Statistical correlations between the return and the indicators of

financial balance. case study: the romanian companies listed on BSE

SIMINICĂ MARIAN, CÎRCIUMARU DANIEL, SIMION DALIA
Department of Finance and Analysis
University of Craiova
Str. A.I.Cuza, no.13
ROMANIA
msiminica@yahoo.com, danielcirciumaru@yahoo.com, daliasimion@yahoo.com

Abstract: - This article is a study on the statistical correlation between the return on assets, as
dependent variable, and a set of 24 indicators that represent the independent variables. The case study
is conducted on a group of 40 companies listed on the Bucharest Stock Exchange, belonging to all
industries, and covers the period 2007-2010. The aim is to highlight how the economic crisis affects
the level and the evolution of the proposed variables and the correlation between them. The results
have revealed the existence of some variables correlated in a large measure with the return on assets.
In this regard, four correlation models have been created, one for each year analyzed. The set of
variables retained was different each year, which means that the economic situation different from one
year to another changes the correlations between the variables studied. The study also highlighted the
fact that some financial ratios are significantly influenced by the economic crisis, while other financial
ratios do not suffer such an influence.

Key-Words: - return on assets, statistical correlation, economic crisis, financial ratios

1 Introduction performance arid can be used to establish a hurdle

The company managers are nowadays rates all new investments must meet for
concerned about the efficiency of the asset approval."(Lindo, David K.).
utilization in an effort to improve the performance A comprehensive analysis of the return on
of the business. The rising pressure exercised by assets was also made by George W. Gallinger. He
shareholders and the limited funds make the firms to developed a model that comprised, as variables,
search the ways to increase the efficiency of the indicators such as the return on sales, the financial
assets, in order to maintain the competitiveness. To leverage, the interest expenses, the return on equity.
achieve this goal, the companies need to properly This allows analyzing a company's asset
assess the return on assets. management and the opportunity to redeploy the
The indicators of return are considered assets in the future (Gallinger, George W.).
among the most important indicators used by the The return of a firm is influenced by many
management of a business. Whatever the form of factors. Knowing these factors is important
expression (return on assets, return on equity, return primarily for the company management, to adopt
on sales), they are found among the set of indicators appropriate measures of growth, and to perform
published by most companies. short or long term forecasts. Also, knowing the
The importance of return on assets as a measure of relationship of dependence between the return and
the firm performance is recognized in the the factors of influence is important for investors,
specialized literature. Thus, David Lindo believes creditors and for other categories of stakeholders
that "Return on Assets (ROA) is the general purpose who have different interests about the firm.
financial ratio used to measure the relationship of M. T. Bosch-Badia performed a study that
profit earned to the investment in assets required to determined "a functional relationship between
earn that profit […]The ROA percent is a baseline ROOA (return on operating assets) and the main
that can be used to measure the profit contribution productivity indicators at a company level: total
required from new anvestments. As such it identifies factor productivity (TFP) and labour productivity.
the rate of return needed to at least maintain current Both productivity indicators, together with price
change of outputs and inputs, are the drivers that - Current liquidity (CL) = Current assets / Short
determine the value of ROOA, as the functional term debts;
relationship we obtain proves. This relationship can - Quick ratio (QR) = (Current assets - Inventories)
be regarded as an extension of the Dupont method / Short term debts;
that expresses ROOA as the product of operating - Overall solvency (OS) = Total assets / Total
margin per asset turnover. "(Bosch-Badia, Maria debts;
Teresa). The author created a model that ROOA, as - Working capital (WC) = Long term capital -
the dependent variable, can be expressed as a Fixed assets;
function between productivity and price change as - The need for working capital (NWC) =
independent variables. Inventories + Receivables – Short term debts;
Patricia Fairfield and Teri Lombardi Yohn - Treasury (T) = Working capital - The need for
have made a study of the return on assets in the working capital;
context of making predictions. They demonstrated - Rate of financing the fixed assets (RFFA) =
