College of Business Administration

University of Rhode Island

2008/2009 No. 6

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Office of the DeanCollege of Business AdministrationBallentine Hall7 Lippitt RoadKingston, RI 02881401-874-2337www.cba.uri.edu

William A. Orme

Xuanjuan Chen, Helen Doerpinghaus, and Tong Yu

Franchise Value and Performance of Property Liability Insurance Firms

  1

Franchise Value and Performance of Property Liability Insurance Firms

Xuanjuan Chen, Helen Doerpinghaus, Tong Yu*

PRELIMINARY DRAFT

 

 

January 2009

Abstract

Insurance firms invest significant resources in developing franchise value, including brand royalty, business networking, and underwriting and claim specialty. Greater franchise value increases insurer ability to charge higher loadings and expand their client bases, thus increasing insurer profitability. On the other hand, to preserve franchise value, high franchise value firms may bypass risky but profitable projects, potentially leading to poorer performance. We develop an analytical framework that jointly considers these two forces of franchise value. In the empirical analyses, we quantify franchise value using company ratings assigned by the A. M. Best Company after controlling for tangible firm characteristics. We find that insurers with greater franchise value have better operating performance three years after the initial sorting year. Further, public insurers with higher franchise value earn a greater stock return in the following year and our results are robust in different stages of insurance underwriting cycles. This suggests that franchise value is an important resource for insurance and financial service firms that investors tend to under-react.

____________________________ * Chen is from College of Business, Kansas State University, email: jchen@ksu.edu. Doerpinghaus is from Moore School of Business, University of South Carolina, email: doerp@moore.sc.edu. Yu is from College of Business and Administration, University of Rhode Island, email: tongyu@uri.edu. All errors are our own. 

  2

Franchise Value and Performance of Property Liability Insurance Firms 1. Introduction

Different from industrial firms delivering consumable products, products delivered by insurance

companies and other financial intermediaries often are a contract or service and its quality is largely

determined by customers’ perception and recognition. Brand loyalty and word-of-mouth reputation are

extremely important for a financial service firm. In the insurance industry, intangible assets such as brand

loyalty, personnel, renewable business, and expertise in claim service and underwriting are generally

recognized as franchise or charter value. Despite its practical importance, there is no direct examination

on the role of franchise value on the performance of insurance firms. A major hurdle on the analysis is the

lack of an effective measure for franchise value. The purpose of this study is to fill this gap. We first

develop a framework to identify the effect of franchise value. Then with a proposed measure of franchise

value, we examine the role of franchise value in determining the performance of insurance companies, i.e.,

operating performance for all insurers and stock performance for publicly-listed insurance companies.

How does insurance firms’ franchise value affect their performance?1 A general perception is that

insurers with greater franchise value would have a competitive edge. In a recent study, Epermanis and

Harrington (2006) find insurer premiums decline in the year and the year after they experience rating

downgrades. Their results can be generally interpreted as the evidence to the effect of franchise value on

insurer profitability. Epermanis and Harrington nonetheless recognize that various factors, such as the

presence of insurance guaranty funds, monitoring and search costs faced by policyholders and regulators,

all present offsetting forces to preserve franchise value. In other words, when policyholders are not highly

risk sensitive, greater risk taking leads to better operating performance even though it lowers franchise

value.

Babbel and Merrill (2005) break down market value of insurer equity into franchise value, market

value of tangible assets, as well as other factors. They note that franchise value is dependent on firm

insolvency risk since solvency means a greater likelihood of capturing all of the economic rents arising

from reputation, renewal business, the distribution network, and so on. They show a non-monotonic

relation between market value of insurers and firm risk, and find that firm value increases with insolvency

risk beyond some moderate level. In their framework, the relation between firm equity value and

franchise value is non-monotonic as well. Also note that Babbel and Merrill’s framework is based on

insurer market value and differs from our research focus on operating and stock return performance.

                                                            1 Despite that prior studies have discussed the importance of intangible assets (or franchise value) on firm value (see Aaker 2001; Chan, Lakonishok and Sougiannis 2001; Barth 1998; Lehmann 2004, for example), the focus of these studies is predominantly on non-financial service. As we lay out later in this study, intangible asset assets take a quite different form in financial service industries than in non-financial industries. As a result, we expect franchise value could affect firm performance differently in the financial service industry.

  3

In this study, we explore the role of franchise value on the operating performance and stock

performance of property liability insurance companies. We present a model to demonstrate the effects of

franchise value on insurers’ operating and stock performance. Regarding operating performance, greater

franchise value increases the insurers’ ability to charge a greater loading and expand their client base thus

increases insurer profitability. On the other hand, to preserve franchise value, high franchise value

insurers may bypass profitable but risky projects, thus having lower profitability. Consequently, the

effect of franchise value on insurers’ operating performance is determined by the relative magnitude of

the two forces. Regarding stock performance, the efficient market theory suggests franchise value should

have no impact on the insurer future stock returns no matter its role on operating performance. The reason

is that stock price has already valued franchise value and its associated risk. However, as insurers’

franchise value is not reported in firms' financial statements. The lack of accounting information generally

complicates the task of equity valuation -- investors may under- (over-) estimate franchise value thus

under- (over-) estimate its effect on firm equity value. Our model shows that if investors under-react to

the effect of franchise value to firm cash flow growth rate (either under-estimating franchise value or

under-estimating the effect of franchise value) and over-estimate the cost of capital for high-franchise

value firms, future stock return would be positively related to franchise value. On the other hand, if

investors over-react to the effect of franchise value to firm cash flow growth rate and under-estimate the

cost of capital for high-franchise value firms, future stock return would be inversely related to franchise

value.

The examination of the effect of franchise value is however challenging as franchise value is

rarely recognized in financial statements. Lev and Zarowin (1999) and others argue that quantifying

intangibles is where the current accounting system fails most seriously in reflecting enterprise value and

performance. Several proxies are used to evaluate franchise value (intangible assets): Tobin’s q (e.g.,

Keeley 1990; Staking and Babbel 1995; Gan 2004) or accounting entries such as research and

development expenses or advertising expenses (e.g., Chan, Lakonishok and Sougiannis 2001). For our

study use of these two proxies is not appropriate for evaluating insurance franchise value since the

majority of insurance firms are privately held and firm market value and other accounting variables are

not publicly available information.2

We construct two measures of franchise value based on A.M. Best’s ratings. Using Best’s ratings

to proxy for franchise value is appealing, however suffers from the critics that ratings simultaneously

capture the effects of tangible and intangible assets. Two insurance companies with the identical rating

may be different in their size, leverage, thus their franchise value may not equate each other. In order to

                                                            2 The nature of the insurance business is substantially different from that of industrial firms and even if these accounting variables were available using them would have serious limitations.

  4

disentangle franchise value we develop two measures here. The first is the benchmark-adjusted rating.

Specifically, we break down all property-liability insurers into 100 (2 x 2 x 5 x 5) groups based on group

affiliation (standalone versus group affiliated), ownership (stocks versus mutual), firm size quintile, and

leverage quintile. For each of the 100 portfolios, we calculate the equal-weighted average rating. This

rating is viewed as the benchmark rating, the average rating of an insurer given its organization form, size,

and financial risk. The difference between the rating of an individual insurer and the benchmark rating of

its group is the benchmark-adjusted rating, or franchise value. Secondly, we follow Yu, Lin,

Oppenheimer, and Chen (2008) and use the residual term in a regression of the Best rating on firm

characteristics as a measure of the insurer’s franchise value. An advantage of this second approach is that

we simultaneously consider multiple characteristics as tangible assets. A limitation of this measure is that

it implicitly assumes a linear relation between rating and all the rating determinants. We utilize both

measures to evaluate franchise value throughout the analysis.

