Banks, Bears, and the Financial Crisis

Warren Bailey and Lin Zheng *

22nd August 2012

Abstract

We test whether short selling is destabilizing comparing distressed financial firms to other firms

using NYSE transactions records covering four years including the recent financial crisis.

Aggressive short-selling is sometimes destabilizing by some measures, but its impact is small,

vanishes quickly, is not necessarily larger for distressed firms or during the crisis, and is

accompanied by other stabilizing effects. The evidence does not validate theoretical predictions

from models of destabilizing speculative or predatory trading. Aggregate short-selling is largely

unrelated to market-wide investor sentiment, credit risk, and ex ante volatility. Aggressive

liquidation of long positions typically has more impact than short selling. Thus, the data cannot

justify the restrictions on short sales of financial stocks imposed in September 2008.

JEL Classifications: G01, G10, G28

Keywords: short selling, banks, financial crisis, speculation, predatory trading

* Johnson Graduate School of Management, Cornell University, Sage Hall, Ithaca, NY 14853-6201, wbb1@cornell.edu; Department of Economics, City College of New York, New York, NY , lzheng@ccny.cuny.edu. We thank Rui Albuquerque,  Kee-Hong Bae, Steve Brown, Peter Chung, Mancang Dong, George Gao, Delroy Hunter, Julapa Jagtiani, Andrew Karolyi, Alok Kumar, Francis Longstaff, Connie X. Mao, Pamela Moulton, David Ng, Paolo Pasquariello, Dilip Patro, Gideon Saar, Carolina Salva, Paul Schultz, Rene Stulz, Robert Whaley, Xiaoyan Zhang, Yinggang Zhou, and seminar participants at SUNY Buffalo, University of South Florida, Cornell University, University of Hawaii, Fifth Biennial McGill Global Asset Management Conference, and 2011 Ohio State Finance Alumni Conference for helpful discussions, comments on earlier drafts, encouragement, and other assistance. © 2010, 2011, 2012 Warren Bailey and Lin Zheng

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“What’s happening out there? It’s very clear to me – we’re in the midst of a market

controlled by fear and rumors, and short sellers are driving our stock down.”

John Mack, Morgan Stanley CEO, 18th September 2008, www.time.com “…the wolf pack trying to pull down the weak deer...” Hank Paulson, former Treasury Secretary, in testimony to the Financial Crisis Inquiry Commission, Bloomberg’s www.businessweek.com, 12:42pm, 6th May 2010

1. Introduction

Short selling in organized stock markets has always been controversial.1 The eruption of

the financial crisis in 2008 has led to further accusations about the destructiveness of short

selling and renewed pressure to further limit the practice.

While much of the existing academic literature suggests that short selling contributes

positively to the workings of financial markets, many practitioners, policymakers, regulators, and

individual investors believe otherwise. The logic behind the indictment of short selling is as

follows. Financial market prices are constructed from estimates of discount rates, future cash

flows, and other information relevant to valuation. Market prices themselves reflect important

information in an environment of asymmetric information, differential processing of information,

and ever-changing investor sentiment. In such an environment, a decline in prices caused by

short sellers can trigger a cascade of selling and further price declines, even if this is not justified

by fundamentals. Declining stock prices in turn lead to increased concerns about

creditworthiness, capital adequacy, and solvency, which can have a severe impact in the real

economy beyond the financial markets and institutions. Predatory trading and trade-based

manipulation can magnify these effects.2

This logic is particularly persuasive during a major financial crisis. For example, it

appears that the recent crisis was precipitated by weaknesses in the legal and regulatory regime

that allowed careless mortgage origination, imprudent writing and trading of credit derivatives,

and reckless decision-making at inadequately-governed financial institutions. Some market

1 Perhaps history’s first ban on short selling was imposed in Amsterdam in 1610 (Bris, Goetzmann, and Zhu, 2007). 2 See, for example, Brunnermeier and Pedersen (2005) and Goldstein and Guembel (2008).

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observers and participants conclude that inadequate legal and regulatory control over equity

market short selling was also part of the problem. However, there is little, if any, direct evidence

on the consequences of short sales of listed equities during this crisis.3

The recent crisis is of particular interest given the characteristics of today’s financial

markets and the manner in which the crisis evolved. The crisis originated outside the stock

market, unlike past episodes like the technology stock crash. However, modern financial

markets and institutions offer a fertile medium for problems on the balance sheets of financial

institutions to propagate and spread. These markets feature ever more automated trading, highly

leveraged trading by hedge funds and derivatives brokers, liquid public securities markets that

contrast with less-liquid markets for securitized assets, and rapid trading across asset classes by

participants ranging from mutual funds to deposit-taking institutions. Given the speed, leverage,

and interconnectedness of this system, a crisis that probably originated in bank assets was

magnified and transmitted very rapidly.4 A crisis can occur even if banks are aware of risk

management objectives and tools.5

Did shorting in the equity market contribute to this crisis? The purpose of our paper is to

examine the behavior of short sales in the U.S. over the period from January 2005 to March

2009. Specifically, we wish to understand associations between short selling and the stock

market and other financial market conditions. Based on theory and previous empirical findings,

we structure tests to answer several questions concerning the potentially destabilizing effects of

short selling of financial stocks prior to and during the financial crisis.

First, we measure whether shorting appears to target certain firm characteristics and types

of events. The popular perception that short sellers are reflexive and predatory when presented

with weak firms or bad news is not confirmed by the data. Second, we measure whether shorting

of individual stocks displays positive feedback trading (that is, follows the market down) or even

leads trading conditions. There is some evidence of destabilizing behavior, but it is small in scale

and vanishes within minutes. Third, we test whether aggregate shorting follows or “causes”

broader aggregate financial market conditions. There is almost no evidence that shorting follows

3 The Panic of 1907, for example, is believed to have originated in a failed short squeeze that raised doubts about the solvency of the banks and brokerages that had enabled the scheme. See Bruner and Carr (2007). 4 See, for example, evidence on contagion across asset classes due to institutional trading in Boyer, Kumagai, and Yuan (2006) and Manconi, Massa, and Yasuda (2012). 5 See Berger, DeYoung, Flannery, Lee, and Öztekin (2008) for pre-crisis evidence on U.S. bank capital ratio management and Minton, Stulz, and Williamson,(2009) for evidence on their use of credit derivatives.

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or leads market-wide sentiment, credit risk premiums, or ex ante volatility. Finally, we test

whether shorting is a particularly damaging form of selling. In contrast to popular perceptions,

we find that aggressive ordinary selling typically has more impact than short selling. Across all

our tests, we find little difference between results for a sample of distressed financial firms and

results for a matched sample of other firms, or across pre crisis versus crisis periods. We

conclude that blame for this stock market crash is difficult to ascribe to short selling, and that

restrictions on short selling were not justified by market behavior observed during the months of

the crisis or the years prior to its outbreak.

Our paper makes several specific contributions. The recent availability of records of

individual short trades allows us to examine several years of short-selling around a major

financial crisis in great detail.6 We develop new evidence on short-selling during a time of great

chaos relative to other periods that previous authors have studied. Our evidence is relevant to the

ongoing regulatory and political struggle to improve the financial system given catastrophic bank

failures and stock price declines, attenuated liquidity, and an environment of rapid, heavy,

leveraged trading. Furthermore, tick data permits us to measure precisely how disturbances arise

and evolve over time. Time-stamped records of trades and quotes permit us to classify

individual short trades as aggressive or passive so we can contrast different shorting tactics and

include buying and selling pressure among our measures of market conditions. We base testable

propositions on previous authors’ theoretical work to aid motivating and interpreting our tests.

Finally, we provide a novel viewpoint by contrasting short selling to aggressive long position

liquidations.

While our study covers a period of over four years that includes September and October

of 2008 when short selling was restricted, several key papers focus on the three weeks of

shorting restrictions in particular. For example, Boehmer, Jones, and Zhang (2009) study short

NYSE and NASDAQ short trades from August to October of 2008. They find that the shorting

ban degrades market quality as measured by liquidity, price impact, and volatility. Battalio and

Schultz (2011) study quotes and trades for equity options from August 2008 to October 2008.

They find that, while there is no apparent migration from short-banned stocks to options, the ban

6 Until the Reg SHO and NYSE short trade data became available, almost all research relied on proxies for shorting such as borrowing costs, short interest, or institutional ownership. For example, a notable contribution to this literature, Jones and Lamont (2002), is based on hand-collected stock borrowing costs from newspapers from December 1919 to October 1933, spanning the October 1929 crash period. Another good example is Liu, Ma, and Zhang (2010), who use monthly NYSE short interest to study shorting around recent mortgage write-down events.

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increases option bid-ask spreads and decreases stock prices implied by put and call prices. Using

daily data, Ni and Pan (2011) find that puts and credit default swaps are not perfect substitutes

for shorting during the ban. 7 With daily data spanning 2008 to June 2009 from 20 countries with

short sale restrictions and 10 without restrictions, Beber and Pagano (2011) conclude that

shorting restrictions reduce liquidity, slow price discovery, and do not prevent stock price

declines. Other authors demonstrate higher prices (Harris, Namvar, and Phillips, 2009) and

migration of trading (Gagnon and Winter, 2009) for short-restricted stocks during the three

weeks of the ban. An SEC memorandum (Aromi and Caglio, 2008) summarizes data from

thirteen trading days prior to the imposition of the shorting ban, finding contrarian behavior of

shorts and more impact for long position liquidations rather than short trades.

This stream of literature documents the impact of short selling around the period when

restrictions were imposed. These papers typically answer the question "what was the impact of

the shorting ban on market quality?". We answer a different question: "were short sale

restrictions justified by sustained, extensive patterns of destabilizing short selling in the years

leading up to the crisis and the many months of the crisis itself?”. While earlier papers document

the consequences of shorting restrictions during the period of a few weeks when they were

imposed, we present evidence of the sort that regulators should have examined prior to deciding

to impose those restrictions. Our evidence offers little if any justification for the imposition of

shorting restrictions in the fall of 2008 which led to a substantial decay in market quality. We

qualify our findings with a discussion of potential limitations ranging from the extent of our data

to the absence of information on shorting costs in the summary and conclusions section.

2. Literature review and empirical predictions

The role of short sellers in financial markets has attracted growing attention in the

academic finance literature. As a starting point, short sales should facilitate the adjustment of

prices to information, particularly to bad news. This results in improved market efficiency.

Perhaps the first model of short selling appears in Diamond and Verrecchia (1987), and it

demonstrates how short selling by informed traders contributes to market efficiency.

7 We examined shorting for ten NYSE-listed ETFs and closed end funds that hold financial stocks and have shorting activity. Summary statistics (available on request) indicate daily aggregate shorting of these securities declined during the crisis. Beyond the markets studied by Battalio and Schultz (2011) and Ni and Pan (2011), other derivatives markets may have provided means for shorting financial stocks indirectly. However, a good example, S&P Financial index futures traded on the CME, has never enjoyed significant trading volume.

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The intuition of this model has been extensively tested. For example, Dechow, Hutton,

Meulbroek, and Sloan (2000) show that short sellers use information in fundamental ratios to

select stocks with lower expected future returns, reversing their short positions once ratios mean-

revert. Desai et al. (2002) find that heavily-shorted Nasdaq firms experience negative abnormal

future returns, and this effect increases with short interest. Reed (2007) successfully employs a

more direct measure of short sale constraints from the equity lending market. Boehmer, Jones

and Zhang (2008a) construct daily short sales using proprietary NYSE order data and find that

short sellers are well-informed, particularly institutional traders who do not use trading

algorithms.8 Cohen, Diether, and Malloy (2007) use proprietary data on stock loan fees and

quantities from a large institutional investor, and confirm that shorting is important for

incorporating private information into prices. Saffi and Sigurdsson (2011) find that, across 26

countries, reductions in lending supplies are associated with deterioration in proxies for market

efficiency. The results of these authors support the conclusion of Diamond and Verrecchia

(1987) that constraining short sales impedes the adjustment of prices to information.

Another recent direction in this literature studies the strategies and performance of short

sellers, in addition to the issue of informativeness. Some examples are as follows. Diether, Lee

and Werner (2009a) study whether short sellers follow certain strategies such as trading on short-

term overreaction, voluntary liquidity provision, or opportunistic risk-bearing. They conclude

that short sellers often time their trades extremely well to exploit overreaction. Zheng (2009)

documents how short sellers tend to correct overreaction to positive earnings news. Engelberg,

Reed, and Ringgenberg (2012) find that non market-maker short trades around public news

releases have the most predictive power for future returns, suggesting that short sellers

effectively process public information.

While there is much support for the beneficial role of short selling, a detrimental impact

of short selling is also plausible, particularly during a severe downturn in the price of a particular

company’s stock or the market as whole. Thus, other authors have looked for evidence that short

selling is destabilizing. Aitken, Frino, McCorry, and Swan (1998) show that publicly-observed

short sales in Australia have a large negative impact on stock prices, especially near information

events. The effect is much weaker for short sales that are more likely to be associated with limit

8 They also report (Table V) that, during the period they study (January 2000 to April 2004), most shorting is conducted by institutional investors. That most comes from hedge funds (Goldman Sachs, 2010) further fuels the controversy about short selling.

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orders, derivatives trading, hedging, and year-end window-dressing. Chen and Singal (2003)

suggest that short sellers cause the weekend effect by closing positions on Fridays and reopening

them on Mondays. Henry and Koski (2010) study daily short selling around seasoned equity

offerings. They find that the size of SEO discounts and the extent of post-offering price recovery

are correlated with the extent of pre-offering short selling. They conclude that market

inefficiency increases with short selling. Shkilko, Van Ness, and Van Ness (2009) suggest that

short selling is destabilizing because aggressive short selling sometimes increases around large

price reversals for Nasdaq stocks in 2005 and 2006.

Our work is also part of a long stream of research on earlier stock market crashes. Harris

(1989) and Kleidon and Whaley (1992) show that linkages between markets for stocks and

related derivatives weakened around the October 1987 crash, while Battalio and Schultz (2006)

show that such linkages held up during the internet stock crash. With a corporate finance angle,

Seyhun (1990) documents bargain-hunting by corporate insiders after the October 1987 crash,

while Ofek and Richardson (2003) relate the internet stock crash to lockup expirations and

insider selling.

Several studies are philosophically close to ours in testing whether particular classes of

traders or trading strategies contributed to market instability. Seguin and Jarrell (1993) find that,

during the October 1987 crash, margin-eligible NASDAQ stocks were more heavily-traded than

other NASDAQ stocks, but experienced smaller price declines. Choe, Kho, and Stulz (1999) find

no evidence that foreign trading destabilized the Korean stock market during the 1997 Asian

Crisis. Bailey, Chan, and Chung (2000) find that intraday NYSE trading of foreign stocks and

closed-end country funds in response to Mexican peso exchange rate news during the Tequila

Crisis affected stock prices but did not precipitate substantial sell-offs of non-Mexican Latin

American equities. Brunnermeier and Nagel (2004) show how hedge funds rode the technology

bubble up and sold on the way down, rather than trading against trends in a manner that would

stabilize the market. Similarly, our paper assesses whether short selling follows and contributes

to stock price declines and sell-offs.

Previous authors present theoretical models or interpretations of empirical evidence that

help us develop predictions and interpretations for our empirical tests. De Long, Shleifer,

Summers, and Waldmann (1990) do not explicitly refer to short selling. However, they sketch

situations where rational investors play a more complex role than the short sellers with private

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information in Diamond and Verrecchia (1987). Specifically, De Long et al (1990) imagine that

rational speculators trigger, or even try to anticipate, noise traders who follow positive feedback

trading strategies motivated by extrapolative expectations or trend-chasing. Allen and Gale

(1992) explicitly model trade-based manipulation where an uninformed trader profits because his

actions suggest to other traders that he may be informed. Thus, rational traders can ignite or

magnify, rather than counteract, the destabilizing actions of noise traders. 9 This suggests a

potential link between destabilizing effects and short trades. Evidence of positive feedback

trading includes shorting as the market declines, that is, with low returns or negative buy-sell

imbalances.

While positive feedback trading is driven by uninformed noise traders, some authors

offer specific predictions or results about the impact of informed or sophisticated traders on

prices and liquidity. They help us interpret our evidence on short selling around the crisis. In

Leland (1992), trading on private information resolves uncertainty and improves the

informativeness of stock prices, at the cost of less liquidity and a temporary increase in volatility.

Aggarwal and Wu (2006) report patterns in evidence from SEC enforcement actions. Buying

manipulation results in temporarily higher prices, higher liquidity, and higher volatility.

Subsequent selling by manipulators occurs when prices and liquidity are higher than when they

buy. The model of Attari, Mello, and Ruckes (2005) reveals that sophisticated market

participants trade against large arbitrageurs whose trades are predictable given capital

constraints. This can weaken the large trader and precipitate asset sales and greater price

volatility. In Brunnermeier and Pedersen (2005), predatory trading which anticipates the order

flow of large, distressed traders magnifies price movements, reduces liquidity, and can spill

across markets and precipitate a crisis. In Carlin, Lobo, and Viswanathan (2007), illiquidity can

arise if cooperation (refraining from predatory trading) breaks down.

The work of these authors suggests two related testable hypothesis:

H1. Trading on private information increases the informativeness of stock prices,

increases the bid-ask spread, and temporarily increases return volatility (Leland, 1992).

9 See, for example, the model of De Long et al (1989) in which noise trading reduces investment and consumption.

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H2. Manipulative or predatory short selling increases the bid-ask spread and return

volatility and causes temporary price decreases (Attari, Mello, and Ruckes, 2005;

Brunnermeier and Pedersen, 2005; Carlin, Lobo, and Viswanathan, 2007).

The subtle differences between H1 and H2 are the permanence of the predicted effects. H1

predicts a sustained price change given the value of the private information being traded on, a

permanent increase in illiquidity given fear of trading against better-informed traders, but only a

temporary increase in volatility given that informed trading resolves uncertainty. In contrast, H2

predicts only a temporary change in stock prices.

Diether, Lee, and Werner (2009a) describe several specific patterns implied by different

motivations for short trading which can be organized as hypotheses: 10

H3a. Short sellers trade on short-term overreaction if they sell short following positive

returns and their trades are followed by negative returns.

H3b. Short sellers are voluntary liquidity providers if they sell short at times of

significant buying pressure followed by declining buying pressure and negative returns.

H3c. Short sellers are opportunistic risk-bearers during periods of elevated asymmetric

information if they sell short at times of high volatility and bid-ask spreads followed by

lower volatility, narrower spreads, and negative returns.

H3d. Short sellers are opportunistic risk bearers during periods of differences of opinion

if they sell short at times of high volatility and narrow spreads followed by lower

volatility, wider spreads, and negative returns.

We can compare these four predictions to H1 and H2. In H3a, short trading pushes the stock

price back towards fundamentals by correcting overreaction. This is similar to enhancing

informativeness as in H1, but differs from H2, which predicts only a temporary change in stock

10 Such predictions can be complex. For example, on page 577, Diether, Lee, and Werner (2009a) state “In a market with wide dispersion in reservations values, limit orders posted by (nonstrategic) competing liquidity providers result in narrower spreads. As opinions converge, volatility should fall and spreads should widen.”

