Good Monitoring, Bad Monitoring

Intro Legal background Hypotheses Data Results Conclusion

Good Monitoring, Bad Monitoring

Yaniv Grinstein Stefano RossiCornell Purdue, CEPR, ECGI

Bocconi, October 15, 2012

Yaniv Grinstein and Stefano Rossi Good Monitoring, Bad Monitoring

Administrator

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Administrator

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Are Courts Effective Monitors of Business Decisions?I U.S. corporate governance relies strikingly little on courts’ scrutiny

(”monitoring”) of business decisions

I In principle, if shareholders believe that their wealth is not beingmaximized, they can sue directors for breach of fiduciary duties

I In practice, courts apply business judgment rule: Common law”presumption that in making a business decision the directors of acorporation acted on an informed basis, in good faith, and in thehonest belief that the action was in the best interest of the company”=⇒ largely avoid second-guessing managerial intentions

I Why are courts so hands-off? Little evidence so far

I Traditional arguments: judges would make poor business decisions;fear of personal liability would reduce managerial risk-taking anddeter talented people from sitting on boards; liability rules would bea poor governance mechanism for public firms

I These arguments assume uniform effect of scrutiny on firms.We argue that effects of scrutiny on firms can be heterogeneous

I Can we measure these effects?



Shareholder Wealth Effects of Courts’ Scrutiny

I How does courts’ scrutiny affect shareholders’ wealth?

I Not obvious that U.S. courts’ hands-off attitude is optimal. In aCoasian world, want to write state-contingent provisions in corporatecharter (e.g. ”fire subordinates if they make improvident loans”),and want courts to be actively enforcing them

I Hard to measure wealth effects of scrutiny/monitoring

I Severe identification problem: correlation of governance mechanismand value not enough to establish causation (directors’ abilityunobservable; corporate governance endogenous)

I To make it even harder, corporate law and regulation change littleover time; often apply uniformly to population of firms



This PaperI We exploit a sharp and unexpected shift in markets’ perception of

business judgment ruleI Delaware Supreme Court controversial landmark decision in Smith v.

Van Gorkom in 1985 held firm directors grossly negligent and liablefor monetary damages for breach of fiduciary duties

I Tighten scrutiny over board decisions, only for Delaware firms

I Unexpected and controversial, significant shift from previousdoctrine. We argue it is exogenous w.r.to shareholder wealth

I We study effect of Supreme Court decision on stock prices andcorporate policies

I We employ matching and differences-in-differences techniques

I Our hypothesis:I Increased scrutiny makes directors of all firms more conservative

I Conservatism is good in low-growth industries (”good monitoring”)Reduce overinvestment - e.g., Jensen (1986)

I Conservatism is bad in high-growth industries (”bad monitoring”)Can cause underinvestment



LiteratureI Board monitoring and firm value

I Hermalin and Weisbach (1998, 2003): empirical studies failed toaccount for endogeneity of boards =⇒ correlations of governanceand firm value not causal

I Large corporate finance literature on effects of Delaware law (e.g.Garvey and Hanka (1999); Bertrand and Mullainathan (2003))

I Focus on anti-takeover statutes; and hard to measure preciselywealth effects of law (leaks, rumors, etc.). Here, we focus onfiduciary duties, and unexpected court decision

I State competition in corporate law; determinants/consequences of(re-)incorporation decision (Daines (2001), Subramaniam (2004),Bebchuk, Cohen and Ferrell (2002))

I Focus on returns or valuations of Delaware and non-Delaware firms,often without exogenous variation or matching, and does not allowfor case law and state law to affect different firms differently

I We show importance of using economic theory to formulatehypotheses of effect of court decisions on firm value (o/w may fail torecognize large effects on stock prices)



Smith v. Van Gorkom: The FactsI Van Gorkom (CEO of Trans Union) agreed to sell at $55 (48%

premium) to raider Jay Pritzker in an LBO

I Disclosed few days later to board. Dissent among seniormanagement; merger agreement is amended and proposal goesahead. Shareholder Alden Smith files a lawsuit

