Kate Litvak

Using a Randomized Experiment to Measure the Impact of Firm Governance

on Capital Raising and Investment

Kate Litvak

Map of This Talk

• Original Goal Here: Do Precisely Nothing– Not really

• Identification Idea• Theory• Unexpected Difficulties and Solutions• Results

Hierarchy of Identification

• Cross-Sectional Regs• Firm Fixed Effects• Exogenous Shock

– Legal Change– Natural Disaster

• Randomized Trial– Gold Standard

Randomized Trial Here• Conducted by the SEC in 2005-2007• Suspends Existing Restrictions on Short Selling• Up to July 2007:

– Rule 10a-1 from 1938– Ok to Sell Short if Price Is

• Above immediately preceding sale, or• At last sale price if it was higher than last diff price

– Goal: Prevent Downward Price Spirals• Restrictions Supported by Firm Managers

– Claim: Short Sellers Opportunistic, Drive Down Prices

Trial Details• True Randomized Experiment

– Not Just Natural Experiment • Based on Size etc.

• Take All Russell 3000 Firms (High Liquidity)– Rank by Trading Volume– Pick Every Third – “Pilot” Firms– Selection Period: June 2003-June 2004

• Suspends Uptick Rule for Pilot Firms• The Rest – Control Group• Trial Period

– May 2, 2005 to July 3, 2007

Prior/Concurrent Studies on Uptick Rule• SEC Office of Economic Analysis (2007)

– Effect of rule on volume of short sales, option trading, prices, liquidity, volatility

• Diether, Lee, Werner (2006)– Effect of rule on spread, volatility, short sale volume

• Alexander and Peterson (2006)– Volume, Volatility Around Announcement and Initiation Date of

Pilot Program• Bai (2007)

– Effect on Price, Volatility, Volume During Mkt Stress• Grullon et al. (2012)

– Increase in Short Selling Causes Prices to Fall– Small Firms Reduce Equity Issues

Prior Studies on Uptick Rule• No impact on

– Daily Return Volatility– Liquidity– Magnitude and Speed of Price Decline – When Stocks Subject to Downward Pressure

• Caused by Earnings Shocks– Options Trading

• Weak Evidence: Overpricing Caused by Selling Restrictions• So, SEC Concluded – Rule Useless

– Repealed it in June 2007• Now, Adopted Different, Narrower Rule

– No Trial There

Broad Research Question

• Interaction of Internal and External Governance

• Examples of Internal Governance– Boards, Procedures, S/h Voting Rules, Fiduciary

Duties Standards• Examples of External Governance

– Share Price– Mkt for Corporate Control– Product Mkt Competition

Research Design• Measures of Internal Governance + External

Governance Outcome• We Want:

– Exogenous Shock to Some Form of Governance • Then, See if Outcome Affected

– Maybe Conditional of Internal Gov’ce• Other Papers:

– Shocks to Internal Governance• Sarbanes-Oxley, Korean Corp Gov’ce Reform, DE Legal Rules

• This Paper: – Shock to External Governance

Identification

• Exogenous Shock to External Gov’ce – Through Randomized Trial

Hypothesis #1• Short Selling Permitted

– Negative Opinions Incorporated into Stock Prices – Prices More Accurate – Investors More Willing to Invest – Cost of Capital Down – Capital Raising Up

• Prediction:– Short Selling Permitted Capital Raising Up

• Theory:– Lintner (1969), Miller (1977), Scheinkman and Xiong

(2003), Gallmeyer and Hollifield (2006)

Hypothesis #2• Short Selling Permitted

– Manipulators Run Down Prices – Panics Up – Stock Prices Artificially Deflated – Capital Raising Down

• Note: – Gov’ce Value of Short Selling Lower than Damage

from Panics and Deflated Stock Prices• Prediction:

– Short Selling Permitted Capital Raising Down

Bottom Line on Hypotheses• Testing Governance Value of Short Selling

Research Design (1)• Classic Diffs in Diffs for Randomized Trials

– Developed for Drug Trials• Treated Firms

– Exempt from restriction on short-selling• Control Firms

– Short-Selling Restricted Under Old Rule• Compare:

– Outcomes of Treated Firms v. Outcomes of Control Firms

– During and Outside Test Period

Research Design (2)• If Randomized Trial Perfectly Executed No

Need for Regressions– Except if Want to Know Cross-Sectional Effects

• But Not Perfectly Executed Here– So, Need Some Extra Work

• Follow-Up– I Will Re-Do Prior Studies on Price, Volatility,

Volume etc

Summary of My Findings (1)

• Short Selling Causes:– Increase in Equity Raising– No Change in Debt Raising– Increase in Capital Investment– Increase in R&D Investment– Increase in Dividend Payments


• What Kinds of Firms Most Responsive to Short Selling Effects?– Worse Internal Governance– Higher Prior Cash Flows


• What Does Not Predict Response?– Prior Financial Constraint

• Relevant for Cash Flow – Investment Sensitivity Literature– Evidence Consistent with KZ, not FHP

• Capital Raising Made Cheaper for Random Firms– Treated Firms generally responded by raising more $

• And invested more– But more fin constrained firms not different from rest– Firs with higher pre-treatment cash flow investment

sensitivity not different from rest

Data: Intended Randomization Firm Characteristic Treated Group,

MeanIntended Control T-stat Treated v.

Intended ControlNumber of Firms 580 1168Asset Size 2958.95 3245.28 0.67Cash and Cash Equivalents 19.99 25.15 0.57

Capital Expenditures 152.80 141.14 0.59

Common Shares Issued 110.78 126.56 1.15

Long-Term Debt 694.13 769.53 0.74Total Liabilities 1772.77 2025.30 0.89Total Dividends Paid 41.18 40.27 0.12EBITDA 396.77 388.97 0.16Number of Employees 10.26 11.31 0.80

PPE Total 1829.85 1835.20 0.09Sales Growth 267.31 295.38 0.56R&D Expenditures 54.87 84.15 1.92Trading Volume, in $ 6.10e+09 6.17e+09 0.10

But There is Category B…

• Created by SEC, Listed on their Page– Not self-selection– Principles of selection not reported

• Prior Papers Assumed: Inconsequential• Rule for Them:

– Exempted From Uptick Rule from 4pm to 8pm– So, Partially Treated!

• Check: – Random?

Data: Actual Assignment (Compliers)Firm Characteristic Treated Group,

MeanTrue Control Group T-stat Treated v.

True ControlNumber of Firms 580 781Asset Size 2958.95 719.68 7.36Cash and Cash Equivalents 19.99 5.47 2.19





Data: Actual Assignment (Non-Compliers)Firm Characteristic Treated Group,

MeanNon-Compliers (Partially Treated)

T-stat Treated v. Partially Control

Number of Firms 580 387Asset Size 2958.95 8303.04 15.84Cash and Cash Equivalents 19.99 64.62 5.30





Kernel Density of Firm Asset Size for Treated v. Combination of Control and Partially-Treated Firms

(Intended by Randomization)0

.000

2.0

004

.000

6.0

008

Den

sity

0 2000 4000 6000 8000 10000at, Winsorized fraction .01

size, treated, as of 2004

size, intended control, as of 2004

kernel = epanechnikov, bandwidth = 171.1019

Kernel density estimate

Kernel Density of Firm Asset Size for Partially-Treated v. Control Firms

(Compliers v. Noncompliers In Control Group)0

.000

5.0

01.0

015

Den

sity

0 2000 4000 6000 8000 10000at, Winsorized fraction .01

size, partially treated, as of 2004size, control, as of 2004



So, Problem

• Non-Randomized Non-Compliance• Cannot Compare Treated v. Intended Controls

– Third of controls are partially treated• Cannot Compare Treated v. Real Controls

– Real controls not randomly chosen among intended controls

Solutions• Developed by Statisticians for Randomized Trials

– How to deal with non-compliers• Inverse Propensity Weighting

– Alone or with trimming of ranges without common support– Unbiased with heterogenous treatment effects– But inefficient

• Inverse Propensity Tilting– Creates exact covariate balance– Biased with heterogenous treatment effects– Unbiased with homogenous treatment effects– Efficient

