Short Selling and Earnings Management: A Controlled Experiment Vivian Fang, University of Minnesota Allen Huang, Hong Kong University of Science and Technology Jonathan Karpoff, University of Washington SEC/Maryland Second Annual Conference on Financial Market Regulation May 1 st, 2015 1
Motivation Does short selling (SS), or its prospect, constrain firms opportunistic reporting behavior? Short sellers can identify earnings manipulation and fraud Dechow, Sloan, and Sweeney (1996), Christophe, Ferri, and Angel (2004), Efendi, Kinney, and Swanson (2005), Desai, Krishnamurthy, and Venkataraman (2006), and Karpoff and Lou (2010) Anecdotal evidence David Einhorn (Founder and president of Greenlight Capital) Fooling some of the people all of the time : Six-year fight with Allied Capital between 2002 and 2008 In June 2007, the SEC found that Allied broke several securities laws relating to the accounting and valuation of illiquid securities. 2
Motivation (Cont d) There exist various incentives for earnings management (EM) Compensation, insider trading, job security, operational flexibility, or control SS imposes one constraint on EM By reducing benefits: SS increases price efficiency (e.g., Boehmer, Jones, and Zhang, 2013; Boehmer and Wu, 2013) MC2 MC1 By increasing costs: SS tracks a firm s discretionary accruals and detects EM (e.g., Cao et al., 2006; Karpoff and Lou, 2010; Hirshleifer, Teoh, and Yu, 2011) L2 L1 MB1 MB2 EM 3
Empirical Approach We exploit Regulation SHO s 202 T pilot program Every third stock in the Russell 3000 index ranked by trading volume within each exchange was drawn and designated as a pilot stock. From May 2, 2005 - August 6, 2007 (as scheduled), pilot stocks were exempted from short-sale price tests. Tick test (for exchange-listed stocks): a short-sale can only occur at a price above the most recently traded price (plus tick) or at the most recently traded price if that price is an uptick from the last different price (zero-plus tick). Bid test (for Nasdaq NM stocks): a short sale can only occur at a price at least one penny above the bid price if the bid price is a downtick from the previous bid. 4
Empirical Approach (Cont d) The pilot program facilitates a diff-in-diff (DiD) analysis 5 Relevance: an economically meaningful decrease in pilot stocks SS costs (increase in SS prospect) relative to non-pilot stocks SEC (OEA, 2007): increase in short-sale trades and short sales-to-shares ratio for pilot stocks / Diether et al. (2009): more order-splitting for NYSE pilot stocks Strong public reaction: Repeal of price tests in July 2007 led to backlash from practitioners and politicians & Reversal of the policy in Feb 2010 drew sharp criticism from HFs and short sellers Exogeneity: the decrease in pilot stocks SS costs is exogenous Controlled experiment with random assignment of treatment and control group No evidence that the firms themselves lobbied for the pilot program The goal was to evaluate the impact of short-sale price tests on market quality
Data Data Sources 2004 Russell 3000 index Pilot stocks are from SEC s first pilot order Excluded are stocks that were not previously subject to price tests and stocks that went public or had spin-offs after April 30, 2004 Sorted by stock s average daily dollar volume from June 2003 through May 2004 Every third stock (starting with the 2 nd one) within each listing market are then picked Compustat Industrial Annual Files for EM and controls Excluded are financial and utilities firms Sample 6 388 pilot and 709 non-pilot firms (balanced sample) over 2001-2003, 2005-2010 The results are similar using an unbalanced sample.
Balanced Sample Treatment Group Control Group None is Significant. (Pilot Firms) (Non-pilot Firms) Variable N Mean Median N Mean Median t- statistic Wilcoxon z-statistic 7 ASSET 388 3,748.61 817.69 709 3,746.25 817.42 0.00 0.51 MB 388 2.75 1.95 709 2.60 1.98 0.68 0.03 ASSETGR 388 13.42 7.88 709 13.22 7.66 0.10-0.30 CAPEX 388 5.55 3.76 709 5.50 3.65 0.14 0.91 R&D 388 4.19 0.00 709 4.04 0.32 0.27-0.94 ROA 388 14.37 14.51 709 14.15 14.29 0.25 0.22 CFO 388 11.36 11.33 709 10.56 10.46 1.10 1.10 LEV 388 29.36 26.46 709 29.80 27.56-0.25-0.24 CASH 388 0.21 0.12 709 0.22 0.11-0.44-0.32 DIVIDENDS 388 0.83 0.00 709 0.73 0.00 1.14 1.07
Pilot-related Variables PILOT: dummy variable to indicate pilot firms PRE: dummy variable to indicate whether FYE falls between January 1, 2001 and December 31, 2003 DURING: dummy variable to indicate whether FYE falls between January 1, 2005 and December 31, 2007 POST: dummy variable to indicate whether FYE falls between January 1, 2008 and December 31, 2010 8
EM Variables Discretionary Accruals (Kothari, Leone, and Wasley, 2005) Discretionary accruals = Total accruals (Earnings Cash Flows) Fitted normal accruals (Performance matched benchmark within industry-year) Likelihood of beating earnings targets (e.g., Graham, Harvey, and Rajgopal, 2005; Bhojraj et al., 2009) BEAT_ALY = 1 if the reported EPS falls between the analyst consensus forecast and that plus 1ct in a quarter and 0 otherwise; BEAT_EPS = 1 if the reported EPS falls between prior year same quarter s EPS and that plus 1ct in a quarter and 0 otherwise. 9