After deriving measures of insurer franchise value, we test the relation between firm franchise

value and firm operating and stock performance for property liability insurers over the period of 1986-

2007. We find that insurers’ franchise value is positively related to their future operating performance.

When ranking insurers into deciles based on their benchmark-adjusted measure we find that the average

return on assets for the D1 group in the subsequent year is 1.20% while that of the D10 group is 3.67%.

The difference (2.47%) is significant at the 1% level. The same performance pattern holds in the

subsequent five years. Using regression based measure yields similar results.

Could this result be explained by other firm characteristics, such as firm size, ownership, and

leverage? We address this concern by regressing firm performance on franchise value, controlling for

alternative firm characteristics. Again we see a significant positive relation between franchise value and

firm operating performance. Our analysis also shows that franchise value play a similar role in different

stages of the insurance underwriting cycles.

We further examine the effect of insurance franchise value using information of publicly traded

property-liability insurance companies. Chan, Lakonishok and Sougiannis (2001) find that the research

and development expenditures (i.e., a measure of franchise value for industrial firms) positively predict

future stock performance. They attribute this finding to investors’ failure to incorporate intangible asset

information into stock prices. We find a similar stock return result in the insurance industry. When we

break insurers into three groups (based on the benchmark intangible asset measure) and control for firm

characteristics we find that the average annual return for the bottom IA group is 6.99% while that of the

top IA is 14.71%. This yields a return spread of 7.73% per year (t=1.85). Yet we find that risk-adjusted

alphas (see Fama-French (1993) and Carhart (1997)) for high-intangible asset firms no longer

significantly outperform those of low-intangible asset firms. One possible explanation for this is that

  5

insurer franchise value are correcting for missing firm characteristics; this leads to better stock

performance before risk adjustment for those high intangible asset firms.

The remainder of the article is organized as follows: Section 2 discusses the model and Section 3

describes data and empirical methodology. Section 4 presents the empirical results and Section 5 presents

our conclusions.

2. The Model

2.1 Franchise Effect on insurers’ profitability

Suppose an insurer collects premiums (pt) in the beginning of year t and pays out all the losses for

the policies written in year t in the year end. The expected claim payment made by the insurer is ct. The

interest rate is set to be zero for simplicity. The net income of an insurer, tπ , equals to the difference of

collected premiums and present value of expected claim payment:

ttt cp −=π (1)

Also assume that, in year t, insurer assets are made up of franchise value (intangible assets) and

tangible assets:

ttt TFA += (2)

Insurance policies can be viewed as risky debt issued by insurance companies to policyholders

(e.g., Cummins, 1991). Here we assume all the losses related to policies written in year t are occurred and

paid at the end of year t.3 The expected amount is Lt. Given insurance companies hold limited liabilities,

tt Lc = , if tt LA > , i.e., the insurer stays solvent

tt Ac = if tt LA ≤ . (3)

Insurance premiums charged by the insurer are the expected losses, Lt, for all policies written in

year t, plus loading λ*lt minus a default discount θ*dt. θ is a number between 0 and 1, measuring

policyholders’ risk sensitivity. We have the below expression for pt,

ttt dLp θλ −+= *)1( ( )1,0(∈θ ) (4)

The default discount can be denoted as the following:

dsALd tl

ttt ∫ −=0

)( (5)

Inserting (3), (4) and (5) in (1) and taking the expectation, we have:

)()(*)1()(00 ∫∫∫

∞+−−−+=

t

tt

l t

l

t

L

tttt daLdaAdsALLExp θλπ

                                                            3 We greatly simplify the analysis when assuming policies are written and claims are paid out in the same year.

  6

∫ −−+= tl

ttt daALL0

)()1(* θλ (6)

The first term represents the insurer’s profit originating from its loading charges. λ is positively

correlated with the insurer’s franchise value, Ft. Insurers with greater franchise value can charge higher

loading, thus franchise value positively affects insurers’ profitability. Also, as greater franchise value

helps insurers expand their client base, insurers with greater franchise value have higher Lt.

The second term on the right-hand side of Equation (6) is the value of a put option with an

exercise price of Lt. Standard option theory predicts that the value of a put option increases in the

volatility of underlying assets. However, taking more risk lowers the insurer’s chance of harvesting the

full economic rents of franchise value. As a result, insurers with greater franchise value typically avoid

projects involving great insolvency risk and the value of the put option is consequently low. Thus, the

higher franchise value will reduce the put option value. Taken the two terms together, it seems that the

role of franchise value on insurer profitability is uncertain, depending on the relative effects on the two

terms. Yet, note that the impact of the default option on insurer profitability depends on policyholders’

risk sensitivity. If policyholders are highly sensitive to insolvency risk (θ is close 1), then the effect of the

default option on profitability is minimal and we propose a positive relation between franchise value and

insurer operating performance. Alternatively, when policyholders are numb to insolvency risk, the effect

of the default risk on insurers’ profitable would be potentially large and the relationship between

franchise value and profitability is not clear.

Taken together, we don’t expect a monotonic relation between franchise value and insurers’

profitability. On the one hand, greater franchise value increases the insurers’ ability to charge higher

loading and expand their client base thus increase profitability. On the other hand, greater franchise value

shrinks insurers’ risk appetite and high franchise value insurers may bypass profitable but risky projects.

2.2 Franchise Value Effect on Stock Returns

Suppose the cash flow to an insurer is CFt+i (i=0, 1, 2, …) and the firm is sustained indefinitely.

All the revenues and costs are cash based and there is no fixed capital investment involved in the

operation. The insurer’s cash flow equates its net income.

ititCF ++ = π (i=0, 1, 2, …) (7)

The firm’s income in year t is πt and the income growth rate in year t is gt. The income growth

rate becomes gt+1 afterward. The discount rate in year t (i.e., the firm’s weighted average cost of capital of

the insurer) for the cash flow is rt.

If investors correctly forecast the insurer’s future cash flows, franchise value would have no

effect on the insurer’s stock returns in the subsequent year. However, as insurers’ franchise value is not

  7

reported in firms' financial statements. The lack of accounting information generally complicates the task

of equity valuation -- investors may either under-react to the effect of franchise value (e.g., Chan,

Lakonishok and Sougiannis 2001) or over-react to firm tangible value (Daniel and Titman, 2001).

Suppose that in year t, the representative investors incorrectly value the firm and expect that cash

flow grows by gt indefinitely. Their belief of the discount rate is rt. Market value to equityholders is

tt

t

tt

ttt grgr

gV−

=−+

= +1)1( ππ ( tt gr > ) (8)

In year t+1, investors update their expectation on the insurer’s cash flow growth rate and the

discount rate to gt+1 and rt+1. Stock price of the insurer in year t+1 is:

11

111

)1(

++

+++ −

+=

tt

ttt gr

gV π (9)

The insurer’s stock return over year t+1 is:

1))(1(Re11

1111 −−

−+

=+−

=++

++++ tt

tt

t

t

tttt gr

grr

VVVt π

(10)

It is important to note that rt+1 and gt+1 are functions of firm franchise value, F. We have

())()((

)1)(()1)((Re2

11

1'

11'

11

FgFrgFrrFg

Ft

tt

ttttt

++

+++++

−+−+

=∂

∂ )tt gr − (11)

Equation (11) yields various cases described below:

Case 1: If )('1 Fgt+ =0 and )('

1 Frt+ =0, i.e., no mispricing in year 0, we have F

tt

∂∂ +1Re

=0.

