction and allied companies listed at the NSE. The study established that current ratio
positively affects the financial performance of construction and allied companies listed at the
NSE. The study also revealed that an increase in operating cash flow ratio positively affects
the financial performance of construction and allied companies listed at the NSE. The study
found that an increase in debt to equity negatively affects the financial performance of
construction and allied companies listed at the NSE. The study found that an increase in total
assets negatively affects the financial performance of construction and allied companies listed
at the NSE. The study illustrated that an increase in total sales positively affects the financial
performance of construction and allied companies listed at the NSE.
Eljelly (2004) on his research on „liquidity – profitability trade-off he endeavours to examine
the cognation between liquidity and profitability utilizing a sample of Saudi joint stock
companies. The study objectives were directing the attention to active management of
liquidity. This aspect was consequential given the number of non-remuneratively lucrative
Saudi companies, and the dire need to ameliorate profitability. Emerging market‟ efficient
liquidity involves orchestrating and controlling current assets and current liabilities to
eliminate the jeopardy of inability to meet short-term obligations due and evade excess
19
investment assets. This was due to reduction of the probability of running out of cash in the
presence of liquid assets. The effect of liquidity was not only on the cash position and the
troubles it causes to financial managers but with all the company's magnification through
profitability. As measure of liquidity, liquidity ratios are acclimated to quantify business‟
facility to meet the payment obligations by comparing the cash and near-cash with the
payment obligations. If coverage was inadequate, it denotes that the business might face
difficulties in meeting its immediate financial obligations and this affects the company's
business operations and profitability.
Bhunia and Das (2012) conducted a study to examine the relationship between the working
capital management structure and the profitability of Indian private sector firms. The
independent variables used in the study were ratios that affect working capital management
and included the following: current ratio, liquid ratio, cash position ratio, debt-equity ratio,
interest coverage ratio, inventory turnover ratio, debtors‟ turnover ratio, creditors‟ turnover
ratio, and working capital cycle. Return on capital employed was used as a measure for
profitability. Using multiple regression analysis, the study found a weak relationship between
all the working capital management constructs and profitability. The study should,
nevertheless, have been extended to identify the other factors that drive profitability in
addition to working capital management.
Florence (2013) fixated on financial distress, liquidation, bankruptcy, leverage, non-
performing loans and insolvency as his objectives. Data was analysed utilizing weighted
mean score and factor analysis for a period of four years and it was established that the main
causes of financial distress are endogenous variables which have a highest weighted mean
score as compared to exogenous variables. It was further noted that the most consequential
causes of distress were incongruous capital decision, inadequacy of capital, access to credit,
20
and shortage of adept manpower, poor accounting records and poor internal management
through factor analysis, finance factor was the main cause of financial distress in comparison
with management, accounting system, policy changes and liquidity factors.
In his study, Nyabwanga (2013) fixated on liquidity, solvency and profitability positions
utilizing ratio analysis and it was evident that liquidity, profitability and solvency position of
Minute and Medium enterprises (SMEs) he ascertained that liquidity position of the SMEs
was on average low; their solvency was low and their financial health was on average not
good. Further, the results showed that there is a consequential impact of current ratio,
expeditious ratio and Debt to Total Assets ratio on Return on Assets (ROA). The results
additionally demonstrated that the liquidity position of the SMEs was well below the
acceptable ecumenical norm of 2 for current ratio and 1 for expeditious ratio. Further, the
results betokened that the financial health of the SMEs needed to be ameliorated.
2.2.2 Solvency and Financial Performance
Leonard, (2012) in his studies fixated on liquidity, solvency, profitability and efficiency as
his objectives. Data was accumulated utilizing secondary data for a period of five years and
analysed utilizing prohit regression model for 47 banks in Kenya and the findings were that
non-performing loans, liquidity, solvency, cash flow, profitability and efficiency were
paramount in detecting corporate failure in banking industry. In his study he only fixated on
banking industry rather manufacturing companies listed in NSE and he never used ratio
analysis to analyse Solvency and financial performance.
Rehman and Khidmat (2014) did a study on the impact of liquidity and solvency on profit
facility chemical sector of Pakistan utilizing ten listed chemical companies of Pakistan and
compiled last 9 years data of these companies from (2001-2009) found that liquidity affects
positively and solvency affects negatively upon the return on assets and return on equity.
21
Ultimately, this designates that when debt to equity ratio increases then performance
decreases. In conclusion, they argued that liquidity, solvency and profitability are
proximately cognate because when one increases the other decreases.
Ozyildirim and Ozdincer (2006) did a research on determining factors of bank performance
predicated on return on solvency and efficiency on Turkish banks with a primarily objective
of taking into account their jeopardy profiles by quantifying their return on solvency utilizing
risk weighted assets as defined in the current Basel Accord. They further quantified the return
on solvency utilizing revenues adjusted for the cost of capital and solvency calculated on risk
weighted assets. They concluded that return on solvency is mainly driven by the falling
market interest rates. Banks seemed to be performing poorly on their activities. They rely on
their non-core banking activities i.e. trading securities for amending the situation.
Laurent and Viviani (2010) assessed the facility of French wineries to prevail over the crisis
of French wine in the year 2000. Over the 2000‐2006 period in spite of sales fluctuations,
French wineries did not increment their financial debt level substantially. Such result fortifies
the traditional static trade‐off theory (TOT). Co‐operatives were able to absorb part of the
impact of the wine crisis at the expense of their members, in incrementing account payables
to member. Corporations have not incremented trade account payables to vine growers. In the
mid‐2000s, the French wine crisis has not been vigorous enough to shake the financial
structure of cooperatives and corporations but co‐operatives look more affected.
2.2.3 Financial Health and Financial Performance
Foo (2015) examined the relationship between the financial health, as quantified by the
Altman Z-Score, and corporate performance, as quantified by the Return on Equity (ROE), of
listed manufacturing companies in these two markets (Hong Kong and Singapore). A linear
regression analysis was conducted between these variables to determine the magnitude and
22
direction of their relationships. The trends of Z-Scores over a fourteen-year period were
analysed from 2000 to 2013(inclusive) and yielded a statistically positive correlation between
ROE and the Z-Score for both markets. Singapore and Hong Kong both registered moderate-
to-high mean and median Z-Scores. However, Hong Kong was found to be comparatively
more salubrious. This finding further fortifies the economic stature of these two markets as
Asian tigers.
Schiozer, Saito, and Saito et al (2011) analysed the relationship between the financial health
and organizational form of private health care providers in Brazil. It withal examined the
major determinants of customer gratification associated with the provider's organizational
form over a sample of 270 private health care providers and their operations over the period
2003-2005. An adjusted Altman's z-score is utilized as a designator of financial health. The
study found that financial distress in local health care insurers may engender an encumbrance
on the public health care system in these communities as many beneficiaries‟ demands
migrate to public accommodations. This finding withal suggests that one possible strategy for
more diminutive health care insurers would be merger or joint-ventures in order to gain more
preponderant negotiation power and achieve economies of scale and charging plausible
premiums without damaging their financial situation. This may be a categorically pertinent
alternative strategy for peregrine investors that incline to merge with more astronomically
immense local indemnification companies. Alternatively, different health care providers may
have distinct strategies and goals. While the managers of a self-managed plan may be
concerned with long-term financial stability and the quality of the accommodation provided,
commercial indemnification companies may drive their efforts mainly towards profitability,
sometimes in the short run.
23
Raghavan, Sunder (2012) did a study on the relationship between financial health and safety
by utilizing the Altman Z-score, which prognosticates bankruptcy for a firm within the next
two years, as a quantification of profitability of an airline. It is felt that Altman Z-score is a
forward looking measure and consequently would be a better soothsayer of the influence of
financial factors on safety. Their preliminary results of the study found that for both major
(Group III) and more diminutive (Group II) carriers though there is a negative relationship
between safety, as quantified by accidents and the Altman-Z score measure, it is not
statistically paramount. These results further imply that airlines in poor financial health do
not compromise on safety
2.2.4 Financial Performance
Hellen (2014) in her study on the effect of financial distress on financial performance of
commercial banks in Kenya, data was collected and obtained from audited financial
statements from Central bank of Kenya for the period of 5 years i.e. 2008-2012. Data was
analysed using microsoft excel and regression analysis was used to establish the effect of
financial distress on financial performance with the return on assets ratio used to measure
financial performance of the firm. The study also showed that financial distress had a
significant effect on financial performance of banks where performance was negatively
affected. Further, Tan (2012 ) in his study on the impact of financial distress on firm‟s
performance utilizing the regression analysis and financial leverage as a proxy for
financial distress found out that financially distressed firms underperform meaning that
firm‟s performance deteriorates during financial distress.
2.3 Research Gap
Florence (2013) in his study fixated on financial distress, liquidation, bankruptcy, leverage,
non-performing loans and insolvency as his objectives. Data was analysed utilizing weighted
mean score and factor analysis for a period of four years and it was established that the main
24
causes of financial distress are endogenous variables which have a highest weighted mean
score as compared to exogenous variables. It was further noted that the most consequential
causes of distress were incongruous capital decision, inadequacy of capital, access to credit,
and shortage of adept manpower, poor accounting records and poor internal management
through factor analysis, finance factor was the main cause of financial distress in comparison
with management, accounting system, policy changes and liquidity factors. The researcher
used return on equity to measure financial performance of banks for a period of four years
while my study will use return on assets to measure financial performance of manufacturing
firms for a period of five years.
Leonard, (2012) in his studies he fixated on liquidity, solvency, profitability and efficiency as
his objectives. Data was accumulated utilizing secondary data for a period of five years and
analysed utilizing prohit regression model for 47 banks in Kenya and the findings were that
non-performing loans, liquidity, solvency, cash flow, profitability and efficiency were
paramount in detecting corporate failure in banking industry. In his study he only fixated on
banking industry rather than manufacturing companies listed in NSE and he never used ratio
analysis to analyse liquidity, solvency and financial performance.
Raghavan and Sunder (2012) did a study on the relationship between financial health and
safety by utilizing the Altman Z-score, which prognosticates bankruptcy for a firm within the
next two years, as a quantification of profitability of an airline. It is felt that Altman Z-score
is a forward looking measure and consequently would be a better soothsayer of the influence
of financial factors on safety. Their preliminary results of the study found that for both major
(Group III) and more diminutive (Group II) carriers though there is a negative relationship
between safety, as quantified by accidents and the Altman-Z score measure, it is not
statistically paramount. These results further evidence that airlines in poor financial health do
25
not compromise on safety. The researcher used Z-score model to measure financial
performance of airline for a period of two years while my study will use return on assets to
measure financial performance of manufacturing firms for a period of five years.
