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http://www.dem.umk.pl/dem 

D Y N A M I C  E C O N O M E T R I C  M O D E L S  
DOI: http://dx.doi.org/10.12775/DEM.2017.007  Vol. 17 (2017) 115−127 

Submitted October 25, 2017  ISSN (online) 2450-7067 

Accepted December 28, 2017 ISSN (print) 1234-3862 

Ewa Majerowska, Magdalena Gostkowska-Drzewicka
*
  

 

Determinants of Corporate Performance:  
Modelling Approach 

A b s t r a c t. This study is to investigate the influence of the selected factors of the capital 

structure on the corporate performance. An empirical analysis covers a sample of 90 non-

financial companies traded on the Warsaw Stock Exchange, in the period of 2000–2015. The 

panel data models for two corporate performance measures such ROA and ROE were esti-

mated. The company’s capital structure negatively affects its performance. It is in line with 

pecking order theory and previous studies on capital structure of Polish companies.  

K e y w o r d s: capital structure, efficiency, panel data modelling, ROA, ROE 

J E L Classification: G32 

Introduction  

Chakravarty (1986) states that superior financial performance is a way to 

satisfy investors and could be represented by profitability, growth in sales 

and market value of the company. These three aspects complement each 

other (Santos, Brito, 2012). Profitability measures a company’s ability to 

generate returns. Growth reflects firm’s ability to enlarge its size. Increasing 

size, even at the same profitability level, lead to increase company’s  abso-

lute profit and cash generation. Larger size also can bring economies of 

                                                 
* Correspondence to: Ewa Majerowska, Faculty of Management, University of Gdańsk, 

ul. Armii Krajowej 101, 81-824 Sopot, Poland, e-mail: ewa.majerowska@ug.edu.pl; Magda-

lena Gostkowska-Drzewicka, Faculty of Management, University of Gdańsk, ul. Armii Kra-

jowej 101, 81-824 Sopot, Poland, e-mail: mgostkowska@wzr.ug.edu.pl. 



Ewa Majerowska, Magdalena Gostkowska-Drzewicka 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 115–127 

116 

scale, leading to enhanced future profitability. Market value represents the 

external assessment and expectation of company’s future performance. It is 

postulated, that company’s value depends on its financing decisions (Jensen 

and Meckling, 1976; Myers, 1977, 1984). It means that capital structure has 

a significant impact on corporate performance. It is also argued that profita-

ble companies are less likely to depend on debt than less profitable ones. 

Moreover, it was found that companies with high growth opportunities have 

a high profitability ratio. Also asset structure and size of the firm were found 

as important factors affecting firm performance level. 

Therefore, one of the most important decisions that must be made by the 

financial managers is to choose the right combination of debt and equity 

capital that optimizes a corporate performance defined in terms of return on 

assets and equity. Moreover, it is necessary to identify the major factors 

influence the relationship between a company’s capital structure and its per-

formance. The purpose of this study is to investigate the influence of the 

selected factors of the capital structure on the corporate performance of 

Polish companies listed on Warsaw Stock Exchange.  

Obtaining clear and comprehensive answers about the nature of the fac-

tors shaping the corporate performance of Polish companies is difficult due 

to the small range of domestic research. Previous research on capital struc-

ture of Polish companies showed that they prefer to finance with internal 

founds (e.g. Hamrol, Sieczko 2006; Wilimowska, Wilimowski 2010; 

Jędrzejczak-Gas, 2014). This is in line with pecking order theory, so it is 

assumed that: 

H1. A company’s leverage is expected to decrease its performance.   

H2. Growth opportunities affect a company’s performance positively.  

H3. There is a positive relationship between size and a company’s perfor-

mance. 

H4. A company’s tangibility has a negative influence on its performance. 

An empirical analysis covers a sample of 90 non-financial companies 

traded on the Warsaw Stock Exchange, in the period of 2000–2015. The 

panel data models for two corporate performance measures such ROA and 

ROE were estimated. The paper is organized in following way: after intro-

duction, the first section discusses the literature review on corporate perfor-

mance. The second section presents the determinants of corporate perfor-

mance. The third section presents dataset and  methodology of the research. 

The fourth section shows the empirical models used to investigate the effect 

of capital structure determinants on corporate performance, and the last one 

concludes the paper. 



