1. No category

International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, August, 2018 www.oircjournals.org
Effectiveness of Market Ratios in
Predicting Financial Distress. Evidence
from Kenya.
1Jackson
Keter, 2Dr. Jared Bogonko, 2Dr. Geoffrey Kimutai
1MBA
2School
Student Kisii University
of Business and Economics
Kisii University
Type of the Paper: Research Paper.
Type of Review: Peer Reviewed.
Indexed in: worldwide web.
Google Scholar Citation: IJFAE
How to Cite this Paper:
Keter, J., Bogonko, J. and Kimutai G. (2018). Effectiveness of Market Ratios in
Predicting Financial Distress. Evidence from Kenya. International Journal of Finance,
Accounting and Economics (IJFAE) 1 (2), 12-24.
International Journal of Finance, Accounting and Economics (IJFAE)
A Refereed International Journal of OIRC JOURNALS.
© Oirc Journals.
This work is licensed under a Creative Commons Attribution-Non Commercial 4.0
International License subject to proper citation to the publication source of the work.
Disclaimer: The scholarly papers as reviewed and published by the OIRC JOURNALS, are
the views and opinions of their respective authors and are not the views or opinions of the
OIRC JOURNALS. The OIRC JOURNALS disclaims of any harm or loss caused due to the
published content to any party.
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
Effectiveness of Market Ratios in Predicting Financial
Distress. Evidence from Kenya.
1Jackson
Keter, 2Dr. Jared Bogonko, 2Dr. Geoffrey Kimutai
1MBA
2School
Student Kisii University
of Business and Economics
Kisii University
ARTICLE INFO
Abstract
Article History:
Received on 24th July, 2018
Received in Revised Form 16th July, 2018
Accepted 23rd August, 2018
Published online 24thAugust, 2018
Financial distress research of companies has
attracted a growing attention in the recent past. This
phenomenon of financial distress in public companies
has been witnessed by a number of corporate failures
and the increase in delisting of listed companies. This
study therefore attempts determine the effectiveness of
market ratios on financial distress of listed firms in
Keywords: Effectiveness, Market ratios, Financial
Nairobi Security Exchange Market, Kenya. Liability
management theory, was reviewed which provides a
Distress, Kenya.
foundation for both liquidity ratio and financial
distress. The study used a panel study is an
observational study. The target population will be 62 listed companies in Nairobi Security Exchange Market as
indicated in from year 2011-2015. The entire population will be used in this study. The study will use document
analysis by getting panel data from listed companies in Nairobi Security Exchange Market. Panel data is a good
indicator or measure of financial distress. Descriptive and inferential statistics method will be used for data analysis
and interpretation. Data was presented using tables and diagrams. Hypotheses were tested at 0.05 level of
significance (95% confidence level) from OLS pooled regression (fixed and random effect) which shows the
relationship between the independent variable and dependent variable. The findings show that market ratio has a
positive and significant effect on financial distress, (β = 0.593; p< 0.05). This study is significantly important in
that it will enhance efficient management and financing of working capital can increase the operating profitability
ratio.
1.0 Introduction
Financial distress research of companies has
attracted a growing attention in the recent past (Liao
& Mehdian, 2016; Mselmi et al., 2017). This
upsurge in research attention could be attributed to
the importance attached to the need of firms to
understanding financial dimensions that are revealed
in moments of crisis (Pindado et al., 2008). Grice &
Ingram (2001) and Agrawal (2015) defined financial
distress as the inability of a firm to satisfy its
financial obligations as and when they fall due. This
is often witnessed whenever the firm’s operational
cash flows are lower than its financial expenses
(Tsun-Siou et al., 2004). Business firms additionally
encounter financial distress when they confront
lacking liquidity to meet their financial liabilities.
The most discernible impact of financial distress is
the suspension of debt premium instalments, cutting
capital costs, exchanging settled resources and
scaling back (Sanz & Ayca, 2006) temporary
insolvency and low liquidity (Jabeur & Fahmi,
2017).
One of the major objectives of financial analysis is
to decrease the degree of risk, to which creditors are
exposed as a result of bankruptcies and defaulting on
debts (Tamari, 1966). One system often used to
scrutinize the financial position of a firm as reflected
in its financial statements is ratio analysis comparison of various data in the balance sheet and
profit and loss statement. The ratio of current assets
to current liabilities, for example, indicates the firm's
capacity to meet such liabilities; the ratio of net
worth to total liabilities shows the owners' share in
the assets of the business; the ratio of net profit to
net worth gives the return on proprietary capital; and
the ratio of net profit to the value of production gives
some indication of the enterprise's pricing policy.
The exact choice of ratios will clearly depend on the
object in view and the information available
(Tamari, 1966). Ratios are among the most popular
and widely used tools of financial analysis. Yet their
13 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
function is often misunderstood and, consequently,
their significance often overrated. A ratio expresses
a mathematical relation between two quantities
(Bersten & Wild, 1999).
Financial ratios are the most commonly used in
analyzing, understanding and interpreting corporate
financial statements and in evaluating and
monitoring company’s performance over time. The
ratios point out changes and identify irregularities,
abnormalities and surprises that would require
further investigation to ascertain the current and
future financial standing of the company (Barry &
Jamie Elliot, 2006). The ratios are based on the
firm's past behavior and are unaffected by any
additional knowledge in the hands of the
investigator on the future of the branch, the business
or social standing of the owner, Government policy,
etc. (Tamari, 1966).
