The Effects of the European Covered Short Sale Van on Market Quality and Price Efficiency

7/28/2019 The Effects of the European Covered Short Sale Van on Market Quality and Price Efficiency

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The effects of European covered short sale ban on

market quality and price efficiency

Valentijn de Neve

Student number: 1478249

Supervisor:

Dr. P.P.M. Smid

This version: 21 juni 2011

University of Groningen

Faculty of Economics and Business

Master Thesis, MSc Business Administration

Specialization: Finance


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Contents

I Literature review .........................................................................................................7

A. The effect of the short sale ban on market liquidity .......................................... 10

B. The effect of the short sale ban on price efficiency ........................................... 15

II Methodology ........................................................................................................... 19

A The effect of the covered short sale ban on market liquidity ............................. 23

B The effect of the covered short sale ban on price efficiency .............................. 27

III Data ........................................................................................................................ 29

IV Empirical results ..................................................................................................... 31

A. The effect of the short sale ban on market liquidity .......................................... 32

B. The effect of the short sale ban on price efficiency ........................................... 37

C. Robustness of the results ................................................................................... 40

V Conclusion ............................................................................................................... 42

References .................................................................................................................. 45

Appendix A: Literature table ...................................................................................... 47

Appendix B: Robustness estimates ............................................................................ 50


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The effects of European covered short sale ban on market

quality and price efficiency

Valentijn de Neve*

ABSTRACT

This thesis examines whether covered short sale bans decrease liquidity and price

efficiency of affected stocks, as is predicted by the model of Diamond and Verrecchia

(1987). Furthermore, the ability of listed options to serve as a substitute for short sales is

investigated. This study is based on data of 385 European financial stocks that were listed

in countries where only part of the financial stocks were affected by the covered short sale

ban. Contrary to the hypothesis, the estimates reveal that stocks affected by the ban are

more liquid and contain more stock specific information than unaffected stocks. The effect

of the availability of options is insignificant.

INTRODUCTION

At the end of 2008 a worldwide wave of short selling restrictions was imposed in the light of

the financial crisis that started about a year earlier. According to financial regulators, these

restrictions were imposed to restore fair and orderly markets and protect investors and

firms from short sellers (Saffi and Sigurdsson, (2011)). Short sellers were singled out as

exacerbating the quick and steep stock price decline many firms and investors were

Valentijn de Neve is a student of MscBa Finance at the Faculty of Economics and Business of

the University of Groningen


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experiencing. This brought back the long standing issue: the role of short selling in financial

markets.

This discussion on the role of short sellers goes back a long way. A famous historical

example is the short sales of the shares of the East India Company in 1906. In anticipation

of the incorporation of a French rival firm, a group of Dutch business men sold shares of the

East India Company forward in a transaction promising future delivery in 1 or 2 years. Over

the next 12 months the share price dropped 12%, increasing the profits of the short sellers

and resulting in anger among the shareholders who found out about the plan. A year later,

the Amsterdam Exchange imposed the first regulation on the prohibition on short sales.

Over the last 400 years there has been no agreement on whether short sales are a good or a

bad phenomenon, and therefore the corresponding short sale regulations have been

changed over time (Bris, Goetzmann and Zhu, (2007)).

At the end of the current decade short sellers were seen as contributing to efficient

stock prices and liquid markets and playing an important role in uncovering overvalued

companies. Regulators in the United States seemed to share this view as they made life

easier for short sellers when various short sale constraints were removed early 2007.

However, the discussion changed course in 2008 at the moment that financial regulators

around the world imposed short sale regulations in the light of the financial crisis in order to

restore orderly and fair markets (Boehmer, Jones and Zhang, (2009)). However, this is in

contrast to the idea that short selling plays an important role in creating efficient prices and

liquid markets. Therefore the main question becomes what the effect of the ban was on the

market liquidity and price efficiency.

Market liquidity refers to how easily securities can be bought or sold in the market.

A security is liquid when there are enough units outstanding for large transactions to occur

without resulting in a substantial change of the price. Essentially, liquidity reflects the impact


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of the order flow on price (Amihud (2002)). Short sellers provide the market with liquidity;

therefore it is important to determine what the effect of the short sale regulations is on

market liquidity.

Price efficiency is the degree to which the prices of assets reflect all the available

information. The restrictions on short sales limit the diffusion of (negative) information and

can thereby reduce the price efficiency of stocks (Bris, Goetzmann and Zhu, (2007)). This

makes it worthwhile to test what the effect of the short sales constraints were on price

efficiency.

The magnitude of the effect on market liquidity and price efficiency depends on the

type of short sale restrictions that are implemented. There is a wide variety in the type of

short sale constraints that were imposed during the financial crisis, but the most important

difference is between a ban on naked short sales and the ban on covered short sales. These

differences can best be analyzed by looking at the process of short sales under different

short sale constraints. In the situation without short sale restrictions, an investor who wishes

to short a particular firm has to locate a share that can be borrowed first, before the share

can be sold short. The transaction is subsequently carried out with the located share or

another share that can be borrowed. At a later point in time the investor will close the short

position out by buying a share and deliver it to the lender. In case of the naked short sale

ban, an investor has to borrow a share first before it can be sold short. Locating the share is

no longer enough. This requirement tends to increase both the search costs and the cost of

borrowing of the shares and thereby influences short selling (Boulton and Braga-Alves

(2010)). In the case of the covered short sale ban it is no longer allowed for investors to

short a share without owning a share, and borrowing a share is no longer possible. Therefore,

the naked short sale ban can be considered as an increase in the restrictions on short selling,


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whereas the covered short sale ban essentially eliminates the possibility of short selling

(Kolasinski, Reed and Thornock, (2010)).

Next to the type of short sale ban that is imposed, the availability of substitutes for

short sales play an important role in the effect of the ban. The availability of listed options

creates the opportunity to construct synthetic short sale positions as will be discussed later.

Therefore the availability of listed options on the banned stock influences the extent to

which a short sale ban has an effect. The focus of this thesis is on the most severe form of

short sale restrictions: the covered short sale ban. Most empirical research focuses on the

effect of the short sale restrictions based on United States evidence; this thesis focuses on

the effects of the covered short sale bans in Europe. The estimates reveal, in contrast to the

hypotheses, that there was an increase in market liquidity and price efficiency of stocks

affected by the short sale ban. Next to that, market liquidity of stocks with listed options

that were affected by the short sale ban decreased relative to stocks without listed options.

There was no significant effect of the availability of listed options on the price efficiency of

the affected stocks.

In the next section, the literature review, theoretical models that study the effect of

short sale constraints are described. Thereafter, the specific effects of the short sale ban on

market liquidity and price efficiency are discussed based on both theoretical and empirical

literature. The methods to test the hypotheses that are developed in the literature review

are subsequently described in the methodology section that is followed by the section

describing the necessary data to estimate the effects. Lastly this thesis concludes with the

interpretation of the results followed by the conclusion.


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I Literature review

In this literature review the effect of the covered short sale ban on the price level is

presented first. Subsequently, based on theoretical and empirical evidence the effects of the

short sale ban on liquidity and price efficiency are discussed in more detail.