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DYNAMIC ECONOMETRIC MODELS 17 (2017) 115–127 

117 

1. The Literature Review on Corporate Performance 

Corporate performance is an ambiguous term and it is used interchange-

ably with terms of business or firm performance. It appears in most branches 

of management and finance and it is of interest to both academic scholars 

and practicing managers. Cameron and Whetten (1983) state that the im-

portance of performance in strategic management can be argued along three 

dimensions, i.e. theoretical, empirical and managerial. Theoretically, the 

concept of business performance is at the center of strategic management 

and finance. Empirically, many research studies employ the construct of 

performance to examine a variety of strategy content and process issues. The 

managerial importance of this category is all too evident in many prescrip-

tions offered for performance improvement. 

Venkatraman and Ramanujam (1986) offered a scheme of three overlap-

ping concentric circles with the largest one which is representing organiza-

tional effectiveness. This broadest domain includes the medium circle repre-

senting business performance. This one includes the inner circle representing 

financial performance. Santos and Brito (2012) state that business perfor-

mance is a subset of organizational effectiveness that covers operational and 

financial outcomes. Corporate performance measures could be either finan-

cial or operational. Financial performance, for example profit maximization, 

maximizing profit on assets and maximizing shareholders' benefits are at the 

core of the firm’s effectiveness. Operational performance could be measured 

by growth in sales and growth in market share. It provide a broad definition 

of performance and focus on the factors that ultimately lead to financial per-

formance (Chakravarthy,1986; Hoffer and Sandberg, 1987). 

Corporate performance could be defined in context of accounting or 

market measures. The most commonly used corporate performance measures 

are return on assets (ROA), return on equity (ROE) and return on investment 

(ROI). They are financial ratios that are assumed to reflect the fulfillment of 

the economic goals of the company (Venkatraman and Ramanujam, 1986). 

These indicators are used by many researchers (e.g. Rajan, Zingales, 1995; 

Nawaz et al., 2011; Addae, Nyarko-Baasi, Hughes, 2013; Gupta, 2015, 

Igbinosa, 2015). 

In the ,,value-based” approach market performance measures are more 

appropriate than accounting-based ratios (Hax and Majluf, 1984). Such indi-

cators as price per share to the earnings per share (P/E), market value of 

equity to book value of equity (MBVR), and Tobin’s Q are often used in 

many studies (e.g. Rajan, Zingales, 1995; Demsetz, Villalonga, 2001; 

Zeitun, Tian, 2007; Van Essen et al., 2015). 



Ewa Majerowska, Magdalena Gostkowska-Drzewicka 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 115–127 

118 

2. Determinants of Corporate Performance 

The capital structure theories suggest that leverage level can have a ma-

jor impact on corporate performance (Bandyopadhyay, Barua, 2016). Some 

of theoretical predictions on this effect are contradictory.  

Under framework of trade-off theory Lewellen and Roden (1995) 

showed that the total debt and the profitability of a company are positively 

related. Hadlock and James (2002) using a sample of 500 non-financial 

United States firms concluded that companies prefer debt financing because 

they anticipate higher returns from a higher debt level. Fama (1985) argued 

that bank borrowings could lead to increase in company’s performance, be-

cause it avoids the high information costs incurred in public debt offerings 

through bonds issuance. Thus, companies relying more on bank loans are 

expected to be more profitable. 

The pecking order theory (Donaldson, 1961; Ross, 1977; Myers and 

Majluf, 1984) proposes a negative relationship, because companies prefer to 

finance with internal funds rather than debt ones. A negative relationship 

between capital structure and corporate performance (profitability) was also 

found by Kester (1986), Rajan and Zingales (1995) Wiwattanakantang 

(1999) and Chen, Strange (2005).  

The assumptions of trade-off theory and pecking order theory are often 

used to explain the factors that determine the capital structure. These factors 

also could be used to describe the corporate performance. Among these fac-

tors the most frequently mentioned are: size of the firm, company’s tangibil-

ity (asset structure), growth opportunities  (e.g. Margaritis and Psilaki, 2010; 

Banerje and De, 2014; Yinusa, et al., 2016).  

A company’s profit is in line with its sales turnover. This is why it could 

be said that if a company’s sales increases, there is a probability that its prof-

it will increase. Wagner (1995) argues that large companies leads to scale 

expansion. As a result of such expansion, a company becomes more profita-

ble. Further research (Harvey et al., 2001) showed that firm size is signifi-

cantly and nonlinearly related to profitability. It suggests that although big-

ger companies are likely to experience higher profitability, revenue growth is 

likely to slow faster in larger firms. 

