Acta Universitatis Danubius. Œconomica, Vol 14, No 4 (2018)
Financial Profitability of Firms and its Determining Factors.
(Case of the trade sector, Vlore region, Albania)
Kristi Dashi^{1}
Abstract: The main purpose of this paper is to study the relationship between financial profitability and factors such as debt structure, liquidity situation, turnover ratios, size and age of companies, in a sample of 49 large businesses operating in the trade sector in the Vlora region, Albania. An econometric model was built, organizing and integrating the data taken from the certified financial statements of these businesses for a period of three years (2014 to 2016), into the multiple regression model in the form of panel data. The model is found to be statistically significant. The findings of the empirical analysis suggest that there is a positive relationship between financial profitability and accounts payable turnover as well as a negative relationship between financial profitability and shortterm debt ratio, longterm debt ratio, inventory turnover, accounts receivable turnover and cash conversion cycle. Both of these relationships result fixed in time, so they can be used for longterm improvement of the entity’s profitability situation.
Keywords: profitability; econometric model; financial statements; financial ratios
JEL Classification: C01; C51; G320
1. Introduction
Albanian businesses operate in an unfavourable environment, but with obvious and continuous improvements. The Albanian market is considered as a potential for trade development considering the fact that the country is still dealing with a transition economy which is trying to find its own path in the international trade markets. The trade sector plays a key role in the economic development of the country. Datas from the Structural Survey of Enterprises, published by INSTAT in 2015, highlight the importance of this sector in the Albanian economy.
Table 1. Enterprises, employees, net sales and investments in the trade sector, 2015
Activity 
Enterprises 
Employees 
Net Sales (mln ALL) 
Investments (mln ALL) 
Trade 
45.093 (43,1%) 
111.848 (25.7%) 
871.076 (48,3%) 
27.615 (11.3%) 
Total 
104.534 
435.437 
1.802.364 
208.24 
Source: Structural Survey of Enterprises, INSTAT
The trade sector includes wholesale, retail and vehicle repair. 43.1% of the total active businesses operate in the trade sector, whose net sales represent 48.3% of the total net sales of the year. Trade employees represent 25.7% of the total number of employees. Based on the valueadded analysis, the trade sector accounts for 24.4% of this indicator, followed by the services sector with 16%.
The trade sector plays an important role in the economic development of Vlora region too. The chart below shows that 46% of the businesses under the administration of the Vlora Regional Tax Directorate, operate in this sector, followed by the service sector (43%) and production (8%).
Chart 1. The distribution of businesses by economic sectors
About 1,000 businesses with a turnover more than 8 mln ALL, classified as large businesses, share their activity between sectors such as trade, services, production and construction.
Table 2. Number of active subjects for the years 2015 and 2016, in Vlora Region
Tax Liability 
2015 
2016 
Large Businesses 
808 
965 
Small businesses with VAT 
649 
900 
Small businesses 
3074 
3826 
Farmers, etc 
300 
596 
Total 
4831 
6287 
Taking into consideration the importance of the trade sector, this paper aims to answer the following research questions:
“What is the nature of the impact of factors such as capital structure, liquidity, asset turnover, size and age of companies, in the financial profitability of large businesses operating in the trade sector?”
“Does this impact tend to be fixed or random over time?”
2. Literature Review
Foreign literature has a considerable number of studies, which try to identify the factors and the extent of their impact on a company’s profitability. In these studies, the main profitability indicators are classified into accounting and market indicators. Meanwhile, in Albania there are few studies, mainly because of the difficulties in providing accurate and true financial information of the companies.
The existing literature about the relationship between the capital structure.
And the financial profitability of a firm suggests that this relationship may be positive, negative, but there are also studies where this relationship appears to be mixed.
(Sadiq & Sher, 2016) study of 19 out of 22 companies in Pakistan in order to determine the impact of the capital structure on their profitability, found out an important negative relationship between these variables. Thus, an increase in debtfinanced capital caused a decrease in the profitability of these firms. (Onaolapo & Kajola, 2010) studied the impact of the capital structure on the financial performance of companies listed on the Nigeria Stock Exchange. This study was conducted for 30 nonfinancial companies operating in 15 different sectors for a period of seven years. The results showed that the capital structure (debt ratio) had a significant negative impact on the profitability of these companies (ROA and ROE). (Zeitun & Tian, 2007) investigated the impact of the capital structure on firm performance using panel data of 167 companies in Jordan for the period 19892003. The capital structure had a significant negative impact on financial profitability, expressed both through market and accounting indicators. (Sarkar & Zapatero, 2003) discovered a positive relationship between profitability and financial leverage ratio for firms taken in their study. (Abor, 2005) reviewed a sample of 22 commercial firms in Ghana and concluded that there was a positive shortterm relationship with the profitability of these firms.
Another study looked at the relationship between the capital structure and profitability in seven Latin American countries for the years 1996 to 2005 for 6766 firms in various sectors. The conclusion drawn was that there was a positive relationship between debt financing and firm growth and a negative relationship between debt financing and profitability for large firms. Those firms which have more tangible assets, have a lower level of profitability and use more debt. (Cespedes, Gonzales, & Molina, 2009)
