Predicting Takeover Targets Case of Croatian Insurance Companies ( )

Transcription

1 Predicting Takeover Targets Case of Croatian Insurance Companies ( ) Dr. Tomislava Pavic Kramaric, University of Split, University Centre for Professional Studies, Croatia ABSTRACT Globalization and fierce competition have forced many business entities to look for alternative ways of improving their competitiveness. One of such ways is particularly interesting and it refers to mergers and acquisitions of companies. M&A activities are conducted within different industries as well as in an insurance industry. This paper investigates the characteristics of target companies on the Croatian insurance market. The data used for these analyses refer to the period from 1998 to Starting from the standpoint that not all insurance companies are equally attractive for M&A, logistic regression analysis was conducted with the aim of exploring characteristics of target insurance companies and finding out the motives for acquiring a particular insurance company. Such an analysis brings an important finding according to which insurance companies are characterized by distinctive characteristics which make them potential targets. The findings show that out of eighteen independent variables included in the model, three of them were statistically significant (leverage, size measured by gross written premium and size measured by total assets) in predicting whether a certain company is about to become a target company. The likelihood of being acquired is significantly and positively related to leverage and size measured by gross written premium, while it is negatively related to size measured by total assets. The results of the analysis show that M&A are to be driven for the most part by economically viable objectives. Keywords: M&A, takeover targets, logistic regression INTRODUCTION The entrance of Croatia in the EU as well as the regulatory changes in terms of liberalization and deregulation with the aim of creating single financial services market will lead to increase of need for efficient performance of insurance companies. Namely, the insurance companies operating on Croatian insurance market will be facing very fierce competition. Therefore, one way to effectively confront with the rivals may be found in M&A activities. Regulatory changes in terms of liberalization enable internationalization of insurance business but they also lead to intensified competition, so many insurance companies will be forced to conduct M&A transactions which enable insurance companies to achieve better position on the market both on national and international level. Croatian insurance companies are also facing regulatory changes in terms of new strict rules regarding capital adequacy. The implementation of Solvency II Directive planned for the beginning of the year 2013 will encourage these operations. All mentioned above is in accordance with industry shock theory which holds that M&A activities within an industry are not merely firm specific phenomena but the result of the adaptation of industry structure to a changing economic environment or industry shocks such as changes in regulation, changes in input costs, increased foreign or domestic competition, or innovations in technology (Cummins and Xie, 2007). When considering the continuation of M&A activities on Croatian insurance market in the future, the current economic situation should be taken into account. Namely, slow economic growth, the collapse of financial markets and growth of unemployment are endangering dynamics of development of Croatian insurance market. All this, as well as competitive pressure from other nontraditional competitors such as banks or open-end investment funds will force insurance companies to look for other ways for improvement of their efficiency where M&A transactions will have primary role. Therefore, the main aim of this paper is trying to explore the financial characteristics of acquired Croatian insurance companies, i. e. the motives of acquiring companies to acquire particular insurance company. This paper The Journal of Global Business Management Volume 8 * Number 1 * February