that "disaggregating return on assets into asset Long term capital / Fixed assets;
turnover and profit margin does not provide - Coverage of capital invested (CCI) = Long term
incremental information for forecasting the change capital/ (Fixed assets + Need for working
in return on assets one year ahead, but that capital);
disaggregating the change in return on assets into - Coverage of need for working capital (CNWC) =
the change in asset turnover and the change in profit Working capital / Need for working capital;
margin is useful in forecasting the change in return - Rate of financing the turnover (RFT) = Working
on assets one year ahead”. (Patricia Fairfield and capital x 365 / Turnover;
Teri Lombardi Yohn). - Rate of need for working capital (RNWC) =
From the above, it is noted in the literature Need for working capital x 365 / Turnover;
the interest shown to analyze the return on assets. In - Average term for paying the suppliers (TS) =
this article, it was conducted a statistical survey of Average balance of commercial debts x 365 /
the relationship between the return on assets, as Turnover;
dependent variable, and a set of economic and - Average term for collecting the commercial
financial indicators, as independent variables. The receivables (TC) = Commercial receivables x
study covered 40 Romanian companies listed on 365 / Turnover;
Bucharest Stock Exchange (BSE) and included a - Average number of turnovers of the current
period of 4 years between 2007 and 2010. The data assets (NCA) = Turnover / Average balance of
required to calculate these indicators were extracted current assets;
from the annual financial statements of these - Average duration in days for the turnover of
companies. Note that the study includes two years current assets (DCA) = Average Balance of
of economic growth for Romania (2007 and 2008) current assets x 365 / Turnover;
and two of severe economic crises (2009 and 2010). - Cash conversion cycle (CCC) = Operating cycle
It is thus expected that most indicators analyzed to - Payment cycle;
worsen in the past two years. - Return on equity (ROE) = Net profit / Equity;
- Return on operating expenses (ROEx) =
2 Concepts and methodology Operating profit / Operating expenses;
The return on assets ROA (the dependent - Return on sales (ROS) = Operating profit /
variable) was calculated as a ratio of the operating Turnover.
results and the employed (invested) capital. The set
of independent variables includes the following 24 3 Results and discussions
indicators: The economical and financial indicators
- Fixed assets ratio (FAR) = Fixed assets / Total were calculated for the period 2007-2010 for all the
assets; 40 companies surveyed. The aim was to analyze the
- Financial stability ratio (FSR) = Long term statistical correlation between the return on assets
capital / Total capital; and the 24 indicators and the influence factors that
- Self-financing ratio (SFR) = Equity / Total best explain the return on assets. Thus, for each of
capital; the four years analysed, it was found a statistical
- Financial leverage (FL) = Borrowed capital / model linking the return on assets as the dependent
Equity; variable and several independent variables
- Capital employed ratio (CER) = Employed considered as relevant. To create these models, it
(invested) capital / Total capital; was used the statistical software SPSS.
 The analysis of correlation between the where: xi – the values of dependent variable (the
return on assets and the indicators of financial return on assets);
balance can be done separately, using the coefficient yi – the values of each independent variable
of correlation (between the dependent variable and (measures of financial balance);
an independent variable), or can be done globally, in n – number of firms analyzed.
the linear regression. The Pearson correlation coefficient takes
After analysing the correlation between the values between -1 and 1, as the positive values
return on assets and the indicators of financial indicate a direct correlation, while the negative ones
balance, the following data was obtained using an inverse correlation (one variable increases as the
SPSS: other decreases). This indicates a dependency
Table 1 between the data the better the more its value is
Correlations closer to 1 or -1 (1 assumes a perfect correlation,
Indepe Pearson Correlation Sig. (1-tailed) which is obtained only when a data set is correlated
ndent
variabil with itself). Also, the significance threshold must be
2007 2008 2009 2010 2007 2008 2009 2010
e less than 0.05 (which means that out of 100
FAR -0.057 - - - 0.36 0.04 0.05 0.35 measures just under maximum 5% the results can be
0.26 0.25 0.06 4 9 5 3
6 7 1
random, due to chance or hazard).
FSR -0.398 - - - 0.00 0.00 0.00 0.24 As seen in Table 1, in 2007, for the 40
0.43 0.51 0.11 6 2 0 0 companies analyzed, the closest value of -1 or +1
