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The Determinants of Financial Performance in the Romanian Insu rance
Market
Article · Februar y 2014
DOI: 10.6007/IJAR AFMS/v4-i1/637
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International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4, No.1, January 2014, pp. 299–308
E-ISSN: 2225 -8329 , P-ISSN: 2308 -0337
© 2014 HRMARS
www.hrmars.com

The Determinants of Financial Performance in the Roma nian Insurance
Market

Ana-Maria B URC A1
Ghiorghe BA TRÎNCA2
1Faculty of Finance, Insurance, Banking and Stock Exchange, Department of Finance, Bucharest University of Economic
Studies, Bucharest, Romania , 1E-mail: [anonimizat]
2Faculty of Navigation and Naval Transport, Department of Economic Engineering in Transports, Constanta Maritime
University , Constanta, Romania , 2E-mail: [anonimizat]

Abstract The financial analysis of a company is an important tool used by actuaries in the process of decision –
making on underwriting and investment activities of the insurance company. The financial performance
of insurance companies is also relevant within the macroeconomic context since the insur ance industry is
one of the financial system’ components, fostering e conomic growth and stability. The financial
performance of insurance companies can be analyzed at micro and macroeconomic level, being
determined both by internal factors represented by s pecific characteristics of the company, and external
factors regarding connected institutions and macroeconomic environment. This study attempts to
analyze the determinants of the financial performance in the Romanian insurance market during the
period 200 8–2012. According to the final results achieved by applying specific panel data techniques, the
determinants of the financial performance in the Romanian insurance market are the financial leverage in
insurance, company size, growth of gross written premiu ms, underwriting risk, risk retention ratio and
solvency margin.
Key words Financial performance, Romanian insurance market, panel data, fixed effects model, random effects
model

DOI: 10.6007/IJARAFMS/v4 -i1/637 URL: http: //dx.doi.org/10.6007/IJARAFMS/v4 -i1/637

1. Introduction
Performance represents a difficult concept, both in terms of definition and quantification. It was
defined as output of activity, and the appropriate measure selected to assess corporate performance is
considered according to the organization type and objectives of evaluation. Researchers in strateg ic
management have offered a variety of models that can be used to analyze financial performance.
Nevertheless, there is no consensus on what constitutes a va lid set of performance criteria (Ostroff and
Schmidt , 1993).
Profitability, defined as proxy of fi nancial performance, is one of the main objectives of insurance
companies’ management. Profit is an essential prerequisite for an increasing competitiveness of a company
that operates in a globalized market. In addition, profit attracts investors and impro ves the level of
solvency, and thus, strengthens consumers’ confidence. The financial analysis of a company is an important
tool used by actuaries in the process of decision -making on underwriting and investment activities of the
insurance company. The fin ancial performance of insurance companies is also relevant within the
macroeconomic context since the insurance industry is one of the financial system’ components, fostering
economic growth and stability.
The financial performance of insurance companies can be analyzed at micro and macroeconomic
level, being determined both by internal factors represented by specific characteristics of the company, and
external factors regarding connected institutions and macroeconomic environment.
Identifying the facto rs that contribute to insurance companies’ profitability is useful for investors,
researchers, financial analysts and supervisory authorities.

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 2014 HRMARS

300 Although there are numerous approaches, generally, insurers’ profitability is estimated through the
examination of premium and investment income and of the underwriting results or of the overall operating
performance. In order to get an accurate picture of insurers’ profitability, it is important to consider the
total loss or benefit resulting from the operations pe rformed during several years, as any insurance
company can have one unprofitable year, which is compensated by a certain form of profitabil ity achieved
over several years (Kearney , 2010).
According to experts, the Romanian insurance market is below its pot ential, but has a significant
value of approximately 2 billion €. In terms of profitability as proxy of financial performance, the insurance
companies from Romania are facing the combined effects of deteriorating market conditions and financial
crisis impa ct.

