Disseratation Propos - Rutgers University

Disseratation Propos - Rutgers University

Financial Sector Integration and Information Spillovers: Effects of Operational Risk Events on U.S. Banks and Insurers J. David Cummins and Ran Wei The Joint 14th Annual PBFEA and 2006 Annual FeAT Conference July 14, 2006 Research Question Do operational risk events cause market value losses (spillovers) to non-announcing firms in the U.S. banking and insurance industry? Main Results Operational risk events have significant intra- and inter-industry spillover effects Negative impact on stock prices of non-announcing firms Spillover effects are information-based

Informed, rather than indiscriminate, re-pricing of stocks Why We Expect Spillovers: Financial Sector Integration Banks and insurers began competing:1970s Deregulation led to further integration: 1980s & 1990s Commercial banks enter investment banking Commercial banks enter insurance markets Retail integration: Insurers, commercial banks, and investment banks compete for retail asset-accumulation business Financial Sector Integration II Wholesale market integration Insurers, commercial banks, and I-banks compete in investment management, corporate pension funds, commercial mortgages, etc. Investment banks and I-bank subs of C- banks & insurers compete in securities underwriting

Significant integration of the previously fragmented markets for financial services Outline Background on operational risk Background on financial sector integration Literature review Hypotheses Data, sample selection and methodology Results Conclusion What is Operational Risk? In theory, operational risk is the banks residual risk after accounting for other sources of risk Market risk Credit risk Interest rate risk Exchange rate risk Systemic risk Basel II Definition of Operational Risk Basel II defines op risk more narrowly as The risk of loss resulting from inadequate

or failed internal processes, people, and systems, or from external events. Basel II definition does not include: Strategic risk Reputational risk Systemic risk Market risk Credit risk Famous Operational Risk Events NASDAQ Odd eighths trading scandal (Christie and Schultz 1994) Barings Bank collapse (1995) $1.3 billion loss due to rogue trader (Nick Leeson) Daiwa Bank (1995) $1.1 billion loss due to unauthorized bond trading (Toshihida Iguchi) Leading US securities brokers fined $1.4 billion (2002) misleading research reports Prudential Insurance (US) fined $2 billion for sales abuses (1990s)

State Farm Insurance loses $1.2 billion for breach of contract (1999) Basel II: Event Types 7 Event types: Internal fraud External fraud Employment practices and workplace safety Clients, products, and business practices Damage to physical assets Business disruption and system failures Execution, delivery, and process management Why is Operational Risk Important? New Basel Capital Accord An explicit capital charge for operational risk Deregulation and globalization Increasing complexity of business

Incompatible system and integration problems (M&A) Advances in technology Increased probability of systems failure Fraud, new and unknown risks from E-commerce Rating firms (Moodys, Fitch, S&Ps) Financial rating partly based on operational risk Basel II Capital Accord: Overview Three Pillars Approach to Bank Solvency Regulation Pillar I: Minimum capital requirements Market risk Credit risk Operational risk Pillar

II: Supervisory review process Pillar III: Market discipline Basel II Capital Accord: Overview II Operational risk capital charge Considers sum of expected loss (EL) and unexpected loss (UL) UL is at the 99.9% probability level based on a one year exposure period Envisions significant quantification of operational risk charge most sophisticated banks will use Advanced Measurement Approaches (AMA) Outline Background on operational risk Background on financial sector integration Literature review Hypotheses

Data, sample selection and methodology Results Conclusion Why Are Spillover Effects Important? Bank failure contagion (bank-runs) - a main reason for bank regulation Important to investigate: Whether there are spillover effects caused by operational losses events, and If so, are the effects Information-based (rational) or Purely contagious (irrational) Financial Sector Integration: 1970s Investment banks vs. commercial banks Checkable money market funds substitute for bank demand deposits Expansion of commercial paper market substitute for bank loans Asset-backed securities move bank assets such as mortgages off-balance-sheet

Financial Sector Integration: 1970s Insurers vs. banks Insurers issue privately placed bonds substitute for securities underwriting through investment banks Insurers introduce single premium deferred annuities and GICs substitute for bank CDs Insurers compete for commercial mortgages Insurers introduce mutual fund families Insurers introduce variable life and annuities Financial Sector Integration: Deregulation of 1980s & 1990s Regulatory restrictions Glass-Steagal Act of 1933 Separated commercial banking and investment banking Restricted inter-ownership between banks and insurance companies

