Citicorp- Traveler Group merger: Challenging barriers between banking and insurance
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Citicorp- Traveler Group merger: Challenging barriers
between banking and insurance
R94723020 陳怡樺R94723054 溫晴婉R94723041 陳筱君
Agenda
The introduction of Citigroup merger event. The regulatory overview involved in the
event. How the Citicorp-Traveler group merger
influence peer institution? Empirical analysis and results. Conclusion
Citigroup Merger Event
Citicorp
Citibank is the largest bank in U.S(1984) and in the world(1929)
Customer accounts in more than 100 countries
Issuing more than 60,000,000 credit cards
the largest credit card and charge card issuer and service in the world(1993)
Travelers Group
Travelers is one of the largest providers of personal insurance products in the United States
three major business : Insurance 、 Brokerage 、 investment banking
Revenues : investment banking(57%) 、 life insurance(12%) 、 property insurance(26%) 、 consumer banking(5%)
Before Merger
$USD Citibank Travelers Group
Sales $ 21.6 billion $ 27.1 billion
Net income $ 4.1 billion $ 3.4 billion
Assets $ 310.9 billion $ 386.6 billion
Stockholders’ equity
$ 21.9 billion $ 22.2 billion
employees 93,700 68,000
Merger event
Announced on April ,1994
merged into Citigroup Inc on October 8
Congeneric Merger
Co-CEO‘s : Reed from Citicorp
Weill from Travelers
Each holds half of the equity of citigroup
exchange rate of stock : Citicorp → 1 : 2.5 ; Travelers → 1 : 1
Merger event
Board of Directors : 9 for Citicorp
9 for Travelers Date of Announcement
Citicorp’s stock price
$142.875 → $180.50
Travelers Group price
$61.6875 → $73.00
This is the biggest illegal merger in U.S.
Synergies
Complementary nature
cost cutting cross selling 3C capital efficiency
Diversification
Net income → $ 7.5 billion
legislation
1933 Glass-Steagall Act
1956 Bank Holding Company (BHC) Act
─ prohibit banks from underwriting insurance
Federal Reserve → a two year trial period before divesting the insurance underwriting business
1999 Gramm-Leach-Bliley Financial Services
Modernization Act
Alan Greenspan
Impact of this merger
Deregulation of bancassurance
→ passing GLB Act & repealing G-S Act
Sanction the creation of one-stop financial supermarkets
A wave of mergers – peer institutions
→ more competitive environment
How the Citicorp - Traveler group merger influence
peer institution?
Background
Merger events
Federal Regulation
Literature review
Merger events
Announcement on the morning of 6 April 1998
Significant abnormal return
Citicorp: 142.875→180.5 (26%)
Traveler: 61.6875→73 (18%)
Federal Regulation
Federal and state laws: Barrier between banks and insurance
National Banking Act: Authorize small bank to sell insurance
Bank Holding Company ( BHC ): Prohibit banks from underwriting insurance
Literature review
Kane: Deregulation result in the responds of peer firms stock price.
Eckbo: There’s no significant stock price reaction for rival firms ( Due to the arising competition )
Benefit from removal of barriers
Reducing risk
Increasing profits
Increasing implicit government guarantee
Statistic hypothesis
Null hypothesis 1 : The Citicorp-Traveler Group merger announcement will not significantly change the stock prices of banks or insurance companies.
Statistic hypothesis
Null hypothesis 2 : The stock price reaction of banks, life insurance companies, health insurance companies, and property/casualty insurance companies, are insignificantly different from each other surrounding the Citicorp-Travelers merger announcement.
Statistic hypothesis
Null hypothesis 3 : The stock price returns of large bank and insurance companies are insignificantly different from the stock price return of small bank and insurance companies surrounding the Citicorp-Travelers Group merger announcement.
Statistic Methodology
Multivariate Regression Model ( MVRM )
Seemingly Unrelated Regression :
each event has one indicator variable
Statistic Model R1t = a1+ b1Rmt+ c1Rmt-1+ d1ΔIt+ f1ΔIt-1
4
+Σγ1,iDi+e1t
i=-5
:
: Rnt = an+ bnRmt+ cnRmt-1+ dnΔIt+ fnΔIt-1
4
+Σγn,i Di+ent
i=-5
Variable Explanation
Rmt: The observed return on the value-weighted market index on day t
ΔIt: The change in the interest rate on day t for the 10 year constant maturity treasury ( It - It-1)
Di: equal to 1 on day i, 0 other wise
γ1,i:the excess return on day i for firm 1
Empirical Results
SUR Results by Industry
Mean abnormal return H0 : (γ1)+(γ2)+…+(γn)=0
Life insurance company : 1.02%(p-value=0.0423)significant
Sign test H0 : P=50% Life insurance company : 67%(Z=1.826)si
gnificant
SUR Results by Industry
One-day event period – 6 April 1998
Hypothesis: (γ1)+(γ2)+…+(γn)=0
Sample size
Mean estimateγi
Probability<F
%Positive
Z-Statistic
National banks 113 0.1089 0.7367 47 -0.607
State banks 117 0.2747 0.4233 57 1.572
Life insurance 30 1.0207 0.0423**
67 1.826*
Health insurance
26 -0.6842 0.4741 42 -0.784
Property/casualty insurance
67 0.1939 0.5893 55 0.855
SUR Results by Industry
Two-day event period – 6-7 April 1998
Hypothesis: (γ1)+(γ2)+…+(γn)=0
Sample size
Mean estimateγi
Probability<F
%Positive
Z-Statistic
National banks 113 -0.3366 0.2027 44 -0.1301
State banks 117 -0.0578 0.8645 50 0.0000
Life insurance 30 -1.3919 0.0247**
53 0.365
Health insurance
26 -1.1845 0.2807 35 -1.569
Property/casualty insurance
67 -0.1458 0.7452 51 0.122
Cross-sectional Analysis
Model 1 and 2 Indicator variables
State-chartered banks, life insurance,
health insurance, property/casualty insurance
hypothesis 2 Assets size : size>$10b, $1b<size<$10b
hypothesis 3
Cross-sectional Analysis
Model 1 Large banks have significant positive abnormal
returns. Life insurance companies have significantly
positive abnormal returns. Life insurance companies have significantly
positive returns than other insurance company.