2 contributes to the literature by providing empirical findings for Croatia which is a small emerging economy with a very small and undeveloped insurance markets. CHARACTERISTICS OF CROATIAN INSURANCE MARKET Until the nineties of the last century, the Croatian insurance market was dominated by a very small number of insurance companies, similarly like in other former communist countries. The explanation for this can be found in the fact that insurance companies in these countries were state owned, while the state was providing for the insurance services mostly through only one insurance company which had guaranteed monopolistic position. The system of the state owned insurance started with the creation of new socialist society, firstly in the Soviet Union where in 1918 insurance a state monopoly was proclaimed. Until the nineties of the last century the eastern European countries like Hungary, Czechoslovakia, Poland, Romania and Bulgaria had only one state owned insurance company and its branches. The main characteristic of these companies is that they were under direct supervision of the state. Until the nineties of the last century insurance services in Croatia were provided by one state owned insurance company and branches of insurance companies from other republics of the former Yugoslavia. The Croatian insurance industry was poorly developed in terms of number of insurance companies operating on the market as well as in terms of lines of business and the number of the insured. Some insurance lines of business have not been developed or have been underdeveloped. Non-life insurance dominated the market, especially mainly mandatory lines of business while there were no incentives for development of life insurance. The lack of competition resulted in lack of motive for innovation and development of certain lines of insurance business so poor quality and poor variety of insurance products characterized the market. As the consequence, the degree of concentration of Croatian insurance market is still moderate as well as the imperfections of the market. It is inherited from the socialism times because the state was protecting big systems by its regulations and in that way it guaranteed their monopolistic positions contrary to countries with market economies which are trying to stop monopolies and enable the higher degree of the competition. Adoption of new regulations relating to insurance industry, including better supervision of insurance companies, has led to creation of free insurance market. This was reflected by the gradual increase in number of insurance companies operating on the market. The strengthening of competition was influenced by entrance of foreign capital in the insurance market. This interest for entering Croatian insurance market was influenced by the low entry barriers as well as by high profits since the market was unsaturated. In 1994 there were total of 12 companies that had license to conduct insurance business. This number had more than doubled in the period of next 16 years. By entrance of new insurance companies substantial growth of certain lines of business is noticeable, particularly life insurance business. The share of life insurance premium in total premium increased from 14.5% in 1998 to 26.6% in 2010 but it is still below the EU average. Until 2007 Croatian insurance market was constantly growing registering double digit growth rates. After that lower growth rates were registered while negative growth rates were registered in 2009 and Unfortunately, unlike Western European countries situation on the Croatian insurance market is not improving influenced by the poor overall economic performance. LITERATURE REVIEW In the last 30 years scientists have generated great number of studies trying to explore the characteristics of target companies. For such purpose the early studies have usually used discriminant analysis (Simkowitz and Monroe; 1971, Stevens, 1973), but starting from the 80ies studies often use probit and logit regression models (Dietrich and Sorensen, 1984, Harris et al., 1982; Palepu, 1986; Cudd and Duggal, 2000). These studies usually cover countries like USA, UK and western European countries but such studies are very rare in eastern European countries. This is particularly true for insurance markets. A few studies exploring characteristics of target insurance companies are given in the following sections. 34 The Journal of Global Business Management Volume 8 * Number 1 * February 2012

3 Cummins and Xie (2007) in their paper analyze the productivity and efficiency effects of mergers and acquisitions in the U.S. property-liability insurance industry during the period They also examine the firm characteristics associated with becoming an acquirer or target through probit analysis. The independent variables in the probit model are firm characteristics lagged one year and include efficiency scores, the loss ratio, the underwriting expense ratio, and pre-tax return on equity (ROE), the capital-to-asset ratio, the geographical and product line Herfindahl indices which are used to proxy for diversification, the proportion of invested assets in stocks. The variables measuring other firm characteristics include size, measured by the log of assets, a mutual dummy for organizational form, an unaffiliated dummy for corporate structure, the growth rate and business mix. The principal finding from the target probit regressions is that poorly performing firms are more likely to be takeover targets and they also find evidence that M&As are motivated to achieve diversification. However, there is no evidence that scale economies played an important role in the insurance M&A wave. The firms characteristics associated with becoming a takeover target were also examined by Cummins, Tennyson and Weiss (1999) on the sample of 137 insurance companies in the US life insurance industry over the period This research which covered life insurance industry also proved that financially vulnerable firms are more likely to be acquisition targets. By analyzing the Spanish insurance industry on the sample period Cummins and Rubio-Misas (2001) estimate probit models to identify in a multi-variate context the firm characteristics associated with the probability of being an acquisition target. Unlike the two previously mentioned papers this research showed that merger activity in Spain has been motivated by the objectives of increasing size and market share rather than by improvement the performance of financially vulnerable targets. The above mentioned studies exploring the probabilities that a particular company becomes a takeover target proves that target companies are characterized by specific characteristics. These characteristics differ from industry to industry, from one observed period to another as well as from one geographical area covered by the analysis to another. DATA, METHODOLOGY AND SPECIFICATION OF VARIABLES With the aim of conducting this analysis two groups of insurance companies were identified. The first one comprises of insurance companies that were targets in M&A activities, while the second group is made of the companies that were not involved in M&A activities. These activities were observed in period When defining the first group made of target companies one transaction was eliminated since the same insurance company was a target company two years in a row. This elimination was done in accordance with methodology applied in Cummins and Xie (2007) paper that eliminated the companies that were involved in another transaction within two years before or two years after the recorded transaction. According to the methodology applied by Cummins, Tennyson and Weiss (1998) the transactions that represented the internal restructuring of an existing insurance group were also not included in the sample as well as the target companies that were inactive or in run-off after the acquisition. Therefore the final sample of target companies consisted of totally 17 companies. The second sample that consisted of the companies that were not involved in M&A activities was made of total of 146 companies. Three companies were excluded from the sample in the year 2004 since those companies reported negative premium (Cummins and Rubio-Misas, 2001). Therefore, the analysis is conducted on total of 163 observations; i. e. little bit less of 30 per variable which confirms the robustness of the analysis. Table 1 shows the frequencies for each year prior the acquisition year. The Journal of Global Business Management Volume 8 * Number 1 * February