• Propensity score (ptreated) is “balancing score” (Rosenbaum &

Rubin, 1983)

– Same propensity same expected covariates• Unbiased estimate with inverse propensity weights (IPW):

: 1 : 0

: 1 : 0

1 1ˆ * *ˆ ˆ(1 )1 1

ˆ ˆ(1 )i i

j j

N Ni i

ATE N Ni w i wi i

j w j wj j

y yp p

p p

Inverse propensity score reweighting

• Short Version: Multiply [standard weights} * p * (1-p)

• Exact Covariate Balance• Biased Estimate if Heterogenous Treatment Effects

Inverse propensity tilt reweighting

Kernel Density of Propensity to be Treated for Treated v. Control Firms

0.5

11.

52

2.5

Den

sity

0 .2 .4 .6 .8 1Pr(onlyaandcontrolgroup)

p treatedp control



Kernel Density of Propensity to be Treated for Partially-Treated v. Control Firms

02

46

8D

ensi

ty

0 .2 .4 .6 .8 1Pr(onlybandcontrolgroup)

p partly treatedp control



Tests (1)

• Use Inverse Prop Tilting – Weighting to Produce Exact Covariate Balance

• Ask: – Do Treated Firms Raise More Capital During

Treatment?• Answer:

– Yes for equity– No for debt

Panel, Inverse Prop Tilting

Equity Issuance Debt IssuanceTreatment Period * Treated Firm 0.0285*** -0.0109

(3.33) (-1.229)Treatment Period -0.0911*** 0.013

(-5.232) (0.85)Ln Assets -0.0877*** 0.0200***

(-16.27) (3.80)Ln Cash Holdings 0.0250*** -0.00488***

(15.36) (-3.331)Ln Closing Price 0.0417*** -0.0126**

(7.28) (-2.233)Ln Trading Volume 0.0107*** 0.000555

(3.15) (0.16)Constant 0.252*** -0.00583

(4.68) (-0.122)Fixed Effects firm, year firm, yearClusters firm firm Observations 8,372 8,163R-sq 0.223 0.016Firms 948 948

Tests (2)

• Use Inverse Prop Tilting – Weighting to Produce Exact Covariate Balance

• Ask: – Do Treated Firms Invest More During Treatment?

• Answer:– Yes for CapX– Yes for R&D– Also Increase Dividends

Panel, Inverse Prop TiltingCapital Expenditur

R&D Expenditures

Dividend Payments

Treatment Period * Treated Firm 23.23*** 26.27*** 15.37***(3.79) (2.92) (3.69)

Treatment Period 9.247 -14.09** -18.90***(0.61) (-2.355) (-3.116)

Ln Assets 22.21*** 30.99*** -1.312(4.63) (3.89) (-0.604)

Ln Cash Holdings -0.427 0.255 0.654(-0.354) (0.58) (1.27)

Ln Closing Price -10.08** -13.68*** -0.192(-2.190) (-2.942) (-0.109)

Ln Trading Volume 9.523*** 1.689 1.566(3.56) (0.73) (1.59)

Constant -241.3*** -131.7*** 17.67(-5.306) (-3.307) (0.98)

Fixed Effects firm, year firm, year firm, yearClusters firm firm firm Observations 8,451 7,755 8,500R-sq 0.069 0.127 0.048Firms 948 939 947

Tests (3)

• Use Inverse Prop Matching with Trimming– Covariate Balance not Exact– But not Biased when Heterogenous Treatment

Effects– Censored, Uncensored, and No Weighting

• Ask: – Do Treated Firms Raise More Equity During


– Yes for equity

Panel, Inverse Propensity MatchingEquity Issuance

With weights, Censored

With weights, Not Censored

No Weights, Not Censored

Treat Period * Treated Firm 0.0144** 0.0184*** 0.0288***(2.11) (3.05) (4.43)

Treatment Period -0.0266*** -0.0759*** -0.0367***(-3.523) (-5.977) (-4.854)

Ln Assets -0.0887*** -0.0800*** -0.0844***(-16.87) (-17.77) (-19.25)

Ln Cash Holdings 0.0208*** 0.0186*** 0.0209***(15.78) (16.47) (18.10)