2.4 Conceptual Framework
Yildirim and Philippatos (2007) theories provide a conceptual framework, so that knowledge,
both existing and new, can be interpreted for empirical application in comprehensive manner.
In this study, the conceptual framework comprise of independent variables and dependent
variables. Cucinelli, (2013) defines a conceptual framework as a graphical or diagrammatical
representation of the relationship between variables in a given study. The research gap can be
determined by the conceptual frame work as shown below. It shows the relationship between
the dependent (ROA) and independent (liquidity, solvency and financial health) variables.
Independent variables Dependent variable
Financial distress
Financial performance
Figure 2. 1: Conceptual Framework
Source: Researcher, 2018
Financial performance of manufacturing firm was determined by liquidity, solvency and
financial health. Whereby liquidity measures a business' ability to meet the payment
Liquidity
Current Ratio=CA/CL
Solvency
Debt to Total Assets
[DTA]
:
Financial health
Z-score model
Return on Asset
Return on Equity
26
obligations by comparing the cash and near-cash with the payment obligations thus current
ratio was used to measure liquidity. Current ratio is a ratio that measures the quick short-term
solvency position of a firm. A high current ratio indicates that the quick short term solvency
position of a firm is good.
Solvency measures the leverage of the firm where debt to total assets is used as a measure.
Debt to Total Assets [DTA] ratio shows the percentage of assets that are being financed by
creditors and more than 50% of a business‟ assets are financed by debt. It is computed by
dividing total debt by total assets. The degree of leverage of the companies is indicated by
this ratio. Financial performance of the firm was computed by Return on Assets. Return on
Assets is a measure of profitability. It is computed by dividing net income by average assets.
The return on asset must be positive and the standard figure of 10%-12%.
27
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Research Design
According to Shukla (2010) a research design is a framework or a blue print for conducting a
research. It provides a clear plan on how the research will be conducted and helps the
researcher in sticking to the plan. The study adopted a descriptive research design on the
manufacturing companies listed in Nairobi securities exchange.The descriptive survey is best
suited for gathering descriptive information, and it is used to obtain information concerning
the current status of phenomena with a purpose of describing “what exist” with respect to
situation variables (Kirwan 2009).
3.2 Target Population
A population is any group of institutions, people or objectives that have at least one
characteristic in prevalence (Kasomo, 2007) and a target population is the population to
which the researcher will generalize the results of a study (Mugenda and Mugenda, 2003).
The population of the study constituted all 9 active manufacturing companies listed in N.S.E
as at 31st December 2016. Census method was adopted where all 9 listed manufacturing
companies represent the sample size. Below is the diagram representing the manufacturing
companies.
28
Table 3. 1 Target Population
________________________________________________________________
NUMBER MANUFACTURING AND ALLIED
___________________________________________________________________
1 B.O.C Kenya Ltd Ord
2 British American Tobacco Kenya Ltd
3 Carbacid Investments Ltd
4 East African Breweries Ltd
5 Mumias Sugar Co. Ltd
6 Unga Group Ltd
7 Eveready East Africa Ltd
8 Kenya Orchards Ltd
9 Flame Tree Group Holdings Ltd
Source: Nairobi Securities Exchange 2017
3.3 Sample Size
Since the sample size was not large enough to be subjected to calculations, the population of
nine manufacturing firms listed in the Nairobi Securities exchange was taken as sample size.
Such a method is referred to census where all observed variables occur in the population.
Each variable was one operational value for each observation and all represented the universe
of observation for this variable.
3.4 Data Collection Instruments and Procedure
The researcher first got an introduction letter from the school of business and economics,
Kisii University to enable him apply for and obtain a research permit from NACOSTI. The
study used secondary data that was provided by Capital Market Authority. Permission to
collect data was also acquired from each of the manufacturing firms listed in NSE from
audited financial reports and key financial information on the variables of concern discerned
from these reports. The secondary data was obtained from published financial statements for
each firm‟s website and covered a period of 5 years annually.
3.5 Data Analysis and presentation
The main objective of any statistical investigation is to assess relationships that make it
feasible to predict one or more variables in terms of other variables. The data was analysed
29
utilizing the software of Statistical Package for Social Science (SPSS) version 21 and
Microsoft Excel. The computed data was then analysed utilizing descriptive statistics tools
(Arithmetic Mean, Standard Deviation) and inferential statistics which involves regression
analysis and analysis of variance (ANOVA). Arithmetic mean was acclimated to set the
average liquidity ratio, solvency and financial health for each firms for the period covered as
average for each year for all the firms. Standard deviation was acclimated to determine the
spread of data values above the mean. Interpretation of the data was then done within the
frame of reference.
Liquidity was measured by Current ratio (CR): This ratio measures the expeditious short-term
solvency position of a firm. A high expeditious ratio denotes that the expeditious short term
solvency position of a firm is good. A Current ratio of 2:1 considered a firm more rigorous
and perforating test of the liquidity position of an organization as compared to the current
ratio of the firm. Expeditious ratio ER = current assets divided by Current liability.
Solvency was quantified by: Debt to Total Assets [DTA]: This ratio shows the percentage of
assets that are being financed by creditors (in lieu of business owners). Generally, no more
than 50% of a business‟ assets should be financed by debt. It is computed by dividing total
debt by total assets. The degree of leverage of the companies is denoted by this ratio. The
higher the percentage of debt to total assets, the more preponderant the jeopardy that the
business may be unable to meet its maturing obligations and vice versa is additionally true
Debt to Total Assets =Total debt divided by Total assets.
Financial performance was quantified by Return on Asset ROA and Return on Equity as
major ratios that denote the profitability of manufacturing firms. ROA is a ratio of Income to
its total asset (Khrawish, 2011). It shows how efficiently the resources of the company are
acclimated to engender the income. It further designates the efficiency of the management of
a company in engendering net income from all the resources of the institution (Khrawish,
30
2011). Wen (2010), state that a higher ROA shows that the company is more efficient in
utilizing its resources.Return on equity is the ratio of net income after taxes divided by total
equity capital.
Regression analysis was acclimated to determine the relationship between liquidity, solvency
and financial health. Multiple regression model was also used.
3.5.1 Analytical Model
The empirical model used in the study to test the the effect of financial distress on financial
performance of manufacturing firms listed in Nairobi Securities Exchange was presented as
follows:
Y = β0 +β1X1+β2X2+β3X3+ε
Where;
β0 +β1+β2+β3= were the regression coefficients established to indicate the magnitude of effect
of independent variables on the dependent variables.
Y= financial performance
X1=Liquidity level
X2= solvency level
X3=financial Health
ε=Error term.
3.5.2 Test of Significance
The test of significance was performed at 95% level of confidence using Analysis of
Variance (ANOVA) and the F- tests were used to determine the significance of the
regression. Correlation analysis was carried out to find the direction of the relationship
between ROA, ROE and the independent variables (liquidity, solvency and financial health of
the firm). The coefficient of determination, R2, was used to determine how much variation in
dependent variable is explained by the independent variables included bin the model.
3.6 Ethical consideration
Ethical considerations are the moral standards that the researcher considers in all stages of the
research. The research study involved the application of ethical principles to scientific
31
research, during data collection ensured that all work used had been acknowledged,
plagiarism is below the required limit of 20%. Clearance letter from the university issued was
used to apply for authority to conduct the study from the National Commission for Science,
Technology and Innovation (NACOSTI). Data collection was guided by the ethical
considerations of confidentiality, anonymity, responsibility, respect, competence, consent,
security and understanding.
32
CHAPTER FOUR
DATA ANALYSIS AND DISCUSSION OF RESEARCH FINDINGS
4.1 Descriptive Statistics
The study highlights the findings of the three objectives from the study. The main research
objective was to assess the effects of financial distress on the financial performance of
manufacturing firms listed in Nairobi securities exchange. The study addressed the following
specific objectives, to determine the effect of liquidity on financial performance of listed
manufacturing firms, to establish the effect of solvency on financial performance of listed
manufacturing firms, and to analyze the effect of financial health on financial performance
listed manufacturing firms. The first section analyses general information from 9
manufacturing companies listed on Nairobi security exchange, the second part presents
financial risk variables (current ratios, solvency ratios and Z-score ratios).
The aim of the study was to assess the effects of financial distress on financial performance.
The study used 9 manufacturing firms listed in NSE from 2011 to 2015. Financial
performance was quantified by Return on Asset ROA as a major ratio that denotes the
profitability of manufacturing firm listed in NSE. Financial distress was measured by current
ratio, solvency ratios and z-scores analysis among companies.
4.2 Liquidity
Liquidity is very important key in ensuring the stability of a company. A firm that cannot pay
its creditors on time and continue not to honor its obligations to the suppliers of credit,
services, and goods can be declared a sick company or bankrupt company. Inability to meet
the short term liabilities may affect the company‟s operations and in many cases it may affect
its reputation too. Lack of cash or liquid assets at hand may force a firm to miss the
incentives given by the suppliers of credit, services, and goods. Loss of such incentives may
result in higher cost of goods which in turn affect the profitability of the business.
33
4.2.1 Current Ratio
The study sought to determine the effect of liquidity using current ratio on financial
performance of listed manufacturing firms in NSE. Current ratio=CA/CL was used to
measure the amount of asset that the firms uses to pay off their current liability. The table 4.1
shows how current ratio affects financial performance of manufacturing firms.
Table 4. 1 Current Ratio
N Minimum Maximum Mean Std. Deviation
BO.C Kenya 5 1.94 2.23 2.0900 .10677
British American Tobacco 5 1.18 1.45 1.2900 .10075
Carbacid investments 5 4.26 10.09 6.8000 2.59477
East African breweries ltd 5 .54 1.05 .7620 .18660
Mumias sugar company ltd 5 .19 2.20 .9800 .79615
Unga group ltd 5 1.53 2.52 2.1200 .39912
Eveready east Africa
limited
5 .98 1.54 1.2460 .21232
Kenya Orchards 5 .00 2.08 .8320 1.13926
Flame Tree group 5 .00 1.64 .8800 .81918
Average Firm Mean 1.889
Researcher (2018)
The results show that current ratio of Carbacid, Unga group and B.O.C during the study
period were satisfactory as their average of 6.8, 2.12 and 2.0 were slightly higher than the
overall industry average of 1.889. The current ratios of British American Tobacco, East
African Breweries, Mumias Sugar Company, Eveready East Africa, Kenya Orchards and
Flame tree group were below the industry average an indication that Carbacid, Unga group
and B.O.C were better off than British American Tobacco, East African Breweries, Mumias
Sugar Company, Eveready East Africa, Kenya Orchards and Flame tree group in terms of
ability to pay debts as they fall due. However, the average current ratios for the six firms:
British American Tobacco(1.29:1), East African Breweries(0.7620:1), Mumias Sugar
Company(0.980:1), Eveready East Africa(1.240:1), Kenya Orchards (0.8320:1) and Flame
tree group (1.64:1)are all below the global norm of 2:1.This implies that the six firms do not
have satisfactory liquidity positions. On aggregate the results show that the manufacturing
firms may not pay their creditors on time and may not continue to honor their obligations to
34
the suppliers of credit, services and goods resulting in losses on account of non-availability of
supplies. Also, the inability to meet the short term liabilities could affect the business's
operations and in many cases it may affect its reputation as well. However, Nazrul and
Shamem (2012) in his study revealed that low current ratios may be a sign of inventory
turnover being much more rapid than the accounts payable becoming due.