2. Literature review
Research papers on performance in insurance industry are scarce, and most of the papers on
financial performance are focused on banks. As for performance in insurance industry, most of the studies
are recent, being performed after 2 000. Among those performed before 2000, we can mention Spiller
1972, Chidambaran et al . 1997, Cummins and Weiss 1998, Genetay 1999.
Within the context of rapid growth and development of offshore financial centres, Adams and Buckle
(2003) examine the determ inants of operational performance in the Bermudian insurance market, during
1993 –1997. By applying a model of panel data to 47 insurance companies, the authors highlight the fact
that firms with high leverage, low liquidity and reinsurers have better oper ational performance than those
situated to the opposite pole. In terms of underwriting risk, contrary to expectations, the results indicate a
positive relationship between this type of risk and insurers’ operational performance. Also, it was shown
that com pany size and scope of activities are not factors with explanatory power.
Shiu (2004) analyzes the determinants of the performance of the UK general insurance companies,
over the period 1986 –1999, by using three key indicators: investment yield, percentage change in
shareholders’ funds and return on shareholders’ funds. Based on a panel data set, the author empirically
tested 12 explanatory variables and showed that the performance of insurers have a positive correlation
with the interest rate, return on eq uity, solvency margin and liquidity, and a negative correlation with
inflation and reinsurance dependence.
Ćurak et al . (2011) examine the determinants of the financial performance of the Croatian composite
insurers, between 2004 and 2009. The determinant s of profitability, selected as explanatory variables
include both internal factors specific to insurance companies and external factors specific to the economic
environment. By applying panel data technique, the authors show that company size, underwritin g risk,
inflation and return on equity have a significant influence on insurers’ profitability. The final results indicate
that the Croatian insurance market has a low level of development, but it is very dynamic.
Nowadays, insurance is one of the most pr ofitable activities in European economies. Based on this
reality, Ikonić et al . (2011) analyze the profitability of the Serbian insurance companies by applying the IMF
CARMEL methodology. Thus, by determining 4 indicators related to the capital adequacy of insurers, the
authors highlight that capital adequacy is vital for a company, as it may generate a good level of
profitability. Their analysis indicates that the Serbian insurance market falls into the category of developed
markets and that there are good perspectives of evolution.
The integration of a country’s financial system within the EU markets significantly affects the
profitability of the insurance sector. Based on these major changes, Kozak (2011) analyzes the
determinants of the profitability of 25 general insurance co mpanies from Poland during 2002 –2009. By
applying a regression model, the author notices that the reduction of motor insurance and simultaneously
the increase of other classes of insurance, growth of gross written premiums, operatin g costs reduction,
GDP growth and growth of the market share of the companies with foreign ownership have a positive
impact on insurance companies during the period of integration. In contrast, providing a wide range of
insurance classes affects negatively the profitability and the expenses efficiency.
For a better understanding of the financial performance of the insurance sector from Pakistan, Malik
(2011) examines 35 insurance comp anies, during the interval 2005 –2009, by applying a multiple regression
with 6 variables. Results emphasize that company size and volume of equity affects positively and