First Finding Univariate Plot First Finding: Pilot firms discretionary accruals (and likelihood of marginally beating earnings targets) decrease during the pilot program, and revert to preexperiment levels when the program ends. 0.002 0.000-0.002 Pre-pilot During-pilot Post-Pilot -0.004-0.006-0.008-0.010-0.012-0.014-0.016 Discretionary Accruals of Pilot Firms Discretionary Accruals of Non-pilot Firms 10
First Finding Multivariate DiD 11 Dependent Variables Discretionary accruals PILOT DURING -0.010 ** (0.004) PILOT POST 0.003 (0.004) PILOT 0.000 (0.003) DURING -0.001 (0.002) POST -0.001 (0.005) Controls Yes # of obs. 9,873 Adjusted R 2 0.40% BEAT_ALY BEAT_EPS -0.079 ** -0.078 * (0.040) (0.044) 0.016 0.001 (0.042) (0.032) 0.019 0.046 * (0.028) (0.023) 0.249 *** -0.114 (0.050) (0.188) -0.109-0.040 (0.129) (0.257) Yes Yes 28,341 59,573 3.40% 1.46% Discretionary accruals is 1 percentage point lower for the treatment group than for the control group during the 3-year pilot period compared to the 3-year period pre-pilot. The likelihood of marginally beating analyst consensus (prior year same quarter s EPS) is 1.8 (0.8) percentage points lower for the treatment group, 11.1% (14.2%) of the unconditional %.
First Finding Robustness Checks Results are robust to using alternative measures of EM Fraud-score of Dechow et al. (2011) Results are not explained by Growth / Investment We consider several ways to control for growth/investment Pilot firms investment levels do not follow the same pattern as discretionary accruals Equity Issuance We show similar results for firms not seeking to issue equity as for the overall sample Market attention We control for various proxies of market attention/investor awareness Reverting pattern is not consistent with a market attention story (Chen et al. 2004) 12
Second Finding Second Finding: For financial misconduct that occurred before the announcement of the pilot program, pilot firms are more likely to be caught after the pilot program starts than non-pilot firms. As we sequentially include frauds initiated after the pilot program begins, the unconditional likelihood of pilot firms getting caught converges monotonically toward that of non-pilot firms. 13 Pr(Caught(t + n), Fraud(t)) = Pr(Fraud(t)) Pr(Caught(t + n) Fraud(t)) Pr(Caught(>May 2005), Fraud(<July 2004)) = Pr(Caught(>May 2005), Fraud(>July 2004)) (1) (2) (3) (4) Dependent Variables Pre-2004 fraud caught Pre-2005 fraud caught Pre-2006 fraud caught Pre-2007 fraud caught PILOT 0.201*** 0.189*** 0.182** 0.141* (0.063) (0.065) (0.073) (0.077) Controls Yes Yes Yes Yes Industry FE Yes Yes Yes Yes # of obs. 2,705 2,708 2,711 2,715 Pseudo R 2 5.41% 5.27% 4.69% 4.07%
Third Finding Current return-future earnings test Third Finding: Pilot firms price efficiency increase during the pilot program. Aggregated earnings for the next 3 years; Coefficient of current return on future earnings (Lundholm and Myers, 2002) 14 Dependent Variable R t X3 t 0.270 *** (0.027) X3 t PILOT DURING 0.158 *** (0.060) X3 t PILOT -0.014 (0.036) X3 t DURING 0.037 (0.030) X t-1-0.676 *** (0.054) X t 0.576 *** (0.044) R3 t -0.125 *** (0.007) INTERCEPT 0.347 *** (0.010) # of obs. 13,844 Adjusted R 2 10.90% Annual buy-andhold return
Third Finding PEAD test 15 Third Finding: Pilot firms price efficiency increase during the pilot program. Boehmer and Wu (2013) Post-earnings announcement drift, CAR (+2, +11) Treatment Group Control Group t-statistics Control Treatment Earnings surprise D1 (Most negative) -0.38% -1.26% *** 2.47 ** D2-0.41% ** -0.41% *** 0.01 D3-0.38% ** -0.57% *** 0.88 D4 0.00% -0.07% 0.35 D5-0.22% -0.24% ** 0.12 D6-0.13% -0.16% 0.13 D7 0.10% -0.10% 0.95 D8-0.02% -0.06% 0.16 D9 0.23% 0.36% ** 0.45 D10 (Most positive) 0.97% *** 0.83% *** 0.43 When investors fail to fully capitalize on earnings information, returns will drift in the same direction as the earnings surprise (Ball and Brown, 1968; Bernard and Thomas, 1989, 1990). Short selling facilitates the incorporation of negative information into stock prices.
Conclusion and Contribution Takeaway: An exogenous reduction in short selling costs leads to less EM, higher conditional probability of detection, and enhanced price efficiency. Contribution: Literature on the real effects of trading in financial markets Bond, Edmans, and Goldstein (2012) for a survey Literature on the determinants of earnings management Dechow, Ge, and Schrand (2010) for a review Literature on short selling and price discovery Add to the policy debate on the costs and benefits of short selling Short selling, or its prospect, improves financial reporting quality even among firms that are not charged with financial reporting violations. 16