4.2.2 Liquidity and Return on Asset
The study sought to examine the effect of liquidity on financial performance of
manufacturing firms. Current ratio was used to measure liquidity and return on asset
measures financial performance as presented in table 4.2.
Table 4. 2. Liquidity and Return on Asset
N Minimum Maximum Mean Std. Deviation
Return on Asset 9 1.00 9.00 .1286 .15651
Current Ratio 9 1.00 9.00 1.8820 1.91540
Valid N (list wise) 9
Researcher (2018) The results indicate that current ratio had a mean of 1.8820 having deviated from mean value
of1.91540; this was higher than return on asset which had a mean of .1286 having deviated
from .15651. The mean value of current ratio was higher than return on assets implied that
liquidity level affect financial performance
4.2.3 Liquidity and Return on Equity
The study sought to examine the effect of liquidity on financial performance of
manufacturing firms. Current ratio was used to measure liquidity and return on equity
measures financial performance as presented in table 4.3
Table 4. 3 Liquidity and Return on Equity
N Minimum Maximum Mean Std. Deviation
Return on Equity 9 1.00 9.00 .1321 .1621
Current Ratio 9 1.00 9.00 1.943 1.92740
Valid N (listwise) 9
Researcher (2018)
35
The results indicate that current ratio had a mean of 1.9430 having deviated from mean value
of1.92740; this was higher than return on equity which had a mean of .1321 having deviated
from .1621. The mean value of current ratio was higher than return on equity implying that
liquidity level affect financial performance.
4.3 Solvency
4.3.1 Debt to Equity Ratio
The study sought to establish the effect of solvency on financial performance of listed
manufacturing firms in Nairobi Security Exchange. Solvency was measured by asset liability
ratio used to measure the amount of total asset that the firms use to pay off total liability. Not
more than 50% of a business‟s assets should be funded by debt. This ratio can be reduced by
paying off debt or increasing the value of the firm‟s assets .The degree of leverage of the
companies is indicated by this ratio. According to, (Nazrul and Shamem, 2012), the higher
the percentage of debt to total assets, the greater the danger a business may be unable to meet
its growth and vice versa. The table 4.4 shows how the solvency ratio was used to control
financial risk of manufacturing firms.
Table 4. 4 Solvency
Manufacturing
Firm 2011 2012 2013 2014 2015
solvenc
y Mean
Percentage
s mean
(100%)
BOC 0.34 0.38 0.27 0.32 0.35 0.332 33.2
BAT 0.87 0.89 0.92 0.92 0.81 0.882 88.2
CARBACID 0.16 0.19 0.13 0.16 0.18 0.164 16.4
EABL 0.67 1.45 1.61 1.52 1.24 1.298 129.8
EVEREADY 2.04 1.76 1.1 1.99 0.82 1.542 154.2
FLAME TREE
2.23 1.21 1.02 0.892 89.2
KENYA ORCHARDS
1.17 2.17 0.668 66.8
MUMIAS 0.43 0.54 0.74 1 2.14 0.97 97
UNGA GROUP 0.48 0.73 1.04 0.6 0.52
67.4
OVERALL
AVERAGE 0.712 0.85 10.005 0.99 10.03 0.825 82.5
Researcher (2018)
36
The findings of the results indicate that seven manufacturing firms surveyed have higher
percentages of the debt to total assets ratio (British American Tobacco=88.2%, East African
Breweries LTD =129.8%, Eveready =154.2%, Flame Tree =89.2%, Kenya Orchards =66.8%,
Mumias =97%, and Unga Group =67.4%) signifying that the firms use debt financing more
than equity to finance their investment in assets. Even though this high use of debt financing
may result to higher profits, safety may be sacrificed. The high percentage of debt to total
assets can also be indicator of a greater risk that the firms may be incapable to encounter their
maturing obligations. The findings also show that the Average debt-equity ratio (DER) was
82.5%.
4.3.2 Solvency and Return on Asset
The study sought to examine the effect of solvency on financial performance of
manufacturing firms. Asset liability ratio was used to measure solvency and return on asset
measures financial performance as presented in table 4.5.
Table 4. 5 Asset liability ratio and Return on Asset
N Minimum Maximum Mean Std. Deviation
Asset liability Ratio 9 1,00 9,00 5,0000 2,73861
ROA values 9 1,00 9,00 4,6667 2,50000
Valid N (list wise) 9
Researcher (2018) The results shows that asset liability ratio had a mean of 5.0000 with a standard deviation of
2.73861, this was higher than return on asset which had a mean of 4.6667 having deviated
from 2.50000. The mean value of asset liability ratio was higher than return on assets
implied that solvency level affect financial performance.
4.3.3 Solvency and Return on Equity
The study sought to examine the effect of solvency on financial performance of
manufacturing firms. Asset liability ratio was used to measure solvency and return on equity
measures financial performance as presented in table 4.6.
37
Table 4. 6 Asset liability ratio and Return on Equity
N Minimum Maximum Mean Std. Deviation
Asset liability Ratio 9 1,00 9,00 6.1321 3.4421
ROE values 9 1,00 9,00 5.4223 3.2145
Valid N (list wise) 9
Researcher (2018) The results shows that asset liability ratio had a mean of 6.1321 with a standard deviation of
3.4421, this was higher than return on equity which had a mean of 5.4223 having deviated
from 3.2145. The mean value of asset liability ratio was higher than return on equity implied
that solvency level affect financial performance
4.4 Financial health using the Z-score model and financial performance
The study sought to find out the outcome of Z-score model on financial performance in each
period. The model was used to examine financial health of the firm expressed as a
mathematics expression that involved in terms of the following financial ratios, a= working
capital / total assets, b= retained earnings / total assets, c= earnings before interest and tax /
total assets, d= market value of equity / total liabilities was used to measure financial health
using z-score model, (McCallum (2010) Z-score= Z-Score = 1.2a + 1.4b + 3.3c + 0.6d). This
was used to define the firm‟s success and failure to bankruptcy potentiality in year of
operations.
Table 4. 7 Z-Score
(Z-Score)
(Z-
Score)
(2010)Z-Score = 1.2a + 1.4b + 3.3c +0.6d
Xa a= working capital / total assets Measure short-term financial health and
liquidity
Xb b= retained earnings / total assets Measure long-term liquidity risk and
solvency
Xc c= earnings before interest and tax / total
assets
Measure long-term financial health and
solvency
Xd d= market value of equity / total liabilities
Measure financial health and short-term
solvency
Researcher (2018)
38
The range of the Z-value for most corporations is between -4 and +8; with financially strong
corporations having Z values above 2.90, while those in serious trouble would have Z value
below 1.23. Those in the middle have question marks that could go either way. The Z values
of the surveyed manufacturing firms were as shown in Table 4.8 below
Table 4. 8 Z -score values using original model
Manufacturing
firm
Year Input Parameters
(CACL)/
Total
Assets
Retained
Earnings/
Total
Assets
EBIT/
Total
Assets
Equity/
Total
Liabilities
Z’Score,
Mean
EABL 2011 0.0288 0.328 0.358 4.05 4.77
2012 0.14 0.622 0.481 2.31 3.55
2013 0.258 0.654 0.357 3.03 4.3
2014 0.223 0.636 0.294 2.55 3.70
2015 0.013 0.645 0.337 2.62 3.62
Z’ Score, Mean 3.99
EVEREADY 2011 0.21 0.194 0.483 0.30 1.19
2012 0.396 0.307 0.151 0.31 1.16
2013 0.482 0.374 0.121 0.62 1.85
2014 0.534 0.024 0.693 0.65 1.90
2015 0.012 0.802 0.115 0.544 1.473
Z’ Score, Mean 1.51
MUMIAS 2011 0.21 0.55 0.43 0.75 1.94
2012 0.07 0.42 0.081 0.48 1.05
2013 0.072 0.379 0.0118 0.28 0.74
2014 0.486 0.349 0.263 0.20 1.3
2015 1.64 0.136 0.933 0.15 2.9
Z’ Score, Mean 1.59
UNGA 2011 0.603 - 0.154 0.21 0.967
2012 0.558 0.389 0.129 1.09 2.17
2013 0.47 0.372 0.091 0.35 1.28
2014 0.521 0.347 0.107 0.57 1.55
2015 0.495 0.289 0.01 0.64 1.434
Z’ Score, Mean 1.48
CARBACID 2011 0.212 0.615 0.221 6.84 7.89
2012 0.263 0.669 0.288 7.08 8.3
2013 0.38 0.729 0.3 10.2 11.61
2014 0.347 0.751 0.251 22.6 23.9
2015 0.319 0.778 0.187 5.27 6.55
Z’ Score, Mean 11.7
BAT 2011 0.23 0.27 1.76 1.21 3.47
2012 0.14 0.27 1.72 3.66 5.79
2013 0.204 0.24 1.77 3.79 6.004
2014 0.19 0.23 1.82 5.33 7.57
2015 0.3 0.21 1.95 4.79 7.25
Z’ Score, Mean 6.02
BOC 2011 0.318 0.797 0.158 2.55 3.82
2012 0.385 0.783 0.196 2.64 4.004
39
2013 0.32 0.593 0.148 2.63 3.69
2014 0.361 0.787 0.159 2.59 3.9
2015 0.456 1.162 0.429 1.97 4.02
Z’ Score, Mean 3.89
FLAME TREE 2011 - - - -
2012 - - - -
2013 0.47 0.39 1.87 - 2.73
2014 0.64 0.3 0.89 0.0012 1.83
2015 0.68 0.56 0.9 0.00011 2.14
Z’ Score, Mean 2.23
KENYA
ORCHARDS
2011 - - - -
2012 - - - -
2013
2014 0.45 3.34 0.144 0.012 3.95
2015 0.34 1.16 0.23 0.01 1.74
Z’ Score, Mean 3.72
Source: Nairobi Securities Exchange (2017)
From table 4.5, EABL was in a safe zone in the years 2011 to 2015 as per the Z-score
calculation. This means that Altman‟s model was applicable to predict non-distress of this
company. For Eveready, Z score fall within a range of between 1.19 and 1.16between 2011
and 2012 indicating that the manufacturing firm was in distress before slightly improving to
1.85 and 1.90 in 2013,2014 respectively. In 2015, the Z score slid to 1.4 giving an average Z-
score of 1.51 indicating the firm‟s financial state was very unhealthy and was likely to go into
bankruptcy. For Mumias manufacturing company Itd had a Z score of 1.94 in 2011, 1.05 in
2012, 0.74 in 2013 and 1.3 in 2014. From these results the firm was in a distress zone. This
was mainly from poor business performance ranging from sugar cane poaching, cheap sugar
smuggling. The same period, the company issued a statement that it was unable to pay its
sugar cane farmers and asked for government financial support to ensure its survival. In 2015
the Z-score rose 2.9 after the government intervened. The Unga Limited Group produced Z
score values of 0.967, 2.17, 1.28, 1.55 and 1.44 between 2011 to 2015 with an average Z-
score of 1.48 implying that the firm was in the grey zone. This research established that
Carbacid had Z scores of 7.89 in 2011, 8.3 in 2012, 11.61 in 2013, 23.9 in 2014 and 6.55 in
2015. This result shows that Carbacid was in the safe zone. BAT had a Z score of 3.47 in
2011, 5.79 in 2012, 6.004 in 2013, 7.57 in 2014 and 7.25 in 2015. From these results and an
40
overall mean of 6.02, the results implied that the firm was in a safe zone. BOC was in a safe
zone in the entire study period producing Z-score values of 3.82, 4.004, 3.69, 3.9 and 4.02
between 2011 and 2015 respectively. Flame Tree firm indicated a mean Z score value of 2.23
hence indicating that the firm is in a safe zone. The study found that Kenya Orchards Z score
fall within a range of between 3.94 and 1.74 between 2014 and 2015 with a mean score of
3.72 indicating that the firm was in a safe zone.