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 201 4 HRMAR S

301 significantly the profitability of insurers, while leverage and loss ratio have a negative influence. The last
variable tested, company age, does not affect th e profitability of insurance companies.
In countries with less developed economy, the insurance industry does not have an essential role in
fostering economic growth due to the weak financial performance of insurers. In order to identify the
factors that affect the financial performance of the Jordanian insurance market, Almajali et al . (2012)
analyze the insurance companies listed on the Amman Stock Exchange during 2002 -2007, by applying tests
and multiple regressions. Their study shows that, in terms of financial performance, liquidity, leverage,
company size and management competence index have a statistical positive effect on insurers. In this
context, their recommendations include increasing of assets’ number and hiring competent managers.
Life insura nce companies manage significant amounts of money and, therefore, supervisory
authorities monitor their financial performance. The first study of the financial performance of the Indian
life insurers belongs to Charumathi (2012), who took into account a nu mber of 6 independent variables. In
India, life insurers’ profitability is significantly and positively influenced by company size and liquidity, while
leverage, growth of gross written premiums and volume of equity have a negative and significant influenc e.
Moreover, it can be noticed that there is no linkage between underwriting risk and profitability. Concluding,
in order to improve the performance of insurance companies, the author provides certain
recommendations regarding the supervisory authority and competition in the insurance market, capital
market participation, streghtening connections with banks and increasing foreign direct investment.
Bosnia – Herzegovina is another developing country whose insurance sector is examined in terms of
performanc e. Pervan et al . (2012) studied the factors that affected the profitability of the insurance
companies between 2005 and 2010, in the context of the radical changes that occurred within this industry.
By using a dynamic panel model with GMM estimator, the e mpirical analysis shows a significant and
negative influence of the loss ratio on profitability and a significant and positive influence of age, market
share and past performance on current performance. It was also found that diversification does not
signi ficantly influence profitability, and foreign -owned companies were more efficient.
In developing countries, the importance of the insurance industry as an essential component of the
financial system it is not fairly appreciated. In this context, Mehari an d Aemiro (2013) assess the impact of
the Ethiopian insurance companies’ characteristics on their performance. The study includes 9 insurance
companies which are analyzed through pa nel data technique, during 2005 –2010. According to the results,
company size , loss ratio, tangibility and leverage represent important determinants of insurers’
performance, while growth of gross written premiums, age and liquidity have an insignificant statistical
power.

3. Data and methodology
This study attempts to analyze the d eterminants of the financial performance in the Romanian
insurance market. For the Romanian insurance market, no study on the insurers’ financial performance was
performed. Therefore, this analysis improves the understanding of the Romanian insurance marke t and can
provide useful information to insurance companies, investors, experts and supervisory authorities.
Depending on data availability, out of the 41 insurance companies that operated in the Romanian
insurance market in 2012, 21 companies were select ed; in terms of total market share, these companies
accounted approximately 70% in 2012. Annual data were collected from the annual reports of the
Insurance Supervisory Commission and from the insurers’ financial statements. The econometric model
was perfo rmed with EViews 7.
In order to determine the factors that influence the financial performance in the Romanian insurance
market during the interval 2008 – 2012, 13 explanatory variables were tested: insurance financial leverage,
company size, number of yea rs since the company operates in the Romanian market, growth of gross
written premiums, equity, total market share, diversification, underwriting risk, investment ratio,
reinsurance dependence, retained risk ratio, solvency margin and growth of GDP/capita. As for the
dependent variable, the financial performance of the insurance companies is measured through the return
on total assets ratio. It is expected that the evolution of the independent variables to explain the evolution
of the dependent variable.

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 2014 HRMARS

302 The return on total assets ratio represents one of the most used methods of quantifying financial
performance. It was developed in 1919 by Dupont and it emphasizes the company’s ability to efficiently use
its assets. ROA (Return on Assets) is computed as the ratio of net income to total assets.
The insurance financial leverage is calculated as the ratio of net technical reserves to equity, and
reflects the potential impact of technical reserves’ deficit on equity in the event of unexpected losses. A
negative linkage between the insurance financial leverage and the insurers’ financial performance is
expected.
Company size is computed as decimal logarithm of total assets of the insurance company. A positive
linkage between company size and its financial perform ance is expected, since larger firms have more
resources, a better risk diversification, complex information systems and a better expenses management.
As for the number of years since the insurer operates in the Romanian insurance market, a positive
linkag e between this variable and the insurer’s financial performance is expected, because the company
gets a certain reputation, a greater experience and designs efficient strategies over the years.
The growth of the gross written premiums is expected to have a positive influence on financial
performance as a result of an increased underwriting activity and market share expansion.
With regard to equity measured through decimal logarithm, a positive connection between their
volume and insurers’ financial perform ance is expected, given that a greater flow of equity generates a
better financial stability and the possibility of expanding the business. In Romania, the Solvency II regime
requires insurance companies to hold adequate equity by properly determining thei r technical reserves,
solvency capital and minimum capital.
Total market share shows the position of the insurance company in the insurance market, and it is
determined based on the amount of the insurer’s gross written premiums. A positive linkage betwee n the
total market share and the insurer’s financial performance is expected, since a good position in the market
provides benefits in terms of expenses, capital, innovation and reputation.
Diversification is measured through Herfindahl index, which is co mputed as:
2
1



