Economic Change and Restructuring
https://doi.org/10.1007/s10644-019-09260-w
Analysing the determinants of financial performance
for UK insurance companies using financial strength ratings
information
Abhijit Sharma1 · Diara Md. Jadi1 · Damian Ward1
Received: 26 July 2019 / Accepted: 21 December 2019
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
We investigate the determinants of financial performance of UK insurance companies based on their financial strength ratings. We use data from the A.M. Best Insurance for 49 UK insurers for 2006–2009. Our findings show that profitability, liquidity, size and organisational form are the significant determinants that affect financial
performance of insurance companies in the UK. We recommend an effective, alternative variable to more effectively measure the size of an insurance company, which
is based on the gross premium written. We find that gross premium written is more
appropriate for measuring company size in the insurance industry.
Keywords Insurance · Financial performance · Financial strength analysis
JEL Classification G22 · G24
1 Introduction
The insurance industry is one of the key players in the financial service sector in
almost all developed and developing nations (Kirkbeşoğlu et al. 2015; Torbati and
Sayadi 2018). The performance of insurance companies contributes towards the
market value of individual firms, industrial growth and the overall macro-economy
of a country. As an important player in the financial system, constant development
and financial resilience of this industry are imperative to foster rapidly growing economic activities of any countries (Barua et al. 2018).
The financial performance of insurance companies is of utmost importance to
various stakeholders such as policy makers, insurance intermediaries and policyholders. Mehari and Aemiro (2013) summarise that evaluating the determinants
* Abhijit Sharma
sharmaabhijit@gmail.com; A.Sharma12@bradford.ac.uk
1
University of Bradford School of Management, Emm Lane, Bradford BD9 4JL, UK
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Economic Change and Restructuring
of insurers’ performance has become an important research theme in the corporate
finance literature which has attracted substantial and growing interest from regulators, financial experts, researchers, managers and the general public (Omondi and
Muturi 2013; Öner Kaya 2015). Theoretically, the financial performance of insurance companies can be estimated by determining their profitability, which is a relative measure of success for a business and acts as a proxy of financial performance
(Mazviona et al. 2017; Alali et al. 2018).
Previous studies related to financial performance concentrate on analysing insolvency risk or predicting insurers’ failure (Doumpos et al. 2012). However, these are
mainly US-based studies, and it is regarded as rigid and very country specific. For
other insurance industries, there is greater concern for assessing the strength and stability of the insurers operating in the industry (Ceccarelli 2003; Sharpe and Stadnik
2007). Several studies have focused on debt rating determinants by measuring profitability, liquidity, capitalisation, interest coverage, debt status and industry indicators
(Altman and Rijken 2004; Amato and Furfine 2004; Grunert et al. 2005). Yet, these
studies mainly assess large financial corporations and banking institutions, and none
about insurance companies. Recently, there are many studies that focus on alternative approaches to insurers’ solvency. For instance, Eling et al. (2009) propose
a model based on company-specific minimum standards for financial performance
and test it on the German non-life insurance companies. The proposed model incorporates individual factors such as equity capital and the probability distribution of
insurance claims.
Financial performance of insurance companies can also be evaluated by assessing
their financial strength ratings (FSRs). FSR is defined as comprehensive measures of
risk which incorporates all relevant risk factors associated with FSR determination
(Florez-Lopez 2007). FSR represents an overall assessment of an insurer’s creditworthiness. It also provides insights into the insurer’s financial strength and capacity to fulfil their ongoing obligations to the policyholder (Eckles and Pottier 2011).
Chen et al. (2018) simplify that insurer’s FSR assesses the ability of an insurer to
pay future claims. Unlike bond ratings which gauge default risk for a specific debt
security, FSR summarises the insolvency risk of the insurance firm as a whole (Pottier and Sommer 1999).
The importance of FSR for an insurer varies according to the type of the insurance buyer. Epermanis and Harrington (2006) demonstrate that insurance ratings are
imperative and directly influence buyer’s purchasing decision. FSR is also the main
source of buyers’ information about the financial quality of insurers; thus, buyers are
willing to pay higher prices to obtain insurance covers from an insurer with higher
ratings (Kartasheva and Park 2013).