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price. H3b is not subsumed by H1 or H2 because it defines liquidity provision as selling into

buying pressure, rather than trading to lower the bid-ask spread. H3c predicts a run-up then

decline in spread and volatility around a short trade. In contrast, H1 predicts a persistent increase

in the spread with trading on private information. H3c also differs from H2, which predicts

permanent increases in spread and volatility with manipulative or predatory trading. H3d predicts

an increase (decline) then decline (increase) in volatility (spread) around a short trade. None of

the other hypotheses predict mirror-image behavior of the spread and volatility. None of the

theories underlying these predictions specifies the exact timing of immediate versus gradual

effects. Thus, we may not be able to detect the predicted patterns in the daily and five minute

intervals we study.

How will the effects described by the hypotheses differ during the crisis compared to

prior to the crisis? The logic underlying theories of predatory trading suggests that, during a

crisis featuring greater volatility, illiquidity, and contagion across markets and traders, the effects

predicted by H2 should be heightened. Similarly, if overreaction, differences of opinion, and

other manifestations of confusion and disagreement are higher during a crisis, the effects

predicted by H3a and H3d should be heightened. In contrast, it is unclear how the predictions of

H1 and H3c are affected by crisis: the flow and value of asymmetric information can increase, or

public news can be more critical to market participants than private information. If short sellers

play a positive role at times of heightened market turmoil, their contribution to market quality is

more pronounced and the effects predicted by H3b are heightened.

3. Data and methodology

3.1 Data

The sample period is January 2005 to March 2009. To illustrate some of the extreme

equity market movements during this crisis, Figure 1 plots daily closing stock prices for two

prominent banks, Bank of America and Citibank, for 2007 to March 2009. It is evident that these

banks experienced enormous declines in equity value during the height of the financial crisis.

Both banks had share prices above 50 dollars at the beginning of 2007. By the middle of March

2009, however, shares of both banks declined to only several dollars per share.

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As detailed later, our empirical measures are based on two primary datasets, short sales of

NYSE stocks from NYSE/Euronext11 and trades and quotes for NYSE stocks on all exchanges

from the NYSE Trade and Quotation (TAQ) database. We define sub-periods to capture short

selling and its impact before and during the financial crisis and also offer results for a matched

sample of non financial firms. We define two sub-periods as follows. The “Pre Crisis” period is

defined as January 2005 to January 2007. Starting in February 2007, indicators of credit risk

clearly signalled the onset of trouble (Brunnermeier, 2009). The “Crisis” period is defined as

February 2007 to March 2009. While it is not the purpose of our study to understand the effect of

changes in short sale regulations, some results isolate the period (18th September 2008 to 8th

October 2008) when shorting of certain financial stocks was severely restricted.12

Our focus is the impact of shorting on financial stocks that came under pressure during

the crisis. Therefore, we begin with the 797 firms on the original shorting ban list announced on

September 18th 2008.13 Of the 797 stocks, 27 were listed on AMEX, 565 on NASDAQ, one on

Arca, and the balance of 204 on the NYSE. Given that our shorting data covers NYSE trades

only, we must confine our study to the 204 NYSE listings. Among those 204 stocks, 14 are

American Depositary Receipts (ADRs), which are excluded from our sample.14 J M P Group

(JMP) is excluded since it was removed from the SEC’s list at the company’s request on

September 23th, 2008. The A and B class shares of Berkshire Hathaway (BRK.A and BAK.B)

are excluded because only BRK.A is available on CRSP but has an extremely high stock price.

This leaves a sample of 175 stocks from the original list of 797 deemed distressed by the SEC.

As detailed below, a control sample of other firms is also constructed.

3.2 Methodology

11 This data is available for purchase from NYSE/Euronext and is distinct from the Regulation SHO database which covers the period from January 2005 to 6th July 2007. 12 Stock and option market makers were allowed to short for certain purposes, so shorting did not vanish entirely. Further sub periods are possible given events such as the suspension of the uptick rule, changes in rules for naked shorting, and expansion of the list of financial firms for which shorting was severely restricted. Early work on our paper included more sub periods but results did not differ any more substantially across periods than our current two-period study. 13 The SEC’s emergency order claimed to cover 799 stocks, but only 797 were actually listed in the order. 14 We also would have excluded closed-end funds, ETFs, and real estate investment trusts, but there were none among the 204 NYSE firms on the SEC’s list.

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Our first task is to create a control sample of firms that were not subject to the shorting

ban, that is, firms that were not identified as distressed or under pressure.15 We construct score

statistics following Cao, Chen, and Griffin (2005), Huang and Stoll (1996), and Cao, Choe, and

Hatheway (1997) to identify a matched sample based on firm size, share price, share volume,

relative shorting volume, and time weighted bid-ask spread, and excluding closed-end funds,

ETFs, REITs, and ADRs. Specifically, for each firm i in the sample and each candidate among

the stocks not included in the sample, we compute a function of the difference between the

January 2005 to January 2007 average daily stock price, trading volume, and market

capitalization of firm i and the potential match. The firm with the lowest score among potential

matching firms is paired with firm i. As Table 1 implies, there are a few cases where a particular

control firm is matched to more than one sample firm.16

For each distressed financial firm and matching firm in our sample, we compute

measures of the extent of short selling for both daily and intraday intervals, plus other measures

of market activity. Daily relative short flow for a particular firm is the total daily shares shorted

from the NYSE/Euronext files divided by daily NYSE (not consolidated) trading volume

computed from TAQ.17 Relative short flow is broken down into “aggressive” or “price-setting”

short trades that execute at the bid versus “passive” short trades that occur when a buyer hits the

ask. The daily “price-setting” or “aggressive” trading imbalance equals buyer-initiated trading

volume minus seller-initiated trading volume, divided by the sum of buyer-initiated trading

volume and seller-initiated trading volume. Quote and trade data from the TAQ database is used

both to compute bid-ask spreads and to classify trades as buyer or seller initiated using the

algorithm of Lee and Ready (1991).18 The daily abnormal price-setting trading imbalance equals

15 Given that most financial institutions were placed on the short restriction list, this implies that matched firms cannot be drawn from the same industries as the sample firms. 16 The table also indicates the control sample includes 11 financial firms that were apparently not distressed and not placed on the shorting ban list in September 2008. Other partitions of the data could define the sample as all financial firms and construct the control sample from non financial firms. However, this is unlikely to affect our results which, as we discuss below, show little difference between the financial and control samples. 17 Boehmer, Jones, and Zhang (2008a) report many findings that are robust to whether shorting is measured with number of short trades, number of shares shorted, or shorting as a proportion of volume. They indicate that shorting scaled by volume minimizes correlation with returns induced by size, book-to-market, and volume effects. 18 For each trade, a trade price below (above) the midpoint of bid-ask a price is classified as seller-initiated (buyer-initiated). If, the trade occurs at the bid-ask midpoint, it is classified as seller initiated (buyer–initiated) if the trade price is lower (higher) than the preceding trade price. The literature remains unsettled as to which classification method is best. For example, Ellis, Michaely, and OHara (2000) find that the Lee and Ready (1991) method is slightly more likely correct (81.05%) than the quote rule (76.4%) or the tick rule (77.66) in classifying Nasdaq trades of 313 firms from September 1996 to September 1997, though all three are less successful at classifying

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the daily price-setting trading imbalance minus the mean daily price-setting trading imbalance in

the Pre-Crisis period, divided by the standard deviation of the daily price-setting trading

imbalance in the Pre-Crisis period. CRSP is the source of daily stock prices and market-adjusted

stock returns. Daily volatility equals daily high price minus daily low price, divided by one-half

of the daily high plus daily low. Bid-ask spread is the time-weighted average spread over the

day. Abnormal volatility and abnormal spread are computed in a manner similar to the abnormal

price-setting trading imbalance while excess stock return is market-adjusted stock return. Note

that raw variables are used in all descriptive tables and (except for market-adjusted returns) for

regressions, while other results use abnormal variables.

To construct intraday variables, we divide each day in the sample period into 78 five-

minute intervals from 9:30 to 16:00. For each interval and each stock, relative short flow (again,

disaggregated into aggressive and passive trades) is the total shares shorted from the

NYSE/Euronext files divided by NYSE trading volume. The aggressive or price-setting trading

imbalance equals buyer-initiated trading volume minus seller-initiated trading volume, divided

by the sum of buyer-initiated trading volume and seller-initiated trading volume. The market-

adjusted intraday return equals the raw return (based on last trades of intervals) minus the

contemporaneous return on the “SPY” index basket ETF times the beta for the stock estimated at

the same time of day during the Pre Crisis period. Abnormal price-setting trading imbalance

equals the price-setting trading imbalance minus the mean price-setting trading imbalance for the

stock at the same time of the day during the Pre Crisis period. The intraday spread and volatility

and their abnormal values are computed similarly.

4. Empirical results

4.1 Summary statistics

Table 1 summarizes characteristics of the sample of financial firms and the set of

matched firms. The average (median) daily proportion of NYSE volume due to NYSE short sales

is slightly higher, 38.87% (38.67%) for sample firms than for matched firms, 38.45% (37.71%),

though not statistically significantly different. The bid-ask spread is economically slightly trades away from quotes, which are more likely with large trades during very active markets. Chakrabarty, Moulton, and Shkilko (2012) confirm that the algorithm works well over more recent INET trades. Finucane (2000) rates the Lee and Ready (1991) and tick tests about equal in classifying NYSE trades for 144 firms from November 1990 to January 1991, though the tick test is better at computing signed volume and effective spreads.

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higher and statistically significantly higher, 0.28% (0.15%) for sample firms than for matched

firms, 0.23% (0.11%). The price-setting trading imbalance (aggressive buys relative to

aggressive sells) suggests more buying pressure for matched firms relative to sample firms. The

average return volatility is substantially higher for sample firms. The average share price and

institutional ownership are broadly similar across the two groups, while market cap is

significantly smaller for sample firms. Thus, sample firms are more heavily shorted, less liquid,

more subject to aggressive selling, experienced more return volatility, and smaller.

4.2 Does shorting target firm characteristics and market-wide news?

Our first set of tests seeks evidence that short sellers target weak firms and respond

aggressively to bad news. Table 2 summarizes regressions of the cross-section of average Crisis

period shorting on Pre Crisis firm characteristics and interactive variables. The purpose of these

regressions is to understand whether short sellers tend to target certain types of firms.

For both sample and matched firms, finance-related stocks have a positive coefficient

which is approximately double for passive shorting relative to aggressive shorting. However, the

estimated coefficients imply that this effect is small, about 2% more aggressive shorting and

about 4% more passive shorting for sample firms. For those firms for which we could find a

credit rating, investment grade sample firms experience less aggressive shorting, while this

pattern is reversed for matched firms. High beta sample and matched firms draw more shorting,

although the FINANCE slope dummy suggests that the effect for financial firms is close to zero.

A particularly large effect is that sample and matched firms with high stock return volatility

experience more aggressive shorting and less passive shorting. For example, a doubling of stock

return volatility is associated with about 16% more aggressive shorting and 40% less passive

shorting. Aggressive shorting is higher for high turnover sample firms but lower for high

turnover matched firms. Higher turnover is associated with more passive shorting for both types.

Collectively, the evidence in Table 2 does not paint a uniform picture of short traders

singling out vulnerable firms. Unlike passive shorting, aggressive shorting is indeed associated

with higher volatility and turnover. Furthermore, there is more aggressive shorting of sample

firms that do not enjoy an investment grade credit rating. However, passive shorting of financial

firms is more extensive than aggressive shorting, and there is no clear pattern of aggressive

14

shorting of sample stocks versus matched stocks. Furthermore, except for volatility and spread,

the size of these effects is small.

Table 3 presents panel regressions of daily firm shorting on dummy variables for three

trading days (days -1 though +1) around principal events of the crisis. The null hypothesis is that

noise traders reflexively short more at times when bad news arrives. The evidence shows that

shorting sometimes increases at times of such news. For example, in a 7 day window in March

2008 including the collapse of Bear Stearns and two Fed rate reductions, abnormal aggressive

shorting was statistically significantly positive. However, shorting increased by only about 2%,

and this is observed for both sample and matched firms. Furthermore, there are also many events

at which shorting recedes. Slope coefficients for passive shorting are sometimes much larger

than those for aggressive shorting, suggesting that passive shorting can be more sensitive to

public news. Furthermore, the responses of aggressive and passive shorting sometimes differ in

sign. This evidence suggests that the behavior of short traders is complex. Bad news is not

necessarily associated with panic and increased short selling, and, whether news is good or bad,

the scale of the change in shorting is not enormous.

4.3 Does shorting follow returns or selling pressure?

Our next set of tests seeks evidence of destabilizing positive feedback trading (De Long

et al, 1990), that is, a pattern in which shorting of a particular stock accelerates when the stock

experiences low returns or high selling pressure. Table 4 presents pooled time-series cross-

sectional regressions to explain daily relative short selling. As described above, all explanatory

variables are used in raw form except for market-adjusted returns. Results are presented

separately for each of our time periods and for sample and matched firms. A simple null

hypothesis, that shorting is destabilizing, predicts that shorting rises with declining returns and

with rising selling pressure and return volatility,

Table 4 shows that slope coefficients on lagged and contemporaneous daily returns are

positive (except for contemporaneous returns and aggressive shorting of sample firms during

Crisis). For example, a 1% increase in {-3,-1} cumulative return is associated with a 0.1139%

increase in Pre Crisis relative aggressive shorting of sample firms. Thus, daily shorting tends to

rise with the stock’s return (potentially correcting overreaction) rather than increasing with poor

stock returns (potentially destabilizing). This stabilizing effect (that is, trading in the opposite

15

direction of the market for the stock) is much larger for passive shorting, for sample firms in the

Pre Crisis, and for matched firms in the Crisis period. This validates one dimension of the

potentially positive role of short-selling: it tends to rise with returns, suggesting that short selling

is not destabilizing but instead may correct overreaction.

Positive slope coefficients on the bid-ask spread are typically large during the Pre Crisis

period, with an increase in the spread associated with an even greater percent increase in relative

shorting of sample firms. This reverses for aggressive shorting during the Crisis period.

Aggressive and passive shorting display different relationships with daily buy-sell order

imbalances. Negative slopes indicate aggressive shorting increases with selling pressure

(destabilizing positive feedback trading). Positive slopes indicate passive shorting decreases as

selling pressure rises (stabilizing).19 However, the scale of these effects is small, with a one

percent change in selling pressure associated with only a fraction of a percent change in shorting.

Except for passive shorting during the crisis, both types of shorting tend to rise with return

volatility. Perhaps passive short traders were averse to taking positions at times of particularly

high volatility during the Crisis. We will have more to say later about whether aggressive

shorting is drawn to volatile, illiquid conditions or whether it leads those conditions.

We offer some interpretation using the testable hypotheses outlined earlier in the paper.

For the Pre Crisis period, positive slopes on lagged or contemporaneous returns suggest that

aggressive shorting of sample stocks is a stabilizing response to overreaction (H3a), although

negative slopes on buy-sell imbalances suggest destabilizing positive feedback trading. Patterns

of slopes on the bid-ask spread and volatility suggest that aggressive shorts are opportunistic

traders on information for sample stocks (H3c) and opportunistic traders on difference of opinion

for matched stocks (H3d). There is no significant difference between shorting of sample firms

and matched firms. Pre Crisis evidence for passive shorting displays some characteristics from

all four of the stereotypes of Diether, Lee, and Werner (2009a). Crisis evidence on aggressive

shorting suggests that beneficial effects recede (no trading against overreaction, H3a) while

potentially destabilizing effects (opportunistic speculation on asymmetric information, H3c)

increase. Finally, short selling at times of high bid-ask spreads and return volatility is consistent

with trading on private information (H1) or manipulative or predatory trading (H2), though these

19 Many of the findings in Tables 5 and 6 are broadly consistent with Comerton-Forde, Jones, Putniņš, (2011): for the period January to August 2008 (part of our Crisis period), passive short traders appear to supply liquidity and trade against market trends.

16

regressions do not allow us to observe the gradual impact of shorting on spread and volatility. In

summary, the evidence of largely positive slopes on returns validates a stabilizing role for

shorting, while the evidence on liquidity and volatility suggests speculation, manipulation, or

private information. Thus, comparing the crisis period to the pre crisis period, beneficial effects

tend to recede and destabilizing effects tend to increase. However, the economic significance of

these effects is typically very small. For example, consider Crisis period aggressive shorting. A

one percent decline in return is associated with a 1.44 basis point increase in aggressive shorting

while a one percent increase in selling pressure is associated with a 2.67 basis point increase in

aggressive shorting. A one percent increase in volatility is associated with an 11.6 basis point

increase in aggressive shorting while a one percent increase in spread is associated with a

0.5078% decrease in shorting.

The table also reports slope dummy terms that isolate associations when (19th September

2008 to 8th October 2008) shorting of sample stocks was severely restricted. Patterns for

aggressive shorting of sample firms appear to weaken during the shorting restriction period. On

balance, Table 4 suggests a mixture of effects, with shorting pushing against some dimensions of

market trends while following or possibly even exploiting others.

For a higher-frequency look at the associations suggested by Table 4, Table 5 presents

pooled time-series cross-sectional regressions to explain intraday relative short selling. The

regressions study five minute intervals, rather than days as in the previous table. All explanatory

variables are used in raw form except for market-adjusted returns.

Relative to the daily results of Table 4, there is more evidence that aggressive shorting

rises with, or after, declines in 5-minute returns during the Crisis period. For example, a one

percent decrease in the contemporaneous return is associated with a 1.36% increase in aggressive

Crisis period shorting of sample firms. This behavior, though perhaps transitory, can be

interpreted as destabilizing as it follows (positive feedback trading), or perhaps even causes

(manipulation or predatory trading) negative returns. Note, however, that this effect is even

higher, -2.24%, for matched firms. Similarly, 5-minute selling pressure (that is, negative values

of the buy-sell imbalance) is often associated with greater aggressive and passive shorting,

particularly during the Crisis. However, it is not necessarily more pronounced for sample firms,

and the elasticity is much less than one. In both periods, aggressive shorting sometimes declines

with the bid ask spread while passive shorting declines as the spread rises. The sign of the

17

relationship of shorting to return volatility depends on the type of shorting, with aggressive

shorting increasing with volatility and passive shorting decreasing with volatility. Finally, the 5-

minute regression slope dummy terms for the period of a few weeks when shorting of sample

firms was restricted yield some significant findings. For aggressive shorting, evidence of

liquidity provision weakens, negative correlation with the spread weakens, and positive

correlation with volatility strengthens. For passive shorting, evidence of trading against

overreaction, shorting when the bid-ask spread is wide, and liquidity provision strengthens. The

coefficients for spread and contemporaneous and lagged volatility are large, and typically larger

for passive shorting.

Across Table 5, the evidence of destabilizing behavior is often similar for sample and

matched firms. The divergent behavior of aggressive versus passive shorting seems more

pronounced. Thus, it is unclear why sample firms were singled out for short-selling restrictions

in the fall of 2008.

Following our testable hypotheses, the Pre Crisis evidence for aggressive shorting

suggests opportunistic speculation (H3c for sample firms, H3d for matched firms) while passive

shorting can be interpreted as supplying liquidity (H3b) with a lag. For the Crisis period, there is

less evidence of correcting overreaction for aggressive shorting (H3a), and some evidence of

liquidity provision (H3b) with a lag for passive shorting. The lack of uniformity in signs on the

bid-ask spread and volatility do not point to private information (H1) or manipulative or

predatory trading (H2). Collectively, the daily and intraday results of Tables 4 and 5 offer mixed

evidence of destabilizing effects. These appear transitory, vanishing or reversing when 5-minute

results are compared to daily results. Some effects are heightened during the crisis, others shrink,

and all are typically economically small. Furthermore, short sellers often contribute to market

stability by trading into higher returns, rather than following returns down.