4 Table 1:Frequencies of the data year-wise Year f % , , , , , , ,6 Total ,0 On the basis of relevant theory and literature and in order to estimate the impact of various factors that may be important in predicting takeover targets eighteen different insurance-specific and industry-specific variables were created. These variables include: return on assets, (ROA), return on equity (ROE), claims ratio, expense ratio, combined ratio, leverage, return on investment, size measured as the natural logarithm of total assets as well as the size measured as the natural logarithm of total gross written premium, growth rate, degree of diversification measured by HHI, technical provisions, business result, guarantee capital, assets per employee, premium per employee, the share of business expenses in earned premium and ownership. A negative influence of variables such as return on assets (ROA), return on equity (ROE), investment result, technical provisions, business result, guarantee capital, assets per employee and gross written premium per employee on the probability that a particular insurance company becomes a takeover target is expected. This is in accordance with the corporate control theory (Cummins and Xie, 2007) which predicts that poorly performing firms are more likely to be acquired and that the performance of targets will improve after the takeover. At the same time a positive influence of the variables such as claims ratio, expense ratio, combined ratio, leverage and share of business expenses in earned premium on the probability that a particular insurance company becomes a takeover target is expected. This is also in accordance with corporate control theory (Cummins and Xie, 2007) which assumes that financially vulnerable insurance companies are more likely to become a takeover targets. A size variable measured on the basis of total assets was introduced into the model according to the size hypothesis (Cudd and Duggal, 2000) stating that larger firms are less likely to become acquisition targets due to the greater costs of absorbing larger targets into the acquiring firms organizational structures, and also due to the ability of larger firms to engage in more prolonged and costly takeover defenses. Therefore, this variable is expected to carry a negative sign in the model of predicting takeover targets. The size variable measured on the basis of total gross written premium is included in the model due to the theory (DePamphillis, 2010) according to which the acquirers are conducting takeover activities with the aim of improving the range of their services as well as with the aim of entering the new markets which leads to the improvement of their competitive position on the market. By taking over insurance companies with higher values of gross written premium the acquiring companies also increase their portfolio and consequently increase their market shares. Therefore, a positive sign of this variable is expected in the model. Variable ownership was introduced in the model in order to examine the influence of ownership structure on predicting takeover targets. It was included as a dummy variable with the value 1 indicating domestic ownership and 0 otherwise. By entrance of foreign capital in this sector, diversity and quality of insurance products and services are improved. Since foreign companies usually have superior access to technical and financial resources but also due to the fact that domestically owned companies have better knowledge of local markets and economic environment (Berger, 2004) it is expected that domestically owned companies are more likely becoming takeover targets. 36 The Journal of Global Business Management Volume 8 * Number 1 * February 2012