The current review of existing literature reveals the existence of a significant relationship between the liquidity situation and the financial profitability of a firm. Despite the large number of studies, the nature of the liquidity impact on profitability is not fully recognized. This is because these studies have produced different results; some of them have shown a negative relationship while some other studies have shown a positive relationship.
Also, since every study is conducted under different economic conditions, their conclusions can not be considered true for every economy.
A study of 131 listed companies on the Athens Stock Exchange for the period 20012004 showed a significant link between the cash conversion cycle and profitability. The Accounts Receivable Turnover, Accounts Payable Turnover and the Inventory Turnover are the three components of the money conversion cycle. Pearson correlation and regression analysis showed that there was a negative relationship between these three indicators and profitability. (Lazaridis & Tryfonidis, 2006) Moreover, managers could make their firms more profitable by managing the money conversion cycle and keeping each of its components at optimal levels.
These findings are also supported by the conclusions of (Deloof, 2003) the Deloof’s study found a negative correlation between gross operating income and the average collection period of accounts receivable, the average payment period and the inventory holding period for Belgian firms.
According to (Gill, Biger, & Mathur, 2010), who extended the study of (Lazaridis & Tryfonidis, 2006) there is a significant relationship between cash conversion cycle and profitability. The study of 88 firms listed on the New York Stock Exchange through regression analysis showed that the profit of a firm will increase if the accounts receivable, accounts payable and inventories are managed effectively.
(Hasan, Akbas, Caliskan, & Durer, 2011) study of companies listed on the Istanbul Stock Exchange for the period 20052009 tried to shed light on the relationship between profitability and the management of working capital. The findings showed that the reduction of cash conversion cycle positively impacted profitability, represented by ROA.
The size of a firm can be defined as the production capacity to provide a variety of goods and services to customers. Usually, larger sized firms are characterized by higher profitability compared to smaller ones because their position allows them to benefit from economies of scale. So, compared to small firms, units can be produced at a lower cost.
(Doğan, 2013), analyzing data from 200 listed companies on the Istanbul Stock Exchange, revealed a positive correlation between firm size and profitability. (Jonsson, 2007) also came to this conclusion. His study found out that large firms have higher profitability than small ones. While (Niresh & Velnampy, 2014) in a study of 15 firms in the trade sector discovered a neutral relationship between the firm's profitability and size. The results of their study showed that firm size had no impact on its profitability.
As for the turnover indicators, as far as the results of empirical studies are concerned, different studies have different results. Literature offers mixed results; positive, negative, or neutral relationship between assets turnover and financial profitability indicators.
(Skolnik, 2002) study found that a steady decline in asset turnover was offset by an increase in operating income, thus not having a significant impact on operational return. The study also showed that profit margins and asset turnover both contribute to profitability and that there is a statistically significant negative correlation between total assets turnover and operating profit margin
(Balili, 2016) in a study of the pharmaceutical companies in Albania revealed: a statistically significant impact of shortterm assets turnover and profitability, a positive and fixed impact of total assets turnover in financial profitability; a fixed and negative impact of the accounts receivable turnover and accounts payable turnover on the profitability of the companies.
3. Methodology
3.1. Source of Data and Population of Study
The initial phase of the study was to identify the activity of large businesses, which would constitute the population of the study. For this reason, a general analysis of all businesses operating in the Vlora Region was conducted.
The distribution of businesses by economic sectors was presented graphically in the introduction section. The two sectors with the highest share are the trade sector and the service sector. Large businesses (businesses with an annual turnover more than 8 mln ALL) operating in the trade sector are chosen because this sector not only has the highest share, but also because it often functions as a provider of the service sector. So, the population of the study consists of all the large businesses operating in the trade sector, which are under the administration of the Vlora Regional Tax Directorate. Within the big business bundle, 49 businesses with the highest total annual sales were selected. The total sales of the selected sample account for approximately 52% of the sales of all the population, so we can say that this is a representative sample. The data used in this paper are secondary data provided by certified financial statements of the surveyed companies, mainly from the balance sheet and the income statement. Subsequently, they are used to calculate the financial ratios which will be used in the econometric model.
3.2. Variables Used in the Study
The dependent variable in this study is the financial profitability of the companies, represented by ROA. The independent variables are chosen taking into consideration the literature study discussed previously, selecting the most commonly used variables in similar econometric models.
Table 3. Variables of the model and their calculation
Variable 
Indicator 
Measurement 
Dependent Variable 
ROA 
Net Income/Average Total Assets 
Independent Variables 
Short term debt (SHTD) 
Short term debt/Total Assets 
Long term debt (LTD) 
Long term debt/Total Assets 

