price inflation due to the 2008 sec short-sale ban
DESCRIPTION
Price Inflation Due to the 2008 SEC Short-Sale Ban . Lawrence E. Harris University of Southern California Ethan Namvar University of California – Irvine Blake Phillips University of Waterloo. The SEC Ban on Short Selling. September 19 to October 8, 2008 (14 trading days) - PowerPoint PPT PresentationTRANSCRIPT
ACCOUNTING & FINANCE
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Price Inflation Due to the 2008 SEC Short-Sale Ban
Lawrence E. HarrisUniversity of Southern California
Ethan NamvarUniversity of California – Irvine
Blake PhillipsUniversity of Waterloo
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The SEC Ban on Short Selling
• September 19 to October 8, 2008 (14 trading days)
• All financial stocks• Later, some other stocks with significant
financial operations• 987 stocks in total
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The SEC Concerns
“We intend these and similar actions to provide powerful disincentives to those who might otherwise engage in illegal market manipulation through the dissemination of false rumors and thereby over time to diminish the effect of these activities on our markets.”
SEC Release No. 34-58592
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The SEC Concerns
• Price manipulation• Short sellers sap confidence• Clients withdraw business• “Liquidity Death Spirals”
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Potential Unintended Consequence• By preventing short sellers from trading, the
SEC created a bias towards higher prices• Thus, buyers could have purchased at prices
above fundamental values• These buyers would face significant losses
when prices ultimately adjust downward towards their true intrinsic values
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• We estimate the price inflation transferred $597M from buyers to sellers for these stocks
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Anecdotal Evidence: Fanny Mae and Freddy Mac
18/09/2008
19/09/2008
20/09/2008
21/09/2008
22/09/2008
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24/09/2008
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30/09/2008
01/10/2008
02/10/2008
03/10/2008
04/10/2008
05/10/2008
06/10/2008
07/10/2008
08/10/2008
09/10/2008
10/10/2008
0
0.5
1
1.5
2
2.5
FREFNM
Price
(USD
)
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Did the Ban Inflate Prices?• One-shot event study• Short period• Lots of other issues– Troubled Asset Relief Program in particular!– Lehman Brothers collapse also occurred just prior
to the ban• The answer may not be knowable, but the
question is very important7
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Our Paper• Take our best shot at estimating inflation• Use a factor analytic model to characterize
return determinants for the banned stocks • Estimate the factors from the not-banned
stocks• Estimate “but-for” prices for the banned
stocks
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Problems
• The factor model must work during the crisis• The signal must be sufficiently large relative to
the extreme noise• The noise cannot be specific to the banned
stocks
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The Factor Analytic Approach• Use time-series regressions to identify factor
loadings for known factors • Six return factors– Fama-French and Carhart– Value-weighted banned stock index– TARP-weighted banned stock index
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The Factor Analytic Approach• Use cross-sectional regression to identify
factors returns– Estimate using only not-banned sample.
• Three stock characteristic factors– Inverse price– 10-day rolling volatility– 10-day rolling turnover
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The Factor Model
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• First Stage: Factor Loadings
• Second Stage: Factor Model Coefficients
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Sample• 987 stocks on the banned list, 88% of which
appeared on the original September 19 list • 4,812 of 7,639 CRSP stocks with – Market cap > $50M on September 18– Complete data over the sample period
• Includes 676 banned stocks of which 127 received TARP funds in 2008
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Model Validation• The estimated returns for the banned stocks
should be highly correlated with the actual returns before and after the period of the ban
• The difference should have zero mean • The estimated factor returns should be highly
correlated with the actual factor returns for those factors that are known
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Model Validation• If stock price inflation results from the ban,
price correction should be realized in a similar timeframe after the ban
• Inflationary influences should be greater for stocks realizing more negative investor sentiment
• Inflation should also be greater for non-optionable stocks
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Validation Results
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ModelCorrelation coefficient, daily actual
value-weighted banned index returns with the corresponding estimated
index return
Paired t-testt-statistic, for equality of
means
Period Pre Post Pre Post
3 Return Factor Model 0.9274 0.9340 0.37 0.47
3 Return Factor Modelwith 3 Stock Characteristic Factors 0.9306 0.9335 0.08 0.09
6 Return Factor Model 0.9824 0.9640 0.19 0.06
6 Return Factor Modelwith 3 Stock Characteristic Factors 0.9829 0.9606 0.37 0.32
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But What About TARP?
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Results: Full Sample
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10.6%
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Results: Option Availability
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1.6%
12.8%
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Dependent Variable = Inflation
Variable Model 1(N=4810)
Model 2(N=4810)
Model 3(N=676)
INTERCEPT 0 0 0(1.80) (2.11) (4.69)
BAN 0.12 0.14(7.77) (5.52)
OPTION -0.019 0.009 -0.14(1.30) (0.54) (3.38)
TARP 0.013 0.010 0.030(0.84) (0.67) (0.80)
SIZE 0.062 0.067 0.040(4.29) (4.19) (1.03)
SHORT -0.023 -0.026 -0.088(1.56) (1.75) (2.00)
AMIHUD 0.019 0.022 0.00(1.32) (1.18) (0.00)
VOLAT 0.084 0.053 0.25(5.76) (3.28) (6.15)
OPTION*BAN -0.10(4.53)
SIZE*BAN -0.006(0.38)
AMIHUD*BAN -0.012(0.61)
VOLAT*BAN 0.098(4.56)
R2 0.03 0.04 0.06
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Volatility and the Ban
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Results: Post-Ban Period
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Results: Post-Ban Period
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Results: Post-Ban Period
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Cost of Inflation• Multiply estimated price inflation times
volume. • Add up across all banned stocks. • $4.9B in our sample ($2.3B for negative
performance sub-sample)
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Conclusion• Results are indicative of substantial inflation
resulting from the short-sale ban.• Short sample with high volatility.• Arbitrage induces a conservative bias.
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