Mergers and Acquisitions (M&A) in the European and U.S. Insurance

Industries: Information Asymmetry and Valuation Effects

J. David Cummins Mary A. Weiss

Temple University

Paul J.M. Klumpes Imperial College London

April 15, 2008

Authors’ contact information: J. David Cummins, Joseph E. Boettner Professor, Temple University, 481 Ritter Annex, 1301 Cecil B. Moore Avenue, Philadelphia, PA 19122. Phone: 610-520-9792, Fax: 610-520-9790, email: cummins@wharton.upenn.edu. Paul J.M. Klumpes, Professor, Imperial College, London. email: p.klumpes@imperial.ac.uk. Phone: +44-207-5949168, Fax: +44-115-929-0156 Mary A. Weiss, Deaver Professor, Temple University, 473 Ritter Annex, 1302 Cecil B. Moore Avenue, Philadelphia, PA 19122. Phone: 215-204-1916, Fax: 610-520-9790, email: mweiss@temple.edu.

Mergers and Acquisitions (M&A) in the European and U.S. Insurance Industries:

Information Asymmetry and Valuation Effects

ABSTRACT The objective of this paper is to determine whether within border and cross border M&As in the European and U.S. insurance markets create value for shareholders by studying the stock price impact of M&A transactions on target and acquiring firms. Various hypotheses motivating M&A transactions are advanced, ranging from assumed market efficiency (no gain) to market power and regulatory hypotheses. We conduct an event study analysis of M&A transactions where an insurance firm was either the target or the acquirer for the sample period 1990-2006. The stock price effect of M&As is measured by looking at cumulative abnormal returns on the transaction event day and surrounding days. The analysis reveals that M&As created small positive CAARs for acquirers. Targets, however, realized substantial positive CAARs in the range of 12% to 15%. For acquirers, there is no clear difference in performance between cross-border and within-border (domestic) transactions. For targets, both cross-border and within-border transactions led to substantial value-creation, thus providing evidence that geographical integration of financial services markets has been successful. Insurers that acquire banks and securities broker/dealers sustain significant market value losses, but intra-insurance industry transactions generate significant gains for the acquiring insurers. Only Canadian and US firm targets benefit significantly from acquisition activity when results are further broken down by size. Multiple regression analysis is conducted to analyze the relationship between firm characteristics and market value gains and losses.

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1. Introduction

Perhaps the most important development in the financial services market of the past two

decades is the integration of the previously separate segments of the financial services industry.

Deregulation, advances in communications and information technology, and economic forces have

led to the breakdown of the ‘firewalls’ that traditionally separated financial intermediaries such as

commercial banks, thrift institutions, investment banks, mutual fund companies, and insurance

companies.

The European Union gradually deregulated the financial services sector through a series of

banking and insurance directives, culminating in the virtual deregulation of financial services

(except for solvency) in the Second Banking Coordination Directive, implemented in the early

1990s, and the Third Generation Insurance Directives, implemented in 1994 (Group of 10, 2001).

The objective of the banking and insurance directives was to create a single European market in

financial services. The introduction of the Euro in 1999 also profoundly changed the economic

landscape for financial services firms in the European market.

European deregulation in insurance was particularly important, because insurers

traditionally had been limited to operating within specific European countries, with little or no

price competition and cross-border transactions mainly limited to reinsurance and some

commercial coverages. The Third Generation Insurance Directives introduced true price and

product competition in European retail insurance markets for the first time.

The result of deregulation and other economic drivers of financial sector integration has

been an unprecedented wave of mergers and acquisitions (M&As) of European financial

institutions. These have also had knock on effects in North American and Asian markets, as

European financial institutions became more aggressive in competing on a world-wide basis.

However, there is little literature on this issue. In general, prior literature on the costs and benefits

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of M&As is equivocal as to their value-creation effects. On the one hand, industry based studies

(e.g., McKinsey and Company, 2007) assert that M&As are value destroying for both the acquirer

and the target. On the other hand, Cummins and Weiss (2004) predict and find that M&A activity

in the European insurance industry over the period 1990-1997 had no noticeable effect on the

acquirers (i.e., M&As were value-neutral) and generally create market value for targets. They used

an event study approach where cumulative abnormal returns of the acquirers’ and targets’ stock

price around the announcement date, relative to the market index. However, their results did not

account for country, industry or sectoral factors that might underlie variations in M&A value

creation. There has been little analysis of international aspects of M&As for financial firms.

The objective of the study is to remedy this limitation in the existing literature by

extending the Cummins and Weiss (2004) results to analyze the effects of M&A transactions on

the market value of target and acquiring firms in the international insurance market. We analyze

M&A transactions over the period 1990-2006, as reported in the Thomson Financial SDC

Platinum Database. The analysis is defined as including all transactions where either the acquiring

firm of the target firm is an insurance company. Included in the analysis are all transactions

reported in SDC that involve a change in control, defined as an acquisition that increases the stake

of the acquiring institution from less than 50% to 50% or more of the ownership shares of the

target institution. Tests are conducted for differences in market value effects of mergers by

country, by whether the transaction is focusing versus diversifying, and by whether the transaction

is cross-border or within-border.

The study analyzes the market value impact of mergers and acquisitions (M&As) in the

European and U.S. insurance industries on both target and acquiring firms. We conduct an event

study analysis to determine the market value effects of the transactions included in our sample.

Specifically, we obtain stock price data from the Datastream database and study the market

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reaction to the M&A transactions on both target and acquirer firms in a series of event windows

surrounding the transaction dates. As argued by Schwert (1981), the use of market value data is

more powerful than other approaches in studying the effects of events such as M&As because

market prices immediately reflect the market’s assessment of new information on the target and

acquiring firms. In effect, conducting an event study enables us to capture the market’s

expectation of the net effect of an M&A transaction on the present value of the expected future

cash flows of the firms involved in the transaction and thus to determine whether M&As tend to

create value for shareholders. Although there are clearly other effects of M&As, such as the

impact on prices, service quality, and product offerings to customers, studying the stock price

effect of the transactions provides one important metric of the degree of value-creation or

destruction resulting from global merger trends.

Studying the market-value effects of European and U.S. insurance mergers is important for

a number of reasons. Analyzing whether M&As create value has implications for future regulatory

policy. The objective of the regulatory changes in Europe was to move away from a restrictive

regulatory system that primarily focused on solvency towards a system that enhances economic

efficiency and provides better value for consumers by harnessing market forces. Because M&A

activity is costly, serious questions would be raised about the efficiency effects of regulatory

policy if the resulting M&As fail to create value or actually destroy value for the firms involved in

the transactions.

Studying M&A transactions also has implications for anti-trust policy. Value-creation can

have both positive and negative effects from an anti-trust perspective. If merged firms gain value

because of market power that allows them to charge super-competitive prices, then positive market

value gains from mergers might be adverse from an anti-trust perspective. On the other hand, if

firms gain value because they become more efficient and competitive and take market share away

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from less efficient rivals, then M&As would not be a serious concern for anti-trust regulators.

Although determining whether any market value gains from M&As are due to market power or

more economically desirable effects is beyond the scope of the present study, our research

contributes by providing evidence on whether market value gains are occurring and on the types of

transactions that are most likely to lead to market value gains.

Finally, studying within and cross-border insurance mergers has important implications for

managers of financial services firms. If mergers tend to be value-creating, then it may be

worthwhile for managers to devote scarce time and resources to further consolidation activities.

On the other hand, if mergers have little or no impact on value or possibly destroy value, then

managerial efforts might be more profitably directed towards other activities such as improving

efficiency and productivity. Also, information on whether some types of transactions are more

likely than others to create value should help managers in formulating M&A strategies.

This study contributes to the literature as the first paper to analyze the market value effects

of global insurance mergers. There have been few market value studies of European and U.S.

insurance financial sector M&As of any kind. The leading study of European insurance mergers,

Cummins and Weiss (2004), analyzed merger transactions in 17 European insurance countries

over the period 1990-1997. In their sample, either the target or the acquiring firm had to be an

insurer. Based on 52 deals that involved a change in control, they found significant market value

for within-country, insurance to insurance transactions, and for transactions where banks acquired

insurance companies. However, they did not find market value gains for cross-border transactions

or transactions involving banks and securities firms.

Similar results for banks are reported by Cybo-Ottone and Murgia (2000) and for Lepetit et

al. (20002). Delong finds that bank mergers in the US are activity and geographically focusing

create value but that diversifying mergers do not create value. By contrast, Akhigbe and Madura

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(2001) find that US insurance mergers are value-creating for both acquirers and targets, although

the value-creating for targets is significantly larger than for acquirers.

Other papers (e.g. Cummins and Rubio-Misas, 2006, and Cummins, et al., 1999) analyse

consolidation in the Spanish and U.S. life insurance markets using book value data to measure

technical, cost and profit efficiency. 1 Both papers find that consolidation led to significant

improvements in efficiency and to price reductions.

In the present paper, the market value impact of M&As is analyzed using an event study

approach, i.e., we analyze the stock price performance of acquirers and target firms during various

windows of time surrounding the dates of the M&A transactions. We develop and test hypotheses

concerning the value creation arising from M&A transactions in the international insurance

industry. Among the specific predictions examined in the study are the following:

• Do acquiring firms gain or lose market value as a result of M&As?

• Do target firms that continue to be traded following the analysis gain or lose value as a result of M&As?

• Are cross-border or within-border M&As more likely to be value creating for targets and acquirers?

• Are within-industry or cross-industry M&As more likely to create value for targets and acquirers? I.e., are transactions within the insurance industry more likely to create value than transactions where a non-insurance firm is the target or acquirer?

• Are within sector or cross-sector M&As more likely to create value, i.e., are transaction where both target and acquirer are life (non-life) insurers more likely to create value than those where one M&A partner is a life insurer and the other is a non-life insurer?

• Does the size of the target and/or acquirer have any impact on the likelihood of value-creation.

• Does the country of origin of the target or the acquirer have any relationship with value-creation?

1 Klumpes and Urdu (2007) also study European M&As during the 1999-2000 boom, using a book value approach, but include an analysis of embedded value of acquirers/takeovers and other economic contingent claims. They also use a novel approach by matching the acquirer and takeover in estimating efficiencies, rather than analyzing the efficiencies separately. Their results are similar to the studies reviewed above.

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• Does risk, return, size or cross-border or industry characteristics explain cross-sectional variation in deal value and abrnormal stock returns for acquiring firms?

The study discriminates among alternative neo-classical and behavioral explanations for

M&A. It covers the most recent decade of M&As, 1990-2006. Although we conducted the

analysis for all regions of the world, North America, Europe, Asia, and all types of insurance, both

life and non-life M&A transactions. However, because the data outside of the U.S. and Europe

were relatively sparse, we chose to focus the analysis of results on the European and U.S. markets.

The findings support the general contention that M&A deals are more likely to be value

creating for targets than for acquiring firms. However these results are not consistent when

decomposed by country, region, industry and sector. Domestic deals, those based in Europe, and

those involving private negotiation can lead to value creation for acquiring firms. By contrast,

private negotiated deals and those involving insurance agents can be less value creating for target

firms. We also find that cross-sectional variation in deal value for acquiring firms is associated

with cross-border takeovers and abnormal returns is assiocated with risk, return and common

industry characteristics for European firms. Following the presentation of results, we identify

further areas where research is needed and identify outstanding issues that need resolution.

The remainder of the paper proceeds as follows. Section 2 discusses the relevant literature

and provides the theoretical and institutional antecedents. Section 3 develops predictions

concerning the likely economic effects of mergers and acquisitions, identifies ways in which

M&As create and destroy value, and specify our hypotheses. Section 4 explains our sample

selection procedure and event study methodology. Section 5 presents the results, and section 6

analyzes the determinants of value creation. The conclusions are discussed in section 7.

2. Theoretical and Institutional Background

Mergers can be somewhat difficult to rationalize in terms of financial theory. According to

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financial theory the value of any asset is equal to the present value of its cash flows. Thus, a

publicly held firm can be considered as a bundle of cash flows expected to be received in the

future. Investors are assumed to hold broadly diversified portfolios including value-weighted

shares of all firms in the economy (the “market portfolio”). In this construct, M&As do not

necessarily add value because they merely combine the rights to cash flows that are already held

by diversified investors. Hence, in theory, investors should be indifferent between receiving

future cash flow streams from two separate firms rather than from one merged firm formed by

combining the two separate firms. To the extent that M&As are costly, investors may actually be

worse off following an M&A transaction.

Of course, perfect markets finance theory rests on a number of assumptions which hold

only as approximations in practice. Among these are the absence of transactions costs, agency

costs, other types of friction costs, informational asymmetries between investors and managers,

taxation, and regulation. The existence of these and other market imperfections can lead to

situations where M&As have the potential to create value. In addition, economic production

theory offers other explanations for firm combinations such as economies of scale and scope that

can provide economic justifications for M&As that are not inconsistent with financial theory.

However, it is important to keep in mind the fundamental insight of finance – that cash flows

determine value – when considering the arguments regarding the economic rationale for M&As

discussed below. I.e., in order for a M&A transaction to create value, it must have a favorable

impact on the amount, timing, or risk of the cash flow streams of the combined institution in

comparison with those of the acquiring and target firms involved in the transaction.

We compare the implications of the neoclassical theory to behavioural theories. Unlike

neoclassical theories, behavioural theories take account of the inherent risk and uncertainty in

reaching consensus on the fair value of insurance business. The four leading behavioural theories

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of M&As are hubris, market misevaluations, agency and integration problems. These theories

regard M&As as departures from neoclassical economic theories. We then introduce an

algternative ‘risk management’ explanation for M&As, i.e. there is a demand for M&As purely to

‘resolve’ severe informational asymmetries in the valuation of insurance firms.

The winner’s curse has a long histoy in the literature on auctions. When there are many

bidders for an object of highly uncertain value, a wide range of bids is likely to result. For

example, suppose that many insurance firms are bidding to take over a block of insurance business.

Given the difficulty of estimating the actual amount of fair value of insurance business, the

estimates of the insurance firm may vary greatly. The highest bidder will therefore typically bid in

excess of the realized fair value of the insurance business embedded in the company. The winning

bidder is, therefore, ‘cursed’ in the sense that its bid exceeds the value of the business, so the firm

loses money. Roll (1986) analyzed the effect of the winner’s curse in takeover activity. Postulating

strong market efficiency in all markets, the prevailing market price of the target already reflected

the full value of the firm. The higher valuation of the bidders (over the target’s true economic

value), he states, resulted from hubris – their excessive self-confidence (price, arrogance). Hubris

is one of the main factors that cause the winner’s curse phenomenon to occur in the acquisition

market. Even if there were synergies, the actual or potential competitiotn of other bidders could

cause the winning bidder to pay too much.

Moeller et al. (2005) find that in a sample of large-losss acquirers ($1 billion or more), the

majority had prior acquisition successes. They suggest this might be interreted as consistent with

hubris. However, Boon and Mulherin (2006) find no significant differences in bidder returns

between multi-bidder auctions and one-on-one negotiations, inconsistent with Roll’s hubris

conjecture. Shleifer and Vishny (2003) present a model of acquisitions that provides a framework

for analyzing the relationship between short-run market misevaluation and the choice of stock or

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cash as a mediaum of payment. The misevaluation is a result of asymmetric information where

managers have perfect information and investors are less informed. This leads to overvalued firms

making acquisitions using their mis-valued stock as a payment instead of correctly-valued cash

when markets misperceive the true value of the synergies generated by the acquisition.

The main implications of their model are that acquisitions for stock are more likely to

occur when the market is overly optimistic about potential synergies and target managers have

short-run horizons or are paid off to accept the offer. Both bidder and gtarget firms may be over-

or under-valued relative to fundamentals in stock deals. Cash deals, in contrast, are more likely

when targets are undervalued relative to fundamentals and markets are overly pessimistic about

potential synergies, according to Shelifer and Vishney (2003).

In the Roll model the financial markets are efficient, but bidders are irrational. In the

Shleifer-Vishny model, financial markets are inefficient, but bidders and targets have perfect

information. Both the winners’ curse theory of Roll and the stock market misevaluations of SV are

types of behavioural finance tehroes. Different behavioural finance assumptions result in different

models and predictions. The neo-classical theory of M&As is that they take place to help firms

adjust to changing environments or to extend captabilities. The neo-classical theory predicts that

the market will reward mergers that make economic sense and punish mergers that do not make

economic sense.

Mitchell and Lehn (1990) show that market forces correct for merger mistakes. Their study

uses a sample of 1,158 public corporateions in 51 industries covered byValue Line, beginning at

the end of 1981. Of their sample, acquiring firms were divided into two groups. 77 firms that

made 113 acquisitions during 1982-1986 subsequently became acquired by other firms. 166

acquiring firms that made 232 acquisitions were not subsequently acquired. The event returns over

various lengths of windows ranging from 3 days to 61 days were sharply different for the firms

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that were subsequently acquired and those that don’t become future targets. The firms that were

subsequently acquired had negative event returns significant t the 1% level. For the firms that were

not subsequently acquired the event returns were significantly positive.

The neo-classical theory predicts that mergers that make economic sense will have positive

event returns, those that do not have a sound basis will have negative event reutnrs and will

subsequently be taken over. Mitchell and Lehn (2001) show consistent with earlier theories, the

financial markets perform a discliplinary role. The stock prices of firms that make sound mergers

will rise, but ‘bad bidders become good targets’.

Another branch of literature directly tests the market-timing prediction of the

misevaluation theories against the industry shock prediction of the neoclassical theories by

examining the causes of merger waves. Harford (2005) shows that merger waves cluster by

industry following exogenous shocks,, but only when accompanied by a sufficient degree of

capital liquidity. Harford distinguishes this liquidity from market run-ups and find that after

accounting for liquidity, market-timing variables have little explanatory power, rejecting the

models of SV and the similar theoretical results presented in Rhones-Kropf et al. (2004). Harford

also points out that Rhodes-Kropf et al’s (2004) empirical tests of the market-timing theory of

merger waves are equally consistent with alternative explanations of their evidence.

An agency problem arises when managers own only a fraction of the ownership shares of

the firm (Jensen and Meckling, 1976). This partial ownership may cause managers to work less

vigorously than otherwise and/or to consumer more perquisites because the majority owners bear

most of the cost. Furthermore, it is argued that in large corporations with widely dispersed

ownership there is not sufficient incentive for individual owners to expand the resources enquired

to monitor the behavior of managers. Hence, managers may use mergers to increase firm size to

increase their own salaries, bonuses and perks. Also managers may be motivated to seek mergers

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because it enables them to cash in on substantial stock option arrangements.

Agency costs are also present in the free cash flow hypothesis (Jensen, 1986). Jensen

defines free cash flow as cash flow in excess of the amounts required to fund all projects that have

positive net present values when discounted at their applicable costs of capital. Managers may

seek to avoid declines in growth by investing free cash in industries they do not understand,

regulting in negative NPV investments. Aggarwal and Samwick (2003) find empirical evidence to

support agency theory, consistent with a managerial desire to increase benefits through

diversification. The neoclassical theory states that potential M&As can help firms build

capabilities and adjust to change. On e of the advantages of M&As is that they permit relatively

rapid adjustments. But a major challenge of mergers is that they require that two formerly different

organizations be combined. The integration of organizations and cultures can be difficult. Hence

we would expect that M&As required by change forces will have uneven successrates.

Hazelkorn et al. (2004) emphasize the frequency distributions of excess stock returns for

acquirers. The extending capabilities theory of M&As is supported by the data that shows that

manay firms that make many small acquisitions achieve superior performance. One advantage is

the experience that is developed from making many acquisitions. Villalonga and McGahan (1995)

find that prior acquisition experience in acquisition leads to a higher probability of completing

future acquisitions. Another benefit is that smaller acquisitions can be in to the operations of the

larger acquiring firms without major restructuring of the organization.

3. Hypotheses

In terms of economic production theory, firms operate with cost, revenue, and profit

functions, all of which could be affected by mergers and acquisitions. One rationale often given

for M&As is economies of scale, usually associated with the cost function. The argument is that

firms operating at sub-optimal scale may be able to achieve scale gains more quickly through

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M&As than through organic growth. Scale economies are almost always given as a rationale for

M&As in the insurance industry and most other industries, usually without any supporting

empirical evidence. Although M&As can permit firms to achieve scale economies, friction costs

arising from post-merger integration problems potentially can offset any scale economy gains that

may be realized. In many cases, organic growth may be superior to M&As as a method for

achieving optimal scale; and other types of inefficiency such as technical and allocative

inefficiency often are much more significant than scale inefficiency.

Economies of scope provide another production theory rationale for mergers and

acquisitions. Scope economies can be present for costs, revenues, and (on net) for profits. If cost

(revenue) economies of scope are present, the cost of producing two outputs jointly in a single

firm will be lower (higher) than if the outputs were produced by two separate firms. Cost

economies of scope generally arise from the joint use of inputs such as managerial expertise,

customer lists, computer technologies, and brand names. Revenue economies of scope are often

said to arise due to reductions in consumer search costs and improvements in service quality from

the joint provision of related products such as life insurance and automobile insurance. This is the

“one-stop shopping” argument often utilized to justify financial sector mergers.

There is some empirical evidence for the existence of economies of scope in insurance,

although findings suggest that economies may exist only for specific types of producers and

specific sub-products within the insurance industry (Berger, et al., 2000). In addition, production

theory arguments for scope economies generally do not recognize that achieving such economies

through M&As can often be defeated by the frictions arising from integrating the corporate

cultures of two previously separate firms offering different products, perhaps using different

distribution systems and information technologies.

Potential gains in X-efficiency provide another production-based rationale for M&As. X-

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inefficiency arises when firms fail to operate on the cost, revenue, or profit frontier but rather incur

higher costs or earn lower revenues because of various types of suboptimal performance. The

principal types of inefficiency include technical inefficiency, failing to operate on the cost

minimizing isoquant, allocative inefficiency, failing to choose cost minimizing combinations of

inputs, and scale inefficiency, the failure to operate with constant returns to scale. Similar

efficiency concepts can be defined with respect to the revenue frontier. A potentially important

justification for a merger or acquisition transaction is to improve the efficiency of the merger

target, e.g., by replacing inefficient managers or introducing superior technology possessed by the

acquiring firm. The efficiency rationale for M&As may be somewhat stronger for focusing rather

than diversifying M&As, however. If the objective is to improve technical or allocative efficiency

of the target, it seems reasonable to expect that such improvements are more likely to be realized if

the managers of the acquiring firm already have considerable expertise in the types of operations

conducted by the target.

One important source of potential efficiency gains from mergers is the possibility of

eliminating duplicate or overlapping production, delivery, or back office systems. For example,

the merger of banks operating in the same geographical area may permit a reduction in the number

of branches and branch office employees without correspondingly degrading customer service.

The same rationale may apply in insurance to the extent that the duplication of agencies, claims

adjustment offices, and data processing facilities can be reduced. This rationale seems to apply

most strongly to intra-country and intra-industry mergers; although diversifying mergers that

permit the sale of insurance through bank branches have the potential to realize scope economies.