For the ratios to be more meaningful, a standard is
required when traditional ratio analysis techniques
are used in analyzing financial statements. Such
standards can be adjusted depending on the
economic conditions obtaining at the time or the
changes in company objectives. The most
commonly used ratio analysis techniques are trend
analysis and cross sectional analysis. Trend analysis
relates the company’s performance over time; the
benchmark therefore could be the previous year’s
financial ratios, the budgeted financial ratios,
budgeted financial ratios for the same period or
financial ratios for other profit centers or cost centers
(Barry & Jamie, 2006). With cross sectional
analysis, the benchmark is the financial ratios of
another company either in the same industry or in a
different industry.
1.2 Statement of the Problem
Kenyan companies have equally been affected by
financial distress. In the recent past Uchumi
Supermarket has suffered financial distress and was
put under receivership (Kipruto, 2013). Companies
listed at the NSE are no exception to financial
distress and bankruptcy (Mohamed, 2012). These
companies are expected to be health financially in
order to maintain investor confidence. Miller (1991)
argues that the bankruptcy on indebted firm will
send a shock wave to the firm’s equally indebted
suppliers leading in turn to more bankruptcies until
eventually the whole economy collapses in a heap.
The financial health of firms listed at the NSE will
influence the transactions conducted at the NSE.
More recently Mumias Sugar Company, Kenya
airways have been hit hard by financial distress and
have asked the government for bailouts (The
Standard Newspaper, June 27 2015). Mamo (2011)
and Kariuki (2013) studied financial distress of the
banking industry in Kenya using the Z – score.
Kipruto (2013) and Shisia et al. (2014) studied
financial distress in Uchumi Supermarkets using the
Altman’s Z – score model. No significant studies
have been done in Kenya on financial distress
prediction. The original Z – score model (Altman,
1968) was developed to predict financial distress
and bankruptcy in large manufacturing firms in the
United States of America. This study therefore
differs from the above studies in that it sought to test
the validity of Altman (1968) model in the Kenyan
context and in particular listed companies at the
NSE.
In his MBA project, Mamo (2011) conducted a
study on financial distress of Kenyan banking
industry. He used Altman (1968) model of
predicting financial distress on 43 banks. The model
was found to be an accurate predictor on 8 out of 10
failed firms, 80% validity for the model. On the
sampled non-failed firms majority of them proved
the Edward Altman’s financial prediction model to
be 90% valid. In another study in the banking
industry Kariuki (2013) sought to establish the
impact of financial distress on commercial banks
performance. She sought to know whether they are
in distress, if so how their performance is affected
and how to rectify the situation.
The findings indicate that most banks under study
had financial distress, non-listed banks suffered
more. Financial distress had significant impact on
financial performance. There is a negative
relationship between financial distress and financial
performance. The study established the need to
reduce financial distress by ensuring financial
stability in banks to ensure shareholders confidence.
Shisia et al. (2014) conducted a study with the
objective of Altman failure prediction model in
predicting financial distress in Uchumi Supermarket
in Kenya. They used secondary data for a period of
five years from 2001 – 2006. The study established
that Altman failure prediction model was
appropriate for Uchumi Supermarket as it recorded
declining Z – score values indicating that it was
suffering financial distress. It is always important
account users will need only a small number of
financial ratios to make crucial decisions about a
company’s state of affairs. Hence, it will be costly
and waste of resources for corporate stakeholders to
focus on the numerous financial ratios in order to
make critical business decisions. There is also the
risk of focusing on less important, ambiguous or the
wrong type of ratios.
There is need to determine which ratios are more
statistically effectives than others in predicting
financial distress. The ratios in themselves may not
be as useful when applied individually. It is
therefore necessary to combine several ratios. The
problem then is to determine which ratios are more
significant in such decision making process. It
would not be viable for analysts, creditors investors
14 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
to apply all the over 50 financial ratios. According
to Polemis et al., (2012) massive fluctuating
environment and the financial crisis highlight the
need for future research on the world trade
implications, as well as individual macroeconomic
variables of each country. This study looks at the
Kenyan companies listed in the Nairobi Stock
Exchange. This study would seek to determine the
predictive power of ratios in all the firms listed in
NSE and thus determine the most appropriate ratios
that can be used to effectively predict the financial
distress.
2.0 Literature Review and Hypothesis
Development
Liability Management Theory
Since the early 1960s, the loan portfolios of banks
have been affected by the emergence of new theory,
which became known as the liability management
theory. This is one of the significant liquidity
management theories and says that there is no need
to follow old liquidity norms like maintaining liquid
assets, liquidity investments etc. lately banks have
focused on liabilities side of the balance sheet.
According to this theory, banks can satisfy liquidity
needs by borrowing in the money and capital
markets. The fundamental contribution of this
theory was to consider both sides of a bank’s balance
sheet as sources of liquidity (Emmanuel, 1997).
Today, banks use both assets and liabilities to meet
liquidity needs. Available sources of liquidity are
identified and compared to expected engagement by
a Bank’s Asset and Liability Management
Committee (ALCO). Key considerations includes
maintain high asset quality and a strong capital base
that both reduces liquidity needs and improves a
bank’s access to funds at low cost. There is a short
run tradeoff between liquidity and profitability. In
the long run, if management is successful in
managing liquidity, then, long term earnings will
exceed other banks earnings, as will the capital and
overall liquidity (Koch and MacDonald, 2003).
This theory is relevant in this study because as
earlier mentioned, firms are using both assets and
liabilities to meet liquidity needs and therefore, the
management of liquidity is relevant. Liquidity
management according to Monnie (1998), means
ensuring that institutions maintains sufficient cash
and liquid assets. Reasons for this are: First, to
satisfy client demand for loans and savings
withdrawals and secondly, to pay the institution’s
expenses. Liquidity management involves a daily
analysis and detailed estimation of the size and
timing of cash inflows and outflows over the coming
days and weeks to minimize the risk that savers will
be unable to access their deposits in the moments
they demand them. For an institution to manage
liquidity the institution must put in place
management information system; which will be able
to generate and compute ratios needed to make
realistic projections on liquidity.