Tangibility refers to a company’s investments in tangible assets. If these 

investments are effective, the company’s performance improves. Ghosh 

(2008) found that the greater asset tangibility, the lower the scope for infor-

mational asymmetries between insiders and outsiders. It allows for higher 

leverage with a concomitant positive effect on profitability. This relationship 



Determinants of Corporate Performance: Modelling Approach 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 115–127 

119 

is U-shaped. It suggests that greater increases in tangibility exerts a positive 

effect on profits, what is in line with trade-off theory. 

In turn, in line with pecking order theory there is a negative relationship 

between tangibility and company’s performance. High profits motivate com-

panies to accumulate surpluses. They are invested in short-term securities. 

These funds can be spent to fund the investment. In such a situation, the 

company does not need to have large amounts assets in the form of collat-

eral, as its investments are financed by cash from the sale of its short-term 

securities. 

Companies in the growth phase have a high performance ratio. They are 

able to generate profit from investment. So, growth opportunities are ex-

pected to be positively related to a company’s performance. 

Literature also indicates other factors of corporate performance. There 

are age of the firm, risk, debt service capacity, dividend pay-out, develop-

ment expenditures, degree of operating leverage, ownership structure and 

ownership type industry affiliation, macro factors (e.g. Bandyopadhyay and 

Barua, 2016; Margaritis and Psillaki, 2010; Banerje and De, 2014; Zeitun 

and Tian, 2007). 

3. Dataset and Methodology 

The object of analysis are companies continuously listed on the main 

market of the Warsaw Stock Exchange in 2000–2015. There were 117 com-

panies, including 6 from the sub-index WIG-20, 17 from the sub-index 

WIG-40, 22 from the sub-index WIG-80 and 72 not included in any of them. 

The affiliation of a company to a given sub-index was determined by its 

composition at the end of December 2015. In this analysis we omitted the 

fact that the company could change its size during the period of the analysis. 

Of the above companies only entities from the non-financial sector were 

included into research. Therefore, 16 companies were excluded from the 

sample. In addition, entities that did not submit complete financial state-

ments were also rejected, i.e. 11 companies. Finally, 90 companies were 

qualified for the study, i.e. 77% of the pre-selected entities and the balanced 

panel was created. 

In this study we used four variables: growth opportunities, firm’s size 

and tangibility (asset structure) as determinants of corporate performance. 

Capital structure is measured by total debt ratio to total assets (LEVER-

AGE). Growth opportunities are represented by growth of sales 

(GROWTH). The SIZE is measured by the growth of company's total assets. 

Asset structure is measured by assets tangibility (TANG), i.e. the ratio of 



Ewa Majerowska, Magdalena Gostkowska-Drzewicka 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 115–127 

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fixed assets to total assets. As a corporate performance measures we used 

return on assets (ROA) and return on equity (ROE). As it was mentioned, in 

the literature there are more factors that could influence the corporate per-

formance. Due to the scope of this research only four factors out of all was 

taken into account. The selected factors are the factors of the capital struc-

ture of companies. So the rest of them was omitted. 

The two variables, ROA and ROE are modelled using the panel data ap-

proach. The proposed model takes a form: 

ititit

titit

TANGSIZE

GROWTHLEVERAGEy









43

210   (1) 

where 
ity  is the explained variable, represented by the ROA or ROE of i’th 

company in time t, 
43210 ,,,,   are the structural parameters and the 

it  

is the error term. The model will be estimated by the pooled OLS and using 

the fixed effects estimator and the random effects estimator. Application of 

three testes, that are the Chow F-test of the joint significance of group ef-

fects, the Breusch-Pagan test and the Hausman test will allow for selecting 

the best model. The first of them tests that the pooled OLS is adequate, in 

favor of the fixed effects alternative, the second one tests that the pooled 

OLS is adequate, in favor of the random effects alternative. The null hypoth-

esis of the last test states that the random effects estimator is consistent and 

more effective than the fixed effect estimator. 

