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Universal Journal of Accounting and Finance 1(3): 95-103, 2013 http://www.hrpub.org
DOI: 10.13189/ujaf.2013.010302

Cash Flow Statement as an Evidence for

Financial Distress
Naz Sayari1,*, F.N. Can Simga Mugan2

1
Department of Business Administration, Middle East Technical University, Ankara, 06800, Turkey
2
Department of Business Administration, Izmir University of Economics, İzmir, 35330, Turkey
*Corresponding Author: nazsayari@yahoo.com

Copyright © 2014 Horizon Research Publishing All rights reserved.

Abstract In this study, the effect of cash flow risk of related firms. To our knowledge, there are few studies
components on financial distress score is examined for 124 that analyze the relation between financial distress and CFO
companies selected from Istanbul Stock Exchange (ISE). It component of cash flows, but no study includes CFI and CFF
is further analyzed whether cash flow components have an in bankruptcy prediction studies. In this respect, we
explanatory effect on bankruptcy risk and financial health of contribute to existing literature by evaluating information
companies. Four separate models are constituted and Linear content of each cash flow component in explaining financial
Regression Model is used to evaluate company age, weakness or healthiness of companies. The study further
company size, cash flow from operations (CFO), cash flow illustrates whether company age and company size has any
from investing (CFI) and cash flow from financing (CFF) significant impact on determination of financial distress
activities as a function of financial distress score of score among companies.
companies. The results show that there is a negative The paper also contributes to the prior literature by
relationship between CFO, company size and financial providing the financial distress score of firms selected from
distress score of companies. On the contrary, CFF is manufacturing and service sectors that are traded in the stock
observed to be positively related with the financial distress market of Turkey. In this respect, the study extends the
score. Meanwhile, it is further observed that the standardized subject of value relevance of cash flow statements as an
coefficient of CFI is statistically insignificant and hence it evidence for financial distress, by illustrating detailed
does not provide any evidence for the financial weakness or financial information about firms in a developing country.
bankruptcy risk of companies.
Keywords Cash Flow Statement, Cash Flow from
Operations, Cash Flow from Investing, Cash Flow from 2. Literature Review
Financing, Financial Distress Score, Bankruptcy Risk, In the prior literature cash flow analysis are examined
Financial Health mainly for two reasons. First reason is to explore whether
cash flow components carry information about financial
health of a company and to use that information to derive
firms’ life cycle stages. Second reason is to analyze the value
1. Introduction relevance of operating, investing and financing cash flows
versus the value relevance of earnings and accruals.
The aim of the article is to examine whether components To start with, Gentry et al.[1] evaluates the contributions
of cash flow statements; cash flows from operations (CFO), of cash flow components to identify financial health of a
cash flows from investing activities (CFI) and cash flows company. The researchers state that, if a company’s cash
from financing activities (CFF), contain relevant information flows from operations (CFO) increase, the financial and
in explaining deterioration or financial health of companies. credit health of the firm would also increase as the firm
We also investigate whether cash flow patterns have any would less likely to need borrowing and cash interest
significant effect on financial distress score of companies. expense. Contrarily, if a company’s CFO declines, it would
Financial distress can be defined as a late stage of corporate be more likely to use interest bearing debt to finance its plans
decline or as an early warning model for financial health, and investments. They employ Helfert approach to analyze
which is used in predicting bankruptcy or liquidation cash flow components and observe that, firms with high CFO
problem of firms. Notwithstanding, the objective of this and cash outflow from investing activities would also likely
paper is to explain whether cash flow pattern of companies to have low credit risk. The results also show that, CFO has
have any explanatory power in predicting the bankruptcy more information content than investing and financing cash
96 Cash Flow Statement as an Evidence for Financial Distress