The short sale ban can lead to higher prices and low subsequent returns. Assuming

that investors have heterogeneous expectations regarding securities, the introduction of

short sale constraints hence implies that the more pessimistic investors are shut out of the

market. The pessimistic investors will no longer participate in the price setting process since,

if they do not possess the shares, they are no longer able to effectively sell shares if they

believe they are overvalued. When in the price setting process, the more optimistic investors

do not take the absence of pessimistic investors into account, this will lead to biased security

prices. Therefore, these prices will be too high. Consequently the short sale ban can lead to

overpricing (Miller (1977)). However, short sale constraints do no longer result in overpricing

as soon as investors take into account in the price setting process that pessimistic investors

are shut out of the market. This can be illustrated with a simple example. Consider an

unconstrained referendum in which voters can choose either yes or no (in financial markets

buy or sell). The outcome of the referendum is governed by the election rule that states that

the motion passes if there are more yes votes than no votes. Next, consider a constrained

referendum in which voters can only vote yes or abstention (in financial markets buy or do

nothing). If in this case the same election rule is applied, the outcome will be biased in

favour of the yes voters. However, if the election rule is changed correspondingly by for

example stating that half of the votes have to be in favour of the motion in order to pass, the

result of the restricted referendum will be unbiased. In conclusion, changing the election

rule as well as the voting constraint can result in unbiased outcomes. Diamond and

Verrecchia (1987) argue that the financial markets are aware of the short sale ban.


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Consequently, optimistic investors will take into account the absence of the pessimistic

investors in the price setting process and therefore the short sale ban will not result in

overpricing. Nevertheless, the short sale ban can have an influence on liquidity and price

efficiency, which will be discussed in more detail in the subsequent subsections.

This result may change as soon as models of manipulative trading are considered

where there are incentives for investors to manipulate prices through short selling. This may

have been the reason that financial authorities around the world imposed the bans.

Goldstein and Guembel (2008) suggest that it is worthwhile for investors to undertake

manipulative short selling since it will decrease stock prices and thereby inform the market

about non-existent negative information. In turn, this news has the ability to have an effect

on the real investment opportunities of the firm. If for example banks believe the non-

existent negative information that is inferred from the stock prices, and hence ask for a

higher interest margin, this can result in the situation that the company will experience real

negative effects. However, if these are only driven by manipulative trading, it is unlikely that

they have a real effect since there is a strong incentive for investors with a long position to

counteract this behaviour (Khanna and Mathews (2009)). Therefore, it is unlikely that this

type of manipulative trading has to be considered to study the effect of the short sale ban.

Consequently, the focus of the rest of the analysis of the effect of the short sale focuses on

the effect of partly removing investors with negative information from the price setting

process.

This brings us to the next point to consider in the analysis of the short sale ban: the

effect of the existence of securities that can be partial substitutes for short selling. In fact the

derivative markets can provide a partial substitute for short selling. An investor with

negative information can obtain an exposure similar to a short position by creating a

synthetic short position, for example by buying a put and selling a call with the same


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maturity and strike price. The position is of course not exactly the same, since there are

differences in for example the trading costs. Although the positions are the same, both

trading positions can be used to trade based on negative information. Therefore, options can

be seen as a partial substitute for short sales (Figlewski and Webb (1993)).

Assuming that options are a substitute for short sales to a certain extent, the effects

of the short sale ban can be different for stocks with and without listed options. Kolasinski,

Reed and Thornock (2010) find that during the naked short selling ban in the US, when short

selling was thus only possible once a share was borrowed, the ban increased the costs of

short selling of firms with listed options, but effectively prohibited short selling of firms

without listed options. This indicates that the availability of listed options reduces the effect

of the short sale constraints. This is in line with earlier research that indicates that options

can be seen as a feasible but expensive substitute for short sales (Blau and Wade (2009),

Danielsen and Sorescu (2001)). Therefore the availability of listed stock options on the

underlying stock will decrease the effects of the short sale ban.

An important remark is that the short sale constraints also have effects on the

option market and hence on the ability of options to serve as a substitute for short selling.

Since market makers that are active in option markets were exempted from the short sale

ban, there is no direct effect (Grunewald, Wagner and Weber (2010)). However, it is possible

that there are spill over effects of the short sale ban from the stock market to the option

market. If the short sale ban decreased market liquidity and increased the bid ask spread, as

is argued in this thesis. These spill over effects will increase the price of trading options and

thereby decreases the ability of options to serve as a substitute for short selling. Battalio and

Schultz (2010) and Grundy, Lim and Verwijmeren (2009) do not find that during the 2008

short sale ban in the US investors did migrate to the option market. Therefore, it is likely that


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options can serve as a substitute for short sales, but that this effect was limited due to the

spill over effects from the stock market to the option market. In conclusion, the availability

of options will decrease the effects of the short sale ban; however, this decrease will

probably be relatively small.

In this first part of the literature review the theoretical overpricing model of Miller

(1977), the theoretical model of Diamond and Verrecchia (1987) and the manipulative

trading model of Goldstein and Guembel (2008) were presented. The overpricing model

does not consider that investors can in their decisions take into account that pessimistic

investors are shut out of the market by the short sale ban. The manipulative trading model is

not useful to analyze the effects of the short sale ban since it does not consider the

possibility that investors with long positions will prevent manipulative trading by investors

with a short position. Therefore, the theoretical model of Diamond and Verrecchia (1987) is

the preferred model and hence will be used to analyze the effects of the short sale ban. In

the next sections this model is described in more detail and the effects of the short sale ban

on market liquidity and price efficiency analyzed.

A. The effect of the short sale ban on market liquidity

As discussed in the introduction, the short sale ban is expected to have impacted

market liquidity. In this section first the model of Diamond and Verrecchia (1987) is

explained. Thereafter, the effect of the short sale ban on the bid-ask spread and the ability

to undertake transactions without resulting in a substantial change of the price are discussed.


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According to Diamond and Verrecchia (1987), short selling constraints can have two

effects on the bid-ask spread: it reduces the total amount of short sales and alters the mix of

informed versus uninformed traders. This model assumes that there are informed and

uninformed traders in the market. Informed traders know the actual value of a risky asset

whereas uninformed traders will make an inference about the value based on public

available information. Both informed and uninformed traders will trade based on their

expected value and the available bid and ask price. The bid and ask price are set by market

makers that earn an expected zero return on each trade. The market makers will lose on the

trades with informed traders since they will only sell when prices are too low and buy when

prices are too high, however they can offset the losses by the profits that they generate

from trades with the uninformed traders. Furthermore, assume that uninformed traders do

not only trade based on their own information, but that they also face liquidity shocks that

are exogenously determined that force them to trade. These shocks could be due to for

example taxes that have to be paid or financial planning. The short sale ban reduces the

amount of short sales. In the most extreme case there are no exemptions from the ban and

as a result the amount of short sales will drop to zero. The reduction in the number of short

sales limits the arrival of negative information to the market that informed traders possess,

since they are no longer able to sell short. Therefore, the slower speed of price discovery

delays the incorporation of information on the fundamental value of stocks and hence

increases the risk for a market maker. In order to compensate the market maker for the

higher risk a higher price in the form of an increased bid-ask spread will be asked.

However, so far the role of options has not been included in the analysis of the

effect of the short sale ban on the bid-ask spread. As discussed, options can be seen as an

expensive substitute for short selling. During the short sale ban, traders who perceive stocks

as being overvalued can turn to the option market to trade, but at a higher cost than in the


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case of short selling. Therefore, the short sale ban can be seen as an increase in the cost of

short selling. This will alter the mix of informed versus uninformed traders. The increased

costs of short selling will drive the less informed investors out of the market since only

investors that are well informed are willing to pay the extra costs. A higher proportion of

informed traders relative to uninformed traders will result in the situation that market

makers have relatively more trades with informed traders. As discussed, the market makers

make on average a loss on transactions with informed traders, thus in order to arrive at a an

average zero profit the market maker has to increase the bid-ask spread of the affected

stocks (Kolasinski, Reed and Thornock, (2010), Diamond and Verrecchia (1987). Therefore,

the availability of listed options on a banned stock is assumed to increase the bid-ask spread

of the stock relative to the spread of stocks that are affected by the ban but do not have

listed options.

