Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`Shackling Short Sellers: The 2008
`Shorting Ban
`
`Ekkehart Boehmer
`EDHEC Business School
`
`Charles M. Jones
`Columbia Business School
`
`Xiaoyan Zhang
`Krannert School of Management, Purdue University
`
`In September 2008, the U.S. Securities and Exchange Commission (SEC) temporarily
`banned most short sales in nearly 1,000 financial stocks. We examine the ban’s effect on
`market quality, shorting activity, the aggressiveness of short sellers, and stock prices. The
`ban’s effects are concentrated in larger stocks; there is little effect on firms in the lower half
`of the size distribution. Although shorting activity drops by about 77% in large-cap stocks,
`stock prices appear unaffected by the ban. All but the smallest quartile of firms subject to
`the ban suffer a severe degradation in market quality.
`(JEL G14)
`
`For the most part, financial economists consider short sellers to be the “good
`guys,” unearthing overvalued companies and contributing to efficient stock
`prices. Even as late as the summer of 2007, regulators in the United States
`seemed to share this view, as they made life easier for short sellers by repealing
`the New York Stock Exchange’s (NYSE’s) uptick rule and other short-sale
`price tests that had impeded shorting activity since the Great Depression (see
`Boehmer, Jones, and Zhang (2009) for an analysis of this event). However,
`short sellers are often the scapegoats when share prices fall sharply, and
`regulators in the United States did a sharp U-turn in 2008, imposing tight new
`restrictions on short sellers as the financial crisis worsened. In September 2008,
`the U.S. Securities and Exchange Commission (SEC) surprised the investment
`
`We thank Frank Hatheway, Robert Battalio, and NASDAQ for providing data. We thank Robert Battalio (a
`referee), Peter Dunne, Tim McCormick, David Musto, Maureen O’Hara, Laura Starks (the editor), Ingrid
`Werner, Avi Wohl, and an anonymous referee for their valuable comments and suggestions. We also appreciate
`feedback from seminar participants at the NASDAQ Economic Advisory Board, the New York Fed, Purdue
`University, University of Illinois, University of North Carolina, University of Washington, Yale University,
`the 2010 AFA and RMA Securities Lending meetings, the Central Bank Conference on Market Microstructure,
`University of Michigan Mitsui Life Symposium, University of Notre Dame Conference on the Future of Securities
`Market Regulation, and the NYSE Euronext TI Workshop on Liquidity and Volatility in Today’s Markets. Send
`correspondence to Ekkehart Boehmer, EDHEC Business School, EDHEC Risk Institute-Asia, One George St.,
`049145 Singapore; telephone: +65.6631.8579. E-mail: ekkehart.boehmer@edhec.edu.
`
`© The Author 2013. Published by Oxford University Press on behalf of The Society for Financial Studies.
`All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
`doi:10.1093/rfs/hht017
`Advance Access publication April 10, 2013
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1363 1363–1400
`
`CFAD VI 1065 - 0001
`CFAD VI v. CELGENE
`IPR2015-01103
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`The Review of Financial Studies / v 26 n 6 2013
`
`community by adopting an emergency order that temporarily banned most
`short sales in nearly 1,000 financial stocks. In this paper, we study changes
`in various liquidity measures, the rate of short sales, the aggressiveness of
`short sellers, and in stock prices before, during, and after the shorting ban. We
`compare banned stocks to a control group of nonbanned stocks to identify these
`effects.
`We find that during the shorting ban, shorting activity in large-cap stocks
`subject to the ban drops by about 77%. All but the smallest stocks subject
`to the ban (those in the smallest size quartile) suffer a severe degradation
`in market quality, as measured by spreads, price impacts, and intraday
`volatility. In contrast, the smallest-quartile stocks see little impact from the
`shorting ban. Stock price effects are difficult to discern, as there is substantial
`contemporaneous, confounding news about the Troubled Asset Relief Program
`(TARP) and other government programs to assist the financial sector. When we
`look at firms that are added later to the ban list (for these firms, confounding
`contemporaneous events are less of a problem), we do not find a price bump at
`all. In fact, these stocks consistently underperform during the whole period the
`ban is in effect. This suggests that the shorting ban did not provide an artificial
`boost in prices.