National Banking Act (NBA) of 1916 restricted commercial banks from selling insurance Financial Sector Integration: Deregulation of 1980s & 1990s Deregulation: Wholesale financial services In 1987 commercial banks permitted to engage in investment banking through Section 20 subsidiaries In 1987, I-banking limited to 5% of gross revenue 1996, I-banking permitted up to 25% of gross revenue 1999, Gramm-Leach-Bliley Act removed all remaining restrictions and permits Financial Holding Companies (FHCs) to engage in all types of financial services through subsidiaries Financial Sector Integration: Deregulation of 1980s & 1990s II

Deregulation: Retail financial services National Banking Act interpreted more liberally allows subs of banks to sell insurance if headquartered in towns of < 5,000 population Office of Comptroller of Currency (OCC) deregulation 1985: OCC allowed banks to sell fixed-rate annuities 1990: OCC allowed banks to sell variable-rate annuities 1996: OCC actions upheld by U.S. Supreme Court 1999: GLB Act permits FHCs to own insurance companies Integration: Cross-sector M&As in US Acquirer Commercial Bank Investment Banks Insurers Target

Inv Banks Insurer & Agencies Comm Banks Insurer & Agencies Comm Banks Inv Banks Total # of Deals 1985-2004 151 229 52 49

22 76 Average # of Deals per Year 1985-1994 2 1.2 1 1.3 0.5 1.7 Average # of Deals per Year 1995-2004 11.2 20.5 3.3

2.4 1.2 4.2 Outline Background on operational risk Background on financial sector integration Literature review Hypotheses Data, sample selection and methodology Results Conclusion Prior Literature Aharony and Swary (1983) Negative information spillover (contagion) effect The spillover effects of events affecting specific firms to others Pure spillover effect (contagion)

Indiscriminant re-pricing of all shares (bank runs) The spillover effect to other firms regardless of the cause of the event and the non-announcing firms risk characteristics Pure spillovers create social costs Information-based spillover effect Informed re-pricing of shares If the cause of event is correlated across firms, only the correlated firms are affected Investors are able to differentiate firms based on risk characteristics No social costs Prior Literature Aharony and Swary (1983) Negative information spillover (contagion) effect Events

affecting specific firms spillover to others. Pure spillover effect (contagion) Indiscriminant re-pricing of all shares (bank runs) The spillover effect to other firms regardless of the cause of the event and the non-announcing firms risk characteristics Pure spillovers create social costs Information-based spillover effect Informed re-pricing of shares If the cause of event is correlated across firms, only the correlated firms are affected Investors can differentiate firms based on risk characteristics No social costs Prior Literature Lang and Stulz (1992)

Competitive effect Announcement of bankruptcy need not only convey negative information Value of rival firms can be increased by redistributing wealth from the announcing firm Industries with similar cash flow characteristics exhibit negative information spillovers (contagion) Competitive effect dominates in highly concentrated industries and cannot occur in a competitive industries Positive and negative spillovers may both be present empirical estimates measure the net effect Cummins, Lewis, and Wei (2006) Research Question What is the effect of operational risk events on market value of announcing banks and insurers? Main Results

OpRisk events have a strong, statistically significant negative stock price impact on announcing firms Moreover, the market value loss significantly exceeds the amount of the operational loss reported Investors price operational risk into their views on the future profitability of a firm Outline Background on operational risk Background on financial sector integration Literature review Hypotheses Data, sample selection and methodology Results Conclusion How Are Spillovers Generated? Arise if events cause investors to revise downward estimates of future cash flows for non-announcing firms Events provide information on previously unknown risks to all institutions Events cause customers to be wary of financial institutions and disintermediate

Events may induce greater regulatory scrutiny Hypotheses Intra-industry Effect Null H1: Announcements of operational loss events have no intra-sector effect. 3 Scenarios: Within insurance industry Within commercial banking industry Within investment banking industry Alternative hypotheses: either negative or positive information spillovers dominate Hypotheses Inter-industry Effect Null H2: Announcements of operational loss events have no inter-sector effect. 4 Scenarios:

Effect of commercial bank events on investment banks Effect of investment bank events on commercial banks Effect of C-bank & I-bank events on insurers Effect of insurance events on C-banks and I-banks Alternative hypotheses: either negative or positive information spillovers dominate Hypotheses Inter-industry Effect: Commercial and investment banking sectors Commercial banks have expanded into the investment banking arena since 1980s The Fed lifted restriction under Section 20 of the Glass-Steagall Act of 1933 But, many investment banks remain largely pure investment banks and do not offer traditional commercial bank products

Thus, investment bank events should affect both nonannouncing commercial and investment banks. Commercial bank events mainly affect non-announcing commercial banks Hypotheses Inter-industry effect: Effect of insurance events on banks Commercial banks enter insurance, mid-1980s Annuities account for 2/3 of banks insurance premiums Premiums from life and P-L insurance also growing rapidly Commercial banks rather than investment banks have been the major players during the banks expansion into the insurance market Thus, insurance events expect to have stronger impact on commercial banks than on investment banks Hypotheses Inter-industry effect: Effect of bank events on insurers Competition with investment banks

Securities issuance Commercial mortgages & mortgage backed bonds Mutual funds Competition with commercial banks Annuities, mutual funds, life insurance SPDAs, GICs Pension plan management No clear prediction on whether insurers respond more strongly to C-bank events or I-bank events Hypotheses - Deceptive Sales I Market conduct (deceptive sales) problems Especially severe for insurers A byproduct of competitive pressures caused by bank entry into annuity and insurance markets Null H3: Non-announcing insurers are not affected by the deceptive sales events of a few

insurers. Alternative hypotheses: either negative or positive information spillovers dominate Hypotheses- Deceptive Sales II Null H4: The banks are not affected by insurer deceptive sales events. Alternative hypotheses: either negative or positive information spillovers dominate Do the deceptive sales problems extend to bank distribution channel? Do banks have differential response to insurer deceptive sales events? Hypotheses Pure vs. InformationBased Spillover Effects Testing for pure vs. information-based spillovers Cross

sectional regression: dependent var = market value loss (CAR in %) Event or firm characteristics as independent variables Information-based: significance of some variables reveals market is penalizing correlated insurers Pure contagion: no significant explanatory variables reveals indiscriminate effect regardless of correlation among firms Outline Background on operational risk Background on financial sector integration Literature review Hypotheses Data, sample selection and methodology Results Conclusion Data OpVar Quantitative Loss database

Data on operational loss events in several industries from the 1970s-present from public sources Compiled by Algorithmics, member of Fitch Group Event date the first public announcement of events Settlement date Description of event Event type and business lines Loss amount final settlement amount Most events (97%) are after 1985 Unique opportunity to study the effect of integration Summary Statistics (Millions $) Mean Median Std Dev

Min Max N Banks All Operational Losses 193.20 101.35 Deceptive Sales Events 156.51 82.28 All Operational Losses 224.14 117.80 377.60 50.16 2,256.75 Deceptive Sales

Events 91 37% 340.55 137.61 572.64 52.72 2,256.75 34 277.90 50.20 2,532.39 247 20% 150.96 51.02 774.54 49 Insurers Operational Loss Severity Distribution 70% 60% Percent of Losses 50%

Banking Insurance 40% 30% 20% 10% 0% 0-50 50-100 100150 150200 200250 250300 300350 Loss Amount ($Millions) 350400 400450 450500

> 500 Operational Loss Events: US Banks 70 60 Number 50 7000 Number Amount 6000 5000 4000 40 30 20 10 0 3000 2000 1000 0 Amount ($ Millions)

80 Operational Loss Events: US Insurers 30 Number 25 6000 Number Amount 5000 4000 20 3000 15 10 2000 5 1000 0 0

Amount ($ Millions) 35 Events by Event Type: US Banks 70% 60% Frequency Severity 50% 40% 30% 20% 10% 0% d d ts ct ts ss on u u a e

c i e t r a a p P ss du Fr Fr oc u A r y o l l r o P is al Pr na na pl r

c , & r D i s te ts te y Em ec x n us In h x e E B i P E Cl Events by Event Type: US Insurers 100% 90% 80%