Ln Closing Price 0.0400*** 0.0317*** 0.0347***(7.36) (6.97) (8.11)

Ln Trading Volume 0.0112*** 0.0150*** 0.0155***(3.64) (5.59) (6.08)

Constant 0.211*** 0.163*** 0.115**(3.83) (3.90) (2.48)

Fixed Effects firm, year firm, year firm, yearClusters firm firm firmObservations 9,563 11,346 12,316R-squared 0.24 0.207 0.21Number of Firms 1,231 1,255 1,395

Tests (4)

• Use Inverse Prop Matching with Trimming– Covariate Balance not Exact– But not Biased when Heterogenous Treatment

Effects– Censored, Uncensored, and No Weighting

• Ask: – Do Treated Firms Raise More Debt During


– No for debt

Panel, Inverse Propensity MatchingDebt Issuance

With weights, Censored

With weights, Not Censored

No Weights, Not Censored

Treat Period * Treated Firm -0.0042 -0.00688 -0.0102(-0.450) (-0.797) (-1.294)

Treatment Period -0.0285* 0.00459 -0.00042(-1.876) (0.33) (-0.0414)

Ln Assets 0.0317*** 0.0217*** 0.0213***(5.78) (4.69) (5.10)

Ln Cash Holdings -0.00438*** -0.00418*** -0.00401***(-3.045) (-3.093) (-3.306)

Ln Closing Price -0.00584 -0.00369 -0.00453(-1.084) (-0.719) (-1.006)

Ln Trading Volume -0.00483 -0.00266 -0.00272(-1.400) (-0.842) (-0.986)

Constant 0.0448 0.0325 0.0371(0.95) (0.74) (0.83)

Fixed Effects firm, year firm, year firm, yearClusters firm firm firmObservations 9,185 11,008 12,041R-squared 0.017 0.014 0.013Number of Firms 1,231 1,255 1,396

Tests (4a)

• Cross-Sectional Results• Ask:

– What Predicts Whether Treated Firm Will Raise Capital During Treatment?

• Possible Candidates:– Pre-Treatment Financial Constraint

• Use Inverse Prop Matching with Trimming

Tests (4b)• Intuition:

– Firm Is Financially Constrained Pre-Treatment – Randomly Given Chance to Raise More Capital – It should take it!

• Ask:– Do Pre-Treatment Financial Constraints Cause Capital Raising During

Treatment?• Fin Constraint = Dividends/Net Income• Use Inverse Prop Matching with Trimming• Answer:

– No– Very Robust– In All Specifications

• Panel, x-section• Linear and Categorical Measures of Fin Constraint• With different weighting, matching, etc.

Impact of Prior Fin Constraint on Equity RaisingX-Section, Before-After Tests

ΔEquityRaised/PPENT



Inverse prop match weighted and trimmed yes yes yesFin Constraint A in 2004 * Treated Firm 0.0121

(0.37)Fin Constraint A in 2004 -0.00956

(-0.562)Treated Firm -0.0167 -0.0196 0.00974

(-1.097) (-1.334) (1.05)Fin Constraint B in 2004 * Treated Firm 0.00909

(0.25)Fin Constraint B in 2004 -0.00856

(-0.336)Fin Constraint C in 2004 * Treated Firm -0.0419

(-0.853)Fin Constraint C in 2004 0.0256

(0.63)Assets, Cash Holdings, Closing Price, Trading Volume Yes Yes Yes Constant 0.0125 0.0421 0.0211

(0.12) (0.43) (0.27)Observations 537 529 32R-squared 0.059 0.062 0.37

Impact of Prior Fin Constraint on Equity RaisingPanel, Inverse Propensity Matching and Trimming


ΔDebtRaised/PPENT

Financial Constraint A * Treatment Group

(0.00) 0.00 (-0.127) (0.22)

Financial Constraint A 0.00 (0.00)(0.08) (-0.202)

Treatment Group (0.02) (0.02) 0.01 (0.00) (0.00) 0.01 (-1.019) (-1.019) (1.05) (-0.472) (-0.472) (0.52)

Financial Constraint B * Treatment Group

(0.00) 0.00 (-0.127) (0.22)