8 7 5 for Pearson's coefficient (-0.602) is encountered for
SFR -0.560 - - 0.18 0.00 0.00 0.00 0.13 the correlation between return on assets and the
0.57 0.43 2 0 0 2 0
9 8 capital employed ratio, which means an indirect
FL -0.405 - - - 0.00 0.01 0.11 0.32 correlation between the two variables. The
0.33 0.19 0.07 5 9 9 7 significance threshold (Sig) has a very low level
1 1 3
(0.000) which shows that the value obtained is
CER -0.602 - - 0.18 0.00 0.00 0.00 0.12
0.67 0.59 7 0 0 0 3
significant.
2 7 The following variables that influence the
CL -0.088 0.03 - 0.01 0.29 0.41 0.26 0.46 return on assets, presented after the intensity of the
7 0.10 5 5 1 2 4 dependence, are: self-financing ratio (-0.560), for
4
which the threshold of significance (Sig) is 0.000,
QR -0.013 0.10 - 0.08 0.46 0.26 0.35 0.29
3 0.06 7 9 4 8 7 leverage (-0.405) with a value of 0.005 for the
0 significance threshold and return on equity (-0.398)
OS -0.128 - - 0.14 0.21 0.34 0.27 0.19 with a significance threshold of 0.006, less than
0.06 0.09 2 5 1 1 1 0.05. It is noted that these two variables are also in
7 9
CCI -0.290 - 0.19 0.25 0.03 0.09 0.10 0.05
inverse correlation with return on assets. The other
0.21 9 8 5 6 9 4 variables analyzed have low levels of Pearson
1 correlation coefficient, and high values for the
NCA - - 0.25 0.18 - 0.41 0.06 0.12 significance threshold Sig (above 0.05), which
0.03 0 3 9 0 9
3 means they have a little influence on the return on
ROEx -0.240 - 0.33 0.48 0.06 0.36 0.01 0.00 assets.
0.05 0 5 8 1 9 1 In 2008 and 2009 the situation didn’t
8 changed too much. The capital employed ratio still
ROS -0.342 - 0.33 0.48 0.01 0.07 0.01 0.00 has a strong inverse correlation with the return on
0.23 9 4 5 4 6 1
2 assets, with a correlation coefficient of -0.672 and -
0.597 respectively, and a significance threshold
The intensity of correlation between the (Sig) of 0.000. This ratio is followed by self-
variables studied is assessed using the Pearson financing ratio and financial stability ratio as
correlation coefficient, calculated with the formula: regarding the intensity of correlation, while the
influence of financial leverage decreases greatly.
n 
n
xi yi  xi yi 
n n n
 In 2010, due to profitability problems
rxy   i1 i1  i1 i1  caused by the economic crisis, the return on assets is
 n 2   n 2  n 2   n 2  no longer correlated with the indicators of financial
n ixi  xi  n yi  yi   balance. The most powerful connections are found
 1  i1   i1  i1   (1) with other two rates of profitability: return on
operating expenses, with a correlation coefficient of
 0.485 (Sig = 0.001), and return on sales, with a Table 2
correlation coefficient of 0.484 (Sig = 0.001). We Variables Entered/Removeda
Variables Variables
Model Entered Removed Method In our study, the first independent variable
entered in the model is capital employed ratio,
1 Forward (Criterion: Probability-
CER . which, as we have seen, has a greater influence on
of-F-to-enter <= ,050)
the return on assets. The next steps consisted in
2 Forward (Criterion: Probability-
FL . introducing the other independent variables such as
of-F-to-enter <= ,050)
leverage, self-financing ratio, quick ratio, overall
3 Forward (Criterion: Probability- solvency, while the last variable entered was the
SFR .
of-F-to-enter <= ,050)
coverage of capital invested. The other independent
4 Forward (Criterion: Probability- variables were not introduced in the model, as their
QR .
of-F-to-enter <= ,050) influence on the return on assets is insignificant.
5 Forward (Criterion: Probability- The following table presents for each
OS .
of-F-to-enter <= ,050) regression model the correlation coefficient (R), the
6 CCI .
Forward (Criterion: Probability- R Square and the standard error.
of-F-to-enter <= ,050) Table 3
a. Dependent Variable: Model Summary
ROA Std. Error
appreciate that the difficulties occurred in this year's R of the
return was not due to financial policy and to Model R Square Adjusted R Square Estimate
financial structure but rather to the decreased profit 1 0.602a 0.362 0.345 20.46269
margin and return on expenses. 2 0.708b 0.501 0.475 18.33085
The linear regression: the link between 3 0.859c 0.738 0.716 13.46623
return on assets and measures of financial 4 0.891d 0.793 0.770 12.13055
balance 5 0.913e 0.833 0.809 11.05861
The linear regression means the calculation 6 0.925f 0.855 0.829 10.46754
of the correlation coefficient for the group of
variables, analyzing the correlation between a a. Predictors: (Constant), CER
dependent variable and a series of independent b. Predictors: (Constant), CER, FL
variables. As in the case of correlation coefficient c. Predictors: (Constant), CER, FL, SFR
above applied, the calculated value should be closer d. Predictors: (Constant), CER, FL, SFR, QR
to 1 in order to assume a strong correlation. e. Predictors: (Constant), CER, FL, SFR, QR, OS
To emphasize the correlation between the