4. Empirical results 

4.1. Performance of Companies Listed on WSE in 2000–2015 

The fluctuations of ROE and ROA ratios in companies listed on the 

Warsaw Stock Exchange in 2000–2015 were similar to each other through-

out the period considered (Table 1). For this reason, the description focuses 

only on one of them, i.e. ROE. The value of the first quartile, calculated for 

ROE in companies listed on WSE, decreased systematically in 2000–2002. 

In 2002 it reached the lowest level in the whole period, i.e. –13,76%. It 

means that 25% of companies achieved a value of ROE not greater than –

13,76% in 2002. Starting from 2003, the ratio was increasing until 2007 

when it reached 4,57%, i.e. the highest value in the whole period analysed. 

In subsequent years, the value of the ROE fluctuated. However, only in 

2012–2013 it was negative (Table 1.). The value of ROE has been increasing 

since 2003 to 2007.  



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Table 1.  Return on assets (ROA) and return on equity (ROE) in companies listed on 

WSE in 2000–2015 

Years  ROA(%)   ROE(%)  

I quartile Median III quartile I quartile Median III quartile 

2000 –2,76 2,71 5,67 –1,11 5,17 10,36 
2001 –5,82 0,99 4,02 –9,12 2,07 6,09 
2002 –4,29 1,76 3,99 –13,76 3,46 7,04 
2003 0,61 3,24 6,11 0,97 5,85 12,64 
2004 1,83 4,04 7,92 2,64 8,33 17,03 
2005 1,03 4,22 9,39 0,54 7,92 16,11 
2006 1,38 4,91 10,38 2,22 10,03 19,42 
2007 2,69 5,49 10,69 4,75 9,50 17,72 
2008 0,72 3,46 6,42 0,92 4,99 12,81 
2009 0,60 3,42 6,91 0,80 4,84 10,39 
2010 0,93 3,32 5,95 1,25 5,50 10,54 
2011 1,05 4,62 6,39 1,61 6,68 10,89 
2012 –0,73 2,43 5,14 –1,63 4,08 8,45 
2013 0,01 2,71 6,88 –0,17 4,50 10,20 
2014 1,33 3,78 6,71 1,25 6,06 11,63 
2015 0,12 2,97 6,85 0,16 5,15 11,67 

 

The median, calculated for ROE in the entities in 2000–2015, ranged 

from 2,07% in 2001 to 10,03% in 2006. A similar situation was noted for the 

third quartile of ROE. In 2001 and 2006, it was at the level of 6,09% and 

19,42% respectively, which means that 25% of the companies achieved ROE 

not lower than the third quartile. The trends described above are related to 

the economic situation in Poland during the period considered. In the years 

when the companies achieved the highest profitability level, there was 

a prosperity in Polish economy. In turn, during the economic downturn prof-

itability ratios ROE and ROA were relatively low (Table 1). 

In the next step the stationarity of the two selected ratios was tested. Sta-

tistics of the tests for the panel data are given in the Table 2. In all cases the 

null hypothesis that the series is integrated in order of one or higher at the 

0.05 level of significance need to be rejected. Consequently, it can be as-

sumed that all series are stationary. 

Additionally, the correlations between analysed series were tested. Only 

one statistically significant correlation at the level of 0.05 appeared (see 

Table 3). That was between ROA and LEVERAGE which means that com-

panies listed on WSE prefer to finance with internal founds what is in line 

with pecking order theory. Similar results could be found in most of previous 

studies for Poland (e.g. Hamrol, Sieczko 2006; Wilimowska, Wilimowski 

2010; Jędrzejczak-Gas, 2014). 



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DYNAMIC ECONOMETRIC MODELS 17 (2017) 115–127 

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Table 2.  Statistics of the stationarity tests of the indicators 

 statistic 

test ROA ROE 

Im-Pesaran-Shin –2.5162 –2.7011 
Choi meta-tests:   

Inverse chi-square 406.944 458.669 
Inverse normal test –9.8127 –11.1538 

Logit test –9.8738 –11.6641 
Levin-Lin-Chu pooled ADF –3.6030 –35.2900 

Table 3.  Correlation coefficients of the indicators, 2000–2015 

 ROA ROE LEVERAGE GROWTH SIZE TANG 

ROA 1 –0.0017 –0.9997 0.0008 0.0039 0.0359 
ROE  1 –0.0004 –0.0002 –0.0013 0.0355 

LEVERAGE   1 –0.0010 –0.0041 –0.0360 
GROWTH    1 –0.0008 0.0506 

SIZE     1 –0.0399 
TANG      1 

4.2. Estimated Models  

 The proposed model (1) was estimated for both ROA and ROE ratios. 