4.4 Does shorting lead returns, selling pressure, and other financial conditions?

The results of the previous regressions (Tables 4 and 5) suggest stacking relative short

selling, returns, and price-setting order imbalances into a three-equation vector autoregression to

confirm aspects of earlier results. This not only gives us an alternative test for positive feedback

trading by short sellers but can also reveal any feedback in the other direction, from short-selling

to returns and buy-sell imbalances. Therefore, we compute individual firm VARs over these

18

three variables, using 5-minute intervals, with three lags, for each of our 175 sample firms and

our 169 matched firms.

Table 6 summarizes these VAR estimates. The most prominent Pre Crisis VAR effect

(half or more of all firms) for both sample and matched firms is that 5-minute shorting tends to

rise after increased buying pressure. During the Pre Crisis period, 65.5% of sample firms and

68.6% of matched firms display a significant slope for the effect of the first lag of imbalance on

aggressive shorting, and the means and medians of these slopes are positive. A positive slope of

the lagged imbalance for the dynamics of shorting suggests that such shorting is stabilizing,

offsetting buying pressure or supplying liquidity (H3b). Similarly frequent positive slopes are

observed for Pre Crisis passive shorting. Median coefficients of about 1% suggest that this effect

is economically small.

The next most prominent Pre Crisis effect is that 5-minute buying pressure tends to rise

after aggressive shorting increases. This is observed for aggressive shorting for almost 60% of

both sample and matched firms, and is even more frequent for passive shorting. This effect is not

consistent with destabilizing positive feedback trading or manipulative or predatory trading (H2)

whereby shorting triggers additional selling. Median coefficients of about 1/2% suggest that this

effect is economically small.

In contrast, the third most prominent Pre Crisis effect is that 5-minute aggressive shorting

tends to rise after returns decline. This is observed for aggressive shorting for 22.9% of sample

firms and 30.67% of matched firms at the first lag, for example. This echoes the findings in

Table 5 for intraday regressions to explain shorting. Shorting that follows weak stock

performance can be interpreted as destabilizing positive feedback trading. Alternatively, it can

be interpreted simply as informed trading that contributes to price discovery if it anticipates

further negative information (Diamond and Verrecchia, 1987). Median coefficients of about one

suggest that this effect is economically small: a -1% return is associated with about a 1%

increase in relative shorting.

During the Crisis period, significant coefficients are less frequent and can change sign

relative to the Pre Crisis period. Interestingly, significant VAR effects in the Crisis period are

more frequent for matched firms than for sample firms. Thus, destabilizing effects are not

necessarily worse for financial firms or heightened during the crisis period.

19

For a look at contemporaneous relationships among key variables in the VAR, Panel C of

Table 6 summarizes Cholesky decomposition coefficients. The table reports means, medians, and

a measure of the frequency of statistically large coefficients based on bootstrapped standard

errors. Across the panel, 90% or more of coefficients are typically large relative to their standard

errors. The signs of coefficients echo many of the intraday findings in the regressions of Table

5. Aggressive Pre-Crisis shorting rises with return, which can be interpreted as stabilizing (H3a).

However the sign changes for the Crisis period, indicating that a healthy dimension of shorting

turns destabilizing. It also tends to decline with buying pressure, though this can occur by

construction. In contrast, passive shorting tends to rise with returns and buying pressure, which is

more unambiguous evidence of a stabilizing effect.

To demonstrate that the VAR effects are small and vanish quickly, Figure 2 summarizes

impulse response plots for aggressive shorting of sample firms during the Crisis period.20 In

contrast to the VARs reported in the table, these are estimated with eight lags rather than three.

Each plot displays the median, upper 5%, and lower 95% of plots for all sample firms.

The plot in the upper left hand corner summarizes the response of shorting to buy-sell

imbalances. It indicates that, for the median firm, there is a small positive increase in shorting at

the time of a buy-sell impulse. This suggests stabilizing behavior. The response tends to vanish

quickly. The spread between 5% and 95% is sufficiently large to suggest that the impulse

responses are typically insignificant, though there are outliers where there is a large, persistent

response. The plot in the lower left hand corner summarizes the response of shorting to returns.

The pattern is similar to what is reported for the response to buy-sell imbalances. Typically, a

small, stabilizing effect vanishes after several intervals, although there are large persistent

outliers.

The plot in the upper right hand corner summarizes the response of buy-sell imbalances

to shorting. The median initial response of buy-sell imbalance is negative (destabilizing), though

this may occur by construction as aggressive short sales contribute to the buy-sell imbalance.

Furthermore, the median response vanishes beyond the first lag. There are two alternative

explanations for these findings: manipulative or predatory speculators trigger noise traders (H2)

to sell, or short sales quickly transmit bad news to the market. However, any such effects are

faint, and fleeting.

20 Comparable plots for passive shorting and for shorting of matched firms are available on request.

20

The plot in the lower right hand corner summarizes the response of returns to shorting. It

is similar to the response of the imbalance to shorting. The initial response is negative, which can

be construed as destabilizing, predatory or manipulative, or informative. However, the response

is inconsequential because it is tiny (a few tenths of a basis point) and converges to zero after the

first interval. While persistent negative and positive outlying responses are evident, they are also

tiny (about one basis point). On balance, the impulse responses for aggressive shorting of

sample stocks suggest both stabilizing and destabilizing effects, and other interpretations, but

they are, in any case, typically small and transitory.

The previous results suggest that a destabilizing impact of short selling is, at most,

confined to certain time periods and certain dimensions of market behavior, is economically

small, and vanishes within minutes. Note that those results are based on all observations for the

entire cross-section and time-series of data for both sample and matched firms. Next, we take an

event study approach to detecting destabilizing behavior, focussing on market conditions around

the most intense periods of short selling for sample firms only.21 Following the study of the

impact of foreign investors on Korea’s stock market during the Asian economic crisis by Choe,

Kho, and Stulz (1999), we focus on market behavior around the largest short sale events.22

Specifically, for each sample stock we identify the ten days with the largest relative short

selling activity. We refer to these as “large short selling events”.23 Table 7 examines excess

returns, abnormal price-setting buy-sell imbalances, abnormal bid-ask spreads, and abnormal

volatility in an 11-day window around such large short sale events. Results are not reported for

the Pre Crisis period because it serves as the benchmark for Crisis period results.

The market conditions around large shorting events appear to differ for aggressive versus

passive shorting. Crisis period aggressive shorting events are associated with contemporaneous

daily stock return declines averaging 70 basis points for sample firms and 38 basis points for

matched firms. In contrast, Crisis period passive shorting events are associated with daily stock

return increases averaging 45 basis points for sample firms and 66 basis points for matched

21 An alternative approach is studying the “permanent price impact” (Linnainmaa and Saar, 2012) over all individual short trades. 22 The formal structure of information available to market participants about shorting is as follows. NYSE has always released a monthly summary of short interest, while a daily summary (see www.nyxdata.com/page/875) only become available in late July 2009 after the end of our sample period. Informal knowledge of shorting activity and other aspects of order flow presumably varies across market participants. 23 Given the ability of traders to break large trades into small orders, it is not relevant to identify large short selling events based on trade size.

21

firms. Furthermore, aggressive shorting events are associated with substantial selling pressure

(close to one-to-one in percent terms, partly by construction for the contemporaneous value)

while passive shorting events are associated with substantial buying pressure. Aggressive

shorting is also associated with particularly large values for abnormal daily return volatility and

bid-ask spreads.

The results of Table 7 suggest that short selling is associated with market downturns,

selling pressure, and high spreads and volatility, particularly for aggressive shorting. While this

is difficult to interpret as positive for market stability, the scale of these effects is small. Even

days of the largest, most aggressive shorting are associated with stock return declines of less than

one percent.

Using the predictions from theory outlined earlier in the paper, we also look for more

subtle effects in Table 7 by looking at leading and lagging behavior around the events. The signs

of returns around aggressive shorting events for sample firms appear to switch sign around the

event, going from insignificant or positive to negative. This is consistent with correction of

overreaction (H3a). This is even more noticeable for passive shorting of sample firms. Sign

changes for both returns and buy-sell imbalances suggest that passive shorting of sample and

matched firms both corrects overreaction (H3a) and supplies liquidity (H3b). Note that bid-ask

spreads and volatility remain high within these event windows. Thus, the evidence on spreads

and volatility is not consistent with predictions about private information, manipulative or

predatory trading, or speculation (H1, H2, H3c, H3d).

Table 8 parallels the event study of Table 7 but reports results for 5-minute intervals,

rather than for days. Again, large short selling events are defined by the amount of shorting

within the interval, rather than by trade sizes. Intraday results give us a sense of the immediate

impact of such events, and comparison of intraday and daily evidence helps identify transitory

versus longer-lived effects.

Table 8 shows that effects of large shorting events are qualitatively similar, though

smaller, for 5-minute intervals relative to daily intervals in the previous table. Large aggressive

shorting events are associated with contemporaneous negative stock returns (destabilizing) while

passive shorting is associated with positive stock returns (correcting overreaction, H3a).

However, the scale of these return effects is small. For example, aggressive shorting is

associated with a 13 basis point negative return for sample firms and a 15 basis point negative

22

return for matched firms. Passive shorting is associated with 8 and 10 basis point positive returns

for sample and matched firms respectively. Thus, these effects are economically small and do

not differ much across sample and matched firms.

As another dimension of the market impact, the abnormal buy-sell imbalance (t-statistic)

associated with a large aggressive short sale event 5 minutes later is -0.155 (-5.99) for sample

firms and -0.3461 (-13.00) for matched firms. Thus, aggressive shorting is associated with times

of selling pressure, but the scale of the effect is much greater for matched firms. The pattern in

abnormal buy-sell imbalance suggests that passive shorting of sample firms supplies liquidity

(H3b). As was the case for daily events, bid-ask spread and volatility are high within the event

window, but there is no evidence of changes at the time of the event consistent with

hypothesized patterns resulting from private information, predatory or manipulative trading, or

speculation (H1, H2, H3c, H3d).

Note that the evidence in both Tables 7 and 8 is stated in excess or abnormal terms, with

the Pre Crisis period as benchmark for all series but excess returns. They address the question

“was market activity around large shorting events significantly different for what is observed in

more normal times?” The daily results in Table 7 suggest some destabilizing effects. The 5-

minute results in Table 8 suggest only economically small, transitory effects of shorting on

returns during the crisis, and some effects which are larger for matched firms than for sample

firms.

4.5. Does shorting follow or lead broad financial market conditions?

To this point, we have measured associations between the shorting of financial stocks,

and a matched sample of other firms, with individual trading conditions for each firm. Another

dimension of the potentially destabilizing impact of short sales of financial stocks is associations

between such trading and broader measures of sentiment, risk premiums, and uncertainty beyond

the direct impact on stock markets that we have documented. Put another way, are destabilizing

effects of the sort predicted by our testable hypotheses observed in the aggregate or triggered by

market-wide conditions? Lead-lag associations suggest the extent and direction of contagion

among these series. Estimating results for both Pre Crisis and Crisis periods suggests whether

correlation between equity shorting and these measures was heightened during the crisis.

23

Longstaff (2010) summarizes three channels through which a shock in one market may

be transmitted to another. With “correlated information”, a shock in one market conveys news

that is relevant for valuation in another market. For example, increased political risk perceived

for the equity market of one country may cause market participants to revise their estimates of

the political risk of another, related country. A “liquidity” channel has a shock in one market

reducing liquidity in a manner that affects other markets. For example, a precipitous price

decline for a particular asset class eventually triggers margin calls that result in sales, and price

declines, for other asset classes. A “risk premium” channel has a shock in one market causing

investors to revise required returns for many markets, triggering common price declines. For

example, an increase in the perceived risk of the banking industry that lowers bank stock prices

and raises expected returns can manifest itself in the prices and expected returns of bank debt or

derivatives. It may be possible to partly distinguish correlated information, liquidity, and risk

premium effects. Responses to correlated information shocks should be observed quickly,

liquidity effects can occur more gradually, and risk premium effects are directly observed in

deposit and bond yields, as we describe below.

With this in mind, we collect or construct several market-wide or aggregate measures.24

Our first measure picks up the sentiment of small investors towards financial stocks. “Financial

Stock CEF Premium” equals daily changes in an equally-weighted average of the closed-end

fund premiums of the small number of financial stock funds with several years of trading

history.25 Data source is Bloomberg. The variable is intended to capture changes in the sentiment

of small individual investors towards financial stocks (Lee, Shleifer, and Thaler, 1991).

Our next set of measures reflects interest rates and bond yields that include premiums for

credit risk. “TED spread” is the three-month Euro-dollar yield minus the three-month Treasury

bill yield. Daily values are downloaded from the Fed Board of Governors data webpage. This

spread is a well-known indicator of the market’s assessment of the risk of the large banks that

make the euro-deposit markets. “Eurodollar futures” is the intraday price of short-maturity

Chicago Mercantile Exchange (CME) Eurodollar futures contracts obtained from The Institute

for Financial Markets. Given that trading in Treasury bill futures is inactive, we are unable to

24 Note that our short sample period and emphasis on daily and intraday data precludes use of monthly sentiment indicators as developed in Baker and Wurgler (2006). For a related application to hedge funds, see Boyson, Stahel, and Stulz (2010). 25 John Hancock Bank and Thrift Opportunity Fund (BTO), Financial Trends Fund (DHFT), and First Trust Specialty Finance (FGB).

24

compute a spread like our daily “TED spread”, but the volatility in the spread is likely dominated

by the Eurodollar yield, not the Treasury bill yield.26 “CP Spread” is the daily three-month yield

spread on commercial paper of non-financial issuers versus Treasury bills, also downloaded from

the Fed. It reflects the short-term credit risk of large industrial companies that issue commercial

paper extensively. “BAA-AAA Spread” is Moody’s daily index of BAA minus AAA corporate,

bond yield spreads, as motivated by Chen, Roll, and Ross (1986) and also downloaded from the

Fed. It is a broad, long-horizon measure of the risk of industrial borrowers and the state of the

business cycle. It has been successfully used both as a business cycle risk factor and, when

lagged, as an ex ante business cycle risk premium in many important empirical asset pricing

papers (Ferson and Harvey, 1991; Chen, 1991). “ABX BBB-” is an index of 20 subprime

mortgage-backed securities computed by Markit (www.markit.com). The ABX indexes reflect

the real estate credit risk that is central to the evolution of the crisis (Brunnermeier, 2009).

Finally, we employ a measure of ex ante aggregate stock market volatility. “VIX spot

index” is the implied volatility index computed from stock index option prices by the Chicago

Board Options Exchange (CBOE). The VIX is a commonly-cited forward-looking indicator of

beliefs about the general level of volatility in the stock market and is beginning to be used by

researchers (Longstaff, 2010). It is frequently quoted on financial-news television channels and

web pages. Source of daily data is Bloomberg while 5-minute data are extracted from intraday

ticks supplied by CBOE.

The test proceeds as follows. First, compute the aggregate relative short flow for all of

the distressed financial stocks. Second, compute an individual VAR for aggregate relative short

flow and each sentiment or uncertainty measure to understand the lead-lag relationship:

11

11

11

N

i

Ait

Ai

N

i

Jit

Ji

At RSSXRSS (1)

21

21

22

N

i

Ait

Ai

N

i

Jit

Ji

Jt RSSXX (2)

26 Knez, Litterman, and Scheinkman (1994) decompose money market yields and find that the 3-month eurodollar yield (and other credit-sensitive yields) displays substantial loading on an unobserved third factor which the 3-month T-bill displays only trivial loading on. Given that we cannot observe intraday Treasury bill yields to match the intraday Eurodollar yields implied in futures prices, the intraday Eurodollar yields may be thought of as a noisy proxy for the TED spread.

25

RSSA is aggregate relative short sales and XJ is the jth sentiment or uncertainty factor. N is the

number of leads and lags, and is set to 3. If short selling has little or no broad impact, the cross-

terms in (2) will largely be insignificant, except perhaps for transitory effects that parallel our

earlier findings. Similarly, if short selling does not exhibit positive feedback trading by

responding to the other variables, the cross-terms in (1) will be insignificant.

Table 9 presents the results of these tests in two panels, one for each period. To save

space, we do not report the autoregressive terms but note that the estimated coefficients on own-

lags (available on request) reflect a good deal of persistence.

Panel A presents results for the Pre Crisis period. The column labelled “Coefficients on X

for RSS (equation 1)” measures feedback from the sentiment and uncertainty indicators to

subsequent values of aggregate relative short selling. The column labelled “Coefficients on RSS

for X (equation 2)” measures feedback in the other direction. There is one sheet for aggressive

shorting and a second for passive shorting.

For aggressive shorting of sample firms, lags of the financial stock CEF premium seem to

affect shorting but the signs of the coefficients do not permit easy interpretation. For aggressive

shorting of matched firms, the first two lags of the daily TED spread and VIX appear to predict

increased shorting. There are few, if any, other significant coefficients. There is even less

evidence of feedback from shorting to the broad financial market indicators. For passive shorting

of sample firms, there is evidence that shorting rises a day after the TED spread or CP spread

rises, and that the TED spread declines a day after shorting rises. There are few other significant

coefficients for passive shorting of either sample or matched firms.

Panel B presents results for the Crisis period. There is little if any evidence of lead-lag

associations for aggressive shorting of sample firms. There is strong evidence that daily lags of

BAA-AAA are associated with subsequent increases in aggressive shorting. The only prominent

effect for passive shorting is feedback with the financial stock CEF premium.

Collectively, Table 9 offers only limited evidence of a destabilizing relationship between

the short selling of financial stocks and broader indicators of financial market conditions. There

is at best weak evidence of leading or lagging feedback effects between short selling and general

financial market conditions. These relationships seem stronger from the macro indicators to

subsequent aggregate short selling, which suggests that short sellers are more likely to respond

to, rather than trigger, adverse developments in the broader economy. It is particularly notable

26

that there is hardly any relationship between aggregate shorting and a proxy, ABX BBB-, for the

mortgage credit risk believed to be at the heart of the crisis. Furthermore, given the spotty nature

of the evidence, it does not appear that we can distinguish correlated information, liquidity, and

risk premium channels. 27 Two even simpler hypotheses, that short sellers follow positive

feedback trading strategies and respond to broader financial and economic forces and that short

sellers trigger changes in those forces, find virtually no support in the results of this test. Finally,

these weak effects are not clearly bigger in the crisis period compared to the pre crisis period.

4.6. Is shorting more destabilizing than other selling?

Given that we find, at most, small, transitory effects from short sales of distressed

financial stocks, we seek another potential cause of the precipitous declines in financial stock

prices during the crisis. Sloan (2010) cites a New York State commission charged with studying

the Panic of 1907 that concluded that unwinding of leveraged long positions, rather than “bear

sales”, had the largest impact on the stock market. This suggests that we examine the impact of

selling not associated with short sales.28 We offer a brief test of this idea as follows.