As discussed, the theoretical model predicts an increase of the bid-ask spread of

stocks that are affected by the short sale ban and that this increase is even larger for the

affected stocks that have listed options. The question is if this is in line with empirical

evidence. Empirical estimates reveal that the bid-ask spread of the stocks affected by the

short sale ban increase significantly (Saffi and Siguerdson (2011), Bris (2008)). These

estimates are in line with the expectation that the short sale restrictions will result in a larger

bid-ask spread. Similar results are obtained by studying the effects of the short sale ban on

the bid-ask spread.

Based on US data, Boehmer, Jones and Zhang (2009) and Kolasinski, Reed and

Thornock (2010) report a significant increase in the bid-ask spread of the affected stocks

during the ban period. Furthermore, Kolasinski, Reed and Thornock (2010) find that the

spread of the affected stocks increased significantly. However, the increase for the affected


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stocks with listed options is relatively larger, but insignificant. In contrast, Beber and Pagano

(2010) studied worldwide short sale constraints and find that effects are especially large for

stocks without listed options. There is thus clear empirical evidence that the short sale ban

increased the bid-ask spread of the affected stocks, but there are conflicting results on what

the effect is of the availability of listed options for the affected stocks. Therefore, based on

the theoretical model of Diamond and Verrecchia (1987) and the empirical evidence

regarding the effect of the short sale ban on the bid-ask spread of affected stocks hypothesis

1 reads:

H1a: The short sale ban increases the bid-ask spread for the stocks that are affected by the

ban.

H1b: The increase in the bid-ask spread for the stocks affected by the short sale ban is larger

for stocks without than with listed stock options.

Next to the effect on the bid-ask spread the short sale ban can have an effect on the

liquidity of the affected securities as discussed in the introduction. The short sale ban is

assumed to decrease the ability to undertake transactions without resulting in a substantial

change of the price. This comes both from the direct and the indirect effect. The direct effect

is that the short sale ban eliminates (part of) the liquidity traders and thereby reduces the

liquidity of the affected stocks. The indirect effect of the short sales ban comes through the

increase in the bid-ask price. A higher bid-ask spread increases trading costs, and increased

trading costs decrease the amount of traders that are willing to buy or sell securities at the

quoted bid and ask spreads. This will in turn decrease the liquidity of a stock (Kolasinski,

Reed and Thornock (2010)).


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Incorporating the availability of listed options can be done in a similar way as in the

analysis of the bid-ask spread. As discussed, the availability of options increases the ratio of

informed traders relative to uninformed traders. A relative increase in this will decrease the

willingness of investors to trade and thereby decreases market liquidity (Kyle (1985)).

Furthermore, if, as is argued in this literature review, the bid-ask spread of stocks with listed

options affected by the short sale ban increases relatively more than the bid-ask spread of

affected stocks without listed options, the decrease in market liquidity is even larger for

affected stocks with listed options than those without listed options.

Based on the analysis above the short sale ban is assumed to decrease market

liquidity, and this decrease in liquidity is expected to be relatively larger for stocks with listed

options that are affected by the ban. Empirical evidence based on the short sale ban in the

United States indicates that the price impact on the order flow as measured by the Amihud

illiquidity ratio increased for the stocks affected by the ban (Boehmer, Jones and Zhang

(2009)). Beber and Pagano (2010) also find that the short sale ban decreased liquidity for the

affected stocks based on a worldwide study on short selling bans. Kolansinksi, Reed and

Thornock (2010) confirm that the US short sale ban decreases liquidity for the affected

stocks. They also test if the availability of listed stock options changed this effect, however

they did not find a significant result. Based on the theoretical model of Diamond and

Verrecchia (1987) and the previous empirical evidence hypothesis 2 is:

H2a: The short sale ban decreases liquidity for the stocks that are affected by the ban.

H2b: The short sale ban decreases liquidity more for the stocks without than with listed stock

options.


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B. The effect of the short sale ban on price efficiency

Besides the influence on the market quality, the short sale ban influences the price

efficiency too. In this part of the literature the effect of the short sale ban on price efficiency

in the form of the speed of adjustment of prices and the level of stock specific information

are discussed. The ability to incorporate information in stock prices has not only an effect on

the speed of adjustment, but can also have an effect on the level of stock specific

information that is incorporated into prices. Assuming that the ratio of firm specific

information to market information is likely to be higher in markets that allow investors to

acquire information and act quickly upon it. As discussed, the short sale ban slowed down

the incorporation of information into stock prices and eliminated the possibility to act upon

negative news in most cases (Diamond and Verrecchia (1987)). Therefore, it is likely that the

short sale ban decreases the amount of stock specific information that can be incorporated

into the stock prices. Based on this idea, Morck, Yueng and Yu (2000) develop the theory

that less efficient markets that constrain the ability of investors to incorporate information

into security prices have a lower degree of idiosyncratic risk. This difference is measured as

the difference in the fit of the estimation of a standard market model. In case that there is

no idiosyncratic risk, in theory the fit of the market model is perfect (Bris, Goetzmann and

Zhu (2007)).

Assume again that options are an expensive substitute for short sales. In that case

the availability of listed options for stocks affected by the short sale ban increases the ability

of investors to act upon negative information by creating synthetic short positions.


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Moreover, as discussed options are more likely to be used by informed investors and

consequently it increases the informativeness of short sales (Diamond and Verrecchia

(1987)). Since both the ability as well as the speed at which investors can trade based on

information increases, this increases the ability to rapidly impound value relevant

information of stocks. Consequently, there will be a higher level of idiosyncratic risk.

Therefore the availability of listed options for stocks affected by the short sale ban is

assumed to increase the degree of idiosyncratic risk.

Empirical evidence based on worldwide short selling constraints shows that short

selling restrictions are associated with lower idiosyncratic risk, indicating that the price

discovery process is harmed by the restrictions (Bris, Goetzman and Zhu, (2007)). This is in

line with the evidence based on the US emergency order that prohibited naked short selling.

The fit of the market model as measured by the R squared increased significantly more for

the stocks affected by the short sale ban than for the stocks that were not affected (Bris

(2008), (Kolansinksi, Reed and Thorntock, (2010)).

Kolansinksi, Reed and Thorntock (2010) also investigate what the effect of the short

sale ban was on the degree of idiosyncratic risk. However, they do not find a significant

change for the stocks that are affected by the ban. Nevertheless, in contrast to the theory

the availability of listed options significantly decreased the level of idiosyncratic risk. Since

the theoretical and empirical evidence are opposite, hypothesis 3b will be based on the

theoretical model of Diamond and Verrecchia (1987). Consequently hypothesis 3 reads:

H3a: The short sale ban decreases the level of idiosyncratic risk for stocks that are affected by

the ban


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H3b: The short sale ban decreases the level of idiosyncratic risk of banned stocks more for

stocks without than with listed options.

As discussed the short sale ban has an effect on the speed of adjustment of prices.

Diamond and Verrecchia (1987) formalize the effect of short sale regulations by

distinguishing between a decrease in the number of traders that can sell short and an

increase in the costs of shorting. The short sale ban can be considered as a decrease in the

number of traders that can sell short, since the ban prohibits short selling apart from the

market makers that are exempted from the ban.

Diamond and Verrecchia (1987) show that the expected numbers of periods

required for the adjustment of prices to bad news and to good news are both decreasing

functions of the short sale restrictions. However, more specifically the expected time for

adjustment of prices to bad news relative to good news is decreasing in the short selling

constraints. Intuitively it makes sense that a short sale ban has a stronger effect on the

incorporation of negative information than positive information into prices, because

investors with negative information are directly limited to trade based on their information

due since short selling is forbidden.