The 90 selected companies in years 2000–2015 constituted the panel data 

sample. Additionally, we propose to estimate the model dividing the sample 

into four subsamples. The first subsample holds large companies, included in 

the WIG20. There were only 4 of them. To the second one contains medium-

size companies (included in the mWIG40). There were 14 of them. The 

group of 21 companies (included in the sWIG80 index) constituted the sub-

sample of the small companies. The rest of companies (51) represents the 

last subsample.  

 The models, for all companies and for subsamples, were estimated using 

three estimators: the pooled OLS, the fixed effects estimator and the random 

effects estimator. Application of such estimators for modelling the corporate 

performance could be found , for example, in Majumdar and Chhibber 

(1999), Berger, Bonaccorsi di Patti E. (2006) or King, Santor (2007). 

 Then the selection of the most appropriate model was based on the basis 

of the three tests, mentioned above. Results are given below (Tables 4 and 

5). It can be noticed that in almost all cases (in four out of five cases) the 

best model for the ROA was the one obtained with the fixed effects estima-

tor, and for ROE the pooled OLS (also in four out of five cases). Also the 

values of the measure of goodness of fit (R-squared), the values of the 

Durbin-Watson statistic (DW) and the Akaike criterion are presented in the 

tables. 



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123 

Table 4.  Estimates of the model of ROA and statistics of the tests for companies 

listed on WSE in 2000–2015 

Variable all companies large 
companies 

medium  
companies 

small 
companies 

rest of 
companies 

fixed effects fixed effects fixed effects random effects fixed effects 

constant 0.1279 0.2334 0.0221 0.1160 0.1139 
LEVERAGE –0.2379*** –0.4011*** –0.0993** –0.1887*** –0.2379*** 
GROWTH 0.0000 0.1051* –0.0001 0.0013 0.0000 

SIZE –0.0006 0.0456 0.1035*** 0.0425*** –0.0006 
TANG –0.0159 –0.0788 0.2123*** –0.0745* –0.0140 

Joint signifi-
cance test 2.2854#) 3.6343#) 7.3432#) 2.7666#) 2.0205#) 
Breusch-

Pagan test 56.2668#) 0.5627 85.3518#) 13.6845#) 19.8526#) 
Hausman test 14.0096#) na 24.2201#) 8.4723 11.1615#) 

R squared 0.9995 0.4203 0.4282 na 0.9996 
DW 1.4859 1.7440 1.5033 na 1.4954 

Akaike crit. –214.2246 –104.6516 –444.6726 –554.2151 265.8595 

*) **) ***)  statistically significant at the level of 0.1, 0.05 and 0.01 respectively; #) the null hypothesis is 
rejected at 0.05 significance level 

 If the ROA ratio is modelled, in all cases the LEVERAGE negatively 

influences its volatility. As it was mentioned such relationship is in line with 

pecking order theory. It is interesting that for large companies the GROWTH 

of the company positively affects the ROA ratio. It means that they are able 

to generate profit from investment, what is also in line with pecking order 

theory. GROWTH is not significant if the medium, small size companies and 

rest of companies are analysed. For the medium size companies the hypothe-

sis of the negative relationship between asset structure (TANG) and their 

performance is not confirmed. In turn, it is confirmed in the case of small 

companies. There is a positive relationship between the size of the company 

(SIZE) and the company’s performance for medium and small size compa-

nies. In the case of the rest of companies the size of the company and its 

tangibility do not affect their performances. 

 Estimated models of ROE for all companies and small companies did not 

point out any statistically significant relationships between explanatory and 

explained variables. For the large, medium-size and the small-size compa-

nies the ROE was negatively influenced by the LEVERAGE and positively 

by the TANG variable. Asset structure (TANG) refers to a company’s in-

vestments in tangible assets. If these investments are effective, the com-

pany’s performance improves. In contrary to previous relationships this one 

is in line with trade-off theory. 