We compute the daily and intraday price-setting buy-sell imbalances excluding

aggressive short sales, for each individual sample stock. Our aggressive long position liquidation

events consist of the ten largest daily or 5-minute realizations of this variable for each firm. We

then use these variables in abnormal form to repeat the event studies of Tables 7 and 8, for large

daily or intraday aggressive long position liquidation events, rather than large daily or intraday

shorting events. Once again, a large “event” is defined by the amount of trading activity within

the day or 5-minute interval, rather than by the size of individual trades.

Table 10 presents daily results for instances of large aggressive liquidation of long

positions during the Crisis period. It should be compared to the findings for aggressive shorting

in Table 7, which examines the effect of large short selling events. Effects are similar: big long

position liquidation events are associated with lower returns, selling pressure, higher volatility,

and higher spread. However, some key effects are larger for long position liquidations than for

aggressive short sales. While aggressive short sale events for sample firms are associated with a

27 Given the 5-minute and daily frequency of our tests, we cannot employ weekly liquidity indicators as in Table 5 of Longstaff (2010). 28 Recent research suggests that short selling can have less impact than ordinary selling. See Comerton-Forde, Carole, Jones, and, Putniņš (2011) and Chakrabarty, Moulton, and Shkilko (2012).

27

70 basis point daily return decline (t=-7.16), aggressive long position liquidation events are

associated with a 93 basis point daily return decline (t=-8.78). The negative abnormal buy-sell

imbalance for large aggressive long position liquidation events is more than double that for large

aggressive short sale events. There is a similarly greater impact of long position liquidations

relative to short sale events for matched firms. Indeed, the impact on matched firm returns,

minus 96 basis points (t=-10.57) may even be slightly larger than for sample firms. There is less

evidence of the trading against overreaction pattern (positive returns followed by negative

returns at and beyond the event) in this table relative to the aggressive shorting events in Table 7.

Table 11 presents 5-minute results for aggressive long position liquidation events, and

should be compared to findings for aggressive shorting in Table 8. As was the case for daily

effects, negative contemporaneous associations with returns and buy-sell imbalances are greater

for large aggressive long position liquidation events than for large aggressive shorting events

(Table 8). For example, the average contemporaneous 5-minute stock return at these events is

minus 21 basis points (t=-8.75) and minus 19 basis points (t=-8.02) for sample and matched

firms respectively. This is higher than the averages for aggressive short sale events in Table 8,

minus 13 and minus 15 basis points respectively. Furthermore, there is much more spillage into

5-minute returns one or two intervals out for aggressive long position liquidation events than for

aggressive short sale events. Finally, compared to Table 8, the buy-sell imbalances within the

window are much larger for these events than for aggressive shorting events.

For a more comprehensive look at how long position liquidations fit into the evolution of

trading, we estimate VARs that stack 5-minute aggressive long position liquidations, short

selling (aggressive and passive separately), returns, and buy-sell imbalances into a system of four

equations. Some of the results are summarized in Table 12.

Results for the Pre Crisis period appear in Panel A. The most prominent finding is that

over 90% of both sample and matched firms show strong positive feedback between aggressive

long position liquidations and short sales of either type. Thus, shorting and ordinary selling

appear strongly related to each other. The second most prominent effect is that a majority of

sample and matched firms display a negative correlation between aggressive long position

liquidations and lagged returns. This can be interpreted as destabilizing: long position

liquidations increase following declines in returns. However, compare this to VAR results in

Table 6. Although a similar negative association between shorting and lagged returns is

28

observed, the frequency and size of the effect is much smaller for shorting relative to what Table

12 reports for aggressive long position liquidations.

Results for the Crisis period appear in Panel B. The strong positive feedback between

shorting and long position liquidations is much weaker for sample firms. The frequency and

strength of the relationship between long position liquidations and returns remains, but it

switches sign to positive. That is, long position liquidations during the Crisis tend to occur on

positive stock returns. Compare this to the VAR results in Table 6. The sign of the relationship

between shorting and lagged returns remains negative. However, the number of firms displaying

a statistically significant coefficient is very small.

Panel C of Table 12 reports selected Cholesky decomposition coefficients from VARs.

They allow us to assess some critical contemporaneous associations among these variables. We

report averages of coefficients and their bootstrapped standard errors, and their ratios cannot be

interpreted as formal significance tests. Nonetheless, in comparing Panel C of this table to Panel

C of Table 6, it appears that there are many more important associations for aggressive long

position liquidations than for shorting. For example, the coefficients that relate long position

liquidations to returns or buy-sell imbalances are typically negative and much larger than their

average standard errors. The negative signs are consistent with destabilizing behavior, and

contrast with the much weaker Cholesky coefficients for shorting in Panel C of Table 6.

In summary, the evidence from Tables 10, 11, and 12 suggests that aggressive

liquidations of long positions are typically bigger and more detrimental than short sales. One

could argue that aggressive liquidations of long positions are typically larger than short sales,29

but that is our exact point: Short sales are simply much less important than aggressive

liquidations of long positions.

5. Summary and Conclusions

Throughout modern financial history, short selling has often been characterized as

damaging to stock markets (Sloan, 2010). At the same time, traders, financial economists, and

others close to the stock markets often believe that short selling can be a healthy influence on

29 For sample firms and daily intervals during the Crisis period, for example, the average big shorting event is about 0.42 for aggressive shorting and 0.49 for passive shorting, while the average big aggressive liquidation event is about 68%. Corresponding averages for matched firms are slightly smaller, 0.39, 0.44, and 0.67 respectively.

29

markets, or, at worst, a trivial influence. 30 We aim to uncover evidence of wide-ranging

destabilizing effects of short selling during the recent financial market crisis. We measure

whether shorting aggressively targets weak firms or the arrival of bad news, displays positive

feedback trading by following negative stock returns, selling pressure, or other key indicators, or

leads market conditions and touches off negative effects.

On some dimensions, we find evidence of destabilizing behavior. For example,

aggressive shorting of distressed financial stocks shows relatively more positive feedback trading

and more impact on trading conditions during the crisis period. However, , these effects are not

large, typically vanish after only a few 5-minute intervals, and are sometimes smaller for

distressed firms compared to matched firms. There is also evidence, particularly for passive

shorting during the period prior to the crisis, that shorting was contrarian rather than following,

or pushing, the market down. Patterns in bid-ask spreads and return volatility around large short-

selling events do not strongly support theoretical predictions about manipulative or predatory

trading. We conclude our empirical tests by comparing short sales to other types of selling, and

find that aggressive liquidations of long positions typically have greater impact than short sales.

The reductio ad absurdum is that, if short sales are to be restricted, sales of long positions should

also be discouraged.

Unhappily for those bankers, politicians, regulators, and journalists who blame short

sellers for the large drops in financial stock prices during this crisis, we find little evidence to

support their accusations. This is probably not surprising to anyone who has studied the evidence

on shorting from earlier time periods. However, it is important to air our results given that

resources are being spent worldwide pondering, drafting, and implementing new laws and

regulations intended to avoid future financial crises. The ongoing crisis of European banking, for

example, was met with shorting restrictions on 11th August 2011.31 There is little if anything in

our evidence to support constraining short selling. As much as we might like to denounce

heartless speculators, wealthy hedge fund managers, and the economic philosophies (and

philosophers) that inspire and enable them, the blame for the crisis probably lies elsewhere.

In fairness to regulators, there is some evidence that our sample firms, which were

subjected to short-selling restrictions, differ from our matching firms. The sample firms are

30 “…their influence (for both good and evil) is a little more than a drop in a bucket and something less than a hill of beans”, Schwed (1940), page 92. 31 “European short-selling ban comes under attack”, www.ft.com, 12th August 2011.

30

generally smaller, have poorer liquidity, and experience more volatile stock returns. These firms

may have been perceived as more vulnerable to aggressive trading. Because the collapse of stock

prices of financial institutions would directly or indirectly adversely affect economic agents

ranging from retail bank depositors to employees and customers of businesses that rely on banks,

the intention of regulators was to minimize potential damage to the growth of the broader

economy and to the savings and confidence of consumers. Modern financial institutions are

highly interdependent and competition can increase their fragility (Berger, Klapper, and Turk-

Ariss, 2009). Thus, the collapse of even a single bank or broker could have disastrous

consequences. The design and implementation of regulations for financial institutions is always

problematic (see for example Kane, 2007; Jarrow, 2007) and cannot anticipate and address all

possible threats to stability.

A number of limitations to our experiment suggest directions for future research. First,

we cannot isolate naked shorting activity, which is subject to even more constraints than short

selling generally.32. Researchers with access to trading records more detailed than ours will be

able to study the effect of naked short selling directly.33 Second, trading records of specific

investment banks, hedge funds, and other sophisticated traders might reveal anecdotes of

successful manipulation or substantial destabilization.34 Third, we have probably not exhausted

the range of daily and intraday measures of investor confidence, credit risk, and ex ante

volatility. More imaginative and aggressive use of data will yield additional insights on this

facet of the effect of short selling. Fourth, the available data confine us to only shorting of NYSE

stocks that occurs on the NYSE. NYSE stocks can be shorted in other venues, and non NYSE

stocks are also of interest. Fifth, our tentative finding that liquidation of long positions may have

had a larger impact on markets than short sales calls for much additional work on this aspect of

the crisis, along with a search for other, more fundamental factors that triggered and fuelled the

huge drops in financial stock prices. 35 With more detailed data, we might infer different

motivations and information across types of investors, and better understand why short sales and

long position liquidations differ. Sixth, we recognize that explicit and implicit costs of shorting

32 See http://en.wikipedia.org/wiki/Naked_short_selling#Developments.2C_2007_to_the_present. 33 Lacking transactions records tagged as naked short sales, some authors proxy for naked shorting with daily SEC records of failures-to-deliver (Fotak, Raman, and Yadav, 2009; Boulton and Braga-Alves, 2010). 34 A recent working paper, Jones, Reed, and Waller (2012), studies the impact of mandatory disclosure of large short positions in three European countries but finds little impact except around rights issues. 35 See, for example, Ben-David, Franzoni, and Moussawi (2012) on hedge fund selling in 2007 and 2008.

31

vary across firms and time (Jones and Lamont, 2002), are difficult to control for, and, therefore,

introduce a potential selection bias into our findings. Finally, we have focussed our study on

short selling and the financial crisis, but it is equally important to understand the cascade of

buying that led to greatly-increased stock prices prior to the crisis.36

36 See, for example, Griffin, Harris, Shu, and Topaloglu (2011) on the tech stock boom and bust.

32

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39

Figure 1.

Bank of America and Citibank Daily Closing Prices

0

10

20

30

40

50

60

Jan-07

Feb-07

Mar-07

Apr-07

May-07

Jun-07

Jul-07

Aug-07

Sep-07

Oct-07

Nov-07

Dec-07

Jan-08

Feb-08

Mar-08

Apr-08

May-08

Jun-08

Jul-08

Aug-08

Sep-08

Oct-08

Nov-08

Dec-08

Jan-09

Feb-09

Mar-09

BA C

40

Figure 2. Impulse response functions for relative shorting, returns, and buy-sell imbalances The Pre Crisis period is January 2005 to January 2007 and the Crisis period is February 2007 to March 2009. The median, 5%, and 95% points of the distribution of individual firm VARs (similar to Table 6 but based on eight lags rather than three) are used to summarize the individual firm impulse response plots. To save space, we report plots only for aggressive shorting of sample stocks. Each plot suggests the distribution of the accumulated response to a two standard error shock occurring 5 minutes earlier.

‐0.05

0

0.05

1 2 3 4 5 6 7 8

Aggressive Shorting

Simple Response of Aggressive Shortingto Impluse in Trading Imbalance

95%

Median

5%

‐4

‐2

0

2

4

6

1 2 3 4 5 6 7 8

Aggressive Shorting

Simple Response of Aggressive Shortingto Impluse in Abnormal Return

95%

Median

5%

‐0.1

‐0.05

0

0.05

1 2 3 4 5 6 7 8Trading 

Imbalance

Simple Response of Trading Imbalanceto Impluse in Aggressive Shorting

95%

Median

5%

‐0.0002

‐0.0001

0

0.0001

0.0002

1 2 3 4 5 6 7 8

Abnormal Return

Simple Response of Abnormal Returnto Impluse in Aggressive Shorting

95%

Median

5%

41

Table 1. Summary Statistics on Characteristics of Sample Firms and Matched Firms Sample stocks include 175 finance-related firms that were subject to the initial short selling ban. Matched firms are selected by constructing score statistics following Cao, Chen, and Griffin (2005) and Huang and Stoll (1996) Time period for computation of statistics is the Crisis period defined as 1st February 2007 to 31st March 2009. Coefficients in bold indicate statistical significance at the 5% level. Sample Firms Matched Firms Test of difference (1) (2) (3) (4) (1) – (3) Mean Median Mean Median (Daily shares shorted)/volume 0.38876 0.38671 0.38450 0.37715 0.00114 (1.47) Daily time weighted average bid-ask spread 0.00284 0.00152 0.00236 0.00114 0.00047 (16.50) Daily price-setting trading imbalance 0.00390 0.00201 0.00708 0.00466 -0.00318 (-4.09) Daily return volatility 0.05345 0.03593 0.04347 0.03109 0.00997 (42.10) Daily close price 43.48853 31.30999 42.31717 34.68999 1.17140 (4.30) Market capitalization ($000) 12593846.19 2642925.01 12756974.63 2928987.97 -163128 (-1.21) Average quarterly institutional ownership 0.69377 0.70976 0.73293 0.78634 -0.03922 (-1.48) Number of firms 175 169 DEPOSITORY INSTITUTIONS 54 2 NONDEPOSITORY INSTITUTIONS 5 3 SECURITY AND COMMODITY BROKERS 27 1 INSURANCE CARRIERS, SERVICE 78 2 REAL ESTATE 0 0 HOLDING AND OTHER INVESTMENT OFFICES

2 3

Other 9 156

42

Table 2. Cross-sectional regressions of daily average Crisis relative short-selling on Pre Crisis firm characteristics The Crisis period is defined as February 2007 to March 2009. Dependent variables are daily average Crisis period aggressive shorting (short trades executed at the prevailing bid) and passive shorting (all other short trades). Credit rating is S&P domestic long term credit rating in Compustat. Credit rating equals 1 for investment grade, otherwise, equals 0. The credit rating dummy equals 1 if the company has a credit rating, otherwise, equals 0. . Dummy variable FINANCE equals 1 if firm’s 2-digit SIC code is 60, 61, or 62, otherwise, equals 0, which excludes insurance and real estate companies. Coefficients in bold indicate statistical significance at the 5% level. Sample Firms Matched Firms Aggressive Shorting Passive Shorting Aggressive Shorting Passive Shorting Coefficient for Slope t-stat Slope t-stat Slope t-stat Slope t-stat Intercept 0.15806 39.67 0.17842 43.43 0.14892 96.78 0.18192 115.16 FINANCE dummy 0.01993 4.73 0.03990 8.43 0.01996 2.72 0.04763 6.71 Credit rating*Credit rating dummy -0.00356 -2.26 -0.00166 -0.95 0.00771 8.41 -0.00381 -4.10 Stock return beta 0.00775 2.63 0.04067 12.73 0.01070 8.71 0.02598 19.07 Stock return beta * FINANCE -0.00985 -3.07 -0.04105 -11.60 -0.00572 -0.98 -0.03506 -6.33 Stock return volatility 0.15912 3.92 -0.40450 -13.11 0.35753 16.02 -0.12040 -6.24 Stock return volatility * FINANCE -0.01313 -0.31 0.19241 5.94 -0.10405 -1.69 0.00782 0.20 Turnover 0.00060 3.81 0.00076 4.63 -0.00033 -5.13 0.00032 3.87 Bid-ask spread 0.00185 0.00 -0.75090 -2.21 0.07439 0.25 -0.24819 -0.86 Book-to-market -0.00216 -2.77 -0.00046 -0.48 -0.00222 -1.91 -0.00467 -5.85 Book-to-market* FINANCE 0.00182 2.30 0.00002 0.02 0.00491 1.94 0.00929 3.67 Adjusted r-squared 0.008613 0.01849 0.02207 0.01659

43

Table 3. Panel regressions of daily relative short-selling on event dummies This table reports panel regressions of daily relative short selling on dummy variables that indicate days of very significant firm-specific or market-wide news stories. The Pre Crisis period is defined as January 2005 to January 2007. The Crisis period is defined as February 2007 to March 2009. Aggressive shorting is defined as short trades executed at the prevailing bid while passive shorting is all other short trades. Unreported intercept dummies isolate the Crisis period (February 2007 to March 2009) and days (September 19th 2008 to October 8th 2008) when shorting of sample stocks was restricted. Regressions are estimated with the method of Schipper and Thompson (1983) to account for correlation in security return residuals. Events are identified from Brunnermeier (2009), New York Fed (www.ny.frb.org/research/global_economy/Crisis_Timeline.pdf), and news stories in The Wall Street Journal. Given overlapping (-1,+1) windows, many individual events are combined into a single event dummy. Coefficients in bold indicate statistical significance at the 5% level. Sample Firms Matched Firms Aggressive Shorting Passive Shorting Aggressive Shorting Passive Shorting Dummy for News Slope t-stat Slope t-stat Slope t-stat Slope t-stat Intercept 2-4 May 2007 S&P, Moodys’ downgrade or downgrade review MBSs,

UBS closes internal hedge fund -0.1051 (-32.49) 0.0246 (3.46) -0.090 (-33.26) 0.0214 (6.09)

22-23 June2007 Fitch puts CDOs on downgrade review, Bear Stearns supports first of two hedge funds

-0.0894 (-21.44) 0.0312 (3.40) -0.0532 (-12.43) 0.0435 (7.80)

10-12 July 2007 Moody’s, S&P, Fitch downgrade or downgrade review additional subprime related MBS and CDO

-0.0091 (-2.97) -0.0172 (-2.42) -0.0161 (-5.92) -0.0152 (-4.31)

24-31 July 2007 Countrywide Financial announces earnings decline, new home sales decline 6.6% year-on-year, IKB rescue begins, American Home Mortgage Investment cannot fund itself

0.0427 (16.67) 0.0002 (0.05) 0.0312 (14.54) -0.0072 (-2.58)

6 Aug 2007 American Home Mortgage Investment bankrupt 0.0248 (5.98) -0.0134 (-1.47) 0.0129 (3.71) -0.0248 (-5.46)

9 Aug 2007 BNP Paribas freezes three funds; illiquidity raises LIBOR -0.0201 (-4.85) -0.0473 (-5.18) -0.0286 (-8.22) -0.0549 (-12.06)

17 Aug 2007 Fed cuts discount rate ½%, eases access to discounting 0.0072 (1.75) -0.0102 (-1.12) -0.0030 (-0.88) -0.0149 (-3.29)

13 Sept 2007 Northern Rock rescued by Bank of England 0.0070 (1.70) -0.0328 (-3.58) 0.0268 (6.27) -0.0071 (-1.28)

18 Sept 2007 Fed cuts FF target by ½% to 4.75% 0.0001 (0.02) -0.0293 (-3.20) -0.0007 (-0.17) -0.0144 (-2.59)

31 Oct 2007 Fed cuts FF target by ¼% to 4.5% 0.0338 (9.39) -0.0092 (-1.16) 0.0200 (6.63) -0.0043 (-1.10)

11-12 Dec 2007 Fed cut FF target by ¼% to 4.25%, Fed offers Term Auction Facility to aid banks

0.0043 (0.60) -0.0290 (-1.84) 0.0133 (2.22) -0.0220 (-2.80)