flows in explaining financial success or failure of a firm. operating environments. Investing cash flows are also
Dickinson[2] examines the cash flow patterns as a proxy negative because of managerial optimism that investment
for firm life cycle that is derived from accounting opportunities are growing. In this stage financing cash flows
information. The researcher indicates that cash flow patterns are expected to be positive since they borrow from creditors
supply a rigid and robust indicator of firm life cycle stage or issue stock. In the growth stage, operating cash flows
and allows researchers to evaluate a firm’s current would be positive since firm’s main purpose is to maximize
performance as well as predicts its future performance their profit margins. Investing cash flows as well as
according to firm’s current life cycle stage. In this respect, financing cash flows are also expected to be positive since
Dickinson divides life cycle of firms into 5 phases namely firms continue to invest and finance their investment in order
introduction, growth, maturity, shake out and decline. The to grow more. In mature stage, operating cash flows are still
classification of life cycle stages are constituted by using positive although profitability decreases. Meanwhile,
firm’s operating, investing and financing cash flows in financing and investing cash flows are negative since the
which firm life cycle is completely separated from firm’s age. firm invests to maintain capital rather than to grow and to
The researcher uses life cycle proxy in order to assess the service its debt rather than to acquire new financing. In the
economic, market and accounting behavior of firms within shakeout stage, cash flow expectations are ambiguous and
each life cycle stage and develops a method for identifying hence cash flows from operating, financing and investing
firm life cycle using the combination of cash flow patterns. can be either positive or negative. Finally, in the decline
First, Dickinson makes an ex ante assumption considering a stage, operating cash flows are expected to be negative and
uniform distribution of life cycle stages across firms and uses investing cash flows are expected to be positive since firms
the sign of the net operating, investing and financing cash are aimed to liquidate their assets and finance their
flows and determines eight possible cash flow patterns operations as well as service their debt. However, firms in
combinations. The study uses NYSE, AMEX and NASDAQ this stage may also seek for additional funds to downturn
firms in determining the sample for the 1989-2005 period. their position so that the sign of financing cash flows is
Dickinson demonstrates several variables such as indeterminable.
profitability, stock returns, financial leverage, risk, tax rates, In 1978 FASB declared that financial reporting should
dividend payments, age and size which have a non linear focus on earnings rather than cash flows. However, because
relationship with firm life cycle. The researcher conducts of increase in business failures and high interest rates, a
probit analysis to explain how these variables are related to debate started regarding the usefulness of accrual accounting
life cycle and uses life cycle proxy to assess profitability in numbers versus cash flow data. In this respect, Bowen et
the financial statement analysis. Dickenson conducts probit al.[4] examine whether cash flow data has information
model to evaluate life cycle stage with respect to thirteen content in explaining stock prices and whether accruals has
variables covering return ratios, earnings per share, sales and incremental value relevance above cash flow numbers. They
dividend payments. The researcher further determines a base define earnings and working capital from operations (WCFO)
model, where changes in return on net operating assets are as accrual based measures and cash flow from operations as a
regressed as a function of current profitability and lagged cash flow measure. The study uses market model to examine
profitability. In the analysis, Chow test is conducted on the expected return in the event period that covers the release
full sample versus life cycle stage subsamples in order to date of financial statements to the public. They also use
measure whether the coefficients in the separate subsamples regression analysis to examine the incremental information
are equal across life cycle stage. As a conclusion it is found content of independent variables where unexpected return is
that, the structural shift among life cycle stages is significant. regressed on unexpected WCFO, unexpected earnings and
It is further observed that, incorporating information about unexpected CFO. The regression results show a significant
firm life cycle improves the explanatory power of future relation between earnings and unexpected returns, while no
profitability. Specifically, current and past profitability, evidence is observed for the incremental explanatory power
growth in net operating assets and the changes in asset of WCFO above information content of earnings. On the
turnover have significant effect in explaining future contrary, the outcomes reveal that unexpected CFO contains
profitability. Consequently the results show that, cash flow incremental value relevant information above unexpected
patterns are robust indicators of firm life cycle stages. earnings.
Gort and Klepper[3] defines the life cycle stages as To examine the information content of accruals and cash
introductory stage-where an innovation is first produced, flows Bernard and Stober[5] decompose earnings as CFO,
growth stage-where the number of producers increases current accruals, WCFO and noncurrent accruals, where
dramatically, maturity stage- where the number of producers current accruals contain increases in inventories and
reaches a maximum, shake out stage-where the number of receivables, decreases in payables, while noncurrent accruals
producers begins to decline and decline stage in which there contain depreciation and deferred income taxes. They
is nearly zero net entry. Inspired by Gort and Klepper’s replicate Wilson’s[6] study and employ regression analysis,
definition of life cycles, Dickinson demonstrates that in the where abnormal return is regressed on unexpected CFO,
introduction stage, net operating cash flows are negative unexpected current accruals and unexpected noncurrent
since firms are initially learning their cost structures and accruals. They further conduct an event study to estimate
 Universal Journal of Accounting and Finance 1(3): 95-103, 2013 97