The results show that 3 out of the 9 manufacturing firms (33%) surveyed are financially
unhealthy and are likely to close door. 1 out of the 9 (11%) are on the grey zone which means
that is on a shaky state and a majority 5 out of 9 (55%) are financially sound since their Z
score values were above 2.90. On aggregate, a mean Altman‟s Z-score of 4.01 was obtained.
This value is higher than 2.90, this implies that on aggregate the manufacturing firm‟s
financial health are financially healthy implying that their financial health is sound.
4.5 Financial performance
The study sought to establish the extent to which financial performance was established
among the manufacturing firms for a five year period between 2011 to 2015.Table 4.8 and
4.9 gives the ROA, ROE summary.
Table 4. 9 ROA values using original model
Manufacturing Firm 2011 2012 2013 2014 2015 ROA MEAN
BOC 0.11 0.13 0.1 0.13 0.09 0.11
BAT 0.37 0.36 0.37 0.38 0.41 0.38
CARBARCID 0.18 0.21 0.23 0.21 0.15 0.98
EABL 0.26 0.35 0.21 0.19 0.23 0.2
EVERREDY 0.35 0.15 0.09 0.5 0.09 0.24
FLAME TREE 0.49 0.29 0.25 0.86
KENYA ORCHARDS 0.46 0.75 0.61
MUMIAS SUGAR 0.1 0.09 0.09 0.21 0.69 0.24
UNGA GROUP 0.11 0.09 0.06 0.07 0.07 0.08
OVERALL MEAN 1.39 0.2 0.2 0.19 0.30 0.41
Source: Nairobi Securities Exchange (2017)
In table 4.8, the descriptive statistics of the entire sample are listed the sampled
manufacturing firms financial performance index for a period between 2011 and 2015 based
on, ROA. A total of 9firms from the Nairobi security exchange financial data were sampled
for the entire 5 year period. The statistics indicates that the mean score for the financial
41
variable, ROA, display a less predictable pattern in terms of rises and falls. The ROA mean
value in 2011 is 1.39 compared to 0.2 in 2012 and 2013 respectively. In 2014, it maintained
at 0.19 before rising to 0.30 in 2015.
Table 4. 10 ROE values using original model
Manufacturing Firm 2011 2012 2013 2014 2015 ROA
MEAN
BOC 0.11 0.13 0.1 0.13 0.09 0.11
BAT 0.37 0.36 0.37 0.38 0.41 0.38
CARBARCID 0.18 0.21 0.23 0.21 0.15 0.2
EABL 0.26 0.35 0.21 0.19 0.23 0.25
EVERREDY 0.35 0.15 0.09 0.5 0.09 0.24
FLAME TREE 0.49 0.29 0.25 0.34
KENYA ORCHARDS 0.46 0.75 0.61
MUMIAS SUGAR 0.1 0.09 0.09 0.21 0.69 0.24
UNGA GROUP 0.11 0.09 0.06 0.07 0.07 0.08
OVERALL MEAN 1.39 0.2 0.2 0.19 0.30 0.41
Source: Nairobi Securities Exchange (2017)
In table 4.9, the descriptive statistics of the entire sample are listed the sampled
manufacturing firms financial performance index for a period between 2011 and 2015 based
on ROE. A total of 9firms from the Nairobi security exchange financial data were sampled
for the entire 5 year period. The statistics indicates that the mean score for the financial
variable, ROE, display a less predictable pattern in terms of rises and falls. The ROE mean
value in 2011 is 1.39 compared to 0.2 in 2012 and 2013 respectively. In 2014, it maintained
at 0.19 before rising to 0.30in 2015.
4.6 Inferential Statistics
The inferential statistics involved the use of multiple linear regression analysis to determine
the significance of the coefficients of the independent variables in explaining the variation in
dependent variables. Model summary was used to determine the proportion of the dependent
variable explained by the explanatory variables while analysis of variance was used to
determine the fitness of the model used in the analysis. Correlation analysis established the
direction of the relationship between the dependent and independent variables. Model
summary was used to determine the proportion of the dependent variable explained by the
42
independent variables while analysis of variance was used to determine the fitness of the
model used in the analysis. Correlation analysis established the direction of the relationship
between the dependent and independent variables.
4.6.1 Correlation Analysis
Correlation analysis shows the trend of the relations between the variables used in the model.
Pearson product-moment correlation coefficient measures the strength of a linear relationship
among two variables denoted by R. The coefficient, R, can take a range of values from +1 to
-1. A value of 0 shows that there is no relationship between the two variables. A value
greater than 0 indicates a positive relationship, that is, as the value of one variable rises so
does the value of the other variable. A value less than 0 indicate a negative relationship, that
is, as the value of one variable rises the value of the other variable fall.
Table 4. 11 Correlations Matrix
Solvency Liquidity
Financial
Health
Return on
Asset
Return on
Equity
Solvency Pearson
Correlation 1 .482 1.000
** .451
** .360
Sig. (2-tailed) .081 .000 .006 .008
N 5 5 5 5 5
Liquidity Pearson
Correlation .482 1 .432 .507 .361
**
Sig. (2-tailed) .081 .081 .005 .009
N 5 5 5 5 5
Financial
Health
Pearson
Correlation 1.000
** .432 1 .371
** .260
Sig. (2-tailed) .000 .081 .006 .003
N 5 5 5 5 5
Return on
Asset
Pearson
Correlation .451
** .507 .371
** 1 .682
Sig. (2-tailed) .006 .019 .006 .205
N 5 5 5 5 5
Return on
Equity
Pearson
Correlation .360 .361
** .260 .682 1
Sig. (2-tailed) .136 .005 .003 .205
N 5 5 5 5 5
**. Correlation is significant at the 0.01 level (2-tailed).
43
The results shows that liquidity and performance were positively correlated at r=0. 507, 0.19,
p <0.001and r=0.361, 005, P<0.001respectively implied that financial distress (liquidity) is
positively related to financial performance by return on asset and return on equity of
manufacturing firms. Financial health r=.371, 0.06, p<0.01 and r=.260, 0.03,
p<0.01respectively which implied that is statistically significant to return on assets and return
on equity. Solvency with r= .451, .006p <0.01 and 360, .008p <0.01. This revealed that there
is a positive relationship between solvency, return on assets and return on equityof
manufacturing firms.
4.6.2 Regression Analysis
The objective of the study was to observe whether the relationship between financial distress
and financial performance of manufacturing firms listed at the Nairobi Securities Exchange
exist. The study examined the effect of liquidity, solvency and financial health on financial
performance of manufacturing firms listed at the Nairobi Securities Exchange (NSE) in
Kenya.
4.7 Financial distress and Return on Asset
4.7.1 Model Summary
In order to examine the relationship between financial distress and return on asset of
manufacturing firms listed at Nairobi Securities Exchange, a multiple regression analysis was
applied to find out the relationship between independent variables and dependent variable.
The study adopted model summary from multiple regressions analysis.
Table 4. 12 Model Summary
Model R R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .998a .996 .983 .147 .996 80.296 3 1 .082
Source: Nairobi Securities Exchange (2017)
44
The Adjusted R-squared is the proportion of variance in the dependent variable which can be
explained by the independent variables and from table 4.12, the model summary shows that
the Adjusted R-squared in this study was 0.983, which means that, liquidity, solvency and
financial health( independent variables), combined can explain up to 98.3% of the changes in
the financial performance ( dependent variable) and other factors not subject of this study
cumulatively contribute to the remaining 1.7% of the financial performance of the
manufacturing firms.
4.7.2 Analysis of Variance
Analysis of Variance was applied to analyze the influence of financial distress on financial
performance of manufacturing firms. To test the significance of the model, ANOVA analysis
was used in table 4.13
Table 4.13 ANOVA for financial distress and ROA
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 5.179 3 1.726 80.296 .082b
Residual .021 1 .021
Total 5.200 4
Source: Nairobi Securities Exchange (2017)
The study used ANOVA statistics to establish the significance of the relationship between
financial distresses on financial performance of the firms listed at NSE. The regression model
is insignificant given the level of significance 0.082 which is above 0.05; to establish the
relationship between financial distress and return on assets under study, the researcher used
regression coefficients in table 4.13.