Shiftability Theory of Liquidity
Shiftability is an approach to keep banks liquid by
supporting the shifting of assets. An explanation of
bank liquidity that holds that a bank’s capacity to
meet liquidity demands is related to the volume of
its assets that can be readily shifted to another bank.
Shiftability theory was pioneered by H. G. Moulton
in 1918, who affirmed that if the institutions
especially of finance maintain a reasonable quantity
of assets that can be exchanged for cash without
losing materials in case of need, then you don’t need
to rely on maturities. In other words, to perfectly
shift an asset, it must be instantly be transferred
without losing capital when the need for liquidity
arises. This is specifically applicable to market
investments which are of short term, such as treasury
bills which can be immediately sold whenever it is
necessary to raise funds by these firms.
The shiftability theory liquidity replaced the
commercial loan theory and was supplemented by
the doctrine of anticipated income. Formally
developed by Harold G, H.G. Moulton in 1918, the
shiftability theory held that banks could most
effectively protect themselves against massive
deposit withdrawals by holding, as a form of
liquidity reserve, credit instruments for which there
existed a ready secondary market. Included in this
liquidity reserve were commercial paper, prime
bankers’ acceptances and, most importantly as it
turned out, treasury bills. Under normal conditions
all these instruments met the tests of marketability
and, because of their short terms to maturity, capital
certainty.
A major defect in the shiftability theory was
discovered similar to the one that led to the
abandonment of the commercial loan theory of
credit, namely that in times of general crisis the
effectiveness of secondary reserve assets as a source
of liquidity vanishes for lack of a market (Casu et
al., 2006). The role of the central bank as lender of
last resort gained new prominence, and ultimately
liquidity was perceived to rest outside the banking
system. Furthermore the soundness of the banking
system came to be identified more closely with the
state of health of the rest of the economy, since
business conditions had a direct influence on the
cash flows, and thus the repayment capabilities, of
bank borrowers. The shiftability theory survived
these realizations under a modified form that
included the idea of ultimate liquidity in bank loans
resting with shiftability to the Banks (Allen and
Gale, 2004).
This theory has certain prerequisites of truth on this
research as shares and debentures of large
15 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
companies are acknowledged as liquid assets along
with treasury bills and bills of exchange. It is more
efficient approach in running firm financial system:
with fewer reserves or investing in long-term assets.
However, this theory has weaknesses. First, it does
not provide liquidity to a firm by mere shiftability of
assets as it completely depends upon the economic
circumstances. Secondly, the theory ignores the fact
that the debentures and shares cannot be shifted on
to other organizations during acute depression. In
such a situation, there are no buyers and all who
possess them want to sell them. Third, a single
institution may have shiftable assets in sufficient
quantities but if it tries to sell them when there is a
run, it may undesirably upset the whole finance
system. Lastly, if all the financial institutions
concurrently start shifting their assets, it would have
devastating effects on both the creditors and debtors.
Market Ratios and Financial Distress
Market value ratio is also call share ownership ratio.
It referred to the stockholders way of analyzing the
present and future investment in a company. In this
ratio the stockholders are interested in the way
certain variables affect the value of their holdings. It
helps the stockholder to be able to analyze the likely
future market value of the stock.
Abu Shanab (2008) examined the impact of returns
and risks on the share prices for a sample of 38
industrial public companies in Jordan listed on
Amman Security Exchange for the period of 2000 to
2007. The results of the study showed that there is
no effect for the returns, risks and dividends on the
market value per share. However, the results
indicated that there is a significant relationship
between cash flow and share prices.
AL Kurdi (2005) study explored the ability of the
published accounting information to predict share
prices for a representative sample of 110 Jordanian
public companies listed in Amman Security
Exchange for the period of 1994 to 2004. The results
informed that there is a relationship between the
published accounting information of the insurance
public companies and their share. The results also
informed that market information have more ability
on predicting share prices compared to the
accounting information. Abu Hasheesh (2003)
examined the role of published accounting
Information in predicting share prices. The study
used a sample of 40 Jordanian public companies
listed in Amman Security Exchange for the year
2003. The results showed that there is a positive
significant positive relationship between the market
price per share with the ratios of net profits to equity,
net profits to total assets, and dividends to net profits
as a total. The results showed also a significant
negative relationship between the market price per
share, with the ratios of fixed assets to total assets,
the creditors total to total of cash sources, and the
wages ratio to total of expenses ratio.
H01: Market ratios does not effectively predict
financial distress of listed companies in Nairobi
Security Exchange Market
Conceptual Framework
Independent Variable
Ratio Analysis
Market ratios
Earnings per share
ratio
Market ratio
Figure 2.1
Dependent Variable
Financial Distress




Liquid Assets
Cummulative Profits
Asset Turn over
Market value of the
firm
Conceptual Framework
3.0 Research Design and Methodology
This study adopted cross sectional research design.
A cross-sectional study is an observational study.
Explanatory research seeks to establish causal
relationship between variables (Saunders et al., 2009
&Robson 2002). According to Kerlinger & Lee
(2000) a cross sectional research design is
appropriate where the researcher is attempting to
explain how the phenomenon operates by
identifying the underlying factors that produce
change in it in which case there is no manipulation
of the independent variable. This study therefore
used panel data research design seeking to establish
the relationship between accounting ratios and firm
financial performance.
