1
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

Does short selling (SS), or its prospect, constrain firms’ opportunistic reporting behavior?

•
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)

2
 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.


 Compensation, insider trading, job security, operational flexibility, or control
3

• By reducing benefits: SS increases price efficiency
(e.g., Boehmer, Jones, and Zhang, 2013; Boehmer and Wu, 2013)
• 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
MC2
MC1
MB1
MB2
EM

4


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.

5
 The pilot program facilitates a diff-in-diff (DiD) analysis
 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

•
•
6




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

388 pilot and 709 non-pilot firms (balanced sample) over 2001-2003, 2005-2010
The results are similar using an unbalanced sample.

Variable N
Treatment Group
(Pilot Firms)
Mean Median
7
ASSET
MB
ASSETGR 388 13.42 7.88
CAPEX 388 5.55 3.76
R&D
388 3,748.61 817.69 709
388 2.75 1.95 709
388 4.19 0.00
709
709
709
ROA
CFO
LEV
CASH
388
388
388
388
14.37
11.36
29.36
0.21
14.51
11.33
26.46
0.12
709
709
709
709
DIVIDENDS 388 0.83 0.00 709
Control Group
None is
Significant.
N
(Non-pilot Firms)
Mean Median tstatistic
Wilcoxon z-statistic
3,746.25 817.42 0.00
2.60 1.98 0.68
13.22
5.50
4.04
7.66
3.65
0.32
0.10
0.14
0.27
14.15
10.56
29.80
0.22
14.29
10.46
0.25
1.10
0.51
0.03
-0.30
0.91
-0.94
0.22
1.10
27.56 -0.25 -0.24
0.11 -0.44 -0.32
0.73 0.00 1.14 1.07

•
•
•
•
8
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

9
•
•
(
Kothari, Leone, and Wasley, 2005
)

Discretionary accruals = Total accruals ( Earnings – Cash Flows ) – Fitted normal accruals (Performance matched benchmark within industry-year)
( 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.

10
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

11
Discretionary accruals Dependent Variables
PILOT
×
DURING
PILOT × POST
PILOT
DURING
POST
Controls
# of obs.
Adjusted R 2
-0.010
**
(0.004)
0.003
(0.004)
0.000
(0.003)
-0.001
(0.002)
-0.001
(0.005)
Yes
9,873
0.40%
BEAT_ALY
-0.079
**
(0.040)
0.016
(0.042)
0.019
(0.028)
0.249
***
(0.050)
-0.109
(0.129)
Yes
28,341
3.40%
BEAT_EPS
-0.078
*
(0.044)
0.001
(0.032)
0.046
*
(0.023)
-0.114
(0.188)
-0.040
(0.257)
Yes
59,573
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 %.

 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
12

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 )

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.


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 ))
Dependent Variables
PILOT
Controls
Industry FE
13
# of obs.
Pseudo R 2
(1)
Pre-2004 fraud caught
0.201***
(0.063)
Yes
Yes
2,705
5.41%
(2)
Pre-2005 fraud caught
0.189***
(0.065)
Yes
Yes
2,708
5.27%
(3)
Pre-2006 fraud caught
0.182**
(0.073)
Yes
Yes
2,711
4.69%
(4)
Pre-2007 fraud caught
0.141*
(0.077)
Yes
Yes
2,715
4.07%

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)
Dependent Variable
X3 t
X3 t
×
PILOT
×
DURING
X3 t
×
PILOT
R t
0.270
***
(0.027)
0.158
***
(0.060)
-0.014
(0.036)
Annual buy-and- hold return
X3 t
× DURING 0.037
(0.030)
14
X t-1
X t
R3 t
INTERCEPT
-0.676
***
(0.054)
0.576
***
(0.044)
-0.125
***
(0.007)
0.347
***
(0.010)
# of obs.
Adjusted R 2
13,844
10.90%

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)
D2
-0.38%
-0.41% **
-1.26% ***
-0.41% ***
2.47
**
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
15
• 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.

•
•
16
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.