Financial Constraint B 0.00 (0.00)(0.08) (-0.202)

Financial Constraint C * Treatment Group (0.04) (0.05)

(-0.853) (-0.633)Financial Constraint C 0.03 0.03

(0.63) (0.52)Constant 0.01 0.01 0.02 0.01 0.01 0.01

(0.10) (0.10) (0.27) (1.05) (1.05) (0.09)Assets, Cash Holdings, Closing Price, Trading Volume yes yes yes yes yes yesObservations 537.00 537.00 32.00 490.00 490.00 27.00 R-squared 0.06 0.06 0.37 0.01 0.01 0.11

More Robustness

• Same results with categorical measures of constraints

Tests (5a)

• Use Cash Flow – Investment Sensitivity as Proxy for Financial Constraint

• Theory: – Firm Cannot Raise Outside Capital – Has to Rely on Internal Cash Flows – Investment Correlated with Cash Flows

Tests (5b)• Replicate Prior Results in FHP• Ask:

– Does Pre-Treatment Investment-Cash Flow Sensitivity Predict Pre-Treatment Financial Constraint?

• Fin Constraint = Dividends/Net Income

• No Treatment, Just Check• Panel• Use Inverse Prop Matching with Trimming• Answer:

– Yes – Higher Fin Constraint More Cash Flow – Investment

Sensitivity

Correlation: Fin Constraint versus Cash Flow Investment Sensitivity (No Treatment)

Panel, Inverse Prop Score Weighted and TrimmedCash Flow

Investment Sensitivity

Cash Flow Investment Sensitivity


Financial Constraint Group 1 0.102***(9.63)

Financial Constraint Group 2 -0.0362*(-1.872)

Financial Constraint Group 3 -0.109***(-9.312)

Constant 0.213*** 0.288*** 0.309***(23.96) (54.28) (56.02)

Inverse prop match weighted and trimmed yes yes yes

Obs 863 863 863R-Squared 0.097 0.004 0.091

Tests (5c)

• Ask:– Does Pre-Treatment Investment-Cash Flow

Sensitivity Cause Capital Raising During Treatment?– Firms Randomly Offered Easy Ways to Raise Capital Do High-Sensitivity Firms Raise More?

• Use Inverse Prop Matching with Trimming• Answer:

– No

Impact of Prior Investment Cash Flow Sensitivity on Equity Raising; X-Section, Before-After Tests

Equity Raising

Debt Raising

Equity Raising

Debt Raising

Cash Flow Investment Sensitivity Pre-Tr * Treated Firm

-0.106 -0.0204* -0.109 -0.0204*(-1.180) (-1.649) (-1.208) (-1.651)

Fin Constraint Group 1 0.00517 -0.00099(0.29) (-0.412)

Fin Constraint Group 2 0.0215 0.00127(0.77) (0.35)

Cash Flow Investment Sensitivity Pre-Treatment

-0.0362 -0.00192 -0.0383 -0.00146(-0.532) (-0.216) (-0.558) (-0.162)

Treated Firm0.0159 0.00486 0.0154 0.00472(0.54) (1.23) (0.53) (1.19)

Assets, Cash Holdings, Closing Price, Trading Volume yes yes yes yesConstant -0.0525 0.00618 -0.0581 0.00599

(-0.556) (0.49) (-0.612) (0.47)Inverse prop match weighted and trimmed yes yes yes yesObservations 486 443 486 443R-squared 0.052 0.015 0.053 0.016

Tests (6)• Ask:

– Doe Pre-Treatment Cash Flows Cause Capital Raising During Treatment?