19-22 Jan 2008 Fitch downgrades monoline insurer Ambac, Fed cuts FF target ¾% to 3.5%

0.0257 (6.17) -0.0180 (-1.96) 0.0263 (7.56) -0.0114 (-2.52)

30 Jan 2008 Fed cuts FF target ½% to 3% 0.0269 (6.48) -0.0112 (-1.23) 0.0199 (4.67) -0.0138 (-2.50)

11-18 Mar 2008 Fed offers Term Securities Lending Facility to banks, Bear Stearns in trouble, propped, then sold, Fed cuts discount rate ¼% (Sunday evening), Fed cuts FF target by ¾% to 2.25%

0.0230 (8.98) -0.0055 (-0.98) 0.0227 (10.58) -0.0022 (-0.79)

20 Apr 2008 Fed cuts FF target by ¼% to 2% 0.0107 (2.11) -0.0114 (-1.02) 0.0014 (0.33) -0.0082 (-1.49)

44

Table 3 continued. 11-14 July 2008 Indy Mac taken over by FDIC ,Treasury guarantees

Fannie, Freddie (Sunday evening) 0.0314 (8.28) 0.0001 (0.01) 0.1035 (32.59) 0.0828 (20.01)

7-9 Sept 2008 Fannie, Freddie taken over by Treasury, Lehman in trouble 0.0667 (20.59) 0.0330 (4.62) 0.0248 (7.11) -0.0050 (-1.11)

15-16 Sept 2008 Lehman bankrupt, AIG debt downgraded, stock declines 90%, Fed props

0.0158 (4.34) -0.0137 (-1.71) 0.0254 (7.27) -0.0124 (-2.72)

19 Sept 2008 Treasury to unveil $700 billion TARP bailout plan -0.0061 (-1.31) -0.0069 (-0.68) -0.0164 (-4.29) -0.0141 (-2.83)

25-29 Sept 2008 Washington Mutual taken over by FDIC, Wachovia to sell banking operation

-0.0050 (-1.17) -0.0024 (-0.25) -0.0193 (-5.05) -0.0192 (-3.86)

8 Oct 2008 Fed cuts FF target ½% to 1.5% -0.0144 (-2.76) -0.0435 (-3.77) 0.0121 (2.81) -0.0251 (-4.46)

29 Oct 2008 Fed cuts FF target ½% to 1% -0.0052 (-1.23) -0.0363 (-3.86) 0.0044 (1.26) -0.0127 (-2.75)

16 Dec 2008 Fed FF target range of 0% to ¼% -0.0051 (-1.21) -0.0266 (-2.84) 0.0117 (3.36) -0.0169 (-3.70)

16 Jan 2009 Fed, Treasury, and FDIC aid Bank of America 0.1192 (27.62) 0.0415 (4.37) 0.0778 (18.15) 0.0003 (0.05)

Adjusted r-squared 0.2473 0.0095 0.3427 0.0149

45

Table 4. Panel regressions to explain daily relative short selling Aggressive shorting is defined as short trades executed at the prevailing bid while passive shorting is all other short trades. The Pre Crisis period is defined as January 2005 to January 2007. The Crisis period is defined as February 2007 to March 2009. Slope and intercept dummy terms isolate days (19 September 2008 to 8 October 2008) when shorting of sample stocks was restricted. Explanatory variables are the average -3 to -1 daily market-adjusted stock return, R{-3,-1}, the contemporaneous market-adjusted stock return, R{t}, the daily average bid-ask spread, Spread{t}, the negative price-setting buy-sell imbalance, imb-{t}, the average -3 to -1 daily negative price-setting buy-sell imbalance, imb-{-3,-1}, average -3 to -1 daily short selling ratio, contemporaneous stock return volatility, volatility{t}, and average -3 to -1 daily volatility {-3,-1}. See Diether, Lee, and Werner (2009a). Coefficients in bold indicate statistical significance at 5% level. All tests are White heteroskedasticity consistent. Standard errors account for clustering by calendar date and by stock (Thompson, 2010). Period Constant R{-3,-1} R{t} Spread{t} Imb-{t} Imb-{-3,-1} Short{-3,-1} Volatility{t} Volatility{-3,-1} Adj. R-Squared Pre Crisis: aggressive shorting Sample Firms 0.02002 0.1139 0.3172 1.6749 -0.0411 -0.0815 0.0451 0.2509 0.0540 0.1188

(1.39) (3.06) (14.20) (3.44) (-7.74) (-17.66) (2.19) (9.20) (2.49)

Matched Firms 0.0026 0.0711 0.2691 -0.6704 -0.0495 -0.0809 0.0707 0.2350 0.0390 0.2176

(2.58) (9.54) (20.39) (-2.25) (-32.35) (-21.20) (54.46) (13.56) (5.41)

Difference 0.0173 0.0427 0.0480 2.3453 0.0083 -0.0006 -0.0255 0.0159 0.0149

T-test (1.20) (1.13) (1.85) (4.12) (1.51) (-0.11) (-1.24) (0.49) (0.65)

Pre Crisis: passive shorting Sample Firms 0.1538 0.3739 0.9115 5.9605 0.0161 0.3436 0.0556 0.3882 0.1112 0.1907

(8.67) (7.57) (24.08) (10.09) (2.49) (88.92) (2.19) (7.83) (3.42)

Matched Firms 0.0810 0.1353 0.8987 4.5398 -0.0004 0.3214 0.1485 0.1385 -0.0919 0.3480

(44.93) (10.69) (35.16) (12.58) (-0.16) (75.91) (65.99) (4.06) (-7.27)

Difference 0.0728 0.2386 0.0128 1.4206 0.0165 0.0223 -0.0929 0.2497 0.2032

T-test (4.08) (4.68) (0.28) (2.05) (2.39) (3.88) (-3.65) (4.15) (5.82)

Crisis: aggressive shorting Sample Firms -0.0081 0.0039 -0.0144 -0.5078 -0.0267 -0.2159 0.1388 0.1106 0.0356 0.3151

(-8.30) (0.93) (-1.96) (-4.70) (-13.95) (-41.10) (171.87) (11.96) (11.26)

Matched Firms 0.0268 0.0356 0.1098 -1.3877 -0.0254 -0.2440 0.1034 0.1781 0.0771 0.3127

(16.37) (4.35) (7.87) (-7.69) (-12.02) (-44.14) (71.74) (10.81) (11.54)

Difference -0.0349 -0.0316 -0.1241 0.8799 -0.0012 0.0280 0.0353 -0.0675 -0.0414

T-test (-18.31) (-3.43) (-7.88) (4.18) (-0.43) (3.67) (21.41) (-3.57) (-5.61)

Crisis: passive shorting Sample Firms 0.1153 0.0528 0.1201 1.5163 0.0300 0.3051 0.1089 -0.0508 -0.1031 0.2545 (102.40) (12.13) (14.82) (6.53) (14.93) (80.91) (121.36) (-5.63) (-25.57) Matched Firms 0.1302 0.0830 0.2666 1.1153 0.0314 0.3147 0.0834 0.0215 -0.0849 0.2598 (94.39) (11.96) (18.79) (6.87) (16.92) (96.20) (67.21) (1.49) (-15.33)

Difference -0.0148 -0.03012 -0.1464 0.4010 -0.0014 -0.0095 0.0255 -0.0723 -0.0182

T-test (-8.33) (-3.68) (-8.96) (1.42) (-0.52) (-1.92) (16.64) (-4.25) (-2.66)

46

Table 4 continued. Crisis short restrict dummies: aggressive shorting Sample Firms 0.0134 0.0271 0.0232 0.2469 0.0017 -0.0896 0.0165 0.0469 0.0049 0.1728

(7.36) (4.37) (2.11) (1.03) (0.45) (-7.53) (5.12) (3.94) (1.25)

Matched Firms 0.0459 -0.0259 0.1875 -0.9402 0.0094 -0.1986 0.1155 -0.0542 -0.0192 0.3772

(6.28) (-1.07) (3.87) (-1.71) (0.77) (-8.69) (19.92) (-1.42) (-1.26)

Difference -0.0325 0.0531 -0.1643 1.1872 -0.0077 0.1090 -0.0990 0.1011 0.0242

T-test (-4.32) (2.12) (-3.31) (1.98) (-0.60) (4.24) (-14.93) (2.54) (1.54)

Crisis short restrict dummies: passive shorting Sample Firms 0.0165 0.0051 0.0318 0.2979 0.0041 0.0552 0.0409 0.0365 -0.0094 0.1292

(8.65) (0.91) (2.61) (1.19) (1.26) (5.60) (8.19) (2.56) (-2.15)

Matched Firms 0.0355 0.0724 0.1805 1.8843 -0.0016 0.2374 0.1410 0.0071 -0.0242 0.4670

(4.97) (2.69) (3.26) (3.16) (-0.13) (16.85) (23.82) (0.21) (-1.58)

Difference -0.0189 -0.067 -0.1486 -1.5864 0.0057 -0.1822 -0.1001 0.0294 0.0148

T-test (-2.56) (-2.45) (-2.62) (-2.46) (0.43) (-10.60) (-12.94) (0.80) (0.93)

47

Table 5. Panel regressions to explain intraday relative short selling Aggressive shorting is defined as short trades executed at the prevailing bid while passive shorting is all other short trades. The Pre Crisis period is defined as January 2005 to January 2007. The Crisis period is defined as February 2007 to March 2009. Slope and intercept dummy terms isolate days (19 September 2008 to 8 October 2008) when shorting of sample stocks was restricted. Intraday periods are 5 minutes in length, with overnight excluded. Explanatory variables are the average -3 to -1 5-minute market-adjusted stock return, R{-3,-1}, the contemporaneous market-adjusted stock return, R{t}, the 5-minute average bid-ask spread, Spread{t}, the negative price-setting buy-sell imbalance, imb-{t}, the average -3 to -1 5-minute negative price-setting buy-sell imbalance, imb-{-3,-1}, average -3 to -1 5-minute short selling ratio, contemporaneous stock return volatility, volatility{t}, and average -3 to -1 5-minute volatility {-3,-1}. See Diether, Lee, and Werner (2009a). Coefficients in bold indicate statistical significance at 5% level. All tests are White heteroskedasticity consistent. Standard errors account for clustering by calendar date and by stock (Thompson, 2010). The “SPY” ETF serves as the market portfolio for intraday market-adjusted returns. Period Constant R{-3,-1} R{t} Spread{t} Imb-{t} Imb-{-3,-1} Short{-3,-1} Volatility{t} Volatility{-3,-1} Adj. R-Squared Pre Crisis: aggressive shorting Sample Firms 0.0206 -0.0857 5.6468 0.26333 -0.0017 -0.1014 0.0135 3.5387 1.0334 0.0147

(72.56) (-1.56) (59.90) (3.05) (-7.73) (-236.42) (113.12) (35.75) (24.39)

Matched Firms -0.0029 -0.5375 3.8579 -2.6509 -0.0090 -0.1041 0.0506 3.9168 1.2340 0.0970

(-19.83) (-24.47) (103.22) (-60.10) (-81.44) (-494.07) (454.71) (93.81) (70.85)

Difference 0.0235 0.4518 1.7888 2.9143 0.0073 0.0026 -0.0371 -0.3781 -0.2006

T-test (67.27) (7.66) (17.71) (27.86) (27.47) (5.09) (-165.39) (-3.46) (-4.34)

Pre Crisis: passive shorting Sample Firms 0.2176 0.5815 10.2912 8.8452 -0.0029 0.2773 0.0312 -4.1745 -1.9719 0.0348

(436.50) (6.06) (62.27) (60.31) (-7.67) (368.70) (149.75) (-24.06) (-26.55)

Matched Firms 0.1447 -1.1527 9.2853 8.0894 -0.0096 0.2649 0.1195 -3.4185 -2.0225 0.2361

(649.60) (-34.54) (163.54) (120.72) (-57.43) (828.00) (707.03) (-53.89) (-76.44)

Difference 0.0728 1.7342 1.0059 0.7558 0.0067 0.0124 -0.0882 -0.7560 0.0505

T-test (120.84) (17.06) (5.78) (4.18) (14.62) (14.05) (-228.25) (-4.02) (0.64)

Crisis: aggressive shorting Sample Firms 0.01605 -0.06084 -1.36051 -2.2303 -0.00516 -0.18594 0.09334 1.17138 0.54840 0.1458

(79.75) (-5.78) (-75.16) (-112.78) (-35.62) (-654.80) (774.02) (62.12) (75.36)

Matched Firms 0.0707 0.1759 -2.2376 -2.4945 -0.0048 -0.2173 0.0277 2.5286 1.4128 0.0530

(269.83) (6.71) (-49.65) (-69.36) (-19.98) (-462.85) (227.36) (54.54) (75.40)

Difference -0.0546 -0.2367 0.8771 0.1367 -0.0004 0.0314 0.0655 -1.3573 -0.8644

T-test (-162.97) (-9.24) (19.92) (6.76) (-1.33) (59.14) (357.43) (-29.86) (-47.63)

Crisis: passive shorting Sample Firms 0.1483 0.2798 1.1045 1.2469 -0.0045 0.2418 0.0923 -0.7890 -0.8976 0.1666

(697.07) (25.13) (57.70) (59.62) (-29.67) (805.32) (724.04) (-39.57) (-116.65)

Matched Firms 0.2018 0.6186 3.3147 1.6564 -0.0045 0.2543 0.0285 0.0327 -0.7110 0.0340

(541.34) (16.60) (51.72) (32.39) (-13.10) (380.80) (163.77) (0.50) (-26.68)

Difference -0.0534 -0.3387 -2.2102 -0.4094 -0.0001 -0.0125 0.0639 -0.8218 -0.1867

T-test (-121.52) (-10.09) (-38.29) (-8.00) (-0.20) (-17.93) (265.70) (-13.79) (-7.85)

48

Table 5 continued. Crisis short restrict dummies: aggressive shorting Sample Firms 0.0050 0.0841 -0.2526 0.4071 0.0025 -0.0385 0.0644 0.3411 0.0319 0.0634

(11.92) (4.99) (-9.18) (6.77) (5.73) (-35.34) (39.84) (11.10) (2.87)

Matched Firms 0.0202 0.2626 -0.6709 -3.0720 0.0023 -0.1889 0.0871 0.9939 0.6103 0.1843

(15.04) (3.52) (-5.48) (-7.77) (1.80) (-57.90) (80.26) (6.62) (10.33)

Difference -0.0152 -0.1785 0.4183 3.4792 0.0002 0.1505 -0.0226 -0.6528 -0.5783

T-test (-4.89) (-1.89) (2.48) (7.55) (0.12) (24.90) (-5.69) (-3.63) (-6.73)

Crisis short restrict dummies: passive shorting Sample Firms 0.0123 0.0664 -0.0241 0.2569 0.0023 0.0182 0.0733 0.1068 -0.0336 0.0820

(41.03) (5.65) (-1.12) (5.34) (7.55) (31.24) (42.37) (4.76) (-4.14)

Matched Firms 0.0775 0.4832 1.2449 1.8265 0.002 0.1969 0.0976 0.3927 -0.1906

(59.87) (7.17) (9.34) (5.82) (2.15) (113.93) (92.07) (2.69) (-3.54) 0.2275

Difference -0.0652 -0.4168 -1.2690 -1.5696 -0.00001 -0.1786 -0.0243 -0.2859 0.1569

T-test (-20.31) (-5.17) (-6.76) (-3.83) (-0.00) (-39.88) (-4.20) (-1.67) (1.92)

49

Table 6. Summary of vector autoregressions for intraday relative short sales, returns, and buy-sell imbalances This table partially summarizes VAR estimates that relate 5-minute short selling, return, and price-setting buy-sell imbalance. There is one VAR for each of 175 sample firms and each of 169 matched firms. Autoregressive coefficients and coefficients that do not involve short selling are not reported. The Pre Crisis period is defined as January 2005 to January 2007. The Crisis period is defined as February 2007 to March 2009. Intercept and slope dummies for 19th September 2008 to 8th October 2008, when shorting of sample stocks was restricted, are unreported. Panel A: Pre Crisis period 175 Sample Firms 169 Matched Firms Coefficient on:

at lag:

For:

Mean

Median

Fraction significant

Mean

Median

Fraction significant

Aggressive shorting Short 1 Return 6.32E-6 -4.59E-7 0.0804 0.00002 9.75E-8 0.0933 Short 2 Return 3.57E-6 2.90E-6 0.0747 -0.00003 1.19E-6 0.0667 Short 3 Return 2.35E-6 5.07E-7 0.0977 0.00002 3.88E-7 0.0533 Short 1 Imbalance 0.00949 0.0019 0.5970 0.0036 0.0026 0.5933 Short 2 Imbalance 0.0168 0.0029 0.5172 0.0185 0.0027 0.5933 Short 3 Imbalance 0.0193 0.0040 0.5919 0.0224 0.0052 0.5933

Return 1 Short -0.0493 -1.0070 0.2298 -0.5857 -0.9139 0.3067 Return 2 Short -0.3173 -0.8016 0.1436 -0.4929 -0.4212 0.2600 Return 3 Short -0.6919 -0.3420 0.1379 -0.3458 -0.4137 0.2400 Imbalance 1 Short 0.0092 0.0129 0.6551 0.0130 0.0146 0.6867 Imbalance 2 Short 0.0106 0.0122 0.6091 0.0106 0.0119 0.6400 Imbalance 3 Short 0.0059 0.0094 0.5689 0.0065 0.0112 0.6533 Passive shorting Short 1 Return 0.00001 1.91E-6 0.3103 0.00003 5.01E-6 0.3733 Short 2 Return 3.482E-6 1.85E-6 0.1206 -3.83E-6 2.31E-6 0.1667 Short 3 Return 0.00001 1.28E-6 0.1609 -6.18E-6 9.12E-7 0.1200 Short 1 Imbalance 0.0321 0.0198 0.6092 0.0239 0.0075 0.8467 Short 2 Imbalance 0.0361 0.0244 0.6149 0.0104 0.0044 0.7267 Short 3 Imbalance 0.0442 0.0319 0.6379 0.0071 0.0042 0.7267 Return 1 Short 0.7167 -0.3558 0.1896 -0.9999 -2.053 0.2400 Return 2 Short -1.0751 -1.0881 0.0977 -1.1063 -0.6064 0.1600 Return 3 Short -1.9983 -2.0429 0.1609 -2.6145 -1.4984 0.2533 Imbalance 1 Short 0.0321 0.0198 0.6091 0.0300 0.0134 0.5867 Imbalance 2 Short 0.0361 0.0244 0.6149 0.0393 0.0232 0.6200 Imbalance 3 Short 0.0442 0.0319 0.6379 0.0387 0.0255 0.6600

50

Table 6 continued. Panel B: Crisis period 175 Sample Firms 169 Matched Firms Coefficient on:

at lag:

For:

Mean

Median

Fraction significant

Mean

Median

Fraction significant

Aggressive shorting Short 1 Return 1.88E-6 -1.89E-7 0.0689 -8.41E-6 -0.00001 0.3800 Short 2 Return -5.43E-6 -8.39E-7 0.0977 -2.62E-6 -7.25E-6 0.1533 Short 3 Return -5.90E-6 -1.25E-7 0.1091 -0.00002 -6.86E-6 0.2133 Short 1 Imbalance -0.0008 -0.0001 0.2241 -0.0188 -0.0137 0.7800 Short 2 Imbalance 0.0002 -0.0001 0.2758 -0.0018 -0.0022 0.3933 Short 3 Imbalance -0.0001 -0.00002 0.1724 0.0017 -0.0004 0.4400 Return 1 Short 2.2677 0.8844 0.1379 0.3136 0.7543 0.3533 Return 2 Short -1.3711 -0.6963 0.1379 0.1485 0.4748 0.3000 Return 3 Short 0.5685 -0.5214 0.1091 0.1859 0.3369 0.2333 Imbalance 1 Short 0.0093 0.0281 0.2011 0.0156 0.0173 0.6133 Imbalance 2 Short -0.0374 0.0005 0.2356 0.0105 0.0130 0.5733 Imbalance 3 Short 0.0094 0.0057 0.1724 0.0060 0.0094 0.5100 Passive shorting Short 1 Return -8.51E-6 3.96E-7 0.1034 0.00004 0.00002 0.3133 Short 2 Return 2.31E-6 -4.05E-7 0.1149 -5.03E-6 1.06E-6 0.1066 Short 3 Return 2.35E-6 -1.14E-8 0.0919 -6.76E-6 -1.33E-6 0.0800 Short 1 Imbalance 0.0016 0.2951 0.2931 -0.0133 -0.0202 0.6200 Short 2 Imbalance 0.0006 0.1033 0.2356 -0.0040 -0.0096 0.5733 Short 3 Imbalance 0.0003 0.0685 0.2011 0.0010 -0.0066 0.5600 Return 1 Short 0.3942 -0.8918 0.1551 -1.2843 -0.7653 0.1867 Return 2 Short -1.3643 -1.5602 0.1666 -1.1583 -0.7622 0.2333 Return 3 Short 0.8221 -0.1624 0.0919 -0.8475 -0.9629 0.2800 Imbalance 1 Short -0.0232 -0.0146 0.2758 -0.0133 -0.0201 0.6200 Imbalance 2 Short -0.0273 -0.0137 0.2183 -0.0040 -0.0096 0.5733 Imbalance 3 Short 0.0095 -0.0101 0.2241 0.0010 -0.0066 0.5600

51

Table 6 continued. Panel C: Pre Crisis and Crisis Cholesky decomposition coefficients (significance from bootstrapped standard errors) 175 Sample Firms 169 Matched Firms Coefficient Period Shorting

type

Mean

Median Fraction >

two standard

errors from 0

Mean

Median

Fraction > two

standard errors from 0

Shorting versus return

Pre Crisis

Aggressive 3.05E-05 4.79E-05

0.8914 4.91E-05 5.79E-05

0.9704

Shorting versus imbalance

Pre Crisis

Aggressive -0.12702 -0.11314

0.9771 -0.08895 -0.10001

0.9822

Shorting versus return

Crisis Aggressive -0.00042 -0.00034

0.9943 -0.06589 -0.04094

0.9704

Shorting versus imbalance

Crisis Aggressive -0.15928 -0.14412

0.9943 0.21848 0.19965 0.9822

Shorting versus return

Pre Crisis

Passive 0.00019 0.000175

0.8914 -0.00015 0.000242

0.9704

Shorting versus imbalance

Pre Crisis

Passive 0.25861 0.261911

0.9771 -0.06589 0.227815

0.9822

Shorting versus return

Crisis Passive 0.00041 0.000363

0.9885 0.00042 0.00036

0.9704

Shorting versus imbalance

Crisis Passive 0.20020 0.18923

0.9885 0.21848 0.210771

0.9763

52

Table 7. Daily returns, buy-sell imbalances, and bid-ask spreads around large Crisis period short sale events The Crisis period is February 2007 to March 2009. For each stock, the ten largest shorting events by firm-day are selected. Shorting equals shares shorted for the firm-day divided by trading volume for the firm day. Imbalance is the price-setting buy-sell ratio previously defined. Spread is the bid-ask spread previously defined. “Abnormal” indicates the variable equals its raw value minus its mean from the Pre Crisis (January 2005 to January 2007) divided by its standard deviation from the Pre Crisis period. T-statistics are reported below each mean. Coefficients in bold indicate statistical significance at the 5% level. -5 -4 -3 -2 -1 0 1 2 3 4 5 {0,+5} Sample firms, aggressive shorting Market-adjusted return -0.0009 0.0005 0.0027 -0.0004 -0.0010 -0.0070 -0.0032 -0.0042 -0.0024 -0.0013 -0.0010 -0.0031 (-0.75) (0.51) (2.33) (-0.37) (-1.04) (-7.16) (-2.89) (-3.14) (-2.01) (-0.99) (-0.76) (-6.22) Abnormal volatility 3.5454 3.5191 3.5211 3.7173 3.6947 4.3237 4.3128 4.4123 4.3958 4.3335 4.6053 4.4314 (20.30) (25.61) (25.98) (23.27) (24.51) (23.43) (24.88) (23.61) (26.96) (26.50) (23.98) (60.31) Abnormal imbalance -0.3034 -0.3034 -0.3443 -0.3599 -0.4072 -1.0186 -0.4779 -0.4065 -0.3602 -0.3849 -0.3412 -0.4969 (-11.32) (-14.64) (-13.82) (-14.51) (-16.13) (-33.92) (-17.39) (-16.17) (-15.17) (-15.03) (-13.25) (-41.8) Abnormal spread 5.5977 6.00282 5.8846 6.2202 6.5409 6.9158 6.6519 6.8772 6.3203 6.4781 6.4038 7.3514 (12.04) (10.97) (11.53) (9.79) (8.91) (8.11) (8.91) (8.52) (13.44) (12.82) (12.36) (23.71) Sample firms, passive shorting Market-adjusted return 0.0007 0.0017 0.0025 0.0012 0.0018 0.0045 -0.0042 -0.0018 -0.0008 -0.00036 -0.0003 -0.0002 (0.99) (1.72) (3.15) (1.64) (2.48) (5.15) (-6.06) (-2.64) (-1.04) (-0.29) (-0.30) (-0.69) Abnormal volatility 1.7307 1.7792 1.7104 1.7255 1.7644 2.0743 1.8947 1.92909 1.8848 2.1750 2.5857 2.1240 (12.91) (15.51) (15.91) (15.45) (16.22) (17.34) (16.72) (18.31) (16.79) (15.63) (16.03) (40.07) Abnormal imbalance -0.0410 -0.0584 0.00383 -0.0320 0.0474 0.6866 -0.0052 -0.0471 -0.0647 -0.0865 -0.1008 0.0709 (-1.47) (-2.10) (0.14) (-1.19) (1.72) (20.89) (-0.18) (-1.66) (-2.44) (-3.18) (-3.79) (5.70) Abnormal spread 2.3774 2.2391 2.2506 2.2464 2.3395 2.2395 2.3884 2.5253 2.5323 2.8038 2.9496 2.8342 (6.50) (7.73) (7.90) (7.72) (7.15) (7.40) (7.27) (7.39) (7.44) (7.32) (8.51) (16.00) Matched firms, aggressive shorting Market-adjusted return 0.0017 0.0017 0.0007 0.0015 0.0014 -0.0038 -0.0006 -0.0008 0.0004 0.0008 -0.0013 -0.0007 (1.78) (2.17) (1.00) (1.59) (1.30) (-4.33) (-0.74) (-1.02) (0.47) (0.98) (-1.40) (-2.11) Abnormal volatility 1.9155 1.9155 1.8744 1.9223 1.7294 2.0666 1.9437 1.9616 1.8824 1.7977 2.0008 1.9574 (20.04) (20.36) (22.41) (22.81) (19.39) (18.50) (21.61) (22.38) (19.69) (19.48) (21.17) (47.95) Abnormal imbalance -0.3580 -0.4264 -0.3454 -0.4117 -0.3413 -1.3045 -0.4658 -0.4404 -0.4027 -0.4008 -0.3644 -0.5481 (-13.12) (-16.87) (-13.05) (-15.53) (-10.98) (-44.72) (-17.37) (-16.52) (-13.57) (-14.68) (-13.14) (-45.62) Abnormal spread 3.0663 2.9083 3.0753 3.1026 3.0429 3.2847 3.1573 3.0191 2.9520 3.1753 3.0061 3.1788 (9.63) (9.72) (9.93) (10.20) (9.69) (9.52) (10.28) (10.88) (10.98) (8.50) (10.23) (22.01) Matched firms, passive shorting Market-adjusted return 0.0001 0.0015 0.0031 0.0041 0.0038 0.0066 0.0001 -0.0023 0.0002 0.0021 -0.0012 0.0010 (0.15) (1.71) (3.87) (4.75) (5.03) (7.66) (0.23) (-2.90) (0.33) (1.86) (-1.42) (2.70) Abnormal volatility 1.6935 1.4213 1.4403 1.6507 1.1599 1.3790 1.2083 1.5034 1.1896 1.2510 1.3835 1.3494 (16.39) (17.70) (17.59) (15.35) (14.89) (15.84) (16.46) (15.76) (16.75) (10.95) (15.73) (33.76) Abnormal imbalance -0.0341 -0.1230 0.0048 0.0042 0.1038 0.9607 -0.0341 -0.0978 -0.1256 -0.1028 -0.1458 0.0702 (-1.13) (-4.16) (0.18) (0.14) (3.10) (28.04) (-1.12) (-3.34) (-3.98) (-3.57) (-4.96) (5.23) Abnormal spread 1.7642 1.5901 1.7886 1.7384 1.6734 1.8587 1.7570 1.7089 1.5524 1.5503 1.5384 1.7021 (7.94) (7.91) (7.54) (7.62) (7.33) (6.95) (7.23) (7.69) (7.96) (7.46) (7.96) (14.82)

53

Table 8. Intraday returns, buy-sell imbalances, and bid-ask spreads around large short sale events for sample firms The Crisis period is February 2007 to March 2009. Intraday intervals are 5 minutes. For each stock, the ten largest shorting events by firm-interval selected. Shorting equals shares shorted for the firm-day divided by trading volume for the firm day. Imbalance is the price-setting buy-sell ratio previously defined. Spread is the bid-ask spread previously defined. “Abnormal” indicates the variable equals its raw value minus its mean from the Pre Crisis (January 2005 to January 2007) divided by its standard deviation from the Pre Crisis period. Market-adjusted return is the raw return adjusted for β which is derived from the stock return and return for the “SPY” ETF in “Pre Crisis” period for the same interval. T-statistics are reported below each mean. Coefficients in bold indicate statistical significance at the 5% level. -5 -4 -3 -2 -1 0 1 2 3 4 5 {0,+5} Sample firms, aggressive shorting Market-adjusted return -0.00004 -0.0001 0.00004 -0.0002 -0.0001 -0.0013 -0.00001 -0.0001 0.0001 0.0002 -0.0001 -0.0007 (-0.25) (-0.54) (0.31) (-0.95) (-0.43) (-6.13) (-0.08) (-0.47) (0.41) (0.93) (-0.53) (-2.32) Abnormal volatility 9.4715 9.6837 9.7465 9.8790 9.78753 10.8206 10.8723 10.9660 11.1599 11.0081 11.3725 11.2477 (44.94) (48.64) (49.42) (44.46) (49.48) (46.68) (48.61) (46.58) (49.22) (50.01) (45.34) (106.36) Abnormal imbalance -0.0970 -0.1130 -0.0408 -0.1059 -0.1758 -0.8477 -0.1555 -0.08214 -0.1120 -0.0942 -0.0695 -0.1032 (-3.87) (-5.38) (-1.67) (-4.25) (-6.94) (-31.28) (-5.99) (-3.27) (-4.53) (-3.73) (-2.77) (-8.99) Abnormal spread 2.4627 2.4304 2.6036 2.6305 2.6146 2.9218 3.2482 3.2741 3.4333 3.1886 3.1886 3.1874 (13.06) (13.04) (12.56) (12.62) (12.18) (13.42) (12.97) (12.89) (12.17) (13.41) (14.65) (31.63) Sample firms, passive shorting Market-adjusted return -0.0001 -0.00004 -0.0003 -0.0003 -0.00002 0.0008 -0.0001 -0.0001 -0.0001 -9.91E-6 -0.0001 0.00003 (-1.36) (-0.41) (-2.37) (-1.48) (-0.14) (4.76) (-0.43) (-0.52) (-0.69) (-0.06) (-0.62) (0.11) Abnormal volatility 6.7421 7.0221 6.8017 6.8626 6.9039 7.3795 7.0141 7.1097 7.0847 7.4867 8.0249 7.4353 (35.06) (37.28) (37.75) (36.59) (39.11) (38.24) (38.62) (40.93) (38.62) (36.86) (36.55) (84.27) Abnormal imbalance -0.0198 -0.0205 -0.0274 -0.0143 0.0182 0.8814 0.0224 -0.0216 -0.0220 -0.0164 -0.0493 -0.0814 (-0.79) (-0.82) (-1.11) (-0.55) (0.72) (37.16) (0.89) (-0.84) (-0.89) (-0.66) (-1.98) (-1.17) Abnormal spread 1.7053 1.6771 1.7899 1.6432 1.7600 2.1763 1.8604 2.5545 2.6276 2.6696 2.5137 2.4806 (8.97) (8.68) (8.58) (8.46) (8.34) (10.64) (9.39) (11.74) (11.91) (12.01) (11.40) (25.01) Matched firms, aggressive shorting Market-adjusted return -0.0001 -0.0001 0.0001 -0.00002 0.0001 -0.0015 0.0002 -0.0001 0.0001 -0.0001 0.0001 0.0001 (-1.06) (-0.77) (0.41) (-0.18) (0.53) (-7.74) (1.33) (-0.41) (0.63) (-0.51) (1.22) (1.23) Abnormal volatility 1.9155 1.9050 1.8744 1.9223 1.7294 2.0666 1.9437 1.9616 1.8824 1.7977 2.0008 1.9301 (20.04) (20.36) (22.41) (22.81) (19.39) (18.50) (21.61) (22.38) (19.69) (19.48) (21.17) (45.70) Abnormal imbalance -0.1209 -0.1318 -0.1431 -0.1851 -0.2225 -0.3720 -0.3461 -0.2154 -0.1230 -0.1451 -0.1382 -0.1940 (-4.63) (-5.07) (-5.29) (-7.06) (-8.72) (-13.30) (-13.00) (-7.98) (-4.47) (-5.25) (-5.24) (-16.00) Abnormal spread 0.9166 1.0633 1.0457 0.9793 1.2045 2.0892 1.5840 1.3054 1.1269 1.0982 1.1075 1.3958 (9.96) (9.57) (10.03) (9.67) (5.86) (7.52) (6.70) (9.37) (10.29) (9.56) (10.24) (15.45) Matched firms, passive shorting Market-adjusted return -8.08E-6 -0.0003 0.0002 0.0001 -0.0001 0.0010 0.0002 -0.00002 -0.0001 -0.0003 -0.00003 0.0001 (-0.05) (-0.95) (2.55) (0.75) (-0.95) (6.88) (0.91) (-0.16) (-0.35) (-1.92) (-0.23) (0.94) Abnormal volatility 1.6935 1.4213 1.4403 1.6507 1.1599 1.3791 1.2083 1.5034 1.1896 1.2510 1.3835 1.3204 (16.39) (17.70) (17.59) (15.35) (14.89) (15.84) (16.46) (15.76) (16.75) (10.95) (15.73) (30.88) Abnormal imbalance -0.0673 -0.0526 -0.0167 -0.0241 0.0438 0.1928 0.1707 0.0931 0.0524 0.0209 -0.0244 0.0632 (-2.56) (-2.08) (-0.65) (-0.92) (1.70) (7.17) (6.59) (3.42) (1.90) (0.77) (-0.91) (5.24) Abnormal spread 0.6567 0.5917 0.6461 0.6580 0.7316 1.2050 0.8245 0.7461 0.7184 0.7048 0.7821 5.5812 (8.55) (8.34) (8.52) (7.86) (7.24) (8.62) (8.19) (7.83) (7.89) (8.58) (8.01) (8.77)

54

Table 9. Vector Auto-Regressions relating short selling to investor sentiment, credit risk, and ex ante volatility This table reports estimates of systems of equations:

11

11

11

N

i

Ait

Ai

N

i

Jit

Ji

At RSSXRSS (1) 2

12

122

N

i

Ait

Ai

N

i

Jit

Ji

Jt RSSXX , J = 1, NJ (2)

RSSA is aggregate relative short sales of our sample of financial stocks. XJ is the jth sentiment, credit risk, or volatility factor. N, the number of

leads and lags, is set at 3.Nj is the number of aggregate financial indicators, XJ , available for a particular data frequency. Reporting of

autoregressive coefficients and all intercepts is suppressed to save space. Coefficients in bold indicate statistical significance at the 5% level. T-

statistics are reported in parentheses beneath each coefficient estimate. Also reported are p-values for F-tests of the hypotheses that βJ11= βJ

21=

βJ31=0 in specification (1) and βA

12= βA22= βA

32=0 in specification (2). “Financial stock CEF premium” is change in the average price premium

over NAV on closed end funds specializing in financial stocks. “TED spread” is three-month Eurodollar deposit yield minus three-month U.S.

Treasury bill yield. “Eurodollar futures” is the price of the short maturity on-the-run futures contract, which trades as one minus the Eurodollar

yield. “CP spread (non financial)” is three-month commercial paper yield for non financial issuers minus three-month U.S. Treasury bill yield.

“BAA-AAA spread” is difference in yield on BAA versus AAA-rated corporate bonds. “ABX BBB-” is Markit ABX.HE index of 20 subprime

mortgage-backed securities. “VIX spot index” is the stock index option implied volatility computed by the CBOE. Coefficients in bold type

indicate statistical significance at the 5% level. The Pre Crisis period is defined as January 2005 to January 2007. The Crisis period is defined

as February 2007 to March 2009. Unreported intercept and slope dummies isolate the period from 19th September 2008 to 8th October 2008

during which shorting of sample stocks is restricted.