expected CFO and WCFO after the earnings announcement In addition operating and financing cash flows have the
date. Contrary to Wilson’s findings stating a larger price expected sign, negative and positive respectively. In the
reaction to cash flows than accruals for a given amount of growth stage, although cash flow measures are value
earnings, they observed no significant reaction either for relevant, earnings are not. In the mature stage, earnings,
cash flows or for accruals data. Regression results also do not operating and financing cash flows are value relevant and
provide any significant evidence regarding value relevance earnings and operating cash flow have the expected sign;
of cash flow or accruals data in explaining stock returns. positive for both indicators. Finally, in the decline stage all
They conclude that, Wilson’s findings cannot be generalized cash flow measures have the expected signs, while the same
since the reaction of prices to the release of cash flow and result cannot be achieved for earnings at this stage.
accruals data is highly contextual to be modeled or there is Regarding Black’s findings that information of earnings
information leakage regarding financial statements before do not possess useful information while cash flow
the announcement date so that the information contained in information is still value relevant in distressed firms, Joseph
these statements generates no surprise in the public. and Lipka[9] reexamine whether informativeness of earnings
Black[7] investigates the value relevance of earnings, really declined in case of financial distress. They employed
operating, financing and investing cash flows in four the following model to investigate alleged deterioration in
different life cycle stages namely start up, growth, maturity information content of earnings and value relevance of CFO:
and decline stage. He demonstrates the differences between Firm value = ƒ(expected performance, adaptation value of
life cycle stages by using the firm value definition of assets)
Myers[8], who divides firm value into two components: The results show that information content of earnings
assets in place and growth opportunities. According to decline because of operating difficulties. However value
Myers, in early life cycle stages, growth opportunities are a relevance of cash flow also declines over the same time
larger component of firm value, whereas in later stages assets period, indicating neither earnings nor cash flows provide
in place become the largest component. As a result Black useful information in explaining stock prices in the
observes that, at least one of the components in cash flow financially distressed periods.
statements (operating, financing and investing) is useful in Livnat and Zarowin[10] examine whether components of
explaining stock returns in each firm life cycle stage. The operating, financing and investing cash flows are
researcher further demonstrates that, the value relevance of a differentially associated with annual security returns and find
particular cash flow component depends on the life cycle similar results with Black[7]. Financing cash flow is
stage of the firm. incrementally value relevant in the growth and maturity
Black states that, in the start up and decline life cycle stages. They further found that disaggregating net income
stages operating cash flow is expected to be negative, since into cash flow from operations and accruals does not
the companies would likely to face financial difficulties contribute to the security returns when it is compared to the
during these stages in funding their obligations. On the contribution of the net income alone. Moreover, they do not
contrary, cash flow from operations is expected to be find any evidence for differential association between the
positive in the growth and mature stages, since the components of cash flows from investing and their
companies would likely to generate cash in these periods. contribution to annual security returns. Dechow[11] also
Meanwhile cash flow from financing is expected to be examines the circumstances under which accruals are
positive in the early life cycle stages and turn out to be predicted to enhance earnings ability to assess firm
negative in the later stages. In addition, cash flow from performance and concludes that, cash flows suffer more
investing is expected to be negative in the early stages while severely from timing and matching problems that reduce
positive in the later stages. Black also includes earnings as an their ability to reflect firm performance.
additional indicator other than cash flow components and Overall, there are mixed evidence regarding the
reveals that, in the start up and decline life cycle stages incremental value relevance of cash flows over accounting
earnings are expected to be negative, while in the growth and earnings in explaining stock price reaction. However, many
mature phases earnings would be positive. The researcher studies reveal that cash flow components contain relevant
classifies life cycle stages considering the sales growth, information in identifying financial health or deterioration of
capital expenditure, dividend payout and age of firms. For companies. In the light of prior literature findings, our aim is
instance, a firm is classified in the growth stage if it is in the to show whether cash flow components have information
highest quintile of the combined score for sales growth and content in determining financial distress score of firms and
capital expenditure and in the lowest quintile of the whether cash flow patterns have any significant effect in
combined score of dividend payout and firm age. On the identifying financial weakness or healthiness of companies.
contrary, a firm is classified in the mature stage if it is in the
middle quintile of the combined score of sales growth and
capital expenditure while in the highest quintile on dividend 3. Methodology and Design
payout and in the middle quintile on firm age. As a
conclusion, Black finds that, start up stage shows limited In the first phase of this study, cash flow statements of 151
usefulness of earnings and cash flow accounting information. firms from manufacturing and service sectors are selected
98 Cash Flow Statement as an Evidence for Financial Distress

