Equity Vesting and Managerial Myopia Alex Edmans, LBS, Wharton, - PowerPoint PPT Presentation

Equity Vesting and Managerial Myopia Alex Edmans, LBS, Wharton, NBER, CEPR, ECGI Vivian W. Fang, Minnesota Katharina A. Lewellen, Dartmouth Bristol-Manchester 3 rd Annual Corporate Finance Conference, September 2014 1

Motivation n Myopia is believed to be a first-order issue n Theories: Narayanan (1985), Stein (1988, 1989), Bebchuk and Stole (1993) … n Policy arguments: Porter (1992), Thurow (1993), Zingales (2000) … n But very difficult to document empirically n In theory models, what matters is horizon of incentives. Max α [ ω P + (1- ω )V] n Standard measures of incentives quantify overall sensitivity to stock price: α , not ω 2

Empirical Approach n αω P is dollar value of CEO’s equity sales n But actual equity sales are (a) endogenous (b) potentially unpredictable n Our approach: use scheduled vesting of equity n Relevance: highly correlated with equity sales n Exclusion: driven by grants several years prior n Available post-2006 SEC rules. Short time series, so Execucomp has little power n Use Equilar 3

Empirical Specification n Δ INVESTMENT t+1 = α + β 1 NEWLYVESTING t+1 + β 2 UNVESTEDADJ t + β 3 ALREADYVESTED t + γ CONTROL t + � n NEWLYVESTING: $ change for 1% rise in price n Control for unvested and already-vested equity n Additional controls: n Largely follow Asker, Farre-Mensa, and Ljungqvist (2013) n Investment opportunities: Q t , Q t+1 , momentum, age, MV n Capacity to finance investment: cash, leverage, retained earnings n Other: ROA (measures both), salary, bonus 4 n Firm FE, year FE, cluster standard errors at firm level

Related Literature n Graham, Harvey and Rajgopal (2005) n Level of incentives and EM: n Cheng and Warfield (2005), Bergstresser and Philippon (2006), Peng and Roell (2008) vs. Erickson, Hanlon, and Maydew (2006) n Vesting horizons n Kole (1997) documents n Johnson, Ryan, and Tian (2009): corporate fraud n Gopalan et al. (2013) introduce “duration” n Varies across industries n Linked to EM n Ladika and Sautner (2013) 5

The Data n Equilar 2006-10 covers Russell 3000 n Shares: “shares acquired on vesting of stock” n Options: uniquely identify each grant using strike price and date n Newly-vesting t+1 = Unvested t + Newly-awarded t+1 – Unvested t+1 gives number of vesting options n Multiply by delta at start of t+1 and sum, to give effective number of shares n Multiply delta by stock price at start of t+1 to give sensitivity : $/% incentives n Sum with shares to give Newlyvesting 6

The Data (cont’d) n Similarly calculate n Alreadyvested n Unvested n Unvestedadj = Unvested – Newlyvesting n Equitysold from Thomson Financial Insider Trading 7

Dependent Variables n Δ RD (scaled by total assets) n Market can’t discern quality n Cohen, Diether, and Malloy (2013): “the stock market appears unable to distinguish between “good” and “bad” R&D investment” n Bushee (1998), Bhojraj et al. (2009) n Δ RDAD n Chan, Lakonishok, and Sougiannis (2001) n Δ Capex, Δ Capexall n Δ RDADCapex, Δ RDADCapexall 8

Predicted Equity Sales and Investment (1) (2.1) (2.2) (2.3) (2.4) (2.5) (2.6) Dependent Δ CAPEX Variables _ EQUITY_ Δ RDAD_ Δ RDAD_ SOLD t CAPEX t ALL t CAPEXALL t Δ RD t Δ RDAD t Δ CAPEX t NEWLYVESTING t 0.328 *** (0.034) FIT_ EQUITYSOLD t -0.942 * -1.192 * -0.625 -2.154 ** -4.252 ** -6.564 ** (0.553) (0.635) (0.585) (1.083) (1.918) (2.631) UNVESTEDADJ t-1 -0.022 -0.054 -0.078 -0.013 -0.139 0.422 0.337 (0.025) (0.073) (0.089) (0.123) (0.193) (0.492) (0.593) VESTED t-1 0.018 *** 0.013 0.020 0.050 ** 0.074 ** 0.098 * 0.136 * (0.002) (0.014) (0.016) (0.023) (0.033) (0.059) (0.078) Controls, year FE, firm FE Yes Yes Yes Yes Yes Yes Yes 6,730 6,730 6,730 6,730 6,730 6,730 Observations 6,730 Adjusted R 2 (R 2 ) 0.421 0.354 0.359 0.304 0.343 0.159 0.138 1 st : $1 increase in NEWLYVESTING -> 33c increase in EQUITYSOLD 2 nd : IV increase associated with 0.25% fall in Δ RD vs. average of 4.6%; 9 equates to $2.2 million

