Daily Return Behavior of the Insurance Industry: The Case of Contingent Commission

Jiang ChengElyas Elyasiani

Tzuting Lin

Temple University

2007 ARIA Annual Meeting, Quebec City

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Motivation

• New York Attorney General Eliot Spitzer filed a civil suit in the State Supreme Court against Marsh & McLennan Cos. for “bid-rigging” and inappropriate use of “contingent commissions” on Oct. 14, 2004.

• We test the market reaction on insurance brokers and property-liability and life-health-accident insurers from the civil action suit using event study methodology within a GARCH framework.

• The bid-rigging event provides a good opportunity to test the effects of contingent commissions on the insurance industry.

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Findings

• The event generated negative effects both within the brokerage sector and for individual brokerage firms, suggesting that the contagion effect dominates the competitive effect.

• The inter-sectoral information spillover effects across the brokerage, property-liability, and life-health sub-sectors of the insurance industry are also significant and mostly negative.

• Our results support the information-based hypothesis against the pure-panic contagion effect as the size of the impact due to the event is highly correlated with firm characteristics.

• ARCH/GARCH effects are significant for both the sectoral portfolios and about half of individual brokers and property-liability insurers.

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Insurance Marketing systems and Contingent Commission

• Direct Marketing Insurers (DMIs) :

direct writer + exclusive agents

• Insurers with Independent Intermediaries (IIIs) :

independent agents + brokers

• Contingent Commission

pros: alignment of interests between insurers and brokers

cons: the potential conflict of interest for brokers and against the buyers

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Literature

• Event Study: the effects of California’s Proposition 103 (Fields et al., 1990; Szewczyk and Varma, 1990; Shelor and Cross, 1990; Grace et al., 1995; and Brockett et al., 1999), the 1989 California earthquake (Shelor et al., 1992), trouble in investment portfolio of First Executive and Travelers (Fenn and Cole, 1994), Hurricane Andrew (Lamb, 1995; Angbazo and Narayanan, 1996), property-liability insurance market pullout (McNamara et al., 1997), the terrorist attacks of September 11, 2001 (Cummins and Lewis, 2003), the European Union Insurance Directives (Campbell et al., 2003), and the impact of operational loss events in the U.S. banking and insurance industries (Cummins et al., 2006a, 2006b).

• Contingent commission (Cummins and Doherty, 2006; Kleffner and Regan, 2007).

• Stock return data often exhibit GARCH properties (Engle, 1982; Bollerslev, 1987; Akgiray, 1989; Lamoureux and Lastrapes 1990).

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List of Hypotheses Outcome of the Test

H1 Announcement of the “bid-rigging” event has no intra-sectoral effect; contagion and competitive effects offset one another exactly.

Rejected.

H2 Announcement of the “bid-rigging” event produces competitive effect which dominates the contagion effect.

Rejected.

H3 Announcement of the “bid-rigging” event has no effect on the insurers.

Rejected.

H4 The response of insurers’ stock prices to announcements of the “bid-rigging” event is independent of the insurers’ marketing system.

Rejected.

H5 Announcement of the “bid-rigging” event does not differentially affect stock prices of insurers with respect to their size.

Rejected.

H6 Announcement of the “bid-rigging” event does not differentially affect stock prices of insurers with respect to their payment of net contingent commission.

Rejected.

H7 Announcement of the “bid-rigging” event does not differentially affect stock prices of insurers with respect to business concentration.

Rejected.

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Methodology

• GARCH (1, 1) model

• Determinants of Abnormal Returns

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Data

• The SIC codes used are: 6331 for property-liability, 6311 for life, and 6320-6321 for health and accident insurers, and 6411 for the broker companies.

• 74 property-liability insurers (excluding AIG, ACE, and Hartford), 40 life-health-accident insurers, and 10 insurance brokers (excluding MMC).

• The market return is measured using the CRSP equally weighted index.

• The property-liability insurers’ financial data is obtained from the Best’s Key Rating Guide and A.M. Best’s Aggregates and Averages.

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Table 1. Estimation of Stock Brokers and Insurers Portfolios Return Sensitivities to the Bid-rigging Event

Stock Portfolio Intercept Market D-1 D0 ARCH0 ARCH1 GARCH1 Persistence

                 

Broker 0.000854(3.00)**

0.7395(21.37)***

-0.0185(-9.27)***

-0.0366(-21.90)***

0.00003036(3.30)***

0.2490(6.00)***

0.3110(2.07)**

0.5600

Property-Liability 0.0000288(0.19)

0.7930(36.25)***

0.00131(0.57)

-0.0162(-6.59)***

0.00000825(5.36)***

0.0377(1.34)

0.3275(2.71)***

0.3652

Life-Health-Accident

0.0000857(0.38)

0.9382(30.40)***

0.000188(0.05)

-0.0164(-4.47)***

0.00000639(1.46)

0.01180(0.47)

0.6915(3.21)***

0.7033

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Table 2. Estimation of Individual Stock Brokers Return Sensitivities to the Bid-rigging Event

Stock Intercept Market D-1 D0 ARCH0 ARCH1 GARCH1 Persistence

Aon Corp. 0.000852 0.8002*** -0.0188*** -0.1935*** 0.000039*** 0.363*** 0.411*** 0.7736