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DYNAMIC ECONOMETRIC MODELS 17 (2017) 115–127 

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Table 5.  Estimates of the model of ROE and statistics of the tests for companies 

listed on WSE in 2000–2015 

Variable all companies large 
companies 

medium  
companies 

small 
companies 

rest of 
companies 

pooled pooled fixed effects pooled pooled 

constant –0.9658 0.1237* 0.0699 1.6421 –0.8189 
LEVERAGE 0.0006 –0.5718*** –0.8700*** –7.0535*** 0.0005 
GROWTH –0.0000 0.2200* 0.0001 0.0794 –0.0000 

SIZE 0.0003 0.0685 0.1685* 0.8597 –0.0009 
TANG 4.3894 0.4979*** 1.0623*** 2.0505 6.1764 

Joint signifi-
cance test 1.0420 2.3126 6.0486#) 1.3991 1.0197 
Breusch-

Pagan test 0.0322 0.0449 21.8961#) 1.1299 0.0023 
Hausman test 0.7468 na 80.6125#) 0.3983 1.0782 

R-squared 0.0012 0.2563 0.3272 0.0331 0.0016 
DW 1.9694 1.7481 1.6607 1.4721 2.0026 

Akaike crit. 13339.5 –24.705 231.6411 2616.291 7980.600 

*) **) ***)  statistically significant at the level of 0.1, 0.05 and 0.01 respectively; #) the null hypothesis is 
rejected at 0.05 significance level 

 Finally, for those models, for which the fixed effects estimator was in 

use, the test for differing group intercept was applied. For all models the null 

hypothesis that the groups have the common intercept was rejected at the 

level of significance of 0.05. It means that there were specific differences 

among companies in the level of ROA and ROE.  

Conclusions   

 On the basis of the empirical analysis in the case of ROA only the first 

hypothesis stated in the paper cannot be rejected. The company’s capital 

structure negatively affects its performance. The rest of the hypotheses need 

to be rejected, with some exceptions. Only for the large companies the se-

cond one, that the growth opportunities lead to the increase of the company’s 

performance, cannot be rejected. Also for the medium size companies the 

third hypothesis cannot be rejected. It means the positive relationship be-

tween the size and the performance of the company. In turn, the forth hy-

pothesis, that a company’s asset structure affects its performance negatively 

need to be rejected. Generally, these results are rather consistent with as-

sumptions of pecking order theory than with trade-off theory. 

 Estimates of the model of ROE for the full panel data and for the sub-

sample of the small companies point the necessity of rejection of all four 

hypotheses. In the case of the large, medium and the small size companies 



Determinants of Corporate Performance: Modelling Approach 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 115–127 

125 

separately the first one cannot be rejected, so the negative relationship be-

tween the company’s capital structure and its performance is found. The last 

hypothesis need to be rejected in all cases, even if for the large companies 

and the medium ones the structural parameters were statistically significant 

at 0.05 significance level. It means no negative influence of tangibility on 

performance. 

 The subsample, called the rest of companies, include the companies of 

different size. The results for this group are close to those for all companies, 

because they show similar volatilities of variables. Both subsample and the 

full sample include large companies. Changes in the size of these companies, 

expressed in total assets growth, are relatively small, so the variable SIZE is 

irrelevant. In other words, this growth is too small to influence the value of 

the explanatory variable. 

 However, we have to remember that the sample size was relatively 

small, especially the number of large companies taken into account and the 

short time series. It is due to the short history of the capital market in Poland 

and lack of the data. It would be interesting to extend the analysis and to 

compare results with outputs from the other markets. One of the ways could 

be to create the non-balanced panel, so more companies would be included 

in the sample.  

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Czynniki kształtujące wyniki finansowe firmy: ujęcie panelowe  

Z a r y s  t r e ś c i. Celem artykułu jest zbadanie wpływu wybranych czynników struktury 

kapitału spółek notowanych na GPW na ich rentowność. Do badań zakwalifikowano 90 

przedsiębiorstw z sektora niefinansowego notowanych na GPW w latach 2000–2015. Osza-

cowano model panelowy dla dwóch miar rentowności, tj. ROA i ROE. Pomiędzy strukturą 

kapitału (dźwignią finansową) a rentownością występuje związek ujemny, co jest zgodne 

z teorią hierarchii źródeł finansowania i wynikami dotychczasowych badań w zakresie struk-

tury kapitału polskich przedsiębiorstw.  

S ł o w a  k l u c z o w e: struktura kapitału, efektywność, modelowanie panelowe, rentowność 

kapitału, rentowność aktywów. 


