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Mergers & Acquisitions, Diversification and Performance in the U.S. Property-Liability Insurance Industry

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Abstract

This paper examines the relationship between mergers & acquisitions (M & As), diversification and financial performance in the U.S. property-liability insurance industry over the period 1989–2004. The risk-adjusted return on assets (ROA), return on equity (ROE), Z-score and total risk measured by earnings volatility are considered as a relevant indicator of performance. We find that acquirers’ financial performance decreases and earnings volatility increases during the gestation period after the M & As perhaps due to increased frictional costs associated with post-merger integration and agency problems. We find that more focused insurers outperform the product-diversified insurers, implying that the costs of diversification outweigh the benefits. These findings are robust to alternative risk and diversification measures. We also find that marginal increases in commercial line share are associated with higher risk-adjusted profits, but these gains are offset by the extra costs from product diversity when its initial share is low. For insurers initially concentrated in commercial line, a marginal increase in commercial line share is related to higher performance due to positive effects of both direct exposure and indirect focus.

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Notes

  1. Hughes et al. (2003) also show that at banks with less entrenched management an increase in acquired assets is associated with improved performance.

  2. Furthermore, Berger and Ofek (1995) document that merging firms trade at an average discount in U.S. stock markets relative to stand-alone firms.

  3. Insurers report capital transfers among affiliates in the Schedule Y (Part 2: Summary of the Insurer’s Transactions with any Affiliates) of the annual statement.

  4. Shareholder dividends are dividends paid to affiliates if they own a portion of the reporting insurer’s shares. Capital contributions are capital transfers from one affiliate to another in the form of cash, securities, real estate and surplus notes.

  5. For example, Sharp ratio (also known as reward-to-volatility ratio) measures excess returns relative to volatility. Sharpe ratio indicates the excess return per unit of risk associated with the excess return and can be obtained by a risk-return frontier.

  6. NAIC’s annual statement indicates that there are approximately 30 different lines of business insurance companies underwrite and these numbers slightly change in any given year. Some lines of business with similar underwriting risks and payout patterns are grouped together in our calculation. For example, Accident and health include Group accident and health (line13 from the Exhibit of Premiums and Losses of annual statement), Credit accident and health (line 14), and other types of accident and health (lines 15.1–15.7). Similar to Liebenberg and Sommer (2008), the final list includes the following 23 lines: Fire and Allied lines, Farmowners’, Homeowners’, Commercial Multi Peril, Mortgage Guaranty, Ocean Marine, Inland Marine, Financial Guaranty, Medical Malpractice, Earthquake, Accident and Health, Workers’ Compensation, Other Liability, Products Liability, Personal Auto, Commercial Auto, Aircraft, Fidelity, Surety, Burglary and Theft, Boiler and Machinery, Credit, and International.

  7. The centering method is widely used in the survival analysis where lifetime is always positive (Lawless 2003).

  8. The input variable (firm size) is centered prior to taking its square.

  9. Direct writing includes exclusive agents and insurer employees. An exclusive agent represents a single insurer, but is not technically insurer’s employee. An independent agent represents more than one insurer. A broker represents the customer, negotiates with multiple insurers and tends to focus more on the commercial lines of business for larger-scale customers. Mixed distribution includes using both independent agency and direct writing or using both brokerage and direct writing.

  10. For example, some insurers merge into inactive firms in other states even merged firms do not operate with no assets or premiums because acquiring firms may want to move their headquarters to other states.

  11. The share of the personal line (PMS) is dropped as the reference category.

  12. We repeat regressions using ROA (ROE) defined as the ratio of net income before policyholder dividends and before taxes to total assets (equity capital). The key results are unchanged.

  13. The turnaround value of asset size can be estimated at the coefficient on firm size over twice the absolute value of the coefficient on the square of firm size. For this purpose, we repeat regressions without centering the values of both firm size and the square of firm size. The result shows that returns can be maximized in a range approximately between $1.2 billion and $4.8 billion in total admitted assets, implying that an increase in asset beyond this range may have a diminishing effect on performance. Our result is consistent with the findings of Hunter et al. (1990) and Noulas et al. (1990) that maximum returns to scale can be achieved with assets between $2 billion and $10 billion in US banks.

  14. To control for changes in the fundamental characteristics of a firm, we modify our basic specification in a way that an M & A indicator takes the value of one if the insurer was involved in a merger in the past three years and a vector of firm characteristics are measured at time t − 1. Similar to Sapienza (2002), we also introduce five new indicator variables that take the value of one if the insurer was involved in a merger one (t = −1), two (t = −2), three (t = −3), four (t = −4), and five (t = −5) years ago, respectively. We repeat our regression analysis with these alternative specifications. The results are unaffected (not reported here).