from Istanbul Stock Exchange (ISE) and the sign of each enough cash from operations to finance capital investment
cash flow component are investigated. The data are collected and repay debt or purchase own shares. Fifth pattern refers to
for the period 2005 – 2009. In the second phase, 27 firms are a company that could not generate enough cash to finance
eliminated from the selected sample since it is observed from operations or capital expansion. As a result new debt or stock
the yearend cash flow statements that some of the cash flow is issued to cover current and long term obligations. Sixth
components of these firms were static. It is further observed one can be regarded as an unusual pattern which is possible
that, these firms possess extreme values of financial distress only with maintaining cash reserves. Similarly seventh
scores so that they are determined as outliers and dropped pattern is also an unusual one, which can be seen in
from the model. In the third phase, linear regression is used companies that could not generate sufficient cash from
to model the effect of company size, company age and cash operations and at the same time finances its operations by
flow pattern on financial distress score for each sector. For selling assets while obtaining loans or issuing stock. Finally,
the first model, the estimated regression model is as follows: eighth pattern demonstrates a company where long term
Y = ß0 + ß1X1 + ß2X2 + ß3X3 + є (1) assets are sold to finance operations and pay off debt.
Considering the first model, it is predicted for the
where;
companies that as the company age and company size grow,
Y = Financial Distress Score
financial distress score of companies would likely to
X1 = Company Age
decrease since financial strength of mature companies are
X2 = Company Size
expected to be higher and bankruptcy risk would likely to be
X3 = Cash Flow Pattern (X3 = 0,1,2…..8)
lower than growing or early stage companies. Additionally,
As mentioned before financial distress is determined as a
financial distress score is expected to be positively related to
late stage of corporate decline or as an early warning model
cash flow pattern since higher cash flow patterns lead to
for financial health of firms and it is used in predicting
weaker financial health and companies that have higher cash
bankruptcy or liquidation problem of related companies.
flow patterns are exposed to bankruptcy risk more than
Financial distress score (FD) is calculated using Zmijewski’s
companies that have lower cash flow patterns. In this respect
model[12] which is assessed as follows:
the hypothesis for the first model is considered as follows:
�� �� ��
�� = −4,803 − 3,6 � � + 5,4 � � − 0,1( ) (1) H01: ß1=ß2=ß3=0
�� �� ��
where NI stands for net income, TA stands for total assets, The frequencies of cash flow patterns illustrated in Table 2
TL stands for total liabilities, CA stands for current assets reveals that 217 of the observations within the years
and CL stands for current liabilities. 2005-2009 correspond to pattern four referring positive CFO,
It should be noticed that negative financial distress score negative CFI and CFF. 154 of the observations correspond to
indicates a relatively stronger financial health, whereas pattern three showing negative CFI, positive CFO and CFF,
positive financial distress score refers to a relatively weaker and 125 of the observations correspond to pattern five
financial health. In other words, as the financial distress showing positive CFF, negative CFO and CFI. The
score increases, the probability of bankruptcy risk also frequency results indicate that, majority of the companies
increases. Company size is the logarithmic function of total show a strong cash flow pattern and finance their capital
assets that are obtained from the yearend financial statements investment through cash flow from operations and are able to
and company age is determined by subtracting year of repay their debt or purchase their own shares. Meanwhile,
incorporation from 2009. In the evaluation of cash flow considering rest of the companies which possess third and
patterns, 8 patterns are determined which are constituted fifth cash flow patterns, it can be stated that the companies
according to the sign of each cash flow component. The cash that are included in the third pattern can be regarded as cash
flow patterns are illustrated in Table 1: generating companies however they further need to generate
Table 1. Cash Flow Patterns
cash through debt or stock issuance.