n
ii
HTPBSPBSI where
iPBS
represents the gross written premiums of the business line “i” of the insurer and TPBS represents the
total gross written premiums of the insurer. The higher the Herfindahl index is, the higher the business
concentration and the lower the diversification is, and vice versa. Diversification is expected to have a
positive influence on the insurers’ financial performance, because it provides various advantages, such as
risk reduction and growth of m arket power.
The underwriting risk emphasizes the efficiency of the insurer’s underwriting activity and it is
measured through the loss rate, which is computed as a ratio of gross claims to gross written premiums. A
negative connection between the underwri ting risk and the insurer’s financial performance is expected,
since taking an excessive underwriting risk can affect the company’s stability through higher expenses.
The investment ratio is computed by dividing investments to total assets, being expected a positive
influence of this variable on the financial performance, as investments generate investment income.
The reinsurance dependence is calculated as ratio of gross written premiums ceded in reinsurance to
total assets. Insurance companies reinsure a certain amount of the risk underwritten in order to reduce
bankruptcy risk in the case of high losses. Although reinsurance improves the stability of the insurance
company through risk dispersion, achievement of solvency requirements, risk profile equili bration and
growth of the underwriting capacity, it involves a certain cost. Therefore, a negative connection between
the reinsurance dependence and the insurer’s financial performance is expected.
The retained risk ratio is computed as ratio of net writte n premiums to gross written premiums, and
reflects the proportion of the underwritten risk retained by the insurer, the difference being ceded in
reinsurance. This variable is expected to have a positive influence on the insurer’s financial performance, as
reinsurance involves a certain cost.
The solvency margin is calculated as ratio of net assets to net written premiums, and represents a
key indicator of the insurer’s financial stability. A positive linkage between this variable and the insurer’s
financia l performance is expected, since the insurer’s financial stability is an important benchmark to
potential customers.

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 201 4 HRMAR S

303 Regarding the last independent variable, the growth of real GDP/capita, it is a macroeconomic
variable, and it is expected to have a posit ive influence on the insurers’ financial performance, since
economic growth improves the living standards and the levels of income, increasing the purchasing power
of population.
This study tests the following hypotheses, which were building based on the connections between
the explanatory variables and the dependent variable:
H1: There is a negative linkage between financial leverage in insurance and return on total assets
ratio.
H2: There is a positive linkage between company size and return on total ass ets ratio.
H3: There is a positive linkage between the number of years since the company operates in the
Romanian insurance market and the return on total assets ratio.
H4: There is a positive linkage between growth of gross written premiums and return on total assets
ratio.
H5: There is a positive linkage between equity and return on total assets ratio.
H6: There is a positive linkage between total market share and return on total assets ratio.
H7: There is a positive linkage between diversification and return on total assets ratio.
H8: There is a negative linkage between underwriting risk and return on total assets ratio.
H9: There is a positive linkage between investment ratio and return on total assets ratio.
H10: There is a negative linkage between rein surance dependence and return on total assets ratio.
H11: There is a positive linkage between retained risk ratio and return on total assets ratio.
H12: There is a positive linkage between solvency margin and return on total assets ratio.
H13: There is a p ositive linkage between growth of real GDP/capita and return on total assets ratio.
Based on these hypotheses, we can build the multiple regression of the econometric model
through which the financial performance of the insurance companies in the Romanian market is analyzed:

       
      
GDP Solvency t Risk insInv LossRatio Div MkShare Equity GWP Age Size Lev ROA
13 12 11 109 8 7 6 5 4 3 2 1 0
Re Re
(1)

Where :
ROA – return on total assets ratio;
Lev – financial leverage in insurance ;
Size – company size ;
Age – number of years since the company operates in the Romanian insurance market ;
GWP – growth of gross written premiums ;
Equity – equity ;
MkShare – total market share ;
Div – diversification ;
LossRatio – underwriting risk ;
Inv – investment ratio ;
Reins – reinsurance dependence ;
RiskRet – retained risk ratio ;
Solvency – solvency margin ;
GDP – growth of real GDP/capita .

All the variables were organized in a balanced panel database, which was analyzed by applying
models with fixed effects and with random effects.
Panel data comprise data sets consisting of multiple observations for each sampling unit. By using
panel data, we can get better estimations and we can test more sophisticated behavioural models, with
less restrictive assumptions (Baltagi , 2008).
Working with panel data allows using various techniques to estimate models with specific effects.
The cross -sectional or cross -temporal specific effects can be identified and analyzed by using techniques for
fixed effects and random effects.

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 2014 HRMARS

304 4. Empirical analysis
This part of the study highlights the results achieved by applying the specific analysis techniques o f
panel data in order to identify the determinants of financial performance in the Romanian insurance
market. In the first instance, the group stationarity of each variable is tested through Levin, Lin & Chu test.
Since the variable real GDP/capita is con stant for all the companies, in its case, the ADF (Augmented
Dickey -Fuller) test is applied. From an economic point of view, shocks to a stationary time series are
temporary and, over time, the effects of the shocks will be absorbed. The absence of a unit root for the
original data was estimated for all the variables, the data being stationary. If the probability is lower than
the significance level of 10%, the variable is stationary (Table 1).

Table 1 . Group stationarity of variables

Variable Statistic Probability
ROA -30.7068 2.307797538599119e -207
Lev -176.378 0
Size -6.11792 4.740271489308685e -10
Age – –
GWP growth -18.7249 1.550529529574853e -78
Loss ratio -8.29454 5.450396026430489e -17
Equity -93.1244 0
Market share -13.5799 2.634855453889327 e-42
Diversification -83.5929 0
Investment -17.1258 4.761868333360074e -66
Reinsurance -8.25772 7.424178081798519e -17
Risk retention -1.91091 0.02800823716090469
Solvency -91.2361 0
GDP per capita (ADF) -3.615401 0.0666452169127152

Source: Own estim ations with Eviews 7

Table 2 shows the descriptive statistics of each variable, computed based on the 105 observations
recorded. It can be noticed that the return on total assets ratio fluctuates between -60.99% and 15.39%,
with an average value of -3.69% , due to the fact that certain insurance companies had negative financial
results. ROA deviates from the average value with about 13.03%.

Table 2. Descriptive statistics of variables

Variable Observations Mean Standard deviation Minimum Maximum
ROA 105 -0.0369 0.1303 -0.6099 0.1539
Lev 105 2.4523 2.7469 0.0019 12.7388
Size 105 8.2051 0.7493 6.9143 9.4048
Age 105 12.2381 5.6322 1 22
GWP growth 105 0.6338 2.901 -0.4925 28.1347
Loss ratio 105 0.3608 0.298 0 1.2758
Equity 105 7.6643 0.5476 6.4946 8.95 32
Market share 105 0.03 0.0402 0 0.1549
Diversification 105 0.5067 0.2457 0.1742 1
Investment 105 0.566 0.2712 0.004 0.9398
Reinsurance 105 0.1077 0.1887 0 1.1648
Risk retention 105 0.8113 0.2181 0.0743 1
Solvency 105 8.7777 50.4535 0.8337 520.0484
GDP per capita 105 0.007 0.0456 -0.064 0.075

Source: Own estimation with Eviews 7

Prior to design panel data models, it is necessary to verify the problem of multicollinearity between
independent variables. In this respect, the matrix of Pearson correl ation coefficients is computed. A value
of the Pearson coefficient higher than 0.7 indicates a strong correlation, which can be identified between
company size and equity, company size and total market share, equity and total market share, reinsurance
depe ndence and retained risk ratio (Table 3).