4.7.3 Regression Coefficients
This statistical control that regression provides is significant because it separates the work of
one variable from others in the regression model. Regression analysis coefficients established
the mean change in dependent variable (Return on asset) for one unit of change in the
45
predictor variable while holding other predictors in the model constant thus the researcher
used regression coefficients in table 4.14
Table 4. 14 Regression coefficient for financial distress and ROA
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
(Constant) 3.919 1.061 3.693 .168
Solvency -2.858 1.341 -.337 -2.131 .279
Liquidity -.629 .241 -.222 -2.612 .233
Financial Health .818 .125 1.134 6.551 .096
Source: Nairobi Securities Exchange (2017)
Other factors held constant, ROA for the firms on the average was 3.919 units between 2011
and 2015. The findings show that solvency negatively impacts on the ROA of the firms listed
at NSE. The effect of solvency on ROA is not statistically significant at 5% level of
significance (t= -2.131, p=0.279, p>0.05). This illustrates that one unit increase in the ratio of
equity to liability will lead to 2.858 unit decrease in financial performance of firms listed at
NSE. The effect of liquidity on ROA is not statistically significant at 5% level of significance
(t= -2.612, p=0.233, p>0.05). This illustrates that one unit increase in liquidity will contribute
to 0.629 unit decrease in ROA of the firms listed at NSE. Financial health positively affects
ROA though the effect is not statistically significant at 5% level (t=6.551, p=.096, p>0.05.
This illustrates that one unit increase in financial health will contribute to 0.818 unit increase
in the manufacturing firm‟s financial performance. A multiple regression analysis was
formulated to determine the relationship between financial distress and the performance of
manufacturing firms. The regression equation (Y = β0 + β1X1 + β2X2 + β3X3+ ε).Therefore,
the proposed regression model will be: Y = 3.919 -2.858X1- 0.629X2 + 0. 818X3
(Where Y = Performance of manufacturing firms, X1 = solvency; X2 = liquidity; X3 =
financial health and ε = error term).
46
4.8 Financial distress and Return on Equity
4.8.1 Model summary
In order to examine the relationship between financial distress and return on equity of
manufacturing firms listed at Nairobi Securities Exchange, a multiple regression analysis was
applied to find out the relationship between independent variables and dependent variable.
The study adopted model summary from multiple regressions analysis.
Table 4. 15 Model Summary
The study show that the adjusted R squared for the model was 0.885 indicated that the
regression model adopted for the research study is true result. The independent variables
showed the 88.5% of the variation on return on equity of listed manufacturing firms was
influenced by return on equity. Only 11.5% of variation on return on equity of listed
manufacturing firms was not showed by the regression model. The regression model
between the variables is showed by R= 0.990 which indicate there was a positive statistical
relationship between the independent variables and dependent variable.
4.8.2 Analysis of Variance
Analysis of Variance was applied to analyze the effect of financial distress on return on
equity of manufacturing firms listed at Nairobi Securities Exchange. The analyzed data were
indicated on the table below.
Table 4. 16 ANOVA for financial distress and ROE
Model Sum of Squares df Mean Square F Sig.
Regression 5.018 3 1.526 72.246 .062b
Residual .017 1 .017
Total 5.167 4
Source: Nairobi Securities Exchange (2017)
The study used ANOVA statistics to establish the significance of the relationship between
financial distresses on financial performance of the firms listed at NSE. The regression model
is insignificant given the level of significance 0.062 which is above 0.05
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .990a .888 .885 .14158
47
4.8.3 Regression Coefficients
This statistical control that regression provides is significant because it separates the work of
one variable from others in the regression model regression analysis coefficients established
the mean change in dependent variable (return on equity) for one unit of change in the
predictor variable while holding other predictors in the model constant.
Table 4. 17 Regression coefficient for financial distress and ROE
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
(Constant) 3.514 1.031 3.523 .128
Solvency -2.358 1.141 -.327 -2.093 .129
Liquidity -.432 .221 -.222 -2.522 .228
Financial Health .756 .109 1.112 6.409 .091
Source: Nairobi Securities Exchange (2017)
Other factors held constant, ROE for the firms on the average was 3.514 units between 2011
and 2015. The findings show that solvency negatively impacts on the ROE of the firms listed
at NSE. The effect of solvency on ROE is not statistically significant at 5% level of
significance (t= -2.093, p=0.129, p>0.05). This illustrates that one unit increase in the ratio of
equity to liability will lead to 2.358 unit decrease in financial performance of firms listed at
NSE. The effect of liquidity on ROE is not statistically significant at 5% level of significance
(t= -2.522, p=0.228, p>0.05). This illustrates that one unit increase in liquidity will contribute
to 0.432 unit decrease in ROE of the firms listed at NSE. Financial health positively affects
ROE though the effect is not statistically significant at 5% level (t=6.409, p=.091, p>0.05.
This illustrates that one unit increase in financial health will contribute to 0.8756 unit
increase in the manufacturing firm‟s financial performance. A multiple regression analysis
was formulated to determine the relationship between financial distress and the performance
of manufacturing firms. The regression equation (Y = β0 + β1X1 + β2X2 + β3X3+
ε).Therefore, the proposed regression model will be: Y = 3.514 -2.358X1- 0.432X2 + 0.756X3
Where Y = Performance of manufacturing firms, X1 = solvency; X2 = liquidity; X3 =
financial health and ε = error term).
48
4.9. Hypothesis Testing
The researcher sought to accept or reject the hypotheses by conducting a test on the null
hypothesis as follows:
H01 Liquidity does not significantly affect financial distress on financial performance of
manufacturing firms listed on the Nairobi Stock Exchange. From the regression coefficient
obtained from tables 4.14 and 4.17 above, an examination of the t-value for liquidity (t = -
2.612; p = 0.233 > 0.05) for ROA, liquidity (t = -2.522; p = 0.228 > 0.05) for ROE, indicates
that liquidity has no statistical significant effect on the performance. This implies that HO1 as
proposed is failed to be rejected.
H02 Solvency does not significantly affect financial distress on financial performance of
manufacturing firms listed on the Nairobi Stock Exchange. From the regression coefficient
obtained from tables 4.14 and 4.17 above, the t-values for solvency (t = -2.131; p = 0.279 >
0.05) for ROA, liquidity (t = -2.093; p = 0.129 > 0.05) for ROE, the findings implies that
solvency has no statistical significant effect on the performance. This implies that HO2 as
proposed is failed to be rejected.
H03 Financial health does not significantly affect financial performance of manufacturing
firms. The regression coefficient obtained from tables 4.14 and 4.17 above, the t-values for
Financial Health (t = 6.551; p = 0.96 > 0.05) for ROA, liquidity (t = 6.409; p = 0.91 > 0.05)
for ROE, the findings implies that financial health has no statistical significant effect on the
performance hence HO3 as proposed is failed to be rejected.
49
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary of the findings
The study and data collected the following summary, conclusion and recommendations were
determined. The researcher had intended to assess the effect of financial distress on financial
performance of manufacturing firms listed at Nairobi Securities exchange in Kenya.The study
addressed the following specific objectives, to determine the effect of liquidity on financial
performance of listed manufacturing firms, to establish the effect of solvency on financial
performance of listed manufacturing firms, and to analyse the effect of financial health on
financial performance listed manufacturing firms. Financial performance was quantified by
Return on Asset ROA as a major ratio that denotes the profitability of manufacturing firms
listed in NSE. Financial risk was measured by current ratio, solvency ratios and z-scores
analysis among companies.
5.1.1 Liquidity level and financial performance
The study sought to determine the effect of liquidity on financial performance of listed
manufacturing firms in NSE. Current ratio was used to measure the amount of asset that the
firm uses to pay off their current liability. Table 4.1, current ratio was used to measure
financial risk of manufacturing firms. Carbacid investments Limited had the highest current
ratio from 2011 to 2015; this revealed that Carbacid investment Limited had the ability to pay
its current liabilities as compared to companies left behind. Further Unga Limited is
following Carbacid investments Limited with positive current ratio while Mumias Sugar
Company limited had lowest current ratio. The study implied that Mumias Sugar Company
has no strong ability to pay its current liabilities.
The findings indicate that level of liquidity in 2011 had a range 8.84, but dropped down in
2012; in 2013 current ratio had increased before decreasing again in the preceding years.
50
However, return on assets in 2011 projected a high ratio, followed by a decrease in 2012
before maintaining a steady rise in the subsequent years. The liquidity of the companies listed
in NSE is not stable from 2011 to 2013 trends is not regular. The study revealed that level of
liquidity affects financial performance of manufacturing firms listed in Nairobi Security
Exchange.
5.1.2 Solvency level and financial performance
The study sought to establish the effect of solvency on financial performance of listed
manufacturing firms in Nairobi Security Exchange. Solvency was measured by asset liability
ratio used to measure the amount of total asset that the firms use to pay off total liability.
A Carbacid investment Limited had the highest debt ratio in the year 2013, and BOC limited
in the year 2013. However, the lowest solvency ratio was Kenya Orchards followed by East
Africa Brewery Limited at and Mumias Sugar Company limited at. This implied that this
Carbacid investment Limited has the ability to pay off its total liabilities in the manufacturing
firms. However, Kenya Orchards limited has the lowest ratio in terms of solvency ratio. The
study implied that Kenya Orchards may not able to pay its totals liabilities in the future.
The results shows that Carbacid Investments had the highest solvency ratio which indicated
how much manufacturing firms were using to finance their solvency relative to total liability
to total assets indicated to the company financial stability. However, Kenya Orchards had the
lowest solvency ratio for two years followed by fame tree group. This implied that there is
need to improve their financial performance relative to debt financing regardless of its asset
ratio
5.1.3 Financial Health and financial performance
The study sought to establish the relationship between financial health and financial
performance (return on asset). From the Z, score results in table 4.5, Carbacid manufacturing
company had the highest score projecting a healthy growth between 2011 to 2015.Unga
51
group limited recorded a mean of 1.48 implying that the company was on a grey zone and
could be headed to bankruptcy. EABL, BOC, BAT and Kenya Orchards recorded a healthy
growth for the five year period.
Correlation analysis established that the relationship between Solvency and ROA is strong,
negative and insignificant to performance. This implied that one unit increase in the ratio of
equity to liability will lead to unit decrease in financial performance of firms listed at NSE.
Liquidity had a moderate negative association with the ROA of the firm but also
insignificant. Financial health had a strong and positive but insignificant relationship with
ROA of firms listed at NSE. The study established that 98.3% of the changes in financial
performance (ROA) of the firms listed at NSE were attributed to the changes in independent
variables (solvency, liquidity and financial health) considered in the model.
5.2 Conclusion of the Study
5.2.1 Liquidity and financial performance
The research sought to establish liquidity level (current ratio) on financial performance. The
study concluded that East African Breweries Ltd had the lowest liquidity mean. The study
established that current ratio positively affect the financial performance of manufacturing and
allied companies listed at the NSE, thus the study concludes that liquidity negatively affect
financial performance of manufacturing and allied companies listed on the NSE. (β= -2.612,
p = 0.233ROA, β= -2.522, p = 0.228 ROE).