Availability of listed stock options leads to an increase in the number of traders that

can sell short. However, this type of short selling can be seen as an expensive substitute for

short sales. An increase in the costs of short selling increases the informativeness of short

sales as is discussed in the first part of the literature review. Therefore both the increase in

the informativeness of short sales as well as an increase in the number of traders that are

able to sell short, increase the speed at which information is incorporated into prices.

Therefore the availability of listed options for stocks affected by the short sale ban is


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expected to decrease the speed at which negative market information is incorporated into

stock prices.

Empirical evidence based on worldwide short selling restrictions shows that the

speed of adjustment is faster in countries that allow short sales (Bris, Goetzmann and Zhu,

(2007)). This is in line with the evidence based on the short sale regulations in the US. Bris

(2008) finds that the emergency order increases the cross-autocorrelation of past market

returns on the returns of the stocks affected by the emergency order more than on the

returns of the stocks that are not affected by the ban. Moreover, the downside cross-

autocorrelation increases for the stocks affected by the emergency order whereas it

increases for the stocks that are not affected. This supports the theory that the short sale

constraints impede the incorporation of negative information into stock prices more than of

positive information.

Similarly, Beber and Pagano (2009) show that price discovery slows down for stocks

that are affected by the short sale ban in case of negative market returns. This is in line with

one of the insights from the model of Diamond and Verrecchia (1987): a short sale ban limits

the process of price discovery asymmetrically, and the effect is stronger for negative

information than for positive information.

There is to my knowledge no literature available that test what the effect is of the

availability of options for stocks affected by the short sale ban on the relative speed of the

incorporation of bad news into security prices. Therefore hypothesis 3b is based on the

theoretical model. Hypothesis 4 reads:

H4a: The short sale ban decreases the speed at which negative information is incorporated in

the prices of stocks that are affected by the ban .


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H4b: The speed at which negative information is incorporated in the prices of stocks that are

affected by a short sale ban decreases more for stocks without listed options than for stocks

with listed options.

II Methodology

This section describes the methods that are employed to identify the effect of the

covered short sale ban. The objective is to determine what the effect of the ban is on the

different measures of liquidity and price efficiency as discussed in the literature review. In

order to do so, first the general approach is discussed. Thereafter the specific estimates to

test the effect on market quality are described in the first subsection and subsequently the

specific estimates to identify the effects on price efficiency are explained in the second

subsection.

The main idea behind the methodology of this thesis is to create a panel dataset that

has a number of differences that can be exploited to determine the effect of the covered

short sale ban. This panel dataset includes firms from the same industry; some of them are

affected by the short sale ban while others are not affected by the ban. This approach is in

line with Beber and Pagano (2010), Bris (2008) and Grundy, Lim and Verwijmeren (2010).

This is in contrast to the other popular method to test the effect of the short sale ban, which

is based on between effect estimates based on a matched sample. The stocks for the

matched sample are determined by minimizing a function of the differences between the

stock affected by the covered short sale ban and the possible matching stock. The function


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consists for instance of variables such as market value of shares outstanding, closing stock

price and return variability (Boulton and Braga-Alves (2010), Boehmer, Jones and Zhang

(2009)). A disadvantage of this method is that a match in trading patterns before the short

sale ban is not necessarily a good match for the period during the short sale ban. During this

time period studied the financial sector was affected more severe or in another way than

other sectors. Therefore better estimates of the effect of the covered short sale ban can be

obtained by studying the differences between the financial stocks that are affected by the

covered short sale ban and those that are not affected. The dataset is created by selecting

the data of all listed firms for the European countries that implemented a short sale ban that

affected a part of the financial sector. The collection of this data results in a panel dataset

incorporating the following differences that can be exploited to estimate the effect of the

covered short sale ban:

Both firms that are affected by the covered short sale ban and firms that are not

affected by the ban are included in the sample.

Data on these firms before during and after the covered short sale ban are included.

Data on firms that were affected by different short sale bans due to their country of

listing are included.

In doing so it is important to keep in mind that the firms that are affected by the

short sale ban are a group with specific characteristics. These firms are chosen for a reason:

they were severely affected by the financial crises and suffered or had a probability of

suffering from financial distress. A dummy variable is included in the regression to capture

these group specific effects. In this way the fundamental differences between the firms that

are affected and that are not affected can be captured. However, that assumes that these

differences are constant over time. That is a strong assumption and may not be realistic.


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Over the course of the period studied new information came to the market and that could

have had a different effect on the firms that were affected by the short sale ban and those

that were not affected. Therefore it is important in the interpretation of the results to take

into account that part of the observed effect of the covered short sale ban could also be

attributed to differences over time between the firms in which short selling was allowed and

those that were affected by the covered short sale ban. Unfortunately this problem is

common for all methodologies that are employed to investigate the effects of the short

selling ban. This problem is also present in case that the analysis is conducted based on

matched pairs as previously described.

Furthermore there could be both stock and time fixed effects that influence the

independent variables. Examples of stock specific characteristics that can have an influence

are for example the stock specific risk, the number of market makers, analyst coverage of

the firm, size of the public float or country characteristics such as insider trading and

regulation (Berber and Pagano, (2010)). Time fixed effects could for example be caused by

the revelation of important market information. These time and stock fixed effects can be

incorporated in the analysis by estimating either fixed or random effects. Both the fixed and

random effects model essentially allow the intercept in the regression model to vary cross-

sectionally but the slope estimates are fixed both cross-sectionally and over time (Brooks

(2008)). Consequently, with these estimation techniques, the firm specific effects discussed

above can be incorporated in the firm specific intercept. The main difference between fixed

and random effects is that the fixed effects model estimates the firm specific intercepts

whereas the random effects model assumes that the firm specific effects arise from a

common intercept plus a random variable that can be estimated. However, this will only

lead to consistent in case that the individual specific effects are uncorrelated with the

independent variables (Hill, Griffiths and Lim (2008)).


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Summarizing one can state that if the random effects assumption holds, the random

effects model is more efficient. However, if this assumption does not hold, the random

effects model is not consistent but the fixed effects model still is (Brooks (2008)).

Consequently, in that case the fixed effects model is the preferred model. I use the Hausman

test to asses whether the random effects model can be used. The Hausman specification test

compares the fixed versus the random effects model under the null hypothesis that the

individual effects are uncorrelated with the other regressors in the model. In case that the

individual effects are correlated with the regressors, the random effects model will result in

biased estimates and the fixed effects model will be used. The standard Hausman test

assumes that the error terms are homoscedastic. In the case of heteroskedasticity the test

statistic can become larger or smaller than the actual statistic. Therefore I use a robust

version of the Hausman test in which the panel estimates are conducted with Driscoll-Kraay

standard errors (Hoechel (2007)).

An essential feature of my approach is that it relies on a panel dataset of daily

financial data on a set of similar financial firms. In these datasets residuals are often

correlated across firms and or across time. The presence of these correlations can bias

standard errors if they are not adjusted for the correlations and thereby lead to incorrect

test statistics and significance estimates (Petersen (2009)). Driscoll Kraay standard errors

can be used to compute standard errors that are robust to disturbances being

heteroskedastic, autocorrelated and cross-sectionally dependent. In many empirical

applications involving combined time-series and cross-sectional data, the residuals from

different cross-sectional units are likely to be correlated with one another. Spatial

correlations among such cross-sections may arise for a number of reasons, ranging from


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observed common shocks to unobserved contagion or neighbourhood effects (Hoechle

(2007)). Driscoll and Kraay () observe that the presence of such spatial correlations in

residuals complicates standard inference procedures that combine time-series and cross-

sectional data since these techniques typically require the assumption that the cross-

sectional units are independent. When this assumption is violated, estimates of standard

errors are inconsistent, and hence are not useful for inference (Petersen (2009)). Driscoll and

Kraay (1998) propose a correction for spatial correlations that does not require strong

assumptions concerning their form and show that it is superior to a number of commonly

used alternatives. In this thesis the presence of both correlation of the residuals across firm

and time is tested and if necessary the adjusted standard errors are be estimated. To test

the presence of group wise heteroskedasticity I use Pesarans cross-sectional dependence

test (2004). The null hypothesis is that the residuals are cross-sectionally uncorrelated. To

test for groupwise heteroskedasticity I use the modified Wald test.