The above condition shows that franchise value would have no effect on the insurer’s stock

returns in the subsequent year.

Case 2: Investors under-react to the effect of franchise value on income growth rate, i.e.,

)('1 Fgt+ >0. Also the discount rate decreases in insurers’ franchise value, i.e., )('

1 Frt+ <0. We have

Ftt

∂∂ +1Re

>0.

The condition )('1 Fgt+ >0 requires the firm income growth rate to increase in franchise value.

When stating “under-reaction”, we implicitly assume that the insurer’s franchise value increases over time,

but investors are unaware of this information initially.

  8

Case 3: Investors under-react to the effect of franchise value on income growth rate, that is

)('1 Fgt+ >0 but the discount rate increases in franchise value )('

1 Frt+ >0. F

tt

∂∂ +1Re

<0 if

)('1 Fgt+ < )

11(*)(

1

1'1

+

++ +

+

t

tt r

gFr .

This differs from case 2 in that we assume greater franchise value insurers take more risks (thus

the greater discount rate). When firms are extremely risk prone (e.g., AIG’s investments in credit default

swaps), they experience long run poor stock performance.

Case 4: Investors over-react to the effect of franchise value on income growth rate, that is

)('1 Fgt+ <0. Higher franchise value insurers are more risk prone, thus )('

1 Frt+ >0. Then, F

tt

∂∂ +1Re

<0.

If in fact investors over-react to the effect of franchise value on income growth rate and insurers are risk

prone, we expect to see high franchise value firms with poor long run performance.

3. Data and Measures

3.1 Data and Sample

We combine the A.M. Best rating database with the National Association of Insurance

Commissioners (NAIC) database to obtain our full sample of all property-liability insurers. The A.M.

Best rating database provides insurers’ ratings and the NAIC database for P&L insurance companies

provides insurers’ financial statement data and other firm information like organization form and group

affiliations. We use the NAIC codes provided by the Best’s Rating data to link these two datasets. These

codes are available for nearly 85 percent of the companies in the NAIC database. When identifying

sample insurers, we employ the following criteria: (1) availability in the A.M. Best rating database, (2)

available financial statement data, (3) total book value is greater than zero, and (4) premium earned is

non-negative. Our sample period is from 1985 to 2005. We consider this the “NAIC-BEST sample” of

insurance firms.

Within the full sample, we further identify a public insurer sample using the CRSP database. We

obtain all public property & liability firms in the CRSP database using the SIC code of 6330 and 6331. As

there is no common identifier between CRSP data and the NAIC sample, we manually match them by

name. The CRSP data are reported at the holding level while the NAIC data are provided at the subsidiary

level with identified group names. If a firm in the NAIC database belongs to an insurance group, we

aggregate the firm level data into the group level in the NAIC database and then match NAIC group

  9

names with CRSP firms. If a firm in the NAIC database is a stand-alone firm, we match it with the CRSP

sample by company name. We consider this the “CRSP sample” of insurance firms.

3.2 Measuring Franchise value

In concept, the intangible asset of an insurer should be the difference between the market value of

the firm and the market value of its tangible value. Our measures of franchise value capture this idea.

To be specific, we derive intangible asset measures from the Best Rating. The Best Company

rates insurers from A++ (superior) to F (in liquidation). Consistent with prior studies like Colquitt,

Sommer, and Godwin (1999), we assign a numerical number to each of the rating ranging from RATING

= 0 for the Best’s Rating of E (under regulatory supervision), F (liquidation), and S (suspended), to

RATING = 13 for the Best’s Rating of A++.4 If multiple ratings are reported for an insurer in a year, we

use the last reported rating in the analysis.

We use two methods to estimate the value of franchise value on the firm rating. First, we use the

benchmark-adjusted ratings as a proxy for the intangible component of the Best rating. To do this, we

control for the effect of four tangible firm characteristics, namely group affiliation, ownership, size and

leverage, on the Best rating and use the benchmark-adjusted rating as the first intangible asset measure. In

each year, we sequentially sort insurers into 100 (2 x 2 x 5 x 5) groups based on group affiliation

(standalone versus group affiliated), ownership (stocks versus mutual), firm size quintile, and leverage

quintile. For each of the 100 portfolios, we calculate the equal-weighted average of their ratings. The

difference between the rating of an individual insurer and the rating of its benchmark group is the

benchmark-adjusted rating. We refer to this intangible asset measure as IA1.

Alternatively, we perform regression analysis and use the regression residual term as the proxy

for franchise value. We follow Pottier and Sommer (1999) to include a set of firm characteristics that may

affect the Best’s rating. We perform an annual cross-sectional regression of insurers’ Best’s ratings with

these firm characteristics and use the residual terms to measure insurers’ franchise value:

RATINGi,t = α0 +∑=

−

10

11,,

jtijj Xβ + εi,t (12)

where 1,, −tijX (j=1, 2, …, 10) are tangible characteristics potentially affecting the firm rating measured at

the end of year t-1. The residuals, εi,t, measure the franchise value of insurance companies. Further details

of the explanatory variables are covered in the Appendix. We refer to this measure as IA2. Relative to

IA1, an obvious advantage of this measure is that it simultaneously controls for more tangible

                                                            4 Specifically, the numerical number for each of the A.M. Best ratings is as below: 0(E, F and S), 1 (D), 2(C-), 3(C), 4(C+), 5(C++), 6(B-), 7(B), 8(B+), 9(B++), 10(A-), 11(A), 12(A+), and 13(A++).

  10

components of the firm rating when estimating the franchise value. A disadvantage is that it assumes a

linear relation between each of the included explanatory variables and the rating.

3.3 Measuring Operating and Stock Performance

We consider three operating performance measures. The first is the combined ratio (CR), which is

the sum of the loss ratio and the expense ratio. We measure the loss ratio as losses incurred divided by

premium earned and the expense ratio as the sum of loss expenses incurred and other underwriting

expenses incurred divided by premiums earned. The combined ratio reflects insurer underwriting

profitability. The second is return on assets (ROA), calculated as net income scaled by beginning period

total assets. The third is return on equity (ROE), calculated as net income scaled by beginning period total

equity. For all of these measures, there are firms with extremely large or small numbers. We remove the

top 1% and bottom 1% observations in each year for each measure to control for outliers.