Current Ratio (CR) 
Current Assets/Current Liabilities 

Quick Ratio (QR) 
(Current AssetsInventory)/Current Liabilities 

Cash Convertion Cycle(CCC) 
Average Collection Period+ Average Inventory PeriodAverage Payment Period 

Working Capital turnover (WCT) 
Net Sales/Working Capital 

Accounts Receivable turnover (ART) 
Net Credit Sales/Average Acounts Receivable 

Accounts Payable Turnover (APT) 
Total Supplier Purchases/Average Accounts Payable 

Inventory turnover (IT) 
Cost of sales/Average inventory 

Age(K) 
Number of years 

Size 
LOG(Net Sales) 
3.3. Empirical Analysis
The model is expected to have the form of a multifactorial linear regression with a generalized form as follows:
Financial profitability = f (explanatory variables i) = f (x_{1i}, x_{2i}, x_{3i}, x_{ni})
All dependent and independent variables are grouped into time series of crosssections. The study contains 49 crosssections crossed out in 3 time periods (20142016) generating matrices of 147 observations. Panel data will be used to estimate the fixed or random time effect that the independent variables have on the dependent variable.
The general form of the panel data model is presented in the following equation:
y_{it} = β_{0} + β_{1xit1} + β_{2xit2} + ... + β_{kxitk} + a_{i} + u_{it}
The fixed effect model
Table 4. Results of the fixed effects model
Dependent Variable: ROA 



Method: Panel Least Squares 



Sample: 2014 2016 



Periods included: 3 



Crosssections included: 49 



Total panel (unbalanced) observations: 145 


Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 

C 
0.282987 
0.054839 
5.160309 
0.0001 

CCC 
0.000304 
0.000169 
1.790224 
0.0006 

K 
0.025324 
0.071700 
0.353200 
0.7248 

LOGS 
0.041139 
0.209362 
0.196499 
0.8447 

IT 
0.000189 
0.001460 
0.129609 
0.0013 

WCT 
0.002489 
0.006313 
0.394332 
0.6943 

ART 
0.000432 
0.000309 
1.396404 
0.0001 

APT 
1.80E05 
2.1E06 
8.345670 
0.0167 

LTD 
0.041428 
0.155259 
0.266831 
0.0058 

SHTD 
0.094958 
0.956746 
0.099251 
0.0071 

CR 
0.000513 
0.001242 
0.413547 
0.6802 

QR 
0.001439 
0.003754 
0.383365 
0.7024 


Effects Specification 



Crosssection fixed (dummy variables) 