To summarize, the methodology relies on studying the differences between financial

firms that are affected by the covered short sale ban and those that are not, before, during

and after the covered short sale ban. In the interpretation of the results it is important to

realise that part of the observed effect might be caused by the differences between the

firms that are affected by the ban and those that are not. In the next two subsections the

specific estimates that are conducted to estimate the effect of the covered short sale ban on

liquidity and price efficiency will be discussed.

A The effect of the covered short sale ban on market liquidity


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As discussed in the literature review the short sale ban is assumed to have

influenced market liquidity both in the form of a higher bid-ask spread as well as a decrease

in the ability to buy and sell securities without causing a price effect. Both the bid-ask spread

as well as the price impact of a stock is influenced by a number of important variables that

are incorporated as control variables in the estimates. In line with Beber and Pagano (2010)

and Kolasinski, Reed and Thorntock (2010) when estimating the effects on the bid ask spread

as well as the Amihud illiquidity ratio the possible presence of a traded stock and the

volatility of a stock is controlled for. Furthermore, in line with Bris (2008), Boulton and

Braga-Alves (2010) and Kolasinski, Reed and Thornock (2010), the pre-ban market

capitalization of the firm is included as a control variable. In order to absorb the group

specific variation on respectively the bid ask spread and the Amihud illiquidity ratio of the

stocks that are affected by the ban a dummy is included that equals one for all the stocks

that will in a point in time be affected by the covered short sale ban. Based on the discussion

above the equation to estimate the effect of the covered short sale ban on the bid-ask

spread reads as follows:

tiitiiiiti

titiitiiti

sizevolatilityoptionbannedoptioneventoption

eventbannedeventbannedoptioneventbannedcspread

,9,8765

4321,

The variable spreadis calculated as the daily percentage bid-ask spread. This is done

by dividing the quoted spread by the average of the quoted ask and quoted bid price. The

dummy variable eventtakes the value of 1 during the period for all stocks during the period

that there was a covered short sale ban in effect in the country of listing and 0 otherwise.

The dummy variable option takes the value of 1 for all stocks on which listed options are

available and 0 otherwise. The last dummy variable, bannedhas the value of 1 for all stocks

1)


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that at a moment in time are affected by the short sale ban and 0 otherwise. As discussed in

the literature review and formulated in hypothesis 1a, the covered short sale ban is assumed

to have increased the bid-ask spread for the affected stocks. Therefore 1 is expected to be

larger than zero. The availability of listed options for banned stocks are assumed to have

increased the bid-ask spread relative to the banned stocks. Therefore 2 is assumed to be

larger than zero as is also formulated in hypothesis 1b. The dummy variable banned can

capture the fixed effects of the group of firms on the independent variable. There is no

reason to believe that the group of firms that are affected by the ban have a fundamental

different spread, therefore the sign of 3 will be determined ex-post. The dummy variable

eventcan capture the variation of the bid-ask spread during the ban in compared to before

and after the ban. There are no specific expectations regarding this effect and therefore the

sign of 4 will be determined ex-post. The dummy variable option can capture the difference

in spread between stocks with and without a listed option. In general options are available

for the more liquid stocks that also tend to have a smaller bid-ask spread. Therefore 5 is

expected to be negative. Since 2 consists of an interaction effect of three consecutive terms,

it is important to include all interactions of the different terms (Brambor, Clark and Golder

(2006)). Therefore both the interaction ofoption x eventand option x bannedare included.

There are no specific expectations regarding these effects and therefore the signs of 5 and

6 will also be determined ex-post. Furthermore as discussed the control variables of

volatility and market size are included. Volatility is an important determinant of the bid ask-

spread. In case of higher volatility, it is well known that market makers are more reluctant to

provide liquidity and demand a higher price in the form of a larger bid-ask spread (Beber

Pagano (2009), Easley, Hvidkjaer, and O'Hara (2002)). Therefore volatility is included as a

control variable and consequently 8 is expected to be larger than zero. Including volatility is

especially important considering that the volatility changed substantially over the course of


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the period studied. Since not all stocks in the sample have options, it is not possible to

include implied volatilities. Therefore the moving standard deviation of the past 20 trading

days is used as a proxy for volatility as is also done by Beber and Pagano (2010). Market

capitalization is assumed to have an effect on the bid-ask spread. Stocks with a higher level

of market capitalization tend to have lower bid-ask spreads. However, this effect is

decreasing in the size of the market capitalization. Therefore the log of the pre-ban market

capitalization is used. The reason that initial pre-ban market sizes are used is that there have

been profound changes in the market sizes of the firms in the sample and these changes are

unrelated to the not able to explain the variation of the bid-ask spread. Because a larger

market capitalization is expected to decrease the bid-ask spread, 9 is expected to be

negative. The equation to estimate the effect of the covered short sale ban on the Amihud

illiquidity ratio is:

tiitiiiiti

titiitiiti

sizevolatilityoptionbannedoptioneventoption

eventbannedeventbannedoptioneventbannedcamihud

,9,8765

4321,

Amihud stands for the Amihud illiquidity ratio and is defined as the absolute value of

the daily stock return divided by its trading volume that day, see Amihud (2002). Therefore a

higher ratio indicates lower liquidity of a stock. The trading volume is defined as the daily

total number of trades multiplied by the closing price. The included variables are exactly the

same as in equation 1. Therefore explanation of the included dummy variables can be found

in the previous section. As discussed in the literature review the covered short sale ban is

expected to decrease liquidity for the affected stocks, thus increase the Amihud illiquidity

ratio. Consequently and according to hypothesis 2a 1 is expected to be larger than zero. The

availability of listed options for stocks that are affected by the ban is expected to increase

2)


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the effect of the ban because the information content of short sales increases. Therefore 2

is expected to be positive. Again there are no expectations regarding the group fixed and

time fixed effects as absorbed by the dummy variables banned and event. Therefore the

signs of 3and 4 will be determined ex-post. The dummy variable option can capture the

difference in illiquidity between stocks with and without a listed option. In general options

are available for the more liquid stocks. Therefore 5 is expected to be negative since stocks

with listed options are assumed to be less illiquid. As in equation 1 the interaction terms of

option x eventand option x banned are included. Again there are no specific expectations

regarding the effects of these interaction variables and therefore the signs of 5and 6 will

also be determined ex-post. Volatility is assumed to decrease the liquidity of the stock

similarly as the effect on the bid-ask spread was discussed. Consequently since an increase in

the volatility decreases liquidity, which means an increase of the Amihud illiquidity ratio, 8

is expected to be positive. Stocks with a higher market capitalization tend to be more liquid.

Therefore the size of the market capitalization is expected to decrease the Amihud illiquidity

ratio and hence 9 expected to be negative.