Pattern 1 2 3 4 5 6 7 8 Table 2. Frequency of Cash Flow Patterns

CFO + + + + - - - - Frequency Percent

CFI + + - - - - + + Pattern 1(+,+,+) 6 1

CFF + - + - + - + - 2(+,+,-) 58 9,4

3(+,-,+) 154 24,8
For the first pattern, cash inflows are said to be
4(+,-,-) 217 33,4
unsustainable and such a pattern can only be maintained
5(-,-,+) 125 20,2
temporarily. Second pattern refers to a company that
generates cash, while it also sells assets to cover debt or 6(-,-,-) 21 3,4
purchase treasury stock. Third pattern demonstrates cash 7(-,+,+) 26 4,2
generating company but also requiring additional funding 8(-,+,-) 13 2,1
through debt or stock issuance. Fourth pattern, Total 620 100
corresponding to a strong cash flow pattern, generates
 Universal Journal of Accounting and Finance 1(3): 95-103, 2013 99

On the contrary, other companies which are included in amounts to total asset values:
the fifth pattern could not generate enough cash to finance Y = ß0 + ß1X1 + ß4X4 + ß5X5 + ß6X6 +є (4)
operations or capital expansion.
To assess whether cash flow patterns at year (t-1) have Where;
significant effect on financial distress score at year t, the lag X4 = Cash Flows from Operations/Total Assets
of cash flow pattern variable is included in the regression X5 = Cash Flows from Investing/Total Assets
model as follows: X6 = Cash Flows from Financing/Total Assets
The hypothesis relating to the fourth model is as follows:
Yt = ß0 + ß1X1t + ß2X2t +ß3X3(t-1) (2)
H04: ß1=ß4=ß5=ß6=0
where X3(t-1) refers to lagged cash flow pattern at year t-1 and
t= (2006, 2007, 2008, 2009) To check the robustness of the data for the first equation,
It is expected that an increase in the cash flow pattern for Pearson Correlation Matrices as well as Coefficient
the previous year would likely to increase financial distress Correlations are derived which indicate that there is no
score of companies. Related hypothesis for the model is as significant correlation between financial distress score as a
follows: dependent variable and company size, company age and cash
flow patterns as independent variables. For the third equation
H02: ß1=ß2=ß3=0
covering CFO, CFI and CFF as separate independent
In order to observe the individual effects of each cash flow variables there is no significant correlation between
component on the financial distress score of companies, third dependent and independent variables either. Furthermore,
regression model is formed. In this model, rather than cash collinearity statistics are also conducted for each of the three
flow patterns, dummy variables are used for cash flows from models. As a consequence, the VIF as well as Tolerance
operations, cash flows from financing and cash flows from measures are found at conceivable levels. On the contrary, in
investing separately. Company size and company age are the fourth model, it is observed in the Pearson Correlation
also included in the model as follows: Test that the correlation between CFO and CFF is -0,614.
Y = ß0 + ß1X1 + ß2X2 + ß4X4 + ß5X5 + ß6X6 (3) However according to the Collinearity Diagnostic Test it can
where; be concluded that, the correlation between CFO and CFF is
Y = Financial Distress Score conceivable since condition index values are at immaterial
X1 = Company Age levels.
X2 = Company Size
X4 = Cash Flows from Operations; if CFO>0; X4 = 1;
otherwise -0- 4. Results
X5 = Cash Flows from Investing; if CFI>0; X5 = 1; otherwise
Descriptive statistics of Model 1 is illustrated in Table 2.
-0-
The regression results of the first model including company
X6 = Cash Flows from Financing; if CFF>0; X6 = 1;
size, company age and cash flow patterns as independent
otherwise -0-
variables indicate that, cash flow pattern and company size
In this model, it is predicted that increase in CFO would
have a significant effect on financial distress score of
have a decreasing effect in financial distress score. It is
companies. The R2 of the model is 0,074 which indicate that
because, CFO is the cash that companies generate through
7,4% of the variation in financial distress score can be
operations and it is defined as revenues less operating
explained by the linear relationship between the company
expenses. Similarly, it is expected that increase in CFI would
size, cash flow pattern, company age and financial distress
have a decreasing effect in financial distress score since CFI
score (See Table 6 in the Appendix).
increases as cash position resulting from any gains from
investments in the financial markets increases, thereby Table 3. Descriptive Statistics of Model 1
improving the financial performance of the company.
Mean Std.Deviation N
Meanwhile, considering the third component of cash flow
statement, CFF is predicted to be positively related to YFD -2,2860 2,8611 755
financial distress score. CFF increases as the companies rely X1age 33,5457 12,5648 755
more on issuing or selling stock, which would result in a X2size 19,0411 1,6163 755
degrading effect in the financial health of firms unless they
X3pattern 4,1536 1,4892 755
are categorized as growing companies. Related hypothesis
for the model is as follows:
The frequency of financial distress score ranges between
H03: ß1=ß2=ß4=ß5=ß6=0 -6,57 and 4,41 which shows that, the healthiest company in
Finally, to disregard the size effect from financial distress financial matters shall obtain the lowest financial distress
score and to normalize the cash flow components, we score of -6,67 and the weakest company would get the
generate Model 4 which excludes company size variable and highest financial distress score of 4,41. ANOVA test is
normalizes CFO, CFI and CFF variables by dividing the conducted at the 0,01 level of significance (See Table 7 in the
100 Cash Flow Statement as an Evidence for Financial Distress