I Two lower courts (including Delaware Chancery court) ruled thatdirectors had acted within boundaries of business judgment

I Upon appeal, Delaware Supreme Court ruled that:

1. Board decision not a product of informed business judgment,because directors did not fully inform themselves before deciding;

2. Proxy statement to shareholders was misleading

I Supreme Court rejected defendants’ argument that premium paidover stock price made it a good deal; and made directors grosslynegligent and liable for monetary damages



Smith v. Van Gorkom and Fiduciary Duties

I Delaware Supreme Court changed perception of business judgmentrule with Smith v. Van Gorkom

I Held managers liable for monetary damages

I Unexpected, exogenous tightening in courts’ scrutinyI New York Times 01/31/1985:

I ”In a major legal decision with broad implications for corporatedirectors, the Supreme Court of Delaware has found that directors ofthe Trans Union Corporation breached their fiduciary duty toshareholders when they agreed in 1980 to sell the company to theMarmon Corporation for $688m This is one of the few times inmodern corporate law history ... that directors have been found liablefor not living up to the standards of the business judgment rule. Thedecision, said Kenneth J. Bialkin of the law firm of Willkie Farr &Gallagher, ’raises significant concerns about the reach andapplicability of the business judgment rule’”



Smith v. Van Gorkom: Unexpected and Controversial

I Smith v. Van Gorkom was unexpected and enormously controversial

I Two lower courts had ruled business judgment rule applied. ”Theoutcome of the case was exactly opposite to what virtually everyobserver of Delaware law would have predicted” (Macey-Miller 1988)

I Split decision 3-2. Minority Judge McNeilly: ”Comedy of errors”.Fischel (1985): ”surely one of the worst decisions in the history ofcorporate law”. Manning (1985): ”atrocious ... exploded a bomb”..

I Taken to be immediately binding only for firms incorporated inDelaware

=⇒ Quasi-natural experiment to estimate value of monitoring



Effect of SVG on Insurance and Market for Directors

I Insurance market (Wyatt survey, Hartmann and Rogers (1991))

I Insurance supply

I 1984 - 32 insurers offered D&O insurance

I In 1985 - 15.6% of those discontinued

I Insurance priceI D&O insurance premiums increased more than 900% following SVG

(191% in the quarter of SVG decision)

I Market for directors

I Korn/Ferry international 1987 survey:

I 22% of responding companies had board candidates who declinedboard service

I 14.2% indicated the reason was legal liability

I Compared with 2% in 1984

I We examine changes in board structure with our data



Overturning Smith v. Van Gorkom by Regulation 102(b)7

I Smith v. Van Gorkom generated such an outcry that state ofDelaware decided to overturn it by means of regulation

I Concern that such insurance crisis would keep many qualifiedindividuals from being willing to serve as directors

I Section 102(b)7, enacted on July 1, 1986, allowed shareholders tovote to relieve directors of liability for breaches of fiduciary duties

I 94% of Delaware firms quickly modified their charters accordingly

I Bradley and Schipani (1989 p. 43): ”If the court were to decideTrans Union today, and if the company had amended its articles ofincorporation to eliminate liability in accordance with section102(b)(7), it is likely the court would exonerate the directors frommonetary liability to the shareholders”

=⇒ Out-of-sample test representing exogenous decrease in monitoring=⇒ Expect opposite effects w.r. to Smith v. Van Gorkom



Timeline



Hypotheses Development

I Manager chooses investment ϕ to maximize:

U (ϕ) = V (ϕ) + B (ϕ)− ϕ

where V (ϕ)− ϕ is shareholder value and B (ϕ) are private benefits

I Shareholders only care about S = V (ϕ)− ϕ

I Managers’ and shareholders’ interests diverge

I Under V (ϕ) = g · ln (ϕ) and B (ϕ) = ln (ϕ):