B The effect of the covered short sale ban on price efficiency

The effect of the covered short sale ban will be tested based on the level of

idiosyncratic risk (hypothesis 3) and on the speed at which information is incorporated in

prices (hypothesis 4). In order to test both of these effects a simple market model is

estimated. This is done based on the method proposed by Bris, Goetzmann and Zhu (2007)

and Morck, Yuens and Yu (2000). The following regression is estimated to determine the

level of idiosyncratic risk as measured by the R-squared as described in the literature review.

3)


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titmitiRR

,,,

In this equation Ri,t stands for the daily stock return whereas Rm,tis the daily market

return. The return of the market is measured by the daily return of the MSCI world index.

This equation is estimated for all stocks for each twenty trading days. The corresponding R-

squared of each model is saved and thereby creates a new dataset. Based on this dataset

the following equation is estimated to estimate the effect of the ban on the level of

idiosyncratic risk:

tiiiiti

titiitiiti

optionbannedoptioneventoption

eventbannedeventbannedoptioneventbannedcR

,765

4321,

In equation 4 Rit is the R-squared of the market model for a specific security based

on 20 subsequent trading days. The higher the R-squared is, the better the fit of the market

model is and consequently the level of stock specific information or idiosyncratic risk is

lower. The short sale ban is expected to decrease the level of idiosyncratic risk as is

formulated in hypothesis 3a. Therefore 1 is expected to be negative. The availability of

listed options is expected to decrease the effect of the short sale ban and thereby increase

the level of idiosyncratic risk of banned stocks with listed options relative to banned stocks

without listed options. Consequently is expected to be positive. The other variables are

the same as in equation 1 and 2 and there are no specific expectations regarding their sign.

These will be determined ex-post.

Another measure of price efficiency is the cross-autocorrelation between one-day

lagged market returns and individual stock returns as proposed by Hou and Moskowitz

4)


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(2005). This is only done for negative market returns, since the effect is larger for negative

news (Bris, Goetzman and Zhu (2007)). The correlations are estimates based on twenty

trading days. The effect of the short sale ban on the speed of adjustment to native

information is estimated with the following equation:

tiiiiti

titiitiiim

optionbannedoptioneventoption

eventbannedeventbannedoptioneventbannedc

,765

4321,

In this equation m-,i is the correlation of the daily stock return with the daily market

return of the previous day. The slower the speed of adjustment is, the higher the correlation

of past market returns with the current stock return will be. As is formulated in hypothesis

4a, the short sale ban is expected to decrease the speed of the incorporation of negative

information in security prices. Therefore 1 is expect to be positive. Listed options are

expected to decrease the effect of the short sale ban and increase the speed of adjustment

to negative information of banned stocks with listed options relative to banned stocks

without listed options. Consequently2 is expected to be negative.

The other variables are the same as in equation 1 and 2 and there are no specific

expectations regarding their sign. These will be determined ex-post.

III Data

In order to estimate the equations that are described in the methodology section, it

is necessary to construct a dataset that includes information for both firms that are affected

5)


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by the short sale ban and those that are not. Therefore data is collected for all listed

financial firms in all European countries that implemented a covered short sale ban on part

of the financial sector. The European countries that implemented such a short sale ban are

the Netherlands, the United Kingdom and Switzerland (Grunewald, Wagner and Weber,

2010). These short sales bans affected a huge number of firms active in the financial sector

such as banks, insurance firms and a broad range of financial service firms. Therefore

information on all listed firms in the Netherlands, the United Kingdom and Switzerland that

are classified according to Thompson Datastream as Banks, Nonlife insurance, Life

insurance and Financial services sector are selected. This results in a sample of 385 firms,

of which 41 are affected by the covered short sale ban. Furthermore there are listed options

available on 22 of the 41 firms that are affected by the short sale ban. For these 378 firms

daily data on the closing price, the bid price, the ask price, daily trading volume and the

availability of listed options is obtained from Thompson Datastream. Furthermore the daily

returns of the MSCI world index are also obtained from Thompson Datastream since they

are necessary to estimate the simple market model as is explained in the methodology

section. Volatility is the moving standard deviation of daily closing prices as is discussed in

the methodology section.

In order to arrive at a clean dataset, all observations with a price below 1 euro, no

trading volume and a negative bid-ask spread are removed. The summary statistics of the

resulting dataset that covers the period march 2008 until December 2009 are displayed in

table 1. This period includes 6 months before the short sale ban was imposed and 6 months

after the last country lifted the short sale ban.


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Tabel 1: Summary statistics

Price is the daily closing price,price askis the daily maximum ask price, price bidis the daily maximum bid price,

Volatilityis the moving average standard deviation of the closing price of the past 20 trading days, trade volume

is the daily number of trades x1000, return is the daily return of the MSCI World index measured as the % change

from the previous closing price, spread is the % spread calculated as (price ask price bid)/((price ask+pricebid)/2),Amihudis the Amihud illiquity measured as daily absolute return of a stock divided by its daily trading

volume.

Variable N Mean St. dev Min Max

Price 57.106 75.24 626.53 1.01 24903.92

Price ask 57.106 75.76 633.25 0.04 25149.28

Price bid 57.106 74.55 620.43 0.03 24903.92

Volatility 57.106 32.10 13.59 16.30 80.86

Trade volume 44.647 18.916 71.834 0.10 1.853.479

Return 57.099 -0.02 4.70 -125 485

Spread 57.106 4.11 9.40 0.01 163

Amihud 44.642 0.001 0.020 0.000 2.585

IV Empirical results

The objective of this thesis is to determine what the effect of the covered short sale

ban was on stocks affected by the ban. Moreover, to investigate the difference of the effects

of the covered short sale ban between affected stocks with listed options and those without.

The regression results of the effect on market liquidity and price efficiency are displayed in

table 2. As discussed in the literature review the short sale ban is assumed to lead to

decreased market liquidity and price efficiency of the affected stocks. As can be seen from

the estimates, market liquidity and price efficiency increased for the affected stocks relative

to the stocks that were not affected by the covered short sale ban. These results will be

discussed in more detail in the subsequent subsections. All regressions are conducted with

panel estimates in order to incorporate stock and time specific effects. Finance panel

datasets are often plagued by group wise heteroskedasticity, cross-sectional dependence

and autocorrelation. As discussed in the methodology section, it is important to take into


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account these effects, calculate robust standard errors and use robust tests in case the data

suffer of the effects above.

Tabel 2 Regressions results:

This table presents panel regressions with fixed effects, the Driscoll and Kraay standard errors are presented in

parenthesis. Column 1 presents the estimates for the independent variable spread, which is calculated as (price-

ask-price bid)/((price ask+ price bid)/2). Column 2 presents the estimatesfor the Amihud Illiquidty, which is the

absolute daily return divided by the daily trading volume. Column 3 presents the estimates of the R-squared of

the market model as is described in equation 3. Column 4 presents the estimates for the correlation between

stock return and lagged market returns. The dummy variable banned has the value of 1 for all stocks that are

affected by the short sale ban at a moment in time. The dummy variable eventhas the value of 1 for a stock

during the period that a covered short sale ban is in place in the country of listing. The dummy variable option

has the value of 1 for all stocks on which listed options exist. The control variable volatilityis based on the 20-daymoving average of the standard deviation of daily returns. The market capitalization is the log of the pre-crises

level of market capitalization of a firm.

Spread Amihud R-squared Corr.