A population is the total collection of elements about
which inferences are made and refers to all possible
cases which are of interest for a study (Sekaran,
2003). A target population is the totality of cases
conforming to the designated specifications as
16 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
required by the study and could be people, events or
things of interest. In this study the target population
comprised all firms listed at the Nairobi Securities
Exchange (NSE). The NSE had 61 firms as at
August 10, 2016.
A = Working capital / Total assets [Measures
the relative amount of liquid assets]
B = Retained earnings / Total assets
[Determines cumulative profitability]
C = Earnings before interest and taxes / Total
assets [measures earnings away from the
effects of taxes and leverage]
D = Market value of equity / Book value of
total liabilities [incorporates the effects of a
decline in market value of a company's
shares]
E = Sales / Total assets [measures asset
turnover]
Census Inquiry
In this study the population of interest were the firms
quoted at the Nairobi securities exchange, and a
census of all firms listed at the Nairobi Securities
Exchange from year 20011-2016 were employed.
This will enable the researcher obtain (68*5)
totalling to 340 observations.
Data Collection Instruments
The study used secondary data, this is data collected
by someone other than the user. Common sources of
secondary data for social science which this study
used include censuses, organizational records and
data collected through qualitative methodologies or
quantitative research. Analysts of social and
economic change consider secondary data essential,
since it is impossible to conduct a new survey that
can adequately capture past change and/or
developments.
The study utilized panel data which consisted of
time series and cross-sectional data. The data for all
the variables in the study was extracted from
published annual reports and financial statements of
the listed companies at the NSE covering the years
2010 to 2015. The data was obtained from the NSE
hand books for the period of reference. Data
extracted included the income statement, statement
of financial position, and notes to the accounts using
a document review guide.
Measurement of the Variables
Dependent variable
Financial distress was measured using the Z-score
for firm i in year t, developed and validated by
Altman (1968) and reviewed by Altman &
Hotchkiss (2006).
Z = 1.2A x 1.4B x 3.3C x 0.6D x 0.99E
The letters in the formula designate the following
measures:
Table 4.1:
Variable
Independent Variable
Market ratios is measured was measured by Price /
Earnings
The multiple regression model used in this study is
given as;
Where,
𝑦𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1 𝑥1𝑖𝑡 + 𝜀𝑖𝑡
Y = Financial Distress
𝛼 = constant.
β1… = the slope which represents the degree in
which firm performance changes as the
independent variable change by one unit variable.
X1 = Market ratios
ε = error term
t = measure of time
i = number of firm observation
4.0 Results and Discussions
Descriptive statistics
In panel data descriptive statistics are a collection of
measurements of two things: location and
variability. In this case, location tells the central
value of the variable (where the mean is the most
common measure). Variability refers to the spread
of the data from the center value (that is, variance,
standard deviation, in this case the standard
deviation is inferred). Consequently, the study
sought to determine the descriptive statistics of the
panel data especially the mean, standard deviation
and maximum and minimum values. The findings
were summarized and presented in Table 4.1.
Descriptive statistics
N
Mean
Std. Dev.
Min
Max
Marketing Ratio
240
0.811
0.171
0.054
1.619
Financial distress
240
1.152
0.113
0.000
0.947
The findings in Table 4.1 showed that assessment of
the liquidity ratio analysis of the firms revealed a
mean of 1.675 with a minimum of 0.000 and a
maximum of 6.209 (std. dev. = 1.029) implying that
majority of the firms were able to meet their current
liabilities comfortably using their current assets. The
17 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
mean for financial distress was 1.152 with a
minimum of 0.000 and a maximum of 0.947 (std.
dev. = 0.113) while the mean for marketing ratios
was 0.811 with a minimum of 0.054 and maximum
of 1.619 (std. dev. = 0.171).
Assumptions of Regression Analysis
Normality
Jarque-Bera (JB) test for normality was used to test
for normality of error terms. According to Brys et
al., (2004) the JB tests the hypothesis that the
distribution of error terms is not significantly
different from normal (H0: E (ε) ~N (μ=0, Var.
=σ2). The results of the tests are presented in Table
4.2. The results show that the significance levels for
the Jarque-Bera statistics were greater than the
critical p-value of 0.05 implying that the errors were
not different from normal distribution (Tanweeer,
2011).
Table 4.3: Test Statistics for Model Residual Normality
JB (Prob).
Model
Z-Scoreit
Model 1
3. 637 (0.239)
Source: Research Data (2018)
The multiple linear regression analysis requires that
the errors between observed and predicted values
(that is, the residuals of the regression) should be
normally distributed.
Multicolinearity
Multiple linear regression assumes that there is no
multicollinearity in the data. Multicollinearity
Table 4.4; Results of Multicollinearity test
Conclusion
Normal
occurs when the independent variables are too
highly correlated with each other. It can be tested
using the Variance Inflation Factor (VIF) - the VIFs
of the linear regression indicate the degree that the
variances in the regression estimates are increased
due to multicollinearity. VIF values higher than 10
indicate that multicollinearity is a problem. In
addition, tolerance values of less than 0.1 indicate
the presence of multicollinearity.
Marketing Ratio
Tolerance
VIF
0.763
1.310
a. Dependent Variable: Financial distress
The findings in Table 4.4 revealed that the VIF
values for all the independent variables were below
10. This means that for all the independent variables,
there was no presence of multicollinearity.
Testing for Unit Roots
Before empirical estimations are conducted, the data
series are subjected to unit root tests to establish their
stationarity conditions, that is, their orders of
integration. Therefore, the series must be primarily
tested for stationarity in all econometric studies
(Granger and Newbold, 1974). In case a series is
found to be non-stationary at levels, it is differenced
until it became stationary (Gujarati, 2004; 2007 and
Baltagi, 2001). Since panel data models were used
Table 4.5; Unit root test
Series
in this study and the data set had a time dimension,
unit root existence was investigated by panel unit
root tests. Maddala and Wu (1999) suggest that
using panel unit root tests yields statistically better
results compared to the results of unit root tests like
Philips-Perron, which are based on a single time
series.