5.2.2 Solvency level and financial performance
The study sought to determine the effect of solvency on performance which was measured by
Debt to Equity ratio. The study examined the effect of solvency on the performance of
manufacturing and allied firms in Kenya. The results showed that solvency had a negative but
insignificant effect on the performance of firms (β= -2.131, p = 0.279ROA, β= -2.093, p =
0.129 ROE).
52
5.2.3 Financial Health and financial performance
The study sought to establish the relationship between financial health and financial
performance. The study used Zscore model to measure the financial health for a five year
period between the years 2011 to 2015. The selected listed manufacturing firms found out
that, individually, one (1) of the selected firms have their average Z-Score between 1.81<Z<2.67
and therefore classified in the grey zone and three (3) firms has its average Z-score below Z<1.81 and
therefore were classified as distressed. The five (5) out of the nine (9) firms representing 55% of the
selected firms are classified as safe.
5.2.4 Financial distress and financial performance
Financial performance was measured by ROA and financial distress was measured by
liquidity, solvency and financial health over the period of study. The study shows that the
adjusted R square was 0.983. This imply that any change in one unit of one shilling of
financial distress causes a positive change (98.3%) in one unit of one shilling on return on
assets of manufacturing firms under study. However, 1.7% can cause variation with other
financial distress not in this study. The calculated p value is more than 0.05 indicating that
there is no direct significant relationship between financial distress and return on assets under
study.
5.3 Recommendations of the Study
5.3.1 Liquidity and financial performance
The study sought to establish liquidity level (current ratio) on financial performance.
Manufacturing firms should formulate strategies to be adopted in order to mitigate against
liquidity better financial performance. The study recommends that there is need for
manufacturing and allied companies listed at the Nairobi Securities Exchange to increase
their current assets so as to increase their liquidity as it was established that an increase in
current ratio positively affect the financial performance.
53
5.3.2 Solvency and financial performance
The study sought to define the effect of solvency which was measured by Debt to Equity
ratio. The study further recommends that there is need for the manufacturing and allied
companies listed at the NSE to decrease their debt to equity ratio as it was found that debt
negatively affects the financial performance of manufacturing and allied companies listed at
the Nairobi Securities Exchange. There is also need for these firms listed on the Nairobi
Securities Exchange to decrease their total assets, through disposing of their underutilized
assets in order to positively influence their financial performance. In addition, short term
borrowing should be converted to long term borrowing so as to ease repayment period.
5.3.3 Financial Health and financial performance
The study sought to determine the effect of financial health on financial performance. The
findings established that by investors having prior know how on the financial health of a firm,
it will help them to make sound financial decisions before putting in more resources. Inflation
being an intervening factor will also shed light on its effect between financial health on the
performance of the manufacturing and allied companies listed at the Nairobi Securities
Exchange. This will benefit investors to take advantage on the investment opportunities
available when these variables vary. The study findings offer valuable inputs to advise the
assessment of the legal framework and influence effective formulation of regulatory policies
5.4 Suggestions for Further Study
A study can be done on the Analysis of Financial Performance and Financial Risk in
Agricultural Companies Listed on the Nairobi Security Exchange and companies from other
sectors listed in the NSE.In the future years, the researcher should be focusing on other
independent variables and dependent variable, so as to check whether the finding will still
remain the same in future. This will give accurate result for decision making on
54
recommendations on the purpose of financial distress on financial performance of
commercial banks listed at Nairobi Securities Exchange
55
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62
APPENDICES
APPENDIX A: APPLICATION FOR RESEARCH PERMIT
63
APPENDIX B: NACOSTI RESEARCH AUTHORIZATION
64
APPENDIX C: NACOSTI RESEARCH CLEARANCE PERMIT
65
APPENDIX D: MANUFACTURING COMPANIES FINANCIAL STATEMENTS
BOC KENYA LIMITED. Continued
FINANCIAL REVIEW (KSHS.’000)(FINANCIAL YEAR END 31ST DECEMBER.)
ASSETS EMPLOYED 2015 2014 2013 2012 2011
Non-current
assets
1,068,704.00
1,117,163.00
1,421,589.00
901,570.00
926,721.00
Current Assets 1,252,252.00
1,183,157.00
1,211,504.00
1,087,971.00
890,082.00
Current Liabilities 606,850.00
553,132.00
544,011.00
523,229.00
458,790.00
Net Current
Assets
645,402.00
630,025.00
667,493.00
564,742.00
431,292.00
Total Net Assets 1,714,106.00
1,747,188.00
2,089,082.00
1,466,312.00
1,358,013.00
FINANCED BY
Share Capital
97,627.00
97,627.00
97,627.00
97,627.00
97,627.00
Share premium 2,554.00
2,554.00
2,554.00
2,554.00
2,554.00
Reserves* 191,219.00
271,369.00
736,144.00
207,212.00
145,553.00
Non-current
Liabilities
13,022.00
11,501.00
29,462.00
Retained
earnings*
1,422,706.00
1,375,638.00
1,239,735.00
1,147,418.00
1,082,817.00
Total Financing 1,714,106.00
1,747,188.00
2,089,082.00
1,466,312.00
1,358,013.00
TURNOVER
1,186,420.00
1,296,679.00
Profit Before
Taxation
221,721.00
277,984.00
308,392.00
286,692.00
214,948.00
Taxation (73,121.00)
(48,359.00)
(105,756.00)
(89,318.00)
(64,344.00)
Net Profit 148,600.00 29,625.00 202,636.00 197,374.00 150,604.00
FINANCIAL
66
RATIOS
Current Ratio 2.06
2.14
2.23
2.08
1.94
Earnings Per
Share
7.61
11.76
10.38
10.11
7.71
Price-to-Earnings
Ratio
13.40
10.63
12.04
9.84
12.96
Dividend Yield % 5.10
4.16
4.16
5.08
6.80
Pay-Out Ratio % 68.32
44.22
50.10
49.96
88.16
Net Asset Value
Per Share
87.79
89.48
107.00
75.10
69.55
Price-to-Book
Value
1.16
1.40
1.17
1.32
1.44
MARKET
INFORMATION
Dividends Per
Share (Kshs)
5.20
5.20
5.20
5.05
6.80
Share Price as at
30 Dec
102.00
125.00
125.00
99.50
100.00
Number of shares
in Issue
19,525,000.00
19,525,000.00
19,525,000.00
19,525,000.00
19,525,000.00
Market
Capitalization
(Kshs)
1,991,550,000.00
2,440,625,000.00
2,440,625,000.00
1,942,737,500.00
1,952,500,000.00
67
BRITISH AMERICAN TOBACCO LTD... Continued
FINANCIAL REVIEW (KSHS.’000)(FINANCIAL YEAR END 31ST DECEMBER.)
ASSETS EMPLOYED 20152014 2013 2012 2011
Non-Current
Assets 9,101,979.0
0 9,281,014.00 8,467,651.00
8,046,667.00 6,770,831.00
Current
Assets 9,579,205.0
0 8,972,496.00 8,518,272.00
7,129,828.00 6,979,714.00
Current
Liabilities 6,600,703.0
0 7,182,905.00 6,781,102.00
6,052,680.00 5,340,629.00
Net Current
Assets 2,978,502.0
0 1,789,591.00 1,737,170.00
1,077,148.00 1,639,085.00
Total Net
Assets 12,080,481.00 11,070,605.00 10,204,821.00
9,123,815.00 8,409,916.00
FINANCED BY
Share Capital 1,000,000.0
0 1,000,000.00 1,000,000.00
1,000,000.00 1,000,000.00
Share
premium 23.00 23.00 23.00 23.00 23.00
Revaluation
reserve 1,416,219.0
0 1,446,433.00 1,488,403.00
1,528,976.00 1,063,978.00
Retained
earnings 1,836,936.0
0 1,780,466.00 1,733,182.00
1,668,918.00 1,648,066.00
Proposed
dividends 4,600,000.0
0 3,900,000.00 3,350,000.00
2,900,000.00 2,700,000.00
Shareholders
fund 8,853,178.0
0 8,126,922.00 7,571,608.00
7,097,917.00 6,412,067.00
Non-Current
Liabilities 3,227,303.0
0 2,943,683.00 2,633,213.00
2,025,898.00 1,997,849.00
Total
Financing 12,080,481.00 11,070,605.00 10,204,821.00
9,123,815.00 8,409,916.00
TURNOVER 22,300,000.00 34,124,565.00 34,915,663.00
30,503,560.00 20,138,122.00
Profit Before
Taxation 7,138,902.0
0 6,095,419.00 5,469,955.00 4,754,302.0
0 4,484,116.00
Taxation (2,162,646.00) (1,840,105.00) (1,746,264.00) (1,483,450.00) (1,386,361.00) Net Profit 4,976,256.0
0 4,255,314.00 3,723,691.00
3,270,852.00 3,097,755.00
FINANCIAL
RATIOS
Current Ratio 1.45 1.25 1.26 1.18 1.31
Earnings Per
Share (Kshs.) 49.76 42.55 37.24 32.71 30.98
Price-to- 15.77 21.15 15.98 15.07 7.94
68
Earnings Ratio
Dividend
Yield % 5.41 4.33 6.22 6.59 12.40
Pay-Out Ratio
% 85.41 91.65 99.36 99.36 98.46
Net Asset
Value Per
share (Kshs)
120.80 110.71 102.05 91.24 84.10
Price-to-Book
Value 0.35 0.35 0.36 0.36 2.93
MARKET
INFORMATIO
N
Dividends Per
Share (Kshs.) 42.50 39.00 37.00 32.50 30.50
Share Price as
at 31 Dec. 785.00 900.00 595.00 493.00 246.00
Number of
shares in Issue 100,000,000.00 100,000,000.00 100,000,000.00
100,000,000.00 100,000,000.00
Market
Capitalization
(Kshs.)