f. Predictors: (Constant), CER, FL, SFR, QR, OS, CCI
return on assets (Y) on the one hand and the
g. Dependent Variable: ROA
financial balance indicators (X1 ... Xn) on the other
hand, we used a multiple linear regression model of
the form: The model 1 shows the dependence between
the return on assets and the capital employed ratio,
Y    1  X1   2  X 2  ...   n  X n (2)
obtaining a correlation coefficient of 0.602 and an R
where: α, β1 ... βn – regression coefficients. Square of 0.362, which means a pretty strong
To identify the best combination of correlation between the two variables, while 36.2%
independent variables that explain the variation of of the variation of return on assets is explained by
the dependent variable, we used the Forward option the change of capital employed ratio.
in SPSS, by which the variables are introduced in In model 2 was introduced the second
the model one by one, in order of their importance, independent variable (leverage), obtaining a
and at each step it is tested whether the regression correlation coefficient of 0.708 and an R Square of
coefficient is zero. The analysis was made for each 0.501. This means that 50.1% of the variation of
year of the period 2007 - 2010 highlighting the return on assets is explained by the variation of
changes in the factors that influenced the return on capital employed ratio, namely financial leverage.
assets of the companies listed on BSE before the Furthermore, by introducing the second independent
economic crisis, and during it. variable in the regression model, the standard error
For 2007, of the 24 variables included in the of estimation decreases from 20.463 to 18.331.
analysis, we selected six variables that explain the Model 3 introduces the third independent
variation of return on assets. variable in the equation, self-financing ratio, leading
to a correlation coefficient of 0.859 and an R Square values while the values of Sig are very small (less
of 0.738. In model 4 quick ratio is introduced into than 0.05), which allows us to reject the hypothesis
the equation, as the correlation coefficient increases that there is no significant connection between the
to 0.891 and R Square to 0.793. The model accuracy variables analyzed, leading to small errors that
is increased by the introduction of the fifth ratio, the might occur due random measurements.
overall solvency, which determines a level of the We note that the influence of the six
correlation coefficient of 0.913 and an R Square of variables selected on the return on assets is good
0.833. The last variable introduced in model 6 is because Sig<0.05. Based on calculated coefficients,
coverage of capital invested, for which is obtained which are found in column B of Table 4, the linear
the highest value of the correlation coefficient multiple regression model identified for the
(0.925) and of R Square (0.855). This model variables studied is as follows:
explained 85.5% of the change of return on assets. ROA  71.749  0.787 CER 50.284 LF -
The regression coefficients calculated for
- 1.569 SFR  0.104 QR  (3)
each of the six models are presented in Table 4.
 0.016 OS - 0.271 CCI.
Table 4 This allows estimating the return on assets
The regression coefficients for 2007 based on the six indicators of financial equilibrium
Standar selected in the model.
dized
Unstandardized Coeffici Collinearity For 2008, the linear regression model
Coefficients ents Statistics explaining the variation of the return on assets
Std. Toleran changes, but there are no significant changes
Model B Error Beta t Sig. ce VIF compared with 2007. Thus, from the 24
1 (Constant 61.621 11.178 5.513 0.000 independent variables analyzed, seven variables
CER -0.687 0.148 -0.602 -4.643 0.000 1.000 1.000 were selected: capital employed ratio, quick ratio,
2 (Constant 65.628 10.091 6.504 0.000 coverage of capital invested, return on operating
CER -0.664 0.133 -0.582 -5.003 0.000 0.997 1.003 expenses, financial leverage, number of turnovers
FL -23.290 7.238 -0.374 -3.218 0.003 0.997 1.003 of current assets and current liquidity. The
3 (Constant 49.718 7.920 6.278 0.000 regression coefficients for this model are listed
CER 0.924 0.295 0.810 3.134 0.003 0.109 9.177 below:
FL -55.870 7.802 -0.897 -7.161 0.000 0.463 2.159 Table 5
SFR -1.503 0.263 -1.539 -5.706 0.000 0.10010.000 The regression coefficients for 2008
4 (Constant 47.681 7.165 6.654 0.000 Standard
CER 0.972 0.266 0.851 3.651 0.001 0.109 9.208 Unstandardized ized
Coefficients Coeffici R
FL -54.201 7.049 -0.870 -7.689 0.000 0.460 2.172 Model ents t Sig. R Squar
SFR -1.668 0.243 -1.708 -6.855 0.000 0.09510.516 e
Std.
QR 0.070 0.023 0.277 3.060 0.004 0.722 1.386 B Error Beta
5 (Constant 48.431 6.537 7.408 0.000 7 (Consta
76,919 5,127 15,002 0,000
CER 1.029 0.243 0.901 4.225 0.000 0.108 9.270 nt)
FL -53.121 6.438 -0.853 -8.252 0.000 0.459 2.180 CER -0,639 0,050 -0,949 -12,753 0,000 0,672a 0,451