With the CRSP insurer sample, we can estimate stock performance of each public insurance

company. Using franchise value evaluated at the end of year t-1, we look at monthly stock returns from

July in year t to June in year t+1. In addition to raw stock returns, we use the Fama-French (1993) three-

factor alpha and Carhart (1994) alpha to estimate a firm’s risk-adjusted stock performance. Alphas are

estimated using the following two steps. First, in each month, we regress monthly stock return on the

corresponding factor models and obtain coefficients on the factors using data during the past 12 months.

ttttftt HMLSMBRMRFRR εβββα ++++=− 321

^ ˆˆˆ (13)

tttttftt UMDHMLSMBRMRFRR εββββα +++++=− 4

^

321

^ ˆˆˆ (14)

where Rt is the stock return in month t. Rft is the risk-free rate proxied by the yield on Treasury bills with

one-month maturity. RMRFt is the market return (CRSP value-weighted index return) in excess of the

risk free rate; SMBt, HMLt, UMDt are size, book-to-market and momentum factors.5 After we obtain the

coefficients, in each month, we estimate the risk-adjusted return using the difference between the actual

stock return and the predicted return using estimated coefficients and the three-factors in the current

month:

)ˆˆˆ( 321

^3

tttt HMLSMBRMRFR βββα ++−= (15)

)ˆˆˆˆ( 4321

^4

ttttt UMDHMLSMBRMRFR ββββα +++−= (16)

                                                            5 Data on RMRF, SMB, HML, and UMD factors are from Ken French’s website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

  11

Table 1, Panel A presents the distribution of A. M. Best ratings for firms having NAIC financial

data and rating information. We get the number of firms for each rating in each year and then compute the

time series average of these numbers. The majority of insurers have a rating better than B. Twenty-four

firms have a rating below B-, out of 1252 firms with matched financial and rating data.

Table 1, Panel B provides the statistics of our intangible rating and operating performance

measures for the NAIC-BEST sample and the CRSP sample. Within the NAIC-BEST sample, the average

raw A.M. Best rating for an insurer is 10.36, corresponding to an A- to A Best rating. The mean IA1 is

0.06 and the mean IA2 is 0.07, both being close to zero. The average ROA is 2.86%, the average ROE is

6.03% and the average of CR is 106.99%. The results on the CRSP insurance sample show that public

insurers have slightly higher rating and better operating performance.

Table 1, Panel C reports the correlations among these variables for the NAIC-BEST sample. The

correlation between the raw rating and IA1 is 0.78 and the correlation between the raw rating and IA2 is

even higher, 0.85. These high correlations between franchise value and ratings may suggest that franchise

value are a more important determinant of insurer ratings than tangible assets are. Panel C also shows that

ROA and ROE are highly correlated, while CR is inversely correlated with ROA and ROE, i.e., poor

performing firms typically have high combined ratios. Both intangible asset measures are positively

correlated with operating performance, measured by ROA and ROE, but inversely correlated with CR.

Panel D reports the correlations among three intangible asset measures within the CRSP sample.

The result suggests that our IA measures and Tobin’s q are positively correlated.6 The correlation between

IA1 and q is 0.18. The evidence indicates that ratings-based measures capture different aspects of

franchise value than does Tobin’s q. It should be noted that the essence of Tobin’s q is to capture the

difference between market value and replacement value of firm assets. Using Tobin’s q to assess

franchise value could be subject to at least the following three criticisms: 1) the market value of firm

assets could be sentiment driven, 2) the market value of firm assets could be affected by non-franchise

value items, such as government subsidies (Gan, 2004)7, and 3) the replacement value of firm assets is not

a good measure of the market value of firm tangible assets. Insurance ratings are made by experts, thus

less sentiment driven and the benchmark-adjustment or regression residual approaches are flexible in

adjusting for tangible assets. As a result, we expect rating-based measures have advantages relative to

Tobin’s q in assessing franchise value.

                                                            6 Tobin’s q is estimated as: q = (MVE + PS + DEBT)/TA, where: MVE (market value of common equity) = Annual closing price x number of common shares outstanding (Compustat item 24 x item 25); PS = Liquidating value of outstanding preferred stocks (item 10); DEBT = Current liabilities (item 4) - Current assets (item 5) + Inventories (item 3) + Long term debt (item 9); and TA = Book value of total assets (item 6). 7

  12

3.4 Measuring Firm Attributes

In addition to franchise value various other firm characteristics play a role in determining

operating performance and stock performance. We construct the following control variables in our

analysis. The first set of variables reflects insurer capitalization. We construct SIZE as the logarithm of

total assets of an insurer. Large firms tend to have greater capacity for natural hedging and consequently

may be able to earn better operating performance. We estimate LEV as the ratio of total liability to total

assets.

The second set of variables covers insurer organizational forms. GROUP is an indicator variable

that equals one for an affiliated firm and zero for standalone firms. STOCK equals one for stock insurers

and zero otherwise. Stock insurers have easier access to new capital and are able to assume higher firm

risk than non-stock insurers. Lamm-Tennant and Starks (1993) find that the underwriting risk of stock

insurers is greater than that of mutual insurers.

Finally, we construct variables capturing insurer business risk. LONGTAIL is net premiums

written (NPW) in long-tail lines of insurance divided by total NPW. Following Sommer (1996) and

Pottier and Sommer (1999), long-tail lines include auto liability, other liability, farmowners/homeowners

/commercial multiple peril, medical malpractice, workers compensation, aircraft, and boiler and

machinery. HERFLINE, measuring line concentration, is the Herfindahl index by line of business. It is

the sum of the squared percentage of net written premiums in each business line to the total written of the

insurer. ∑= 2)/( TPWNPWHERFLINE i where PWi is the net premiums written in line i (i=1,2,…,

and 34),8 and TPW is the insurer’s total net premiums written. The greater the Herfindahl index, the less

diversified a firm is in its business. REINSUR is reinsurance ceded divided by the sum of direct premiums

written and reinsurance assumed.

4. Empirical Results

4.1 Franchise value and Operating Performance

To see the effect of franchise value on operating performance, we sort insurers into deciles based

on their franchise value in year t-1 and see the average operating performance of each decile portfolio in

the subsequent five years, year t, through t+4. Table 2 presents the results, where we label the subsequent

five years as Y1 through Y5 and Y0 for the portfolio formation year t-1.

In Table 2, Panel A, we sort insurers into deciles based on their benchmark-adjusted franchise

value, IA1. Insurers’ operating performance generally increases as franchise value rank increases. For

example, in Y1, ROA for D1 (lowest intangible rating) insurers is 1.07%, while that of the D10 (highest                                                             8 By-line net premiums written data are reported in the “Underwriting and Investment Exhibit”, Part 2B, which categorizes property liability insurance business into 34 lines.

  13

intangible rating) insurers is 3.57%. Their difference of 2.49% is significant at the 1% level (t=4.11). The

result stays unchanged when we use ROE to measure operating performance. Consistent with the result of

these firm profitability measures, we find that high IA firms have lower combined ratios; the average CR

of D1 insurers is 114.94% while that of D10 insurers is 105.02%. The difference is also significant at the

1% level.

In Figure 1, we plot the spreads of ROA and ROE between D10 and D1 insurers for each of our

sample years. We observe positive spreads for all sample years. The ROA spreads are low in the

beginning years and peak around year 1998 to 2000. The ROE spread is more evenly distributed over

time, with the lowest spread in 2000 and the highest in 2001. Overall our evidence suggests that superior

performance of high franchise value is stable over time.

Panel A also shows that the outperformance of D10 insurers decays slowly in the subsequent

years. Measuring insurer operating performance using ROA, we find D10 insurers outperform D1

insurers in ROA by 1.80% in Y2, 1.77% in Y3, 1.57% in Y4, and 1.14% in Y5. These are all significant

at the 1% level. A similar pattern holds for ROE and CR.

Panel A results support our baseline argument that high IA firms have better future performance.

Several caveats remain. First, when estimating IA1, we only consider size, leverage, ownership and group

affiliation to form benchmark groups. It is possible that important tangible components are omitted. Panel

B addresses this concern by using the regression approach in constructing the intangible asset measure,

IA2. We find that, after controlling for a large of tangible firm characteristics, our intangible asset

measure still strongly positively predicts future operating performance, although the magnitude is smaller.