Manager chooses: ϕ∗ = g + 1Shareholders wish: ϕs = g

=⇒ Manager overinvests

I g represents growth opportunities



Hypotheses - The Effects of Smith v. Van GorkomI Smith v. Van Gorkom tightens monitoring, imposes cost on

managers (not shareholders): investment cost is now mϕ, m > 1.Now manager chooses:

ϕ∗ =g + 1

m

I Change in shareholder value is:

∆S =m− 1

m(g + 1)︸︷︷︸

Benefits from reduced

overinvestment

− g · ln (m)︸︷︷︸Costs from

underinvestment

I ∆S monotonically decreasing in g, equals 0 for g = g∗:

g∗ =m− 1

m ln (m)− (m− 1)



Shareholder Utility v. Managerial Utility

Low-growth industries

Effect of Smith v. Van Gorkom: Increase m

Expect: CAR>0



Shareholder Utility v. Managerial Utility (cont.)

High-growth industries

Expect: CAR<0

Effect of Smith v. Van Gorkom: Increase m



Testable Hypotheses

I Expect ∆S < 0 if high growth opportunities, g > g∗

I Too much monitoring stifles growth in industries with high growthopportunities

I Expect ∆S > 0 if low growth opportunities, g < g∗

I Increased monitoring reduces private benefits and boosts value inindustries with low growth opportunities

I Expect the opposite for decrease in monitoring from Section 102(b)7



Methodology

I Examine abnormal returns on portfolios long Delaware-incorporatedfirms and short appropriately matched non-Delaware firms

I Matched sample methodology introduced by Barber and Lyon (1997)and Barber, Lyon and Tsai (1999)

I Akin to differences-in-differences w.r. to stock prices

I Separately for high-growth and low growth industries

I Focus on equally-weighted portfolios to see effect for average firm

I We also provide value-weighted returns



Table 1 - Sample Construction

Selection criterion Number of firmsCompustat firms with market cap > $100m as of 1984and historical SIC codes from CRSP as of 1/2/1985. 1284Excluding firms without relevant data for financialvariables (assets, book leverage, cash flow).Excluding financials (SIC 6xxx) 103Excluding regulated industries (SIC 4xxx) 209Excluding firms with missing incorporation data 12Final Sample 960



Table 2 - State of IncorporationI We rely on Moody’s (1985) for information on state of incorporation

I Compustat does not keep historical incorporation information.Whenever a firm re-incorporates, Compustat discards old information

Compustat Moody’sNumber of non-missing observations 831 960Number of firms incorporated in Delaware 469 388Misclassifying non Delaware as Delaware 138Misclassifying Delaware as non Delaware 18

I Out of 469 firms that Compustat reports as incorporated inDelaware in 1985, only 331 were actually incorporated in Delaware in1985. Remainder reincorporated in Delaware afterward

I Compustat misclassifies 40% [138/(469-138)] of Delaware firms=⇒ cannot rely on Compustat, need Moody’s (1985)



Table 3 - Summary Statistics

Panel A - Industry distribution

All Delaware Non-Delaware Difference(n = 960) (n = 388) (n = 572)

Agriculture 0.5% 0.5% 0.5%Mining, construction 7.8% 8.8% 7.2% ∗

Manufacturing A 26.7% 28.9% 25.2% ∗∗

Manufacturing B 40.6% 36.3% 43.5% ∗∗∗

Wholesale, retail 13.5% 11.6% 14.9% ∗∗∗

Services 8.6% 11.3% 6.8% ∗∗∗

Health 2.2% 2.6% 1.9% ∗∗∗

I Large differences in industry compositions =⇒ Need to control foridiosyncratic industry shocks

I E.g. large oil price drop after OPEC pact signed on January 30

I We match by industry (4- or 3-digit SIC)