55

Table 9 continued. Panel A: Pre Crisis period aggressive shorting Sample Firms Matched Firms Coefficients on X for RSS

(equation 1)

Coefficients on RSS for X (equation 2)

Coefficients on X for RSS (equation 1)

Coefficients on RSS for X (equation 2)

X series Lag3 Lag2 Lag1 P Lag3 Lag2 Lag1 P Lag3 Lag2 Lag1 p Lag3 Lag2 Lag1 P Financial stock CEF premium

Daily 34.9624 -171.3288 115.2892 0.00

1.44E-6 -0.0001 0.0001 1.47E-7 10.6255 -22.5668 10.4236 0.36 0.00003 -0.0003 0.0002 0.01

(0.60) (-2.29) (1.98) (0.04) (-1.94) (1.40) (0.84) (-1.42) (0.82) (0.18) (-1.76) (1.25)

TED spread Daily -7.5373 -7.0700 19.6382 0.07 0.0001 0.0001 -0.0003 0.00 -0.0103 0.1468 0.7364 0.01 0.0015 0.0020 -0.0018 0.00

(-0.92) (-0.61) (2.25) (0.26) (1.58) (-1.36) (-0.23) (2.62) (16.21) (1.26) (1.38) (-1.53)

VIX spot index

Daily -0.2845 1.0306 -0.7414 0.39 0.0071 -0.0030 -0.0012 0,00 0.0081 0.1494 0.7436 0.92 -0.0102 0.0236 -0.0116 0.00

(-0.57) (1.48) (-1.50) (1.53) (-0.63) (-0.27) (0.18) (2.70) (16.62) (-0.52) (0.97) (-0.59)

CP spread (non financial)

Daily -7.5059 -3.3993 15.2951 0.07 -0.0001 0.0005 -0.0003 0.00 -1.2515 2.4504 -0.4235 0.06 0.0011 0.0018 -0.0017 0.00

(-0.89) (-0.29) (1.70) (-0.42) (1.78) (-1.06) (-0.74) (1.06) (-0.25) (0.98) (1.29) (-1.49)

BAA-AAA spread

Daily 31.0360 11.9618 -37.5901 0.02 -0.00004 -0.00003 -2.98E-6 9.12E-9 0.8678 0.1887 0.3268 0.02 0.0004 0.0003 -0.0004 0.00

(1.41) (0.38) (-1.69) (-0.39) (-0.31) (-0.03) (0.19) (0.03) (0.07) (0.64) (-0.89) (-0.93)

ABX BBB- Daily 0.0278 -0.0362 0.0062 0.72 0.0335 0.0617 -0.1297 0.00 -0.0091 0.0074 0.0009 0.55 1.1640 -0.3277 -0.0553 0.00

(0.66) (-0.53) (0.15) (0.30) (0.42) (-1.12) (-1.15) (0.58) (0.12) (2.03)** (-0.53) (-0.09)

Eurodollar futures

5-minute

646.6805 467.5646 39.0510 0.00 -8.63E-9 6.02E-8 -4.33E-8 0.00 -287.9047 -204.4094 -138.6146 0.00 -8.49E-8 2.74E-8 4.213E-8 0.00

(0.80) (0.57) (0.05) (-0.16) (1.18) (-0.81) (-0.74) (-0.52) (-0.36) (-1.44) (0.44) (0.71)

VIX spot index

5-minute

-3.3169 -4.6366 -1.2805 0.00 -5.64E-6 3.43E-6 -8.24E-6 0.00 1.0001 -4.0417 1.3876 0.00 -2.22E-6 -5.61E-6 5.34E-6 0.00

(-0.82) (-1.14) (-0.31) (-1.05) (0.58) (-1.38) (0.49) (-1.95) (0.67) (-0.38) (-0.91) (0.88)

56

Table 9 continued. Panel B: Pre Crisis period passive shorting Sample Firms Matched Firms Coefficients on X for RSS

(equation 1)

Coefficients on RSS for X (equation 2)

Coefficients on X for RSS (equation 1)

Coefficients on RSS for X (equation 2)

X series Lag3 Lag2 Lag1 P Lag3 Lag2 Lag1 P Lag3 Lag2 Lag1 p Lag3 Lag2 Lag1 P Financial stock CEF premium

Daily -45.7873 -149.3604 184.7940 4.09E-8

5.89E-6 -0.00001 8.54E-6 4.42E-7 21.2824 -54.5874 37.1537 0.05 0.0001 0.0001 0.00003

0.01

(-0.29) (-0.75) (1.19) (0.40) (-1.00) (0.59) (1.17) (-2.38) (2.05) (0.79) (0.85) (0.30)

TED spread Daily -23.7913 -25.6605 55.5453 0.01 0.0001 0.0002 -0.0003 0.00 -5.2012 4.1536 2.3371 0.00 0.00001 0.0001 -0.0013 0.00

(-1.14) (-0.87) (2.49) (0.66) (1.62) (-2.56) (-2.24) (1.32) (1.00) (0.01) (0.07) (-1.48)

VIX spot index

Daily -0.9200 2.4765 -1.6048 0.49 0.0023 -0.0001 -0.0006 0.00 0.1023 -0.3970 0.2392 0.13 -0.0296 0.0179 -0.0009 0.00

(-0.73) (1.40) (-1.28) (1.28) (-0.07) (-0.33) (0.71) (-1.97) (1.67) (-2.17) (1.17) (-0.07)

CP spread (non financial)

Daily -23.6173 -14.3667 43.6456 0.09 -8.83E-6 0.0002 -0.0002 0.00 -5.5535 2.5476 4.1593 0.00 -0.0003 0.0002 -0.0011 0.00

(-1.11) (-0.48) (1.91) (-0.08) (1.64) (-1.84) (-2.28) (0.77) (1.71) (-0.44) (0.24) (-1.41)

BAA-AAA spread

Daily 102.0365 4.8599 -100.1966 0.02 -0.00004 -5.50E-6 0.00001 4.47E-9 8.8048 -10.9539 2.7417 0.02 0.0003 0.0003 -0.0003 0.00

(1.82) (0.06) (-1.77) (-1.21) (-0.13) (0.32) (1.34) (-1.18) (0.41) (0.86) (0.64) (-0.89)

ABX BBB- Daily 0.0227 -0.0319 0.006576 0.95 0.0292 0.0144 -0.0627 0.00 0.0181 -0.0341 0.0215 0.00 -0.1866 -0.1257 0.4998 0.00

(0.26) (-0.23) (0.08) (0.53) (0.19) (-1.12) (1.48) (-1.73)* (1.76)* (-0.50) (-0.33) (1.35)

Eurodollar futures

5-minute

1921.75 -2251.45 -1519.87 0.00 3.82E-8 -3.92E-8 2.44E-8 0.00 -473.5298 -450.1708 -624.2832 0.00 6.63E-8 -4.46E-8 6.17E-8

0.00

(1.21) (-1.41) (-0.96) (1.45) (-1.47) (0.91) (-0.67) (-0.63) (-0.88) (1.18) (-0.77) (1.04)

VIX spot index

5-minute

-2.1797 -37.6364 -0.3660 0.00 -5.43E-6 -1.63E-6 2.36E-6 0.00 -6.1058 -7.1091 -1.5098 0.00 -2.22E-6 -5.62E-6 5.35 E-6

0.00

(-0.23) (-3.91) (-0.04) (-1.85) (-0.54) (0.79) (-1.32) (-1.520 (-0.320 (-0.38) (-0.91) (0.88)

57

Table 9 continued. Panel C: Crisis period aggressive shorting Sample Firms Matched Firms Coefficients on X for RSS

(equation 1) Coefficients on RSS for X

(equation 2) Coefficients on X for RSS

(equation 1) Coefficients on RSS for X

(equation 2) X series Lag3 Lag2 Lag1 P Lag3 Lag2 Lag1 P Lag3 Lag2 Lag1 p Lag3 Lag2 Lag1 P Financial stock CEF premium

Daily -46.1176 27.5799 16.8722 0.14 0.00003 0.00005 -0.0001 0.36 -17.9486 -6.9868 23.9794 0.002 0.0002 0.0003 -0.0005 0.00

(-1.24) (0.58) (0.46) (0.47) (0.82) (-1.71) (-1.12) (-0.35) (1.58) (1.53) (1.49) (-2.79)

TED spread Daily 3.1204 -0.9945 -2.1406 0.23 -0.0001 -0.0001 0.0001 0.00 1.3881 -0.1893 -1.2007 0.0002 -0.0003 -0.0002 0.0016 0.00

(1.09) (-0.22) (-0.75) (-0.16) (-0.13) (0.09) (1.22) (-0.10) (-1.04) (-0.17) (-0.10) (0.70)

VIX spot index

Daily -0.3468 0.3421 0.0555 0.19 -0.0028 0.0022 -0.0067 0.00 0.0359 -0.0851 0.0703 0.46 -0.0046 -0.0186 0.0179 0.00

(-1.46) (1.05) (0.23) (-0.37) (0.28) (-0.88) (0.33) (-0.58) (0.61) (-0.25) (-0.80) (0.77)

CP spread (non financial)

Daily 1.7572 -1.6912 -0.2476 6.88 E-10

-0.000166 -0.0002 0.0003 0.00 0.9421 0.4207 -1.3786 0.0004 -0.0002 -0.0003 0.0007 0.00

(0.80) (-0.55) (-0.11) (-0.20) (-0.27) (0.30) (1.17) (0.37) (-1.69) (-0.45) (-0.72) (1.52) 0.00

BAA-AAA spread

Daily 0.1991 -3.0611 3.3381 6.9 E-10

-0.0000915

-0.0001 -0.00003 0.00 0.1628 0.3809 0.3553 0.002 2.2428 -1.8234 -0.9211 0.004

(0.02) (-0.16) (0.24) (0.70) (-0.90) (-0.20) (3.98) (7.25) (6.67) (5.83) (0.47) (-0.26)

ABX BBB- Daily 0.0002 0.0007 -0.0008 0.25 -0.6914 2.0317 -0.5695 0.31 -0.0010 0.0019 -0.0008 0.05 4.0986 4.5520 -6.0965 0.00

(0.12) (0.28) (-0.45) (-0.63) (1.78)* (-0.52) (-1.48) (1.85) (-1.16) (1.43) (1.24) (-1.65)

Eurodollar futures

5-minute

298.4691 16.8866 -117.3352 0.00 -8.496E-8 2.74E-8 4.21E-8 0.00 -300.9963 -5.3157 8.1340 0.00 -1.05E-7 4.61E-8 3.88E-8 0.00

(0.55) (0.03) (-0.22) (-1.44) (0.44) (0.71) (-0.55) (-0.01) (0.01) (-1.46) (0.61) (0.53)

VIX spot index

5-minute

3.2245 5.7912 -4.9142 0.00 -2.848E-7 4.33E-6 -8.6E-6 0.00 -1.2835 1.8053 3.1102 0.00 5.07E-6 -8.48E-6 -4.21E-6 0.00

(0.66) (1.17) (-0.99) (-0.05) (0.72) (-1.5) (-0.28) (0.39) (0.67) (0.88) (-1.45) (-0.74)

58

Table 9 continued. Panel D: Crisis period passive shorting Sample Firms Matched Firms Coefficients on X for RSS

(equation 1) Coefficients on RSS for X

(equation 2) Coefficients on X for RSS

(equation 1) Coefficients on RSS for X

(equation 2) X series Lag3 Lag2 Lag1 P Lag3 Lag2 Lag1 P Lag3 Lag2 Lag1 p Lag3 Lag2 Lag1 P Financial stock CEF premium

Daily -43.6979 28.5430 35.4525 0.05 0.00005 0.00003 -0.00002 0.52 -17.3379 -11.6139 37.4172 1.14E-6 0.0003 0.0003 -0.0001 0.00

(-1.13) (0.59) (0.92) (0.89) (0.64) (-0.35) (-1.29) (-0.70) (3.00) (2.03) (1.40) (-0.29)

TED spread Daily 1.7419 -1.3550 -2.4605 0.001 -0.0004 -0.0001 -0.0007 0.00 0.1418 -1.1861 0.3764 3.66E-6 -2.3809 5.7283 -3.2617 0.00

(0.59) (-0.29) (-0.84) (-0.64) (-0.18) (-1.12) (0.15) (-0.77) (0.39) (-0.44) (0.71) (-0.54)

VIX spot index

Daily -0.1979 0.1194 0.0402 0.62 -0.0030 0.0004 -0.0118 0.00 -0.0262 -0.0365 0.0331 0.06 -0.0177 -0.0218 -0.0253 0.00

(-0.80) (0.35) (0.16) (-0.40) (0.06) (-1.60) (-0.29) (-0.30) (0.34) (-0.90) (-0.76) (-0.90)

CP spread (non financial)

Daily 0.3013 -2.4793 -0.4342 0.001 -0.0005 -0.0005 -0.0007 0.00 -0.5247 -0.1879 -0.1125 0.00 -0.0002 -0.0003 0.0001 0.00

(0.13) (-0.78) (-0.19) (-0.59) (-0.52) (-0.83) (-0.78) (-0.20) (-0.17) (-0.43) (-0.50) (0.25)

BAA-AAA spread

Daily 10.2074 -1.4232 -9.5562 0.34 -0.0000 -0.0001 -0.0001 0.00 2.9683 -2.3241 -1.0175 0.02 -0.0002 -0.0003 0.0007 0.00

(0.75) (-0.07) (-0.65) (-0.28) (-0.94) (-0.77) (0.65) (-0.34) (-0.20) (-0.45) (-0.72) (1.52)

ABX BBB- Daily 0.0005 0.0006 -0.0011 0.67 -0.5191 1.3630 -0.3197 0.63 0.0003 0.0005 -0.0008 0.09 3.7024 1.1532 -3.5575 0.00

(0.27) (0.28) (-0.60) (-0.49) (1.24) (-0.30) (0.46) (0.60) (-1.37) (1.20) (0.25) (-0.80)

Eurodollar futures

5-minute

-362.7988 -85.7002 306.2114 0.00 -4.88E-8 -3.72E-9 3.54E-8 0.00 -524.3273 -524.3273 -258.3045 0.00 -3.6E-10 -7.45E-8 4.87E-8 0.99

(-0.76) (-0.18) (0.64) (-0.73) (-0.05) (0.53) (-0.83) (-0.67) (-0.41) (-0.00) (-0.79) (0.53)

VIX spot index

5-minute

-4.9304 -1.6210 3.8161 0.00 -6.59E-6 8.61E-6 -4.93E-6 0.00 -3.5908 -0.4740 -5.2667 0.00 -4.89E-6 1.067E-6 5.44E-7 0.00

(-1.07) (-0.35) (0.82) (-1.09) (1.38) (-0.82) (-0.98) (-0.13) (-1.42) (-0.93) (0.20) (0.07)

59

Table 10. Daily returns, buy-sell imbalances, and bid-ask spreads around large Crisis period aggressive long position liquidation events

The Crisis period is February 2007 to March 2009. For each stock, the ten largest aggressive long position liquidation events by firm-day are selected. Aggressive long position liquidation equals price-setting sells minus shares shorted for the firm-day divided by trading volume for the firm-day. Imbalance is the price-setting buy-sell ratio previously defined. Spread is the bid-ask spread previously defined. Aggressive long position liquidation for the firm-day equals minus the price-setting buy-sell imbalance excluding short trades, divided by trading volume for the firm-day. “Abnormal” indicates the variable equals its raw value minus its mean from the Pre Crisis (January 2005 to January 2007) divided by its standard deviation from the Pre Crisis period. T-statistics are reported below each mean. Coefficients in bold indicate statistical significance at the 5% level. -5 -4 -3 -2 -1 0 1 2 3 4 5 {0,+5} Sample firms, Market-adjusted return 0.0031 0.0010 0.0048 0.0026 -0.0020 -0.0093 -0.0006 -0.0019 0.0001 -0.0012 0.0029 -0.0017 (2.35) (0.84) (3.62) (2.02) (-1.63) (-8.78) (-0.55) (-1.82) (0.08) (-0.85) (2.12) (-2.75) Abnormal volatility 4.2671 4.0476 4.4026 4.3425 4.2622 4.1374 4.4393 3.9113 4.2193 4.0858 4.0494 4.3141 (19.09) (20.62) (20.07) (19.77) (19.67) (19.83) (19.78) (19.51) (19.49) (21.41) (21.19) (39.74) Abnormal imbalance -0.3120 -0.3642 -0.3795 -0.4616 -0.6368 -2.34141 -0.6590 -0.4568 -0.4217 -0.3884 -0.3717 -0.7940 (-12.27) (-13.73) (-14.49) (-17.79) (-21.24) (-106.32) (-22.22) (-17.41) (-16.58) (-15.27) (-14.54) (-20.45) Abnormal spread 7.0328 6.8502 7.4041 7.3394 7.6520 7.7501 0.8245 0.7461 0.7184 0.7048 0.7821 10.9714 (13.95) (13.94) (12.86) (12.64) (14.45) (14.13) (8.19) (7.83) (7.89) (8.58) (8.01) (3.75) Matched firms, Market-adjusted return 0.0023 -0.0003 -0.0007 -0.0011 -0.0031 -0.0096 0.0012 -0.0011 0.0003 0.0004 -0.0005 -0.0015 (2.73) (-0.34) (-0.78) (-1.27) (-3.49) (-10.57) (1.46) (-1.46) (0.31) (0.55) (-0.61) (-4.03) Abnormal volatility 1.7725 1.5287 1.6296 1.5396 1.4856 1.7205 1.4926 1.5395 1.5949 1.5175 1.5121 1.5844 (16.80) (17.01) (17.67) (17.12) (16.28) (16.53) (15.98) (15.29) (16.43) (17.41) (17.15) (37.44) Abnormal imbalance -0.3879 -0.4043 -0.4062 -0.4673 -0.5174 -2.4017 -0.5315 -0.4314 -0.4039 -0.3790 -0.3825 -0.79402 (-13.73) (-15.19) (-15.35) (-17.35) (-18.54) (-119.60) (-18.68) (-16.20) (-15.34) (-13.97) (-14.05) (-23.65) Abnormal spread 1.5698 1.61495 1.6149 1.6791 1.6779 1.9790 1.8172 1.7821 1.8394 1.8512 1.6968 1.8410 (13.72) (13.54) (14.41) (13.33) (13.04) (9.37) (7.97) (10.58) (11.88) (10.01) (10.72) (23.32)

60

Table 11. Intraday returns, buy-sell imbalances, and bid-ask spreads around large Crisis period aggressive long position liquidation events The Crisis period is February 2007 to March 2009. For each stock, the ten largest aggressive long position liquidation events by firm-interval are selected. An interval is 5 minutes and the overnight interval is excluded. Aggressive long position liquidation equals price-setting sells minus shares shorted for the firm-interval divided by trading volume for the firm-interval. Imbalance is the price-setting buy-sell ratio previously defined. Spread is the bid-ask spread previously defined. Market-adjusted return is the raw return adjusted for β derived from the stock return and return for the “SPY” ETF in “Pre Crisis” period for the same interval. Long position liquidation for the firm-day equals minus the price-setting buy-sell imbalance excluding short trades, divided by trading volume for the firm-day. “Abnormal” indicates the variable equals its raw value minus its mean from the Pre Crisis (January 2005 to January 2007) divided by its standard deviation from the Pre Crisis period. T-statistics are reported below each mean. Coefficients in bold indicate statistical significance at the 5% level. -5 -4 -3 -2 -1 0 1 2 3 4 5 {0,+5} Sample firms, Market-adjusted return -0.0001 -0.00002 0.0003 0.0002 -0.0002 -0.0021 -0.0004 -0.0003 -0.0001 0.0002 -0.0002 -0.0005 (-1.09) (-0.17) (1.97) (1.28) (-0.94) (-8.75) (-2.06) (-1.82) (-0.41) (0.98) (-0.80) (-4.99) Abnormal volatility 2.4344 2.4990 2.4205 2.8402 3.0020 4.6195 4.2939 3.5673 3.1975 2.8922 2.7854 3.6419 (15.40) (15.62) (15.08) (14.53) (16.44) (23.94) (20.75) (18.60) (18.44) (18.37) (18.31) (40.34) Abnormal imbalance -0.1342 -0.1195 -0.1101 -0.1435 -0.2392 -2.1855 -0.3142 -0.1635 -0.1618 -0.1140 -0.1233 -0.5565 (-5.53) (-4.90) (-4.59) (-5.91) (-9.07) (-326.85) (-11.39) (-6.67) (-6.52) (-4.53) (-5.04) (-41.99) Abnormal spread 1.2996 1.3189 1.3414 1.3938 2.347 6.1941 3.5534 2.4053 2.1956 2.0023 1.9798 1.8409 (9.88) (9.90) (9.90) (9.73) (8.87) (17.59) (13.22) (15.24) (14.03) (13.93) (13.68) (23.32) Matched firms, Market-adjusted return -0.0001 -0.00003 -0.0001 -0.0001 0.0002 -0.0019 0.0001 0.0005 -0.0001 -0.0001 -0.0003 -0.0003 (-1.33) (-0.17) (-0.79) (-0.72) (1.15) (-8.02) (0.68) (2.88) (-0.60) (-0.33) (-1.63) (-3.69) Abnormal volatility 1.9224 2.1098 2.0470 1.9649 2.1071 4.5904 3.6560 3.2355 2.5658 2.6415 2.3546 3.1713 (21.55) (18.25) (17.83) (21.61) (19.21) (27.12) (25.33) (22.98) (26.76) (22.75) (23.39) (59.07) Abnormal imbalance -0.1021 -0.1306 -0.1423 -0.1496 -0.2080 -2.2641 -0.2231 -0.1715 -0.12863 -0.1434 -0.1725 -0.5168 (-3.51) (-4.64) (-5.21) (-5.27) (-7.35) (-272.55) (-7.40) (-5.80) (-4.38) (-4.84) (-5.92) (-37.37) Abnormal spread 0.1310 0.1090 0.0943 0.0451 0.0766 3.7481 1.3129 0.9495 0.7564 0.6773 0.6501 1.3529 (3.76) (3.11) (2.97) (1.68) (2.07) (14.09) (18.79) (13.69) (15.70) (15.14) (14.06) (26.63)