Another industrial organization hypothesis about M&As is that consolidation allows firms

to acquire varying degrees of monopoly power, permitting them to increase cash flows by raising

prices. This rationale would seem to apply most strongly to mergers that increase concentration

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within specified geographical or product markets. Empirical evidence based on U.S. banking

provides some support for the market power hypothesis, especially for large banks, but the

quantitative effect on bank profits tends to be small (Berger, 1995). Empirical evidence also has

been presented that consolidation in the Spanish insurance market during the 1990s led to price

reductions (Cummins and Rubio-Misas, 2006), contrary to the market power hypothesis.

If one relaxes the assumptions of perfect markets finance theory, some additional

rationalizations for M&As are provided. One important assumption is the absence of costs of

financial distress. In real world markets, especially in those such as financial services where

stringent solvency regulation is the norm, firms face significant financial distress costs. Insurers

that are over-leveraged or in weakened financial condition for other reasons incur increased

regulatory costs and potential operating restrictions. Moreover, because buyers of insurance are

especially sensitive to insolvency risk, insurers in deteriorating financial health are likely to lose

their best customers to rivals. Deteriorating financial condition is also likely to trigger financial

ratings downgrades with accompanying higher costs of capital. Finally, firms with relatively high

insolvency risk also face the loss of relationships with key employees and suppliers.

Because larger insurers are known to have lower insolvency probabilities, mergers can be

beneficial to the extent that increases in scale are accompanied by reductions in income volatility

due to enhanced diversification. This reasoning applies both to within-industry mergers and to

cross-industry mergers between institutions such as insurers and banks, providing a possible

rationale for both focusing and diversifying M&A activity. The potentially favorable effect of

M&As on expected bankruptcy costs is generally called the earnings diversification hypothesis.

Deregulation also provides a potential motive for value enhancing M&A transactions. For

example, the insurance industry in Europe traditionally was subject to stringent regulation

affecting pricing, contractual provisions, the establishment of branches, solvency standards, etc.. A

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separate market existed in every European country, and cross-border transactions were rare, except

for reinsurance and some commercial coverages. Competitive intensity was generally low, with

minimal price and product competition (Swiss Re, 2000). The implementation of the European

Union’s (EU’s) Third Generation Insurance Directives, beginning on July 1, 1994, represented a

major step in creating conditions in the EU resembling a single deregulated national market.

The Third Generation Directives have three key components: (1) The establishment of a

single EU license (the “single passport”), whereby an insurer is required to obtain only one license

to operate in the EU rather than being licensed in each member nation. (2) The principle of home

country supervision, whereby an insurer is regulated only by the nation which issued its license

and not by each host country where it operates. And (3) the abolition of “substantive insurance

supervision,” meaning that regulation is limited to solvency control and that pricing, contracting,

and other insurer operations are effectively deregulated. Thus, insurers were allowed to engage in

true price competition in personal lines for the first time and also to compete more freely in

products and services.

The opening of the European market provided a powerful rationale for M&As as

companies that previously operated in specific national markets sought to expand throughout the

EU. Expanding into other national markets by acquiring firms located in these market is likely to

be more effective than organic growth because local firms have superior knowledge of the

language, culture, and legal system of their home country. Thus, cross-border M&As are likely to

be value-enhancing. On the other hand, the “liability of foreignness” hypothesis suggests the

opposite, i.e., that domestic companies are likely to have an advantage over foreign competitors,

even if the foreign competitor acquires a domestic insurer.

The existence of corporate income taxation also provides a rationale for M&As as a

possible mechanism for increasing net cash flows. Firms can reduce expected taxes by reducing

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earnings volatility to the extent that corporate tax schedules are convex, or to the extent that they

can exploit inter-country tax arbitrage or utilize tax loss carryovers.

Another rationale sometimes given for M&As based on relaxation of the assumptions of

perfect markets financial theory is the creation of internal capital markets. The argument is that

informational asymmetries between managers and capital markets tend to make capital markets

somewhat inefficient in allocating capital among alternative uses and also may lead to higher costs

of capital. Managers are said to be able to utilize their superior knowledge of the firm’s

investment opportunities to allocate capital efficiently among projects, thereby maximizing firm

value. However, the extensive literature on the diversification discount, i.e., the tendency of

diversified firms to have lower values than their subsidiaries taken independently, as well as

theoretical research casts considerable doubt on the internal capital markets hypothesis.2 This

hypothesis may have a somewhat stronger justification in Europe than in the U.S. because

European firms have traditionally relied relatively more on bank financing and less on capital

market financing than U.S. firms, suggesting that capital markets may be somewhat less efficient

in Europe. However, based on existing empirical and theoretical evidence, we do not find the

internal capital markets hypothesis to be very convincing.

There are also non-value-maximizing motives for consolidation. Contrary to perfect

markets finance theory, considerable evidence exists that real world managers do not always act in

the best interests of shareholders but rather tend to pursue their own interests to varying degrees.

Instead of taking actions to maximize firm value, managers may act to maximize their own net

worth and income, engage in excessive perquisite consumption, and take other actions not

consistent with value maximization. These agency conflicts may lead managers to forgo

2The existence of a diversification discount has been widely documented in the literature.

See, for example, Comment and Jarrell (1995) and Berger and Ofek (1995). Theoretical research on internal capital markets has been conducted by Scharfstein and Stein (2000).

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profitable but risky projects that may threaten their job security. Moreover, and of special

relevance for M&As, managers may engage in projects of questionable value that increase the

scale of the firm to increase their compensation and prestige. Managers may also engage in

defensive acquisitions designed to head off hostile takeovers of the firm that would threaten their

jobs. To the extent that managers engage in non-value-maximizing acquisitions, M&As can be

expected to have adverse market value effects.

M&As also may reduce value to the extent that firms are not very successful in conducting

post-merger integration. Post-merger integration is likely to be especially difficult for cross-

country and cross-industry mergers due to larger national and corporate cultural differences that

must be overcome.3

The net result of this analysis is that the theoretical prediction with regard to the impact of

M&As on market values is ambiguous. A large number of factors come into play which could

affect the success of any given M&A transaction, making generalized predictions very difficult.

One general result that emerges from the discussion as well as from past empirical work, however,

is that focusing mergers are somewhat more likely to create efficiency gains than diversifying

mergers. Focusing can be defined either geographically or in terms of activities such as banking,

life and non-life insurance, or securities operations. Thus, we first predict that within-industry and

within-country mergers are more likely to create value than activity or geographically diversifying

mergers. The predictions of the study are summarized in Table 1.

4. Methodology And Sample Selection

4.1. -Data and Sample Selection

We are not conducting a stratified random sample but rather are capturing the universe of

3Evidence that difficulties in integrating data processing systems is an impediment to

efficiency gains in some financial sector mergers is provided in Rhoades (1998).

19

all transactions during the sample period 1990 through 2006 where either the acquirer or target

was an insurance company. We decided to use the universe of transactions rather than a sample

because the statistical power of our tests will be improved with a larger sample size. The

beginning of the sample period was selected to provide a few years of observations prior to the

introduction of the European Union’s Third Generation Insurance Directives in 1994, because

many European countries introduced deregulatory measures prior to the Third Directives to

provide time for their domestic insurers to prepare for the overall European deregulation.

The data on M&A transactions were obtained from the Thomson Financial SDC Database.

To focus on insurance M&As, we identified all transactions in SDC in which an insurance

company was either the acquirer or the target. Insurance companies were defined as all firms with

two-digit Standard Industrial Classification (SIC) codes in the following categories:

SIC Codes Used For Selecting Acquirers and Targets

SIC code Definition:

6311 Life Insurance

6321 Accident & Health Insurance

6331 Fire, Marine & Casualty Insurance

6399 Insurance Companies, NEC

6411 Insurance Agents, Brokers & Service

Because either the target or the acquirer (not both) had to be an insurer, transactions are

included in the sample where insurers are acquired by non-insurance firms such as banks, other

financial firms, and industrials, and where insurance firms acquire non-insurers, as well as within

the insurance industry (insurer-to-insurer) transactions. The study focuses primarily on

transactions in member countries of the European Union, in Western Europe, resulting in the

exclusion of a small number of East European Transactions. We also study the North American

(US and Canadian) market, resulting in the exclusion of a small number of Latin American

transactions. The Asian-Pacific study initially covered the six ASEAN countries, plus India, China,

20

South Korea, and Taiwan. However, we have chosen to focus the discussion of results on the

North American and European transactions. Some of the Asian results are included in the tables

for purposes of comparison and completeness.

The first pass through the SDC database produced a substantial number of transactions

involving minority stakes. We decided that it was useful to include these transactions in order to

parallel our results with those of the G10 (2001) and because we thought it would be interesting to

look at the entire portfolio of transactions. However, we conduct the market value analysis using

the sub-sample of transactions that represent a change in control, which we define as a transaction

where the acquirer stake changes from less than 50% to 50% or more of the target firm’s shares.

The stock price data for the event study are obtained from the Datastream Database. Using

the SDC sample as the transactions database, we then identified all transactions where either the

acquirer or the target firm was also was present in Datastream and obtained Datastream stock price

data for the periods needed to conduct the event study.

4.2. Methodology Outline

The steps followed in conducting the study are summarized as follows:

(1) Identify M&A transactions using Thomson Financial’s SDC Platinum database.

The analysis included the countries shown in Table 2 and the SIC codes listed above.

(2) Identify M&A transactions where the target and/or the acquirer have corresponding

stock price data in the Datastream database. Because some transactions are private, this step

significantly reduces the sample size. This step is very time consuming because SDC Platinum

and Datastream do not use the same company identification codes. Thus, the companies had to be

matched by company name.

(3) Conduct tabular analysis of SDC data (pivot tables of company characteristics) to

provide summary statistics on the extent of M&A activity.

21

(4) Conduct standard event study to measure the market value effects of M&As. The

results are summarized by country, for cross-border and within-border transactions, for cross-

industry and within-industry transactions, etc.

As discussed above, we are capturing the universe of M&A transactions that are reported

in SDC Platinum, and we conduct the market value analysis on the subset of firms for which

Datastream data are present.

4.3. Event Study Methodology

The standard market model event study methodology is used. For each transaction

included in the study, the event study methodology computes the abnormal return associated with

a specified event, controlling for the predicted return on the stock on the same day. The predicted

return is computed using the market model. The procedure is described in more detail in the

remainder of this section.

The event study approach assumes that the returns of the underlying securities are jointly

multivariate normal and independently and identically distributed through time (MacKinlay,

1997). The analysis involves computing the returns for each of the transactions in our sample

using data from the Datastream database. Using this approach, the expected return of any given

insurer security is obtained from the market model, defined as follows:

Rjt = αj + Βj Rmt + εjt (1)

where Rjt is the actual dividend-adjusted return on security j on day t [log((Pricet +

Dividendt)/Pricet-1], Rmt is the rate of return on the Datastream General Market Index for the

country of the target or acquiring firm, αj is the idiosyncratic return on security j, Βj is the beta

coefficient of security j, and εjt is the error term of the regression. Under the assumption of joint

normality and independently and identically distributed returns, the error of the regression is well-

behaved, i.e.,

22

2( ) 0 ( )jjt jtE Var εε ε σ= = (2)

The market model (equation (1)) is estimated for each of our companies based on the

security’s returns over the 250 trading-day period ending 30 days prior to the event date. Using the

parameters estimated from this market model and the movement of the market index during the

event period, we compute the expected return on each stock during each day of the event window.

The daily unexpected or abnormal return (AR) for each security is obtained by subtracting the

expected return from the actual return on each day. We utilize several event windows for the

study, extending a maximum of 15 days before and after the event date. The notation for an event

window extending m days prior to the event date and p days following the event date is (-m,+p),

with the event date as day 0.

Thus, the abnormal return on day t in the event window for security j can be expressed as

the estimated disturbance term of the market model calculated out of sample as follows:

)ˆˆ( mtjjjtjt RRAR βα +−= (3)

where Rjt = the rate of return on security j on event day t, and

Rmt = the rate of return on the value-weighted index on event day t.

We compute daily abnormal returns for each firm over various windows during the period t = -15

to t = +15. The cumulative abnormal return (CAR) over the event window (-m,+p) is defined as:

p

j jtt m

CAR AR=−∑= (4)

The mean cumulative abnormal return for a sample of N stocks is:

1

1 N

jj

CAR CARN =

∑= (5)

The mean cumulative abnormal return is expected to be zero in the absence of abnormal

performance. The Dodd and Warner (1983) mean standardised cumulative abnormal return can be

23

used to test the significance of any prediction error. This test statistic is calculated by

standardising the daily prediction by its standard deviation (sjt):5

jt

jtjt s

ARSAR = (6)

and then cumulating the standardised abnormal return over the period K to J:

1

L jt

jtt K

SARSCAR

L K=∑=

− + (7)

For a sample of N securities, the appropriate test statistic is:

N j

j 1

SCAR z

N=∑= (8)

SARjt and z will be normally distributed with a unit root if there is no abnormal performance.

5. Empirical Results

This section reports the empirical analysis. Section 5.1 describes the data in more detail.

Section 5.2 presents the results of the event study analysis. Section 5.3 provides sensitivity tests.

Section 5.4 reports the results of cross-sectional regressions on the determinants of acquirer deal

value and post-announcement abnormal returns.

5.1. Insurance M&As: Descriptive Statistics, Deals and Deal Volume

The number of deals by year is shown in Figure 1. The deals shown in the figure are those

where the acquirer held at least 50% of the stock of the target following the transaction, i.e., these

are transactions involving a clear change of control. There are at least 150 deals in each year of

the sample period with a total of 4,068 deals over the entire sample period. The number of deals

peaked during the mid-1990s with more than 300 transactions taking place each year from 1996

through 2000. The number of transactions in the market value study is significantly smaller

because many of the deals reported by SDC do not have traded stocks that appear in Datastream.

The deal volume in millions of U.S. dollars is shown in Figure 2. Deal volume exceeded $120

24

billion per year from 1997-2001 and exceeded $100 billion in 2003, 2005, and 2006. The total

deal value for the entire period covered by the study is more than $1.3 trillion.

The number of deals by region is shown in Table 3. The largest number of transactions

were within the Americas (2,149). The next largest number, 1,152, were within Europe. Overall,

there were 3,712 within-region transactions and 301 cross-region transactions. However, at this

level of aggregation, the tabulation masks a significant number of cross-border transactions within

the principal regions.

The number of deals by country of the acquirers and targets is shown in Table 4, Panel A.

Acquiring countries are shown as rows in the table, while targets are shown in columns.4 Only the

largest 13 countries are shown; M&A transactions in other countries are too small to be separately

listed and are classed under ‘other.’ As expected, the largest number of transactions were within

the United States (51%), followed by transactions within the United Kingdom (11%), Canada

(4.7%), and France (2.7%). Cross-border deals dominate M&A activity only in Switzerland, the

Netherlands, and Bermuda. Overall, 82.6% of the transactions were within-border and 17.4%

were cross-border. The total number of cross-border transactions is 344.

Table 4, Panel B shows the value of deals in millions of U.S. dollars by country of the

acquirers and targets. Relative to the results reported in Panel A, the United States dominates with

just over 51% of total worldwide deal value, followed by the United Kingdom (10%) and France

(5%), followed by Italy (2.3%), Belgium (2%), the Netherlands and Canada (1.7%), and

Switzerland (1.5%). Again, cross-border deals by value dominate in only in Switzerland, the

Netherlands, Germany and Bermuda. The analysis also suggests that cross-border deals are much

more common in Europe and Australia than in the North American markets. Overall, 77.7% of

the deal volume ($568.5 billion) represented within-border transactions.

4 The total number of deals is smaller than shown in Figure 1 because the region of the target and acquirer is not reported for some transactions.

25

Table 5 reports on M&A deals broken down by industry type; where either the acquirer or

takeover target must be an insurance firm. Panel A reports the results by number of deals; panel B

reports results by deal value. The analysis in Table 5 reveals that just under 40% of all deals by

number and 60% by value involve life insurance acquirers. Of these, 43% by number and 50% by

value involve within business transactions. Interestingly, 17% of all deals by value involve life

insurance firms acquiring commercial banks, although these are only 1.5% by number.

Conversely, 6% of all deals involve commercial banks acquiring life insurers, which is the highest

proportion of all deals involving non-insurance acquirers during the study period.

Table 6 reports the winners and losers of M&A transaction, both by acquirer and by target.

Panel A reports the results by reference to the major insurance markets; Panel B reports the results

by line of business. Winners were defined as the two top deals in each country (by country of the

acquirer) in terms of market value gains (value-creation), and losers were defined in terms of the

two deals with the largest market value losses, again by country of the acquirer. Relevant details of

the transaction, including the transaction date, acquirer and target industry/country and deal value,

as well as the one-day CAR (see discussion in the results).

The analysis indicates that the largest gains and losses in percentage terms occurred in the

United States. However, the most significant deals involving large gains in total value by country

of origin occurred in the Netherlands and the UK (see Panel A), with US cross-border deals

initiated by ING Group in particular involving large gains for both the acquirer (takeover of

Equitable of Iowa) and target (Reliastar). At the other end of the spectrum, the most significant

losses also occurred in the Netherlands (Nationale Nederlanden takeover of NMB Post).

Analysis by line of business reveals different patterns emerging, with 70% of all winners

and losers involving US-only transactions (see panel B of Table 6). The most significant winners

arise in the life insurance industry, with both acquirer (Financial Industries Corp) and target

26

(Condor Services) benefiting most from M&A transactions. By contrast, the largest losers from

M&A transactions involved non-insurance acquisitions or targets.

. Table 7 reports the largest M&A deals both by country (Panel A) and by line of Business

(Panel B). The largest transaction by far is the Travelers Group acquisition of Citicorp in 1998

(10% of all deals during the period), followed by CGU’s takeover of Norwich Union in 2000 to

form Aviva, the largest UK life insurance firm. The largest transactions also occurred in the life

insurance sector.

Table 8 reports the most active M&A insurance firms by country for the countries with the

most active M&A markets. The most active firm in terms of deal volume is Travelers, which

engaged in more than $81 billion in M&A transactions over the period. Also very active in terms

of deal volume were Aviva ($29 billion) and AXA ($22 billion). The most active firms in terms

of numbers of transactions are Aon with 20 transactions and Aviva with 19. Swiss Re was also

quite active, with 18 transactions totaling $16.5 billion.

5.2. Event Study Results: Overview

The first stage in the event study analysis was to match SDC transactions with Datastream

codes in order to capture the Datastream data on M&As for traded insurers in the overall SDC

sample. The latest SDC search indicate that there were 10,532 transactions involving insurance

companies in our sample. The latest SDC search indicated there were 10,532 transactions

involving insurance companies in our sample countries from 1/1/1990 to 12/31/2006.

This means that there were a maximum of 21,064 (10,532*2) companies involved

in these transactions, since each transaction has an acquirer and a target. After eliminating

countries that were not in our sample and cross-checking with our existing database, the number of

companies left to look up was reduced to 9,890. Further cross-checking reduced the sample

further to 8,035 companies. Elimination of transactions in which at least 50% control was not

27

achieved reduced the sample of companies to be looked up to 7,047 companies. This is the final

sample of companies used to begin the Datastream analysis..

To provide an initial overview of the results, Table 9 reports the 1-day post announcement

CARs across the sample by country (Panel A) and by line of business (Panel B).5 The averages are

reported separately for acquirers and for targets. The minimum and maximum CARs and the

number of transactions are also shown in Table 9. The transactions shown summarized in the

table are those resulting in a change in control.

The targets have substantially higher average CARs than acquirers in all countries and

business lines (except for commercial banks), and most of the acquirer CARs are not statistically

significant. However, the maximum CARs show that it is possible for acquirers to gain significant

value from M&A transactions. The country level analysis shows that there is little variation in

average (0,+1) event window CARs for acquirers. By contrast, the average (0,+1) day window

CARs for targets varies considerably with US, Swiss, and Dutch targets benefiting considerably

from the M&A transactions.

The line of business level analysis by contrast indicates that acquirers classed as other

financial institutions and other insurers experienced slightly negative 1 day CARs. However, for

the other categories of insurers and agents, M&As appear to be close to value-neutral for acquirers.

On the other hand, bank acquirers registered statistically significant market value gains of 2.6% on

average for the (0,+1) window.

5.3. Event Study Results: Detailed Analysis

This section provides a more detailed analysis of the event study results. This analysis

investigates several additional event windows besides the (0,+1) window discussed above. The

results also are broken down by country, by industry, and in terms of cross-border versus within

5 I.e., the results in Table 9 are for the (0,+1) window.

28

border transactions. The study focuses only on transactions that resulted in a change in control,

i.e., where the buyer’s ownership share in the target increased from less than 50% to 50% or more

as the result of the transaction. Prior research has shown that change in control transactions

provide the most meaningful results in the analysis of M&As.

5.3.1. All Transactions

The event study results for all transactions in the Datastream sample are shown in Table 10.

The acquirer transactions analyzed in this study have a small positive effect on market value for

the acquiring firms, depending upon the event window selected. The positive effect is small on

average (less than 1%) but is statistically significant at the 5% or 10% level for the (0,+x), based

on both the Patel and SCZ statistics. However, there is apparently some pre-event information

leakage, because the mean CARs for the (-10.+10) and (-15,+15) windows are negative. However,

overall the results support a finding of small market value gains for acquirers. The finding of a

small positive reaction for acquirers is not unusual in the event study M&A literature, although the

standard finding in the finance literature is that acquirers lose but targets gain from M&As.

In contrast to the acquirers, the target transactions summarized in Table 10 are

characterized by significant value creation. The CAARs are statistically significant at the 1% level

or better for all windows studied. Based on the (-1,+1) window, stocks of acquisition targets

gained 10.8% on average; and based on the (-15,+15) window, the average gain is 15.6%. Again

the findings for targets are supportive of the predictions of hypothesis H1, and generally consistent

with the prior M&A event study literature, i.e., targets tend to gain value in an acquisition.

However, the magnitude of the gains shown in our study is significantly larger than shown in most

of the prior literature, including Cummins and Weiss (2004).

Table 11 breaks down the overall acquirers’ results into cross-border (Panel A) and within-

border (domestic) (Panel B) transactions. Whereas the results for cross-border transactions (Panel

29

A) are not negative across any windows (except -15, +15), they are also not statistically significant

except for the shortest windows shown in the table (e.g., (-1,+1) and (-2,+2). By contrast, the

results for domestic transactions are positive and statistically over all post-M&A periods (0,+x),

except for the 15 day window. However, there appears to be negative information leakage, leading

to significant negative CAARs for the (-10,+10) and (-15,+15) windows. Hence the information

presented in Table 11 does not permit us to draw conclusions about whether domestic or cross-

border transactions are more profitable for acquirers.

Table 12 reports the equivalent cross-border and within-border (domestic) transaction

results for targets. In both cases, the CAARs are large, positive, and statistically significant for

nearly all windows shown. The results do not support the predictions of hypothesis H3b that cross-

border transactions are more likely than domestic transactions to be value creating for target firms.

On average, both cross-border and domestic transactions create significant market value gains for

target firms.

5.3.2. Country / Regional Analysis

Table 13 provides a further regional breakdown of acquirers. The panels report on regional

effects (Asia/Pacific; Panels A-E), North America (Canada/US; Panels F-H) and Europe

(UK/Non-UK; Panels I-K). In interpreting these results, it is important to keep in mind that the

levels of statistical significance are likely to drop off considerably due to the reductions in sample

size in many of the regions analyzed.