`Given this backdrop, it is not surprising that several papers contemporane-
`ously address the recent short sale bans. Most are complementary, focusing on
`different aspects of the shorting restrictions. For example, our paper focuses on
`intraday data to shed light on the U.S. ban’s effects on equity trading activity
`and market quality, whereas Battalio and Schultz (2011) study individual
`equity options markets during the ban (see also Grundy, Lim, and Verwijmeren
`2012). Harris, Namvar, and Phillips (2013) gauge stock price effects, whereas
`Kolasinski, Reed, and Thornock (2013) study naked shorting prohibitions and
`analyze stock price responses to short interest announcements during 2008.
`Bailey and Zheng (2013) show that short selling has a stabilizing effect on
`prices during the crisis periods that surround the shorting ban. Ni and Pan
`(2011) show that it takes longer for negative information to be incorporated
`into share prices during the ban.
`Closest to our analysis is the contemporaneous work by Beber and Pagano
`(2013), who look at an international panel of stocks that are subject to different
`types of shorting bans. Their main result is that shorting bans increase end-
`of-day bid-ask spreads, implying a decline in stock liquidity when shorting
`constraints are more severe. They also find some evidence of slower price
`discovery during shorting bans but detect no effect on share prices. Our study
`on the U.S. shorting ban complements Beber and Pagano’s (2013) cross-country
`analysis well. Their data are broader as they cover thirty different countries, but
`this breadth confines the analysis to broadly available data. Specifically, Beber
`and Pagano (2013) use prices and the indicative (and possibly nonbinding) end-
`of-day quoted spreads from Datastream, rather than actual intraday transaction
`costs. They cannot measure short-selling activity across countries and therefore
`
`1364
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1364 1363–1400
`
`CFAD VI 1065 - 0002
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`Shackling Short Sellers: The 2008 Shorting Ban
`
`do not know to which extent shorting bans were actually enforced across
`countries. In contrast, we use intraday data on trades and binding quotes to
`compute the standard measures of market quality (including effective spreads,
`realized spread, price impact, and intraday volatility) and link them to ban-
`induced changes in short-selling intensity. We also employ daily data on actual
`shorting flows to gauge the extent to which the ban is effective in reducing short
`selling across stocks and how this reduction affects market quality.Additionally,
`we use metrics of how difficult it is to borrow a stock and whether a stock is
`heavily traded by algorithmic traders to examine channels that potentially link
`the shorting ban to market quality in the affected stocks.
`Owing mostly to these differences in the nature of the underlying data, Beber
`and Pagano’s (2013) tests primarily describe how the effects of shorting bans
`differ across countries and how bans on naked shorting and bans on covered
`shorting have different effects. In contrast, we analyze one market in depth for
`which we can precisely measure changes in the quantity of shorting (a variable
`not available to Beber and Pagano 2013) and then link these changes to variation
`in the market quality of affected stocks. In terms of methodology, we construct
`difference-in-differences tests that allow us to isolate the effects of the ban,
`whereas Beber and Pagano (2013) employ a firm-day panel that gives more
`weight to firms in countries that experience longer bans than to firms in countries
`with short bans (such as the United States). Moreover, Beber and Pagano (2013)
`restrict their main parameters to be the same across countries in the interest of
`parsimony. This comes at the cost of ignoring cross-country differences, such as
`differences in financial market development, information environment, investor
`protection regulation, etc. In contrast, our one-country study is complementary
`in the sense that it neither requires subjective decisions on how to weight each
`observation nor suffers from cross-country heterogeneity. Instead, it allows a
`much more detailed look at the nature of equity trading before, during, and
`after the ban.
`Other regulatory restrictions on shorting have been studied as well. Jones
`(2012) studies a variety of restrictions in the United States during the Great
`Depression and observes large stock price effects but only modest effects on
`liquidity. Diether, Lee, and Werner (2009) and Boehmer, Jones, and Zhang
`(2009) find small market-quality effects associated with the repeal of the U.S.
`uptick rule in 2005 and 2007. Bris, Goetzmann, and Zhu (2007) find slower
`adjustment to negative information in countries with more severe shorting
`restrictions, as predicted by Diamond and Verrecchia (1987), and Ho (1996)
`finds that shorting restrictions in Singapore increase volatility. Rhee (2003)
`finds some evidence of price effects in Japan following imposition of an uptick
`rule there.