This study conducted unit root test for the variables
using the Augmented Dickey Fuller unit root test. As
shown in Table 4.5 the p-values for the Augmented
Dickey Fuller Chi-square statistic were less than the
critical values of 0.05 for financial distress, and
Marketing. This implies that these variables/ panels
(had no unit roots) and therefore suitable for
modelling and forecasting. To correct for non
stationarity in profitability, the first difference of the
variables [D (var)] was used in the regression model.
(χ2)
P-value
Conclusion
Financial Distress
396.84
0.000
Do not Reject H0
Marketing
-2.842
0.013
Do not Reject H0
Source: Research data (2018)
18 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
Correlation Analysis
Correlation analysis is usually used to establish the
level to which two variables converge or diverge
together depending on the case so as to determine
the significance of the relationship. Normally, the
Pearson's Product Moment Correlation Coefficient
is used to make inference about the existing
relationship between two variables. Generally,
Table 4.6:
Correlation analysis
Financial
Distress
correlation analysis depicts to a certain degree, the
aspect of how one factor influences another.
However, correlations do not imply or infer a causeeffect relationship. Consequently, a correlation
analysis of the independent factors and the
dependent factor (Financial distress) was conducted
and the findings were summarized and presented in
Table 4.6.
Market
Ratios
Financial
Distress
Market Ratios
1
1
-0.186**
0.004
* Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
The findings in Table 4.5 revealed that market ratio
has a negative and significant relationship with
financial distress, ρ = -0.186, p-value = 0.004 and
this means that there is 18.6% chance that financial
distress will decrease with increase in the market
ratio.
The findings also showed significant inter-factor
relationships between profitability ratios and
liquidity ratios, ρ = 0.135, p-value = 0.036 and
between market ratios and leverage ratios, ρ = 0.408,
p-value = 0.000. Although these findings do not
imply a cause-effect relationship, they point to the
existence of a cause-effect linear relationship
especially between the response and explanatory
variables.
Regression Results
Model Specification Tests Statistics
In this study the random effects model was used in
constructing the panel regression models. The
decision for using random effects models in this
study was based on the Hausman specification test
(Wooldridge, 2002; Greene, 2002). According to
Gujrat (2004) Hausman specification test should be
used to determine between random and fixed effects.
Accordingly, the null hypothesis is rejected when
Prob.>χ2 is less than the critical p-value and in such
a case the fixed effects regression is appropriate. All
the models were run on random effects since the
significance levels were greater than the critical
value of 0.05.
Table 4.7: Model Specification Test Statistics for Z score
Model
χ2 Statistic
χ2 d.f.
Model 1
4.578
14
Source: Research data (2018)
Hypothesis Testing
To test the various hypotheses the various predictor
variables were regressed against the response
variable. Random effects regression models were
run for all the models and the results presented. The
F-statistics was used to test the regression models
(Blackwell III, 2005) or simply the goodness of fit
(Hoe 2008).The test-test was used to test
significance of then regression parameters at five
percent significance level using the following
criteria; H0;Bj=0 and Ha:Bj≠0, ith H0 being rejected
if Bj≠0;p-value ≤0.05).
The Null Hypothesis H01 stated that market ratios do
not have significant effect financial distress of banks
Prob.
0.764
Appropriate Model
Random Effects
listed in the NSE. The findings showed that liquidity
analysis ratio has a positive and significant effect on
financial distress, β1 = 0.593, p < 0.05. This means
that hypothesis 3 was rejected. This imply that with
each unit increase in liquidity analysis ratio,
financial distress would increase by 0.593 units.
These findings correspond with literature that
liquidity ratios determine the organization’s ability
to pay debt in short term. Thus, an increase in market
ratios enhance firms’ financial position. Any failure
to meet these can damage its reputation and
creditworthiness and in extreme cases even lead to
bankruptcy.
19 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
Table 4.8:
Regression Results
Group variable: firm
Number of obs
= 353
Number of groups = 35
R-sq: within = 0.477
between = 0.435
overall = 0.475
Obs per group: min = 8
avg = 10.1
max = 11
Wald chi2(6)
Prob> chi2
corr(u_i, X) = 0 (assumed)
= 313.6
= 0.000
Financial distress
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
_cons
0.885
0.684
1.29
0.197
-0.462
2.232
Market ratio
-1.115
0.374
-2.98
0.003
-1.852
-0.378
5.0 Summary, Conclusions and
Recommendations
meaningful when taken alongside market ratios of
other companies within the same sector.
Effectiveness of Market Ratios in Predicting
Financial Distress of Firms Listed on the NSE
The mean market ratio was 0.811 with a minimum
of 0.054 and a maximum of 1.619. The findings
revealed that market ratio has a negative and
significant effect on financial distress controlling for
firm age and firm size. The market ratio plays a
pivotal role in investment practices and it has been
found to reflect the market’s expectation of future
growth and is associated with reduced financial
distress. The market ratio is used to estimate the cost
of equity capital and is also heavily used by financial
analysts to justify their stock recommendations.
Valuations and growth rates of companies may often
vary wildly between sectors due both to the differing
ways companies earn money and to the differing
timelines during which companies earn that money.
As such, one should only use market ratios as a
comparative tool when considering companies
within the same sector, as this kind of comparison is
the only kind that will yield productive insight.
Thus, the negative effect found here is against a
background of comparing firms in different sectors.