78,500,000,000.0 90,000,000,00
0.00
59,500,000,000.0
0
49,300,000,000.00
24,600,000,000.0
0
0
69
CARBACID INVESTMENTS LIMITED...Continued
FINANCIAL REVIEW (KSHS. ‘000) (FINANCIAL YEAR END 31 JULY)
ASSETS EMPLOYED 2015 2014 2013 012 011 010
Non-current assets 1,854,036.00 1,552,475.00 1,312,332.00 1,373,428.00 1,335,872.00 1,127,061.00
Current Assets 1,114,691.00 980,688.00 892,062.00 639,388.00 404,113.00 385,105.00
Current Liabilities 247,126.00 155,757.00 88,417.00 150,166.00 45,698.00 66,558.00
Net Current Assets 867,565.00 824,931.00 803,645.00 489,222.00 358,415.00 318,547.00
Total Net Assets 2,721,601.00 2,377,406.00 2,115,977.00 1,862,650.00 1,694,287.00 1,445,608.00
FINANCED BY
Share Capital 254,852.00 254,852.00 169,902.00 169,902.00 169,902.00 169,902.00
Reserves* 27.00 27.00 - - 255,680.00 228,061.00
Retained earnings 2,118,508.00 1,784,246.00 1,545,035.00 1,245,458.00 1,041,783.00 895,794.00
Revaluation surplus 103,639.00 121,041.00 209,492.00
Proposed dividends 237,410.00
Shareholders‟ funds 2,477,026.00 2,160,166.00 1,924,429.00 1,652,770.00 1,467,365.00 1,293,757.00
Non-current Liabilities 244,575.00 217,240.00 191,553.00 209,880.00 226,922.00 151,851.00
Total Financing 2,721,601.00 2,377,406.00 2,115,982.00 1,862,650.00 1,694,287.00 1,445,608.00
TURNOVER 809,719.00 826,630.00 952,836.00 921,753.00 576,092.00 620,083.00
Profit before Taxation 580,467.00 597,262.00 634,686.00 535,444.00 374,210.00 438,041.00
Taxation (186,604.00) (106,621.00) (159,145.00) (146,157.00) (72,015.00) (130,649.00)
Profit after Taxation 393,863.00 490,641.00 475,541.00 389,287.00 302,195.00 307,392.00
MARKET INFORMATION
Dividends Per Share (Kshs.) 0.70 0 .70 6.00 6.00 5.00 5.00
Share Price as at 31 July 16.95 149.00 140.00 125.00 91.50 156.00
Number of shares in Issue 254,851,988 33,980,265.00 33,980,265.00 33,980,265.00 33,980,265.00
33,980,265.00
Market Capitalization (Kshs.) 4,319,741,196.60 5,063,059,485.00 4,757,237,100.00 4,247,533,125.00
3,109,194,247.50 5,300,921,340.00
70
EAST AFRICAN BREWERIES LTD...Continued
FINANCIAL REVIEW (KSHS.’000)(FINANCIAL YEAR END 30TH JUNE.)
ASSETS EMPLOYED2016 2015 2014 2013 2012 2011
Non-Current Assets 40,190,000.00 41,448,623.00 43,058,789.00 39,127,360.00 36,113,498.00 33,391,673.00
Current Assets 21,556,000.00 25,491,155.00 19,807,154.00 18,593,102.00 18,057,773.00 16,320,457.00
Current Liabilities 27,969,000.00 24,930,769.00 27,460,650.00 26,606,846.00 22,483,782.00 15,509,186.00
Net Current Assets (6,413,000.00) 560,386.00 (7,653,496.00) (8,013,744.00) (4,426,009.00) 811,271.00
Total Net Assets 33,777,000.00 42,009,009.00 35,405,293.00 31,113,616.00 31,687,489.00
34,202,944.00
FINANCED BY
Share Capital 1,581,547.00 1,581,547.00 1,581,547.00 1,581,547.00 1,581,547.00
1,581,547.00 Share Premium 1,691,151.00 1,691,151.00 1,691,151.00 1,691,151.00
1,691,151.00 1,691,151.00 other reserves (27,059,824.00) (18,292,037.00) (18,292,037.00)
(18,292,037.00) Translation reserve (1,927,000.00) 246,531.00 177,666.00 188,391.00
284,091.00
Reserves 1,285,324.00 1,285,324.00 1,281,545.00 1,281,545.00
Retained earnings 27,105,032.00 22,501,939.00 20,352,473.00 19,717,366.00 11,202,570.00
Proposed Dividends 9,938,000.00 10,172,000.00 73,387.00 71,373.00 67,046.00 4,942,340.00
Shareholders fund 11,283,698.00 15,021,761.00 9,018,977.00 6,874,443.00 6,330,709.00
21,300,971.00
Non-Current Liabilities 22,910,000.00 27,325,000.00 26,304,445.00 23,515,016.00
23,384,654.00 7,314,817.00
Minority Interests (417,000.00) (337,752.00) 81,871.00 724,157.00 1,972,126.00 5,587,156.00
Total Financing 33,776,698.00 42,009,009.00 35,405,293.00 31,113,616.00 31,687,489.00
34,202,944.00
-
TURNOVER 64,322,000.00 64,420,000.00 61,292,176.00 59,061,875.00 55,522,166.00
44,895,037.00
Profit Before Taxation 13,619,000.00 14,151,000.00 10,406,619.00 11,114,919.00 15,253,049.00
12,258,989.00
Taxation (5,598,000.00) (4,616,000.00) (3,548,011.00) (4,592,719.00) (4,066,936.00)
(3,235,329.00)
Profit / Loss After Taxation 8,021,000.00 9,535,000.00 6,858,608.00 6,522,200.00
11,186,113.00 9,023,660.00
EVEREADY EAST AFRICA LIMITED. Continued
ASSETS EMPLOYED 20152014 2013 2012 2011
Non-
Current Assets
871,045.00
166,700.00
257,826.00
274
,686.00
283,200.00
71
Current
Assets
640,620.00
763,357.00
683,971.00
876,043.00
727,664.00
Total
Assets
1,511,665.00
930,057.00
941,797.00
1,150,729.00
1,010,864.00
Current
Liabilities
651,306.00
572,293.00
444,019.00
695,764.00
652,383.00
Net
Current Assets
(10,686.00)
191,064.00
239,952.00
180,279.00
75,281.00
Total Net
Assets
860,359.00
357,764.00
497,778.00
454,965.00
358,481.00
FINANCED BY
Share Capital
210,000.00
210,000.00
210,000.00
210,000.00
210,000.00
Retained Earnings
(70,716.00)
8,463.00
185,915.00
139,489.00
69,405.00
Reserves
667,004.00
Proposed Dividends
-
-
-
-
-
Shareholders fund
806,288.00
218,463.00
395,915.00
349,489.00
279,405.00
Non-Current Liabilities
54,071.00
139,301.00
101,863.00
105,476.00
79,076.00
Total Financing
860,359.00
357,764.00
497,778.00
454,965.00
358,481.00
TURNOVER
1,132,136.00
1,216,580.00
1,415,395.00
1,374,789.00
1,374,847.00
Profit Before Taxation
(98,912.00)
(248,013.00)
60,113.00
68,914.00
(173,208.00)
Taxation
21,202.00
70,424.00
(15,021.00)
1,170.00
49,214.00
Profit for the year
(77,710.00)
(177,589.00)
45,092.00
70,084.00
(123,994.00)
FINANCIAL RATIOS
Current Ratio
0.98
1
.33
1
.54
1.2
6
1
.12
Earnings Per Share
(Kshs.)
(0.37)
(0.85)
0.21
0.33
(0.59)
Price Earnings Ratio
(8.24)
(
4.32)
1
2.57
5.99
(
2.96)
72
Dividend Yield %
-
-
-
-
-
Pay-Out Ratio %
-
-
-
-
-
Net Asset Value/Share
(Kshs.)
4.10
1.70
2.37
2.17
1.71
Price-to-Book Value
0.74
2.14
1.14
0.92
1.03
MARKET
INFORMATION
Dividends Per Share
(Kshs.)
-
-
-
-
-
Share Price as at 30
September
3.05
3.65
2.70
2.00
1.75
Number of shares in Issue
210,000,000.00
210,000,000.00
210,000,000.00
210,000,000.00
210,000,000.00
Market Capitalization
(Kshs.)
640,500,000.00
766,500,000.00
567,000,000.00
420,000,000.00
367,500,000.00
73
FTG HOLDINGS LTD...Continued
FINANCIAL REVIEW FINANCIAL YEAR END 31ST DECEMBER
ASSETS EMPLOYED 2015 20142013
Non-current assets
318,725,526.00
248,732,588.00
185,674,313.00
Current Assets
1,053,504,227.00
805,722,217.00
690,135,062.00
Current Liabilities
641,999,959.00
518,494,707.00
572,191,112.00
Net current assets
411,504,268.00
287,227,510.00
117,943,950.00
Total Net Assets
730,229,794.00
535,960,098.00
303,618,263.00
FINANCED BY
Share Capital
133,540,084.00
133,540,084.00
113,502,902.00
Share premium
152,450,453.00
152,450,453.00 -
Retained earnings
291,913,572.00
114,181,310.00
84,992,893.00
Legal Reserves 3,665,461.00 2,549,637.00
1,595,016.00
Translation reserve
46,050,797.00
5,064,873.00
(1,963,093.00)
Shareholders‟ funds
627,620,367.00
407,786,357.00
198,127,718.00
Non-current liabilities
102,609,427.00
128,173,741.00
105,490,545.00
Total Financing
730,229,794.00
535,960,098.00
303,618,263.00
TURNOVER
2,283,151,865.00
1,764,847,673.00
1,601,356,664.00
Profit /(Loss) Before Taxation 198,387,446.00 144,798,997.00 173,23
6,259.00
Tax credit/(charge)
(19,539,360.00)
8,327,201.00
(24,188,870.00)
Profit /(Loss) for the year
178,848,086.00
153,126,198.00
149,047,389.00
74
INVESTOR RATIOS
Current Ratio 1.64 1.55 1.21
Earnings per Share-Kshs 1.10 0.95 0.92
Price to Earnings Ratio 7.69 8.72 -
Dividend yield % 88.2
4
21.82 -
Payout Ratio % 678.
79
190.27 -
Net Asset Value Per Share-Kshs. 4,51
1.30
3,311.12
1,875.73
Price to Book Value
MARKET INFORMATION
0.00
0.00
-
Dividend per Share 7.50 1.80 -
Share Price as at 30 June 8.50 8.25 -
Number of Shares in issue
161,866,804.00
161,866,804.00
161,866,804.00
Market Capitalization-Kshs.
1,375,867,834.00
1,335,401,133.00
-
75
KENYA ORCHARDS LTD... Continued
FINANCIAL REVIEW FINANCIAL YEAR END 31ST DECEMBER
ASSETS EMPLOYED 2015 2014
Non-current assets 44,61
9,344.00
21,004,
803.00
Current Assets 34,11
1,879.00
29,197,
374.00
Current Liabilities 16,43
3,745.00
16,460,
677.00
Net current assets 17,67
8,134.00
12,736,
697.00
Total Net Assets 62,29
7,478.00
33,741,
500.00
FINANCED BY
Share Capital 57,22
8,746.00
57,228,
746.00
Share premium - -
Retained earnings
(51,751,616.00)
(80,61
2,264.00)
Revaluation Reserves 493,4
22.00
493,42
2.00
Proposed Dividends 55,00
0.00
55,000.