This paper investigates the determinants of financial performance of insurance
companies operating in the UK by evaluating their financial strength ratings’ performance. To the best of our knowledge, the most significant study that evaluates
UK insurers’ FSR performance is the one conducted by Adams et al. (2003). Their
study compares insurer rating performance between two rating agencies and the
likelihood of being rated by a particular rating agency. Our paper attempts to extend
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Economic Change and Restructuring
the previous research (Adams et al. 2003; Gaver and Pottier 2005) by identifying
the key financial determinants that have greater influence and effect on financial
strength ratings as reflected in the analysis. We extend the time horizon and employ
an enhanced set of variables, which best captures the process of determining the
financial performance-related outcomes. In addition, we adopt an alternative way of
measuring the size of an insurance company, by using its gross premium written as
suggested by Van Gestel et al. (2007).
The structure of this paper is as follows. Section 2 discusses the data used in the
study. Section 3 introduces and explains the model, and Sect. 4 presents the empirical results. Section 5 discusses the findings, while the final section provides some
concluding remarks.
2 Data
We employ secondary data obtained from the A.M. Best Insurance Report Online Non-US Database. Due to data availability limitation, our final sample consists of 49
insurers, which covers the period of 2006–2009. Following earlier studies (Adams
et al. 2003; Gaver and Pottier 2005; Eckles and Pottier 2011; Kartasheva and Park
2013), our study relies on one commonly used measure of FSR performance, which
is the rating grades (RATING) assigned by AM Best to insurers over the four-year
period.
Like bond ratings, insurer FSR is inherently an ordered variable. A linear transformation of the FSR ratings to numerical scales in implemented which resulted in
an ordinal variable. Similar rating conversions (linear transformation) have been
used extensively in previous work (Gaillard 2012; Gu et al. 2014; Boumparis et al.
2017). In our study, rating grades are combined following the verbal descriptions
provided by Best. Table 1 illustrates the linear transformation of FSR ratings and
provides details of the rating frequencies for each category.
However, the linear transformation of rating grades to numerical scales which
implies that the distance between subsequent ratings is equally spaced has been
subjected to criticism (Afonso et al. 2007; Boumparis et al. 2017). This assumption could lead to significant bias which could affect the overall result. Thus, Afonso
et al. (2007) suggest an alternative to the common linear transformations which is
Table 1 Linear transformation
of FSR, 2006–2009 (190
observations). Source: Author’s
compilation based on AM Best
Classifications
Rating
Description
Numerical
value
No. of
observations
A++
Superior
6
18
A+
Superior
5
46
A
Excellent
4
63
A−
Excellent
3
46
B++
Very good
2
6
B and below
Fair
1
11
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Economic Change and Restructuring
known as the logistic transformation of credit ratings. Boumparis et al. (2017) adopt
logistic transformation in their study in order to examine the robustness of their data
set. The underlying theory of logistic transformation is that in the middle of the
scale, ratings could rise quickly due to financial improvements. Nevertheless, movements at the bottom and top of the scale are slower, due to a more stringent requirement. The logistic transformation approach is further discussed in the methodology
section.
We choose explanatory variables based on their theoretical relationship with the
dependent variables, as well as our examination of the prior empirical literature. Our
dependent variable, explanatory (independent) variables, their measurement and their
expected relationship to FSR are summarised in Table 2. Altogether, there are eight
explanatory variables to be tested, of which six are financial variables and two are
dummy variables. The financial variables are leverage, profitability, liquidity, size, reinsurance and growth. Subsequently, the dummy variables are the business type and the
organisational form.