70% 60% 50% 40% 30% 20% 10% 0% Frequency Severity s d d ct ts ss on u u a et c i e t r a a

s p P s du Fr Fr oc u A r y o l l r o P is Pr al na na pl r c , & r D i

s c s te ts te y Em e x n u In h x E B ie P E Cl Events by Business Line: US Banks 40% 35% 30% 25% 20% 15% 10% 5%

0% Frequency Severity Mean CARs: Announcing Banks and Insurers 1% 0% -1% -2% -3% Insurers Banks -4% Insurer losses larger and emerge over wider window. -5% Study Design: Spillover Effects Impact on non-announcing publicly traded banks and insurers around each event OpVar,

CRSP, Compustat Non-announcing firms Commercial banks: SIC 602, 6711 Investment banks: SIC 621, some 6282 Insurers: SIC 631 (life) and 633 (P-L) Large Events exceeding $50 million Methodology Event study is conducted to measure the effect of op risk events on stock prices Standard market model R jt j j Rmt jt Abnormal return AR jt R jt a j b j Rmt Cumulative abnormal return

T2 CAR(T1 ,T2 ) j AR jt t T1 Significance Tests Since all non-announcing firms are paired with each event, and some events happen on the same day, clustering of events is present Jaffees (1974) calendar time t-test used to correct for cross-sectional dependence caused by clustering Other tests also conducted to check robustness Variance adjusted z-test Non-parametric (generalized sign z) test Outline Background on operational risk Background on financial sector integration Literature review Hypotheses

Data, sample selection and methodology Results Conclusion Banking Industry: Intra-Sector Effect All events: Mean CAR Days N Mean CAR Variance adjusted z-stat Calendar time t-test Generalized sign z-test Commercial bank events: (-1,+1) 158 -0.06%

-12.275 *** -1.784 $ -5.727 *** (-10,+10) 158 -0.51% -14.402 *** -1.745 $ -4.518 *** (-1,+10) -0.38% -18.233 *** -2.382 * -7.678 *** 158 Investment bank events: (-1,+1)

89 -0.59% -8.512 *** -1.354 -5.098 *** (-10,+10) 89 -0.68% -3.772 *** -0.462 -1.492 $ (-1,+10) 89 -1.45% -9.844 *** -1.169

-5.683 *** Intra-Sector Effect: Banks 0.00% -0.20% -0.40% -0.60% (-1,+1) (-1,+5) (-1,+10) -0.80% -1.00% -1.20% -1.40% -1.60% C-Banks I-Banks Banking Industry: Intra-Sector Effect All Events: Mean CAR (-10,+10) (-10,10): -0.47%*** 0.20% Affected Banks:

0.10% (-10,10): -1.27%** CAR 0.00% Spillover Effect: 37% -0.10% -0.20% -0.30% -0.40% -0.50% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 Days 2 3 4 5 6

7 8 9 10 Inter-Sector Effect: Banks 0.00% -0.10% -0.20% -0.30% -0.40% -0.50% -0.60% -0.70% -0.80% -0.90% (-1,+1) (-1,+5) (-1,+10) C-Banks on I-Banks I-Banks on C-Banks I-Banks events have strong effect on C-Banks. C-Bank effect on I-Banks dissipates rapidly. Insurance Industry: Intra-Sector Effect All Events: Mean CAR

Days N Mean CAR Variance adjusted z-stat (-1,+1) 91 -0.20% -3.981 *** -1.561 -4.212 *** (-5,+5) 91 -0.36% -3.601 ***

-1.916 $ -2.519 ** (-10,+10) 91 -0.68% -3.902 *** -2.080 * 0.012 (-15,+15) 91 -0.96% -4.480 *** -2.194 * -0.443 (-15,-1) 91

0.03% (-1,+5) 91 -0.37% -4.697 *** -2.280 * -2.956 ** (-1,+10) 91 -0.64% -6.384 *** -2.600 * -1.535 $ (-1,+15) 91

-1.02% -7.393 *** -3.536 *** -5.304 *** 0.616 Calendar time t-test 0.449 Generalized sign z-test 2.944 ** Intra-Sector Effect: Insurers 0.00% -0.20% -0.40% (-1,+1) (-1,+5) (-1,+10) (-1,+15) -0.60%