Rsquared 
0.855107 
Mean dependent var 
0.135586 

Adjusted Rsquared 
0.797522 
S.D. dependent var 
0.653220 

S.E. of regression 
0.032603 
Akaike info criterion 
2.266896 

Sum squared resid 
0.082910 
Schwarz criterion 
3.498648 

Log likelihood 
104.3500 
HannanQuinn criter. 
2.767399 

Fstatistic 
1.031446 
DurbinWatson stat 
1.953294 

Prob(Fstatistic) 
0.000000 



According to the Fisher test, this model is statistically significant (p> 5%).
The model has a high determination coefficient, R^{2} corrected is about 89%.
The random effects model
Table 5. Results of the random effects model
Dependent Variable: ROA 



Method: Panel EGLS (Crosssection random effects) 

Sample: 2014 2016 



Periods included: 3 



Crosssections included: 49 



Total panel (unbalanced) observations: 145 


Swamy and Arora estimator of component variances 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 

C 
1.319722 
0.333763 
3.954059 
0.0001 

CCC 
0.000412 
0.000129 
3.184177 
0.0317 

K 
0.028597 
0.013281 
2.153318 
0.0331 

LOGS 
0.081981 
0.085615 
0.957551 
0.3400 

IT 
0.000296 
0.000975 
0.303716 
0.0045 

WCT 
0.000493 
0.005372 
0.091803 
0.9270 

ART 
7.60E06 
4.16E05 
0.182643 
0.0132 

APT 
5.56E06 
4.37E05 
0.127330 
0.0003 

LTD 
0.055660 
0.231863 
1.538002 
0.0000 

SHTD 
0.034959 
0.036189 
1.871668 
0.0001 

CR 
0.000956 
0.001011 
0.946460 
0.3456 

QR 
0.004165 
0.002397 
1.737157 
0.0847 


Effects Specification 






S.D. 
Rho 

Crosssection random 
0.123347 
0.0349 

Idiosyncratic random 
0.649052 
0.9651 


Weighted Statistics 



Rsquared 
0.507956 
Mean dependent var 
0.128835 

Adjusted Rsquared 
0.447705 
S.D. dependent var 
0.640238 

S.E. of regression 
0.679772 
Sum squared resid 
54.28103 

Fstatistic 
8.430758 
DurbinWatson stat 
1.178858 

Prob(Fstatistic) 
0.000003 





Unëeighted Statistics 



Rsquared 
0.627390 
Mean dependent var 
0.135586 

Sum squared resid 
72.60002 
DurbinWatson stat 
2.924679 
The random effect model finds some of the independent variables statistically significant at p <5% (based on ttest, except of K, LOGSH, WCT, CR). The level of determination with R squared is approximately 45%.
Both models are statistically significant, so the Hausman test will be used. The Hausman test is performed to determine which model is most appropriate between the fixed and random effects model. The null hypothesis is that the preferred model is the random effects model. The alternative hypothesis is that the preferred model is the fixed effects model.
Ho = Random effect
Ha = Fixed effect
Table 6. Hausman Test
Correlated Random Effects  Hausman Test 


Equation: Untitled 



Test crosssection random effects 


Test Summary 
ChiSq. Statistic 
ChiSq. d.f. 
Prob. 