  15. Similar to Stiroh and Rumble (2006), indirect and direct effects are estimated at a 1% increase in the commercial line share.

  16. The estimates of direct effect do not change across percentiles of commercial line share because the estimated relationship is linear. See Stiroh and Rumble (2006).

  17. Column 1 repeats the estimates of risk-adjusted ROA from Table 5 for comparison.

  18. The values of the variance-inflating factor (VIF) indicate that there is no serious multicollinearity problem in our interaction regressions.

  19. Although product diversity (PRODDIV) may absorb some of the effect of M & A, variations in product diversity and in the share of business lines might occur due to reasons other than M & As, such as an insurer’s expanding its operations to potentially profitable lines or closing unprofitable lines strategically.

  20. See the “Appendix” and Shim (2010) for details of the estimation of this risk measure.

  21. Personal property lines of business include Homeowners, Farmowners, Earthquake, and Auto Physical Damage. Personal liability line includes Private Passenger Auto Liability. Commercial property lines include Fire, Allied Lines, Commercial Multiple Peril, Mortgage Guaranty, Inland Marine, Financial Guaranty, Group Accident and Health, Credit and Other Accident and Health, Fidelity, Surety, Burglary and Theft, Credit. Commercial liability lines include Medical Malpractice, Other Liability, Product Liability, Workers’ Compensation, Ocean Marine, Commercial Auto Liability, Aircraft, Boiler and Machinery, International, Reinsurance.

  22. We also repeat regressions using a discrete measure of product diversity which is commonly used in the diversification-performance literature (e.g., Berger and Ofek 1995; Servaes 1996; Liebenberg and Sommer 2008). The level of product diversification is identified by calculating the number of insurance business lines a firm operates. We find that the key results are still unchanged.

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Acknowledgements

The author is very grateful to an anonymous referee and Haluk Unal (the editor) for insightful comments and suggestions, which have led to substantial improvements in the paper. Any remaining errors or omissions are the sole responsibility of the author.

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  1. Illinois Wesleyan University, P.O. Box 2900, Bloomington, IL, 61702, USA

    Jeungbo Shim

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Appendix

Appendix

1.1 Estimation of portfolio risk (SIGMA)

According to Cummins and Sommer (1996) and Myers and Read (2001), the insurer’s portfolio risk (SIGMA) is quantified from the respective volatility of asset returns (\( \sigma_V^2 \)) and liability returns (\( \sigma_L^2 \)) and covariance of asset and liability returns (σ VL ):\( {\hbox{SIGMA}} = \sqrt {{\sigma_V^2 + \sigma_L^2 - 2{\sigma_{VL}}}} \). The corresponding volatilities and covariance can be estimated using the following:

$$ \begin{array}{*{20}{c}} {\sigma_V^2 = \sum\limits_{i = 1}^N {\sum\limits_{j = 1}^N {{y_i}{y_j}{\rho_{{V_i}{V_j}}}{\sigma_{{V_i}}}{\sigma_{{V_j}}}} }, } \\{\sigma_L^2 = \sum\limits_{i = 1}^M {\sum\limits_{j = 1}^M {{x_i}{x_j}{\rho_{{L_i}{L_j}}}{\sigma_{{L_i}}}{\sigma_{{L_j}}}} }, } \\{\sigma_{VL} = \sum\limits_{i = 1}^N {\sum\limits_{j = 1}^M {{y_i}{x_j}{\rho_{{V_i}{L_j}}}{\sigma_{{V_i}}}{\sigma_{{L_j}}}} }, } \\\end{array} $$

where \( {y_i} = {V_i}/V \)is the proportion of assets from asset type i, \( {x_i} = {L_i}/L \) is the proportion of liabilities from line i, ρ ViVj is the correlation between log asset type i and log asset type j, ρ LiLj is the correlation between log line i liabilities and log line j liabilities, ρ ViLj is the correlation between log asset type i and log line j liabilities, σ Vi is the volatility of asset type i, and σ Lj is the volatility of log line j liabilities. N(M) represents the number of asset categories (lines of insurance business).

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Shim, J. Mergers & Acquisitions, Diversification and Performance in the U.S. Property-Liability Insurance Industry. J Financ Serv Res 39, 119–144 (2011). https://doi.org/10.1007/s10693-010-0094-3

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