Banned x event -0.029*** -0.019** -0.13** 0.000028*

(-7.58) (-2.90) (-3.02) (-2.09)

Option x banned x event 0.024*** 0.013** -0.061 0.00008

(-5.98) (-2.88) (-0.58) (-1.7)

Event -0.0024 -0.0099 -0.03 0.000037*

(-1.29) (-1.42) (-1.40) (-2.29)

Option x event -0.022*** -0.020** 0.024 -0.000032

(-7.93) (-2.62) (-0.28) (-1.13)Volatility 0.85*** 0.98*

(-7.77) (-2.18)

Market capitalization 0.0023*** -0.0029*

(-6.55) (-2.05)

Constant 0.24*** -0.0000011

(-18.95) (-0.15)

Observations 87.871 70.373 869 1.185

Number of groups 120 119 79 79

Lags 6 6 2 2

R squared within 0.057 0.018 0.1 0.027

t statistics in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001

A. The effect of the short sale ban on market liquidity

The effect of the covered short sale ban on liquidity is measured by both the effect

of the ban on the bid-ask spread and the Amihud illiquidity ratio. The estimates of the effect


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of the short sale ban on the bid-ask spread is displayed in column 1 and on the Amihud

illiquidity ratio in column 2 of table 2. Both estimates are conducted with fixed effects,

Driscoll-Kraay standard errors and autoregressive disturbances. The choice for this

specification is discussed in the next two paragraphs. The estimates of the bid-ask spread

and the Amihud illiquidity ratio exhibits both group wise heteroskedasticity and cross-

sectional dependence, as is indicated by the Modified Wald test and the Pesaran test for

heteroskedasticity. As is described in the methodology section, in that case it is important to

use the robust Hausmann test to determine whether estimates should be conducted with

fixed effects. For both the estimates of the bid-ask spread and the Amihud illiquidity ratio

the null hypothesis that the random effects model is valid is rejected at a significance level of

5%. This indicates that the fixed effect model is the correct model. Moreover, as discussed

in the literature review there are theoretical reasons to believe that there are firm specific

effects that influence both the liquidity and the bid-ask spread of stocks. Since there are

theoretical reasons to assume that there are stock fixed effects and the Hausmann test

indicates the presence of them, the estimates are conducted with stock fixed effects. Next to

stock specific effects the presence of time fixed effects is tested. No time dummies are

included for both estimates since the F-test of the time dummies is rejected at a significance

level of 5 %. As discussed, the data exhibits both group wise heteroskedasticity as well as

cross-sectional dependence. Therefore, robust standard errors have to be calculated that

take into account the group wise heteroskedasticity and the cross-sectional dependence. As

discussed in the methodology section, this can be done in the case of fixed effect estimates

with Driscoll-Kraay standard errors.

Hypothesis 1a is that the bid-ask spread for stocks affected by the short sale ban has

increased relative to the stocks that are not affected by the ban. Hypothesis 2a is that the

illiquidity of stocks affected by the short sale ban increased relative to the stocks that are not


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affected by the ban. Therefore, for both the estimates of the bid-ask spread and the Amihud

illiquidity ratio, banned x eventis assumed to be positive. As can be seen in the estimates in

column 1 and 2 of table 2, the coefficients are significantly negative. The estimates show

that the percentage bid-ask spread of the affected stocks decreased by 2.9%, which indicates

that the absolute spread of the banned stocks decreased on average by 0.9 eurocent1. This

can be seen as a considerable decrease in the trading costs. Unfortunately it is difficult to

calculate a meaningful statistic for the Amihud illiquidity ratio. The estimates reveal thus

that, contrary to the hypothesis, the bid-ask spread of the affected stocks decreased and the

liquidity increased. There are two possible explanations. First of all, the interpretation of the

short sale ban as a decrease in the number of trades is too simple and maybe the change in

the ratio of informed versus uninformed investors has to be considered. Secondly, there may

have been events that influenced the stocks affected by the ban during the ban period,

which resulted in the decrease of the bid-ask spread and increase of the liquidity of the

affected stocks. So far the short sale ban is interpreted as a decrease in the number of short

sales. In order to incorporate the change in the ratio of informed versus uninformed traders,

the short selling activity before and during the ban have to be analyzed. The period before

the short sale ban cannot be seen as a normal short selling market. Short selling costs were

at a historical high level and are likely to have increased the ratio of informed traders

relative to uninformed traders (Diamond and Verrecchia (1987)). During the short sale ban

all short sales were eliminated except for market makers. Market makers were allowed to

sell short to hedge their positions and provide liquidity. Both these types of trades can be

seen as uninformed trades. Therefore, it is likely that the short sale ban has decreased the

1This is calculated as: estimated coefficient X average bid-ask spread x average stock price banned

stocks.


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ratio of informed traders relative to uninformed traders since there were only uninformed

trades. Consequently, the short sale ban decreased the number of short sales but also

decreased the ratio of informed trades to uninformed trades. The decrease in the number of

short sales is expected to increase the bid-ask spread and decrease liquidity, whereas the

decrease in the ratio of informed traders is expected to decrease the bid-ask spread and

increase liquidity. In case that the latter effect is stronger than the effect of the reduction of

the number of short sales, the short sale ban will have decreased the bid-ask spread and

increased liquidity as the estimates also show. The other explanation is that specific

information for the stocks affected by the ban during the ban period occurred. During the

period of the ban, several rescue packages were announced that were mainly targeted at

the stocks that were affected by the ban (Beber and Pagano (2010)). These rescue packages

increased the clarity about fundamentals of the value of these firms. Consequently, this

could have decreased the bid-ask spread and increased liquidity of these stocks. Therefore,

the decrease in the bid-ask spread and increase in liquidity could be caused by the

announcement of the rescue packages instead of by the short sale ban.

Hypothesis 1b is that stocks affected by the short sale ban with listed options will

have an increase in the bid-ask spread relative to the stocks that are affected by the short

sale ban without listed options. Hypothesis 2b is that stocks affected by the short sale ban

with listed options will have an increase in the Amihud illiquidity ratio relative to the stocks

that are affected by the short sale ban without listed options. Therefore, the coefficient of

option x banned x eventis expected to be positive for both estimates. This is the case as both

coefficients are significantly positive at a 1% significance level. The estimates reveal that the

percentage bid-ask spread of the affected stocks with listed options increased by 2.4%

relative to the stocks affected by the ban without listed options. This resembles an increase


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in the absolute bid-ask spread of about 0.7 eurocent2, which can be seen as an economic

significant increase. There is thus clear evidence for hypothesis 1b and 2b, that the

availability of listed options on banned stocks increases the bid-ask spread and the illiquidity

of the stock. This is in line with the theoretical model of Diamond and Verecchia (1987) that

argues that if the ratio of informed traders relative to uninformed traders increases, the bid-

ask spread will increase and the liquidity of the stock will decrease. Therefore, these

estimates support the idea that the availability of options during the short sale ban is likely

to have increased the ratio of informed traders relative to the uninformed traders. For the

estimates of the bid-ask spread and the Amihud illiquidity ratio there are no coefficients for

banned, option and option x banned. These variables are omitted due to near perfect

collinearity. This nearly perfect collinearity comes probably from the stock fixed effects

which absorb these stock specific effects. All these 3 variables are stock specific elements

that are constant over time and in estimates that are conducted with random effects, these

variables are not omitted3

. The constant is also omitted due to nearly perfect collinearity.

For both estimates the coefficients ofeventand option x eventare negative; indicating that

the average spread (resp. Amihud illiquidity ratio) for all stocks in the sample was smaller

during the period of the ban. This effect was even stronger for the stocks with listed options

as option x event is negative. However, the coefficients of event are not significantly

different from zero and therefore nothing really can be said about this estimated coefficient.

The control variable volatility is as expected positive, more volatile stocks tend to have a

wider spread and be less liquid. Furthermore, market capitalization is expected to be

negative, as stocks with a larger market capitalization tend to have smaller spreads and a

2This is calculated as: estimated coeffient X average bid-ask spread x average stock price banned

stocks.3 These estimates are available on request to the author.