Table 3 - Summary Statistics

Panel B - Financial variablesAll Delaware Non-Delaware Difference

(n = 960) (n = 388) (n = 572)Mean Median Mean Median Mean Median t-test Wilcoxon

Mkt cap 1098 342 1174 420 1047 306 ∗∗∗

Assets 1777 454 2206 557 1486 385 ∗∗ ∗∗∗

Leverage 0.48 0.49 0.50 0.51 0.47 0.48 ∗∗∗ ∗∗∗

Q 1.55 1.29 1.48 1.24 1.59 1.32 ∗∗ ∗∗

Cashflow 0.05 0.07 0.05 0.06 0.05 0.07

I Large differences in size, leverage and Q=⇒ Within industries need to match by size

I Verify that other characteristics are matched, too, after industry andsize matching

I To test our hypotheses, need to split sample by industry Q. For now,take g∗ to be median; later, examine robustness to other thresholds



Unmatched Cumulative Abnormal Returns

I Large differences in predicted directions

I However, appropriate statistical testing requires matching



Table 4 - Matching Procedure

Panel A - High-growth industries (Matching 174 Delaware firms)

Delaware Non-Delaware DifferenceMean Median Mean Median t-test Wilcoxon

Market cap ($m) 1018 438 805 319Assets ($m) 1050 412 865 395 ∗

Leverage 0.48 0.48 0.45 0.47Tobin’s Q 1.73 1.47 1.76 1.46Cash flow 0.04 0.06 0.06 0.06



Table 4 - Matching Procedure

Panel B - Low-growth industries (Matching 178 Delaware firms)

Delaware Non-Delaware DifferenceMean Median Mean Median t-test Wilcoxon

Market cap ($m) 1449 468 981 499 ∗∗∗

Assets ($m) 3615 927 1828 850 ∗∗∗

Leverage 0.52 0.52 0.52 0.51Tobin’s Q 1.19 1.06 1.30 1.15 ∗∗ ∗∗

Cash flow 0.05 0.06 0.06 0.07



Returns to Tighter Monitoring (Smith v. Van Gorkom)

-3.00%

-2.50%

-2.00%

-1.50%

-1.00%

-0.50%

0.00%

0.50%

1.00%

1.50%

2.00%

High-growth industries Low-growth industries

NY Times Coverage

Smith v. Van Gorkom



Table 5 - Equally-Weighted CARs, Smith v. Van Gorkom

Panel A - High-growth industries

Window Delaware Non-Delaware Difference t-stat Wilcoxon[0, 1] 0.8% 0.9% −0.1%[0, 2] 1.3% 1.7% −0.4%[0, 3] 1.0% 2.1% −1.0% ∗∗ ∗∗∗

[0, 4] 0.4% 1.6% −1.2% ∗∗ ∗∗∗

[0, 5] 1.3% 2.7% −1.4% ∗∗ ∗∗∗

[0, 6] 2.0% 3.9% −1.9% ∗∗∗ ∗∗∗

[0, 7] 2.4% 4.7% −2.3% ∗∗∗ ∗∗∗

[0, 8] 3.3% 5.9% −2.6% ∗∗∗ ∗∗∗

[0, 9] 4.0% 6.0% −2.1% ∗∗ ∗∗∗

[0, 10] 3.2% 5.3% −2.1% ∗∗ ∗∗∗

No firms 174 174



Table 5 - Equally-Weighted CARs, Smith v. Van Gorkom

Panel B - Low-growth industries

Window Delaware Non-Delaware Difference t-stat Wilcoxon[0, 1] 0.6% −0.1% 0.7% ∗∗∗ ∗∗∗

[0, 2] 0.9% 0.4% 0.5% ∗ ∗∗

[0, 3] 1.4% 0.4% 1.0% ∗∗∗ ∗∗∗

[0, 4] 1.1% 0.2% 0.9% ∗∗ ∗∗

[0, 5] 2.2% 0.8% 1.3% ∗∗∗ ∗∗∗

[0, 6] 2.8% 1.5% 1.3% ∗∗ ∗∗∗

[0, 7] 3.1% 1.9% 1.1% ∗∗ ∗∗∗

[0, 8] 4.0% 2.7% 1.3% ∗∗ ∗∗

[0, 9] 4.1% 2.5% 1.5% ∗∗ ∗∗

[0, 10] 3.4% 2.0% 1.4% ∗∗ ∗

No firms 178 178



Bootstrapping

I Under random assignments, 3-day CARs around SVG occur withprobability 0.01 (0.03) in high- (low-) growth industries