-0.80% -1.00% -1.20% All Insurance Insurance Industry: Intra-Sector Effect All Events: Mean CAR 0.50% CAR 0.00% -0.50% (-1,15): -1.02%*** Affected insurers: (-1,15): -3.88%** -1.00% Spillover Effect: 26% -1.50% -15 -10 -5

0 Days 5 10 15 Insurance Industry: Intra-Sector Effect Deceptive Sales Events: Mean CAR 0.4% 0.2% Non-Deceptive Sales Events 0.0% CAR -0.2% -0.4% -0.6% -0.8% -1.0% Deceptive Sales Events -1.2%

-1.4% -15 -10 -5 0 Days 5 10 15 Inter-Sector Effect: Bank Events on Insurers Days N Mean CAR Variance adjusted z-stat Calendar time t-test

Generalized sign z-test Impact of commercial banks events: (-1,+1) 158 -0.07% -3.996 *** -1.013 -0.509 (-10,+10) 158 -0.39% -6.077 *** -1.713 $ 0.761 (-1,+10)

158 -0.37% -8.068 *** -1.929 $ -1.276 Impact of investment banks events: (-1,+1) 89 -0.15% -6.016 *** -2.339 * 0.178 (-10,+10) 89 -0.23% -5.336 ***

-1.812 $ 2.005 * (-1,+10) 89 -0.76% -14.988 *** -3.141 ** -5.758 *** Inter-Sector Effect: Bank Events on Insurers 0.00% -0.10% -0.20% -0.30% -0.40% -0.50% -0.60% -0.70% -0.80% (-1,+1) (-1,+5) (-1,+10)

C-Bank Events on Insurers I-Bank Events on Insurers I-Bank events affect insurers more strongly than C-bank events. Inter-Sector Effect: Insurance Events on Banks Days N Mean CAR Variance adjusted z-stat Calendar time t-test Generalized sign z-test Impact on commercial banks: (-1,+1) 91

-0.12% -4.471 *** -1.109 -0.100 (15,+15) 91 -1.52% -21.884 *** -1.900 $ -10.490 *** (-1,+15) 91 -1.21% -23.760 *** -2.852 **

-11.771 *** Impact on investment banks: (-1,+1) 91 -0.18% (15,+15) 91 -0.15% (-1,+15) 91 -0.69% -1.898 * 0.261 -2.564 ** 0.327 -2.136 * 0.751

1.771 * -0.400 -1.021 Inter-Sector Effect: Insurance Events on Banks 0.00% -0.20% -0.40% -0.60% (-1,+1) (-1,+10) (-1,+15) -0.80% -1.00% -1.20% -1.40% Insurer Events on C-Banks Insurer Events on I-Banks Insurer events have only weak effects on I-Banks. Insurer events affect C-banks as strongly as insurers.

Regression Analysis Pure versus information based effects Regression Hypotheses Pure vs. Information-Based Spillover Effects Size of operational loss events Negative spillover effect Indicate possible size of future loss of non-announcing firms Large losses less frequent more likely to convey new information Competitive effect Indicate the severity of losses for announcing firm Larger losses lead to larger gains in market value for rivals Null

Hypothesis 5: Size of operational loss has no relation with the market value impact on non-announcing firms Regression Hypotheses Pure vs. Information-Based Spillover Effects II Firms growth prospects If announcement of events changes investors expectation about the future cash flows of nonannouncing firms Firms with higher growth prospects are likely to have a more severe effect More likely to have to forego positive-NPV projects due to future operational losses Null Hypothesis 6: Market-value losses of nonannouncing firms are unrelated to their growth prospects. Regression Hypotheses Pure vs. Information-Based Spillover Effects III Insolvency risk: Prediction ambiguous Firm

with low equity-to-assets ratios more likely to enter into financial distress from possible future losses inverse relationship of E/A and MV loss Deep Pockets theory of liability: firm with low equityto-assets ratio are less likely to be sued direct relationship of E/A and MV loss Option theory: stock price of a firm with low equity-toassets ratio is less sensitive to new information direct relationship of E/A to MV loss Null Hypothesis 7: MV loss of non-announcing firms not related to insolvency risk. Regression Hypotheses Pure vs. Information-Based Spillover Effects IV Market conduct problems Reputation is a very valuable intangible asset of financial service firms These events might influence firm value more than other types events due to: Reputational damage Increase in compliance costs Events