Table 2 Variables used in empirical estimations
Variable(s)
Measured by
Dependent variable (DV)
Rating
Ordinal DV
Ordinal variable categorised into the following:
(FSR)
1 = If the firm is assigned a Best rating of B or lower
2 = If the rating is B++ or B+
3 = If the rating is A−
4 = If the rating is A
5 = If the rating is A+
6 = If the rating is A++
Explanatory variables (IVs) (expected sign) and its definition
LEV
Leverage (−)
Accumulated reserve divided by total assets
PROFIT
Profitability (+)
Net underwriting expenses and losses divided by net
premium earned
LIQUID
Liquidity (+)
Current assets divided by current liabilities
LNSIZE
Size (+)
Natural log of gross premium written
REINS
Reinsurance (+)
Annual reinsurance ceded divided by net premiums
written
GROWTH
Growth (+)
Change in surplus
TYPE
Business type (±)
Dummy variable
0 = General insurer, 1 = life insurer
FORM
Organisational form (±)
Dummy variable
0 = Stock insurer; 1 = mutual insurer
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Economic Change and Restructuring
3 Methodology
3.1 Logistic transformation of the rating grades into numerical scales
The logistic transformation assumes that the functional form that explains the relationship between rating, Ri, is normalised to grade each of the insurers on a scale of zero
to one; zero represents the least creditworthy rating and one represents the most creditworthy rating, together with the set of explanatory variables, X, as the standard conventional logistic form:
�
R=
e𝛽 X
1 + e𝛽 � X
(1)
where the vector β includes all parameters of the exogenous variables. Subsequently,
following Boumparis et al. (2017), the logistic transformation is given as follows:
(
)
Li = ln[Ri ∕ 1 − Ri ]
(2)
where Ri=(2i − 1)/(2nc), nc is the number of categories, which equals 6, and the rating grades are i =1, 2, 3, 4, 5, 6. Table 3 illustrates the alternative values that we
used in the logistic transformation, and Fig. 1 compares the linear and the logistic
transformation.
As illustrated in Fig. 1, the overall fit of the data seems very good even though
slightly lower than the one obtained through linear transformation. In the logistic
Table 3 Logistic transformation
of the rating grades
Rating
A++
A+
A
A−
B++
B
Linear
6
5
4
3
2
1
Logistic
2.40
1.10
0.34
− 0.34
− 1.10
− 2.40
Fig. 1 Linear and logistic transformation
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Economic Change and Restructuring
transformation, the differences between categories are not constant, but are still imposed
a priori, which is a deductive reasoning derived from self-evident propositions.
3.2 Ordered probit regression model
The ordered probit regression model (OPM) is appropriate in cases where there are
more than two outcomes of an ordinal dependent variable and is widely used in similar analyses (Jackson and Perraudin 2000; Greene 2004). For example, Afonso et al.
(2007) employ the ordered probit model to estimate the determinants of sovereign debt
rating. Rating grades are discrete variables and reflect an order in terms of probability
of default. The underlying foundation of ordinal outcomes is that there is a latent continuous metric (defined as R*) underlying the observed responses by the rating agency.
Subsequently, R* is an unobserved variable and can only be known once it crosses certain thresholds (Salisu 2016). The latent variable has a linear form and relies on the
same set of variables as before. In this instance, to model the determinants of insurers’
performance, we consider a latent variable model given as:
R∗it = 𝛼0 + 𝛼1 x1 + 𝛼2 x2 + ⋯ + 𝛼k xk + e
�
R∗it = xi 𝛼 + ei
R∗it = jifuj−1 < R∗it ≤ uj
(3)
where i = 1, … , N
We assume that Rit = (1, 2, 3, 4, 5, 6) ≈ (B, B++, A−, A, A+, A++)
Thus, the choice rule is:
Rit = 1 if R∗it ≤ u1
Rit = 2 if u1 < R∗it ≤ u2
Rit = 3 if u2 < R∗it ≤ u3
Rit = 4 if u3 < R∗it ≤ u4
Rit = 5 if u4 < R∗it ≤ u5
Rit = 6 if R∗it > u5
(4)
Since the DV used in this study conforms to the ordinal variable definition,
OPM is used for estimations. Alternatively, ordered logit regression model
(OLM) could also be used in the estimation. However, Torres-Reyna (2012)
establishes that there is no significant difference between OPM and OLM, and
both models provide similar results. On the other hand, ordinal DV cannot be
estimated consistently using the ordinary least square regressions (OLS) (Greene
and Hensher 2010).
Our empirical approach attempts to improve the methods used in previous
research such as those by Gaver and Pottier (2005), by using panel data analysis and extending the time horizon by studying a 4-year data, instead of a
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Economic Change and Restructuring
single-year basis. OPM is used to identify the relationship between the financial
strength rating performance of insurance companies and leverage, profitability,
liquidity, company size, reinsurance, growth, business type and organisational
form. In particular, we model FSR ratings (­ Ratingit) as follows:
Ratingit = 𝛽0 + 𝛽1 LEVit + 𝛽2 PROFITit + 𝛽3 LIQUIDit + 𝛽4 LnSIZEit
+ 𝛽5 REINSit + 𝛽6 GROWTHit + 𝛽7 TYPEit + 𝛽8 FORMit + 𝜀it
(5)
where Ratingit is the ordinal, dependent variable (DV) and it is coded on a six-point
scale from 1 to 6. The independent variables are LEV, PROFIT, LIQUID, LNSIZE,
REINS, GROWTH, LNSIZE and FORM (defined in Table 2). The estimated model
also includes εit that represents the error term.