Robustness Checks / Additional Analyses n Reduced-form on NEWLYVESTING directly n Performance-based vesting (Bettis et al. (2010)) not a concern if price-based, is a concern if earnings-based n Stronger for options, for which PBV is very rare n Using delta of 0.7 for all options or assuming all options are ATM n Controlling for duration or vega n But cannot make strong claims about causality or efficiency 10

Earnings Announcements 11

Earnings Announcements (cont’d) (1) (2.1) (2.2) (2.3) Dependent Variables EQUITY_SOLD t BEAT q BEAT_ BELOW1 q BEAT_ABOVE1 q NEWLYVESTING t 0.451 *** (0.015) FIT_ EQUITYSOLD t 10.829 ** 14.596 *** -1.760 (4.863) (5.576) (4.541) Controls, year FE, Yes Yes Yes Yes industry FE Observations 17,173 17,173 17,173 17,173 2.70 11.60 2.52 Wald-statistics 0.10 <0.01 0.11 p-value Similar results using CUTANDBEAT : dummy = 1 if firm beats the forecast but would have missed it if R&D same as previous year 12

Earnings Announcement Returns n Stein (1989): market rationally takes into account managers’ myopic tendencies and discounts announced earnings n Alternatively, market may not discount, as n Lacks information on managers’ incentives n Is inefficient (von Lilienfeld-Toal and Ruenzi (2014)) n Earnings surprises suggest that analysts don’t take myopic incentives into account 13

Earnings Announcements (cont’d) (1) (2.1) (2.2) Dependent Variables EQUITYSOLD t CAR q (-1, +1) NEWLYVESTING t 0.420 *** (0.026) FIT_ EQUITYSOLD t 76.350 ** 44.418 (29.820) (30.145) DIF q 0.329 (0.290) BEAT q 6.327 *** (0.200) Industry Fixed Effects Yes Yes Yes Observations 18,686 18,686 18,686 Adjusted R 2 (R 2 ) 0.306 0.007 0.088 14

Conclusion n NVE is negatively related to R&D, advertising, capex; narrowly beating earnings n Frydman and Jenter (2010): n “Compensation arrangements are the endogenous outcome of a complex process … this makes it extremely difficult to interpret any observed correlation between executive pay and firm outcomes as evidence of a causal relationship” 15

Other Consequences of Vesting Equity n Edmans, Goncalves-Pinto, Wang, and Xu (2014): “Strategic News Releases in Equity Vesting Months” n Why is news important? n Real decision makers base decisions on news (or stock prices affected by news): Bond, Edmans, and Goldstein (2012) n Reduces information asymmetry among investors (cf. Regulation FD) n News is not mechanically triggered by events, but a strategic decision by the CEO 16

Equity Vesting and Equity Sales 17

News Releases in Vesting Month Column 3: firms release 5% more discretionary news in vesting month n 18

Positivity of News Releases 19

Abnormal Returns to News Releases 20

Economic Significance, Returns n 16-day CAR of 28 bps to discretionary news in VM n $14,504 applied to average CEO equity vesting of $5.18m n Meulbroek (1992): median gain to illegal insider trading of $17,628 ($33,968 in 2007 dollars) n Martha Stewart avoided losses of $45,673 when she sold ImClone shares in 2001 21

Economic Significance, Volume n CEO’s equity sale (on a sale day) is n 6.2% of the average daily volume n 0.165% of shares outstanding n A discretionary news item generates abnormal trading volume of 0.32% of shares outstanding 22

Time From News Until CEO’s First Sale n Median time to full sale is 7 days for DN released in vesting months n Consistent with short-lived return and volume increase 23