ACCOUNTING & FINANCE School of ACCOUNTING & FINANCE School of 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

ACCOUNTING & FINANCE 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 2

ACCOUNTING & FINANCE 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

ACCOUNTING & FINANCE School of The SEC Concerns Price manipulation Short sellers sap confidence Clients withdraw business Liquidity Death Spirals 4

ACCOUNTING & FINANCE 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 5

ACCOUNTING & FINANCE School of We estimate the price inflation transferred $597M from buyers to sellers for these stocks 6 Anecdotal Evidence: Fanny Mae and Freddy Mac

ACCOUNTING & FINANCE School of 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 7

ACCOUNTING & FINANCE 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 8

ACCOUNTING & FINANCE 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 9

ACCOUNTING & FINANCE 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 10

ACCOUNTING & FINANCE 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 11

ACCOUNTING & FINANCE School of The Factor Model 12 First Stage: Factor Loadings Second Stage: Factor Model Coefficients

ACCOUNTING & FINANCE 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

ACCOUNTING & FINANCE 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 14

ACCOUNTING & FINANCE 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 15

ACCOUNTING & FINANCE School of Validation Results 16 Model Correlation coefficient, daily actual value- weighted banned index returns with the corresponding estimated index return Paired t-test t-statistic, for equality of means Period PrePostPrePost 3 Return Factor Model Return Factor Model with 3 Stock Characteristic Factors Return Factor Model Return Factor Model with 3 Stock Characteristic Factors

ACCOUNTING & FINANCE School of Validation Results 17 Model Correlation coefficients, actual return factors with estimated return factors ExMktHMLSMBMOMBANTARP Pre Period (N=254) 3 Return Factor Model Return Factor Model with 3 Stock Characteristic Factors Return Factor Model Return Factor Model with 3 Stock Characteristic Factors Post Period (N=58) 3 Return Factor Model Return Factor Model with 3 Stock Characteristic Factors Return Factor Model Return Factor Model with 3 Stock Characteristic Factors

ACCOUNTING & FINANCE School of Results: Full Sample %

ACCOUNTING & FINANCE School of Results: Option Availability % 12.8%

ACCOUNTING & FINANCE School of Volatility and the Ban 20

ACCOUNTING & FINANCE School of 21 Dependent Variable = Inflation Variable Model 1 (N=4810) Model 2 (N=4810) Model 3 (N=676) INTERCEPT00 0 (1.80)(2.11)(4.69) BAN (7.77)(5.52) OPTION (1.30)(0.54)(3.38) TARP (0.84)(0.67)(0.80) SIZE (4.29)(4.19)(1.03) SHORT (1.56)(1.75)(2.00) AMIHUD (1.32)(1.18)(0.00) VOLAT (5.76)(3.28)(6.15) OPTION*BAN-0.10 (4.53) SIZE*BAN (0.38) AMIHUD*BAN (0.61) VOLAT*BAN0.098 (4.56) R2R

ACCOUNTING & FINANCE School of Results: Post-Ban Period 22

ACCOUNTING & FINANCE School of Results: Post-Ban Period 23

ACCOUNTING & FINANCE School of Results: Post-Ban Period 24

ACCOUNTING & FINANCE 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) 25

ACCOUNTING & FINANCE School of Conclusion Results are indicative substantial inflation resulting from the short-sale ban. Short sample with high volatility. Arbitrage induces a conservative bias. 26