Our regression analysis will be conducted based on an ordered probit model
that explains the performance on the basis of robust standard error estimation.
This is to test for the robustness of the analysis following the approach used in
the literature (Grunert et al. 2005; Eckles and Pottier 2011). We will also regress
our model by incorporating the marginal effects and time fixed effects into the
analysis in order to capture the unobserved changes over time.
4 Empirical results
4.1 Summary statistics of the dataset
See Table 4.
Table 4 Summary statistics of
dataset
Variable
Min
Mean
Max
SD
Rating
1
3.953
6
1.23
Leverage
− 0.09
0.528
0.94
0.228
Profitability
− 0.53
0.611
29.50
2.195
89.845
Liquidity
0.08
29.360
751.42
Size
6.29
12.400
15.34
1.734
Reinsurance
− 75.80
1.76
58.90
8.640
22.702
Growth
− 100.00
10.200
142.41
Business type
0
0.137
1
0.345
Organisational form
0
0.042
1
0.201
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Economic Change and Restructuring
4.2 Ordered probit regression models (OPM)
Afonso et al. (2007) emphasise that OPM is able to provide additional insights into
the determinants of sovereign ratings. Additionally, this model allows to ease the
rigid assumption on the shape of the rating schedule by generating threshold values
estimation and allowing for the evaluation of the shape of the rating curve.
We estimate OPM for all insurers using (1) robust standard errors (Table 5). In
this instance, there will be two sets of estimations; the first one refers to the linear
transformation of rating grades, and the second one refers to the logistics transformation of rating grades. In addition, the marginal effects by each level of the ordered
dependent variables will also be computed (Table 6). We also re-estimate the model
using (2) fixed effects in order to test for time fixed effects (Table 7).
As for the chosen fixed-effects model, we had initially estimated both fixedeffects and random-effects models which is then tested using the Hausman specification test. Under the Hausman test assumption, the null hypothesis (H0) is that the
preferred model is random effects (Greene 2008). The test yields a Prob > χ2 = 0.000,
which is compared as p < 0.05; thus, ­H0 is rejected, and the fixed-effects model is
preferred over the random effects.
Table 5 shows that using robust standard errors for estimation produces differences in standard errors and statistical values. Allison (1995) highlights that robust
standard errors can be used to treat heteroscedasticity and will also generate more
Table 5 Ordered probit regression model (robust standard error)
Linear scales
Coef.
Logistic scales
p value
Sig.
Coef.
p value
Sig.
Leverage
0.424
0.369
0.424
0.369
Profitability
0.078
0.000
***
0.078
0.000
***
Liquidity
0.002
0.073
*
0.002
0.073
*
Size
0.240
0.000
***
0.240
0.000
***
Reinsurance
− 0.001
0.833
− 0.001
0.833
Growth
− 0.002
0.529
− 0.002
0.529
Business type
− 0.148
0.493
Organisational form
− 0.803
0.000
/cut1
1.436
1.436
/cut2
1.700
1.700
/cut3
2.753
2.753
/cut4
3.756
3.756
/cut5
4.694
4.694
LogPseudoLik
− 273.987
− 273.987
0.000000
0.000000
0.0697
0.0697
Prob > χ2
Pseudo-R2
Observations
190
Equal differences
123.88
− 0.148
0.493
− 0.803
0.000
190
(0.000)
Significant levels ***p < 0.01; **p < 0.05; *p < 0.1
13
***
123.88
(0.000)
***
Economic Change and Restructuring
Table 6 Marginal effects
Rating grades
B
(1)
B++
(2)
A−
(3)
A
(4)
A+
(5)
A++
(6)
Leverage
− 0.04
(0.390)
− 0.017
(0.341)
− 0.077
(0.375)
0.000
(0.997)
0.070
(0.350)
0.066
(0.397)
Profitability
− 0.008***
(0.003)
− 0.003**
(0.019)
− 0.014***
(0.000)
6.88e − 06
(0.997)
0.013***
(0.000)
0.012***
(0.000)
Liquidity
− 0.0002*
(0.090)
− 0.0001
(0.156)
− 0.0004*
(0.073