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 201 4 HRMAR S

305 Table 3. The matrix of Pearson correlation coefficients

Variable Lev Size Age GWP Loss ratio Equity Mk share Div Inv Reins Risk ret Solvency GDP
Lev 1 0.6566 0.2181 -0.1309 -0.0854 0.4225 0.3981 -0.089 0.2062 -0.2888 0.4137 -0.1047 0.0251
Size 1 0.5795 -0.1904 0.4352 0.9335 0.8328 -0.3654 0.1053 -0.1545 0.2507 -0.1431 -0.0298
Age 1 -0.275 0.4576 0.6138 0.5806 -0.4756 -0.1767 -0.128 0.2351 -0.2144 -0.0338
GWP growth 1 -0.1739 -0.149 -0.1278 0.1692 0.0858 -0.0641 0.0794 0.0027 -0.0843
Loss ratio 1 0.4941 0.403 -0.5394 -0.1978 0.0516 -0.104 -0.1378 -0.0647
Equity 1 0.8594 -0.358 0.1754 -0.1644 0.2335 -0.0873 -0.0567
Market share 1 -0.4262 -0.0794 -0.074 0.1093 -0.0972 -0.0094
Diversificatio n 1 0.3353 -0.1458 0.1638 0.2164 -0.0803
Investment 1 -0.2134 0.2006 0.1331 -0.0098
Reinsurance 1 -0.8768 -0.0452 0.0117
Risk retention 1 0.0722 -0.029
Solvency 1 0.1403
GDP 1

Source: Own esti mations with Eviews 7

As the issue of multicollinearity may affect the final results of the models, the variables equity, total
market share and reinsurance dependence will be eliminated.

Table 4. Fixed effects model

Variable Coefficient Std. Error t-Statistic Prob.
C -2.040832 0.486596 -4.194100 0.0001
LEV? -0.018332 0.007975 -2.298838 0.0243
SIZE? 0.228612 0.060112 3.803114 0.0003
AGE? -0.000457 0.005680 -0.080430 0.9361
GWP? -0.008147 0.002669 -3.051985 0.0032
LOSS_RATIO? -0.101678 0.048133 -2.112433 0.0380
DIV? 0.010003 0.057023 0.175416 0.8612
INVEST? -0.027209 0.083204 -0.327016 0.7446
RISK_RET? 0.278866 0.098000 2.845573 0.0057
SOLVENCY? 0.000610 0.000159 3.825790 0.0003
GDP -0.099621 0.148344 -0.671551 0.5040
R-squared = 0.827822; A djusted R -squared = 0.758020;
F-statistic = 11.85958; Prob(F -statistic) = 0.000000.
Source: Own estimations with Eviews 7

Table 4 shows the results of the fixed effects model. It can be noticed that the variables number of
years since the company operates in the Romanian insurance market; diversification, investment ratio and
growth of real GDP/capita are not statistically significant, because the probabilities associated to
coefficients are higher than the significance level of 10%. The value of R -squared shows that the
independent variables explain 82.78% of the entire panel’s variations. The model is appropriate because F –
statistic has a value of 11.86% at a significance level of 1%.