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higher liquidity. In contrast, the estimates show a significant positive estimate for the bid-ask

spread and a significant negative estimate for the Amihud illiquidity ratio. It is puzzling why

stocks with a larger market capitalization have larger spreads according to these estimates.

B. The effect of the short sale ban on price efficiency

The effect of the short sale ban on price efficiency is measured by both the level of

idiosyncratic risk and the autocorrelation of past market returns with... The estimates of

the effect of the short sale ban on the level of idiosyncratic risk is displayed in column 3 and

on the autocorrelation of past market returns in column 4 of table 2. Both estimates suffer

again from group wise heteroskedasticity, cross-sectional dependence and autocorrelation.

The robust Hausmann test rejects the null hypothesis of unbiased estimates. Therefore, the

estimates are again conducted with fixed effects, Driscoll-Kraay standard errors and

autoregressive disturbances.

Hypothesis 3a is that the level of idiosyncratic risk is lower for stocks affected by the

short sale ban. This is due to the increased difficulty to absorb stock specific information due

to the short sale ban. Additionally, hypothesis 3b is that this decrease for the affected stocks

was smaller if there are listed options, since it facilitates the incorporation of information

into the stock prices by serving as an expensive substitute for short sales. As can be seen in

column 3 table 2 is that the coefficient ofbanned x eventis negative and significant, contrary

to what is expected based on the hypothesis. This indicates that the level of stock specific

information was larger for stocks that were affected by the ban. However, this effect is

relatively small. The average R-squaredduring the event was 15% and for the affected stocks

it decreased to 13%. This is clearly a relatively small effect, but it is difficult to convert this

directly to an economic sensible number. The coefficient of option x banned x event is as


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expected negative, but insignificant. Consequently, it is not possible to either accept or

reject hypothesis 3b based on this estimate. A possible explanation for the increase of stock

specific information could be that besides the short sale ban there are other factors that

changed for the affected stocks during the ban period, and thereby masked the effects of

the ban. For example, if the short sale ban limited the incorporation of stock specific

information, but there was also more stock specific information for the stocks affected by

the short sale ban, then there may be no overall effect at all. During the short sale ban there

was, as discussed, a considerable amount of stock specific information for the affected firms

in the form of the rescue packages and news regarding the financial position of these firms.

This information has probably moved the stock prices of the firms affected by the short sale

ban more than other financial firms. The reason is that the firms affected by the ban were

affected more by the financial crisis than other financial stocks. Consequently, if the effect of

the increase in the amount of stock specific information dominates the effect of the

decrease in the ability to incorporate stock specific information, the estimate can become

negative as is the case. In this case the problem is that the firms affected by the ban were

not selected at random, firms that were affected by the ban were those that suffered most

from the financial crises. Therefore it is possible that this biased groups leads to biased

results. The reason that the hypothesized effect of the availability of listed options is

insignificant could be that there are more substitutes for short sales available. Next to

options futures can be used as substitutes and thereby can facilitate the incorporation of

stock specific information. This could then mask the effect of both the short sale ban and the

availability of listed options.

Hypothesis 4a states that the short sale ban decreased the ability to incorporate

information in stock prices and thereby increased the autocorrelation of past returns.

Furthermore, hypothesis 4b states that the availability of listed options increases the ability


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to incorporate the information and therefore the effect will be smaller for the affected

stocks with listed options. As can be seen in column 4 of table 2 the coefficient of banned x

eventis as expected positive and significant. However, the effect is very small. For example,

if a stock has a price decrease of 10 percent a banned stock will experience another 0.0003

percent decrease the next day. Since the average stock price of banned stocks is 4.79 euro

this equals only 0.00001 euro. Therefore, it is clear that this estimate is economically

insignificant. Furthermore, the estimate of option x banned x event is statistically

insignificant. This reveals that the availability of options did not have a significant effect on

the autocorrelation of past returns of the banned stocks. The lack of a significantly larger

autocorrelation of negative market returns and stock prices of the banned stocks could

either be present but masked by other effects or the hypothesized effect did not exist. The

reason that the effect is not visible could be, as already discussed, that there was a lot of

stock specific information for the banned stock during the ban. These price movements can

possibly have masked the autocorrelation of past market returns. The other possibility is

that the incorporation of negative information was simply not hindered by the short sale ban.

This could either be the case if the hypothesized effect does not exist, or in case that there

are other substitutes available than options that thereby reduce the effect of the short sale

ban. The insignificance of the effect of the availability of listed options for the stocks

affected by the short sale ban makes sense. In case that there was no significant effect

following the ban, the availability of options can no longer decrease the effect of the short

sale ban. For both the estimate of the R-squared as well as the autocorrelation of past

negative market returns there was a prior no expectation for the signs of eventand option x

event. As can be seen in column 3 and 4 these effects are relatively small and most of them

are insignificant. However, since these variables are part of the interaction effect it is


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important to keep them in the equation. The next step is to test the robustness of these

estimates. This is done in the next section.

C. Robustness of the results

In this section the robustness of the estimates with respect to the use of different

time periods, removal of outliers and a different frequency of the measurement is described.

The estimates for the robustness of the results can be found in table 4 for the bid-ask spread,

table 5 for the amihud illiquidity ratio. In table 6 the estimates of for the R-squared are

presented and table 7 contains the estimates of the the correlation of past market returns.

In all of these tables, column 1 is the standard estimate of the dependent variable that is

also in displayed in table 2. The second column displays the estimates that are based on the

period before and during the short sale ban. The third column displays the estimates for the

period during and after the ban. Column 4 presents the estimates in case identified outliers

are omitted from the analysis. Furthermore, in column 5 for the bid-ask spread and the

Amihud illiquidity ratio the estimates based on weekly instead of daily data are presented.

This is not done for the r squared and the correlation of past market returns since these are

already based on twenty trading day data, and increasing this timeframe would result in a

loss of the precision of measurement of the ban period. This is due to the fact that the

different ban dates can than not easily be incorporated in the analysis. The reason that the

robustness of the estimates with respect to different timeframes is that the concerned

period involved a considerable amount of market activity. Especially since the sort sale ban

introduction coincided with the announcement of several rescue packages for financial firms.

In the preceding period there was a lot of uncertainty in the market about the measures that

would be taken. Therefore, it can be useful to estimate the effect of the ban for the different


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time periods and check if the outcomes are robust. Based on this discussion, the result of the

estimates based on the period during and after the ban are expected to be cleaner since less

market noise will be in the estimates. As can be seen in tables 4 to 7, all estimates based on

the data obtained during and after the ban result in a little more significant effect than the

estimates based on the period before and during the ban. Furthermore, the estimates of the

market liquidity as measured by the bid-ask spread and the Amihud illiquidity ratio have a

larger estimated effect than if they are estimated for the period during and after the ban

compared to before and during. The effect of the availability of listed options is also robust

to the sample period that is chosen for the estimates. The effect of the availability of listed

options is larger for the period during and after than before and during for the estimates of

the spread and the Amihud illiquidity ratio. The availability of listed options has an

insignificant effect no matter which sample period is chosen. The conclusion is that the

results are robust to changes in the sample period chosen.

In some cases results are largely driven by outliers, especially since with the least

squares estimates the objective is to minimize the sum of the squared residuals. Therefore

data points with ad leverage are able to influence the estimates considerable (Brooks

(2008)). Therefore the robustness of the estimates with respect to outliers is examined. The

estimates exclude all observations that are identified with the method proposed by Hadi

(2004). These estimates reveal how robust the results are once the outliers are removed

The Effects of the European Covered Short Sale Van on Market Quality and Price Efficiency

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