`Most previous theoretical and empirical work on shorting restrictions focuses
`on share price effects. There is less theory linking shorting restrictions to market
`quality. Diamond and Verrecchia (1987) point out that short sellers are more
`likely to be informed, as they would never initiate a short sale for liquidity
`
`1365
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1365 1363–1400
`
`CFAD VI 1065 - 0003
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`The Review of Financial Studies / v 26 n 6 2013
`
`reasons.1 Based on this insight, their model predicts that if shorting is banned,
`bid-ask spreads will actually narrow, because liquidity providers will face less
`adverse selection. In contrast to their hypothesis, a shorting ban could hurt
`market quality if short sellers are important liquidity providers. Banning short
`sellers could reduce competition in liquidity provision, worsening the terms
`of trade for liquidity demanders. Our empirical investigation distinguishes
`between these two competing hypotheses.
`The paper is organized as follows. A detailed time line of events related
`to the shorting ban is the subject of Section 1. Section 2 discusses the data,
`including proprietary intraday NYSE, NASDAQ, and BATS data on short sales,
`as well as our matching procedures. Section 3 discusses the methodology we
`use, particularly the firm fixed effects models used to isolate the effect of the
`shorting ban. Main empirical results are discussed in Section 4 with analysis of
`changes in shorting activity, changes in effective spreads, short-term volatility,
`and other market quality measures, as well as effects on share prices. Section 5
`provides more analysis of the end of the ban and on interactions of the ban with
`hard-to-borrow stocks and algorithmic trading. Section 6 concludes.
`
`1. Time Line of Events
`
`The temporary ban on the shorting of financial stocks is the broadest and, at
`the time, probably the most unexpected, in a sequence of regulatory efforts to
`throw sand in the gears of short sellers and make it more difficult or costly
`to take a short position in embattled financial stocks. The first move in this
`direction took place in July 2008, when the SEC issued an emergency order
`restricting naked shorting (where the short seller fails to borrow shares and
`deliver them to the buyer on the settlement date) in nineteen financial stocks.2
`After the emergency order expired in mid-August, the SEC returned on the
`evening of Wednesday, September 17, with a permanent ban on naked shorting
`in all U.S. stocks, effective at 12:01 a.m. (EST) on Thursday, September 18. On
`Thursday, September 18, the United Kingdom’s Financial Services Authority
`(FSA) instituted a temporary ban on short sales in thirty-two financial stocks,
`effective the next day (Friday, September 19). The FSA shorting ban was
`accompanied by a requirement to disclose short positions in these stocks that
`were in excess of 0.25% of the shares outstanding. Both measures were to
`remain in force until January 16, 2009.
`That same day (Thursday, September 18, 2008), after the U.S. market closed
`for the day, the SEC matched the FSA, surprising the market with a temporary
`
`1 Empirical evidence finds that short sellers are well informed and enhance price discovery. See, for example,
`Dechow et al. (2001), Desai, Krishnamurthy, and Venkataraman (2006), Boehmer, Jones, and Zhang (2008),
`Boehmer and Wu (2013), Saffi and Siggurdsson (2011), and Aitken et al. (1998), among others.
`2 Market makers were exempt from the July 2008 emergency order for naked short sales executed as a result of
`bona fide market-making activity. Kolasinski, Reed, and Thornock (2013) show that the July 2008 emergency
`order made it more costly to borrow shares in the affected stocks and reduced shorting activity in those stocks.
`
`1366
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1366 1363–1400
`
`CFAD VI 1065 - 0004
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`Shackling Short Sellers: The 2008 Shorting Ban
`
`ban on all short sales in 797 financial stocks.3 The SEC’s emergency order
`(release no. 34-58592) was issued pursuant to its authority in Section 12(k)(2)
`of the Securities Exchange Act of 1934, and it was effective immediately. The
`initial order covered ten business days, terminating at 11:59 p.m. (EST) on
`October 2, 2008, but could be extended under the law to last for a maximum
`of thirty calendar days.4
`The details of the shorting ban are important for understanding the effect of
`the event. For example, the last time shorting was banned in the United States
`was in September 1931, when the NYSE banned all short sales in the wake of
`England’s announcement that it was abandoning the gold standard. As Jones
`(2012) recounts, all short sales were banned in that case, including short sales
`by specialists and other market makers, which provoked something akin to a
`short squeeze by buyers who realized that at least in the short-term there would
`be few that could stand in the way of their efforts to drive up prices.