Using ROA as the operating performance measure, D10 insurers outperform D1 by 1.44% (t=3.57) in Y1

and 1.30% in Y5 (t=2.99).

Further, in Table 1, Panel A, we show that insurers are disproportionally distributed across firm

ratings. The majority of insurers have a rating better than B which few insurers have a rating of B or

below B. The uneven distribution of firm rating gives rise to our second concern that our prior result

could be driven by outliers with poor insurance rating. To address this concern, we exclude an insurer in a

year if its rating is B or below. Not tabulated, the results are consistent with those reported in Table 2. We

also exclude insurers having a rating below A- and obtain similar results.

In Table 2, we see insurer operating performance persist over time. A natural expectation is that

such persistence in operating performance is driven by the persistence in insurer franchise value. Figure 2

is consistent with this. Figure 2 shows a transition matrix for D10 and D1 insurers sorted by their

franchise value in Y0. It shows what fraction of the D10/D1 insurers remains in the lowest IA1 decile in

the subsequent years. After one year, 53% of D10 firms remain in the same decile and after five years

33% of D10 firms stay in D10, showing a high percentage for D10 insurers tend to remain in the highest

  14

decile. Panel B reports the transition matrix for D1 insurers. We observe a similar pattern. Figure 2

provides evidence that franchise value are persistent, potentially contributing to the persistent

outperformance of D10 firms in Y0.

Will the positive relation between franchise value and operating performance be explained by

firm characteristics that affect insurer operating performance? We perform Fama-MacBeth (1973) style

regressions, controlling for the effect of other firm characteristics on performance. That is, we regress

insurer performance on lagged year franchise value and control variables in each year, and then compute

the time-series averages of the coefficients. Table 3 presents the results of estimation of the following

regression equation:

PERF i,t= Intercept+α1IAi,t-1 + α2SIZEi,t-1 + α3LEVi,t-1 + α4GROUPi,t-1 + α5STOCKi,t-1

+ α6LONGTAILi,t-1+ α7HERFLINEi,t-1+ α8REi,t-1 + εi,t (17)

The first three columns of Table 3 reports results for IA1 and the next three columns are for IA2

results. In the first column (where ROA is used as the measure of performance) the coefficient on IA1 is

0.006, with a t-statistic of 3.87, suggesting franchise value are significantly positively related to next

year’s performance, controlling for other factors. In the third column, the coefficient on IA1 is -0.10, with

a t-statistic of -2.86. The evidence suggests that insurers with higher intangible asset have much lower

combined ratios. We obtain similar results using IA2.

It should be noted that the relation between operating performance and firm characteristics, such

as SIZE, LEVERAGE, GROUP and STOCK is not stable across different operating performance

measures. The coefficient on leverage is negative and statistically significant when we measure operating

performance using ROA, but loses statistical significance when we measure performance using ROE and

CR.

4.2 The Effect of Insurance Cycle

The property casualty insurance industry is characterized by underwriting cycles. During soft

market conditions premium rates are stable or falling and insurance is readily available, and during hard

markets rates rise, coverage becomes more difficult to find, and insurer profits increase (Venezian, 1985;

Cummins and Outreville, 1987; Harrington and Yu, 2003; and Leng 2006). Our specific interest is to see

if the effect of franchise value differs in different stages of the insurance cycle.

We quantify insurance underwriting cycles in two ways. One is to capture the industry dynamic

using the loss ratio of the property casualty insurance industry. Data on loss ratio is obtained from the

Best Averages and Aggregates database. We classify years with above-than-average loss ratio as a soft

market year and the other years as hard market years. Using this criterion, hard market years include year

  15

1985, 1986, 1989-1992, 1994, and 2000-2002. This is consistent with the market classification used in the

underwriting cycle literature.

The other way that we measure the underwriting cycle is by the change in net premium written

for the industry (ChgNPW). For each firm in the sample if ChgNPWt is less than the median of

ChgNPW during our sample period, it is classified as a soft market year. Otherwise it is a hard market

year. 9

We perform portfolio analysis and present results in Table 4. Panel A reports the results based on

IA1 sorting. In the soft market, the ROA difference between D10 and D1 insurers is 2.47% per year, with

a t-stat of 3.49; the ROE difference is 9.00%, with a t-stat of 6.06; the PM difference is 7.07%, with a t-

stat of 1.99. In the hard market, the ROA difference is 2.30%, which is similar to the result for the soft

market. the result for the hard market is much higher when we use ROE: the ROE difference is 8.62%.

Panel B reports the results based on IA2 sorting and the results are similar. Panel C and D use the

percentage change in NPW to estimate market condition. The results are similar. When all of the evidence

is taken together Table 4 shows that franchise value affect operating performance in both soft and hard

markets. There is no obvious difference in the effect of franchise value on firm performance in hard or

soft markets.

4.3 Franchise value and Stock Performance

We evaluate the effect of franchise value on insurers’ future stock performance in this section. As

there are around 30-50 public insurers in each of our sample years, we sort them into three groups based

on franchise value. Specifically, at the end of year t-1, all CRSP sample firms are sorted into 3 portfolios

based on their intangible asset in year t-1. We then compute annualized raw stock return, three-factor risk-

adjusted returns, and four-factor risk adjusted returns in the following year since July in year t to June in

year t+1. We first compute the cross-sectional average performance in each year and then calculate the

time-series averages. Reported in Table 5, when sorted by IA1, the spread in raw stock return is 7.73%

per year, with a t-stat of 1.85.

Interestingly, the return spread using risk-adjusted returns are indifferent from zero. For example,

the annual spread using three-factor adjusted returns of insurers when sorted by IA1 is 2.04%, with a t-

stat of 0.14. The annual spread using four-factor adjusted returns of insurers when sorted by IA1 is -

4.24%, with a t-stat of -0.89. This result seems to suggest that insurers with high franchise value have

higher systematic risk. Chan, Lakonishok and Sougiannis (2001) find that high R&D firms have higher

stock return volatility. Our result is consistent with their findings. We also plot the stock return spreads

between D10-D1 insurers in each sample year in Figure 2. The figure is consistent with Table 5 results.                                                             9 Under this method, 1986-1986, 1993, 1996, and 2001-2004 are classified as hard market.

  16

Further, we control for other firm characteristics and look at if franchise value still predict future

stock return. We perform the following Fama-MacBeth (1973) regression in each year of stock

performance on lagged franchise value and firm characteristics.

Reti,t= Intercept+ α1IAi,t-1 + α2SIZEi,t-1 + α3LEVi,t-1+ α4GROUPi,t-1 + α5LONGTAILi,t-1

+α6HERFLINEi,t-1+α7REi,t-1+ εi,t (18)

where Ret is measured by annualized stock returns, Fama-French three factor risk-adjusted returns, and

Charhart four factor risk-adjusted returns from July year t to June year t+1.

Table 7 shows the time-series averages of the coefficients of regressions of stock returns, three-

factor adjusted returns, and four-factor adjusted returns using our two franchise value measures. In the

first column where we look at raw return, the coefficient on IA1 is 0.03, with a t-stat of 2.42. This result

suggests that insurers with higher franchise value on average have higher stock performance in the

following year after controlling for cross-sectional difference in firm characteristics including firm size,

organizational form, and business risk. In the second column where we look at three-factor risk adjusted

return, the coefficient on IA1 is 0.01, with a t-stat of 1.05. The association between franchise value and

future stock return disappear after risk-adjustment.