2.01e − 07
(0.997)
0.0003*
(0.074)
0.0004*
(0.078)
Size
− 0.024***
(0.004)
− 0.010**
(0.026)
− 0.044***
(0.000)
0.0002
(0.997)
0.040***
(0.000)
0.037***
(0.000)
Reinsurance
0.0001
(0.833)
0.0001
(0.834)
0.0002
(0.834)
− 1.15e − 07
(0.997)
− 0.0002
(0.833)
− 0.0002
(0.834)
Growth
0.0002
(0.529)
0.0001
(0.536)
0.0003
(0.533)
− 1.42e − 07
(0.997)
− − 0.0003
(0.529)
0.0002
(0.532)
Business type
0.015
(0.497)
0.006
(0.459)
0.027
(0.499)
− 0.0001
(0.997)
− 0.025
(0.476)
− 0.023
(0.518)
0.032***
(0.006)
0.146***
(0.000)
− 0.0001
(0.997)
− 0.133***
(0.000)
− 0.124***
(0.000)
Coef.
p value
Sig.
Organisational form 0.079***
(0.000)
Significant levels *** p < 0.01, ** p < 0.05, * p < 0.1
The p value is in parentheses
Table 7 Fixed-effects model
(with time fixed effect)
Leverage
1.214
0.074
*
Profitability
− 0.039
0.087
*
Liquidity
− 0.001
0.850
Size
− 0.393
0.001
Reinsurance
− 0.003
0.924
Growth
− 0.003
0.852
Year 2007
0.031
0.738
Year 2008
0.100
0.294
Year 2009
0.035
0.739
_cons
8.171
0.000
***
Business type
Organisational form
R2: within
R2: between
R2: overall
0.115
0.133
0.092
F (9. 132)
1.910
Prob > F
0.056
Significant levels ***p < 0.01; **p < 0.05; *p < 0.1
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Economic Change and Restructuring
consistent results. The validity of the models estimated in Table 5 is confirmed by
the computed Prob > χ2 value of 0.000.
As shown in Fig. 1, the overall fit between linear and logistic transformations is
very good with slight differences in the numerical values. The logistic transformation is conducted as robustness checks for our regression model. It is further confirmed through the estimations in Table 5, which produce the exact results for both
linear and logistic transformation of the ordinal dependent variables. As in both,
the same core variables are selected as the relevant determinants that influence the
financial strength ratings: profitability, liquidity, size and organisational form.
Table 5 shows that profitability, size and organisational form are positively related
to rating and are statistically significant (p value of 0.0000). Liquidity also has a
positive influence on the ratings, but at the 10% level of significance. Assuming that
the ceteris paribus assumption holds, we conclude that:
1. Larger insurers (size) with greater profitability and liquidity will have a higher
probability of obtaining a higher rating grade.
2. Mutual insurers are more likely to be assigned higher rating grades than stock
insurers.
We find that leverage, reinsurance, growth rates and business type have a statistically insignificant relationship with rating. Thus, these variables do not affect rating
performance.
Table 6 addresses the marginal effects issue. In this study, we compute the average marginal effect (AME) by calculating the marginal effect of each variable x for
each observation (taking into consideration any covariates). Then, we calculate the
average. In this study, the marginal effects are computed using Stata [Stata command: margins, dydx(*)]. Marginal effects show the change in the explained or
dependent variable when the predictor or independent variable increases by one
unit (Torres-Reyna 2012). The dependent variable has six categories; thus, there are
six sets of marginal effects, one for each category (Katchova 2013). The marginal
effects can be interpreted as: one unit increase in “size” is associated with being
2.4% less likely to obtain a B rating grade and 3.7% more likely to obtain an A++
rating grade [refer to the results for explanatory variable “size”, column (1) and column (6)]. Alternatively, it could be said that larger companies have better chances to
obtain good grades.