`In 2008, the SEC did not repeat the NYSE’s earlier mistake. The emergency
`order contained a limited exception for market makers (defined in the
`emergency order as “registered market makers, block positioners, or other
`market makers obligated to quote in the over-the-counter market”) that were
`selling short as part of bona fide market making activity. Also, the shorting ban
`became effective on a so-called “triple witching day,” the last day of trading
`before expiration of index options, equity options on individual stocks, and
`index futures. Barclay, Hendershott, and Jones (2008) provide some recent
`evidence on the very large order imbalances and excess volatility in the equity
`market that are present on these days. To prevent large price swings around these
`expirations, the SEC decided to grant options market makers a 24-hour delay
`so that they too could sell short as part of their market-making and hedging
`activities.
`The ban was implemented quite hastily, and many details evolved over time.
`On Sunday, September 21, the SEC announced (in release 34-58611) technical
`amendments to the original ban, all of which were effective immediately.
`There were three main elements. First, the SEC delegated all decisions about
`the ban status of a listed firm to the exchanges. Listing markets were to
`designate the individual financial institutions to be covered and were authorized
`to exclude firms from the ban list on their request. Second, options market
`makers were to remain exempt from the shorting ban for the duration of
`the emergency order, and the SEC clarified that all registered market makers
`were exempt, including over-the-counter (OTC) market makers and those
`making markets in exchange traded funds (ETFs). Third, the SEC stated that
`“a market maker may not effect a short sale … if the market maker knows
`
`3 The emergency order claimed to cover 799 stocks, but only 797 were actually listed in the order.
`4 At the same time, the Commission announced that all institutional short sellers would have to report their daily
`shorting activity, and the Commission announced aggressive investigations into possible manipulation by short
`sellers.
`
`1367
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1367 1363–1400
`
`CFAD VI 1065 - 0005
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`The Review of Financial Studies / v 26 n 6 2013
`
`that the customer’s or counterparty’s transaction will result in the customer or
`counterparty establishing or increasing an economic net short position (i.e.,
`through actual positions, derivatives, or otherwise) in the issued share capital
`of a firm covered by this Order.” This language seems designed to discourage
`the use of listed or OTC derivatives to take a bearish position in the covered
`stocks, though its main result may have been to provide market makers with
`considerable incentives to avoid knowledge of a customer or counterparty’s net
`positions.
`On Monday, September 22, the three major exchanges announced a number
`of additions to the list of banned stocks. For example, the NYSE added thirty-
`two stocks to the list on this day and forty-four stocks on the following
`day. Many of these additions were clearly financial stocks that were simply
`overlooked by the SEC as it drew up its initial list, but industrial firms with a
`large finance subsidiary (such as General Motors and General Electric) were
`added to the shorting ban list as well. Additions continued on subsequent
`days at a slower pace. For example, the NYSE added 13, 9, and 7 stocks
`on Wednesday, Thursday, and Friday, respectively. Also, four NYSE firms and
`four NASDAQ firms asked to be removed from the shorting ban list on various
`days. These removals included real estate investment trusts (REITs) as well
`as a few broker-dealers and asset managers, who may have been concerned
`about looking hypocritical given that at least some of their revenues relied on
`the continued viability of short sales. For some of our tests, we examine these
`withdrawing firms separately.
`On October 2, 2008, at the end of the initial ten-day effective period, the
`SEC extended the ban to the earlier of October 17, 2008 or three business days
`following enactment of TARP (formally known as H.R. 1424, the Emergency
`Economic Stabilization Act of 2008). President Bush signed the bill into law on
`the afternoon of Friday, October 3, immediately after it passed both houses of
`Congress, and the SEC then announced that the ban would expire at 11:59 p.m.
`(EST) on Wednesday, October 8, 2008. As of October 9, shorting was again
`permitted in all listed stocks as long as market participants complied with the
`requirement to borrow shares in advance, as mandated by the naked shorting
`ban, which continued to remain in effect.