– Yes– More Pre-Treatment Cash Flows More Capital

Raising During Treatment• All Adjusted for PPENT

– Opposite of Theories Using Cash Flow Investment Sensitivity as Proxy for Fin Constraint

Impact of Prior Cash Flows on Equity RaisingX-Section, Before-After Tests


ΔDebtRaised/PPENT

Cash Flows in 2004 * Treated Firm 0.0258*** 0.0009***(9.80) (2.61)

Treated Firm -0.021 -0.00096(-1.560) (-0.530)

Assets, Cash Holdings, Closing Price, Trading Volume Yes Yes

Constant 0.0618 0.0149(0.66) (1.18)

Inverse prop match weighted and trimmed yes yesObservations 537 490R-squared 0.203 0.02

Tests (7)

• Ask:– What Are Firms Doing with Extra Capital They Raise

During Treatment Because of Treatment?• Use Inverse Prop Matching with Trimming• Answer:

– Invest It

Impact of Equity Raising During Treatment on InvestmentX-Section, Before-After Tests

ΔCapx/PPENT

ΔCapx/PPENT

ΔEquityRaised * Treated Firm 0.641**(2.52)

ΔEquityRaised 0.221(1.09)

ΔDebtRaised * Treated Firm 6.637***(2.99)

ΔDebtRaised 0.0589(0.04)

Treated Firm 0.0299 0.00582(0.69) (0.13)

Assets, Cash Holdings, Closing Price, Trading Volume

Yes Yes

Constant 0.264 -0.00873(0.88) (-0.0283)

Inverse prop match weighted and trimmed yes yesObservations 537 490R-squared 0.146 0.107

Tests (8)• Ask:

– Does Pre-Treatment Internal Gov’ce Affect Capital Raising During Treatment?

• Interaction of Internal and External Gov’ce


– Low-Gov’ce Firms More Equity Raising• Substitute External Gov’ce for Internal

– Firms with Higher Risk for Min S/hs More Equity Raising

Internal Governance Predicts Reaction to Changes in External Gov’ce, Panel, Inverse Prop Score Weighted and Trimmed

Equity Issuance Debt Issuance Equity Issuance Debt Issuance

Yes Blockhold No Blockhold Yes Blockholders

No Blockholders

E-Index Bad Gov

E-Index Good Gov

E-Index Bad Gov

E-Index Good Gov

Test Period * Control Group 0.0277*** -0.00881 -0.00957 -0.0176 0.0415*** 0.00908 -0.0272** -0.0023

(2.78) (-0.642) (-0.955) (-0.830) (2.78) (0.89) (-2.060) (-0.190)

Test Period -0.0477*** -0.00552 -0.00584 0.0531* -0.117*** -0.0311*** -0.00272 -0.0227

(-4.290) (-0.357) (-0.412) (1.88) (-3.643) (-2.650) (-0.160) (-1.070)

Assets, Cash Holdings, Closing Price, Trading Volume yes yes yes yes yes yes yes

Constant 0.214*** 0.127 0.0399 -0.183 0.196** 0.278*** -0.0973 0.0655

(3.11) (0.93) (0.69) (-1.511) (2.10) (3.65) (-1.237) (0.88)

Observations 6,743 1,780 6,516 1,773 3,568 4,955 3,483 4,806

R-squared 0.252 0.152 0.016 0.046 0.239 0.227 0.028 0.02

Number of gvkey 780 168 780 168 526 578 523 577

Other Internal Gov’ce Predictors of Responses to Changes in External Gov’ce

• Shareholder Votes to Amend Charter – Worse Gov’ce More Equity Raised

• Cumulative Voting– No Cumulative Voting More Equity Raised

• But Some RiskMetrics Measures Opposite• So, Not Conclusive

Bottom Line• Use Randomized Experiment

• Test Impact of Short Selling in Capital Raising and Investment

• Short Selling Permitted More Capital Raising More Investment

• No Evidence that Fin Constraints Affect Capital Raising and Affect Investment

• Some Evidence that External Gov’ce Is Substitute for Internal Gov’ce

Correlation: Fin Constraint versus Cash Flow Investment Sensitivity (No Treatment)

Panel, Inverse Prop Score Weighted and TrimmedCash Flow

Investment Sensitivity



Financial Constraint Group 1 0.102***(9.63)

Financial Constraint Group 2 -0.0362*(-1.872)

Financial Constraint Group 3 -0.109***(-9.312)

Constant 0.213*** 0.288*** 0.309***(23.96) (54.28) (56.02)

Inverse prop match weighted and trimmed yes yes yes

Obs 863 863 863R-Squared 0.097 0.004 0.091

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Kate Litvak

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