Citicorp Traveler Group merger Challenging barriers between banking
Citicorp- Traveler Group merger: Challenging barriers between banking and insurance R 94723020 陳怡樺 R 94723054 溫晴婉 R 94723041 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 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 3 C ¡ 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 ¡ R 1 t = a 1+ b 1 Rmt+ c 1 Rmt-1+ d 1ΔIt+ f 1ΔIt-1 4 +Σγ 1, i. Di+e 1 t i=-5 : : ¡ Rnt = an+ bn. Rmt+ cn. Rmt-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 ¡ γ 1, i: the excess return on day i for firm 1
Empirical Results
SUR Results by Industry ¡ Mean abnormal return H 0:(γ 1)+(γ 2)+…+(γn)=0 Life insurance company: 1. 02%(p-value=0. 0423) significant ¡ Sign test H 0:P=50% Life insurance company: 67%(Z=1. 826) significant
SUR Results by Industry One-day event period – 6 April 1998 Hypothesis: (γ 1)+(γ 2)+…+(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/casual ty insurance 67 0. 1939 0. 5893 55 0. 855
SUR Results by Industry Two-day event period – 6 -7 April 1998 Hypothesis: (γ 1)+(γ 2)+…+(γn)=0 Sample size Mean estimateγi Probability <F % Z-Statistic Positive 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/casualt y 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>$10 b, $1 b<size<$10 b hypothesis 3
Cross-sectional Analysis ¡ Model 1 Large banks have significant 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 -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 -$10 billion -0. 4521(0. 3394) 0. 0321(0. 9545) Insurance >$10 billion -0. 1227(0. 8569) -0. 0946(0. 9070) 373 2. 166 3. 513 Prob>F 0. 0294 0. 0006 R 2 0. 0454 0. 0717 Adjusted R 2 0. 0245 0. 0513 Intercept Sample size F-statistic
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 -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) 0. 1055(0. 4643) 0. 1229(0. 4870) 373 2. 926 3. 580 Prob>F 0. 0035 0. 0006 R 2 0. 0604 0. 0716 Adjusted R 2 0. 0398 0. 0512 Intercept Property/casualty log of assets Sample size F-statistic
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 -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) -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) 373 2. 595 4. 155 Prob>F 0. 0091 0. 0001 R 2 0. 0540 0. 0837 Adjusted R 2 0. 0332 0. 0635 Intercept % of revenues in life insurance Sample size F-statistic
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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