The F test in the fixed-effects model defines ­H0 as the differences between categories are equal for all categories, and all the coefficients are equal to zero. As
shown in Table 7, the Prob > F is greater than 0.05 (95% confidence level); thus,
the ­H0 cannot be rejected. Thus, it could be inferred that the fixed-effects model
might not be appropriate to estimate models using ordered dependent variable, as in
this study. In addition, the fixed-effects model is also regressed for time fixed effect.
In Stata, the test is done by using the “testparm” command. Based on the test, the
Prob > F = 0.7568. In this instance, the Prob > F is greater than 0.05; thus, we failed
to reject H0 that the coefficients for all years are jointly equal to “0”. Consequently,
no time fixed effects are required in this case.
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Economic Change and Restructuring
As a further robustness check, we consider alternative financial measures of size,
growth and form. In particular, the variables selected are defined using different sets
as defined in Table 8. The alternative measures for these variables are incorporated
in the estimations as in Table 9.
Table 9 is estimated to provide comparative results and to establish whether there
is a significant difference in estimation results by using different variable measurements/proxies. This model is re-estimated using alternative measures for the following variables: size, growth and organisational form.
Model A shows estimations using a different approach to measure company size.
Results indicate that variables such as leverage, profit and form are the key financial determinants that influence rating performance of an insurer. However, comparing the predictive ability for “size” with the estimated output in OPM (Table 5)
(coefficient 0.240, sig. at 1%), we find the results for “size” in this regression is
insignificant.
Table 8 Alternative definitions for selected variables
Variable(s)
Original measure (as in this study)
Alternative measure
Size
ln (gross premium written)
ln (total assets)
Growth
Changes in surplus
Changes in total assets
Organisational form
Dummy variable
Dummy variable
0 = stock insurer
0 = publicly traded insurer
1 = mutual insurer
1 = privately traded insurer
Table 9 Ordered probit regression model (alternative)
Model A
Model B
Model C
Alternative SIZE
Alternative GROWTH
Alternative FORM
Coeff. Sig.
Coeff. Sig.
Coeff. Sig.
Leverage
.9090 [.052]*
.4225 [.372]
.4195 [.364]
Profitability
.0326 [.040]**
.0780 [.000]***
.0780 [.000]***
Liquidity
.0016 [.145]
.0023 [.071]*
.0024 [.064]*
Size
.0201 [.621]
.2385 [.000]***
.2430 [.000]***
Reinsurance
.0010 [.903]
− .0012 [.846]
− .0015 [.803]
Growth
.0022 [.420]
− .0005 [.536]
− .0012 [.632]
Business type
.1939 [.392]
− .1375 [.525]
− .1164 [.576]
Organisational form
.9436 [.000]***
− .7938 [.000]***
− .0381 [.873]
No. of Obs.
190
190
190
LogPseudoLik
− 284.959
− 274.043
− 276.110
109.58
127.64
29.61
0.0000
0.0000
0.0002
0.0325
0.0696
0.0625
Wald χ2 (8)
Prob > χ2
2
Pseudo-R
Significant levels ***p < 0.01; **p < 0.05; *p < 0.1
The p value is in parentheses
13
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Model B shows estimations using a different approach to evaluate growth.
Instead of using changes in surplus, growth is measured by the changes in total
assets (Weiss 1998). Our results show that even though the measurement has been
changed, growth remains as an insignificant determinant of rating performance. A
similar result is obtained from the regression in Table 5. We therefore conclude that
growth is not a key factor influencing rating changes.
Model C provides an alternative measure of organisational form. Form is a
dummy variable to indicate the structure of insurance companies. In this model,
organisational form reflects whether an insurance company is a publicly traded company or a privately traded company. After these adjustments, our results indicate that
organisational form loses its predictive ability to influence rating performance.
5 Discussion of results and policy implications
Interestingly, in all OPM estimations except in Model C (Table 9), organisational
form (FORM) remains as the most significant determinant influencing rating performance (at 1% significance level). Theoretically, managerial decision-making in
the insurance industry is influenced by the organisational forms (stock or mutual
insurer). In addition, risk-taking, investment and product-mix strategies differ
according to their ownership structure, contracting interest and internal governance. Mutual insurers exercise more caution in their operations as compared to stock
insurers. Both insurers tend to obtain ratings in order to promote their good reputation to the public and to protect their market shares (Pottier 1997). This might be a
possible justification to explain the importance of organisational form in explaining
rating performance.