Table 5. Random effects model

Variable Coefficient Std. Error t-Stati stic Prob.
C -0.841411 0.226298 -3.718155 0.0003
LEV? -0.014830 0.006284 -2.359864 0.0203
SIZE? 0.103133 0.029926 3.446268 0.0009
AGE? 0.002244 0.003288 0.682577 0.4966
GWP? -0.011426 0.002554 -4.473593 0.0000
LOSS_RATIO? -0.141981 0.041382 -3.430990 0.0009
DIV? 0.034498 0.047699 0.723244 0.4713
INVEST? 0.026104 0.050794 0.513926 0.6085
RISK_RET? -0.010992 0.063258 -0.173767 0.8624
SOLVENCY? 0.000414 0.000149 2.774601 0.0067
GDP -0.185837 0.144363 -1.287291 0.2012
R-squared = 0.343337; Adjusted R-squared = 0.273480;
F-statistic = 4.914811; Prob(F -statistic) = 0.000011.

Source: Own estimations with Eviews 7

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 2014 HRMARS

306 As for the presence of cross -sectional effects, Table 5 illustrates the results of the random effects
model. It can be noticed that the va riables number of years since the company operates in the Romanian
insurance market, diversification, investment ratio; retained risk ratio and growth of real GDP/capita are
not statistically significant, because the probabilities associated to coefficient s are higher than the
significance level of 10%. The value of R -squared shows that the independent variables explain 34.33% of
the entire panel’s variations. The model is appropriate because F -statistic has a value of 4.91% at a
significance level of 1%.
Despite the fact that the results of the fixed effects model are better than those of the random
effects model, since both models are valid, the Hausman test will be performed. According to Table 6, the
results of the fixed effects model are better than tho se of the random effects model, as the Chi -Sq. value of
37.09 is significant at a significance level of 1%.

Table 6. The Hausman test

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross -section random 37.085363 10 0.0001

Source: Own estimations w ith Eviews 7

Since there are no cross -sectional effects, the fixed effects model is appropriate in this case. In order
to get relevant results, the model will be re -estimated by eliminating variables statistically insignificant,
namely, number of years si nce the company operates in the Romanian insurance market, diversification,
investment ratio and growth of real GDP/capita (Table 7).

Table 7. Fixed effects model re -estimated

Variable Coefficient Std. Error t-Statistic Prob.
C -2.047366 0.407892 -5.019 389 0.0000
LEV? -0.019566 0.007501 -2.608503 0.0109
SIZE? 0.227831 0.047016 4.845774 0.0000
GWP? -0.007997 0.002533 -3.157237 0.0023
LOSS_RATIO? -0.099219 0.045565 -2.177532 0.0325
RISK_RET? 0.276965 0.089010 3.111615 0.0026
SOLVENCY? 0.000599 0.0001 44 4.169928 0.0001
R-squared = 0.826189; Adjusted R -squared = 0.768252;
F-statistic = 14.26012; Prob(F -statistic) = 0.000000.

Source: Own estimations with Eviews 7

According to Table 7, all variables are statistically significant and explain 82.62% of the entire panel’s
variations. The model is appropriate because F -statistic has a value of 14.26% at a significance level of 1%.
In order to test the cross -sectional effects, the random effects model will be computed, according to
the new number of variab les taken into account (Table 8).

Table 8. Random effects model re -estimated

Variable Coefficient Std. Error t-Statistic Prob.
C -0.905023 0.200049 -4.524005 0.0000
LEV? -0.016692 0.005994 -2.784747 0.0064
SIZE? 0.115936 0.025089 4.620987 0.0000
GWP? -0.011166 0.002455 -4.548109 0.0000
LOSS_RATIO? -0.138941 0.039693 -3.500399 0.0007
RISK_RET? 0.014089 0.059628 0.236283 0.8137
SOLVENCY? 0.000408 0.000139 2.925267 0.0043
R-squared = 0.326368; Adjusted R -squared = 0.285125;
F-statistic = 7.913333; Prob(F -statistic) = 0.000001.

