accounting & finance school of accounting & finance school of price inflation due to the...

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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School of

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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School of

The SEC Concerns

• Price manipulation• Short sellers sap confidence• Clients withdraw business• “Liquidity Death Spirals”

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School of

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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School of

• 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

18/09/2

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19/09/2

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0.5

1

1.5

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2.5

FREFNM

Price

(USD

)


School of

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


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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School of

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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School of

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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School of

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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School of

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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,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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School of

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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School of

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


School of

But What About TARP?

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School of

Results: Full Sample

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10.6%


School of

Results: Option Availability

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1.6%

12.8%


School of

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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


School of

Volatility and the Ban

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School of

Results: Post-Ban Period

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School of


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School of


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School of

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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School of

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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accounting & finance school of accounting & finance school of price inflation due to the...

Documents

return factor model0

factor analytic model

estimated factor

actual factor

factor model coefficients

banned stocks

factor loadings

actual return factors