Model 2 Two-day event period provides better explanatory
power.
Cross-sectional Analysis: Model 1 and 2
Independent variables Model 1 Apr.6 Model 2 Apr.6-7
Intercept -0.2425(0.3922) -0.8526(0.0117)
State chartered bank 0.3124(0.3150) 0.4970(0.1792)
Bank $1-10 billion 0.2449(0.4633) 0.3206(0.4195)
Bank >10$ billion 1.3983(0.0031) 2.1473(0.0001)
Life insurance 1.4849(0.0174) 2.2632(0.0024)
Health insurance -0.2825(0.6178) -0.3309(0.6303)
Property/casualty 0.6277(0.1720) 0.7009(0.1997)
Insurance $1-$10billion -0.4521(0.3394) 0.0321(0.9545)
Insurance >$10billion -0.1227(0.8569) -0.0946(0.9070)
Sample size 373 373
F-statistic 2.166 3.513
Prob>F 0.0294 0.0006
R2 0.0454 0.0717
Adjusted R2 0.0245 0.0513
Cross-sectional Analysis
Model 3 and 4 To replace the size indicator variables with the log
of assets for the firm. An improvement in model fit. Results
The indicator coefficient for life insurance companies is significant.
Large banks have significant positive excess returns. Returns do not significantly vary with size for insurance
company industry.
Cross-sectional Analysis: Model 3 and 4
Independent variables Model 3 Apr.6 Model 4 Apr.6-7
Intercept -1.9948(0.0017) -3.1014(0.0001)
State chartered bank 0.4186(0.1245) 0.5668(0.0900)
Life insurance 5.3909(0.0030) 7.6917(0.0006)
Health insurance 2.1643(0.1242) 2.7844(0.1068)
Property/casualty 1.3054(0.2765) 2.1225(0.1494)
Banks log of assets 0.2778(0.0005) 0.3654(0.0002)
Life insurance log of assets -0.2885(0.1501) -0.4067(0.0983)
Health ins. log of assets -0.1007(0.5885) -0.0578(0.8001)
Property/casualty log of assets 0.1055(0.4643) 0.1229(0.4870)
Sample size 373 373
F-statistic 2.926 3.580
Prob>F 0.0035 0.0006
R2 0.0604 0.0716
Adjusted R2 0.0398 0.0512
Cross-sectional Analysis
Model 5 and 6 Does concentration of business in life
insurance products respond more positively? Omit the insignificant size coefficients. Add three product mix variables.
(Percent of the firm’s total revenue derived form three insurance products.)
Degree of concentration Insignificant
Cross-sectional Analysis: Model 5 and 6
Independent variables Model 5 Apr.6 Model 6 Apr.6-7
Intercept -1.9948(0.0017) -3.1014(0.0001)
State chartered bank 0.4186(0.1245) 0.5668(0.0900)
Life insurance 3.1694(0.0016) 5.4610(0.0001)
Health insurance 1.2112(0.2373) 1.0676(0.3908)
Property/casualty 1.6947(0.1832) 0.9893(0.5219)
Banks log of assets 0.2778(0.0005) 0.3654(0.0002)
% of revenues in life insurance -0.3532(0.7705) -2.4207(0.1002)
% of revenues in health ins. 0.4857(0.6541) 2.1085(0.1098)
% of revenues in property/casualty 0.4385(0.7501) 2.6109(0.1190)
Sample size 373 373
F-statistic 2.595 4.155
Prob>F 0.0091 0.0001
R2 0.0540 0.0837
Adjusted R2 0.0332 0.0635
Conclusion
Peer institutions benefit. Despite the increased competitive threat This was not a wealth transfer
Life insurance companies benefit. Management of risks Cross-product sales revenues Lower distribution costs
Large banks benefit. too-big-to-fail Implicit government guarantees Economies of scope
Thanks for your attention!
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