61

Table 12. Summary of vector autoregressions for intraday aggressive long position liquidations, relative short sales, returns, and buy-sell imbalances This table partially summarizes VAR estimates that relate 5-minute short selling, aggressive long position liquidations, returns, and price-setting buy-sell imbalances. There is one VAR for each of 175 sample firms and each of 169 matched firms. Autoregressive coefficients and coefficients that do not involve short selling are not reported. The Pre Crisis period is defined as January 2005 to January 2007. The Crisis period is defined as February 2007 to March 2009. Intercept and slope dummies for 19th September 2008 to 8th October 2008, when shorting of sample stocks was restricted, are unreported. Panel A: Pre Crisis period 187 Sample Firms 169 Matched Firms Coefficient on: at lag:

For:

Mean

Median Fraction significant

Mean

Median Fraction significant

Aggressive shorting Long sale 1 Short 0.1265 0.0876 0.9195 0.1083 0.0766 0.9533 Long sale 2 Short 0.1283 0.0963 0.9310 0.1081 0.0698 0.9200 Long sale 3 Short 0.1563 0.1042 0.9482 0.1049 0.0810 0.9400 Long sale 1 Return 0.00004 0.00003 0.38503 0.0001 0.00004 0.3866 Long sale 2 Return -2.74E-6 -2.25E-6 0.1149 1.53E-6 -3.39E-6 0.1400 Long sale 3 Return 0.00003 -8.81E-6 0.1666 -0.00002 -0.00001 0.0869 Long sale 1 Imbalance 0.0522 0.0341 0.7701 0.0464 0.0316 0.7000 Long sale 2 Imbalance 0.0454 0.0421 0.7356 0.0377 0.0320 0.6868 Long sale 3 Imbalance 0.0598 0.0487 0.7126 0.0428 0.0418 0.7800 Short 1 Long sale 0.0368 0.0084 0.9425 0.0392 0.0144 0.9533 Short 2 Long sale 0.0220 0.0097 0.9310 0.0212 0.0117 0.9067 Short 3 Long sale 0.0163 0.0091 0.9137 0.0195 0.0119 0.9200 Return 1 Long sale -1.4742 -1.7091 0.7126 -0.4562 -1.0817 0.7067 Return 2 Long sale -1.5835 -1.6819 0.5977 -0.8895 -1.2776 0.6533 Return 3 Long sale -0.9946 -1.0202 0.4712 -0.4247 -0.5869 0.5400 Imbalance 1 Long sale 0.0392 0.0338 0.8908 0.04615 0.0446 0.9533 Imbalance 2 Long sale 0.0369 0.0281 0.9310 0.0422 0.0396 0.9333 Imbalance 3 Long sale 0.0371 0.0297 0.9137 0.0476 0.0409 0.9733 Passive shorting Long sale 1 Short 0.3314 0.2870 0.9482 0.2847 0.2033 0.9667 Long sale 2 Short 0.3634 0.2753 0.9482 0.2486 0.1840 0.9333 Long sale 3 Short 0.3484 0.3026 0.9597 0.2674 0.1983 0.9400 Long sale 1 Return 0.00004 0.00003 0.3563 0.00004 0.00003 0.3800 Long sale 2 Return -7.59E-6 -8.33E-6 0.1321 -6.61E-6 -0.00001 0.1533 Long sale 3 Return 0.00002 -0.00001 0.2241 -0.00002 -0.00002 0.1467 Long sale 1 Imbalance 0.0394 0.0264 0.6551 0.0372 0.0213 0.6600 Long sale 2 Imbalance 0.0304 0.0311 0.7356 0.0294 0.0217 0.5933 Long sale 3 Imbalance 0.0486 0.0388 0.7011 0.0339 0.0321 0.7133 Short 1 Long sale 0.0067 0.0026 0.8735 0.0048 0.0023 0.7933 Short 2 Long sale 0.0096 0.0033 0.9540 0.0083 0.0034 0.9000 Short 3 Long sale 0.0106 0.0037 0.9770 0.0110 0.0044 0.9600 Return 1 Long sale -1.3428 -1.6351 0.6954 -0.2681 -1.0106 0.7066 Return 2 Long sale -1.5260 -1.5861 0.5919 -0.8962 -1.2504 0.6466 Return 3 Long sale -0.9428 -0.9772 0.4597 -0.4742 -0.5839 0.5066 Imbalance 1 Long sale 0.0362 0.0328 0.8735 0.0435 0.0437 0.9333 Imbalance 2 Long sale 0.0345 0.0269 0.8965 0.0399 0.0381 0.9333 Imbalance 3 Long sale 0.0350 0.0286 0.9022 0.0463 0.0409 0.9600

62

Table 12 continued. Panel B: Crisis period 175 Sample Firms 169 Matched Firms Coefficient on:

at lag:

For:

Mean

Median

Fraction significant

Mean

Median

Fraction significant

Aggressive shorting Long sale 1 Short 0.0911 0.0922 0.2298 0.1590 0.1173 0.9867 Long sale 2 Short 0.0191 0.0681 0.2873 0.1476 0.1076 0.9733 Long sale 3 Short 0.1501 0.1288 0.3390 0.1282 0.1068 0.9333 Long sale 1 Return 0.0001 0.0001 0.2356 9.04E-6 0.00002 0.1333 Long sale 2 Return -0.0001 -0.00004 0.1379 -0.00002 -0.00002 0.0933 Long sale 3 Return -0.0001 -0.0001 0.2011 -0.00003 -0.00003 0.1333 Long sale 1 Imbalance 0.0166 0.0152 0.4540 0.0132 0.0105 0.4200 Long sale 2 Imbalance -0.00635 -0.0063 0.2931 0.0119 0.0094 0.3200 Long sale 3 Imbalance -0.0071 -0.0084 0.2758 0.0106 0.0086 0.2867 Short 1 Long sale 0.0013 0.0001 0.1436 0.0381 0.0252 0.9733 Short 2 Long sale 0.0008 0.0001 0.1609 0.0221 0.0173 0.9733 Short 3 Long sale 0.0009 0.0001 0.1839 0.0176 0.0139 0.9400 Return 1 Long sale 1.2107 0.9613 0.7299 1.6361 1.4142 0.8067 Return 2 Long sale 0.9282 0.8061 0.7528 1.1009 1.0092 0.6733 Return 3 Long sale 0.8736 0.7087 0.7413 1.1173 0.9508 0.6867 Imbalance 1 Long sale 0.0645 0.0651 0.9252 0.0693 0.0739 0.9667 Imbalance 2 Long sale 0.0536 0.0593 0.9482 0.0617 0.0719 0.9733 Imbalance 3 Long sale 0.05981 0.0642 0.9712 0.0583 0.0676 0.9733 Passive shorting Long sale 1 Short 0.0874 0.0910 0.3045977011 0.1625 0.1170 0.9800 Long sale 2 Short 0.0739 0.0963 0.3045977011 0.1473 0.1135 0.9400 Long sale 3 Short 0.1511 0.1355 0.3620689655 0.1469 0.1124 0.9467 Long sale 1 Return 0.0001 0.0001 0.2356321839 -8.74E-7 3.21E-6 0.1333 Long sale 2 Return -0.0001 -0.00004 0.132183908 -0.00004 -0.00003 0.1200 Long sale 3 Return -0.0001 -0.0001 0.2183908046 -0.00006 -0.00005 0.2067 Long sale 1 Imbalance 0.0152 0.0143 0.4425287356 0.0008 -0.0002 0.2467 Long sale 2 Imbalance -0.0076 -0.0075 0.2701149425 -0.0013 -0.0018 0.2467 Long sale 3 Imbalance -0.0085 -0.0094 0.2816091954 -0.0036 -0.0049 0.2133 Short 1 Long sale 0.0003 0.0001 0.1149425287 0.0126 0.0061 0.8600 Short 2 Long sale 0.0005 0.0001 0.1494252874 0.0156 0.0089 0.9667 Short 3 Long sale 0.0007 0.0002 0.2068965517 0.0153 0.0091 0.9800 Return 1 Long sale 1.2088 0.9550 0.7298850575 1.5973 1.4141 0.8000 Return 2 Long sale 0.9278 0.8026 0.7528735632 1.0829 0.9701 0.6733 Return 3 Long sale 0.8734 0.7100 0.7298850575 1.1058 0.9751 0.6867 Imbalance 1 Long sale 0.0643 0.0642 0.9195402299 0.0632 0.0710 0.9533 Imbalance 2 Long sale 0.0534 0.0591 0.9367816092 0.0571 0.0664 0.9533 Imbalance 3 Long sale 0.0596 0.0641 0.9655172414 0.0547 0.0632 0.9733

63

Table 12 continued. Panel C: Pre Crisis and Crisis Cholesky decomposition coefficients (significance from bootstrapped standard errors) 175 Sample Firms 169 Matched Firms Coefficient Period Shorting type

Mean

Median Fraction > two standard errors

from 0significant

Mean

Median

Fraction > two standard errors

from 0significant

Long sale versus shorting

Pre Crisis Aggressive 0.05544 0.05030

0.9714 0.02153 0.01431

0.9053

Long sale versus return

Pre Crisis Aggressive -5.3E-05 -4.7E-05

0.7257 -0.00012 -0.00011

1.0000

Long sale versus imbalance

Pre Crisis Aggressive -0.16005 -0.15592

1.0000 -0.18615 -0.17967

1.0000

Long sale versus shorting

Crisis Aggressive 0.06934 0.071748

0.9886 0.07215 0.069166

0.9822

Long sale versus return

Crisis Aggressive -0.00072 -0.00061

0.9943 -0.00051 -0.00043

1.0000

Long sale versus imbalance

Crisis Aggressive -0.22231 -0.23615

1.0000 -0.23501 -0.24978

1.0000

Long sale versus shorting

Pre Crisis Passive -0.01549 -0.09021

1.0000 -0.02015 -0.02236

0.9704

Long sale versus return

Pre Crisis Passive -5.4E-05 -4.7E-05

0.8171 0.00011 -0.00011

0.9112

Long sale versus imbalance

Pre Crisis Passive -0.16547 -0.15739

1.0000 -0.18223 -0.18522

1.0000

Long sale versus shorting

Crisis Passive -0.02868 -0.03033

0.9600 -0.0357 -0.03738

0.9645

Long sale versus return

Crisis Passive -0.00072 -0.00061

0.9943 -0.00051 -0.00043

0.9941

Long sale versus imbalance

Crisis Passive -0.22466 -0.23805

1.0000 -0.23646 -0.25065

1.0000

64

Appendix: Sample firms ABK AMBAC FINANCIAL GROUP INC FAC FIRST ACCEPTANCE CORP ACE ACE LTD FAF FIRST AMERICAN CORP CALIF AEL AMERICAN EQUITY INVT LIFE HLDG C FBC FLAGSTAR BANCORP INC AET AETNA INC NEW FBP FIRST BANCORP P R AF ASTORIA FINANCIAL CORP FED FIRSTFED FINANCIAL CORP AFG AMERICAN FINANCIAL GROUP INC NEW FFG F B L FINANCIAL GROUP INC AFL A F L A C INC FFH FAIRFAX FINL HOLDINGS LTD AGO ASSURED GUARANTY LTD FHN FIRST HORIZON NATIONAL CORP AGP AMERIGROUP CORP FII FEDERATED INVESTORS INC PA AHD ATLAS PIPELINE HOLDINGS L P FNB F N B CORP PA AHL ASPEN INSURANCE HOLDINGS LTD FNM FEDERAL NATIONAL MORTGAGE ASSN

AIG AMERICAN INTERNATIONAL GROUP INC FRE FEDERAL HOME LOAN MORTGAGE CORP

AIZ ASSURANT INC GBL GAMCO INVESTORS INC ALL ALLSTATE CORP GCA GLOBAL CASH ACCESS HOLDINGS INC AMG AFFILIATED MANAGERS GROUP INC GHL GREENHILL & CO INC AMP AMERIPRISE FINANCIAL INC GNW GENWORTH FINANCIAL INC AOC AON CORP GS GOLDMAN SACHS GROUP INC AWH ALLIED WORLD ASSUR CO HLDGS LTD HCC H C C INSURANCE HOLDINGS INC AXS AXIS CAPITAL HOLDINGS LTD HMN HORACE MANN EDUCATORS CORP NEW BAC BANK OF AMERICA CORP HNT HEALTH NET INC BBT B B & T CORP HS HEALTHSPRING INC BBX BANKATLANTIC BANCORP INC HUM HUMANA INC BEN FRANKLIN RESOURCES INC ICE INTERCONTINENTALEXCHANGE INC BK BANK OF NEW YORK MELLON CORP IFC IRWIN FINANCIAL CORP BLX BANCO LATINOAMERICANO DE EXP SA IHC INDEPENDENCE HOLDING CO NEW BNS BANK OF NOVA SCOTIA IMP IMPERIAL CAPITAL BANCORP INC BOH BANK OF HAWAII CORP ITC I T C HOLDINGS CORP

BXS BANCORPSOUTH INC ITG INVESTMENT TECHNOLOGY GP INC NEW

C CITIGROUP INC JEF JEFFERIES GROUP INC NEW CB CHUBB CORP JNS JANUS CAP GROUP INC CBC CAPITOL BANCORP LTD JPM JPMORGAN CHASE & CO CBU COMMUNITY BANK SYSTEM INC KEY KEYCORP NEW CFR CULLEN FROST BANKERS INC KFS KINGSWAY FINANCIAL SERVICES INC CI C I G N A CORP L LOEWS CORP CIA CITIZENS INC LAB LABRANCHE & CO INC CMA COMERICA INC LAZ LAZARD LTD CAN C N A FINANCIAL CORP LEH LEHMAN BROTHERS HOLDINGS INC CNB COLONIAL BANCGROUP INC LNC LINCOLN NATIONAL CORP IN CNC CENTENE CORP DEL LUK LEUCADIA NATIONAL CORP CNO CONSECO INC MBI M B I A INC CNS COHEN & STEERS INC MCY MERCURY GENERAL CORP NEW CPF CENTRAL PACIFIC FINANCIAL CORP MER MERRILL LYNCH & CO INC CVH COVENTRY HEALTH CARE INC MFC MANULIFE FINANCIAL CORP DB DEUTSCHE BANK AG MI MARSHALL & ILSLEY CORP NEW

DFG DELPHI FINANCIAL GROUP INC MIG MEADOWBROOK INSURANCE GROUP INC

DSL DOWNEY FINANCIAL CORP MKL MARKEL CORP ENH ENDURANCE SPECIALTY HOLDINGS LTD MOH MOLINA HEALTHCARE INC

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EV EATON VANCE CORP MRH MONTPELIER RES HOLDINGS LTD EVR EVERCORE PARTNERS INC MS MORGAN STANLEY DEAN WITTER & CO MTG M G I C INVESTMENT CORP WIS STT STATE STREET CORP NAL NEWALLIANCE BANCSHARES INC STU STUDENT LOAN CORP NCC NATIONAL CITY CORP SUR C N A SURETY CORP NFS NATIONWIDE FINANCIAL SERVICES IN SWS S W S GROUP INC NMX NYMEX HOLDINGS INC TCB T C F FINANCIAL CORP NSH NUSTAR G P HOLDINGS LLC THG HANOVER INSURANCE GROUP INC NYB NEW YORK COMMUNITY BANCORP INC TMK TORCHMARK CORP NYM N Y M A G I C INC TRH TRANSATLANTIC HOLDINGS INC NYX N Y S E EURONEXT UB UNIONBANCAL CORP OFG ORIENTAL FINANCIAL GROUP INC UBS U B S AG ONB OLD NATIONAL BANCORP UNH UNITEDHEALTH GROUP INC OPY OPPENHEIMER HOLDINGS INC UNM UNUM GROUP ORH ODYSSEY RE HOLDINGS CORP USB U S BANCORP DEL ORI OLD REPUBLIC INTERNATIONAL CORP UTR UNITRIN INC PFG PRINCIPAL FINANCIAL GROUP INC VLY VALLEY NATIONAL BANCORP PFS PROVIDENT FINANCIAL SVCS INC VR VALIDUS HOLDINGS LTD

PJC PIPER JAFFRAY COMPANIES WAL WESTERN ALLIANCE BANCORPORATION

PL PROTECTIVE LIFE CORP WB WACHOVIA CORP 2ND NEW

PMI P M I GROUP INC WBS WEBSTER FINL CORP WATERBURY CONN

PNC P N C FINANCIAL SERVICES GRP INC WCG WELLCARE HEALTH PLANS INC PNX PHOENIX COS INC WDR WADDELL & REED FINANCIAL INC PRA PROASSURANCE CORP WFC WELLS FARGO & CO NEW PRE PARTNERRE LTD WHG WESTWOOD HOLDINGS GROUP INC PRU PRUDENTIAL FINANCIAL INC WHI W HOLDING CO INC PTA PENN TREATY AMERICAN CORP WL WILMINGTON TRUST CORP PTP PLATINUM UNDERWRITERS HLDGS LTD WLP WELLPOINT INC RDN RADIAN GROUP INC WM WASHINGTON MUTUAL INC RE EVEREST RE GROUP LTD WPL STEWART W P & CO LTD RF REGIONS FINANCIAL CORP NEW WTM WHITE MOUNTAINS INS GROUP INC RGA REINSURANCE GROUP OF AMERICA INC XL X L CAPITAL LTD RJF RAYMOND JAMES FINANCIAL INC Y ALLEGHANY CORP DE RLI R L I CORP ZNT ZENITH NATIONAL INSURANCE CORP RNR RENAISSANCERE HOLDINGS LTD RY ROYAL BANK CANADA MONTREAL QUE SAF SAFECO CORP SBP SANTANDER BANCORP SCA SECURITY CAPITAL ASSURANCE LTD SF STIFEL FINANCIAL CORP SFG STANCORP FINANCIAL GROUP INC SLF SUN LIFE FINANCIAL INC SLM S L M CORP SNV SYNOVUS FINANCIAL CORP SOV SOVEREIGN BANCORP INC STI SUNTRUST BANKS INC STL STERLING BANCORP