Crosssection random 
34.921606 
11 
0.0000 
As we see from the results of the Hausman test, the value of the statistics is less than the value of chi square table. This means that Ha stands, so the best model to use is the fixedeffect model (p <5%).
We conclude that the variables: accounts receivable turnover, accounts payable turnover, inventory turnover, cash conversion cycle, long term debt, short term debt give a fixed impact on ROA, which means that these results can be used for further forecast.
According to the results of Table 3, the estimated equation will be:
ROA=0.2829870.000304CCC0.025324K+0.041139LOG(S)0.000189IT0.002489WCT0.000432ART+0.00001.8APT0.041428LTD0.094958SHTD0.000513RK+0.001439QR
4. Conclusion
This paper tried to assess the relationship between financial profitability and factors such as debt structure, liquidity situation, turnover indicators, size and age of companies in a sample of 49 large businesses operating in the trade sector. An econometric model was built, organizing and integrating the data into the multiple regression model in the form of panel data, in order to answer the research questions.
In a more detailed way, the empirical findings suggest the following conclusions:
The results of the model were in line with the initial expectations related to the longterm and shortterm debt variables and confirmed the negative relationship between these variables and ROA. Among these two variables, the short term debt ratio had the highest impact with a coefficient of approximately 9.49%. So, an increase of 1% in SHTD will cause a steady decrease of ROA by 9.49%. As for the long term debt, the 4.14% coefficient shows that an increase of 1% of LTD will cause a fixed decrease of 4.14% of ROA. This may result due to the fact that the activity of these companies relies more on shortterm financing from customers rather than on longterm borrowing.
Regarding the turnover indicators taken in consideration, the model presents the following results:
The impact of the accounts payable turnover is statistically significant, but with a low positive impact level. APT had a positive effect on ROA with a low coefficient of 0.000018. The impact is low, but as it is a fixed impact, managers can increase the company’s ROA in the future by speeding up the accounts payable turnover.
The results of the model show that the inventory turnover is a statistically significant variable with a p <5% level and has given a fixed negative impact on ROA with a coefficient of 0.000189. The level of impact is still low, but being a fixed effect it can be used to improve the profits. Turnover of accounts receivable shows how many times the receivables are received on average over the year. According to the model results, the impact of this variable on the profitability of the companies taken in the study is considered significant, but with a low negative impact. A decrease in the turnover of receivables that may directly result from the increase in the average collection period would positively influence ROA. Regarding the liquidity situation only the cash conversion cycle indicator turned out to be statistically significant. This result is expected since the CCC is considered one of the most important dynamic variables of liquidity. The negative statistical relation between ROA and CCC shows that the lower the CCC, the higher the ROA. A oneday reduction in the money conversion cycle will bring a ROA increase of 0.0304%. More specifically, if we refer to the calculation of this indicator (CCC = Average Collection Period+Average Inventory PeriodAverage Payment Period), the company may consider reducing Average Collection Period and Average Inventory Period and prolonging the Average Payment Period in order to increase profitability.
The age indicator of the firms is measured by the number of years from the moment of their establishment. This indicator has not resulted to be statistically significant, so these entities are profitable regardless of the time when they were founded.
Other indicators included in the model, such as the firm size represented by net sales logs as well as some liquidity indicators such as quick ratio, current ratio and working capital turnover not only resulted numerically negligible but are statistically insignificant (with a value of p> 5%). For these variables the model results did not match with those of the reviewed literature. This discrepancy may exist because of the quality of the data stated in the financial statements. It may also be necessary to expand the population surveyed or extend the study period to reach more accurate conclusions. Therefore, we can conclude that these findings were partially in line with the reviewed literature.
5. Recommendations
Recommendations for future studies that will have the same focus on the issues of this study:
Consider other dimensions and indicators of profitability in order to provide a more complete picture of financial profitability.
Make efforts to collect the most reliable and valuable data, not based solely on the financial statements of the entities, the accuracy of which is questioned, especially in the Albanian reality.
Select a larger sample or extend their study over time to reach more accurate results and closer to theoretical conclusions.
Recommendations could also be given for the companies that made up the sample of this study and those represented by this sample. These companies can improve their profitability and financial position by improving the management process of their activity. Based on the conclusions drawn from the results of empirical models, it is suggested that liquidity management relies more on dynamic rather than static indicators.
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1 MSc, University of Tirana, Faculty of Economy, Albania, Address: Sheshi Nënë Tereza, no. 183, Tiranë, Shqipëri, Albania, Corresponding author: kristi.dashi111@gmail.com.
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