`
`2. Data
`
`Most of the analysis covers the period from August 1 through October 31, 2008.
`We also examine stock returns through the end of 2008. We merge data from
`six different sources. Stock returns are from the Center for Research in Security
`Prices (CRSP), and the TAQ database is used to calculate market quality and
`other intraday measures. The NYX and NASDAQ Web sites provide dates and
`details about stocks initially included on, added to, and/or deleted from the
`shorting ban list. From the NYSE, NASDAQ, and BATS, we have data on
`
`1368
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1368 1363–1400
`
`CFAD VI 1065 - 0006
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`Shackling Short Sellers: The 2008 Shorting Ban
`
`all executed short sales from August 1, 2008 through October 31, 2008.5 The
`format is the same as the data required to be made public from January 2005
`to July 2007 under Regulation SHO. For each transaction executed on one of
`these venues involving one or more short sellers, a record identifies the time
`of the transaction, the ticker symbol, the trade price, and the share volume that
`involves a short seller. Finally, we use “easy-to-borrow” lists provided by a
`major prime broker. Each morning, these lists indicate which stocks can be
`shorted without restrictions on that day. The typical list in fall 2008 contains
`the vast majority of listed stocks, around 5,300 names. Being included on this
`list tells traders that there are no particular impediments to shorting this stock
`on that day. Consequently, we classify stocks that are not included on this list
`as hard to borrow. These reports are available to us from September 2, 2008
`through September 17, 2008, covering the two weeks just before the shorting
`ban was imposed.
`To be included in the sample, stocks must be listed on the NYSE or NASDAQ
`from December 31, 2007 through October 31, 2008, because we create a
`matched sample based on trading activity during the first half of 2008. Based
`on the match to CRSP, we retain only common stocks (CRSP share codes
`10 and 11), which means we exclude securities such as warrants, preferred
`shares, American depositary receipts (ADRs), closed-end funds, and REITs.
`After applying these filters, there are 665 stocks in the sample out of the original
`SEC list of 797 stocks subject to the shorting ban, and an additional 62 stocks
`in our sample later become subject to the shorting ban, for a total of 727 NYSE
`and NASDAQ common stocks in the sample that are subject to the shorting
`ban at some point. Table 1, Panel A, provides details on the filters applied.
`We create a matched control sample of 727 stocks for which shorting was
`never banned. These stocks are matched by listing exchange, the presence or
`absence of listed options, market capitalization at the end of 2007, and dollar
`trading volume from January through July 2008. As a distance metric, we
`compute the absolute value of the proportional market-cap difference between
`the nonbanned match candidate and the banned stock plus the analogous
`absolute value of the proportional dollar trading volume difference. For each
`stock subject to the ban, we choose with replacement the nonbanned stock that
`is listed on the same exchange, has the same options listing status, and has the
`smallest distance measure. For each ban stock and each matched firm, we then
`obtain all trade and quote information from TAQ during our sample period.
`Panel B of Table 1 characterizes the quality of the matching procedure. We
`present results for the full sample and four size quartiles, to better illustrate
`differences across size groups. For each firm-size quartile, we report the
`average percentage distance of the two matching variables. Market cap is
`
`5 Based on October 2008 market share statistics reported by the exchanges, these three organizations account for
`over 76% of total equity trading volume. The NYSE Group’s market share statistics include trading on ARCA,
`but we do not have ARCA short sale data, so we probably have somewhat less than 76% of total shorting activity.
`
`1369
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1369 1363–1400
`
`CFAD VI 1065 - 0007
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`presentthemeanandmedianforbanandmatchfirms,andweprovidep-valuesforthedifferencebetweenbanandmatchfirms.