The analysis for Asia Pacific is somewhat equivocal. Based on the region as a whole,

including or excluding Japan (panels A and B), takeovers appear to generate negative value for

acquirers; but the effects are not statistically significant. When Australia is also excluded, the

negative effect of takeovers becomes statistically significant over the (0,+10) and (0,+15)

windows. The acquirers results for Australia (Panel D) are basically insignificant. The only Asian

30

transactions that appear to generate significant value gains are the Japanese transactions (panel E).

E.g., Japanese acquirers gain 3.97% in the (0,+10) window.

Panel F and G of Table 13 suggest that the North American market appears to generate

some positive effects for acquirers, although these effects dissipate over longer-event windows.

When the US is included, there is a statistically negative effect in some windows including the

period prior to the takeover (Panel G), suggesting some negative information leakage prior to the

events. The U.S. market itself (Panel H) appears to generate slightly positive CAARS for acquirers

over all (0,+x) windows; and the results are statistically significant. However, once again, there

appears to be some negative information leakage prior to the event (e.g., in the (-10,0) window).

The European takeovers (panel I) are positive and statistically significant only for

relatively short windows surrounding the event (e.g., the (-1,+1) and (-2,+2) windows). However,

the European results for most windows are not statistically significant. The results are slightly

stronger when the U.K. is included (panel J). The U.K. results (Panel K) imply that there are

significant positive results for U.K. acquirers over short windows (e.g., (0,+1), (0,+5), and

(0,+10)); but these effects dissipate and become negative over longer horizons, although the

negative returns are not statistically significant.

Table 14 provides the equivalent regional and country breakdown by target. With the

exception of Canada, all of the panels show that M&A transactions yield positive CAARs for

targets, but the magnitude and significance varies considerably across regions and countries.

Panels A to C show significant gains for Asian targets, with positive information leakage prior to

the event. E.g., panel A shows a gain of 7.12% in the (-15,+15) window and a gain of 4.66% in

the (0,+15) window. The highest gains for Asian transactions are in Japan (panel E), but it is

important to point out that only four Japanese transactions are included in the target analysis.

The Canadian transactions show statistically insignificant negative CAARs in most

31

windows. The action in North America is in the U.S. transactions, which show substantial and

significant positive CAARs in all windows. U.S. targets are the world leaders in terms of market

value gains from M&A transactions. European targets also benefit from M&A activity, with

results significant across all windows (Panel I and J). These results are even more pronounced in

the U.K. (Panel K), with positive and statistically significant results across all event windows. In

fact, the U.K. transactions show larger market value gains than the Continental European

transactions in nearly all windows. Thus, the U.S. and U.K. transactions appear to be the most

profitable in terms of market value gains for targets.

4.3.3. Cross-Industry Analysis

To investigate whether the sources of value creation in M&As are related to within-

industry or cross-industry sources, this section presents the results for cross-industry M&A

transactions. We first consider the broadly defined case where the acquirer is within the insurance

industry and the target is in some other industry and then consider inter-industry transactions

involving insurance acquisitions of banks and securities broker dealers and bank and broker/dealer

acquisitions of insurance firms.

Table 15 presents the results for acquirers where the acquirer is an insurance firm and the

target is a non-insurance firm. Panel A shows the results for transactions where the acquirer is an

insurance company and the target is not. The mean CAARs are negative for most windows and

are rarely statistically significant. Thus, insurance company acquirers do not show significant

gains or losses from non-insurance acquisitions.

The results differ when the acquirer is an insurance agent or broker and the target is a non-

insurance firm (panel B of Table 15). Here there is evidence of significant value creation for the

acquirers, which tends to occur primarily prior to the event day. E.g., the (0,+1) window shows a

significant market value gain of 0.37%, while the (-1,+1) window shows a significant gain of

32

1.12%. Hence, there is some evidence that cross-industry transactions can be beneficial for

insurance agents and brokers.

Panel C of Table 15 shows the CAARs for cases where non-life insurance companies

acquire non-insurance firms. Here there is some evidence of market value gains in short windows

surrounding the event day. However, the results become negative for the (0,+10) and (0,+15)

windows. Hence, there is not much evidence that cross-industry transactions are beneficial for

non-life insurance acquirers.

Table 16 shows the CAAR results for targets for transactions where the acquirer is within

the insurance industry and the target is not. The results reveal substantial market value gains

across the board for the non-insurance target firms. Focusing on the widest window (-15,+15), the

results are especially strong for non-insurance targets acquired by non-life insurers, where the

mean CAAR is 22.8%. The corresponding results for targets acquired by insurance companies in

general and by insurance agents and brokers are 14.0% and 12.0%, respectively. This may

provide some evidence that insurance firms over-pay for non-insurance acquisitions.

Table 17 considers bank-insurance and insurance-bank transactions. The results in Table

17, panel A, suggest that transactions where banks acquire insurance companies or agents tend to

generate positive market value gains for the acquirers in relatively short windows surrounding the

acquisition date (panels A and B). However, the effects dissipate over the longer windows shown

in the table. However, when insurance companies acquire banks (panel C and D), market value

losses are generated for the acquiring insurers, and the effects are statistically significant in many

of the windows. These results support the contention in hypothesis H4a that insurers are more

likely to lose value when acquiring banks but banks are less likely to lose and may gain value

when acquiring insurance firms. This is most likely because sales of insurance products, especially

annuities and life insurance, are a natural extension of their normal operations for banks, whereas

33

banking is a relatively unfamiliar activity for insurers.

Table 18 reports the equivalent effects for targets where either banks or insurance

companies are acquirers in cross-industry transactions. The results provide strong evidence that

targets gain market value as the result of being acquired. However, the main message from Table

18 is that bank targets acquired by insurance companies gain substantially more value than

insurance targets acquired by banks (compare panels A and C). For example, in the (-15,+15)

window, insurers acquired by banks register market value gains of 4.16%, whereas banks acquired

by insurers register gains of 7.90%. The provides suggestive evidence that insurers may be

overpaying when trying to enter the banking market through acquisitions.

. Table 19 reports the acquirer results for cross-industry M&As where security dealers or

brokers acquire insurance companies or insurance agents (Panel A) and/or insurance companies

only (Panel B). The results generally indicate market value losses for the acquirers in these

transactions, although the CAARs are not statistically significant in most windows.

Panels C and D of Table 19 report the equivalent results for the acquirers when insurance

companies and/or insurance agents and insurance companies only acquire security dealers or

brokers. Panel C indicates no significant value creation or destruction. However, when the

insurance agent transactions are eliminated (Panel D), there are statistically significant market

value losses in most windows for the acquiring insurance companies. Hence, insurance companies

lose value when acquiring securities broker/dealers.

Table 20 reports the equivalent cross-industry M&A results for targets where security

dealers/brokers acquire insurance companies or insurance agents (Panel A) and/or insurance

companies only (Panel B). The results indicate positive value creation for the target insurance

firms, and the results are strongly significant in most windows. Panels C and D show significant

market value gains for targets in transactions where the acquirer is an insurance firm and the target

34

is a securities broker/dealer. However, most of the gains occur in the period preceding the event

date, indicating significant information leakage for these transactions.

5.3.4. Within-Insurance-Industry Analysis

This section discusses the results when the transactions occur within the insurance industry.

Because this paper focuses on insurers, the within-industry analysis does not consider transactions

where both the acquirer and target was a bank and/or securities broker/dealer.

Table 21 reports the acquirer results for within-insurance-industry deals, i.e., where both

the acquirer and target belong to the insurance sector. Panel A of Table 21 reports the results for

the sector as a whole (i.e. insurance companies and/or insurance agents). The overall results

indicate value creation for acquirers, especially over the (0,+x) windows. Similar results are

obtained when insurance agents are excluded from the analysis (panel B). Combined with the

results of Table 17 on insurer acquisitions of banks, these results provide evidence that focusing

transactions are more likely to create value than diversifying transactions. However, panel C of

Table 21 shows that the within-sector results are driven primarily by insurance company to

insurance company transactions. The results for insurance agent-to-agent transactions are much

weaker and are negative for several windows.

Panels D and E of Table 21 repeat the analysis for acquirers within the non-life and life

sectors, respectively. This analysis considers only insurance companies, not agents or brokers.

The results imply that transactions within the non-life insurance sector of the industry are more

likely to create value than transactions within the life insurance sector. The non-life results (panel

D) show significant market value gains in most windows for the acquirers, whereas the life

insurance industry results (panel E) are significant in fewer windows and generally smaller than

the non-life insurance gains.

Table 22 present the within-insurance-industry analysis for the targets. The results show

35

significant market value gains for targets in all comparisons shown in the table. The results are

especially strong for broker-to-broker transactions (panel C), but the findings are based on only

three transactions. Consequently, it is not clear that they generalize to future transactions. Panels

D and E show that market value gains are generally stronger for life insurer to life insurer

transactions than for transactions when both the target and acquirer are non-life insurers. However,

large market value gains are generated by both types of transactions.

5.3.5. Other Effects: Large versus Small Firms

We also test the hypothesis that M&A activity is more productive for larger than smaller

firms, by evenly splitting the sample by size of the firm to identify large and small sub-samples.

These results are presented in tables available from the authors. For small firms taking over large

firms, acquiring small firms do not appear to gain significantly from M&A activity. These results

are consistent when broken down by region. By contrast, large target firms benefit significantly

from acquisition, although these results are more equivocal for long-event windows for European

firms and are generally less significantly positive for UK large target firms.

For large firms taking over large firms, overall results suggest that large acquiring firms do

gain significantly from taking over large firms but only over longer-event post announcement

windows. These results are particularly stronger for large US acquiring firms, but only for 1 to 5

day post announcement windows. By contrast, neither European nor UK acquirers appear to

benefit from taking over other large firms. Overall, results for larger firms taken over by large

firms indicate positive gains from M&A activity. However a further regional breakdown of these

results suggests that only large US targets benefit significantly from being taken over by other

large firms. By contrast, there is virtually no benefit accruing to large European or UK firms from

being taken over by other large firms.

For large firms taking over small firms acquirer firms do not gain from takeover activity.

36

While overall results show that small targets gain significantly, these results are only consistent for

Canadian and US small firms.

For small firms taking over other small firms, overall results suggest that small acquirers

appear to gain substantially from 1 to 10 days after the announcement date. However these results

are not consistent when broken down by region, with European acquiring small firms not showing

any significant gain in any post announcement date windows. Once again, the overall significant

gains reported for the small targets appear to be entirely attributable to the Canadian and US small

targets only; small targets in other regions do not show significant gains.

Overall, the results indicate that size of the acquirer and/or target does affect the extent to

which M&A activity is deemed beneficial, and that these results are further conditioned by region.

In particular, takeover activity only appears to derive significant benefit for large US firms taking

over other large firms, and small US and, at least initially, small UK firms taking over other small

firms. Results for target firms are also equivocal when broken down by size. While overall results

for targets are generally consistent with those reported elsewhere in this report, this result is only

robust to various types of size breakdown for Canadian and US target firms. By contrast, only

large European and UK targets appear to benefit from being taken over by small firms.

5.4. Determinants of Deal Value and Post-announcement abnormal returns

In this section we report further results of cross-sectional variations in both deal value and

abnormal stock price returns for acquiring firms. Specifically, we examine whether risk (proxied

by Beta, leverage and risk capital), investment opportunity sets (tobin’s Q) size of acquirer

(market value of equity) and profitability (as measured by return on equity). We also control for

dummy variables associated with the degree of commonality of the acquirer and target, number of

takeovers, and cross-border effects.

In order to control for reliability of continuous data for surviving firms, the cross-sectional

37

analysis focuses only on the most recent five year period of takeovers (2001-2006). Acquiring

firms were included in this analysis if they had sufficient financial, credit rating and stock price

data available on Compustat. This reduced the sample size to 166 firms, of which 86 were north

American, 54 european (of which 15 are UK) and 9 Asia-pacific firms. Table 23 reports the

descriptive statistics.

Table 24 reports the results of the determinants of deal value, both for the entire sample

and for regional samples. Model 1 reports the results of the determinants, while model 2

incorporates dummy variables representing acquirer and/or target characteristics. The overall

results support the proposition that deal value is positively associated with both Tobin’s Q and the

cross-border takeover. However these results are subject to regional variations. While none of the

factors affect North American deal size, Tobin’s Q, and return are relevant to deal size European

firms; and cross-boarder takeovers and the commonality of the acquirer and target when industry

controls are included. While the Asia-Pacific sample is too small to conduct full cross-sectional

analysis, acquirer size and profitability are associated with deal value. For UK firms, risk

(measured as beta) additionally explains deal value.

Table 25 reports the results for determinants of acquirer abnormal returns immediately

following the takeover. Factors explaining cross-sectional variations in abnormal returns are only

significant for European firms. Profitability is negatively associated with abnormal returns, while

there is a positive association with risk capital and the extent of commonality between acquirer

and target. For UK firms, there is negative association between abnormal returns and risk, as

measured by beta and risk capital.

6. Conclusions

This paper presents an empirical analysis of M&A transactions in the insurance sector with

a focus on results for Europe and the U.S. The M&A transactions included in the study are those

38

where either the acquirer or the target is in the insurance industry. We examine the universe of

transactions reported in the Thomson SDC Platinum database for which stock return data exist in

the Datastream international stock price database. We examine the effect of M&A transactions on

both the acquiring firm and the target firm by analyzing how market prices of the equity of the

relevant entity behaved by reference to the overall market during the period immediately

preceding and following the announcement, over various event windows. The analysis extends

across the U.S. and European insurance markets and breaks down the results by country/region, by

line of business, and by whether the transaction was intra or inter-industry. Some results also are

presented for the Asian insurance market.

The findings of the event study analysis can be summarized as follows:

• An analysis of all acquirers shows small value gains surrounding the event day (approximately 0.5%). Thus, M&As are modestly value-enhancing for acquirers on average.

• The analysis of all targets shows substantial and highly significant market value that are sustained in the widest event windows included in the analysis. E.g., on average targets show market value gains of 12.8% in the (-10,+10) window.

• For acquirers, there is no clear difference between cross-border and within-border (domestic) transactions. In both cases, there are small market value gains in short windows surrounding the events but the gains dissipate in the wider windows.

• For targets, there is no clear difference between cross-border and domestic transactions – targets tend to realize large, statistically significant market value gains from both types of transactions. This provides evidence that geographical integration of the financial services sector has been successful.

• Regional analysis of the results shows that M&As tend to destroy value for acquirers in Asia, when Japan and Australia are excluded from the analysis. M&As create value for Japanese acquirers, although the results are based on only a few transactions. For Canada, the U.S., Continental Europe, and the U.K., there are small, statistically significant value gains in short windows surrounding the events, but these gains are not sustained in the wider windows.

• Regional analysis also shows that market value gains for targets are highest in the U.S., the U.K., and Japan, although the Japanese sample is very small. Continental European and other Asian targets also show significant market value gains, but Canadian targets register market value losses.

39

• Cross-industry analysis provides virtually no evidence that insurance companies gain value by acquiring non-insurance firms. However, insurance agents and brokers gain significant value by acquiring non-insurance targets.

• Non-insurance target acquired by insurance companies and agents show large and significant market value gains. The gains are especially large when the acquirer is a non-life insurance company.

• Transactions where banks acquire insurance companies or agents generate significant market value gains for the acquiring banks in short windows surrounding the event date, but these gains are not sustained over the wider windows. However, insurers that acquire banks sustain significant market value losses. This provides some evidence that more synergies are generated when banks expand into the insurance industry through M&As than when insurers expand into banking.

• Both bank and insurance targets gain significant market value in cross-industry transactions. However, bank targets acquired by insurers gain significantly more value than insurance targets acquired by banks, suggesting that insurers may be overpaying in their acquisitions of banking firms.

• Insurance companies that acquire securities broker/dealers sustain significant market value losses, providing further evidence that insurers should stick with focusing transactions.

• Intra-insurance-industry transactions generate significant market value gains for acquiring insurance companies, reinforcing the conclusion that product focusing transactions are better than diversifying transactions for insurers. Transactions between non-life insurers are more likely to create value for the acquirer than transactions between life insurers. The results for insurance agent-to-agent transactions are much weaker than for insurance companies and are negative for several windows.

• All types of intra-insurance-industry M&A transactions generate significant market value gains for targets. However, market value gains to targets are generally larger for life-to-life insurance transactions than for non-life-to-non-life transactions.

• Further subdividing the overall sample of acquirers, we find that acquirers sustain significant market value losses when the target is a private company but achieve significant market value gains when the target is a subsidiary of another company. This may reflect information asymmetries such that more information is available on subsidiaries of public firms than on private firms.

• The size of the acquirer and/or target also affects the extent to which M&A activity. Large US and Asian firms acquiring other large firms, and small US and small UK firms taking over other small firms. Only Canadian and US target firms consistently benefit from takeover activity when results are broken down by size. By contrast, only large Asian firms appear to benefit significantly from being taken other by other large firms, while only large European and UK targets appear to benefit significantly from being taken over by small firms.

40

• For European firms, cross-sectional variations in deal value are associated with cross-border deals and the extent of commonality between acquirer and target. Cross-sectional variations in abnormal returns are more associated with both risk and return characteristics of the acquirer for European firms and commonality of acquirer.

Our research findings are subject to the various limitations documented in this report. Further research is also needed to identify the longer-term effects of M&A transactions and to incorporate some information concerning the level of disclosure, regulation and management corporate governance effectiveness immediately during and after the deals were effected.

41

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43

Figure 1: Insurance Mergers and Acquisitions

Total Deal Count By Year for Europe, Asia, and the Americas

0

50

100

150

200

250

300

350

400

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Nu

mb

er

of

Deals

44

Figure 2: Insurance Mergers and Acquisitions

Deal Value by Year For Europe, Asia, and the Americas

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

$ M

illio

ns

45

Table 1: Predictions of the Study

No. Relationship Predicted effect

1 Do acquiring firms gain or lose market value as a result of M&As

H1 Acquiring firms lose value

2 Do target firms that continue to be traded following the transaction gain or lose value as a result of M&As

H2 Target firms gain value

H3a: Domestic transactions more likely to create value for acquirers

3 Are domestic or cross-border transactions more likely to create value?

H3b: Cross-border transactions more likely to create value for targets

H4a: Cross-industry M&As are more likely to create value for bank acquiring insurance firms

4 Are within industry or cross-industry M&As more likely to create value?

H4b: Within industry M&As are more likely to create value for insurance firms acquired by banks

H5a: Within sector M&As are more likely to create value for acquirers

5 Are within sector or cross-sector M&As more likely to create value?

H5b: Cross-sector M&As more likely to create value for targets

H6a: Relatively large targets are more likely to create value for acquirers

6 Does the size of the target and/or acquirer have any impact on the likelihood of value creation?

H6b: Relatively small acquirers are more likely to create value for targets

H7a: European firms taking over cross-border firms is more likely to create value for acquirers

7 Does the country of origin of the target or the acquirer have any relationship with value creation?

H7b: European firms taking over Asian firms is more likely to create value for targets

46

Table 2: Countries Included in Study

Europe/United Kingdom Asia

Belgium Australia

Denmark Bangladesh

Finland Cambodia

France Hong Kong

Germany India

Ireland Indonesia

Italy Japan

Luxembourg Laos

Netherlands New Zealand

Norway Pakistan

Portugal Philippines

Spain Singapore

Sweden South Korea

Switzerland Sri Lanka

United Kingdom Taiwan

Thailand

North America Vietnam

Canada

United States

47

Table 3

Insurance M&A Deal Count By Region, Transactions Resulting in At Least 50% Control

Americas

Asia (Except-

Japan) Europe Japan Total

Americas 2,149 9 100 4 2,262

Asia (Except-Japan) 21 342 44 9 416

Europe 78 16 1,152 1,246

Japan 15 5 69 89

Grand Total 2,263 367 1,301 82 4,013

48

Table 4: Deals by Country – Insurance Acquirer or Target: Deals Involving a Change in Control

Panel A: Number of Deals

Target Country

Aus Bel Ber Cad Dnk Fra Ger Itl Jap Net Oth Swi UK US Total

Australia 8 1 6 2 17

Belgium 9 4 2 2 3 1 21

Bermuda 9 2 1 6 18

Canada 93 1 4 10 112

Denmark 17 1 18

France 5 3 1 53 2 4 1 3 4 6 82

Germany 2 39 1 3 2 4 51

Italy 4 47 2 7 3 1 64

Japan 19 1 20

Netherlands 5 2 3 1 12 2 1 1 27

Other 1 3 1 5 2 6 4 4 1 7 76 12 10 3 135

Switzerland 1 1 3 4 14 23

UK 11 2 4 1 2 1 5 2 7 222 29 286

USA 1 16 22 3 3 3 5 3 11 17 1,010 1,094

Total 20 23 32 127 22 75 59 63 20 38 81 66 278 1,069 1,968

* Other includes Finland (12), Hong Kong (3), Ireland (4), Indonesia (2), India (1), Norway (4), Phillipines (4), Portugal (4), Singapore (8), Spain (14), Sweden (6), South Korea (6), Taiwan (4), Thailand (9).

49

Table 4 (continued): Deals by Country – Insurance Acquirer or Target: Deals Involving a Change in Control

Panel B: Deal value (USD millions)

Target Country

Aus Bel Ber Cad Dnk Fra Ger Itl Jap Net Oth Swi UK US Total

Australia 418 817 81 77 1393

Belgium 14,845 3,524 45 36 175 8 18,633

Bermuda 3,148 139 913 4,200

Canada 12,448 375 567 523 13,913

Denmark 4,465 4,465

France 23 1,212 137 23,914 5,118 1,139 1,283 2,147 525 35,498

Germany 1,859 2,337 90 417 4,703

Italy 1,308 17,381 397 74 19,160

Japan 262 196 458

Netherlands 1,473 1,311 12,765 283 53 157,95

Other 517 451 485 509 1,380 637 49 267 2,153 17,521 540 11,144 16 35,669

Switzerland 441 236 1,224 10,906 12,807

UK 2,033 172 313 1,960 105 663 843 40,027 75,189 3,642 121,305

USA 2,629 8,025 4,268 60 7,080 322 18,490 1 16,036 3,223 377,817 437,951

Total 2,968 18,970 13,449 17,790 4,974 33,202 16,152 20,115 529 36,729 18,365 69,737 93,099 379, 725,950

* Other includes Finland (12), Hong Kong (3), Ireland (4), Indonesia (2), India (1), Norway (4), Phillipines (4), Portugal (4), Singapore (8), Spain (14), Sweden (6), South Korea (6), Taiwan (4), Thailand (9).