Recommendations
Financial ratios can provide small business owners
and managers with a valuable tool with which to
measure their progress against predetermined
internal goals, a certain competitor, or the overall
industry. In addition, tracking various ratios over
time is a powerful means of identifying trends in
their early stages. Ratios are also used by bankers,
investors, and business analysts to assess a
company's financial status. Ratios are aids to
judgment and cannot take the place of experience.
But experience with reading ratios and tracking
them over time will make any manager a better
manager. Ratios can help to pinpoint areas that need
attention before the looming problem within the area
is easily visible. It is important to keep in mind that
financial ratios are time sensitive; they can only
present a picture of the business at the time that the
underlying figures were prepared. Determining
which ratios to compute depends on the type of
business, the age of the business, the point in the
business cycle, and any specific information sought.
However, in this study, the firm age and firm size
were controlled.
Conclusion
The study successfully extended knowledge by
studying and testing whether financial ratios predict
financial distress. Based on the findings of this
study, the following conclusions can be drawn;
Overall, the study is suggesting that the financial
ratios plays a critical role in the prediction of
financial distress.
Market ratio has been showed to have a negative and
significant effect on financial distress of a firm. An
individual company’s market ratio is much more
Suggestions for Further Research
The study determined effectiveness of four
accounting ratios, liquidity analysis ratios, activity
analysis ratios, capital structure analysis ratios and
cash ratio on financial distress. The study only
covered companies listed in the NSE, particularly
those which have been consistently trading for the
last six years. However, the firms listed on the NSE
were from various sectors of the economy. This
could lead to different and in some cases biased
ratios. It is thus, recommended to carry out a similar
20 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
study on firms from the same industry such as
agriculture etc.
REFERENCES
Allen, F., and D. Gale (2003). “Financial Fragility, Liquidity and Asset Prices.” Working Paper 01-37-B,
Wharton Financial Institutions Center
ALLEN, G. and GALE, D. (2004), “Financial Intermediaries and Markets”, Econometrica, 72, 1023–1061.
Altman, E. (1983): Corporate Financial Distress and Bankruptcy, 1st Edition, New York: John Wiley & Sons.
Altman, E. I., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy,
analyze and invest in distressed debt. Hoboken: Wiley Finance.
Altman, E., & Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy: Predict and Avoid
Bankruptcy, Analyze and Invest in Distressed Debt. 3rd Edition, New Jersey: John Wiley & Sons.
Altman, E.I. (1993).Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting and avoiding
Distress and Profiting from Bankruptcy‘, John Wiley and Sons Limited, New York.
Altman, Edward I. (1968). Financial ratios, discriminant analysis and the predication of corporate bankruptcy.
The Journal of Finance.
Anggraini, D. (2014) Financial Distress Model Prediction for Indonesian Companies. International Journal of
Management and Administrative Sciences (IJMAS.
Aziz, M. & Dar, H. (2006). Predicting Corporate Financial Distress: Whither Do We Stand? Corporate
governance, 6(1), 18-33. Doi: 10.1108/147207006106494 36
Aziz, M. & Dar, H. (2006). Predicting Corporate Financial Distress: Whither Do We Stand? Corporate
governance, 6(1).
Casu, B. and C. Girardone, (2006), “Bank Competition, Concentration and Efficiency in the Single European
Market”, The Manchester School, 74(4), pp. 441-468.
Casu, B., Girardone, C. and P. Molyneyx, (2006), Introduction to Banking, FT Prentice Hall, Pearson Education,
1st Edition, Harlow, England.
Central Bank of Kenya (2009). Bank Supervision Annual Report 2009. Central Bank of Kenya, Nairobi.
Chang, J.F., Lee, M.C., & Chen J.F. (2008). Establishment forecasting financial distress model using Fuzzy ANN
mehod, proceeding of the 10th Joint Conference.
Chang-e, S. (2006). The Causes and Salvation Ways of Financial Distress Companies‘: An Empirical Research
on the Listed Companies in China.
Coats, P., and Fant L., (1992). A neural network approach to forecasting financial distress. The Journal of Business
Forecasting Methods & Systems 10(4).
Cooper, D. (2010). Company Insolvencies Rise Dramatically in 2009 With Little Hope of Improvement For 2010.
Cooper Matthews 2010 Debt Articles. Available at http://coopermatthews.com/articles.html
Daily, C.M., Dalton, D.R. and Canella, A.A. (2003). Corporate Governance: Decades of Dialogue and Dat.
Academy of Management Review, 28, (3), 371-382
Damodaran, A. (2002): Investment Valuation. 2nd Edition, New York: Wiley Finance.
Duda, Matej, & Schmidt, Henning, (2009) 'Evaluation of Various Approached to Value at Risk', Masters Program
in Finance, Lund University.
Emmanuel, N.R, (1997), Commercial Banking in an Era of Deregulation, Greenwood Publishing Group, 3rd Ed.
European Distressed Debt: Market Outlook (2007). Survey, Cadwalader, Wickersham & Taft LLP and Rotschild.
Fama, E., French, K. (1992): The Cross-Section of Expected Stock Returns. In: The Journal of Finance, 47(2),
427-465.
Fama, E., French, K. (1993): Common Risk Factors in the Returns on Stocks and Bonds. In: Journal of Financial
Economics, 33, 3-56.
Fama, E., French, K. (1995): Size and Book-to-Market Factors in Earnings and Returns. In: The Journal of
Finance, 50(1), 131-155.
Fama, E., French, K. (1996): Multifactor Explanations of Asset Pricing Anomalies. In: The Journal of Finance,
51(1), 55-84.
Fama, E., French, K. (2004): New Lists: Fundamentals and Survival Rates. In: Journal of Financial Economics,
73(2), 229-269.