SFR -1.862 0.232 -1.906 -8.024 0.000 0.08711.501 QR 0,030 0,018 0,341 1,666 0,105 0,748b 0,559

QR 0.063 0.021 0.248 2.989 0.005 0.711 1.406 CCI -0,351 0,041 -0,781 -8,570 0,000 0,881c 0,776

OS 0.018 0.006 0.268 2.849 0.007 0.552 1.812 ROEx 0,346 0,090 0,313 3,854 0,001 0,907d 0,823
FL -7,111 2,672 -0,168 -2,662 0,012 0,920e 0,847
6 (Constant 71.749 12.173 5.894 0.000
NCA 2,009 0,703 0,188 2,855 0,007 0,931f 0,866
CER 0.787 0.255 0.689 3.089 0.004 0.08811.330
g
CL 0,040 0,017 0,484 2,392 0,023 0,942 0,887
FL -50.284 6.226 -0.808 -8.077 0.000 0.440 2.275
a. Dependent Variable: ROA
SFR -1.569 0.256 -1.606 -6.129 0.000 0.06415.627
QR 0.104 0.027 0.409 3.829 0.001 0.384 2.601
OS 0.016 0.006 0.237 2.622 0.013 0.538 1.857
Based on calculated coefficients, the linear
CCI -0.271 0.122 -0.271 -2.224 0.033 0.296 3.381
multiple regression model explaining the variation
in the return on assets in 2008 is as follows:
The T test and the value of Sig are used to ROA 76.919- 0.639CER 0.030QR-
test the regression coefficients, i.e. the assumption - 0.351 CCI 0.346 ROEx- 7.111 FL
that between the dependent variable and (4)
 2.009 NCA 0.040CL.
independent variables there is no significant link. In
our study, the t test for each variable takes high
 Compared with 2007, there were retained Among the variables included in the model
four variables in the model, while other two were of the year 2008, two rates were kept: capital
eliminated (self-financing ratio and overall employed ratio and the number of turnovers of
solvency). Instead, three other variables were current assets as the return on operating expenses
introduced: return on operating expenses" number was replaced with return on sales. However the
of turnovers of current assets and current liquidity. rates of liquidity and the coverage of capital
The most important influence is still held invested disappeared from the model.
by capital employed ratio for which the correlation Although the capital employed ratio
coefficient was 0.672, explaining 45.1% of the continues to have the strongest influence on the
variation of return on assets. By introducing into return on assets, the influence decreased as the
the model the second variable, quick ratio, the correlation coefficient is 0.597, which explains
correlation coefficient increased to 0.748, and the 35.6% of the return on assets. The second variable
two variables together explain 55.9% of the change introduced in the model, return on sales, caused a
in return on assets. As the other variables are growth of the correlation coefficient to 0.805, and
introduced in the model, we find that the the degree of explanation of variation to 64.7%,
correlation coefficient increases, reaching 0.942, while the last two variables had a smaller
and all the seven variables explain 88.7% of the influence, and the R Square increased to 0.756.
variation of return on assets. We note that in 2009, the share of the
It is noted that among the variables return on assets remained unexplained due to the
introduced into the model, in 2008 we find a rate of change of the 24 variables increased, which means
return and a rate of turnover, which means a shift an increase of the influence of external random
in the factors that influence the return on assets factors that can not be controlled by the company
from the indicators of financial structure towards management.
the indicators of business administration. The The linear regression model for 2010
explanation of these changes can be found in the includes also four rates: return on operating
fact that following the economic crisis, the expenses, financial stability ratio, capital employed
economic profitability of firms has become more ratio and coverage of invested capital. The
fragile, being more sensitive to the current regression coefficients for this model are listed
management of the business. below:
For 2009, the model explaining the Table 7
variation of return on assets includes only four The regression coefficients for 2010
rates: employed capital ratio, return on sales, fixed Standard
Unstandardized ized
assets ratio and number of turnovers of current Coefficients Coeffici R
assets. The regression coefficients for this model Model ents t Sig. R
Square
are listed below: Std.
Table 6 B Error Beta
The regression coefficients for 2009 4 (Consta
-4,671 14,189 -0,329 0,744
nt)
Standar
Unstandardized dized ROEx 0,841 0,215 0,458 3,916 0,000 0,485a 0,235
Coefficients Coeffici R FSR -1,527 0,234 -1,321 -6,539 0,000 0,571b 0,326
Model ents t Sig. R
Square CER 1,202 0,206 1,196 5,830 0,000 0,785c 0,617
Std.
CCI 0,196 0,094 0,236 2,087 0,044 0,812d 0,659
B Error Beta
a. Dependent Variable: ROA
4 (Constant) 31,758 3,703 8,576 0,000
a
CER -0,281 0,035 -0,711 -8,033 0,000 0,597 0,356
ROS 0,438 0,068 0,576 6,440 0,000 0,805b 0,647 The linear regression model for 2010 is as
FAR -0,160 0,045 -0,313 -3,534 0,001 0,842c 0,709 follows:
NCA 1,860 0,719 0,226 2,586 0,014 0,869d 0,756 ROA  -4.671 0.841 ROEx- 1.527 FSR 
(6)
a. Dependent Variable: ROA  1.202 CER 0.196 CCI.
We find that the importance of the return
The regression model explaining the on operating expenses increased, as it is first
variation in return on assets in 2009 is as follows: selected within the model, but in only explains
ROA  31.758 - 0,.81 CER  0.438 ROS  23.5% of the variation of return on assets. The
- 0.160 FAR  1.860 NCA. (5) capital employed ratio, found in the models
developed for previous years, also remains in 2010,
but its importance decreased, as it is the third ratio
selected. Overall, the four selected variables were [9] Siminică M., Diagnosticul financiar al firmei,
able to explain only 65.9% of the change of return Ed. Universitaria, Craiova, 2008
on assets, while the rest up to 100% is generated by
random external factors.