50

Table 5: Deals by Industry: Insurance Acquirer or Target: Deals Involving a Change in Control

Panel A: Number of Deals Comm Bank Oth Fin Life Ins P&L Ins Oth Ins Ins Agent Oth Ind Unknown Total

Commercial Bank

28 28

Other Financial

13 9 5 27

Life Insurance 93 36 338 61 45 45 90 708

P&L Insurance

6 3 32 41 16 7 47 152

Other insurance

3 2 98 19 63 15 44 244

Ins Agency 83 25 101 32 20 148 76 485

Other Industries

161 49 67 47 324

Unknown

Total 185 66 771 211 216 262 213 1,968

Panel B: Value of Deals ($Millions) Comm Bank Oth Fin Life Ins P&L Ins Oth Ins Ins Agent Oth Ind Unknown Total

Commercial Bank

123,059 320 123,379

Other Financial

330 361 23 323 1,037

Life Insurance

42,623 18,180 208,759 7,051 31,584 324 14,985 323,506

P&L Insurance

2,638 0 18,030 31,820 55,997 146 5,168 113,799

Other Insurance

165 39 8,050 22,993 3,542 324 14,824 49,937

Ins Agency 25.708 102 20,786 630 10,762 5,667 1,383 39,330

Other Industries

23,647 23,094 2,513 49,254

Unknown

Total 71,134 18,321 402,661 85,949 101,908 9,617 36,360 725,950

51

Table 6: Winners and Losers

Panel A: Major Insurance Markets Country Description Transaction Date Acquirer Name Acquirer Industry Target Name Target Industry Deal

Value ($m)

1 day CAR 15 day CAR

Winner – Acquirer

30/4/1999 April Group SA Life insurance GMP Gestion Agent N.A. 0.1182 +0.112

Winner – Target 26/9/1995 SCOR P&C Insurance SCOR US Life insurance 60 0.3465 +0.374

Loser – Acquirer

14/6/2000 Credit Lyonnais Commercial Bank

UAF Life insurance 25 -0.0754 +0.012

France

Loser – Target 14/4/2000 ELMONDA Life insurance World Access Inc (US)

Other industry N.A. -0.011 -0.139

Winner – Acquirer

8/8/2000 Cor AG Insurance Technologies

Agent Infexpert Holding AG (SZ)

Other Insurance N.A. 0.1090 -0.236

Winner – Target 30/9/1994 Allianz Other financial institution

Lloyd Adriatico Life Insurance 466 0.329 +0.376

Loser – Acquirer

8/1/1992 Aachener und Muenchener

Life insurance Fondiara Life Insurance 398 -0.064 +0.120

Germany

Loser – Target 29/3/2003 Rolf Gerling Other industry Gerling Konzern Life Insurance N.A. -0.071 -0.354

Winner – Acquirer

13/9/2000 ING Group Life insurance Equitable of Iowa (US)

Life Insurance 2226 +0.087 -0.003

Winner – Target 8/7/1997 ING Group Life insurance Reliastar (US) Life Insurance 4973 +0.594 +0.650

Loser – Acquirer

5/11/1990 Nationale Nederlanden

Life insurance NMB Post Other financial Institution

7457 -0.104 -0.703

Netherlands

Loser – Target 28/1/2000 Fortis Life insurance Banque Generale Luxembourg

Commercial Bank 1628 -0.010 -0.113

Winner – Acquirer

23/9/1995 Schweiz Life insurance UBS Life Agent N.A. +0.146 +0.203

Winner – Target 3/12/2003 Schweizerische Lebens

Life insurance Lloyd Continental (FR)

Life insurance 543 +0.753 +0.685

Loser – Acquirer

12/7/2001 Zurich FS Life insurance Neckura (DE) Life insurance N.A. -0.052 -0.014

Switzerland

Loser – Target 3/12/2003 Swiss Life Life insurance Schweizerische Lebens

Life Insurance 165 -0.032 -0.114

Winner – Acquirer

10/12/2001 Berry Birch an d New

Agent Berkeley Financial Agent 63 +0.411 +0.425

Winner – Target 22/2/2000 Royal London Mutual

Life insurance United Assurance Group

Life Insurance 2462 +0.509 +0.127

Loser – Acquirer

25/2/1999 Legal and General Life insurance Grosvenor Central Other industry 133 -0.1428 -0.198

United Kingdom

Loser – Target 12/11/1999 Limit plc Other industry Torch Holdings Agent 17 -0.403 -0.402

Winner – Acquirer

17/9/1999 Preferred employer Agent Not applicable Unknown 16 +0.741 +0.772

Winner – Target 13/6/1990 Unity Mutual Life Insurance Co

Life Insurance Empire State Life Insurance Co

Life Insurance 1 +1.003 +1.018

Loser – Acquirer

2/12/1992 Pantheon Industries Other insurance Continental A Other industry 5 -0.601 +1.009

United States

Loser - Target 27/4/2005 Leucadia P&C Insurance MK Resources Other industry 10 -0.520 -0.404

52

Panel B: Line of Business Acquirer Line of Business

Description Transaction Date

Acquirer Name Acquirer Country

Target Name Target Country

Target Industry Deal Value ($m)

1 day CAR 15 day CAR

Winner – Acquirer

19/12/2001 Greater Bay Bancorp

United States ABD Insurance and Financial Services

United States Life Insurance 193 +0.139 +0.128

Winner – Target

15/5/1995 Den Norske Banken Norway Vital Forsikring A/S Norway Life Insurance 399 +0.091 +0.060

Loser – Acquirer

21/12/2000 FNB Corp United States OneSource Inc United States Agent N.A. -0.171 +0.530

Commercial bank

Loser – Target 1/5/2002 Regions Finanical Corp

United States ICT Group LLC United States Agent N.A. -0.023 -0.299

Winner - Acquirer

13/1/2005 Equus Resources Inc

United States Dunnottar Insurance Co

United States Life Insurance N.A. +0.417 +0.276

Winner - Target 16/1/1997 Lowndes Lambert Group Holdings

United Kingdom

Fenchurch plc United Kingdom

Agent 47 +0.175 +0.148

Loser - Acquirer

9/8/2005 Equus Resources Inc

United States 1st Metro Insurance Inc

United States Agent N.A. -0.351 -0.443

Other financial institution

Loser - Target 24/7/2000 Capital.com Inc Canada Pethealth Inc Canada Other Insurance

N.A. -0.182 +0.375

Winner – Acquirer

20/2/1991 Financial Industries Corp

United States Family Life Insurance Co

United States Other industry 114 +0.289 +0.386

Winner - Target 1/12/1995 Amwest Insurance Group

United States Condor Services Inc United States Agent 18 +0.880 +0.953

Loser - Acquirer

6/11/1996 Meadowbrook insurance Group

United States Association Self Insurance Services

United States Other industry N.A. -0.376 -0.108

Life insurance

Loser - Target 16/3/2000 Nutramine inc United States Plus International Corp

Canada Other industry 1 -0.280 -0.738

Winner – Acquirer

25/9/1990 White Mountains Insurance Gp

Bermuda CGU Insurance group

United States P& L insurance 2100 +0.323 +0.026

Winner – Target

25/10/2000 State Auto Mutual United States Meridian Insurance United States Life Insurance 125 +0.512 +0.543

Loser – Acquirer

18/2/1992 Vesta Insurance Group Inc

United States Floriday Select Insurance Holdings

United States Life insurance 65 -0.1023 +0.084

Property and Casualty Insurance

Loser - Target 27/4/1995 Leucadia National Corp

United States MK Resources United States Other 10 0.027 -0.404

Winner – Acquirer

24/3/2000 Foundation Health Corp

United States Western- Universal Insurance

United States Life insurance 2 +0.192 +0.082

Winner – Target

17/3/1993 Preferred Employer United States Preferred Employer United States Agent 16 +0.741 +0.772

Loser –Acquirer

2/12/1992 Pantheon Industries Inc

United States Continental Acceptances Mtg Co

United States

Other industry 5 -0.601 +1.009

Other insurance

Loser - Target 17/3/1993 GPG plc United Kingdom

Brown Shipley Holdings

United Kingdom

Other Financial institution

11 -0.078 -0.119

Winner – Acquirer

17/9/1999 Preferred Employers Hldg

United States Preferred Employers Hldg

United States Agent 16 +0.740 +0.772

Winner – Target

As above As above As above As above As above As above As above

As above As above

Loser - Acquirer

30/9/2004 NWD Group plc United Kingdom

Clinic Group Ltd United Kingdom

Other industry 4 -0.132 -0.083

Agents

Loser - Target 17/3/1993 GPG plc United Kingdom

Brown Shipley Holdings

United Kingdom

Other insurance 10 -0.784 -0.119

53

Table 7: Largest M&A Deals

Panel A: By country Country Transaction date Acquirer Name Acquirer

Business Line Target Name Target Business

Line Deal Value ($m)

CAR1 CAR15

France 12/11/1996 AXA Life insurance UAP Life insurance 10605 -0.006 -0.085

Germany 24/8/1992 Allianz Other financial institution

Allianz ercos de seguros

Life insurance 9446 +0.018 +0.038

Netherlands 18/12/1999 Aegon Life insurance Transamerica Commercial bank

9690 -0.042 +0.222

Switzerland 13/10.1997 Zurich Financial Services

Life insurance BAT industries financial

Agent 17055 +0.016 +0.001

United Kingdom

21/12/2000 CGU plc Life insurance Norwich Union plc

Life insurance 28756 N.A. NA

United States 6/4/1998 Travellers group Inc

Life insurance Citicorp Commercial Bank

72558 N.A. N.A.

Panel B: By line of business Business Line Transaction date Acquirer Name Acquirer

Country Target Name Target country Deal Value

($m) CAR1 CAR 15

Commercial banks

23/6/1999 Lloyds TSB Group plc

United Kingdom

Scottish Widows

United Kingdom

11119 +0.001 -0.093

Other financial institutions

17/11/1997 Allianz AG Germany AGF France 5116 -0.026 -0.043

Life insurance 6/4/1998 Travellers group Inc

United States Citicorp United States 72558 N.A. N.A.

P&C insurance 3/4/2001 AIG United States American General Corp

United States 23398 -0.016 -0.110

Other insurance 27/10/2003 Anthem United States

WellPoint Health Network

United States 16441 -0.111 -0.131

Agents 25/8/1998 Marsh& McLennan Cos Inc

United States Sedgwick Group plc

United Kingdom

2129 -0.411 +0.026

Other industry 10/11/1994 Shareholders United States Allstate Corp United States 11761 N.A. N.A.

54

Table 8: Most Active M&A Firms

Panel A: By number of transactions

Country Registered

Firm name Primary business line

Number of Deals 1990-2007

% Cross border Deals

Deal Value ($m)

CAR1 average CAR15 average

France AGF Life insurance 17 5 3464 -0.004 -0.026

Germany Allianz Other financial 14 10 9445 -0.011 +0.005

Netherlands Aegon Life insurance 15 10 11361 +0.001 -0.005

Switzerland Swiss RE: Reinsurance 18 17 16523 -0.002 -0.009

United Kingdom

Aviva plc Life insurance 19 13 28756 -0.001 -0.014

United States Aon Other insurance 20 9 2289 -0.007 +0.002

Panel A: By deal value

Country Registered

Firm name Primary business line

Number of Deals 1990-2007

% Cross border Deals

Deal Value ($m)

CAR1 average CAR15 average

France AXA Life insurance 13 8 22028 -0.004 -0.010

Germany Allianz Other financial 14 10 9445 -0.011 +0.005

Netherlands ING Life insurance 14 11 17860 -0.004 -0.015

Switzerland Zurich Financial

Other financial 17 13 19366 -0.003 +0.009

United Kingdom

Aviva plc Life insurance 19 13 28756 -0.001 -0.014

United States Travellers P&C insurance 5 0 81475 +0.019 +0.014

55

Table 9: Descriptive Statistics on (0,+1) Event Window CAARs: Transactions by Type

Panel A: Major Insurance Markets Country Type Number of

transactions Minimum CAAR 1 Maximum CAAR1 Standard deviation

CAAR1 Average CAAR1

Acquirers 74 -0.075 0.118 0.029 0.002 France

Targets 26 -0.053 0.345 0.087 0.029

Acquirers 58 -0.064 0.109 0.031 0.006 Germany

Targets 23 -0.070 0.329 0.101 0.026

Acquirers 27 -0.104 0.087 0.033 -0.001 Netherlands

Targets 12 -0.047 0.594 0.223 0.143

Acquirers 61 -0.052 0.146 0.031 0.004 Switzerland

Targets 6 -0.032 0.754 0.297 0.156

Acquirers 277 -0.143 0.411 0.053 0.005 United Kingdom

Targets 40 -0.403 0.509 0.168 0.087

Acquirers 1073 -0.601 0.741 0.064 0.005 United States

Targets 139 -0.520 1.003 0.231 0.156

Panel B: Line of Business Business Line Type Number of

transactions Minimum CAAR1 Maximum CAAR1 Standard deviation

CAAR1 Average CAAR1

Acquirers 177 -0.023 0.091 0.004 0.026 Banks

Targets 15 -0.171 0.138 0.034 0.004

Acquirers 65 -0.351 0.418 0.085 -0.006 Other Financial

Targets 15 -0.182 0.175 0.092 0.017

Acquirers 780 -0.376 0.288 0.046 0.002 Life insurers

Targets 120 -0.382 1.003 0.196 0.108

Acquirers 210 -0.102 0.323 0.051 0.011 P&L insurers

Targets 37 -0.519 0.514 0.194 0.101

Acquirers 234 -0.601 0.192 0.072 -0.002 Other insurers

Targets 38 -0.160 0.880 0.233 0.210

Acquirers 270 -0.132 0.140 0.069 0.010 Agents

Targets 17 -0.078 0.740 0.227 0.213

Acquirers 227 -0.402 0.366 0.144 0.038 Other industry

Targets 36 -0.452 0.300 0.078 0.070

56

Table 10: Cumulative Abnormal Returns Across Event Windows:

All Transactions, Market Model, Equally Weighted Index Panel A: Acquirers: All Years 1990: 2007 Days N Mean CAAR Precision Weighted

CAAR Positive: Negative Patel Z SCS Z Generalized Sign Z

(-1,+1) 1790 0.52% 0.40% 925:865 6.158*** 4.432*** 4.357

(-2,+2) 1790 0.55% 0.32% 898:892 2.821** 1.551$ 3.078**

(-5,+5) 1790 0.27% 0.27% 877:913 -0.264 -0.077 2.083*

(-10,+10) 1790 -0.14% 0.18% 886:904 -1.290$ -0.422 2.509**

(-15,+15) 1790 -0.15% 0.18% 864:926 -1.195 -0.446 1.466$

(-1,0) 1790 0.26% 0.21% 887:903 4.157*** 2.945** 2.556**

(-2,0) 1790 0.32% 0.14% 877:913 1.651* 1.179 2.083*

(-5,0) 1790 0.12% 0.04% 869:921 -3.477*** -0.691 1.703*

(-10,0) 1790 -0.05% -0.01% 881:909 -3.130*** -0.833 2.272*

(-15,0) 1790 0.07% -0.05% 858:932 -2.913** -0.922 1.182

(0,+1) 1789 0.48% 0.36% 905:884 6.636*** 4.306*** 3.432

(0,+2) 1790 0.45% 0.35% 882:908 4.610*** 2.809** 2.319*

(0,+5) 1790 0.37% 0.40% 908:882 4.933*** 3.585*** 3.551

(0,+10) 1790 0.12% 0.36% 875:915 2.632** 2.190* 1.988*

(0,+15) 1790 0.00% 0.40% 863:927 2.287* 1.868* 1.419$

Panel B: Targets: All Years 1990-2007

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative Patel Z SCS Z Generalized Sign Z

(-1,+1) 309 10.84% 10.20% 216:93 45.513*** 5.924*** 8.730***

(-2,+2) 309 11.49% 10.96% 218:91 38.030*** 6.259*** 8.959***

(-5,+5) 309 12.33% 11.81% 219:90 27.617*** 6.502*** 9.073***

(-10,+10) 309 12.77% 13.05% 215:94 21.760*** 6.795*** 8.616***

(-15,+15) 309 15.64% 15.21% 223:86 20.609*** 7.413*** 9.530***

(-1,0) 309 7.30% 7.34% 189:120 39.033*** 4.369*** 5.644***

(-2,0) 309 7.75% 7.92% 207:102 34.748*** 4.707*** 7.701***

(-5,0) 309 8.74% 8.65% 208:101 26.997*** 5.081*** 7.816***

(-10,0) 309 9.74% 9.87% 207:102 22.617*** 5.624*** 7.701***

(-15,0) 309 10.66% 10.91% 213:96 20.600*** 6.049*** 8.387***

(0,+1) 309 10.12% 9.43% 210:99 51.490*** 5.510*** 8.044***

(0,+2) 309 10.32% 9.61% 206:103 42.901*** 5.585*** 7.587***

(0,+5) 309 10.17% 9.73% 204:105 30.778*** 5.564*** 7.358***

(0,+10) 309 9.60% 9.75% 208:101 22.655*** 5.394*** 7.816***

(0,+15) 309 11.56% 10.86% 213:96 20.905*** 5.747*** 8.387***

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control.

57

Table 11: Cumulative Abnormal Returns Across Event Windows:

All Acquirer Domestic and Crossborder Transactions

Market Model, Equally Weighted Index Panel A: Acquirers: Cross-border Transactions for All Years 1990-2006 Days N Mean CAAR Precision

Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 378 0.46% 0.46% 198:182 3.377*** 2.346*** 2.117*

(-2,+2) 378 0.74% 0.53% 188:190 3.063** 2.255* 1.292$

(-5,+5) 378 0.17% 0.24% 177:201 0.873 0.751 0.158

(-10,+10) 378 0.00% 0.34% 181:197 0.803 0.724 0.57

(-15,+15) 378 -0.04% 0.08% 167:211 -0.013 -0.012 -0.874

(-1,0) 378 0.53% 0.37% 189:189 3.490*** 2.495** 1.395$

(-2,0) 378 0.51% 0.33% 193:185 2.454** 1.883* 1.808*

(-5,0) 378 0.34% 0.19% 183:195 0.995 0.897 0.776

(-10,0) 378 0.17% 0.17% 194:184 0.633 0.605 1.911*

(-15,0) 378 0.23% -0.02% 187:191 -0.174 -0.168 1.189

(0,+1) 378 0.44% 0.30% 182:196 2.517** 1.590$ 0.673

(0,+2) 378 0.55% 0.41% 173:205 3.021** 2.031* -0.255

(0,+5) 378 0.17% 0.25% 176:202 1.302$ 1.013 0.055

(0,+10) 378 0.17% 0.37% 173:205 1.295$ 1.058 -0.255

(0,+15) 378 0.06% 0.31% 168:210 0.857 0.704 -0.771

Panel B: Acquirers’ Results from Domestic Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 1412 0.49% 0.42% 730:682 5.241*** 3.809*** 3.865***

(-2,+2) 1412 0.50% 0.16% 711:701 1.639$ 0.852 2.851***

(-5,+5) 1412 0.30% -0.13% 701:711 -0.734 -0.193 2.318*

(-10,+10) 1412 -0.18% -0.40% 704:708 -1.858* -0.547 2.478**

(-15,+15) 1412 -0.18% -0.34% 695:717 -1.333$ -0.45 1.998*

(-1,0) 1412 0.19% 0.18% 700:712 2.922** 2.066* 2.264*

(-2,0) 1412 0.27% 0.05% 665:727 0.631 0.442 1.464$

(-5,0) 1412 0.06% -0.52% 687:725 -4.403*** -0.781 1.571$

(-10,0) 1412 -0.10% -0.60% 687:725 -3.822*** -0.912 1.571$

(-15,0) 1412 0.02% -0.60% 672:740 -3.186*** -0.906 0.771

(0,+1) 1412 0.49% 0.40% 723:688 6.221*** 4.073*** 3.518***

(0,+2) 1412 0.42% 0.28% 710:702 3.676*** 2.190* 2.798**

(0,+5) 1412 0.43% 0.56% 733:679 4.893*** 3.495*** 4.025***

(0,+10) 1412 0.11% 0.37% 703:709 2.293* 1.916* 2.425**

(0,+15) 1412 -0.02% 0.43% 695:717 2.146* 1.749* 1.998*

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all change in control transactions reported in the SDC Database for which corresponding Datastream stock returns exist. Results are for the entire sample period.

58

Table 12: Cumulative Abnormal Returns Across Event Windows: All Target Domestic and Crossborder Transactions

Market Model, Equally Weighted Index

Panel A: Targets: Cross-border transactions for All Years 1990-2006 Days N Mean CAAR Precision

Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 72 10.57% 10.79% 48:24 29,483*** 4.219*** 3.549***

(-2,+2) 72 11.54% 11.76% 50:22 24.835*** 4.495*** 4.022***

(-5,+5) 72 11.54% 11.77% 48.24 16.684*** 4.336*** 3.549***

(-10,+10) 72 14.34% 13.10% 47:25 13.106*** 4.399*** 3.312***

(-15,+15) 72 17.07% 15.94% 53:19 12.885*** 5.002*** 4.731***

(-1,0) 72 8.15% 9.37% 48:24 31.333*** 3.752*** 3.549***

(-2,0) 72 8.48% 9.71% 52:20 26.611*** 3.934*** 4.495***

(-5,0) 72 9.09% 10.06% 50:22 19.480*** 4.063*** 4.022***

(-10,0) 72 12.74% 12.03% 50:22 16.983*** 4.494*** 4.022***

(-15,0) 72 12.36% 12.66% 55:17 14.681*** 4.626*** 5.025***

(0,+1) 72 9.92% 10.17% 44:26 34.190*** 4.006*** 2.603**

(0,+2) 72 10.56% 10.80% 43:29 29.528*** 4.121*** 2.366**

(0,+5) 72 9.95% 10.46% 39:33 20.210*** 3.850*** 1.420$

(0,+10) 72 9.11% 9.81% 41:31 13.836*** 3.472*** 1.893*

(0,+15) 72 12.21% 12.02% 42:30 13.880*** 3.950*** 2.130*

Panel B: Targets: Domestic Transactions for All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 237 10.92% 8.41% 168:69 35.719*** 4.535*** 7.996***

(-2,+2) 237 11.48% 9.05% 168:69 29.737*** 4.776*** 7.996***

(-5,+5) 237 12.58% 10.16% 171:66 22.341*** 5.121*** 8.388***

(-10,+10) 237 12.30% 11.29% 168:69 17.623*** 5.394*** 7.996***

(-15,+15) 237 15.22% 12.99% 172:65 16.434*** 5.795*** 8.519***

(-1,0) 237 7.04% 5.24% 141:96 27.299*** 3.009** 4.471***

(-2,0) 237 7.53% 5.86% 155:82 25.009*** 3.318*** 6.299***

(-5,0) 237 8.64% 6.69% 158:79 29.090*** 3.687*** 6.691***

(-10,0) 237 8.84% 7.50% 157:80 16.467*** 4.041*** 6.560***

(-15,0) 237 10.15% 8.56% 158:79 15.434*** 4.459*** 6.691***

(0,+1) 237 10.18% 7.65% 166:71 39.947*** 4.174*** 7.735***

(0,+2) 237 10.25% 7.67% 163:74 32.711*** 4.184*** 7.344***

(0,+5) 237 10.24% 7.95% 165:72 24.006*** 4.278*** 7.605***

(0,+10) 237 9.76% 8.26% 168:69 18.240*** 4.276*** 7.996***

(0,+15) 237 11.37% 8.91% 171:66 16.219*** 4.418*** 8.388***

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all change in control transactions reported in the SDC Database for which Datastream returns exist. Results are for entire sample period,

59

Table 13: Cumulative Abnormal Returns Across Event Windows: Acquirer Transactions by Region/Country

Market Model, Equally Weighted Index

Panel A: Acquirers: Asia (Including Japan) Transactions for All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 57 0.10% 0.23% 30:27 0.416 0.382 0.933