Fama, E., French, K. (2007): The Anatomy of Value and Growth Stock Returns. Working Paper, Chicago
University.
Fama, E., MacBeth, J. (1973): Risk, Return, and Equilibrium: Empirical Tests. In: The Journal of Political
Economy, 81, 607-636.
Fama, Eugene F., (1998), Market efficiency, long-term returns, and behavioral finance, Journal of Financial
Economics 49, 283-306.
21 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
Ferguson, J. (2004): Distressed Debt Europe: The Next Frontier for US Distressed? In: High Yield Report, July
5, 8-9.
Field, A. (2005). Discovering statistics using SPSS, London, Sage.
Fuller, C. M., Biros, D. P., &Delen, D. (2011).An investigation of data and text mining methods for real world
deception detection. Expert Systems with Applications,
Gilbert, L., Menon, K., Schwartz, K. (1990): Predicting Bankruptcy for Firms in Financial Distress. In: Journal
of Business Finance & Accounting, 17, 161-171.
Griffin, J., Lemmon, M. (2002): Book-to-Market Equity, Distress Risk, and Stock Returns. In: The Journal of
Finance, 57, 2317-2336.
Gruszczynski, M. (2004), Financial distress of companies in Poland‘, Warsaw School of Economics, and
Abridged version published in International Advances in Economic Research, 10(4).
Gujrati N. D. (2004). Basic Econometrics, Fourth Edition New York: McGraw-Hill.
Hayes, R., Hillegeist, S. (2006): Financial Distress Risk and CEO Compensation Contracts. Working Paper, Davit
Eccles School of Business, University of Utah.
Hill, N. T., Perry, S. E., & Andes, S. (1996). ―Evaluating firms in financial distress: An event history analysis‖.
Journal of Applied Business Research, 12(3).
Ho, C.-T., & Wu, Y.-S. (2006). Benchmarking performance indicators for banks, benchmarking.An International
Journal.
Holsapple, C. W., & Wu, J. (2011). An elusive antecedent of superior firm performance: The knowledge
management factor. Decision Support Systems, 52(1), 271–283.
Horrigan, James O. (1965). Some empirical bases of financial ratio analysis. The Accounting Review, 40(3), 558–
568.
Hotchkiss, E., and T. Ronen, (2002), ‘‘The Informational Efficiency of the Corporate Bond Market: An Intraday
Analysis,’’ Review of Financial Studies, 15(5), 1325–1354.
Hotchkiss, E., G. Jostova, and A. Warga, (2005), ‘‘Determinants of Corporate Bond Trading: A Comprehensive
Analysis,’’ Working Paper, Boston College.
Hotchkiss, Edith, (1995), Post-bankruptcy performance and management turnover, Journal of Finance 50, 3-22.
Hui, H., & Jhao, J.J. (2008). Relationship between corporate governance and financial distress: An empirical
analysis of the distressed companies in China, International Journal of Management, 25.
James, C. (1996): Bank Debt Restructurings and the Composition of Exchange Offers in Financial Distress. In:
The Journal of Finance, 41(2), 711-727.
John, K. Lang, L., Netter, J. (1992): The Voluntary Restructuring of Large Firms in Response to Performance
Decline. In: The Journal of Finance, 47(3), 891-917.
Kalckreuth u.v (2005) A wreckers theory of financial distress on Deutsche Bundesbank . Bundesbank Series 1
Discussion Paper No. 2005,40 ISBN 3–86558–099–8
Kalckreuth, U. (2006): Wrecking Behavior and the Overpricing of Distressed Companies. Working Paper,
Economic Research Center, Deutsche Bank.
Kaplan, S., Stein, J. (1993): The Evolution of Buyout Pricing and Financial Structure in the 1980s. In: The
Quarterly Journal of Economics, May, 313-357.
Kariuki, E. K. (2013). The Effect of Financial Distress on Financial Performance of
Commercial Banks In
Kenya. Economics Management Innovation Journal EMI, Vol.
6, Issue 3, ISSN: 1804-1299.
Kipruto, E. K. (2013). The validity of Altman’s failure prediction model in predicting corporate financial distress
in Uchumi Supermarket in Kenya. Unpublished MBA Project, University of Nairobi.
Koch, T.W. (1995). Bank Management.3rd edn. The Dryden Press. London
Koch, T.W., MacDonald, S.S., (2003). Bank Management. South-Western, Mason, OH.
Lee, K. C., Han, I., & Kwon, Y. (1996). Hybrid neural network models for bankruptcy predictions. Decision
Support Systems, 18(1), 63–72
Lind, D. A., Marchal, W. G., and Wathen, S. A. (2015). Statistical Techniques in
Business
and
Economics, 16th Ed., New York: McGraw-Hill Foundation.
Loh, W. Y., & Shih. Y. S. (1997). Split selection methods for classification trees.
Loo, M. A, (2007).‟A survey of liquidity management approaches and their effect on profitability of commercial
banks
in
Kenya.‟
Retrieved
from
http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/23005
Maksimovic, V., Phillips, G. (1998): Asset Efficiency and Reallocation Decisions of Bankrupt Firms. In: The
Journal of Finance, 53(5), 1495-1532.
McDonald, S. & Koch, T. (2006). Management of Banking (6th ed.). Thomson Publishers.
Mohamed, S. (2013). Bankruptcy prediction of firms listed at the Nairobi Securities Exchange. Unpublished MBA
Project, University of Nairobi.
22 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
Monnie, B. (1998). Operational Guidelines for the Development and Early Stages of Credit Union Operations,
World Council of Credit Unions, Inc.
Moulton, H. G. (1918). Commercial Banking and Capital Formation, Journal of Political Economy, Vol. 26.
