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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.

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Kate Litvak

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

  2. Map of This Talk • Original Goal Here: Do Precisely Nothing • Not really • Identification Idea • Theory • Unexpected Difficulties and Solutions • Results

  3. Hierarchy of Identification • Cross-Sectional Regs • Firm Fixed Effects • Exogenous Shock • Legal Change • Natural Disaster • Randomized Trial • Gold Standard

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. Identification • Exogenous Shock to External Gov’ce • Through Randomized Trial

  11. 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)

  12. 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

  13. Bottom Line on Hypotheses • Testing Governance Value of Short Selling

  14. 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

  15. 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

  16. 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

  17. Summary of My Findings (2) • What Kinds of Firms Most Responsive to Short Selling Effects? • Worse Internal Governance • Higher Prior Cash Flows

  18. 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

  19. Data: Intended Randomization

  20. 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?

  21. Data: Actual Assignment (Compliers)

  22. Data: Actual Assignment (Non-Compliers)

  23. 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)

  24. Kernel Density of Firm Asset Size for Partially-Treated v. Control Firms (Compliers v. Noncompliers In Control Group)

  25. 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

  26. 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

  27. 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):

  28. Inverse propensity tilt reweighting • Short Version: Multiply [standard weights} * p * (1-p) • Exact Covariate Balance • Biased Estimate if Heterogenous Treatment Effects

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

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

  31. 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

  32. Panel, Inverse Prop Tilting

  33. 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

  34. Panel, Inverse Prop Tilting

  35. 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

  36. Panel, Inverse Propensity Matching

  37. 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

  38. Panel, Inverse Propensity Matching

  39. 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

  40. 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.

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

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

  43. More Robustness • Same results with categorical measures of constraints

  44. 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

  45. 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

  46. Correlation: Fin Constraint versus Cash Flow Investment Sensitivity (No Treatment) Panel, Inverse Prop Score Weighted and Trimmed

  47. 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

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

  49. 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

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

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