(-2,+2) 57 0.72% 0.75% 35:22 1.062 1.121 2.261*

(-5,+5) 57 0.99% 1.23% 30:27 1.179 1.192 0.933

(-10,+10) 57 -1.03% 0.58% 23:34 0.393 0.436 -0.926

(-15,+15) 57 -0.65% 0.86% 27:30 0.594 0.614 0.136

(-1,0) 57 -0.15% 0.22% 24:33 0.386 0.428 -0.661

(-2,0) 57 -0.07% 0.46% 29:28 0.708 0.855 0.667

(-5,0) 57 -0.10% 0.75% 27:30 0.947 1.057 0.136

(-10,0) 57 -0.70% -0.01% 22:35 -0.037 -0.049 -1.192

(-15,0) 57 -0.74% -0.17% 30:27 0.075 0.086 0.933

(0,+1) 57 0.07% 0.07% 29:28 0.164 0.144 0.667

(0,+2) 57 0.60% 0.35% 31:26 0.787 0.800 1.198

(0,+5) 57 0.90% 0.54% 32:25 0.707 0.744 1.464$

(0,+10) 57 -0.51% 0.64% 24:33 0.591 0.599 -0.661

(0,+15) 57 -0.09% 1.09% 27:30 0.821 0.821 0.136

Panel B: Acquirers: Asia (excluding Japan) Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 41 0.18% 0.59% 22:19 0.918 0.847 1.265

(-2,+2) 41 0.44% 0.75% 25:16 0.824 0.828 2.209*

(-5,+5) 41 -0.11% 0.61% 19:22 0.396 0.389 0.321

(-10,+10) 41 -2.67% -0.44% 13:28 -0.36 -0.394 -1.568$

(-15,+15) 41 -4.15% -1.79% 14:27 -0.902 -1.008 -1.253

(-1,0) 41 -0.31% 0.27% 17:24 0.373 0.393 -0.309

(-2,0) 41 -0.34% 0.44% 19:22 0.522 0.600 0.321

(-5,0) 41 -0.83% 0.47% 18:23 0.415 0.426 0.006

(-10,0) 41 -0.82% 0.08% 14:27 -0.001 -0.002 -1.253

(-15,0) 41 -2.22% -1.10% 17:24 -0.693 -0.749 -0.309

(0,+1) 41 0.08% 0.27% 21:20 0.538 0.456 0.950

(0,+2) 41 0.37% 0.26% 22:19 0.475 0.444 1.265

(0,+5) 41 0.32% 0.08% 21:20 0.024 0.025 0.950

(0,+10) 41 -2.26% -0.58% 13:28 -0.595 -0.583 -1.568$

(0,+15) 41 -2.33% -0.74% 15:26 -0.643 -0.827 -0.938

60

Table 13 (continued): Cumulative Abnormal Returns Across Event Windows: Acquirer Transactions by Region/Country

Market Model, Equally Weighted Index

Panel C: Acquirers: Asia (excluding Japan and Australia) Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 28 0.44% 0.42% 16:12 0.433 0.448 1.407$

(-2,+2) 28 0.27% 0.44% 17:11 0.274 0.274 1.788*

(-5,+5) 28 -0.23% 0.18% 12:16 -0.046 -0.048 -0.116

(-10,+10) 28 -3.66% -2.08% 8:20 -1.12 -1.404$ -1.639$

(-15,+15) 28 -4.52% -3.33% 8:20 -1.362$ -1.675* -1.639$

(-1,0) 28 -0.49% -0.13% 12:16 -0.449 -0.505 -0.116

(-2,0) 28 -0.27% 0.06% 13:15 -0.204 -0.25 0.265

(-5,0) 28 -1.04% -0.31% 12:16 -0.46 -0.551 -0.116

(-10,0) 28 -1.55% -0.98% 9:19 -0.75 -1.071 -1.258

(-15,0) 28 -2.04% -2.09% 11:17 -1.134 -1.175 -0.497

(0,+1) 28 -0.06% -0.10% 13:15 -0.183 -0.182 0.265

(0,+2) 28 -0.45% -0.27% 13:!5 -0.281 -0.26 0.265

(0,+5) 28 -0.19% -0.16% 14:14 -0.257 -0.271 0.646

(0,+10) 28 -3.11% -1.76% 8:20 -1.307$ -1.681* -1.639$

(0,+15) 28 -3.46% -1.89% 10:18 -1.199 -1.927* -0.877

Panel D: Acquirers: Australia Transactions for All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 13 -0.39% 0.94% 6:7 0.996 0.743 0.181

(-2,+2) 13 0.80% 1.32% 8:5 1.061 1.058 1.299$

(-5,+5) 13 0.16% 1.47% 7:6 0.771 0.677 0.740

(-10,+10) 13 -0.54% 2.64% 5:8 1.004 0.93 -0.379

(-15,+15) 13 -3.36% 1.09% 6:7 0.397 0.381 0.181

(-1,0) 13 0.08% 1.02% 5:8 1.322$ 1.278 -0.379

(-2,0) 13 -0.49% 1.16% 6:7 1.226 1.281 0.181

(-5,0) 13 -0.39% 1.97% 6:7 1.411$ 1.185 0.181

(-10,0) 13 0.76% 2.08% 5:8 1.098 1.077 -0.379

(-15,0) 13 -2.61% 0.75% 6:7 0.433 0.525 0.181

(0,+1) 13 0.39% 0.94% 8:5 1.224 0.814 1.299$

(0,+2) 13 2.15% 1.18% 9:4 1.257 1.223 1.858*

(0,+5) 13 1.40% 0.52% 7:6 0.42 0.431 0.74

(0,+10) 13 -0.44% 1.58% 5:8 0.862 0.806 -0.379

(0,+15) 13 0.10% 1.36% 5:8 0.617 0.610 -0.379

61

Table 13 (continued): Cumulative Abnormal Returns Across Event Windows: Acquirer Transactions

by Region/Country, Market Model, Equally Weighted Index

Panel E: Acquirers: Japan Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 16 -0.09% -0.70% 8:8 -0.685 -0.622 -0.254

(-2,+2) 16 1.44% 0.77% 10:6 0.686 0.814 0.748

(-5,+5) 16 3.79% 2.74% 11:5 1.592$ 1.774* 1.249

(-10,+10) 16 3.19% 3.04% 10:6 1.317$ 1.578$ 0.748

(-15,+15) 16 8.32% 7.78% 13:3 2.566** 2.727** 2.251*

(-1,0) 16 0.25% 0.11% 7:9 0.13 0.165 -0.755

(-2,0) 16 0.63% 0.56% 10:6 0.501 0.681 0.748

(-5,0) 16 1.77% 1.49% 9:7 1.123 1.708* 0.247

(-10,0) 16 -0.40% -0.28% 8:8 -0.067 -0.133 -0.254

(-15,0) 16 3.05% 2.12% 13:3 1.250 2.013* 2.251*

(0,+1) 16 0.03% -0.49% 8:8 -0.552 -0.528 -0.254

(0,+2) 16 1.19% 0.52% 9:7 0.726 0.969 0.247

(0,+5) 16 2.40% 1.56% 11:5 1.295$ 1.374$ 1.249

(0,+10) 16 3.97% 3.63% 11:5 2.067* 1.888* 1.249

(0,+15) 16 5.65% 5.98% 12:4 2.579** 1.983* 1.750*

Panel F: Acquirers: Canada Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 120 1.38% 1.25% 66:52 3.771*** 2.958** 2.610**

(-2,+2) 120 1.38% 1.32% 71:49 2.941** 2.787** 3.160***

(-5,+5) 120 0.60% 0.96% 60:60 1.433$ 1.429$ 1.141

(-10,+10) 120 -0.36% 1.03% 60:60 0.99 0.886 1.141

(-15,+15) 120 -0.83% 0.98% 62:58 0.702 0.638 1.508$

(-1,0) 120 0.98% 0.75% 76:44 3.212*** 2.225* 4.078***

(-2,0) 120 1.00% 0.69% 64:56 2.135* 1.967* 1.875*

(-5,0) 120 0.54% 0.43% 61:59 0.964 0.977 1.325$

(-10,0) 120 0.38% 0.55% 66:52 0.826 0.918 2.610**

(-15,0) 120 0.40% 0.55% 68:52 0.647 0.838 2.610**

(0,+1) 120 1.55% 1.29% 69:51 4.844*** 3.176*** 2.793**

(0,+2) 120 1.54% 1.42% 67:53 4.158*** 3.009** 2.426**

(0,+5) 120 1.22% 1.33% 66:54 2.707** 2.199* 2.242*

(0,+10) 120 0.42% 1.28% 56:64 1.805* 1.353$ 0.407

(0,+15) 120 -0.07% 1.23% 61:59 1.397$ 1.042 1.325$

62

Table 13 (continued): Cumulative Abnormal Returns Across Event Windows: Acquirer Transactions by Region/Country

Market Model, Equally Weighted Index

Panel G: Acquirers: Canada or US Transactions for All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 1120 0.53% 0.37% 583:537 3.878*** 3.091*** 3.700***

(-2,+2) 1120 0.62% 0.12% 577:543 1.072 0.535 3.340***

(-5,+5) 1120 0.28% -0.37% 559:561 -1.780* -0.424 2.262*

(-10,+10) 1120 -0.08% -0.54% 556:564 -2.050* -0.547 2.082*

(-15,+15) 1120 -0.04% -0.43% 558:562 -1.533$ -0.471 2.202*

(-1,0) 1120 0.12% 0.07% 557:563 1.074 0.817 2.142*

(-2,0) 1120 0.29% -0.06% 546:574 -0.559 -0.407 1.483$

(-5,0) 1120 0.06% -0.84% 548:572 -5.858*** -0.932 1.603$

(-10,0) 1120 -0.12% -0.87% 545:575 -4.499*** -0.965 1.423$

(-15,0) 1120 0.14% -0.79% 538:582 -3.694*** -0.949 1.004

(0,+1) 1120 0.62% 0.44% 584:536 5.598*** 4.041*** 3.760***

(0,+2) 1120 0.54% 0.32% 557:563 3.413*** 2.052* 2.142*

(0,+5) 1120 0.43% 0.62% 599:521 4.435*** 3.210*** 4.658***

(0,+10) 1120 0.25% 0.46% 558:562 2.331** 2.021* 2.202*

(0,+15) 1120 0.02% 0.50% 560:560 2.067* 1.729* 2.322*

Panel H: Acquirers: US Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 1000 0.43% 0.29% 515:485 2.798** 2.238* 3.013*

(-2,+2) 1000 0.53% 0.01% 506:494 0.115 0.055 2.443**

(-5,+5) 1000 0.25% -0.50% 499:501 -2.380* -0.538 1.999*

(-10,+10) 1000 -0.05% 0.70% 496:504 -2.513** -0.637 1.809*

(-15,+15) 1000 0.05% -0.56% 496:504 -1.866* -0.545 1.809*

(-1,0) 1000 0.02% 0.00% 481:519 0.024 0.019 0.859

(-2,0) 1000 0.20% -0.13% 482:518 -1.331$ -0.948 0.922

(-5,0) 1000 0.00% -0.97% 487:513 -6.533*** -0.983 1.239

(-10,0) 1000 -0.18% -1.00% 477:523 -5.047*** -1.025 0.605

(-15,0) 1000 0.11% -0.92% 470:530 -4.133*** -1.006 0.161

(0,+1) 1000 0.50% 0.36% 515:485 4.315*** 3.142*** 3.013**

(0,+2) 1000 0.42% 0.21% 490:510 2.172* 1.284$ 1.429$

(0,+5) 1000 0.34% 0.54% 533:467 3.755*** 2.685** 4.154***

(0,+10) 1000 0.23% 0.38% 502:498 1.841* 1.630$ 2.190*

(0,+15) 1000 0.03% 0.43% 499:501 1.704* 1.447$ 1.999*

63

Table 13 (continued): Cumulative Abnormal Returns Across Event Windows: Acquirer Transactions by Region/Country

Market Model, Equally Weighted Index

Panel I: Acquirers: Europe Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 334 0.50% 0.38% 175:159 3.045** 2.342** 1.774*

(-2,+2) 334 0.37% 0.25% 166:168 1.678* 1.326$ 0.788

(-5,+5) 334 0.13% 0.07% 162:172 0.347 0.324 0.349

(-10,+10) 334 0.05% 0.07% 168:166 0.224 0.211 1.007

(-15,+15) 334 -0.19% -0.06% 151:183 -0.126 -0.116 -0.856

(-1,0) 334 0.44% 0.37% 174:160 3.716*** 2.950** 1.664*

(-2,0) 334 0.43% 0.35% 174:160 2.850** 2.252* 1.664*

(-5,0) 334 0.16% 0.12% 170:164 0.716 0.633 1.226

(-10,0) 334 0.19% 0.11% 172:162 0.469 0.442 1.445$

(-15,0) 334 0.18% 0.13% 162:172 0574 0.529 0.349

(0,+1) 334 0.19% 0.11% 152:181 0.910 0.641 -0.695

(0,+2) 334 0.07% 0.01% 159:175 0.111 0.085 0.021

(0,+5) 334 0.10% 0.06% 146:188 0.326 0.298 -1.404$

(0,+10) 334 -0.01% 0.06% 157:177 0.282 0.258 -0.199

(0,+15) 334 -0.25% -0.09% 152:182 -0.358 -0.325 -0.746

Panel J: Acquirers: Europe or UK Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 583 0.50% 0.44% 292:291 4.487*** 2.852** 1.713*

(-2,+2) 583 0.29% 0.27% 269:314 2.171* 1.511$ -0.197

(-5,+5) 583 0.05% 0.17% 269:314 0.910 0.698 -0.197

(-10,+10) 583 -0.33% -0.01% 289:294 -0.064 -0.051 1.463$

(-15,+15) 583 -0.43% -0.17% 261:322 -0.541 -0.438 -0.861

(-1,0) 583 0.52% 0.40% 291:292 5.114*** 3.283*** 1.630$

(-2,0) 583 0.38% 0.29% 287:296 2.988** 2.054* 1.297$

(-5,0) 583 0.16% 0.16% 278:305 1.207 0.927 0.550

(-10,0) 583 0.05% 0.06% 296:287 0.279 0.235 2.045*

(-15,0) 583 -0.05% -0.09% 275:308 -0.306 -0.261 0.301

(0,+1) 583 0.22% 0.24% 271:311 2.839** 1.618$ 0.008

(0,+2) 583 0.15% 0.18% 276:307 1.868* 1.204 0.384

(0,+5) 583 0.13% 0.21% 258:325 1.479$ 1.103 -1.111

(0,+10) 583 -0.14% 0.13% 277:306 0.722 0.563 0.467

(0,+15) 583 -0.13% 0.12% 261:322 0.438 0.340 -0.861

64

Table 13 (continued): Cumulative Abnormal Returns Across Event Windows: Acquirer Transactions by Region/Country

Market Model, Equally Weighted Index

Panel K: Acquirers: UK Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 249 0.49% 0.52% 117:132 3.339*** 1.776* 0.565

(-2,+2) 249 0.19% 0.28% 103:!46 1.378$ 0.84 -1.218

(-5,+5) 249 -0.05% 0.31% 107:142 0.990 0.633 -0.708

(-10,+10) 249 -0.83% -0.13% 121:128 -0.357 -0.242 1.074

(-15,+15) 249 -0.75% -0.37% 110:139 -0.682 -0.481 -0.326

(-1,0) 249 0.64% 0.45% 117:132 3.521*** 1.865* 0.565

(-2,0) 249 0.33% 0.20% 113:136 1.271 0.758 0.056

(-5,0) 249 0.17% 0.23% 108:141 1.018 0.677 -0.581

(-10,0) 249 -0.12% -0.03% 124:125 -0.116 -0.086 1.456$

(-15,0) 249 -0.37% -0.43% 113:136 -1.132 -0.887 0.056

(0,+1) 249 0.26% 0.42% 119:130 3.288*** 1.550$ 0.820

(0,+2) 249 0.26% 0.43% 117:132 2.730** 1.488$ 0.565

(0,+5) 249 0.18% 0.42% 112:137 1.887* 1.167 -0.072

(0,+10) 249 -0.31% 0.24% 120:129 0.778 0.519 0.947

(0,+15) 249 0.02% 0.40% 109:140 1.084 0.721 -0.454

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

65

Table 14: Cumulative Abnormal Returns Across Event Windows: Target Transactions by Region

Market Model, Equally Weighted Index

Panel A: Target: Asia (Including Japan) Transactions for All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 21 6.87% 6.41% 14:7 7.370*** 2.741** 1.758*

(-2,+2) 21 8.82% 7.73% 14:7 6.774*** 2.510** 1.758*

(-5,+5) 21 13.24% 10.23% 13:8 6.036*** 2.724** 1.321$

(-10,+10) 21 10.28% 8.33% 12:9 3.432*** 2.410** 0.884

(-15,+15) 21 7.12% 6.76% 14:7 2.089* 1.896* 1.758*

(-1,0) 21 2.23% 3.04% 14:7 4.339*** 1.947* 1.758*

(-2,0) 21 2.41% 3.22% 13:8 3.731*** 2.064* 1.321$

(-5,0) 21 6.04% 4.92% 13:8 3.968*** 2.486** 1.321$

(-10,0) 21 6.42% 5.39% 13:8 3.177*** 2.305* 1.321$

(-15,0) 21 3.90% 4.11% 11:10 1.756* 1.425$ 0.447

(0,+1) 21 6.08% 5.61% 12:9 7.955*** 2.299* 0.884

(0,+2) 21 7.86% 6.75% 12:9 7.671*** 2.151* 0.884

(0,+5) 21 8.65% 7.55% 12:9 6.174*** 2.192 0.884

(0,+10) 21 5.30% 5.18% 10:11 3.012*** 1.782* 0.010

(0,+15) 21 4.66% 4.88% 10:11 2.342** 1.660* 0.010

Panel B: Target: Asia (excluding Japan) Transactions for Al Years 1990-2006

eeDays N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 17 3.55% 4.56% 10:7 4.819*** 2.027* 0.828

(-2,+2) 17 3.72% 5.15% 11:6 4.100*** 2.138* 1.314$

(-5,+5) 17 7.61% 7.18% 10:7 3.807*** 2.174* 0.828

(-10,+10) 17 7.37% 7.05% 10:7 2.821** 1.918* 0.828

(-15,+15) 17 5.85% 6.59% 11:6 1.864* 1.668* 1.314$

(-1,0) 17 1.89% 3.20% 11:6 4.141*** 1.705* 1.314$

(-2,0) 17 1.96% 3.39% 10:7 3.551*** 1.811* 0.828

(-5,0) 17 5.31% 4.61% 9:8 3.395*** 2.066* 0.343

(-10,0) 17 6.20% 5.47% 10:7 2.964** 2.057* 0.828

(-15,0) 17 4.72% 4.84% 9:8 1.997* 1.566$ 0.343

(0,+1) 17 2.58% 3.62% 8:9 4.764*** 1.565$ -0.142

(0,+2) 17 2.67% 4.03% 9:8 4.171*** 1.633$ 0.343

(0,+5) 17 3.23% 4.84% 9:8 3.515*** 1.572$ 0.343

(0,+10) 17 2.08% 3.84% 8:9 1.982* 1.233 -0.142

(0,+15) 17 2.04% 4.01% 8:9 1.710* 1.21 -0.142

66

Table 14 (continued): Cumulative Abnormal Returns Across Event Windows: Target Transactions by Region

Market Model, Equally Weighted Index

Panel C: Target: Asia (excluding Japan and Australia) Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 14 2.68% 3.55% 7:7 3.391*** 1.658* -0.100

(-2,+2) 14 2.89% 4.19% 8:6 2.977** 1.627$ 0.434

(-5,+5) 14 8.13% 6.85% 9:5 3.308*** 1.921* 0.969

(-10,+10) 14 7.08% 5.63% 8:6 1.892* 1.413$ 0.434

(-15,+15) 14 6.39% 5.32% 9:5 1.319$ 1.231 0.969

(-1,0) 14 0.73% 1.88% 8:6 2.203* 1.342$ 0.434

(-2,0) 14 0.78% 2.02% 7:7 1.902* 1.214 -0.100

(-5,0) 14 5.46% 3.73% 8:6 2.547** 1.952* 0.434

(-10,0) 14 5.45% 3.59% 8:6 1.824* 1.498$ 0.434

(-15,0) 14 4.32% 2.99% 7:7 1.088 1.02 -0.100

(0,+1) 14 1.90% 2.51% 6:8 3.008** 1.139 -0.634

(0,+2) 14 2.05% 3.02% 7:7 2.780** 1.234 -0.100

(0,+5) 14 2.62% 3.96% 7:7 2.569** 1.174 -0.100

(0,+10) 14 1.57% 2.88% 6:8 1.275 0.828 -0.634

(0,+15) 14 2.01% 3.18% 6:8 1.165 0.832 -0.635

Panel D: Target: Australia Transactions for All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 3 7.61% 9.57% 3:0 4.146*** 1.060 2.268*

(-2,+2) 3 7.59% 9.96% 3:0 3.330*** 1.350$ 2.268*

(-5,+5) 3 5.23% 9.67% 1:2 1.917* 0.841 -0.126

(-10,+10) 3 8.72% 14.56% 2:1 2.151* 1.331$ 1.071

(-15,+15) 3 3.32% 13.55% 2:1 1.588$ 1.088 1.071

(-1,0) 3 7.28% 9.60% 3:0 5.098*** 1.067 2.268*

(-2,0) 3 7.47% 10.01% 3:0 4.344*** 1.383$ 2.268*

(-5,0) 3 4.61% 9.56% 6:7 2.581** 0.827 -0.126

(-10,0) 3 9.70% 14.88% 2:1 3.115*** 1.416$ 1.071

(-15,0) 3 6.61% 14.20% 2:1 2.402** 1.183 1.071

(0,+1) 3 5.78% 9.11% 2:1 4.844*** 0.990 1.071

(0,+2) 3 5.57% 9.08% 2:1 3.924*** 0.970 1.071

(0,+5) 3 6.07% 9.24% 2:1 2.816** 1.010 1.071

(0,+10) 3 4.46% 8.80% 2:1 1.961* 0.928 1.071

(0,+15) 3 2.15% 8.48% 2:1 1.554$ 0.926 1.071

67

Table 14 (continued): Cumulative Abnormal Returns Across Event Windows: Target Transactions by Region/Country

Market Model, Equally Weighted Index

Panel E: Target: Japan Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 4 20.95% 14.02% 4.:0 6.953*** 1.982* 2.345**