Brooke Corp. 0.004776 1.2433*** 0.0041 0.0029

Brown & Brown 0.001246 0.6906*** 0.0007 -0.0719***

Gallagher Arthur 0.000065 0.4013*** 0.00571* -0.0261*** 0.000040*** 0.516*** 0.239* 0.7545

Hilb Rogal 0.000738 0.9180*** -0.00299 -0.0817***

Hub Intl. Ltd. 0.000239 0.2611** 0.0021 -0.0258*

National Fin. 0.001385 1.0209*** 0.0173* 0.0162* 0.000222*** 0.305*** 0.0224 0.3271

Quotssmith Com. 0.000964 0.4933** -0.0340 0.0041

U S I Holdings 0.000555 0.6417*** 0.0138* -0.0566*** 0.000064*** 0.305*** 0.466*** 0.7710

Willis Group 0.000597 0.5780*** -0.0139 -0.0676***

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Table 3. Brokers Ranks, Revenues, Market Share and Contingent Commissions as Percent of Revenues

Brokerage Industry Rank

2004 Revenues ($Millions)

Marker Share %

Percentage of Contingent Commissions to Revenues %Stock

Aon 2 3105.9 16.60% 2.00%

Brooke Co 32 65.907 0.40% 3.10%

Brown & Brown Inc 7 638.267 3.40% 6.00%

Gallagher Arthur J & Co 3 1192.68 6.40% 3.00%

Hilb Rogal & Hamilton Co 8 601.734 3.20% NA

Hub Intl Ltd 12 231.44 1.20% 6.00%

National Financial Partners Co NA NA NA NA

Quotesmith Com Inc NA NA NA NA

U S I Holdings Co 10 405.82 2.20% 5.00%

Wollis Group Holdings Limited 4 1036.35 5.50% 4.00%

Marsh 1 5804.4 31.10% 7.30%

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Table 4. Estimation of Individual Stock Property-Liability Insurers Return Sensitivities to the Bid-Rigging Event

Stock Intercept Market D-1 D0 ARCH0 ARCH1 GARCH1

21st Century Group -0.000541 1.3005*** -0.0233 -0.0152

21st Century Holding -0.000306 1.1955*** -0.002572 0.016 0.000427*** 0.3506*** 0.4522***

ACE Ltd. -0.000385 0.7916*** 0.007412 -0.0678*** 0.0000745*** 0.1839** 0.3421*

AIG -0.000289 0.7914*** 0.0107** -0.0786*** 0.0000321** 0.2397** 0.4230*

ALFA -0.000115 1.1232*** -0.0115 -0.004559

Alleghany 0.001177* 0.3698*** 0.0123 0.0353 0.00000426 0.0907** 0.8720***

Allianz -0.000832 1.3843*** 0.00731 -0.007909 0.0000212 0.1262 0.7340***

Allmerica -0.0011 1.5249*** -0.0040251 -0.013

Allstate 0.000391 0.6477*** -0.000404 -0.001638

American Financial Group 0.000219 0.7863*** 0.001352 -0.0114

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Appendix B. Descriptive Statistics for Property-Liability Insurers

Variables and Definitions MeanStd. Deviation

Abnormal return on the event day, October 14, 2004 -0.0154 0.0180

Abnormal return on one day before the event day, October 13, 2004 0.0002 0.0124

Cumulative abnormal return of the event day and one day before -0.0075 0.0186

Marketing dummy variable equal to one if the insurer is an III, and zero if the insurer is a DMI 0.7973 0.4048

Size=Log of the total admitted assets for insurer 14.4319 1.6016

Contingent= ratio of insurer’s total payment of Net Contingent Commission to its Net Premium Written 1.0960 1.67728

Commercial=ratio of insurer’s premium written in commercial lines to total premiums written from all lines 0.5525 0.35618

The interaction term of the above two ratio: (Commercial*Contingent) 0.5175 0.88578

Leverage= ratio of insurers’ premium written to surplus 1.4951 0.83178

Return is the insurer’s return on policyholders’ surplus 8.1525 15.46658

Multi-line dummy=1 if the insurer has business in Life-Health-Accident insurance lines, and zero otherwise 0.2162 0.4145

Regulation dummy=1 if the insurer regulatory location is New York, and zero otherwise. 0.0676 0.25275

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Table 5. Determinants of the size of abnormal returns (Cross-Sectional Analysis)

Variables Coefficient t-ratio

Intercept 0.03002 ( 1.24)

Marketing dummy variable equal to one if the insurer is an III, and zero if the insurer is a DMI -0.01450 ** (-2.28)

Size= Log of the total admitted assets for insurer -0.00245 * (-1.71)

Contingent= ratio of insurer’s total payment of Net Contingent Commission to its Net Premium Written 0.00920 *** ( 2.98)

Commercial= ratio of insurer’s premium written in commercial lines to total premiums written from all lines 0.01299 ( 1.58)

The interaction term of the above two ratio: (Commercial*Contingent) -0.01941 *** (-3.34)

Leverage= ratio of insurers’ premium written to surplus -0.00219 (-0.77)

Return is the insurer’s return on policyholders’ surplus -0.00013 (-0.77)

Multi-line dummy=1 if the insurer has business in Life-Health-Accident insurance lines, and zero otherwise -0.00077 (-0.14)

Regulation dummy=1 if the insurer regulatory location is New York, and zero otherwise. -0.00030 (-0.03)

Number of observations 74

Adi. R-square 0.1522

F-statistic 2.28 **

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Comments and Suggestions?

Thank you!