at announcing firms could drive customers to non-announcing firms, producing competitive effect Null H8: Market conduct problems have no differential effects compared with other events. Regressions Bank Events Dependent Variable: CAR(-10,10) All Bank Event on Banks Intercept 0.001 LogMve -0.002 *** Log Loss 0.003 *** Q Ratio Equity/Assets Deceptive Deceptive* IBankEvt CBank IBank

IEvt ComBank Evt on Banks 0.004 -0.001 *** 0.001 InvBank Evt on Banks All Bank Event on Insurers -0.025 *** 0.011 *** -0.003 *** -0.002 *** 0.007 *** 0.002 *** -0.003 *** -0.003 *** -0.004 ***

-0.012 *** 0.012 ** 0.016 ** 0.015 * 0.007 * -0.002 * -0.003 ** 0.023 *** 0.025 *** -0.001 0.015 *** -0.004 * -0.008 *** -0.006 *** Bank Event Regressions: Implications I Log(MVE) < 0 implies larger banks have

larger market value loss More Log(Loss) > 0 implies larger losses cause lower MV loss at non-announcing firms vulnerable due to complex operations Some evidence of competitive effect E/A > 0 implies lower MV loss for better capitalized firms: Fin. distress dominant Bank Event Regressions: Implications II Q < 0 implies firms with stronger growth prospects have larger MV loss Deceptive sales (DS) dummy implies Commercial bank DS events have negative information spillovers to banks Investment bank DS events have positive spillovers to banks (competitive effect)

Bank DS events due not have differential spillover effect on insurers Regressions Insurance Events Dependent Variable: CAR(-15,15) Insurance Events Insurance Events on Insurers on Banks Intercept 0.079 *** 0.113 *** LogMve -0.004 *** -0.002 *** Log Loss -0.007 *** -0.008 *** Q Ratio -0.023 *** -0.060 ***

0.016 * 0.054 ** Equity/Assets Deceptive -0.011 *** ComBank Life Deceptive*ComBank -0.001 -0.017 *** 0.001 -0.001 Insurer Event Regressions: Implications Log(MVE) < 0 implies larger insurers have larger market value loss Log(Loss) < 0 implies larger losses cause higher MV loss at non-announcing firms

Evidence of contagion effect E/A > 0 implies lower MV loss for better capitalized insurers and banks Financial distress effect dominant Insurer Event Regressions: Implications II Q < 0 implies firms with stronger growth prospects have larger MV loss Deceptive sales dummy implies Insurer deceptive sales events cause higher loss to other insurers than other types of events Insurer events do not differentially affect banks Insurer events affect C-banks more than I-banks Conclusions: Negative Information Spillovers? Banks Insurers

Bank Events Yes Yes Insurance Events Yes Yes Commercial Banks Investment Banks Commercial Bank Events Yes Yes for (-1,+1), then dies out Investment Bank Event

Yes Yes Conclusions: Information Based Spillovers Evidence on information-based contagion Firms with high growth potential are more adversely affected Financially vulnerable firms are more adversely affected Insurance deceptive sales event have more adverse effect then other types of events but only within insurance industry Insurance events affect C-banks more than on I-banks Conclusions: Information Based Spillovers II Evidence on information-based contagion For commercial and investment banks, intraindustry spillovers are significantly larger than the inter-industry spillovers

Investment bank events negatively affect both commercial and investment banks, Commercial events mainly negatively affect commercial banks I bank response for (-1,+1) but dies out rapidly Conclusions: Overall Implications Negative information spillovers are information based and hence not likely to cause social costs or panics Strong inter-sector effects provide evidence that the U.S. financial sector has achieved significant integration Information spillovers imply that market discipline is an effective regulatory tool The End Thank you! Back-up Slides Convergence: Cross-sector M&As Acquirer Commercial Bank Investment Banks Insurers