Source: Own estimations with Eviews 7

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 201 4 HRMAR S

307 According to Table 8, the variable retained risk ratio is statistically insignificant. The independent
variables explain 32.64% of the entire panel’s variations. The model is appropriate because F -statistic has a
value of 7.91% at a significance level of 1%.
Obviously, the results of the fixed effects model are superior to those of the random effects model,
also confirmed by the Hausman test (Table 9), as the Chi -Sq. value of 38.44 is sig nificant at a significance
level of 1%.
Table 9. The Hausman test

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross -section random 38.445097 6 0.0000

Source: Own estimations with Eviews 7

Therefore, the fixed effects model is relevant to our an alysis, confirming the absence of the cross –
sectional effects. In this case, the performance of the insurance companies in the Romanian market,
measured through the return on total assets ratio, can be illustrated as follows:

Solvency t RiskLossRatio GWP Size Lev ROA
* 0.000599 Re * 0.276965 * -0.099219)( * -0.007997)( * 0.227831 * -0.019566)( 2.047366-
      
(2)

With regard to the hypotheses tested, H1, H2, H8, H11 and H12 are valid; H4 is rejected, while for
the other hypotheses, the related variables do not have significant linkages with the insurers’ financial
performance. As for the hypothesis H4, the linkage b etween the growth of gross written premiums and
insurers’ financial performance is negative, as in some cases, an excessive growth of underwritings
generates a higher underwriting risk and the necessity to increase the volume of technical reserves. More,
Chen and Wong (2004) argue that the obsession to excessively increase the volume of the gross written
premiums may lead to self -destruction, as other important objectives, such as selecting profitable
investment portfolios, could be neglected. According to the final results, the determinants of the financial
performance in the Romanian insurance market are the financial leverage in insurance, company size,
growth of gross written premiums, underwriting risk, risk retention ratio and solvency margin.

5. Conclus ions
The financial performance of insurance companies can be analyzed at micro and macroeconomic
level, being determined both by internal factors represented by specific characteristics of the company, and
external factors regarding connected institutions and macroeconomic environment.
This analysis improves the understanding of the Romanian insurance market and can provide useful
information to insurance companies, investors, experts and supervisory authorities.
According to the final results achieved by applying specific panel data techniques, the determinants
of the financial performance in the Romanian insurance market are the financial leverage in insurance,
company size, growth of gross written premiums, underwriting risk, risk retention ratio and so lvency
margin. The insurance financial leverage reflects the potential impact of technical reserves’ deficit on
equity in the event of unexpected losses and has a negative influence on the insurers’ financial
performance. As for the company size, there is a positive linkage between this variable and the insurers’
financial performance, since larger firms have more resources, a better risk diversification, complex
information systems and a better expenses management. The linkage between the growth of gross w ritten
premiums and insurers’ financial performance is not positive, as expected, as in some cases, an excessive
growth of underwritings generates a higher underwriting risk and the necessity to increase the volume of
technical reserves. The underwriting r isk emphasizes the efficiency of the insurer’s underwriting activity
and it is measured through the loss rate, which is computed as a ratio of gross claims to gross written
premiums. The underwriting risk has a negative influence on the insurer’s financial performance, since
taking an excessive underwriting risk can affect the company’s stability through higher expenses. The
retained risk ratio has a positive influence on the insurer’s financial performance, as reinsurance involves a
certain cost. As for th e solvency margin, there is a positive linkage between this variable and the insurer’s

International Journal of Academic Research in Accounting, Finance and Management Sciences
Vol. 4 (1), pp. 299–308, © 2014 HRMARS

308 financial performance, because the insurer’s financial stability is an important benchmark to potential
customers.
The interpretation of this analysis’ results should b e made considering the fact that the insurance
companies from Romania are facing the combined effects of deteriorating market conditions and financial
crisis impact. Nevertheless, despite the fact that the effects of the financial crisis are still present and that
the Romanian insurance market is below its potential, the insurance industry has interesting perspectives
of evolution.

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