Mugenda, O. &Mugenda, A., (2003).Research Methods: Quantitative and Qualitative approaches. Nairobi, Acts
Press.
Muhammed, Y.N. (2007). Effects of Liquidity Management approaches and their effects on profitability in Banks:
An Empirical study of Commercial Banks in Kenya.
Natalia, O. (2007). Corporate Financial Distress: An Empirical Analysis of Distress Risk (Doctoral Dissertation
No. 3430, University of St. Gallen, St. Gallen, Switzerland).
Ndung’u, N. and Ngugi, R.W., (2000). Banking Sector Interest Rate Spreads in Kenya, Kenya Institute for Public
Policy Research and Analysis (KIPPRA), Discussion Paper, No. 5.
Ntoiti, J. (2013). Determinants of Financial Distress Facing Local Authorities in Service Delivery in Kenya.
JKUAT Thesis.
Nyabwanga, R. N. (2013). An Empirical Analysis of the Liquidity, Solvency and Financial Health of Small and
Medium Sized Enterprises in Kisii Municipality, Kenya. European Journal of Business and
Management.ISSN 2222-2839. Vol.5, No.8
Opler, T., Titman, S. (1994): Financial Distress and Corporate Performance. In: The Journal of Finance, 49(3),
1015-1040.
Oracle financial services. (2011). Asset Liability Management: An Overview, An Oracle White Paper.
Pandey, I M,. (2010), Financial Management, New Delhi: Vikas Publishing House. Princeton, NJ: Princeton
University Press.
Pandey, I. M. (2010). Financial Management (10th Ed). New Delphi, India, Vikas Publishing Popiel, P.A. (1994).
Financial Systems in Sub Saharan Africa: A Comparative Study, World Bank, Discussion Paper,
No.260, Africa Technical Department Series, Washington, DC, World Bank
Pindado, J., Rodrigues, L. (2005): Determinants of Financial Distress Costs. In: Financial Markets and Portfolio
Management, 19(4), 343-359.
Platt, H. & M. B. Platt (2002). Predicting Corporate Financial Distress: Reflections on Choice Based Sample Bias.
Journal of Economics and Finance, Vol. 26, No. 2, 184-199.
Platt, H. & M. B. Platt (2006). Understanding Differences between Financial Distress and Bankruptcy. Review
of Applied Economics, Vol. 2, No. 2, 211-227.
Platt, H. D. & Platt, M. B. (1994). Bankruptcy Prediction with Real Variables. Journal of Business Finance &
Accounting. 21(4), 491-510.
Ross, Stehen A., Westerfield, Randolph W., & Jordan, Bradford D. (2003). Fundamentals of corporate finance
(6th ed.). New York: The McGraw-Hill Companies.
Schmidt, Henning and Duda, Matej (2010). Bankruptcy Prediction, discrete-time hazard model, static
logit, multi-period logit, macro dependent baseline function. Journal of Forecasting, Volume 27, Issue
6.
SPSS. (2007). Clementine12 User Manual, Chicago, IL. StatisticaSinica, 7, 815–840.
Tabari, N., Ahmadi, M & Emami, M. (2013). The Effect of Liquidity Risk on the Performance of Commercial
Banks. International Research Journal of Applied and Basic Sciences. Vol, 4 (6): 1624-1631,
Science Explorer Publications.
Taffler, R. J. (1983). The Assessment of Company Solvency and Performance Using a Statistical Model.
Accounting and Business Research, 15(52), 295-308.
Tam, K. Y., & Kiang, M. Y. (1992). Managerial Applications of Neural Networks: The Case of Bank Failure
Predictions. Management Science, 38(7), 926-947. http://dx.doi.org/10.1287/mnsc.38.7.926
Tan, T. K. (2012). Financial Distress and Firm Performance: Evidence from the Asian Financial Crisis. Journal
of Finance & Accountancy.
Von Kalckreuth, Ulf (2008), Panel Estimation of State Dependent Adjustment When the Target is Unobserved.
Deutsche Bundesbank Discussion Paper Series 1: Economic Studies, No 09.
Von Kalckreuth, Ulf, (2004) Financial Constraints for Investors and the Speed of Adaptation: Are Innovators
Special? Deutsche Bundesbank Discussion Paper Series 1: Economic Studies, No 20.
Von Kalckreuth, Ulf, (2005). "A "wreckers theory" of financial distress," Discussion Paper Series 1: Economic
Studies 2005, 40, Deutsche Bundesbank.
Von Kalckreuth, Ulf, (2006), Financial Constraints and Capacity Adjustment: Evidence from a Large Panel of
Survey Data. Economica, Vol. 73, 691-724.
Ward, T., Foster, B. (1997): A Note on Selecting a Response Measure for Financial Distress. In: Journal of
Business Finance and Accounting, 24(6), 869-879.
Whitaker, R .(1999). ‗The Early Stages of Financial Distress‟: Journal of Economics and Finance. 23(2).
23 | P a g e
Keter et al., (2018)
www. oircjournal.org
International Journal of Finance, Accounting and Economics (IJFAE)
ISSN: 2617-135X Vol. 1 (2) 12-24, June 2018 www.oircjournals.org
Whitaker, R. (1999). The Early Stages of Financial Distress. Journal of Economics and Finance. 23(2), 123-133.
Wruck, K. (1990). ‗Financial Distress, Reorganization, and Organizational Efficiency’: Journal of Financial
Economics, 27.
Zulkarnain, M. (2009). ‗Prediction of Corporate Financial Distress‘: Evidence from Malaysian Listed Firms
during the Asian Financial Crisis
24 | P a g e
Keter et al., (2018)
www. oircjournal.org