00
Shareholders funds 6,025
,552.00
(22,83
5,096.00)
Non-current liabilities 56,27
1,926.00
56,576,
596.00
Total Financing 62,29
7,478.00
33,741,
500.00
TURNOVER 60,97
4,312.00
58,062,
204.00
Profit /(Loss) Before Taxation 4,328
,873.00
1,471,4
48.00
Tax credit/(charge) 24,58
6,775.00
(26,73
2,995.00)
Profit /(Loss) for the year 28,91
5,648.00
(25,26
1,547.00)
INVESTOR RATIOS
76
Current Ratio 2.08 1.77
Earnings per Share-Kshs 0.99 1.99
Price to Earnings Ratio 98.99 55.28
Dividend yield % - -
Payout Ratio % - -
Net Asset Value Per Share-Kshs. 4,841
.22
2,622.1
0
Price to Book Value
MARKET INFORMATION
0.02 0.04
Dividend per Share - -
Share Price as at 30 June 98.00 110.00
Number of Shares in issue 12,868,124.00 12,868,124.00
Market Capitalization-Kshs. 1,261,076,152.00 1,415,493,640.00
77
MUMIAS SUGAR COMPANY LTD...Continued
FINANCIAL REVIEW (KSHS. 000’S)(FINANCIAL YEAR END 30TH JUNE
ASSETS EMPLOYED 2015 2014 20132012 2011
Non-current assets
17,860,015.00
19,209,782.00
20,222,053.00
20,167,253.00
16,664,857.00
Current Assets 2,543,549.00
4,353,304.00
7,059,940.00
7,232,860.00
6,511,659.00
Current Liabilities
13,640,591.00
10,635,149.00
8,408,773.00
5,720,655.00
2,961,691.00
Net current assts
(11,097,042.00)
(6,281,845.00)
(1,348,833.00)
1,512,205.00
3,549,968.00
Total Net Assets 6,76
2,973.00
12,927,937.00
18,873,220.00
21,679,458.00
20,214,825.00
FINANCED BY
Share Capital
3,060,000.00
3,060,000.00
3,060,000.00
3,060,000.00
3,060,000.00
Revaluation Surplus 1,95
5,580.00
3,071,442.00
3,173,432.00
3,350,880.00
3,552,456.00
Retained earnings 916,
464.00
4,510,363.00
7,149,058.00
9,191,706.00
7,863,551.00
Dividends Proposed - -
-
-
-
Shareholders‟ funds 5,93
2,044.00
10,641,805.00
13,382,490.00
15,602,586.00
14,476,007.00
Non-current liabilities 830,
929.00
2,286,132.00
5,490,730.00
6,076,872.00
5,738,818.00
Total Financing 6,76
2,973.00
12,927,937.00
18,873,220.00
21,679,458.00
20,214,825.00
TURNOVER 5,53
1,357.00
13,075,912.00
11,957,823.00
15,542,686.00
15,795,300.00
Profit /(Loss) Before
Taxation
(6,307,257.00)
(3,405,046.00)
(2,222,699.00)
1,764,029.00
2,646,575.00
Tax credit/(charge) 1,66
2,456.00
698,451.00
562,293.00
248,650.00
(713,350.00)
Profit /(Loss) for the
year
(4,644,
801.00)
(
2,706,595.00)
(
1,660,406.00)
2
,012,679.00
1,
933,225.00
INVESTOR
RATIOS
78
Current Ratio 0.19
0.41
0.84
1.26
2.20
Earnings per Share-
Kshs
(3.0
4)
(1.77)
(1.09)
1.32
1.26
Price to Earnings
Ratio
(0.7
7)
(1.61)
(3.87)
4.64
5.66
Dividend yield % - - -
8.20
6.99
Payout Ratio % - - -
38.01
39.57
Net Asset Value Per
Share-Kshs.
4.42 8.4
5
1
2.34
14.17
13
.21
Price to Book Value 0.53
0.34
0.34
0.43
0.54
MARKET
INFORMATION
Dividend per Share - - -
0.50
0.50
Share Price as at 30
June
2.35
2.85
4.20
6.10
7.15
Number of Shares in
issue
1,530,000,000.00
1,530,000,000.00
1,530,000,000.00
1,530,000,000.00
1,530,000,000.00
Market
Capitalization-Kshs
3
,595,500,000.00
4,360,500,000.00
6,426,000,000.00
9,333,000,000.00
10,939,500,000.00
79
UNGA GROUP LTD... Continued
FINANCIAL REVIEW (KSHS ‘000) (FINANCIAL YEAR END 31 DECEMBER)
ASSETS EMPLOYED 20112012 20132014 2015
Non-Current Assets
1,622,280.00
1,754,938.00
2,272,647.00
2,54
1,402.00
3,219,069.00
Current Assets
4,086,617.00
4,644,891.00
5,835,732.00
4,934,209.00
5,452,719.00
Current Liabilities
1,618,796.00
2,431,941.00
3,817,078.00
2,172,393.00
2,302,165.00
Net current Assets
2,467,821.00
2,212,950.00
2,018,654.00
2,761,816.00
3,150,554.00
Total Net Assets
4,090,101.00
3,967,888.00
4,291,301.00
5,303,218.00
6,369,623.00
FINANCED BY
Share Capital
378,535.00
378,535.00
378,535.00
378,535.00
378,535.00
Share Premium
73,148.00
73,148.00
73,148.00
73,148.00
73,148.00
Reserves*
2,078,952.00
201,689.00
121,060.00
550,600.00
1,428,956.00
retained earnings 1
,544,540.00
1,595,723.00
1,840,932.00
1,840,932.00
Shareho
lders‟ Funds
2,530,635.00
2,197,912.00
2,168,466.00
2,843,215.00
3,721,571.00
Non-Current Liabilities
345,150.00
463,988.00
650,214.00
987,381.00
1,014,344.00
Minority interests
1,214,316.00
1,305,988.00
1,472,621.00
1,472,622.00
1,633,708.00
Total Financing
4,090,101.00
3,967,888.00
4,291,301.00
5,303,218.00
6,369,623.00
TURNOVER
13,214,442.00
15,976,763.00
15,142,017.00
17,002,302.00
18,723,250.00
Profit/(
Loss) Before Taxation
6
31,070.00
5
12,569.00
3
89,458.00
567,
735.00
6
35,695.00
Taxation
(190,027.00)
(164,374.00)
(124,685.00)
(184,968.00)
(205,914.00)
Net Profit/ Loss
441,043.00
348,195.00
264,773.00
382,767.00
429,781.00
80
INVESTOR RATIOS
Current Ratio
2.52
1.91
1.53
2.27
2.37
Earnings/(Loss) Per
Share (Kshs)
5.83
4.60
3.50
5.06
5.68
Net Asset Value Per
Share (Kshs)
54.03
52.41
56.68
70.05
84.14
Dividend Yield
2.08
1.0
8
2
.21
1.89 2
.14
Price-to-Earnings Ratio
6.18
15.11
9.72
14.83
17.62
Pay-Out Ratio %
12.87
16.31
2.14
14.83
17.62
Price-to-Book Value
0.67
1.33
0.60
0.57
0.56
MARKET
INFORMATION
Dividends Per Share
(Kshs)
0.75
0.75
0.75
0.75
1.00
Share Price as at 30
June
36.00
69.50
34.00
39.75
46.75
Number of Shares in
Issue
75,706,986.00
75,706,986.00
75,706,986.00
75,706,986.00
75,706,986.00
Market Capitalization
(Kshs)
681,362,874.00
5,261,635,527.00
2,574,037,524.00
3,009,352,693.50
3,539,301,595.50
81
APPENDIXE: COMPANIES LISTED
IN THE NSE
AGRICULTURE
Eaagads ltd ord 1.25
Kapchorua tea co. Ltd ord 5.00
Kakuziord 5.00
Limuru tea co. Ltd ord 20.00
Rea vipingo plantations ord 5.00
Sasini ltd ord 5.00
Williamson tea kenya ltd ord 5.00
AUTOMOBILES AND
ACCESSORIES
Car and General (K) Ltd Ord 5.00
Sameer Africa Ltd Ord 5.00
Marshalls (E.A.) Ltd Ord 5.00
BANKING
Barclays Bank Ltd Ord 0.50
CFC Stanbic Holdings Ltd ord.5.00
I&M Holdings Ltd Ord 1.00
Diamond Trust Bank Kenya Ltd Ord
Housing Finance Co Ltd Ord 5.00
Kenya Commercial Bank Ltd Ord
National Bank of Kenya Ltd Ord 5.00
NIC Bank Ltd 0rd 5.00
Standard Chartered Bank Ltd Ord 5.00
Equity Bank Ltd Ord 0.50
The Co-operative Bank of Kenya Ltd
COMMERCIAL AND SERVICES
Express Ltd Ord 5.00
Kenya Airways Ltd Ord 5.00
Nation Media Group Ord. 2.50
Standard Group Ltd Ord 5.00
TPS Eastern Africa (Serena) Ltd Ord
Scangroup Ltd Ord 1.00
Uchumi Supermarket Ltd Ord 5.00
Hutchings Biemer Ltd Ord 5.00
Longhorn Kenya Ltd
Atlas Development and Support
Services
CONSTRUCTION AND ALLIED
Athi River Mining Ord 5.00
Bamburi Cement Ltd Ord 5.00
Crown Berger Ltd 0rd 5.00
E.A.Cables Ltd Ord 0.50
E.A.Portland Cement Ltd Ord 5.00
ENERGY AND PETROLEUM
KenolKobil Ltd Ord 0.05
Total Kenya Ltd Ord 5.00
KenGen Ltd Ord. 2.50
Kenya Power & Lighting Co Ltd
Umeme Ltd Ord 0.50
INSURANCE
Jubilee Holdings Ltd Ord 5.00
Pan Africa Insurance Holdings Ltd 0rd
Kenya Re-Insurance Corporation Ltd
Liberty Kenya Holdings Ltd
British-American Investments
Company
CIC Insurance Group Ltd Ord 1.00
INVESTMENT
Olympia Capital Holdings ltd Ord 5.00
Centum Investment Co Ltd Ord 0.50
Trans-Century Ltd
Home Afrika Ltd Ord 1.00
Kurwitu Ventures
MANUFACTURING AND ALLIED
B.O.C Kenya Ltd Ord 5.00
British American Tobacco Kenya Ltd
Carbacid Investments Ltd Ord 5.00
East African Breweries Ltd Ord 2.00
Mumias Sugar Co. Ltd Ord 2.00
Unga Group Ltd Ord 5.00
Eveready East Africa Ltd Ord.1.00
Kenya Orchards Ltd Ord 5.00
A.Baumann CO Ltd Ord 5.00
Flame Tree Group Holdings Ltd Ord
TELECOMMUNICATION AND
SERVICE
Safaricom Ltd Ord 0.05
REAL ESTATE INVESTMENT
TRUST
StanlibFahari I-REIT
82
APPENDIX F: PUBLICATION
83
APPENDIX G : PLAGARISM REPORT