Our study uses a different approach to measure company size. Instead of using the
total assets, we measure company size based on its gross premium written. It is justified that due to operational and accounting differences within insurance companies,
using total assets to measure size might not be appropriate. This notion further supports our decision to measure size of an insurer based on its gross written premium.
Our results also confirm the findings by Van Gestel et al. (2007) who claim that size
is an important rating determinant and its measure should appropriately reflect company operations. It seems highly likely that the insurance companies’ operations are
markedly different as compared to banks and other financial institution. Thus, the
appropriate measure of size should be gross premium written.
It is noteworthy that one of our findings contradicts the empirical theory that serves
as the basis of our hypotheses development. We hypothesise that stock insurers are
more likely to obtain a higher rating grade than mutual insurers. However, our empirical results suggest that the opposite takes place. There is a negative, but significant relationship between organisational form and rating performance. As such, mutual insurers are more likely to obtain a higher rating grade than stock insurers (p value 0.000).
Nonetheless, the relationship between organisational form and rating has not been conclusively established in previous studies.
One notable finding relates to the impact of growth on potential ratings. Cole et al.
(2011) contends that the impact is ambiguous. A strong growth position might reduce
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Economic Change and Restructuring
uncertainty and help to convey information relating to a favourable financial position.
Our study investigates the impact of growth on rating performance and finds that the
growth factor is not significant in explaining rating changes.
Rating agencies suffered a major setback during the global financial crisis in terms
of the credibility, assessment and transparency of their rating assignments. As a result,
the aftermath of the financial crisis perceives less reliance on insurer’s rating grades
and insurance companies move towards internal, risk-based assessment (Ciumas et al.
2015). Premium income, growth, capital, profitability, liquidity and solvency of insurance companies are the most affected variables during the early years of the global
financial crisis, in 2008 and 2009 (Alexander 2010). However, since the subprime
crisis is more prominent in the banking industry, insurance companies are quick to
rebound from the effect. By 2010, most insurers have managed to recover their capital
and net premium income (Simpson 2013). National Association of Insurance Companies (NAIC) and state regulators continued to address and monitor the performance of
insurance companies prior to the global financial crisis. In terms of its risks and capital adequacy, NAIC developed and implemented the Own Risk and Solvency Assessment in 2015 and reformed the Insurance Holding Company System Regulatory Act to
address the transparency issue and oversight of holding companies’ activities (Simpson
2013).
Our study is restricted to a limited data availability issue. We retrieved ratings and
financial data from the AM Best database, and we had difficulties to obtain larger sample size. There are many insurers opted out from the rating activity, thus leaving us with
unbalanced panel data. This might be the aftermath of the global financial crisis, where
insurers and stakeholders no longer trust the credibility of rating agencies and its rating
assessment (Ciumas et al. 2015). In addition, all the financial variables affected during
the financial crisis (as in Alexander 2010) are also relevant to the variables used in our
measurements. Thus, the impact of the crisis is reflected in the financial data obtained,
where almost all insurers in our sample selection suffered from sharp decline particularly in their total investment, gross premium written and total assets.
6 Conclusions
This paper examines the determinants of financial performance of UK insurance
companies by using the financial strength ratings (FSR) information from 2009 to
2009. Our empirical analysis confirms that the FSR performance, as reflected by
rating grades, is positively and significantly explained by profitability (Adams et al.
2003), liquidity (Almajali et al. 2012; Omondi and Muturi 2013), company size
(Charumathi 2012) and negatively influenced by organisational form (Kartasheva
and Park 2013). However, leverage, reinsurance, growth and type of business do not
reflect a statistically significant relationship with rating grades.
Our study contributes to the area of insurance and financial performance of insurance companies in the following ways. First, we empirically investigate and determine the key financial determinants that explain rating grades assigned to insurers.
Rating grades are significantly influenced by profitability, liquidity, company size
and organisational form. Second, we find evidence confirming the suitability of a
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Economic Change and Restructuring
theoretical measure of company size which is highly appropriate for the insurance
sector. Within the insurance industry, company size is better captured by using
gross premiums written instead of total assets. Third, we fill a gap in the literature
by extending previous research to incorporate longer period of investigation and
robust empirical measurement of key variables. The models used are applicable to
all insurers and can be used to provide an internal assessment of a firm’s own financial strength performance.
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