5. Conclusions

In this paper, we explore the impact of franchise value on insurers’ operating and stock

performance. We develop an analytical framework on the role of franchise value in insurer performance.

Two forces are considered: Greater franchise value increases insurer ability to charge higher loadings and

expand their client bases, thus increasing insurer profitability. Alternatively, to preserve franchise value,

high franchise value firms may bypass risky but profitable projects, potentially leading to poorer

performance. As a result, the effect of franchise value on insurers’ operating performance is determined

by the relative magnitude of the two forces. The model also shows that when investors under-react to the

effect of franchise value, future stock return would be positively related to franchise value.

We provide empirical evidence that firm performance is positively correlated with franchise value.

We first quantify franchise value using benchmark adjusted A.M. Best ratings and regression residual

A.M. Best ratings. These two measures take into account the special business nature of insurance firms

and capture the intangible value of an insurer. We find that insurers with higher franchise value have

better operating performance for up to five years after the initial sorting. The significant relationship

between franchise value and operating performance remains strong after controlling for other firm

characteristics. We find that public insurers with higher franchise value tend to earn higher stock returns.

We also find that risk-adjusted alphas of high intangible asset insurers no longer significantly outperform

those of low intangible asset insurers.

  17

It should be noted that our analysis is not limited to the insurance industry. Our framework and

franchise value measures are applicable to other financial service firms where franchise value plays a

significant role in their operations.

An interesting extension of our analysis is to see if franchise value becomes more or less

important as the value of tangible assets change and solvency issues arise. Exploration of this issue would

give us a better understanding of the role of franchise value in firm performance.

  18

Appendix: Constructing Regression Residual Intangible Asset Measure

Following Pottier and Sommer (1999), we perform cross-sectional regression analysis using insurer Best’s ratings using the following firm characteristics and the residual terms then are used to measure insurer franchise value:

RATINGi,t = α0 +∑=

−

10

11,,

jtijj Xβ + εi,t (1)

where 1,, −tijX (j=1, 2, …, 10) are tangible characteristics potentially affecting firm rating measured at the end of year t-1. The residuals, εi,t, measure the franchise value of insurance companies. X includes the following variables:

• SIZE: the logarithm of total assets • LEVERAGE: liability divided by total assets • CHGNPW: the difference between the net written premiums in this period and in the prior

period, scaled by lagged net written premium • ROA: an insurer’s return on assets, net income divided by total assets • HERFLINE: Herfindahl index by business line. Specifically it is the sum of squared

percentage of written premiums in each business line to the total written of the insurer.

∑= 2)/(_ TPWPWlineHerfindahl i where i =1, 2, …, 34 • LONGTAIL: net premiums written (NPW) in long-tail lines of insurance divided by total

NPW. Following Sommer (1996) and Pottier and Sommer (1999), we include auto liability, other liability, farmowners/homeowners /commercial multiple peril, medical malpractice, workers compensation, aircraft, and boiler and machinery as long-tail lines.

• REINSURANCE: reinsurance ceded divided by the sum of direct premiums written and reinsurance assumed.

• STOCK: equity investment divided by invested assets • JUNK: risky bond investment divided by invested assets • CASH: cash divided by invested assets CHGNPW is not available from 1986 and JUNK is not available from 1986 to 1988. As a result,

they are not included in the cross-sectional regression in these years when we estimate franchise value.

  19

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Table 1: Summary Statistics

Panel A reports the number of sample insurers in each of A.M. Best rating. Panel B reports summary statistics of the NAIC-BEST (full) sample and CRSP (public) insurance firm sample. Panel C shows the correlations among ratings and insurers’ operating performance for the NAIC-BEST sample. Panel D shows the correlations among three franchise value measures for the CRSP sample. Rating is obtained from A.M. Best database with converting letter rating to numerical ratings. IA1 is the benchmark-adjusted insurance rating. IA2 is regression-adjusted insurance rating. Return on assets is the ratio of net income to total assets. Return on equity is net income divided by total surplus. Combined ratio is the sum of loss ratio (loss/premium earned) and expense ratio (underwriting expenses/premium earned). Tobin’s q is the ratio of book value of assets divided by market value of assets. The sample period is from 1985 to 2005.

Panel A: Distribution of Best Ratings

Rating A++ A+ A A- B++ B+ B B- C++ C+ C C- D E,F,S Mean 78 269 344 260 106 104 48 19 6 6 4 2 3 3

Panel B: Insurance Franchise value and Operating Performance

NAIC-BEST Sample CRSP Sample Mean Median Mean Median

10.38 10.37 10.73 10.73 0.06 0.06 0.11 0.09 0.07 0.05 0.12 0.10

2.86% 2.84% 3.07% 2.94% 6.03% 6.31% 7.50% 8.33%

Rating Benchmark-adjusted Rating (IA1) Regression residual Rating (IA2) Return on Assets (ROA) Return on equity (ROE) Combined Ratio (CR) 106.99% 106.65% 106.89% 106.42%

Panel C: Correlations among Rating Measures and Operating Performance for NAIC-BEST Sample

Rating IA1 IA2 ROA ROE IA1 0.78 IA2 0.85 0.83 ROA 0.12 0.09 0.07 ROE 0.12 0.11 0.08 0.89 CR -0.09 -0.10 -0.10 -0.46 -0.42

Panel D: Correlation among Intangible Asset Measures for CRSP Insurance Sample

IA1 IA2 Tobin’s Q 0.19 0.18 IA1 0.86

  22

Table 2: Insurers’ Performance Sorted by Lagged Intangible Measures

This table reports insurers operating performance (in percent) in yeart+1 to year t+3 across franchise value deciles sorted in year t. At the end of year t, all sample firms are sorted into 10 portfolios based on their intangible asset in year t. We then compute the return on asset, return on equity and combined ratio in year t+1 to year t+3. We first compute the cross-sectional average performance in each year and then calculate the time-series averages. We also report the performance difference between top insurers (D10) and bottom insurers (D10 and their corresponsive t-statistics. Panel A sorts firms by IA1, the benchmark-adjusted rating, and Panel B sorts firms by IA2, regression residual rating. The sample period is from 1985 to 2005. Panel A: Insurers Sorted by IA1

Y1 Y2 Y3 Y4 Y5

RANK ROA ROE CR ROA ROE CR ROA ROE CR ROA ROE CR ROA ROE CR 1 1.20 2.27 115.93 1.54 2.96 116.09 1.42 2.92 116.39 1.76 3.33 115.79 1.92 3.84 114.50

2 2.43 5.50 107.95 2.53 5.57 107.61 2.37 5.12 109.04 2.47 5.02 109.64 2.25 4.64 112.38

3 3.08 6.20 107.51 2.93 6.24 108.57 2.64 5.72 108.47 2.43 4.87 110.12 2.40 4.68 109.87

4 2.75 5.92 107.90 2.43 5.57 107.96 2.75 5.46 108.51 2.50 4.68 108.55 2.81 5.17 109.08

5 3.24 6.89 104.51 2.89 5.48 105.37 2.69 5.19 105.96 2.76 5.39 107.69 2.56 4.42 106.14

6 3.22 6.26 104.57 2.88 5.51 106.31 2.97 5.43 106.13 3.06 5.41 106.43 2.71 5.05 108.22

