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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.

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slide1

Using a Randomized Experiment to Measure the Impact of Firm Governance on Capital Raising and Investment

Kate Litvak

map of this talk
Map of This Talk
  • Original Goal Here: Do Precisely Nothing
    • Not really
  • Identification Idea
  • Theory
  • Unexpected Difficulties and Solutions
  • Results
hierarchy of identification
Hierarchy of Identification
  • Cross-Sectional Regs
  • Firm Fixed Effects
  • Exogenous Shock
    • Legal Change
    • Natural Disaster
  • Randomized Trial
    • Gold Standard
randomized trial here
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
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
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
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
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
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
Identification
  • Exogenous Shock to External Gov’ce
    • Through Randomized Trial
hypothesis 1
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), Scheinkmanand Xiong (2003), Gallmeyer and Hollifield (2006)
hypothesis 2
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
Bottom Line on Hypotheses
  • Testing Governance Value of Short Selling
research design 1
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
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
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
summary of my findings 2
Summary of My Findings (2)
  • What Kinds of Firms Most Responsive to Short Selling Effects?
    • Worse Internal Governance
    • Higher Prior Cash Flows
summary of my findings 3
Summary of My Findings (3)
  • 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
but there is category b
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?
slide23

Kernel density estimate

.0008

size, treated, as of 2004

size, intended control, as of 2004

.0006

Density

.0004

.0002

0

0

2000

4000

6000

8000

10000

at, Winsorized fraction .01

kernel = epanechnikov, bandwidth = 171.1019

Kernel Density of Firm Asset Size for Treated v. Combination of Control and Partially-Treated Firms (Intended by Randomization)
slide24
Kernel Density of Firm Asset Size for Partially-Treated v. Control Firms (Compliers v. Noncompliers In Control Group)
so problem
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
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
inverse propensity score reweighting
Inverse propensity score reweighting
  • Propensity score (ptreated) is “balancing score” (Rosenbaum & Rubin, 1983)
    • Same propensity  same expected covariates
  •  Unbiased estimate with inverse propensity weights (IPW):
inverse propensity tilt reweighting
Inverse propensity tilt reweighting
  • Short Version: Multiply [standard weights} * p * (1-p)
  • Exact Covariate Balance
  • Biased Estimate if Heterogenous Treatment Effects
kernel density of propensity to be treated for partially treated v control firms
Kernel Density of Propensity to be Treated for Partially-Treated v. Control Firms
tests 1
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
tests 2
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
tests 3
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 Treatment?
  • Answer:
    • Yes for equity
tests 4
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 Treatment?
  • Answer:
    • No for debt
tests 4a
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
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 raising panel inverse propensity matching and trimming
Impact of Prior Fin Constraint on Equity RaisingPanel, Inverse Propensity Matching and Trimming
more robustness
More Robustness
  • Same results with categorical measures of constraints
tests 5a
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
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
slide46
Correlation: Fin Constraint versus Cash Flow Investment Sensitivity (No Treatment) Panel, Inverse Prop Score Weighted and Trimmed
tests 5c
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
Impact of Prior Investment Cash Flow Sensitivity on Equity Raising; X-Section, Before-After Tests
tests 6
Tests (6)
  • Ask:
    • Doe Pre-Treatment Cash Flows Cause Capital Raising During Treatment?
  • Use Inverse Prop Matching with Trimming
  • Answer:
    • 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
tests 7
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 investment x section before after tests
Impact of Equity Raising During Treatment on InvestmentX-Section, Before-After Tests
tests 8
Tests (8)
  • Ask:
    • Does Pre-Treatment Internal Gov’ce Affect Capital Raising During Treatment?
      • Interaction of Internal and External Gov’ce
  • Use Inverse Prop Matching with Trimming
  • Answer:
    • Low-Gov’ce Firms  More Equity Raising
      • Substitute External Gov’ce for Internal
    • Firms with Higher Risk for Min S/hs More Equity Raising
slide54
Internal Governance Predicts Reaction to Changes in External Gov’ce, Panel, Inverse Prop Score Weighted and Trimmed
other internal gov ce predictors of responses to changes in external gov ce
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
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 Raisingand Affect Investment
  • Some Evidence that External Gov’ce Is Substitute for Internal Gov’ce
slide57
Correlation: Fin Constraint versus Cash Flow Investment Sensitivity (No Treatment) Panel, Inverse Prop Score Weighted and Trimmed