4 Conclusion
We conclude that the profitability of the
Romanian firms declined as a result of the
economic crisis. Before crisis (2007) it was
significantly influenced by the financial structure
and the financial balance. After the crisis, the
importance of business administration indicators
(as profit margin and rates of turnover) increased,
but also of the random external factors,
uncontrollable by the management. Also, please
note that this study mainly used the statistical
methodology and its limitations may affect the
findings and the assessments made.
ACKNOWLEDGMENTS:
This work was supported by the strategic grant
POSDRU/89/1.5/S/61968, Project ID61968 (2009),
co-financed by the European Social Fund within the
Sectorial Operational Program Human Resources
Development 2007 – 2013.

References:

[1] Achim, MV.. Analiza economico-financiara.

Risoprint Publishing House (Cluj-Napoca)
2010.
[2] Bosch-Badia M.T., Connecting Productivity to
Return on Assets through Financial Statements:
Extending the Dupont Method, International
Journal of Accounting and Information
Management, vol. 18, issue 2, pg. 92-104, 2010
[3] Buse L, Siminica M, Circiumaru D, Simion D,
Ganea M., Analiza economico-financiara.
Sitech Publishing House (Craiova) 2010.
[4] Fairfield P.M., Teri L. Y., Using Asset
Turnover and Profit Margin to Forecast
Changes in Profitability, Review of Accounting
Studies, 6, 2001, pg. 371-385
[5] Gallinger, G.W., A framework for financial
statement analysis part 1: Return-on-asset
performance, Business Credit, vol. 102, issue 2,
pg. 40-43, 2000
[6] Lala-Popa I., Miculeac Melania, Analiza
economico-financiară. Elemente teoretice si
studii de caz, Editura Mirton, Timisaora, 2009
[7] Lindo D.K., Asset Management is Your Job,
SuperVision, vol. 69, issue 1, pg. 14-18, 2008
[8] Radu F., Cîrciumaru D., Bondoc A., Analiză şi
diagnostic economico-financiar, Ed. Scrisul
Românesc, Craiova, 2008