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

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Presentation on theme: "Using a Randomized Experiment to Measure the Impact of Firm Governance on Capital Raising and Investment Kate Litvak."— Presentation transcript:

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 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Shocks – Options Trading Weak Evidence: Overpricing Caused by Selling Restrictions So, SEC Concluded – Rule Useless – Repealed it in June 2007 Now, Adopted Different, Narrower Rule – No Trial There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), Scheinkman and 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 Firm Characteristic Treated Group, Mean Intended Control T-stat Treated v. Intended Control Number of Firms Asset Size Cash and Cash Equivalents Capital Expenditures Common Shares Issued Long-Term Debt Total Liabilities Total Dividends Paid EBITDA Number of Employees PPE Total Sales Growth R&D Expenditures Trading Volume, in $6.10e e

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) Firm Characteristic Treated Group, Mean True Control Group T-stat Treated v. True Control Number of Firms Asset Size Cash and Cash Equivalents Capital Expenditures Common Shares Issued Long-Term Debt Total Liabilities Total Dividends Paid EBITDA Number of Employees PPE Total Sales Growth R&D Expenditures Trading Volume, in $6.10e e

22 Data: Actual Assignment (Non-Compliers) Firm Characteristic Treated Group, Mean Non-Compliers (Partially Treated) T-stat Treated v. Partially Control Number of Firms Asset Size Cash and Cash Equivalents Capital Expenditures Common Shares Issued Long-Term Debt Total Liabilities Total Dividends Paid EBITDA Number of Employees PPE Total Sales Growth R&D Expenditures Trading Volume, in $6.10e e

23 Kernel Density of Firm Asset Size for Treated v. Combination of Control and Partially-Treated Firms (Intended by Randomization) Density at, Winsorized fraction.01 size, treated, as of 2004 size, intended control, as of 2004 kernel = epanechnikov, bandwidth = Kernel density estimate

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 Propensity score (p treated ) is “balancing score” (Rosenbaum & Rubin, 1983) – Same propensity  same expected covariates  Unbiased estimate with inverse propensity weights (IPW): Inverse propensity score reweighting

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

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 Equity IssuanceDebt Issuance Treatment Period * Treated Firm *** (3.33)(-1.229) Treatment Period ***0.013 (-5.232)(0.85) Ln Assets ***0.0200*** (-16.27)(3.80) Ln Cash Holdings *** *** (15.36)(-3.331) Ln Closing Price *** ** (7.28)(-2.233) Ln Trading Volume *** (3.15)(0.16) Constant 0.252*** (4.68)(-0.122) Fixed Effects firm, year Clusters firm Observations 8,3728,163 R-sq Firms 948

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 Capital Expenditur R&D Expenditures Dividend Payments Treatment Period * Treated Firm 23.23***26.27***15.37*** (3.79)(2.92)(3.69) Treatment Period **-18.90*** (0.61)(-2.355)(-3.116) Ln Assets 22.21***30.99*** (4.63)(3.89)(-0.604) Ln Cash Holdings (-0.354)(0.58)(1.27) Ln Closing Price **-13.68*** (-2.190)(-2.942)(-0.109) Ln Trading Volume 9.523*** (3.56)(0.73)(1.59) Constant ***-131.7***17.67 (-5.306)(-3.307)(0.98) Fixed Effects firm, year Clusters firm Observations 8,4517,7558,500 R-sq Firms

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 Equity Issuance With weights, Censored With weights, Not Censored No Weights, Not Censored Treat Period * Treated Firm **0.0184***0.0288*** (2.11)(3.05)(4.43) Treatment Period *** *** *** (-3.523)(-5.977)(-4.854) Ln Assets *** *** *** (-16.87)(-17.77)(-19.25) Ln Cash Holdings ***0.0186***0.0209*** (15.78)(16.47)(18.10) Ln Closing Price ***0.0317***0.0347*** (7.36)(6.97)(8.11) Ln Trading Volume ***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 Clusters firm Observations 9,56311,34612,316 R-squared Number of Firms 1,2311,2551,395

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 Debt Issuance With weights, Censored With weights, Not Censored No Weights, Not Censored Treat Period * Treated Firm (-0.450)(-0.797)(-1.294) Treatment Period * (-1.876)(0.33)( ) Ln Assets ***0.0217***0.0213*** (5.78)(4.69)(5.10) Ln Cash Holdings *** *** *** (-3.045)(-3.093)(-3.306) Ln Closing Price (-1.084)(-0.719)(-1.006) Ln Trading Volume (-1.400)(-0.842)(-0.986) Constant (0.95)(0.74)(0.83) Fixed Effects firm, year Clusters firm Observations 9,18511,00812,041 R-squared Number of Firms 1,2311,2551,396