5 The Table 2 shows description of variables that are used in the research and way of their calculation. Table 2: Description of the variables used in the logistic regression Variable Description Return on assets ROA is calculated by dividing a company's after tax annual profits by its total assets Return on equity ROE is calculated by dividing a company's after tax annual profits by its total equity Calculated as a ratio of sum of claims paid, changes in provisions for claims and Claims ratio changes in other technical provisions (including changes in life insurance technical provisions when policyholder bears a risk of investment) and earned insurance premium (multiplied by 100), with net value of reinsurance included into calculation. Calculated as ratio of sum of operating expenses (acquisition cost and administrative Expense ratio costs), other technical charges, income from commissions and fees and gross written premium, so that such amounts included into calculation are reduced by premiums ceded to reinsurance (multiplied by 100) Combined ratio Calculated as the sum of expense ratios and claims ratios Leverage The share of total liabilities and total assets Return on investment Calculated as the ratio of income from investments reduced by investment expenses to the amount of investments (multiplied by 100) Size on the basis of gross This variable is calculated as a natural logarithm of total gross written premium premium Growth rate This variable is calculated in a following way ( GWPt GWPt 1 ) / GWPt 1 Degree of diversification Measured by Herfindahl-Hirschman index This variable is calculated as a sum of provisions for unearned premiums, provisions for Technical provisions bonuses and rebates, provisions for claims outstanding and other technical provisions as well as equalization reserves and mathematical provisions Business result Net income or loss after tax Guarantee capital The calculation of guarantee capital is prescribed by Insurance Act (Official Gazette No 151/05, 87/08 and 82/09) Assets per employee Total assets divided by total number of employees within an insurance company Premium per employee Total gross written premium divided by total number of employees within an insurance company Size on the basis of assets This variable is calculated as a natural logarithm of total assets Share of business expenses This variable is calculated as a ratio of total business expenses and earned premium in earned premium Ownership This variable is included in the model as a dummy variable (1 indicating domestically owned company and 0 otherwise) RESULTS OF THE ANALYSIS With the aim of identifying the multivariate context of characteristics of insurance companies associated with the probability of becoming a takeover target the logistic regression was conducted were the dependant variable is set equal to 1 for target insurance companies and to 0 for insurance companies with no M&A activity. The independent variables in the logistic regression model are insurance companies' characteristics lagged one year, i.e., the regressors represent year t-1 so that insurance companies' characteristics prior to the acquisition year are associated with what occurs during the acquisition year. The analysis was conducted in a way that each step of the analysis eliminated the variables that did not have significant effect in predicting potential target insurance companies in M&A activities. Therefore, the following variables were chosen: return on assets (ROA), return on equity (ROE), leverage (LEV), size measured by gross written premium (ln_gwp), The Journal of Global Business Management Volume 8 * Number 1 * February

6 business result (BUSS_RES), and size measured by total assets (ln_asset). Since some of the variables used in the logistic regression model have the same denominator, like gross written premium per employee and assets per employee, there is a problem of multicollinearity between certain variables that causes inefficiently estimated parameters and high errors, what in turn results with many insignificant variables. With the aim of controlling this problem, all variables that were not highly correlated with target outcome were eliminated from the analysis. Table 3 shows the level of multicollinearity between variables (ROA, ROE, leverage, size measured by gross written premium, business result and size measured by total assets) used in logistic regression model. Table3:The level of multicollinearity between variables used in the logistic regression model Target ROA ROE Leverage Size (ln_gwp) Business result Size (ln_assets) Target 1 -,177* -,185*,233*,451** -,179* -,281** ROA -0,177 1,881** 0,002-0,125,522**,312** ROE -0,185 0, ,027 -,248**,611**,252** Leverage 0,233 0,002-0, ,136-0,029 0,005 Size (ln_gwp) 0,451-0,125-0,248 0, ,148 0,107 Business result -0,179 0,522 0,611-0,029-0,148 1,507** Size (ln_assets) -0,281 0,312 0,252 0,005 0,107 0,507 1 ** corellation statistically significant with p <.01 * corellation statistically significant with p <.05 The Table 3 above suggests the possible problem of multicollinearity between variables business result with variables ROA and ROE, but regression analysis solves that problem in a way that it eliminates variable business result from the model. As it can be seen from the Table 3 the variables ROE and ROA are highly correlated. Therefore, it would be justified to omit one of them from the analysis but the model with both ROA and ROE variables included gives better prediction. This means that variable included in the model does not bring direct benefits but it restricts the degrees of freedom. The correlations between other variables do not indicate the possible problems with multicollinearity. The second test for multicollinearity was done by Variance Inflation Factors VIFs, where linear regression of one discriminating variable was run, while all other variables were used as explanatory variables. This auxiliary regression model resulted with VIFs less than 5, which indicates that the estimated model of logistic regression is free of multicollinearity. Table 4: VIF values Variable VIF ROA 4,911 ROE 5,898 Leverage 1,020 Size (ln_gwp) 1,159 Business result 2,085 Size (ln_assets) 1,480 Therefore, the final model was formed in two steps, while the variable business result was eliminated since it does not help in prediction of possible takeover targets. Table 5 shows the significance of variables used in the model in predicting takeover targets. 38 The Journal of Global Business Management Volume 8 * Number 1 * February 2012