Appendix). Consequently, H01 is rejected and concluded that financial health of companies. The outcomes also indicate
there is a statistically significant relation between financial that, when a company’s net cash flow resulting directly from
distress score and cash flow patterns as well as company size. its regular operations as well as total assets of that company
The linear regression analysis show that, an increase in the increase, bankruptcy risk decreases while improving
cash flow pattern from pattern 1 through pattern 8 would financial strength of the related organization. Considering
result in an increase in the financial distress score meaning the third model, the results further indicate that cash flows
that firms having higher patterns in the cash flow analysis from investment (CFI) and company age do not have any
would likely to have weaker financial health and exposed to statistically significant relation with financial distress score
higher bankruptcy risk. The standardized coefficient of of companies.
company size (X2) and cash flow pattern (X3) are -0,166 and Descriptive statistics of Model 4 is illustrated in Table 5.
0,142 respectively (See Table 8 in the Appendix). The results
Table 5. Descriptive Statistics of Model 4
indicate that, one unit increase in the company size would
likely to decrease financial distress by 0,166 units. Similarly Mean Std.Deviation N
one unit increase in the cash flow pattern would likely to YFD -2,4484 1,7561 620
increase financial distress score by 0,142 units. Meanwhile,
X1age 34,0484 13,2704 620
from the coefficient test it is observed that p-value of X1 is
above 0,01 significance level of significance which indicate X31o 0,0550 0,1354 620
that the relation between company age and financial distress X32i -0,0481 0,0990 620
score is not statistically significant. X33f 0,0043 0,1350 620
Second model, including lagged cash flow patterns, shows
that, previous years’ cash flow patterns do not have any R2 for the fourth model is calculated as 0,152, indicating
significant effect on current years’ financial distress scores 15,2% of the variation in financial distress score can be
for the sample period (See Table 9, Table 10, Table 11 and explained by the linear relationship between company age,
Table 12 in the Appendix). Extension of time period and CFO, CFI, CFF (cash flow components are normalized and
panel data analysis are needed to obtained significant results divided by total assets) and financial distress score (See
for the lagged model. Table 16 in the Appendix). H04 is rejected and consequently
Descriptive statistics of Model 3 is illustrated in Table 4. it is concluded that company age, CFO are statistically
Table 4. Descriptive Statistics of Model 3
significant and have a negative relation with financial
distress score of companies. In other words, one unit increase
Mean Std.Deviation N in the company age and CFO would result in 0,12 and 0,17
YFD -2,2860 2,8611 755 units of increase in the financial distress score respectively.
X1age 33,5457 12,5648 755
In addition, according to coefficient estimates, CFF is also
statistically significant and has a positive relation with
X2size 19,0411 1,6163 755
financial distress score such that, one unit increase in the
X31odummy 0,6795 0,4670 755 CFF would lead to a 0,23 units of increase in the financial
X32idummy 0,1815 0,3857 755 distress score of a company (See Table 17 and Table 18 in
X33fdummy 0,4742 0,4997 755
the Appendix). The results can be interpreted such that,
incase the companies are likely to increase external activities,
In the third regression model, R2 is calculated as 0,147 such as adding loans or issuing and selling more stock,
which indicates that 14,7% of the variation in financial companies’ bankruptcy risk increase.
distress score can be explained by the linear relationship
between the company size, company age, CFO, CFI, CFF
(cash flow components are calculated as dummy variables) 5. Conclusion
and financial distress score (See Table 13 in the Appendix).
H03 is rejected, showing that company size, cash flow from In this paper, it is investigated whether cash flow
operations and cash flow from finance are significantly components have any effect on predicting financial distress
related to financial distress score. CFO and company size score of companies. In this respect, the study includes 124
have negative coefficients which indicate that, one unit companies from manufacturing and service sectors that are
increase in the operational cash flow amount would likely to quoted in the Istanbul Stock Exchange for the period
decrease financial distress score nearly 0,11 units and 2005-2009. Four different models are derived and linear
similarly one unit increase in the company size would likely regression analyses are conducted for each equation in which
to decrease financial distress score about 0,16 units (See financial distress score is determined as a function of
Table 14 and Table 15 in the Appendix). On the contrary, company age, company size, CFO, CFI and CFF. For the
since the standardized coefficient of CFF is negative, one first model, it is observed that, cash flow patterns and
unit increase in CFF causes 0,24 units increase in the company size have significant effect on financial distress
financial distress score, meaning to a deterioration in the score of companies. Meanwhile, for the second model no
 Universal Journal of Accounting and Finance 1(3): 95-103, 2013 101