I Matching unlikely to produce tests of incorrect size

I Matching unlikely to produce inappropriate control groups

I Bootstrapping confirms that our results reflect the economicimpact of Smith v. Van Gorkom



Table 6 - Value-Weighted CARs, Smith v. Van Gorkom


Window Delaware Non-Delaware Difference t-stat[0, 1] 0.9% 1.4% −0.5% ∗

[0, 2] 1.0% 2.0% −1.0% ∗∗

[0, 3] 0.6% 2.1% −1.5% ∗∗∗

[0, 4] 0.0% 1.5% −1.6% ∗∗∗

[0, 5] 1.3% 2.9% −1.5% ∗∗

[0, 6] 1.5% 3.6% −2.0% ∗∗∗

[0, 7] 1.4% 4.0% −2.6% ∗∗∗

[0, 8] 2.3% 4.9% −2.6% ∗∗∗

[0, 9] 2.9% 5.3% −2.4% ∗∗∗

[0, 10] 2.1% 4.6% −2.5% ∗∗∗

No firms 174 174



Table 6 - Value-Weighted CARs, Smith v. Van Gorkom


Window Delaware Non-Delaware Difference t-stat[0, 1] 0.8% 0.2% 0.6% ∗∗

[0, 2] 1.0% 1.0% 0.0%[0, 3] 1.6% 1.0% 0.6%[0, 4] 1.1% 0.6% 0.5%[0, 5] 1.9% 1.3% 0.7%[0, 6] 2.7% 1.9% 0.8%[0, 7] 2.5% 2.5% 0.0%[0, 8] 3.3% 3.2% 0.2%[0, 9] 3.4% 3.3% 0.2%[0, 10] 2.7% 2.6% 0.2%No firms 178 178



Table 7 - Smith v. Van Gorkom - Low growth high cashflow

Window Equally-weighted t-stat Value-weighted t-stat[0, 1] 0.4% 0.3%[0, 2] 0.5% −0.7%[0, 3] 1.0% −0.2%[0, 4] 0.9% −0.5%[0, 5] 1.1% −0.5%[0, 6] 1.0% 1.0%[0, 7] 1.2% 0.6%[0, 8] 1.2% 0.3%[0, 9] 1.4% 0.0%[0, 10] 1.6% 0.2%No firms 74 74



Table 8 - Smith v. Van Gorkom - Unconditional CARs

Window Equally-Weighted Value-Weighted[0, 1] 0.29% 0.08%[0, 2] 0.04% -0.47%[0, 3] 0.00% -0.32%[0, 4] -0.10% -0.37%[0, 5] -0.04% -0.29%[0, 6] -0.28% -0.40%[0, 7] -0.55% -1.14%[0, 8] -0.61% -1.04%[0, 9] -0.23% -0.96%[0, 10] -0.32% -0.99%No firms 352 352



Table 9 – Sensitivity of results to different cutoffs g∗


Window 0% 30% 40% 50% 60% 70% 100%[0, 1] 0.29% 0.06% 0.00% -0.14% -0.21% -0.25% .[0, 2] 0.04% -0.01% -0.20% -0.45% -0.47% -0.48% .[0, 3] 0.00% -0.13% -0.56% -1.01%∗∗ -1.16%∗∗ -0.92% .[0, 4] -0.10% -0.23% -0.69% -1.18%∗∗ -1.31%∗∗ -1.01% .[0, 5] -0.04% -0.33% -0.88%∗∗ -1.43%∗∗ -1.53%∗∗ -1.32% .[0, 6] -0.28% -0.79% -1.33%∗∗ -1.93%∗∗∗ -2.35%∗∗∗ -2.23%∗ .[0, 7] -0.55% -1.19%∗∗ -1.66%∗∗∗ -2.34%∗∗∗ -3.01%∗∗∗ -2.97%∗∗ .[0, 8] -0.61% -1.33%∗∗ -1.85%∗∗∗ -2.66%∗∗∗ -3.31%∗∗∗ -3.27%∗∗ .[0, 9] -0.23% -0.99% -1.38%∗∗ -2.10%∗∗∗ -2.74%∗∗∗ -2.70%∗ .[0, 10] -0.32% -1.02% -1.39%∗∗ -2.09%∗∗ -2.94%∗∗∗ -2.73%∗ .No firms 352 238 206 174 132 99 0