7 3.42 7.41 104.67 3.28 6.44 104.75 3.21 6.09 105.30 3.06 5.91 106.34 3.27 5.85 107.67

8 3.68 7.91 102.93 3.44 7.57 103.81 3.22 6.92 105.52 3.37 7.09 106.14 3.31 6.25 107.47

9 3.53 8.09 104.98 3.42 7.55 105.35 3.46 7.58 105.30 3.41 7.12 104.59 3.51 6.85 105.02

10 3.67 8.37 105.16 3.45 7.59 105.48 3.38 7.04 105.77 3.28 6.72 107.19 3.13 6.61 106.29

10-1 2.47 6.10 -10.76 1.91 4.63 -10.61 1.96 4.12 -10.62 1.52 3.39 -8.59 1.20 2.77 -8.21

t 4.50 12.48 -6.08 4.06 10.06 -5.76 2.95 6.74 -4.83 3.15 4.66 -6.05 3.42 5.83 -6.10

  23

Panel B: Insurers Sorted by IA2 Y1 Y2 Y3 Y4 Y5 RANK ROA ROE CR ROA ROE CR ROA ROE CR ROA ROE CR ROA ROE CR

1 1.63 3.49 114.31 1.91 3.61 114.76 2.02 3.51 114.12 2.14 4.03 113.53 2.05 4.14 114.35

2 2.81 5.74 108.45 2.44 5.29 108.50 2.20 4.91 110.75 2.22 4.50 110.28 2.25 4.50 111.14

3 3.12 6.75 107.31 3.02 6.45 108.07 2.49 5.39 108.41 2.48 4.84 110.38 2.52 4.86 109.63

4 2.85 6.18 108.10 2.57 5.43 108.61 2.57 5.52 109.22 2.51 4.88 108.87 2.37 4.41 110.78

5 3.12 6.53 105.93 2.90 6.18 106.15 2.77 5.41 106.54 2.81 5.29 107.56 2.58 4.85 108.86

6 2.99 6.30 104.42 2.76 5.83 105.96 2.84 5.61 106.51 2.97 5.65 107.09 2.92 5.40 107.53

7 3.28 6.69 105.00 2.99 6.11 105.49 2.96 6.18 106.43 3.02 6.11 106.78 3.06 5.65 106.42

8 3.51 7.41 104.05 3.48 7.01 103.94 3.56 6.85 104.64 3.28 6.33 105.79 3.33 6.00 106.06

9 3.55 7.90 103.98 3.34 7.10 105.00 3.26 6.74 104.49 3.23 6.75 106.13 3.36 6.69 106.35

10 3.35 7.76 104.58 3.37 7.40 104.75 3.42 7.26 105.43 3.44 7.22 105.98 3.41 6.82 105.34

10-1 1.72 4.26 -9.73 1.46 3.79 -10.02 1.41 3.74 -8.69 1.30 3.20 -7.55 1.36 2.68 -9.01

t 4.02 8.21 -5.69 4.03 6.31 -5.61 3.58 6.07 -4.77 3.40 4.94 -5.09 3.20 5.54 -7.10

  24

Table 3: Regression of Operating Performance on Franchise value

This table reports the time-series averages of the Fama-MacBeth regression of firm performance on lagged year franchise value, firm characteristics and tangible factors. In each year t, we perform the following cross-sectional regression: PERF i,t= Intercept+α1IAi,t-1 + α2SIZEi,t-1 + α3LEVi,t-1 + α4GROUPi,t-1 + α5STOCKi,t-1 + α6LONGTAILi,t-1+ α7HERFLINEi,t-1+ α8REi,t-1 + εi,t where PERM is measured by ROA, ROE, or CR.

ROA ROE CR ROA ROE CR INTERCEPT 0.05 0.01 0.99 0.05 0.01 0.99

(9.71) (0.85) (59.67) (9.48) (0.70) (58.57) IA1 0.01 0.01 -0.02

(3.86) (8.66) (-5.99) IA2 0.01 0.01 -0.02

(3.11) (6.87) (-5.37) SIZE 0.01 0.01 0.01 0.01 0.01 0.01

(5.82) (6.48) (1.84) (5.61) (6.93) (1.20) LEV -0.08 -0.01 -0.01 -0.08 0.01 -0.02

(-10.20) (-0.03) (-0.42) (-9.75) (-0.02) (-0.50) GROUP 0.01 -0.01 0.02 0.01 -0.01 0.03

(0.39) (-0.11) (2.67) (0.67) (-1.68) (4.18) STOCK 0.01 0.01 -0.00 0.01 0.02 -0.01

(5.86) (2.75) (-0.64) (6.22) (3.12) (-1.40) LONGTAIL -0.01 -0.03 0.05 -0.01 -0.03 0.05

(-2.16) (-6.08) (3.67) (-2.12) (-6.06) (3.88) HERFINE 0.02 0.04 -0.01 0.01 0.04 0.00

(6.12) (4.89) (-1.02) (4.98) (3.76) (-0.25) REINSURANC -0.01 -0.02 0.10 -0.01 -0.02 0.09

(-2.05) (-4.08) (5.19) (-1.58) (-2.94) (4.85)

ADJ R2 0.11 0.06 0.04 0.10 0.05 0.04

  25

Table 4: Effect of Franchise value on Performance in Soft and Hard Markets

This table reports insurers operating performance (in percent) in yeart+1 to year t+3 across franchise value deciles sorted in year t in both soft market and hard market. Panel A and B classify market condition based on aggregated loss ratio. We define soft market and hard market by means of the aggregated industry loss ratio. If the industry loss ratio in a year is less than the median loss ratio over our sample period, we define this year as a soft market. Otherwise it is a hard market. Panel C and D classify market condition using aggregated change in percentage growth of net premium written (NPW). We define soft market and hard market by means of the growth rate of net premium written. If the average change in NPW in a year is less than the median value over our sample period, we define this year as a soft market. Otherwise it is a hard market. Panel A: Using Loss Ratios to Classify Insurance Markets, Performance of Insurers Sorted by IA1

Soft Market Hard Market RANK ROA ROE CR ROA ROE CR

1 2.29 4.46 116.99 0.52 0.89 114.87 2 3.42 7.13 105.47 1.30 3.43 110.56 3 3.44 7.26 106.81 2.23 4.83 111.00 4 3.05 6.63 106.21 1.57 4.11 110.38 5 3.44 6.90 103.90 2.15 3.53 107.39 6 3.58 6.82 104.20 1.91 3.70 109.21 7 3.97 7.97 102.92 2.34 4.33 107.28 8 4.06 8.91 101.87 2.59 5.72 106.48 9 4.06 8.77 102.63 2.54 5.88 109.08

10 4.16 9.11 104.15 2.47 5.50 107.31

10-1 1.87 4.65 -12.83 1.95 4.61 -7.56 t 2.77 7.26 -4.82 2.92 6.60 -3.58

Panel B: Using Loss Ratios to Classify Insurance Markets, Performance of Insurers Sorted by IA2

Soft Market Hard Market RANK ROA ROE CR ROA ROE CR

1 2.78 5.45 114.85 0.70 1.09 114.64 2 3.05 6.50 106.47 1.61 3.64 111.29 3 3.55 7.60 106.46 2.31 4.86 110.28 4 3.09 6.53 108.18 1.85 3.91 109.20 5 3.50 7.34 104.64 2.07 4.59 108.22 6 3.37 6.93 104.26 1.92 4.31 108.29 7 3.63 7.14 104.09 2.10 4.68 107.41 8 4.23 8.60 101.61 2.45 4.82 107.14 9 4.02 8.61 102.08 2.39 5.03 109.01