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 Raising X-Section, Before-After Tests ΔEquityRaised /PPENT ΔEquityRaised /PPENT ΔEquityRaised /PPENT Inverse prop match weighted and trimmed yes Fin Constraint A in 2004 * Treated Firm (0.37) Fin Constraint A in (-0.562) Treated Firm (-1.097)(-1.334)(1.05) Fin Constraint B in 2004 * Treated Firm (0.25) Fin Constraint B in (-0.336) Fin Constraint C in 2004 * Treated Firm (-0.853) Fin Constraint C in (0.63) Assets, Cash Holdings, Closing Price, Trading Volume Yes Constant (0.12)(0.43)(0.27) Observations R-squared

42 Impact of Prior Fin Constraint on Equity Raising Panel, Inverse Propensity Matching and Trimming ΔEquityRaised /PPENT ΔDebtRaised /PPENT Financial Constraint A * Treatment Group (0.00)0.00 (-0.127)(0.22) Financial Constraint A0.00(0.00) (0.08)(-0.202) Treatment Group(0.02) 0.01(0.00) 0.01 (-1.019) (1.05)(-0.472) (0.52) Financial Constraint B * Treatment Group (0.00)0.00 (-0.127)(0.22) Financial Constraint B0.00(0.00) (0.08)(-0.202) Financial Constraint C * Treatment Group(0.04)(0.05) (-0.853)(-0.633) Financial Constraint C0.03 (0.63)(0.52) Constant (0.10) (0.27)(1.05) (0.09) Assets, Cash Holdings, Closing Price, Trading Volumeyes Observations R-squared

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 Cash Flow Investment Sensitivity Financial Constraint Group *** (9.63) Financial Constraint Group * (-1.872) Financial Constraint Group *** (-9.312) Constant0.213***0.288***0.309*** (23.96)(54.28)(56.02) Inverse prop match weighted and trimmedyes Obs863 R-Squared

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 Equity Raising Debt Raising Equity Raising Debt Raising Cash Flow Investment Sensitivity Pre-Tr * Treated Firm * * (-1.180)(-1.649)(-1.208)(-1.651) Fin Constraint Group (0.29)(-0.412) Fin Constraint Group (0.77)(0.35) Cash Flow Investment Sensitivity Pre- Treatment (-0.532)(-0.216)(-0.558)(-0.162) Treated Firm (0.54)(1.23)(0.53)(1.19) Assets, Cash Holdings, Closing Price, Trading Volumeyes Constant (-0.556)(0.49)(-0.612)(0.47) Inverse prop match weighted and trimmedyes Observations R-squared

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 Raising X-Section, Before-After Tests ΔEquityRaised /PPENT ΔDebtRaised /PPENT Cash Flows in 2004 * Treated Firm ***0.0009*** (9.80)(2.61) Treated Firm (-1.560)(-0.530) Assets, Cash Holdings, Closing Price, Trading Volume Yes Constant (0.66)(1.18) Inverse prop match weighted and trimmed yes Observations R-squared

51 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

52 Impact of Equity Raising During Treatment on Investment X-Section, Before-After Tests ΔCapx /PPENT ΔCapx /PPENT ΔEquityRaised * Treated Firm 0.641** (2.52) ΔEquityRaised (1.09) ΔDebtRaised * Treated Firm 6.637*** (2.99) ΔDebtRaised (0.04) Treated Firm (0.69)(0.13) Assets, Cash Holdings, Closing Price, Trading Volume Yes Constant (0.88)( ) Inverse prop match weighted and trimmed yes Observations R-squared

53 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

54 Internal Governance Predicts Reaction to Changes in External Gov’ce, Panel, Inverse Prop Score Weighted and Trimmed Equity IssuanceDebt IssuanceEquity IssuanceDebt Issuance Yes BlockholdNo Blockhold Yes Blockholders No Blockholders E-Index Bad Gov E-Index Good Gov E-Index Bad Gov E-Index Good Gov Test Period * Control Group0.0277*** *** ** (2.78)(-0.642)(-0.955)(-0.830)(2.78)(0.89)(-2.060)(-0.190) Test Period *** *-0.117*** *** (-4.290)(-0.357)(-0.412)(1.88)(-3.643)(-2.650)(-0.160)(-1.070) Assets, Cash Holdings, Closing Price, Trading Volume yes Constant0.214*** **0.278*** (3.11)(0.93)(0.69)(-1.511)(2.10)(3.65)(-1.237)(0.88) Observations6,7431,7806,5161,7733,5684,9553,4834,806 R-squared Number of gvkey

55 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

56 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

57 Correlation: Fin Constraint versus Cash Flow Investment Sensitivity (No Treatment) Panel, Inverse Prop Score Weighted and Trimmed Cash Flow Investment Sensitivity Financial Constraint Group *** (9.63) Financial Constraint Group * (-1.872) Financial Constraint Group *** (-9.312) Constant0.213***0.288***0.309*** (23.96)(54.28)(56.02) Inverse prop match weighted and trimmedyes Obs863 R-Squared


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