`statusandthesamelistingexchange;thematchthenminimizesthesumofabsolutepercentagedeviationsinDecember2007marketcapitalizationandJanuary–July2008dollarvolume.We
`listofstocksinwhichshortsellingisnotallowedduringthe2008ban.PanelBpresentsacomparisonofthe727banfirmswith727matchedfirms.Matchesrequirethesameoptionlisting
`Thistabledescribessampleselectionandmatchingprocedure.PanelApresentsthesampleselectionprocedure.WebeginwithallstocksthatappearoneithertheSEC’sortheexchanges’
`
`5.8
`8,751.0
`8,726.7
`
`1.1
`4,606.5
`4,881.3
`
`0.55
`0.04
`4,117.3
`25,553.7
`29,671.0
`
`−0.2
`490.8
`480.0
`
`0.68
`0.21
`1.0−2,867.5
`473.1
`20,434.7
`481.0
`17,567.1
`
`182
`
`0.34
`0.61
`−2.8
`977.7
`974.9
`
`0.77
`0.98
`0.1
`565.2
`565.3
`182
`
`−0.8
`33.1
`30.0
`
`0.3
`140.7
`138.9
`
`0.00
`0.12
`−2.5
`105.5
`103.0
`
`0.64
`0.03
`−3.4
`153.0
`149.6
`182
`
`−0.5
`11.7
`6.0
`
`−0.2
`47.8
`46.5
`
`0.00
`0.00
`−4.3
`23.3
`19.0
`
`0.38
`0.06
`−3.0
`51.6
`48.5
`181
`
`−0.6
`194.1
`189.9
`
`0.1
`250.0
`242.5
`
`0.00
`0.04
`1,028.3
`6,674.2
`7,702.5
`
`0.84
`0.20
`−719.5
`5,308.3
`4,588.9
`
`727
`
`p-value(Wilcoxon)
`p-value(t-test)
`Difference
`Control
`Ban
`
`p-value(Wilcoxon)
`p-value(t-test)
`Difference
`Control
`Ban
`
`Median
`
`Mean
`
`Median
`
`Mean
`
`Median
`
`Mean
`
`Median
`
`MedianMean
`
`Mean
`
`Sizequartile4(largest)
`
`Sizequartile3
`
`Sizequartile2
`
`Sizequartile1(smallest)
`
`Fullsample
`
`January–July2008
`volumeinroundlots
`
`Consolidatedmonthlytrading
`
`Dec.2007marketcap($millions)
`
`Numberofstocks
`
`The Review of Financial Studies / v 26 n 6 2013
`
`62
`665
`727
`−29
`−113
`−55
`−7
`931
`
`PanelB:Matchingstatistics
`
`Samplestocksaddedtothebanlistlater
`Samplestocksontheoriginalbanlist
`
`Stocksinfinalsample
`RemoveAMEXcommonstocks
`Removeotherthancommonstocks
`StockswithoutinformationonCRSP
`Lostbecausetickersambiguouslyrefertomultipleshareclasses
`TotalnumberofstocksonSECandexchangebanlists
`
`PanelA:Sampleselection
`
`Securitiessubjecttothe2008shortingban
`Table1
`
`1370
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1370 1363–1400
`
`CFAD VI 1065 - 0008
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`Shackling Short Sellers: The 2008 Shorting Ban
`
`barely distinguishable between the ban firms and the matched control firms
`within each quartile (and is statistically indistinguishable for the entire sample,
`which is not tabulated). The median pairwise size difference is less than 0.4%
`in each quartile and is not significantly different from zero, except in one case.
`Dollar volumes are also well matched, although significant differences remain
`for the two smaller size quartiles. Even in the smallest quartile, however, the
`median difference is only 5%, and it is only 2.2% in the next smallest quartile.
`Overall, the two samples appear to be well matched during the preban period,
`and matching quality tends to be better in the larger size quartiles. Note that
`in the regression tests, the set of control variables also includes these pairwise
`differences in market cap and dollar volume, to ensure that the results are not
`driven by differences in these stock characteristics between the two groups.
`Table 1, Panel B, also reveals that most financials subject to the ban are
`quite small. The median December 2007 market caps for quartiles 1 and 2 are
`only $46.5 million and $138.9 million, respectively. In fact, all of the stocks in
`these two quartiles are in the bottom market cap decile based on NYSE break
`points. Similarly, the median stock in quartile 3 would find itself in the ninth
`NYSE market cap decile. Only the largest quartile of banned stocks would not
`be considered small-cap. The median stock in quartile 4 would be in the fourth
`NYSE decile. Of course, there are quite a few large cap financials, and in some
`of our tests, we consider these large financial firms separately.