(-2,+2) 4 30.51% 18.48% 3:1 7.069*** 1.507$ 1.332$

(-5,+5) 4 37.16% 22.73% 3:1 5.982*** 1.744* 1.332$

(-10,+10) 4 22.65% 12.85% 2:2 2.461** 1.381$ 0.320

(-15,+15) 4 12.51% 5.99% 3:1 0.943 0.788 1.332$

(-1,0) 4 3.68% 2.31% 3:1 1.405$ 1.128 1.332$

(-2,0) 4 4.30% 2.48% 3:1 1.229 1.112 1.332$

(-5,0) 4 9.14% 6.46% 4:0 2.093* 1.319$ 2.345**

(-10,0) 4 7.35% 4.90% 3:1 1.169 0.932 1.332$

(-15,0) 4 0.41% 0.12% 2:2 -0.092 -0.086 0.32

(0,+1) 4 20.96% 13.82% 4.:0 8.406*** 1.862* 2.345*

(0,+2) 4 29.90% 18.12% 3:1 8.978*** 1.478$ 1.332$

(0,+5) 4 31.72% 18.39% 3:1 6.901*** 1.587$ 1.332$

(0,+10) 4 18.99% 10.07% 2:2 2.815** 1.350$ 0.320

(0,+15) 4 15.79% 7.99% 2:2 1.842* 1.199 0.320

Panel F: Target: Canada Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 9 -2.76% -1.89% 3:6 -0.846 -0.830 -0.207

(-2,+2) 9 -4.53% 1.10% 5:4 0.776 0.538 1.175

(-5,+5) 9 -13.36% -3.12% 5:4 -0.436 -0.288 1.176

(-10,+10) 9 -7.02% -2.08% 5:4 -0.101 -0.068 1.176

(-15,+15) 9 -2.96% -1.37% 4:5 0.046 0.036 0.484

(-1,0) 9 -1.49% 1.04% 3:6 0.687 0.494 -0.207

(-2,0) 9 -4.35% 2.00% 5:4 1.080 1.146 1.175

(-5,0) 9 -5.06% 1.14% 5:4 0.461 1.046 1.176

(-10,0) 9 6.69% 2.76% 5:4 0.764 1.121 1.176

(-15,0) 9 0.84% 1.92% 4:5 0.564 0.704 0.484

(0,+1) 9 -1.98% -1.68% 3:6 -1.132 -0.828 -0.207

(0,+2) 9 -0.89% 0.15% 5:4 0.443 0.253 1.176

(0,+5) 9 -9.01% -3.21% 5:4 -0.566 -0.318 1.176

(0,+10) 9 -14.41% -3.79% 5:4 -0.579 -0.412 1.176

(0,+15) 9 -4.51% -2.25% 5:4 -0.220 -0.191 1.176

68

Table 14 (continued): Cumulative Abnormal Returns Across Event Windows: Target Transactions by Region/Country

Market Model, Equally Weighted Index

Panel G: Target: Canada or US Transactions for All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 152 15.17% 14.57% 115:37 41.849*** 8.675*** 7.645***

(-2,+2) 152 15.57% 15.58% 121:31 34.653*** 8.783*** 8.624***

(-5,+5) 152 15.90% 16.07% 119:33 23.856*** 8.434*** 8.298***

(-10,+10) 152 15.77% 16.77% 115:37 17.663*** 8.382*** 7.645***

(-15,+15) 152 17.60% 18.76% 121:31 15.897*** 8.952*** 8.624***

(-1,0) 152 9.74% 9.51% 98:54 33.498*** 6.570*** 4.873***

(-2,0) 152 10.26% 10.49% 108:44 30.323*** 6.825*** 6.504***

(-5,0) 152 11.11% 11.11% 111:41 22.587*** 7.010*** 6.993***

(-10,0) 152 11.90% 12.03% 106:44 17.832*** 7.353*** 6.177***

(-15,0) 152 13.14% 13.90% 114:38 16.864*** 8.008*** 7.482***

(0,+1) 152 14.53% 14.06% 116:36 49.517*** 8.385*** 7.808***

(0,+2) 152 14.41% 14.10% 115:37 40.542*** 8.462*** 7.645***

(0,+5) 152 13.89% 13.96% 112:40 28.405*** 8.114*** 7.156***

(0,+10) 152 12.96% 13.74% 114:38 20.409*** 7.883*** 7.482***

(0,+15) 152 13.56% 13.86% 114:38 16.945*** 7.912*** 7.482***

Panel H: Target: United States Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 143 16.30% 15.30% 112:31 43.358*** 8.892*** 7.925***

(-2,+2) 143 16.84% 16.23% 116:27 35.532*** 8.884*** 8.596***

(-5,+5) 143 17.74% 16.90% 114:39 24.705*** 8.687*** 8.260***

(-10,+10) 143 17.20% 17.58% 110:33 18.235*** 8.642*** 7.589***

(-15,+15) 143 18.90% 19.62% 117:26 16.379*** 9.224*** 8.764***

(-1,0) 143 10.44% 9.92% 95:48 34.364*** 6.599*** 5.069***

(-2,0) 143 11.18% 10.90% 103:40 30.991*** 6.821*** 6.413***

(-5,0) 143 12.13% 11.59% 106:37 23.171*** 7.038*** 6.917***

(-10,0) 143 12.23% 12.48% 101:42 18.193*** 7.347*** 6.077***

(-15,0) 143 13.91% 14.46% 110:33 17.245*** 8.053*** 7.589***

(0,+1) 143 15.57% 14.77% 113:30 51.355*** 8.594*** 8.092***

(0,+2) 143 15.38% 14.72% 110:30 41.688*** 8.586*** 7.589***

(0,+5) 143 15.33% 14.69% 107:36 29.427*** 8.345*** 7.085***

(0,+10) 143 14.69% 14.49% 109:34 21.187*** 8.133*** 7.421***

(0,+15) 143 14.70% 14.54% 109:34 17.525*** 8.117*** 7.421***

69

Table 14 (continued): Cumulative Abnormal Returns Across Event Windows: Target Transactions by Region/Country

Market Model, Equally Weighted Index

Panel I: Target: Europe Transactions for Al Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 87 5.39% 5.60% 53:34 16.643*** 3.707*** 2.981**

(-2,+2) 87 6.26% 6.45% 50:37 14.814*** 3.900*** 2.334**

(-5,+5) 87 6.34% 6.93% 53:34 10.751*** 3.659*** 2.981**

(-10,+10) 87 8.37% 9.48% 54:33 10.479*** 3.965*** 3.196***

(-15,+15) 87 13.23% 12.22% 53:34 11.101*** 4.124*** 2.981***

(-1,0) 87 3.58% 3.99% 43:44 14.484*** 2.924* 0.826

(-2,0) 87 4.13% 4.47% 51:36 13.261*** 3.28*** 2.550**

(-5,0) 87 4.11% 4.52% 48:39 9.587*** 3.143*** 1.903*

(-10,0) 87 5.05% 5.73% 52:35 8.894*** 3.367*** 2.765**

(-15,0) 87 5.31% 5.67% 50:37 7.305*** 3.235*** 2.334**

(0,+1) 87 5.25% 5.30% 50:37 19.513*** 3.715*** 2.334**

(0,+2) 87 5.58% 5.67% 46:41 17.023*** 3.649*** 1.472$

(0,+5) 87 5.67% 6.10% 47:40 12.840*** 3.535*** 1.688*

(0,+10) 87 6.77% 7.43% 52:35 111.596*** 3.500*** 2.765*

(0,+15) 87 11.37% 10.25% 55:32 13.174*** 3.826*** 3.412***

Panel J: Target: Europe or UK Transactions for All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 134 6.67% 5.31% 68:48 21.558*** 2.077* 4.386***

(-2,+2) 134 7.40% 5.78% 82:52 18.067*** 2.228* 3.692***

(-5,+5) 134 8.14% 6.62% 86:48 13.944*** 2.480** 4.386***

(-10,+10) 134 9.77% 8.49% 87:47 12.732*** 2.978** 4..559***

(-15,+15) 134 14.81% 11.00% 89:45 13.434*** 3.589*** 4.906***

(-1,0) 134 5.41% 4.40% 76:58 21.855*** 1.762* 2.650**

(-2,0) 134 5.87% 4.70% 85:49 19.027*** 1.878* 4.212***

(-5,0) 134 6.59% 5.35% 83:51 15.350*** 2.109* 3.865***

(-10,0) 134 7.89% 6.66% 87:47 14.018*** 2.543** 4.559***

(-15,0) 134 9.01% 7.29% 87:47 12.594*** 2.721** 4.559***

(0,+1) 134 5.85% 4.42% 81:53 22.237*** 1.769* 3.518***

(0,+2) 134 6.11% 4.58% 78:56 18.769*** 1.815* 2.998*

(0,+5) 134 6.13% 4.79% 79:55 13.771*** 1.858* 3.171***

(0,+10) 134 6.47% 5.34% 84:50 11.345*** 1.992* 4.039***

(0,+15) 134 10.38% 7.23% 88:46 12.651*** 2.531** 4.733***

70

Table 14 (continued): Cumulative Abnormal Returns Across Event Windows: Target Transactions by Region/Country

Market Model, Equally Weighted Index

Panel K: Target: United Kingdom Transactions for Al Years 1990-2006

Days N Mean CAAR Precision

Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 47 9.05% 4.84% 33:14 13.757*** 0.832 3.349***

(-2,+2) 47 9.49% 4.72% 32:15 10.351*** 0.810 3.057**

(-5,+5) 47 11.47% 6.11% 33:14 8.917*** 1.028 3.349***

(-10,+10) 47 12.35% 6.98% 33:14 7.241*** 1.148 3.349***

(-15,+15) 47 17.72% 9.08% 36:11 7.581*** 1.460$ 4.227***

(-1,0) 47 8.79% 4.94% 33:14 17.197*** 0.862 3.349***

(-2,0) 47 9.09% 4.97% 34:13 14.084*** 0.864 3.642***

(-5,0) 47 11.18% 6.46% 35:12 12.874*** 1.107 3.935***

(-10,0) 47 13.14% 7.91% 35:12 11.569*** 1.341$ 3.935***

(-15,0) 47 15.87% 9.42% 37:10 11.327*** 1.572$ 4.520***

(0,+1) 47 6.95% 3.15% 31:16 10.999*** 0.546 2.764**

(0,+2) 47 7.09% 3.01% 32:15 8.531*** 0.521 3.057**

(0,+5) 47 6.99% 2.91% 32:15 5.782*** 0.500 3.057**

(0,+10) 47 5.90% 2.32% 32:15 3.380*** 0.396 3.057**

(0,+15) 47 8.55% 2.91% 33:14 3.438*** 0.489 3.349***

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

71

Table 15: Cumulative Abnormal Returns Across Event Windows: Acquirer Cross-Sector Transactions

Market Model, Equally Weighted Index

Panel A: Acquirer: Acquirer is Insurance Company and Target is Not: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 491 -0.02% 0.12 239:252 0.987 0.676 0.821

(-2,+2) 491 0.32% 0.20% 245:246 1.136 0.933 1.364$

(-5,+5) 491 -0.49% 0.18% 246:245 0.678 0.608 1.454$

(-10,+10) 491 -0.71% 0.02% 228:263 -0.082 -0.073 -0.174

(-15,+15) 491 -1.25% -0.44% 238:255 -1.030 -0.980 0.550

(-1,0) 491 -0.12% -0.06% 247:244 -0.335 -0.228 1.545$

(-2,0) 491 -0.09% -0.07% 245:246 -0.436 -0.338 1.364$

(-5,0) 491 -0.17% 0.03% 243:248 0.202 0.174 1.183

(-10,0) 491 -0.41% -0.14% 238:253 -0.578 -0.523 0.731

(-15,0) 491 -0.94% -0.55% 228:263 -1.882* -1.598$ -0.174

(0,+1) 491 0.10% 0.22% 238:253 1.991* 1.199 0.731

(0,+2) 491 0.41% 0.30% 243:248 2.248* 1.644$ 1.183

(0,+5) 491 -0.31% 0.18% 244:247 0.939 0.807 1.274

(0,+10) 491 -0.31% 0.19% 240:251 0.621 0.530 0.912

(0,+15) 491 -0.31% 0.14% 221:270 0.341 0.297 -0.807

Panel B: Acquirer: Acquirer is Insurance Broker or Agent and Target is Not: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 115 1.12% 1.19% 58:57 3.649*** 2.537** 0.737

(-2,+2) 115 0.57% 0.95% 57:58 2.257* 1.586$ 0.550

(-5,+5) 115 2.41% 1.95% 59:56 3.056** 2.296* 0.924

(-10,+10) 115 1.33% 0.97% 58:57 1.022 0.874 0.737

(-15,+15) 115 2.63% 1.75% 54:61 1.278 1.065 -0.010

(-1,0) 115 1.49% 1.33% 63:52 5.036*** 3.124*** 1.671*

(-2,0) 115 1.80% 1.47% 61:54 4.489*** 3.171*** 1.298$

(-5,0) 115 1.99% 1.49% 60:55 3.189*** 2.549** 1.111

(-10,0) 115 2.14% 1.31% 61:54 2.016* 1.777* 1.298$

(-15,0) 115 4.13% 2.27% 59:58 2.486*** 1.700* 0.924

(0,+1) 115 0.37% 0.74% 61:54 2.785** 1.752* 1.298$

(0,+2) 115 -0.49% 0.36% 58:57 1.183 0.772 0.737

(0,+5) 115 1.17% 1.34% 60:55 2.901** 2.018* 1.111

(0,+10) 115 -0.08% 0.54% 55:60 0.839 0.643 0.177

(0,+15) 115 -0.77% 0.36% 50:65 0.503 0.404 -0.758

72

Table 15 (continued): Cumulative Abnormal Returns Across Event Windows: Acquirer Cross-Sector Transactions

Market Model, Equally Weighted Index

Panel C : Acquirer: Acquirer is Insurance Company (excluding Life Insurance) and Target is Not: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 287 0.47% 0.65% 150:137 3.318*** 2.474** 2.262*

(-2,+2) 287 0.81% 0.66% 154:133 2.555** 2.123* 2.736*

(-5,+5) 287 -1.00% 0.28% 141:146 0.656 0.591 1.195

(-10,+10) 287 -0.89% 0.25% 145:142 0.429 0.379 1.669*

(-15,+15) 287 1.67% 0.14% 149:138 0.058 0.052 2.143*

(-1,0) 287 0.25% 0.23% 147:140 1.681* 1.097 1.906*

(-2,0) 287 0.24% 0.23% 148:139 1.195 0.926 2.025*

(-5,0) 287 -0.59% 0.09% 135:152 -0.297 -0.266 0.484

(-10,0) 287 -0.50% -0.14% 138:149 -0.342 -0.330 0.839

(-15,0) 287 -1.06% -0.30% 138:149 -0.871 -0.864 0.839

(0,+1) 287 0.39% 0.72% 150:136 4.458*** 3.003** 2.320*

(0,+2) 287 0.73% 0.74% 141:146 3.756*** 2.648** 1.195

(0,+5) 287 -0.25% 0.67% 153:134 2.349** 1.942* 2.617**

(0,+10) 287 -0.22% 0.70% 149:138 1.787* 1.517$ 2.143*

(0,+15) 287 -0.44% 0.75% 142:145 1.598$ 1.292$ 1.314$

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

73

Table 16: Cumulative Abnormal Returns Across Event Windows: Target Cross-Sector Transactions, Market Model, Equally

Weighted Index

Panel A: Target: Acquirer is Insurance Company and Target is Not: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 93 10.62% 8.75% 66:27 23.195*** 5.822** 5.232***

(-2,+2) 93 11.34% 9.44% 60:33 19.528*** 5.746*** 3.979***

(-5,+5) 93 11.01% 9.61% 64;29 13.254*** 5.403*** 5.023**

(-10,+10) 93 11.04% 10.23% 64:29 10.055*** 5.108*** 4.815***

(-15,+15) 93 13.99% 12.45% 62:31 9.903*** 5.230*** 4.397***

(-1,0) 93 8.99% 7.87% 58:35 25.671*** 5.394*** 3.561***

(-2,0) 93 9.68% 8.59% 60:33 23.044*** 5.268*** 3.979***

(-5,0) 93 10.15% 8.89% 64:29 16.735*** 5.285*** 4.815***

(-10,0) 93 10.74% 9.39% 61:32 12.937*** 5.105*** 4.188***

(-15,0) 93 11.62% 10.38% 62:31 11.788*** 5.168*** 4.397***

(0,+1) 93 9.82% 7.81% 62:31 24.456*** 5.482*** 4.397***

(0,+2) 93 9.85% 7.78% 61:32 20.847*** 5.642*** 4.188***

(0,+5) 93 9.05% 7.65% 60:33 14.471*** 5.192*** 3.979***

(0,+10) 93 8.49% 7.78% 66:27 10.840*** 5.074*** 5.232***

(0,+15) 93 10.56% 9.02% 67:26 10.255*** 4.856*** 5.441***

Panel B: Target: Acquirer is Insurance Broker or Agent and Target is Not: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 15 17.59% 16.42% 14:1 17.764*** 4.200*** 3.698***

(-2,+2) 15 20.42% 17.42% 13:2 14.570*** 4.095*** 3.180***

(-5,+5) 15 21.74% 18.22% 11:4 10.193*** 4.166*** 2.143*

(-10,+10) 15 13.56% 12.93% 11:4 5.148*** 2.492** 2.143*

(-15,+15) 15 11.96% 12.14% 12:3 3.847*** 2.480** 2.662**

(-1,0) 15 4.45% 5.96% 11:4 7.994*** 1.991* 2.143*

(-2,0) 15 5.48% 6.44% 12:3 7.161*** 2.230* 2.662**

(-5,0) 15 6.79% 7.49% 12:3 5.763*** 2.421** 2.662**

(-10,0) 15 5.93% 6.46% 9:6 3.725*** 2.116* 1.107

(-15,0) 15 5.43% 6.70% 11:4 2.949** 2.050* 2.143*

(0,+1) 15 16.71% 15.94% 13:2 21.041*** 3.868*** 3.180***

(0,+2) 15 18.52% 16.46% 12:3 17.702*** 3.680*** 2.662**

(0,+5) 15 18.53% 16.21% 12:3 12.439*** 3.614*** 2.662**

(0,+10) 15 11.21% 11.94% 10:5 6.600*** 2.260* 1.625$

(0,+15) 15 10.10% 10.92% 10:5 5.017*** 2.166* 1.625$

74

Table 16 (continued): Cumulative Abnormal Returns Across Event Windows: Target Cross-Sector Transactions

Market Model, Equally Weighted Index

Panel C : Target: Acquirer is Insurance Company (excluding Life Insurance) and Target is Not: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 48 17.00% 16.18% 38:10 26.258*** 5.292*** 5.057***

(-2,+2) 48 19.00% 18.26% 39:9 22.974*** 5.201*** 5.348***

(-5,+5) 48 17.63% 18.47% 37:11 15.495*** 4.803*** 4.766***

(-10,+10) 48 18.86% 18.80% 35:13 11.214*** 4.764*** 4.182***

(-15,+15) 48 22.79% 24.63% 38:10 11.829*** 5.276*** 5.057***

(-1,0) 48 13.05% 12.43% 32:16 24.768*** 4.764*** 3.308***

(-2,0) 48 14.70% 14.23% 36:12 23.353*** 4.647*** 4.474***

(-5,0) 48 14.56% 14.75% 35:13 16.868*** 4.648*** 4.182***

(-10,0) 48 16.18% 14.83% 32:16 12.471*** 4.604*** 3.308***

(-15,0) 48 16.26% 16.15% 34:14 11.149*** 4.608*** 3.891***

(0,+1) 48 16.80% 15.70% 38:10 31.261*** 5.158*** 5.057***

(0,+2) 48 17.16% 15.98% 38:12 25.982*** 5.247*** 4.474***

(0,+5) 48 15.92% 15.66% 34:14 18.049*** 4.955*** 3.891***

(0,+10) 48 15.53% 15.91% 37:11 13.385*** 4.949*** 4.765***

(0,+15) 48 19.39% 20.42% 38:9 14.004*** 5.347*** 5.348***

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

75

Table 17: Cumulative Abnormal Returns Across Event Windows:

Acquirer Cross Industry Transactions: Insurance and Insurance Brokers v Banks

Market Model, Equally Weighted Index

Panel A: Acquirers: Acquirer is Bank and Target is Insurance Company or Insurance Agent: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 193 0.44% 0.48% 109:84 2.731*** 2.368** 2.440**

(-2,+2) 193 0.44% 0.50% 102:91 2.123* 2.076* 1.431$

(-5,+5) 193 0.54% 0.46% 101:92 1.315$ 1.534$ 1.287$

(-10,+10) 193 -0.23% -0.21% 103:90 -0.453 -0.516 1.575$

(-15,+15) 193 -0.19% -0.28% 94:99 -0.52 -0.61 0.278

(-1,0) 193 0.32% 0.32% 95:98 2.234* 1.959* 0.423

(-2,0) 193 0.36% 0.38% 95:98 2.060* 1.927* 0.423

(-5,0) 193 0.38% 0.40% 97:96 1.543$ 1.570$ 0.711

(-10,0) 193 -0.16% -0.04% 93:100 -0.146 -0.170 0.134

(-15,0) 193 -0.33% -0.24% 87:106 -0.581 -0.649 -0.731

(0,+1) 193 0.26% 0.24% 92:101 1.763* 1.434$ -0.010

(0,+2) 193 0.22% 0.19% 94:99 1.156 1.039 0.278

(0,+5) 193 0.31% 0.14% 102:91 0.602 0.655 1.431$

(0,+10) 193 -0.07% -0.09% 98:95 -0.24 -0.25 0.855

(0,+15) 193 0.28% 0.04% 96:97 0.055 0.063 0.567

Panel B: Acquirers: Acquirer is Bank and Target is Insurance Company: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 95 0.91% 0.84% 53:42 3.569*** 2.722** 1.578$

(-2,+2) 95 0.56% 0.61% 50:45 1.953* 1.814* 0.962

(-5,+5) 95 0.55% 0.56 49:46 1.170 1.365$ 0.756

(-10,+10) 95 0.07% 0.04% 50:45 0.063 0.082 0.962

(-15,+15) 95 -0.50% -0.38% 44:51 -0.301 -0.355 -0.271

(-1,0) 95 0.67% 0.56% 48:47 2.929** 2.409* 0.551

(-2,0) 95 0.65% 0.62% 49:46 2.590** 2.415** 0.756

(-5,0) 95 0.47% 0.52% 49:46 1.503$ 1.544$ 0.756

(-10,0) 95 0.05% 0.15% 49:46 0.306 0.395 0.756

(-15,0) 95 -0.23% -0.19% 48:47 -0.098 -0.128 0.551

(0,+1) 95 0.54% 0.49% 40:55 2.628** 1.903* -1.093

(0,+2) 95 0.20% 0.20% 44:51 0.872 0.682 -0.271

(0,+5) 95 0.39% 0.24% 51:44 0.735 0.724 1.167

(0,+10) 95 0.33% 0.10% 51:44 0.247 0.271 1.167

(0,+15) 95 0.03% 0.02% 48:47 0.058 0.070 0.561

76

Table 17 (continued): Cumulative Abnormal Returns Across Event Windows:

Acquirer Cross Industry Transactions: Insurance and Insurance Brokers v Banks

Market Model, Equally Weighted Index

Panel C: Acquirers: Acquirer is Insurance Company or Insurance Broker or Agent and Target is Bank: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 28 -0.01% -0.13% 14:14 -0.256 -0.139 0.162