Table 9 – Sensitivity of results to different cutoffs g∗


Window 0% 30% 40% 50% 60% 70% 100%[0, 1] . 0.76%∗∗ 0.69%∗∗∗ 0.70%∗∗∗ 0.59%∗∗∗ 0.49%∗∗ 0.29%[0, 2] . 0.14% 0.37% 0.51%∗ 0.34% 0.24% 0.04%[0, 3] . 0.27% 0.79%∗ 0.99%∗∗∗ 0.70%∗∗ 0.36% 0.00%[0, 4] . 0.17% 0.74% 0.96%∗∗ 0.63%∗ 0.26% -0.10%[0, 5] . 0.56% 1.14%∗∗ 1.33%∗∗∗ 0.86%∗∗ 0.46% -0.04%[0, 6] . 0.75% 1.18%∗∗ 1.32%∗∗∗ 0.95%∗∗ 0.47% -0.28%[0, 7] . 0.77% 0.99% 1.19%∗∗ 0.91%∗ 0.39% -0.55%[0, 8] . 0.87% 1.10% 1.38%∗∗ 0.98%∗ 0.42% -0.61%[0, 9] . 1.34% 1.37%∗∗ 1.58%∗∗ 1.26%∗∗ 0.73% -0.23%[0, 10] . 1.11% 1.17% 1.40%∗∗ 1.23%∗∗ 0.62% -0.32%No firms 0 114 146 178 220 253 352



Equally-Weighted CARs to Low-High Growth Portfolio as aFunction of g∗



Taking Stock

I Firms in low-growth industries gain from tightened scrutiny

I Firms in high-growth industries lose from tightened scrutiny

I What is the source of these effects?

I Examine changes in investment and financial policies, and in boardstructure



Table 10 - Investment and Capital Structure around SVG

Panel A - High-growth industriesDependent variable: CapEx Acquisitions R&D Debt Cash Holdings

(1) (2) (3) (4) (5)Delaware * After 0.0008 0.0029 0.0027 -0.0222* -0.0136*

(.0053) (.0078) (.0025) (.0130) (.0074)Firm dummies? Yes Yes Yes Yes YesTime dummies? Yes Yes Yes Yes Yes

Adjusted R2 0.87 0.57 0.93 0.83 0.92No firms 479 479 479 479 479

Panel B - Low-growth industriesDependent variable: CapEx Acquisitions R&D Debt Cash Holdings

(1) (2) (3) (4) (5)Delaware * After -0.0090* -0.0128* -0.0003 0.0003 0.0092

(.0049) (.0077) (.0006) (.0083) (.0060)Firm dummies? Yes Yes Yes Yes YesTime dummies? Yes Yes Yes Yes Yes




Table 11 - Board Structure and Compensation around SVG

Panel A - High-growth industriesDependent variable: Board Size % Turnover % Turnover Meeting Fees Annual Fees

(overall) (outsiders) ($) ($)(1) (2) (3) (4) (5)

Delaware * After -0.2 -0.1 0.2 -31.2 48.8(0.3) (1.6) (1.2) (52.0) (864.4)

Firm dummies? Yes Yes Yes Yes YesTime dummies? Yes Yes Yes Yes Yes


Panel B - Low-growth industriesDependent variable: Board Size % Turnover % Turnover Meeting Fees Annual Fees