`In robustness tests, we also consider noncommon stocks and matches based
`on industry. Specifically, we take all three-digit SIC codes for which at least
`one firm appears on the ban list and at least one firm does not. Then we exclude
`ADRs, closed-end funds (but not REITs), ETFs, and partnerships. For each
`of the sixty-two ban list firms in this subset, we then find a matching firm
`that is listed on the same exchange and minimizes our distance metric based
`on market cap and volume. This subsample is small, because in most of the
`financial industries, all stocks were subject to the ban. Thus, this matching
`procedure yields a sample that is dominated by firms in nonfinancial industries
`with modest financial arms. It also differs from the base sample in that securities
`other than common stocks are included.
`To create a subset of large, systemically important firms for separate analysis,
`we identify the nineteen large financials that were subject to the SEC’s
`temporary emergency ban on naked shorting in July 2008. These firms included
`all of the primary dealers in Treasury securities as well as Fannie Mae and
`Freddie Mac, so this list includes the largest investment and commercial banks
`with the most extensive debt securities market operations. Eight institutions
`on this list survive our filters, including Bank of America, Goldman Sachs,
`Morgan Stanley, Citigroup, and J.P. Morgan Chase. These firms were probably
`the ones expected to receive the most government assistance, and we refer to
`this group as the “largest TARP firms.” We examine them separately, because it
`appears the shorting ban was designed in part to assist these large, systemically
`important firms.
`
`1371
`
`[10:12 24/4/2013 RFS-hht017.tex]
`
`Page: 1371 1363–1400
`
`CFAD VI 1065 - 0009
`
`

`
`Downloaded from
`
`http://rfs.oxfordjournals.org/
`
` at University of Georgia Libraries on August 7, 2015
`
`The Review of Financial Studies / v 26 n 6 2013
`
`3. Methodology
`
`We describe the effects of the shorting ban graphically and in firm-pair fixed
`effects panel regressions. Most of the figures compare the 665 sample stocks
`on the original ban list to the 665 matched control stocks for which shorting is
`never banned. We use this subset of banned stocks in the figures because the
`event dates are the same for all of them, making it easy to visually identify
`the effects of imposing and ending the ban by comparing banned stocks to
`otherwise similar nonbanned stocks.
`Our panel regression analyses incorporate all 727×2= 1,454 stocks in the
`sample, including stocks that were added to the ban list after September 19 and
`the matching control stocks. Using this sample and various subsets, we estimate
`the following fixed effects model for a variety of left-hand side variables Yit
`measured for matched pair i on day t:
`
`Yit = αi + βDBAN
`it + θ Xit + εit ,
`
`(1)
`
`where Yit is the measured quantity Y for the banned stock less the measured
`quantity for its nonbanned match. On the right-hand side, a matched pair fixed
`effect is present, and DBAN is an indicator variable set equal to one if and only
`if the shorting ban is in effect for the banned stock in matched pair i on day t.
`Also included is Xit , a vector of pairwise differences for the following control
`variables: market cap, dollar trading volume, the proportional daily range of
`transaction prices, and the daily volume-weighted average share price (VWAP).
`The matched pair fixed effect means that we take out any differences between
`two stocks in a pair that are present during the nonban period. The control
`variables are designed to pick up time-variation in the matching variables as
`well as any effects due to volatility or share price level, though it turns out that
`none of those effects are important—all of our inference is unchanged when we
`exclude these control variables. Thus, our overall strategy is to identify the effect
`of the ban on a particular quantity Y by comparing banned stocks to matching
`nonbanned stocks during the ban versus at other times. Said another way, this
`panel is a differences-in-differences methodology that can accommodate the
`staggered introduction and removal of the shorting ban across stocks.6
`Statistical inference is conducted using Thompson (2011) standard errors.
`This technique allows for both time-series and cross-sectional correlation of
`the regression errors, as well as heteroscedasticity. In general, we find that
`these robust standard errors are very similar to ordinary least squares standard
`
`6 As a robustness check, we use a Fama-MacBeth approach that we construct as follows. We estimate model (1)
`using only the 665 firms on the original ban list and their matched control firms. We omit the ban dummy and
`instead add day fixed effects to the model. Fourteen of the day fixed effects represent days during the ban period
`and forty represent nonban days. Their respective means are an estimate of the conditional ban and nonban paired
`differences between ban and control firms. We use a two-sample t-test to see whether the mean time fixed effects
`coe