10 4.21 9.19 102.43 2.22 4.95 107.94

10-1 1.42 3.73 -12.42 1.52 3.87 -6.71 t 2.82 4.83 -4.59 2.76 3.81 -4.34

 

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Panel C: Using Net Premiums Written Growth Rate to Classify Insurance Markets, Performance of Insurers Sorted by IA1

Soft Market Hard Market RANK ROA ROE CR ROA ROE CR

1 1.41 3.19 117.58 1.69 2.70 114.44 2 2.76 5.59 107.23 2.27 5.55 108.03 3 3.22 5.75 107.68 2.60 6.78 109.56 4 2.41 5.31 108.62 2.45 5.86 107.24 5 3.46 6.07 104.69 2.26 4.83 106.12 6 3.21 5.44 108.12 2.50 5.58 104.29 7 3.88 6.73 105.00 2.62 6.11 104.48 8 3.85 7.30 104.52 2.99 7.87 103.02 9 3.94 7.62 106.01 2.84 7.48 104.61

10 4.06 7.48 104.92 2.77 7.71 106.11

10-1 2.65 4.29 -12.67 1.08 5.01 -8.34 t 3.25 7.73 -4.43 4.68 6.54 -3.86

Panel D: Using Net Premiums Written Growth Rate to Classify Insurance Markets, Performance of Insurers Sorted by IA2

Soft Market Hard Market RANK ROA ROE CR ROA ROE CR

1 1.85 3.75 115.81 1.96 3.46 113.60 2 2.54 4.92 108.94 2.33 5.71 108.02 3 3.54 6.45 106.65 2.45 6.44 109.65 4 2.92 5.44 108.64 2.17 5.41 108.57 5 3.17 6.00 107.04 2.60 6.38 105.16 6 2.97 6.08 105.53 2.53 5.55 106.43 7 3.38 6.51 106.39 2.54 5.66 104.49 8 4.00 7.07 104.64 2.90 6.95 103.17 9 3.91 7.32 105.44 2.70 6.86 104.51

10 3.89 6.95 104.96 2.80 7.90 104.50

10-1 2.03 3.20 -10.84 0.83 4.45 -9.10 t 3.29 4.94 -4.21 3.59 4.26 -3.52

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Table 5: Insurers’ Stock Performance Sorted by Franchise value

This table reports insurers’ annualized stock performance (in percent) from July in year t to June in year t+1 across franchise value groups sorted in year t-1. At the end of year t-1, all sample firms are sorted into 3 portfolios based on their intangible asset in year t-1. We then compute annualized raw stock return, three-factor risk-adjusted returns, and four-factor risk adjusted returns in the following year since July in year t to June in year t+1. We first compute the cross-sectional average performance in each year and then calculate the time-series averages. We also report the performance difference between top insurers (R3) and bottom insurers (R1) and their corresponsive t-statistics. The sample period is from 1985 to 2005.

IA1 IA2 RANK Raw 3F Alpha 4F Alpha Raw 3F Alpha 4F Alpha

1 6.99 -0.81 3.41 6.71 -3.09 2.35 2 12.27 0.37 1.20 13.58 1.02 1.79 3 14.71 1.23 -0.83 14.63 -0.94 -0.61

3-1 7.73 2.04 -4.24 7.92 2.15 -2.96 t-stat 1.85 0.14 -0.89 1.75 0.60 -0.54

 

  28

 Table 6: Regression of Stock Returns on Franchise value

This table reports the time-series averages of the Fama-MacBeth regression of firm performance on lagged franchise value, market condition, firm characteristics and tangible factors. In each year t, we perform the following cross-sectional regression: Reti,t= Intercept+ α1IAi,t-1 + α2SIZEi,t-1 + α3LEVi,t-1+ α4GROUPi,t-1 + α5LONGTAILi,t-1+α6HERFLINEi,t-1 +α7REi,t-1+ εi,t. Ret is measured by annualized stock returns, Fama-French three factor risk-adjusted returns, and Charhart four factor risk-adjusted returns from July year t to June year t+1.

Raw 3F Alpha 4F Alpha Raw 3F Alpha 4F Alpha INTERCEPT 0.22 0.24 0.41 0.22 0.24 0.44

2.04 1.75 2.66 1.95 1.77 2.75 IA1 0.03 0.01 0.01

2.42 1.05 0.22 IA2 0.03 0.01 0.00

2.16 0.71 -0.12 SIZE 0.00 -0.01 -0.04 0.01 -0.01 -0.03

-0.13 -0.81 -1.67 0.35 -0.53 -1.33 LEV 0.02 0.00 0.02 -0.01 -0.04 -0.11

0.10 0.03 0.11 -0.03 -0.19 -0.53 GROUP -0.04 -0.04 -0.02 -0.06 -0.04 -0.01

-0.72 -0.65 -0.26 -1.15 -0.80 -0.12 LONGTAIL -0.03 -0.02 -0.08 -0.03 -0.02 -0.07

-0.47 -0.19 -0.60 -0.49 -0.19 -0.58 HERFINE -0.04 -0.19 -0.18 -0.08 -0.21 -0.20

-0.47 -2.15 -1.76 -0.83 -2.32 -1.88 REINSURANC

E-0.13 -0.17 -0.25 -0.13 -0.15 -0.24

-1.35 -1.47 -1.90 -1.28 -1.41 -1.93

ADJ R2 0.14 0.09 0.05 0.16 0.12 0.09

 

 

  29

 

Figure 1: Spreads in ROA and ROE between Top and Bottom Decile Insurers Sorted by Franchise value

1988 1990 1992 1994 1996 1998 2000 2002 20040

2

4

6

8D10-D1 Spreads in Return on Assets

1988 1990 1992 1994 1996 1998 2000 2002 20040

5

10

15

20D10-D1 Spreads in Return on Equity

 

We calculate the equal-weighted average ROA/ROE in year t+1 in each decile of insurers sorted by IA1 in year t. This plot shows the time-series of the spread in ROA and ROE in each sample year.

  30

Figure 2: Persistence of Franchise value for Top and Bottom Decile Insurers

1 2 3 4 5 6 7 8 9 101 2 3 4 5

0

0.5

IA Rankt+1

D10 Firms

Year

Pro

b

1 2 3 4 5 6 7 8 9 101 2 3 4 5

0

0.5

IA Rankt+1

D1 Firms

Year

Pro

b

 

In each year t, we sort insurers into deciles based on their IA1. We look at the percentage distribution of these firms in the deciles of IA1 in the following five years.

  31

Figure 3: Spread in Stock Performance between Top and Bottom Decile Insurers Sorted by Franchise value

1988 1990 1992 1994 1996 1998 2000 2002 2004-5

0

5

Spreads in Raw Returns

1988 1990 1992 1994 1996 1998 2000 2002 2004-5

0

5Spreads in 3-Factor Alphas

1988 1990 1992 1994 1996 1998 2000 2002 2004-5

0

5Spreads in 4-Factor Alphas

We calculate the equal-weighted average monthly raw stock return and risk-adjusted return from July in year t+1 to June in year t+1 in each tri-groups of insurers sorted by IA1 in year t. This plot shows the time-series of the spread in raw stock returns and risk-adjusted returns in each sample year.

 

 

 

  

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