(-2,+2) 28 -0.88% -0.78% 9:19 -1.297$ -0.715 -1.729*

(-5,+5) 28 -0.82% -0.99% 12:16 -1.020 -1.010 -0.594

(-10,+10) 28 -1.26% -1.74% 9:19 -1.356$ -1.719* -1.729*

(-15,+15) 28 -4.48% -4.45% 10:18 -2.843** -2.849** -1.351$

(-1,0) 28 0.30% 0.42% 13:15 1.149 0.841 -0.216

(-2,0) 28 0.09% 0.13% 12:16 0.288 0.211 -0.594

(-5,0) 28 0.31% 0.03% 12:16 0.068 0.066 -0.594

(-10,0) 28 0.36% -0.68% 13:15 -0.780 -0.771 -0.216

(-15,0) 28 -2.28% -2.76% 10:18 -2.500** -2.208* -1.351$

(0,+1) 28 -0.84% -0.96% 7:21 -2.555** -1.419$ -2.485**

(0,+2) 28 -1.51% -1.31% 10:18 -2.858** -1.805% -1.351$

(0,+5) 28 -1.66% -1.42% 9:19 -2.092* -2.489** -1.729*

(0,+10) 28 -2.16% -1.46% 9:19 -1.587$ -1.836* -1.729*

(0,+15) 28 -2.74% -2.10% 9:19 -1.912* -1.442$ -1.729*

Panel D: Acquirers: Acquirer is Insurance Company and Target is Bank: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 25 -0.09% -0.14% 13:12 -0.271 -0.144 0.332

(-2,+2) 25 -0.88% -0.74% 8:17 -1.241 -0.652 -1.669*

(-5,+5) 25 -1.55% -1.31% 9:16 1.367$ -1.338$ -1.269

(-10,+10) 25 -2.99% -2.50% 6:19 -1.958* -2.908** -2.469**

(-15,+15) 25 -5.81% -5.11% 7:18 -3.273*** -3.308*** -2.069*

(-1,0) 25 0.30% 0.43% 12:13 1.176 0.816 -0.069

(-2,0) 25 0.02% 0.10% 10:15 0.241 0.167 -0.869

(-5,0) 25 -0.26% -0.23% 9:16 -0.308 -0.297 -1.269

(-10,0) 25 -1.19% -1.41% 10:15 -1.577$ -1.813* -0.869

(-15,0) 25 -3.43% -3.36% 7:18 -3.073** -2.766** -2.089*

(0,+1) 25 -0.92% -0.96% 6:19 -2.569** -1.424$ -2.469**

(0,+2) 25 -1.43% -1.24% 9:16 -2.712** -1.645* -1.269

(0,+5) 25 -1.83% -1.47% 8:17 -2174* -2.468** -1.669*

(0,+10) 25 -2.33% -1.49% 7:18 -1.617$ -1.774* -2.069*

(0,+15) 25 -2.90% -2.14% 8:17 -1.954* -1.394$ -1.669*

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

77

Table 18: Cumulative Abnormal Returns Across Event Windows:

Target Cross Industry Transactions: Insurance and/or Insurance Agent v. Banks

Market Model, Equally Weighted Index

Panel A: Target: Acquirer is Bank and Target is Insurance Company or Insurance Agent/Broker: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 16 2.47% 2.59% 11:5 3.971*** 2.634** 1.870*

(-2,+2) 16 2.82% 2.74% 10:6 3.340*** 2.259* 1.17

(-5,+5) 16 2.36% 2.48% 9:7 1.962* 1.764* 0.669

(-10,+10) 16 3.48% 4.37% 9:7 2.520** 1.347$ 0.669

(-15,+15) 16 4.16% 4.91% 10:6 2.114* 1.512$ 1.170

(-1,0) 16 1.89% 2.25% 8:8 4.245*** 2.086* 0.169

(-2,0) 16 2.81% 2.95% 9:7 4.538*** 2.428** 0.669

(-5,0) 16 2.94% 3.19% 9:7 3.396*** 2.335** 0.669

(-10,0) 16 6.39% 5.40% 9:7 4.279*** 2.043* 0.669

(-15,0) 16 6.33% 5.27% 9:7 3.295*** 1.996* 0.669

(0,+1) 16 1.37% 1.43% 9:7 2.686* 1.603$ 0.669

(0,+2) 16 1.01% 0.88% 7:9 1.519$ 0.933 -0.332

(0,+5) 16 0.22% 0.38% 9:7 0.476 0.441 0.669

(0,+10) 16 -2.11% 0.05% 10:6 0.119 0.059 1.170

(0,+15) 16 -1.37% 0.73% 9:7 0.451 0.298 0.669

Panel B: Target: Acquirer is Bank and Target is Insurance Company: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 15 2.47% 2.59% 11:5 3.971*** 2.634** 1.870*

(-2,+2) 15 2.82% 2.74% 10:6 3.340*** 2.259* 1.17

(-5,+5) 15 2.36% 2.48% 9:7 1.962* 1.764* 0.669

(-10,+10) 15 3.48% 4.37% 9:7 2.520** 1.347$ 0.669

(-15,+15) 15 4.16% 4.91% 10:6 2.114* 1.512$ 1.170

(-1,0) 15 1.89% 2.25% 8:8 4.245*** 2.086* 0.169

(-2,0) 15 2.81% 2.95% 9:7 4.538*** 2.428** 0.669

(-5,0) 15 2.94% 3.19% 9:7 3.396*** 2.335** 0.669

(-10,0) 15 6.39% 5.40% 9:7 4.279*** 2.043* 0.669

(-15,0) 15 6.33% 5.27% 9:7 3.295*** 1.996* 0.669

(0,+1) 15 1.37% 1.43% 9:7 2.686* 1.603$ 0.669

(0,+2) 15 1.01% 0.88% 7:9 1.519$ 0.933 -0.332

(0,+5) 15 0.22% 0.38% 9:7 0.476 0.441 0.669

(0,+10) 15 -2.11% 0.05% 10:6 0.119 0.059 1.170

(0,+15) 15 -1.37% 0.73% 9:7 0.451 0.298 0.669

78

Table 18 (continued): Cumulative Abnormal Returns Across Event Windows:

Target Cross Industry Transactions: Insurance and/or Insurance Agent v. Banks

Market Model, Equally Weighted Index

Panel C: Target: Acquirer is Insurance Company or Insurance Agent/Broker and Target is Bank: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 12 9.58% 5.95% 7:3 12.377*** 2.875** 1.795*

(-2,+2) 12 9.12% 8.66% 10:2 8.886*** 2.909** 2.372**

(-5,+5) 12 9.20% 8.78% 10:2 6.212**** 2.281* 2.372**

(-10,+10) 12 10.18% 9.21% 10:2 4.657*** 2.090* 2.372**

(-15,+15) 12 7.90% 6.81% 9:3 2.998** 1.602$ 1.795*

(-1,0) 12 6.24% 5.45% 8:4 8.945*** 2.084* 1.217

(-2,0) 12 6.30% 5.32% 8:4 7.098*** 2.023* 1.217

(-5,0) 12 6.68% 5.33% 8:4 5.203*** 1.834* 1.217

(-10,0) 12 7.15% 5.95% 8:4 4.209*** 2.012* 1.217

(-15,0) 12 5.82% 4.56% 7:5 2.931** 1.501$ 0.640

(0,+1) 12 9.67% 9.37% 9:3 15.311*** 2.688** 1.795*

(0,+2) 12 9.16% 8.88% 9:3 11.825*** 2.715** 1.795*

(0,+5) 12 8.86% 8.99% 9:3 8.682*** 2.306* 1.795*

(0,+10) 12 9.37% 8.80% 10:2 6.271*** 2.028* 2.372**

(0,+15) 12 8.41% 7.78% 10:2 4.492*** 2.002* 2.372**

Panel D: Target: Acquirer is Insurance Company and Target is Bank: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 10 6.64% 5.95% 7:3 7.079*** 2.427** 1.513$

(-2,+2) 10 6.50% 5.66% 8:2 5.714*** 2.464** 2.147*

(-5,+5) 10 6.20% 5.33% 8:2 3.467*** 1.487$ 2.147*

(-10,+10) 10 7.36% 5.82% 8:2 2.705** 1.312$ 2.147*

(-15,+15) 10 4.68% 3.09% 7:3 1.322$ 0.791 1.513$

(-1,0) 10 6.75% 5.65% 7:3 8.238*** 2.184* 1.513$

(-2,0) 10 6.88% 5.57% 7:3 6.603*** 2.098* 1.513$

(-5,0) 10 7.07% 5.22% 7:3 4.599*** 1.788* 1.513$

(-10,0) 10 7.66% 6.02% 7:3 3.770**** 1.976* 1.513$

(-15,0) 10 6.00% 4.39% 6:4 2.479** 1.369$ 0.878

(0,+1) 10 6.48% 5.77% 7:3 8.398*** 2.237* 1.513$

(0,+2) 10 6.20% 5.55% 7:3 6.590*** 2.237* 1.513$

(0,+5) 10 5.71% 5.57% 7:3 4.901*** 1.564$ 1.513$

(0,+10) 10 6.28% 5.26% 8:2 3.473*** 1.266 2.147*

(0,+15) 10 5.26% 4.16% 8:2 2.210* 1.234 2.147*

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

79

Table 19: Cumulative Abnormal Returns Across Event Windows:

Acquirer Cross Industry Transactions: Insurance and/or Insurance Agents v Security Dealers

Market Model, Equally Weighted Index

Panel A: Acquirers: Acquirer is Security Dealer/Broker and Target is Insurance Company or Insurance Agent/Broker: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 33 -0.57% -0.54% 15:18 -1.230 -1.132 -0.296

(-2,+2) 33 -1.18% -0.22% 15:18 -0.388 -0.354 -0.296

(-5,+5) 33 0.47% 0.06% 15:18 0.015 0.014 -0.296

(-10,+10) 33 1.37% 0.91% 18:15 0.626 0.606 0.750

(-15,+15) 33 -0.37% -0.05% 14:19 -0.072 -0.065 -0.644

(-1,0) 33 -0.39% -0.72% 11:22 1.890* -1.524$ -1.689*

(-2,0) 33 -1.15% -0.68% 14:19 -1.502$ -1.458$ -0.644

(-5,0) 33 -0.52% -0.89% 13:20 -1.367$ -1.565$ -0.993

(-10,0) 33 0.79% 0.72% 17:16 0.688 0.694 0.401

(-15,0) 33 1.28% 0.66% 15:18 0.555 0.555 -0.296

(0,+1) 33 -0.91% -0.48% 17:16 -1.147 -0.902 0.401

(0,+2) 33 -0.77% -0.19% 14:19 -0.275 -0.197 -0.544

(0,+5) 33 0.26% 0.30% 16:17 0.428 0.313 0.053

(0,+10) 33 -0.15% -0.46% 16:17 -0.499 -0.464 0.053

(0,+15) 33 -2.38% -1.36% 13:20 -1.214 -1.120 -0.993

Panel B: Acquirers: Acquirer is Security Dealer/Broker and Target is Insurance Company: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 25 0.91% -0.41% 12:13 -0.867 -0.766 0.050

(-2,+2) 25 -1.06% -0.82% 11:14 -1.184 -1.187 -0.350

(-5,+5) 25 -0.43% -1.17% 9:16 -1.163 -1.120 -1.151

(-10,+10) 25 0.31% -0.75% 13:12 -0.531 -0.674 0.451

(-15,+15) 25 -0.61% -2.15% 8:17 -1.240 -1.259 -1.552$

(-1,0) 25 0.76% 0.43% 9:16 -1.134 -0.828 -1.151

(-2,0) 25 -0.86% -0.65% 12:13 -1.372$ -1.227 0.050

(-5,0) 25 -1.26% -1.08% 8:17 -1.535$ -1.627$ -1.552$

(-10,0) 25 0.34% 0.05% 13:12 0.016 0.025 0.451

(-15,0) 25 1.48% -0.45% 10:15 -0.355 -0.484 -0.751

(0,+1) 25 0.00% -0.57% 13:12 -1.299$ -0.982 0.451

(0,+2) 25 -0.35% -0.76% 10:15 -1.265 -0.945 -0.751

(0,+5) 25 0.68% -0.67% 11:14 -0.874 -0.662 -0.350

(0,+10) 25 -0.18% -1.39% 12:13 -1.332$ -1.291$ 0.050

(0,+15) 25 -2.24% -2.29% 9:16 -1.879* -1.645$ -1.151

80

Table 19 (continued): Cumulative Abnormal Returns Across Event Windows:

Acquirer Cross Industry Transactions: Insurance and/or Insurance Agents v Security Dealers

Market Model, Equally Weighted Index

Panel C: Acquirers: Acquirer is Insurance Company or Insurance Agent/Broker and Target is Security Dealer/Broker: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 44 0.39% 0.01% 20:24 0.062 0.039 -0.335

(-2,+2) 44 0.68% 0.12% 18:26 0.214 0.138 -0.939

(-5,+5) 44 0.27% -0.06% 18:26 -0.083 -0.065 -0.939

(-10,+10) 44 -0.48% -0.98% 19:25 -0.845 -0.794 -0.637

(-15,+15) 44 1.62% -0.57% 19:225 -0.344 -0.358 -0.637

(-1,0) 44 0.26% 0.10% 18:26 0.293 0.171 -0.939

(-2,0) 44 0.55% 0.19% 22:22 0.453 0.278 0.268

(-5,0) 44 0.65% 0.33% 18:26 0.548 0.395 -0.939

(-10,0) 44 -0.31% -0.49% 18:26 -0.610 -0.579 -0.939

(-15,0) 44 0.54% -0.21% 19:25 -0.207 -0.203 -0.637

(0,+1) 44 0.50% 0.05% 17:27 0.170 0.090 -1.241

(0,+2) 44 0.51% 0.07% 21:23 0.199 0.118 -0.034

(0,+5) 44 -0.01% -0.25% 19:25 -0.416 -0.304 -0.637

(0,+10) 44 0.21% -0.35% 20:24 -0.41 -0.363 -0.335

(0,+15) 44 1.46% -0.22% 16:28 -0.174 -0.168 -1.543$

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level

Panel D: Acquirers: Acquirer is Insurance Company and Target is Security Dealer/Broker: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 40 -0.77% -0.77% 17:23 -1.694* -2.074* -0.734

(-2,+2) 40 -0.68% -0.90% 16:24 -1.571$ -1.730* -1.050

(-5,+5) 40 -1.22% -1.16% 16:24 -1.368$ -1.483$ -1.050

(-10,+10) 40 -1.88% -2.00% 17:23 -1.667* -1.913* -0.734

(-15,+15) 40 0.43% -1.57% 17:23 -0.995 -1.216 -0.734

(-1,0) 40 -0.71% -0.54% 16:24 -1.516$ -1.478$ -1.050

(-2,0) 40 -0.53% -0.59% 20:20 -1.346$ -1.316$ 0.215

(-5,0) 40 -0.57% -0.60% 16:24 -0.963 -1.055 -1.050

(-10,0) 40 -1.53% -1.36% 16:24 -1.15$ -2.049* -1.050

(-15,0) 40 -0.60% -1.06% 17:23 -1.019 -1.194 -0.734

(0,+1) 40 -0.52% -0.60% 14:26 -1.659* -1.690* -1.683*

(0,+2) 40 -0.62% -0.69% 18:22 -1.524$ -1.544$ -0.418

(0,+5) 40 -1.11% -0.94% 17:23 -1.511$ -1.510$ -0.734

(0,+10) 40 -0.82% -1.02% 18:22 -1.191 -1.302$ -0.418

(0,+15) 40 0.57% -0.89% 14:26 -0.807 -0.925 -1.683*

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

81

Table 20: Cumulative Abnormal Returns Across Event Windows:

Target Cross Industry Transactions: Insurance and/or Insurance Agent v Security Dealers

Market Model, Equally Weighted Index

Panel A: Target: Acquirer is Security Dealer/Broker and Target is Insurance Company or Insurance Agent/Broker: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 11 2.77% 4.23% 4:7 3.673*** 1.462$ -0.240

(-2,+2) 11 2.20% 2.90% 5:6 1.942* 0.916 0.376

(-5,+5) 11 8.94% 6.41% 6:5 2.752** 1.370$ 0.992

(-10,+10) 11 18.83% 19.91% 7:4 6.232**** 1.806* 1.607$

(-15,+15) 11 36.92% 22.64% 7:4 5.582*** 2.025* 1.607$

(-1,0) 11 2.82% 3.86% 5:6 4.119*** 1.236 0.376

(-2,0) 11 1.06% 2.27% 5:6 1.956* 0.745 0.376

(-5,0) 11 5.59% 5.07% 5:6 3.030** 1.198 0.376

(-10,0) 11 8.51% 6.86% 7:4 3.007** 1.344$ 1.607$

(-15,0) 11 12.55% 7.28% 7:4 2.719** 1.311$ 1.607$

(0,+1) 11 3.39% 4.52% 5:6 4.817*** 1.676* 0.376

(0,+2) 11 4.57% 4.79% 6:5 4.172*** 1.726* 0.992

(0,+5) 11 6.78% 5.49% 6:5 3.257*** 1.445$ 0.992

(0,+10) 11 13.75% 17.20% 6:5 7.659*** 1.626$ 0.992

(0,+15) 11 27.80% 19.51% 7:4 7.140*** 1.859* 1.607$

Panel B: Target: Acquirer is Security Dealer/Broker and Target is Insurance Company: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 9 1.65% 2.88% 3:6 2.475** 1.018 -0.300

(-2,+2) 9 -0.37% 2.44% 3:6 1.616$ 0.684 -0.300

(-5,+5) 9 6/03% 3.88% 4:5 1.626$ 0.854 0.386

(-10,+10) 9 15.15% 17.41% 5:4 5.423*** 1.451$ 1.072

(-15,+15) 9 21.83% 17.53% 5:4 4.704*** 1.501$ 1.072

(-1,0) 9 1.14% 2.31% 3:6 2.436** 0.758 -0.300

(-2,0) 9 -1.09% 1.25% 3:6 1.071 0.383 -0.300

(-5,0) 9 3.07% 1.90% 4:5 1.110 0.61 0.386

(-10,0) 9 3.48% 2.60% 5:4 1.155 0.757 1.072

(-15,0) 9 2.66% 1.51% 5:4 0.813 0.587 1.072

(0,+1) 9 2.72% 3.60% 4:5 3.799*** 1.284$ 0.386

(0,+2) 9 2.93% 4.22% 4:5 3.635*** 1.372$ 0.386

(0,+5) 9 5.17% 5.02% 4:5 2.932** 1.172 0.386

(0,+10) 9 13.88% 17.84% 4:5 7.861*** 1.513$ 0.386

(0,+15) 9 21.37% 19.05% 5:4 6.929*** 1.622$ 1.072

82

Table 20 (continued): Cumulative Abnormal Returns Across Event Windows:

Target Cross Industry Transactions: Insurance and/or Insurance Agent v Security Dealers

Market Model, Equally Weighted Index

Panel C : Target: Acquirer is Insurance Company or Insurance Broker or Agent and Target is Security Dealer or Broker : All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 12 8.06% 2.86% 6:6 3.490*** 1.006 0.074

(-2,+2) 12 8.42% 2.62% 5:7 2.469** 0.829 -0.503

(-5,+5) 12 8.87% 2.90% 6:6 1.828* 1.014 0.074

(-10,+10) 12 10.91% 3.70% 7:5 1.692* 1.030 0.652

(-15,+15) 12 14.55% 6.36% 8:4 2.328** 1.698* 1.229

(-1,0) 12 7.43% 2.67% 7:5 4.008*** 1.031 0.652

(-2,0) 12 8.42% 2.76% 6:6 3.380*** 0.927 0.074

(-5,0) 12 9.27% 3.31% 8:4 2.852** 1.208 1.229

(-10,0) 12 9.79% 3.58% 7:5 2.271* 1.179 0.652

(-15,0) 12 10.52% 3.70% 7:5 2.014* 1.124 0.652

(0,+1) 12 5.07% 1.29% 5:7 1.947* 0.548 -0.503

(0,+2) 12 4.44% 0.96% 5:7 1.175 0.418 -0.503

(0,+5) 12 4.05% 0.69% 5:7 0.596 0.297 -0.503

(0,+10) 12 5.57% 1.22% 7:5 0.826 0.471 0.652

(0,+15) 12 8.48% 3.76% 6:6 1.933* 1.226 0.074

Panel D : Target: Acquirer and Target is Security Dealer or Broker : All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 11 9.50% 3.21% 6:5 3.964*** 1.109 0.530

(-2,+2) 11 10.02% 3.01% 5:6 2.871** 0.934 -0.075

(-5,+5) 11 10.07% 3.14% 6:5 1.999* 1.069 0.530

(-10,+10) 11 11.04% 3.51% 6:5 1.626$ 0.944 0.530

(-15,+15) 11 14.78% 6.17% 7:4 2.286* 1.592$ 1.134

(-1,0) 11 8.02% 2.72% 6:5 4.139*** 1.018 0.530

(-2,0) 11 9.05% 2.80% 5:6 3.469*** 0.909 -0.075

(-5,0) 11 9.18% 3.08% 7:4 2.683** 1.084 1.134

(-10,0) 11 8.48% 2.90% 6:5 1.864* 0.936 0.530

(-15,0) 11 9.26% 3.01% 6:5 1.683* 0.905 0.530

(0,+1) 11 6.29% 1.60% 5:6 2.452** 0.668 -0.075

(0,+2) 11 5.78% 1.33% 5:6 1.649* 0.570 -0.075

(0,+5) 11 5.71% 1.18% 5:6 1.028 0.504 -0.075

(0,+10) 11 7.37% 1.72% 7:4 1.162 0.649 1.134

(0,+15) 11 10.33% 4.27% 6:5 2.223* 1.388$ 0.530

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

83

Table 21: Cumulative Abnormal Returns Across Event Windows: Acquirer Within Sector Transactions

Market Model, Equally Weighted Index

Panel A: Acquirer: Acquirer and Target are Insurance Company or Insurance Agent/Broker: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 973 0.68% 0.53% 510:463 5.574*** 4.213*** 3.834***

(-2,+2) 973 0.47% 0.47% 481:492 3.535*** 2.763** 1.969*

(-5,+5) 973 0.29% 0.41% 478:495 2.269* 1.989* 1.777*

(-10,+10) 973 -0.13% 0.43% 474:499 1.564$ 1.412$ 1.519$

(-15,+15) 973 -0.21% 0.56% 465:508 1.564$ 1.482$ 0.941

(-1,0) 973 0.32% 0.28% 495:478 3.945*** 2.905** 2.870**

(-2,0) 973 0.17% 0.14% 474:499 1.652* 1.345$ 1.519$

(-5,0) 973 0.02% 0.09% 478:495 0.772 0.686 1.777*

(-10,0) 973 -0.16% 0.08% 482:491 0.288 0.271 2.034*

(-15,0) 973 -0.33% -0.01% 484:509 -0.215 -0.206 0.876

(0,+1) 973 0.72% 0.48% 512:580 6.329*** 4.178*** 3.994***

(0,+2) 973 0.66% 0.41% 490:483 5.500*** 3.891*** 2.548**

(0,+5) 973 0.62% 0.55% 593:480 4.152*** 3.229*** 2.741**

(0,+10) 973 0.39% 0.61% 483:480 3.258*** 2.690** 2.098*

(0,+15) 973 0.48% 0.81% 486:487 3.564*** 2.979** 2.291*

Panel B: Acquirer: Acquirer and Target are Insurance Companies: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 613 0.66% 0.54% 335:278 4.708*** 3.450** 4.110***