significant relation observed between the lagged cash flow that there are only 620 observations in each variable since the
components and the financial distress score of firms. In the time period of the data covers only 5 years. If time period
third model, when the dummy variables of cash flow could be extended, the R2 of the models would likely
components are included in the model, it is observed that increase.
company size, CFO and CFF are statistically significant and For future research it is suggested to extend the study by
explain 14,7% of the variation in financial distress score. examining the data according to life cycle stages of
Finally fourth equation, in which cash flow components are companies such that the financial distress score can be
normalized by dividing the data to total assets, shows that defined as a function of cash flow patterns and life cycle
company age, CFO and CFF is significantly related to stage. Inclusion of life cycle stages would provide
financial distress score of companies. It can be further information about cash flow pattern differences between
concluded that CFI does not have any significance in early stage, growing and mature companies and further
explaining the variation in the financial distress score in any explore whether these differences have any effect on
of the models. bankruptcy risk in the related firms.
The limitation of the study could have arisen from the fact

6. Appendix
Table 6. Summary Statistics of Model 1

Change Statistics
R Adjusted R Std. Error of the
Model R R Square F Sig. F Durbin-Watson
Square Square Estimate df1 df2
Change Change Change
1b 0,195a 0,038 0,034 2,8117 0,038 9,909 3 751 0,000 1,597

a. Predictors: (Constant), X3pattern, X1age, X2size

b. Dependent Variable: YFD

Table 7. ANOVA of Model 1

Model Sum of Squares df Mean Square F Sig.

b
1 Regression 235,010 3 78,337 9,909 0,000a
Residual 5937,176 751 7,906
Total 6172,186 754

a. Predictors: (Constant), X3pattern, X1age, X2size

b. Dependent Variable: YFD

Table 8. Coefficients of Model 1

Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics

Model
B Std. Error Beta Zero-Order Partial Tolerance VIF
a
1 Constant 1,914 1,355 1,413 0,158
X1age -0,007 0,008 -0,033 -0,882 0,378 -0,073 -0,032 0,942 1,061
X2size -0,276 0,067 -0,139 -3,663 0 -0,17 -0,132 0,888 1,126
X3pattern 0,178 0,071 0,093 2,502 0,013 0,13 0,091 0,937 1,068

Dependent Variable: YFD

Table 9. Coefficients of Model 2 (t=2006)

Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics

Model
B Std. Error Beta Zero-order Partial Tolerance VIF
a
2 Constant 1,408 1,973 0,714 0,477
X106 -0,013 0,012 -0,099 -1,108 0,27 -0,171 -0,101 0,933 1,072
X206 -0,226 0,095 -0,215 -2,368 0,020 -0,269 -0,211 0,907 1,103
X305 0,205 0,133 0,137 1,540 0,126 0,198 0,139 0,944 1,060
Dependent Variable: Y06
102 Cash Flow Statement as an Evidence for Financial Distress

Table 10. Coefficients of Model 2 (t=2007)

Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics

Model
B Std. Error Beta Zero-order Partial Tolerance VIF
2a Constant 2,123 1,864 1,139 0,257
X107 -0,017 0,011 -0,135 -1,524 0,130 -0,188 -0,138 0,955 1,047
X207 -0,248 0,093 -0,236 -2,658 0,009 -0,277 -0,236 0,945 1,058
X306 0,153 0,115 0,115 1,331 0,186 0,146 0,121 0,988 1,012
Dependent Variable: Y07
Table 11. Coefficients of Model 2 (t=2008)

Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics

Model
B Std. Error Beta Zero-order Partial Tolerance VIF
a
2 Constant 1,695 2,347 0,722 0,472
X108 -0,018 0,014 -0,113 -1,251 0,213 -0,145 -0,109 0,944 1,060
X208 -0,206 0,117 -0,161 -1,768 0,080 -0,216 -0,155 0,916 1,091
X307 0,242 0,129 0,166 1,866 0,064 0,191 0,163 0,965 1,036
Dependent Variable: Y08
Table 12. Coefficients of Model 2 (t=2009)

Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics

Model
B Std. Error Beta Zero-order Partial Tolerance VIF
a
2 Constant 0,425 2,028 0,210 0,834
X109 -0,014 0,012 -0,112 -1,224 0,223 -0,176 -0,111 0,919 1,088
X209 -0,150 0,096 -0,148 -1,568 0,120 -0,216 -0,142 0,868 1,152
X308 0,163 0,117 0,130 1,398 0,165 0,198 0,127 0,888 1,126
Dependent Variable: Y09
Table 13. Summary Statistics of Model 3

Change Statistics
R Adjusted R Std. Error of the
Model R R Square F Sig. F Durbin-Watson
Square Square Estimate df1 df2
Change Change Change
3b 0,291a 0,085 0,079 2,7464 0,085 13,863 5 749 0,000 1,586
a. Predictors: Constant, X1age, X2size, X31odummy, X32idummy, X33fdummy
b. Dependent Variable: YFD

Table 14. ANOVA of Model 3

Model Sum of Squares df Mean Square F Sig.

b
3 Regression 522,797 5 104,559 13,863 0,000a
Residual 5649,389 749 7,543
Total 6172,186 754
a. Predictors: Constant, X33fdummy, X2size, X32idummy, X1age, X31odummy
b. Dependent Variable: YFD
Table 15. Coefficients of Model 3
Unstandardized Standardized Collinearity
t Sig. Confidence Interval
Coefficients Coefficients Statistics
Model
Lower Upper
B Std. Error Beta Tolerance VIF
Bound Bound
3a Constant 2,706 1,223 2,214 0,027 0,306 5,106
X1age -0,005 0,008 -0,024 -0,661 0,509 -0,022 0,011 0,937 1,067
X2size -0,249 0,067 -0,141 -3,731 0,000 -0,381 -0,118 0,856 1,168
X31odummy -0,658 0,244 -0,107 -2,699 0,007 -1,137 -0,179 0,772 1,296
X32idummy -0,311 0,270 -0,042 -1,152 0,249 -0,841 0,219 0,924 1,083
X33fdummy 0,937 0,223 0,164 4,196 0,000 0,498 1,375 0,804 1,244
Dependent Variable: YFD
 Universal Journal of Accounting and Finance 1(3): 95-103, 2013 103

Table 16. Summary Statistics of Model 4

Change Statistics
R Adjusted R Std. Error of the
Model R R Square F Sig, F Durbin-Watson
Square Square Estimate df1 df2
Change Change Change
4b 0,389a 0,152 0,146 1,623 0,152 27,471 4 615 0,000 1,718
a. Predictors: Constant, X1age, X31o, X32i, X33f
b. Dependent Variable: YFD

Table 17. ANOVA of Model 4

Model Sum of Squares df Mean Square F Sig.

4b Regression 289,345 4 72,336 27,471 0,000a
Residual 1619,433 615 2,633
Total 1908,777 619
a. Predictors: Constant, X1age, X31o, X32i, X33f
b. Dependent Variable: YFD

Table 18. Coefficients of Model 4

Unstandardized Standardized Collinearity
Correlations
Coefficients Coefficients Statistics
t Sig.
Toler
B Std. Error Beta Zero-order Partial VIF
Model ance
4a (Constant) -1,707 0,189 -9,050 0,000
X1age -,016 0,005 -0,123 -3,278 0,001 -0,138 -0,131 0,978 1,023
X31o -2,135 0,739 -0,165 -2,889 0,004 -0,335 -0,116 0,425 2,354
X32i 1,716 0,837 0,097 2,050 0,041 0,055 0,082 0,620 1,613
X33f 3,027 0,749 0,233 4,042 0,000 0,319 0,161 0,416 2,404
Dependent Variable: YFD

Accruals and Cash Flows: Combined Evidence at the

Earnings Announcement and Annual Report Date. Journal of
Accounting Research. 165-200, 1986.

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