7 Step 1 Table 5: Significance of variables used in the model in predicting takeover targets B S.E. df p ROA -0,247 0, ROE 0,108 0, Leverage 8,881 3, Size measured by GWP 2,519 0, Business result Size measured by assets -3,071 0, The final model is statistically significant which is also proved by Omnibus test and Hosmer/Lemeshow test which as shown by Table 6. Table 6: Omnibus and Hosmer/Lemeshow test Omnibus test of coefficients Hosmer / Lemeshow test Final c 2 df p c 2 df p model Model s classification accuracy for target companies was 70.6%, for non M&A companies 97.9%, while total accuracy was 95.1%. Since the sample that is made of the companies that were target companies in M&A transactions is much smaller than the sample that consists of the companies not involved in M&A activities this result is expected. This is shown by Table 7. Table 7: Classification of accuracy Estimated category Real category Target % accuracy Target % accuracy 95.1 Table 8: Results of the empirical analysis in the logistic regression model Variable B S.E. df p ROA ROE Leverage Size measured by GWP Size measured by assets Results of the logistic regression show that the likelihood of being acquired is significantly related to variables leverage, size measured by gross written premium and size measured by total assets. This is shown by Table 8. Likelihood of being acquired is positively related to leverage and size measured by gross written premium, while it is negatively related to size measured by total assets. This means that acquirers were focused on more leveraged insurance companies as well as on larger insurance companies measured by gross written premium, while at the same time the acquirers were focused on smaller insurance companies measured by total assets which as in accordance with predictions. The result of the analysis that shows that more leveraged insurance companies are more likely becoming takeover targets is in accordance with corporate control theory (Cummins and Xie, 2007) which assumes that financially The Journal of Global Business Management Volume 8 * Number 1 * February

8 vulnerable insurance companies are more likely to become a takeover targets because of the possibility of improving their business operations after the merger. Theory of corporate control according to which financially vulnerable firms are more likely becoming takeover targets was proved by studies of Cummins and Xie (2007) and Cummins, Tennyson and Weiss (1998). But variable leverage when calculated for insurance companies also includes technical provisions. Since technical provisions are formed from gross written premium, the larger insurance companies measured by gross written premium have higher leverage coefficient. According to the hypothesis that larger insurance companies measured by total gross written premium are more likely to become takeover targets due to the higher range of the insured they would get by merger, one can say that more leveraged companies are more likely becoming takeover targets due to this reason as well. The results of the analysis that show that smaller insurance companies measured by total assets are more likely becoming target companies is in accordance with size hypothesis due to the larger costs of absorbing larger targets in the organizational structure of the acquirer as well as due to the fact that larger companies have the ability to engage in more prolonged and costly takeover defenses. The result of the analysis that indicate that larger insurance companies measured by gross written premium are more likely becoming takeover targets is in accordance with hypothesized theory by DePamphillis (2010) which assumes that the acquirers are conducting takeover activities with the aim of improving the range of their services as well as with the aim of entering the new markets which leads to the improvement of their competitive position on the market. By taking over insurance companies with higher values of gross written premium the acquiring companies also increase their portfolio and consequently their market shares. CONCLUSION With the aim of exploring characteristics of Croatian target insurance companies, i.e. motives for acquisition of particular insurance companies logistic regression analysis was done. For the purpose of the analysis two groups of insurance companies were identified. The first one comprises of insurance companies that were targets in M&A activities, while the second group is made of the companies that were not involved in M&A activities. These activities were observed in period The final sample of target companies consisted of totally 17 companies while the second sample that consisted of the companies that were not involved in M&A activities was made of total of 146 companies. In order to estimate the impact of various factors that may be important in predicting takeover targets eighteen variables were used including: ROA, ROE, claims ratio, expense ratio, combined ratio, leverage, return on investment, size measured as the natural logarithm of total assets as well as the size measured as the natural logarithm of total gross written premium, growth rate, degree of diversification measured by HHI, technical provisions, business result, guarantee capital, assets per employee, premium per employee, the share of business expenses in earned premium and ownership variable. The results of the logistic regression show that the likelihood of being acquired is significantly and positively related to variables leverage and size measured by gross written premium, while it is significantly and negatively related to variable size measured by total assets. The results of the analysis prove the existence of different, but economically justified motives for conducting M&A activities. The fact that the results of this analysis have the impact on development strategy of insurance companies especially on the Croatian insurance market as well as on the regulators is unquestionable. The importance of understanding M&A activities increases in the context of forthcoming entrance of the Republic of Croatia in EU when it will face increased competition on the European single market. As a consequence a certain number of insurance companies will exit the market, and some of them will exit the market through M&A activities. At the same time regulators are under pressure with the aim of avoiding possible anticompetitive effects that such transactions may cause. 40 The Journal of Global Business Management Volume 8 * Number 1 * February 2012

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