Target Inv Banks Insurer & Agencies Comm Banks Insurer & Agencies Comm Banks Inv Banks Total # of Deals 1985-2004 151 229 52 49

22 76 Average # of Deals per Year 1985-1994 2 1.2 1 1.3 0.5 1.7 Average # of Deals per Year 1995-2004 11.2 20.5

3.3 2.4 1.2 4.2 Bank Share of Individual Annuity Premium ($ billion) Year Total Bank Bank Share (%) 1995 98.7 14.2 14.4% 1996 111.4 17.2

15.4% 1997 126.1 19.3 15.3% 1998 131.5 19.7 15.0% 1999 156.5 26.4 16.9% 2000 190.5

31.0 16.3% 2001 184.5 38.3 20.8% 2002 217.9 48.9 22.4% 2003 210.8 50.1 23.8% 2004 217.9

48.3 22.2% Convergence of financial services: Commercial and investment banking Sectors Regulatory restrictions Glass-Steagal Act of 1933 separated commercial banking from investment banking Deregulation: Wholesale financial services In 1987 commercial banks permitted to engage in limited investment banking through Section 20 subsidiaries 1987, I-banking limited to 5% of gross revenue 1996, I-banking permitted up to 25% of gross revenue 1999, Gramm-Leach-Bliley Act removed all remaining

restrictions and permits Financial Holding Companies (FHCs) to engage in all types of financial services through subsidiaries Regressions Bank Events Dependent Variable: CAR(-10,10) All Bank Event on Banks Intercept 0.001 LogMve -0.002 *** Log Loss 0.003 *** Q Ratio Equity/Assets Deceptive Deceptive* IBankEvt CBank IBank IEvt ComBank Evt on Banks 0.004

-0.001 *** 0.001 InvBank Evt on Banks All Bank Event on Insurers -0.025 *** 0.011 *** -0.003 *** -0.002 *** 0.007 *** 0.002 *** -0.003 *** -0.003 *** -0.004 *** -0.012 *** 0.012 **

0.016 ** 0.015 * 0.007 * -0.002 * -0.003 ** 0.023 *** 0.025 *** -0.001 0.015 *** -0.004 * -0.008 *** -0.006 *** Operational Loss Events: US Banks 70 60 Number 50 7000 Number

Amount 6000 5000 4000 40 30 20 10 0 3000 2000 1000 0 Amount ($ Millions) 80 Operational Loss Events: US Insurers 30 Number 25 6000 Number

Amount 5000 4000 20 3000 15 10 5 0 2000 1000 0 Amount ($ Millions) 35 CARs by Window: Announcing Banks (0,0) 0.0% -0.2% -0.4% -0.6% -0.8% -1.0% -1.2% -1.4%

-1.6% -1.8% -2.0% (-1,+1) (-2,+2) (-3,+3) (-5,+5) (-10,+10) (-15,+15) (-20,+20) CARs by Window: Announcing Insurers (0,0) 0.0% -0.5% -1.0% -1.5% -2.0% -2.5% -3.0% -3.5% -4.0% -4.5% (-1,+1) (-1,+2) (-1,+3)

(-1,+5) (-1,+10) (-1,+15) (-1,+20) Hypotheses Pure vs. InformationBased Spillover Effects I Pure contagion effect The spillover effect to other firms regardless of cause of the event or the risk characteristics of non-announcing firms Information-based contagion effect If the cause of event is correlated across firms, only the correlated firms are affected Investors are able to differentiate across firms with different risk characteristics Regressions Bank Events Dependent Variable: CAR(-10,10) Bank Response To All Bank Evt Intercept

0.001 LogMve -0.002 *** Log Loss 0.003 *** Q Ratio Equity/Assets Deceptive Deceptive* IBankEvt CBank IBank IEvt ComBank Evt on Banks 0.004 -0.001 *** 0.001 InvBank Evt on Banks All Bank Event on Insurers

-0.025 *** 0.011 *** -0.003 *** -0.002 *** 0.007 *** 0.002 *** -0.003 *** -0.003 *** -0.004 *** -0.012 *** 0.012 ** 0.016 ** 0.015 * 0.007 * -0.002 * -0.003 **

0.023 *** 0.025 *** -0.001 0.015 *** -0.004 * -0.008 *** -0.006 ***

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