ACCOUNTING & FINANCE School of ACCOUNTING & FINANCE School of Price Inflation Due to the 2008 SEC...

ACCOUNTING & FINANCE

School of ACCOUNTING & FINANCE

School of

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


School of

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


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Did the Ban Inflate Prices?• One-shot event study• Short period• Lots of other issues– TARP in particular!– Lehman Brothers collapse also occurred just prior

to the ban• The answer may not be knowable, but the

question is very important

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School of

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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titiitiitii

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VOLATTURNInvP

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,5,5,4,4,3,3,2,2,1,1,


School of

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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School of

Validation Results

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Model

Correlation 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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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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Volatility and the Ban

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Dependent Variable = Inflation

VariableModel 1(N=4810)

Model 2(N=4810)

Model 3(N=676)

INTERCEPT 0 00

(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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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 substantial inflation

resulting from the short-sale ban.• Short sample with high volatility.• Arbitrage induces a conservative bias.

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