(-2,+2) 613 0.48% 0.43% 305:308 2.989** 2.280* 1.681

(-5,+5) 613 0.18% 0.35% 307:306 1.594$ 1.411$ 1.843*

(-10,+10) 613 0.19% 0.60% 320:293 1.892* 1.736* 2.896**

(-15,+15) 613 -0.11% 0.71% 305:308 1.758* 1.626$ 1.681*

(-1,0) 613 0.38% 0.32% 317:296 3.475*** 2.546** 2.653**

(-2,0) 613 0.28% 0.22% 315:298 1.935* 1.555$ 2.491**

(-5,0) 613 -0.02% 0.11% 308:305 0.688 0.578 1.924*

(-10,0) 613 -0.04% 0.17% 312:301 0.727 0.665 2.248*

(-15,0) 613 -0.32% 0.16% 301:312 0.519 0.488 1.357$

(0,+1) 613 0.61% 0.42% 319:293 4.463*** 2.918** 2.854**

(0,+2) 613 0.53% 0.42% 300:313 3.708*** 2.56** 1.276

(0,+5) 613 0.53% 0.45% 309:304 2.764** 2.186* 2.005*

(0,+10) 613 0.56% 0.64% 312:301 2.838** 2.499** 2.248*

(0,+15) 613 0.54% 0.75% 321:292 2.722** 2.375** 2.977**

84

Table 21 (continued): Cumulative Abnormal Returns Across Event Windows: Acquirer Within Sector Transactions

Market Model, Equally Weighted Index

Panel C : Acquirer: Acquirer and Target are Insurance Broker or Agent: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 147 1.03% 0.46% 68:79 1.797* 1.419$ -0.086

(-2,+2) 147 0.45% -0.08% 68:81 -0.188 -0.162 -0.417

(-5,+5) 147 0.17% -0.11% 57:90 -0.21 -0.209 -1.905*

(-10,+10) 147 -0.48% -0.09% 58:89 -0.227 -0.228 -1.740*

(-15,+15) 147 -0.97% 0.02% 61:86 -0.130 -0.151 -1.244

(-1,0) 147 -0.06% -0.11% 62:84 -0.381 -0.367 -0.913

(-2,0) 147 -0.43% -0.57% 58:91 -2.090* -1.941* -2.070*

(-5,0) 147 -0.47% -0.59% 62:85 -1.512$ -1.827$ -1.078

(-10,0) 147 -0.53% -0.56% 64:83 -1.123 -1.265 -0.748

(-15,0) 147 -0.90% -0.84% 65:82 -1.458$ -1.727* -0.582

(0,+1) 147 1.16% 0.55% 78:69 2.605** 1.835* 1.567$

(0,+2) 147 0.96% 0.48% 73:74 1.770* 1.401$ 0.741

(0,+5) 147 0.71% 0.45% 70:77 1.189 0.993 0.245

(0,+10) 147 0.13% 0.45% 61:86 0.825 0.685 -1.244

(0,+15) 147 0.01% 0.84% 56:81 1.251 1.138 -0.417

Panel D: Acquirer: Acquirer and Target are Insurance Companies Other than life insurers: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 131 0.54% 0.52% 74:57 1.808* 1.146 2.232*

(-2,+2) 131 0.88% 0.71% 68:63 1.839* 1.251 1.182

(-5,+5) 131 1.29% 1.32% 67:64 2.321* 1.949* 1.006

(-10,+10) 131 1.49% 1.46% 75:56 1.784* 1.775* 2.407*

(-15,+15) 131 1.28% 1.86% 68:63 1.655* 1.617$ 1.182

(-1,0) 131 0.28% 0.30% 71:60 1.284$ 0.877 1.707*

(-2,0) 131 0.48% 0.37% 71:60 1.223 0.946 1.707*

(-5,0) 131 0.79% 0.90% 69:52 2.095* 1.505$ 1.357$

(-10,0) 131 0.97% 1.00% 73:58 1.680* 1.428$ 2.057*

(-15,0) 131 1.11% 1.67% 67:64 2.088* 1.826* 1.006

(0,+1) 131 0.61% 0.58% 71:60 2.531** 1.373$ 1.707*

(0,+2) 131 0.75% 0.71% 61:70 2.469** 1.382$ -0.044

(0,+5) 131 0.85% 0.78% 71:60 1.993* 1.365$ 1.707*

(0,+10) 131 0.87% 0.82% 71:60 1.472$ 1.330$ 1.707*

(0,+15) 131 0.52% 0.56% 70:61 0.802 0.708 1.532$

85

Table 21 (continued): Cumulative Abnormal Returns Across Event Windows: Acquirer Within Sector Transactions

Market Model, Equally Weighted Index

Panel E: Acquirer: Acquirer and Target are Life Insurers: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 293 0.64% 0.45% 157:135 2.822** 2.203* 2.380**

(-2,+2) 293 0.28% 0.24% 141:152 1.250 1.021 0.507

(-5,+5) 293 0.07% 0.26% 150:143 0.819 0.744 1.561$

(-10,+10) 293 0.05% 0.87% 151:142 2.024* 1.835* 1.678*

(-15,+15) 293 -0.32% 0.77% 144:149 1.627$ 1.563$ 0.858

(-1,0) 293 0.41% 0.26% 148:145 2.011* 1.611$ 1.327$

(-2,0) 293 0.23% 0.11% 148:145 0.679 0.582 1.327$

(-5,0) 293 0.05% 0.09% 153:140 0.309 0.308 1.912*

(-10,0) 293 -0.05% 0.30% 150:143 0.937 0.874 1.561$

(-15,0) 293 -0.36% 0.12% 148:145 0.566 0.559 1.327$

(0,+1) 293 0.50% 0.25% 146:147 1.852* 1.274 1.092

(0,+2) 293 0.32% 0.20% 138:155 1.274 0.994 0.155

(0,+5) 293 0.30% 0.24% 143:150 1.045 0.853 0.741

(0,+10) 293 0.38% 0.64% 148:145 2.082* 1.841* 1.327$

(0,+15) 293 0.32% 0.72% 151:142 1.927* 1.755* 1.678*

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

86

Table 22: Cumulative Abnormal Returns Across Event Windows: Target Within Sector Transactions

Market Model, Equally Weighted Index

Panel A: Target: Acquirer and Target are Insurance Company or Insurance Agent/Broker: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 147 13.94% 13.31% 108:39 46.586*** 7.373*** 6.869***

(-2,+2) 147 15.12% 14.56% 115:32 39.313*** 7.850*** 8.029***

(-5,+5) 147 16.01% 15.79% 113:34 28.657*** 7.851*** 7.697***

(-10,+10) 147 16.91% 17.12% 108:39 22.025*** 8.083*** 6.869***

(-15,+15) 147 18.65% 19.03% 112:35 19.849*** 8.038*** 7.532***

(-1,0) 147 9.54% 9.76% 98:49 41.812*** 5.865*** 5.212***

(-2,0) 147 10.24% 10.52% 109:38 36.837*** 6.340*** 7.034***

(-5,0) 147 10.87% 11.42% 106:41 28.242*** 6.620*** 6.537***

(-10,0) 147 11.86% 12.80% 106:41 23.110*** 7.042*** 6.537***

(-15,0) 147 12.62% 13.77% 109:38 20.466*** 7.420*** 7.034***

(0,+1) 147 13.24% 12.59% 105:42 54.198*** 7.034*** 6.372***

(0,+2) 147 13.73% 13.07% 107:40 45.819*** 7.176*** 6.703***

(0,+5) 147 13.98% 13.41% 104:43 33.301*** 7.080*** 6.206***

(0,+10) 147 13.90% 13.38% 103:44 24.244*** 6.916*** 6.040***

(0,+15) 147 14.87% 14.30% 104:43 21.410*** 6.685*** 6.206***

Panel B: Target: Acquirer and Target are Insurance Companies: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 132 13.44% 13.07% 98:34 44.375*** 6.954*** 6.627***

(-2,+2) 132 14.32% 14.21% 103:29 37.187*** 7.292*** 7.501***

(-5,+5) 132 15.33% 15.58% 100:32 27.430*** 7.319*** 6.976***

(-10,+10) 132 16.52% 17.11% 96:36 21.337*** 7.593*** 6.277***

(-15,+15) 132 18.66% 19.17% 101:31 19.479*** 7.629*** 7.151***

(-1,0) 132 8.99% 9.35% 88:44 38.779*** 5.401*** 4.879***

(-2,0) 132 9.50% 10.00% 98:34 33.975*** 5.798*** 6.627***

(-5,0) 132 10.22% 10.99% 93:39 28.345*** 6.070*** 5.753***

(-10,0) 132 11.59% 12.63% 96:36 22.085*** 6.587*** 6.277***

(-15,0) 132 12.52% 13.57% 98:34 19.668*** 6.968*** 6.627***

(0,+1) 132 12.73% 12.42% 95:37 51.939*** 6.728*** 6.102***

(0,+2) 132 13.10% 12.88% 96:36 43.785*** 6.801*** 6.277***

(0,+5) 132 13.40% 13.30% 94:38 32.034*** 6.718*** 5.927***

(0,+10) 132 13.21% 13.18% 92:40 23.203*** 6.512*** 5.578***

(0,+15) 132 14.43% 14.30% 94:38 20.781*** 6.354*** 5.927***

87

Table 22 (continued): Cumulative Abnormal Returns Across Event Windows: Target Within Sector Transactions

Market Model, Equally Weighted Index

Panel C : Target: Acquirer and Target are Insurance Broker or Agent: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 3 40.21% 24.37% 2:1 12.745*** 1.627$ 0.846

(-2,+2) 3 40.85% 25.52% 3:0 10.193*** 1.856* 1.802*

(-5,+5) 3 44.07% 26.85% 3:0 7.172*** 2.114* 1.802*

(-10,+10) 3 40.82% 25.54% 3:0 4.940*** 2.832** 1.802*

(-15,+15) 3 44.96% 30.23% 3:0 4.631*** 5.310*** 1.802*

(-1,0) 3 15.58% 16.51% 2:1 10.540*** 1.023 0.646

(-2,0) 3 16.65% 17.99% 2:1 9.408** 1.169 0.646

(-5,0) 3 19.06% 18.31% 3:0 6.719*** 1.281 1.802*

(-10,0) 3 15.19% 17.38% 2:1 4.721*** 1.391$ 0.646

(-15,0) 3 19.04% 22.56% 3:0 4.871*** 1.940* 1.802*

(0,+1) 3 36.99% 20.07% 2:1 12.884*** 1.053 0.646

(0,+2) 3 36.56% 19.75% 2:1 10.247*** 1.022 0.646

(0,+5) 3 37.37% 20.76% 2:1 7.527*** 1.132 0.646

(0,+10) 3 37.99% 20.38% 2:1 5.451*** 1.102 0.646

(0,+15) 3 38.29% 19.89% 2:1 4.376*** 1.011 0.646

Panel D: Target: Acquirer and Target are Insurance Companies Other than life insurers: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 27 11.92% 11.58% 21:6 14.912*** 4.010*** 3.466***

(-2,+2) 27 10.71% 11.30% 23:4 11.159*** 3.879*** 4.241***

(-5,+5) 27 10.72% 12.06% 20:7 8.087*** 3.995*** 3.079**

(-10,+10) 27 10.42% 12.80% 18:9 6.077*** 4.293*** 2.305*

(-15,+15) 27 9.32% 13.59% 20:7 5.460*** 4.646*** 3.079**

(-1,0) 27 8.91% 8.00% 19:8 12.303*** 2.811** 2.692**

(-2,0) 27 7.94% 7.80% 18:9 9.738*** 2.736** 2.305*

(-5,0) 27 8.69% 9.08% 21:6 8.057** 3.161*** 3.466***

(-10,0) 27 10.17% 10.91% 20:7 7.131*** 3.671*** 3.079**

(-15,0) 27 9.72% 11.62% 20:7 6.673*** 3.893*** 3.079**

(0,+1) 27 12.36% 12.23% 22:5 19.394*** 4.247*** 3.853***

(0,+2) 27 12.12% 12.15% 22:5 15.623*** 3.924*** 3.466***

(0,+5) 27 11.38% 11.63% 21:6 10.790*** 3.924*** 3.466***

(0,+10) 27 9.59% 10.53% 19:8 7.075*** 3.569*** 2.692**

(0,+15) 27 8.95% 10.62% 21:6 5.821*** 3.873*** 3.466***

88

Table 22 (continued): Cumulative Abnormal Returns Across Event Windows: Target Within Sector Transactions

Market Model, Equally Weighted Index

Panel E: Target: Acquirer and Target are Life Insurers: All Years 1990-2006

Days N Mean CAAR Precision Weighted CAAR

Positive: Negative

Patel Z SCS Z Generalized Sign Z

(-1,+1) 73 13.11% 12.37% 50:23 34.929*** 4.715*** 4.098***

(-2,+2) 73 14.87% 14.05% 52:21 30.585*** 5.224*** 4.569***

(-5,+5) 73 16.50% 15.87% 53:20 23.166*** 5.355*** 4.805***

(-10,+10) 73 18.10% 17.38% 53:20 17.966*** 5.593*** 4.805***

(-15,+15) 73 21.07% 19.43% 54:19 16.473*** 5.496*** 5.040***

(-1,0) 73 8.37% 9.12% 44:29 31.543*** 3.701*** 2.686**

(-2,0) 73 9.37% 9.97% 53:20 28.189*** 4.078*** 4.805***

(-5,0) 73 10.14% 10.91% 49:24 21.846*** 4.234*** 3.863***

(-10,0) 73 11.58% 12.38% 52:21 18.032*** 4.636*** 4.569***

(-15,0) 73 12.14% 12.79% 53:20 15.441*** 4.703*** 4.805***

(0,+1) 73 11.93% 11.28% 48:27 39.307*** 4.36*** 3.157***

(0,+2) 73 12.69% 12.12% 48:25 34.265*** 4.452*** 3.627***

(0,+5) 73 13.55% 12.99% 48:25 25.854*** 4.623*** 3.627***

(0,+10) 73 13.72% 13.01% 47:26 19.004*** 4.527*** 3.392***

(0,+15) 73 16.12% 14.67% 49:”4 17.793*** 4.492*** 3.863***

***Significant at 0.1% level, **Significant at 1% level, *Significant at 5% level, $Significant at 10% level Key: CAAR = cumulative average abnormal return, SCS Z = standardized cross-sectional Z score, Generalized sign Z = non-parametric test statistic. Note: This table reports results for all transactions reported in the SDC Database for which corresponding Datastream stock returns exist, where the transaction resulted in a change in control. Results are for the entire sample period,

89

Table 23

Descriptive Statistics

This table summarises the results of further analysis of the sample of acquiring firms. To be included in the sample, acquiring firms had to be continuously listed on the Compustat database, and relevant stock price, market, financial and credit rating data had to be available. Data relates to over the study period 1 June 2001 to 30 April 2006. Deal value is the average deal values for all deals undertaken in study period. Beta is based on 60 month observations over the study period. Tobin’s Q is defined as the relation of total market value of assets divided by replacement cost of assets. Market value of equity is the number of outstanding shares on issue multiplied by the adjusted share price of common stock averaged over the period. Leverage is defined as total long term debt divided by common shareholders equity. The EBITDA is defined as earnings before interest, taxes, amortization and depreciation and is averaged over the period. The Risk Capital% is defined as the smallest amount that can be invested to insure the net assets of the firm, as a percent of total shareholders equity. Following Merton and Perold (1993, 242), risk capital is approximated by 0.4 x the gross assets (invested at a risk free rate) x the volatility of percentage changes in the ratio of gross assets to long-term liabilities.

Entire Sample (n =166) North America (n=86) Europe (n = 54) Asia-Pacific (n=9) UK (n = 15)

Mean Standard Deviation

Mean Standard Deviation

Mean Standard Deviation

Mean Standard Deviation

Mean Standard Deviation

1 day post takeover return 0.220 2.733 0.005 0.029 0.659 4.761 0.009 0.033 -0.008 0.018

Deal value 528.259 31.013 570.617 1683.912 534.33 1008.21 107.39 134.843 516.060 1545.605

Beta 1.416 4.359 0.850 1.986 0.744 3.121 12.208 11.525 0.560 2.265

Tobin’s Q 3.341 10.042 4.299 13.356 2.369 3.984 0.939 0.768 2.778 4.140

Market Value of Equity 26149 115624 12338 33007 14187.08 21989.49 250667 432847 6982.38 11122.63

Leverage 2.051 5.077 2.237 6.749 2.234 2.239 0.577 1.430 1.209 0.887

EBITDA/Equity 21.7% 0.568 28.02% 0.683 22.11% 0.164 7.55% 0.061 18.96% 0.178

Risk Capital% 1.828 8.652 1.038 2.592 2.001 6.914 0.411 0.695 1.918 3.216

% Insurance acquirers 54.82% 0.498 50.00% 0.502 59.26% 0.495 33.33% 0.500 86.37% 0.351

% Life insurance acquirer 42.17% 0.496 33.72% 0.475 51.85% 0.504 33.33% 0.500 66.67% 0.487

% Insurance target 71.08% 0.450 66.28% 0.475 85.19% 0.358 77.78% 0.440 53.33% 0.516

% Life insurance target 47.00% 0.500 32.56% 0.471 75.93% 0.431 44.44% 0.527 33.33% 0.488

% Agent target 24.70% 0.434 26.74% 0.445 16.67% 0.376 44.44% 0.527 33.33% 0.488

No. of takeovers in period 2.774 4.410 2.634 4.452 3.296 4.729 1.111 0.781 2.667 3.792

% common industry of acquirer and target

56.63% 0.520 53.49% 0.502 74.07% 0.520 22.22% 0.441 40.00% 0.507

% cross-border takeovers 28.31% 0.453 17.44% 0.381 40.74% 0.495 22.22% 0.441 53.33% 0.516

90

Table 24

Regression – Deal Value

This table summarises the results of further analysis of the sample of acquiring firms. To be included in the sample, acquiring firms had to be continuously listed on the Compustat database, and relevant stock price, market, financial and credit rating data had to be available. Data relates to over the study period 1 June 2001 to 30 April 2006. Deal value is the average deal values for all deals undertaken in study period. Beta is based on 60 month observations over the study period. Tobin’s Q is defined as the relation of total market value of assets divided by replacement cost of assets. Market value of equity is the number of outstanding shares on issue multiplied by the adjusted share price of common stock averaged over the period. Leverage is defined as total long term debt divided by common shareholders equity. The EBITDA is defined as earnings before interest, taxes, amortization and depreciation and is averaged over the period. The Risk Capital% is defined as the smallest amount that can be invested to insure the net assets of the firm, as a percent of total shareholders equity. Following Merton and Perold (1993, 242), risk capital is approximated by 0.4 x the gross assets (invested at a risk free rate) x the volatility of percentage changes in the ratio of gross assets to long-term liabilities.

Entire Sample (n =166) North America (n=86) Europe (n = 54) Asia-Pacific (n=9) UK (n = 15)

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

Beta 17.225 27.529 13.208 22.603 -0.801 -17.510 -2.004 652.390*** 896.247**

Tobin’s Q 63.101*** 58.339*** 21.110 30.671 160.634*** 158.801*** -11.598 -44.314 -182.103

Market Value of Equity 0.000 -0.001 0.014 0.013 0.005 0.006 -0.000*** -0.011 0.080

Leverage 3.894 -17.088 169.614 291.698 -34.321 -79.533 3.849 -35.339 553.312

EBITDA/Equity 48.999 48.666 -883.246 -1493.077 1463.196*** 718.959 2072.445* 553.921 1850.927

Risk Capital% -4.981 -4.889 -30.588 -28.513 -23.457 -8.254 -88.752 11.131 23.207

% Insurance acquirers 400.992 97.323 -82.528 354.805

% Life insurance acquirer -246.053 727.631 -412.936 -992.255

% Insurance target 63.807 -51.138 -415.391 264.124

% Life insurance target 220.726 -146.350 259.716 -2230.515

% Agent target -23.621 180.980 -53.815 525.005

No. of takeovers in period -19.869 1.365 -20.396 147.924

% common industry of acquirer and target

293.006 -716.586 562.228** -3801.64*

% cross-border takeovers 593.992** 703.216 441.942* 1798.466*

Constant 292.565** -197.957 303.524 243.362 -118.618 11.203 -22.424 232.420 -12.506

R2 0.176 0.225 0.179 0.157 0.519 0.547 0.469 0.884 0.993

Note: the dependent variable is deal value in USDM.***Significant at 1% level** Significant at 5% level* Significant at 10% level.

91

Table 25

Regression – Abnormal Returns

This table summarises the results of further analysis of the sample of acquiring firms. To be included in the sample, acquiring firms had to be continuously listed on the Compustat database, and relevant stock price, market, financial and credit rating data had to be available. Data relates to over the study period 1 June 2001 to 30 April 2006. Deal value is the average deal values for all deals undertaken in study period. Beta is based on 60 month observations over the study period. Tobin’s Q is defined as the relation of total market value of assets divided by replacement cost of assets. Market value of equity is the number of outstanding shares on issue multiplied by the adjusted share price of common stock averaged over the period. Leverage is defined as total long term debt divided by common shareholders equity. The EBITDA is defined as earnings before interest, taxes, amortization and depreciation and is averaged over the period. The Risk Capital% is defined as the smallest amount that can be invested to insure the net assets of the firm, as a percent of total shareholders equity. Following Merton and Perold (1993, 242), risk capital is approximated by 0.4 x the gross assets (invested at a risk free rate) x the volatility of percentage changes in the ratio of gross assets to long-term liabilities.

Entire Sample (n =166) North America (n=86) Europe (n = 54) Asia-Pacific (n=9) UK (n = 15)

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

Beta -0.006 -0.002 0.001 -0.001 0.009 0.051 13.208 -0.003* -0.009

Tobin’s Q -0.005 -0.005 -0.004 0.001 0.033 -0.031 21.110 0.002 0.008

Market Value of Equity -0.001 -0.002 -0.004 0.001 -0.007 -0.003 0.014 -0.006 -0.035

Leverage -0.029 -0.053 -0.003 0.001 -0.087 -0.020 169.514 -0.004 -0.029

EBITDA/Equity 0.054 0.175 0.015 -0.001 -10.580** -21.559*** -883.245 -0.006 -0.016

Risk Capital% 0.033 0.023 -0.005 0.001 0.302*** 0.484*** -30.588 -0.003* -0.001

% Insurance acquirers 0.175 0.007 -0.495 0.001

% Life insurance acquirer -0.271 -0.004 0.120 0.024

% Insurance target -0.803 0.006 1.697 0.020

% Life insurance target 0.824 0.009 -0.095 0.056

% Agent target 1.119* -0.012 1.523 -0.044

No. of takeovers in period -0.100* 0.000 0.021 -0.002

% common industry of acquirer and target

1.439*** -0.002 6.192*** 0.091

% cross-border takeovers -0.542 -0.025 -0.150 -0.057

Constant 0.226 -0.212 -1.590 0.006 2.553** -0.440 303.524 0.003 0.001

R2 0.012 0.026 0.017 0.127 0.051 0.379 0.179 0.288 0.470

Note: the dependent variable is abnormal stock returns over one day after the takeover.***Significant at 1% level** Significant at 5% level* Significant at 10% level.