(overall) (outsiders) ($) ($)(1) (2) (3) (4) (5)

Delaware * After 0.1 -1.5 0.2 18.2 -54.8(0.4) (1.9) (1.2) (39.4) (657.2)

Firm dummies? Yes Yes Yes Yes YesTime dummies? Yes Yes Yes Yes Yes




Returns to Tighter Monitoring (Smith v. Van Gorkom)

-3.00%

-2.50%

-2.00%

-1.50%

-1.00%

-0.50%

0.00%

0.50%

1.00%

1.50%

2.00%


NY Times Coverage

Smith v. Van Gorkom

I Out-of-sample test [Section 102(b) 7]: Expect opposite pattern

I However, no clear-cut announcement date (rumors, leaks, etc.)



Returns to Decreased Monitoring (Section 102(b)7)

-3.00%

-2.50%

-2.00%

-1.50%

-1.00%

-0.50%

0.00%

0.50%

1.00%

1.50%

6/3/

1986

6/5/

1986

6/7/

1986

6/9/

1986

6/11

/198

6

6/13

/198

6

6/15

/198

6

6/17

/198

6

6/19

/198

6

6/21

/198

6

6/23

/198

6

6/25

/198

6

6/27

/198

6

6/29

/198

6

7/1/

1986

7/3/

1986

7/5/

1986

7/7/

1986

7/9/

1986

7/11

/198

6

7/13

/198

6

7/15

/198

6

7/17

/198

6

7/19

/198

6

7/21

/198

6


Enactment of S 102(b)7



Results Support Monitoring Hypothesis

I Delaware firms reduce investment relative to non-Delaware firms

I Low growth industries: lower CapEx & M&A (curb overinvestment)

I High growth industries: raise less capital (conservatism)

I Firms in low-growth industries gain from tightened scrutiny, andlose from loosened scrutiny (”good monitoring”)

I Firms in high-growth industries lose from tightened scrutiny, andgain from loosened scrutiny (”bad monitoring”)

I We also consider 2 alternative hypotheses

1. Changes in risk profiles

2. Increased overall uncertainty



1. Did Risk and Expected Returns Change around SVG?

High Growth Low Growth High Growth Low Growth(1) (2) (3) (4)

α –0.0010* 0.0011** –0.0012* 0.0012**(.0006) (.0005) (.0007) (.0005)

RMRF –0.1122 –0.0520 –0.0088 –0.0993(.1633) (.1247) (.3349) (.2529)

RMRF ∗ After –0.1159 0.0835(.3820) (.2885)

SMB –0.0109 –0.3897*** 0.1231 –0.4223*(.1728) (.1320) (.3249) (.2454)

SMB ∗ After –0.1941 –0.0165(.3755) (.2835)

HML –0.0551 0.3396** –0.0594 0.1840(.2259) (.1726) (.3974) (.3000)

HML ∗ After 0.1352 0.2634(.5203) (.3928)

Momentum –0.2970 –0.1241 –0.2708 –0.0061(.1810) (.1383) (.3048) (.2302)

Momentum ∗ After 0.0858 –0.3059(.4283) (.3234)

No observations 41 41 41 41



2. Did Volatility Change around SVG?

F-test (Levene) of equality of variance of returns (2 months before / 2months after SVG announcement) shows no significant differences invariances



Conclusion - Takeaways

I Firms in high growth industries lost and firms in low growthindustries gained from exogenous tightening in monitoring bySupreme Court

I Tight monitoring in industries with high growth opportunities stiflesgrowth and decreases shareholder value (”bad monitoring”)

I Tight monitoring in industries with low growth opportunities curbsexcesses and boosts shareholder value (”good monitoring”)

I When regulation reversed Supreme Court decision, effects alsoreversed

I General implication: business judgment rule prevents courts frommaking poor business decisions, which are most harmful in fastestgrowing sectors

I Regulation may remedy failure of courts to solve disputes cheaply,predictably and impartially


Documents

Good Monitoring, Bad Monitoring