Short Selling Bans and Institutional Investors' Herding Behavior:

Evidence from the Global Financial Crisis Martin T. Bohla, Arne C. Kleina and Pierre L. Siklosb

a Department of Economics, Westphalian Wilhelminian University of Münster, Germany

b Department of Economics, Wilfrid Laurier University and Viessmann European Research Centre, Canada

Testable Hypotheses• Does herding become more relevant during a

financial crisis? In other words, are regulators desired to displace SS during a crisis because herding is exacerbated during falling markets?– YES? Herding implies investors’ difference of opinion

is relatively small– NO? Divergences of opinion increase during a crisis.

Therefore, adverse herding is a possibility• SENTIMENT plays a role

• Is the evidence for/against herding similar across countries?

Contribution to the literature

• Do short sales constraints (SSC) have a significant impact on herding behavior?

Contribution to the literature

• Do short sales constraints (SSC) have a significant impact on herding behavior?

• The answer, in turn, entails important information for stock market regulators

Contribution to the literature

• Do short sales constraints (SSC) have a significant impact on herding behavior?

• The answer, in turn, entails important information for stock market regulators

• and deepens insights into institutional investors’ trading behavior

Markets under consideration

Setting and Data: Short Sale Constraints in

The United States• 07/15/2008 – 08/12/2008

Naked short sales in (18) selected stocks United Kingdom• 09/19/2008 - 01/16/2009

All economic short positions in (32) selected stocks Germany• 09/22/2008 – 01/31/2010

Naked short sales in (10) selected stocks

Markets under consideration

France• 09/22/2008 – 01/31/2010

Short Sales in (12) selected stocks South Korea• 09/30/2008

All short sales• 06/01/2009

Lifted for non-financials

Markets under consideration

Australia• 09/22/2008

Naked short sales• 11/19/2008

Lifted for non-financials being member of the S&P/ASX 200 and APRA-regulated business

• 05/24/2009Ban expires

Markets under consideration

US UK GER FR ROK AUS

No. Stocksbanned

(N)

18 29 10 12 16 44

No. StockControl group

18 29 10 12 16 44

T 17 83 343 347 317 127

Markets under consideration

• US & UK > 200%

• GER, FR, AUS > 100%

• ROK ≈ 90%

In 2007 (Gonnard (2008))

Institutional investors holdingsGDP

Literature Review

• Miller (1977), Diamond and Verrecchia (1987)

• Short selling bans

Miller (1977)

• Divergence of opinion

Miller (1977)

• Divergence of opinion

• SSC deter pessimists from expressing their beliefs

Miller (1977)

• Divergence of opinion

• SSC deter pessimists from expressing their beliefs

• therefore, market prices are build upon optimists’ valuation

Miller (1977)

• Divergence of opinion

• SSC deter pessimists from expressing their beliefs

• therefore, market prices are build upon optimists’ valuation

Overvaluation

Diamond and Verrecchia (1987)

SSC reduce informational efficiency:

new information is impounded into prices with a delay

Diamond and Verrecchia (1987)

SSC reduce informational efficiency:

new information is impounded into prices with a delay

• this holds for both positive and negative news

Diamond and Verrecchia (1987)

SSC reduce informational efficiency:

new information is impounded into prices with a delay

• this holds for both positive and negative news

• but the effect is stronger for negative information

Crisis related Bans

Previous literature on the short selling bans reports strong evidence for deteriorations in market quality and

liquidity

• Bris (2008), Boulton and Braga-Alves (2010) and Kolanski et al. (2010) for the July/August ban in the US

• Boehmer et al. (2009) and Kolanski et al. (2010) for the September/October ban in the US

Crisis related Bans

• Marsh and Payne (2010) for the UK

• Helmes et al. (2010) for Australia

• A broad international perspective incl. 30 countries is given in Beber and Pagano (2011)

Beber and Pagano (2011)

• Their results for 30 countries underscore negative effects on market liquidity

Beber and Pagano (2011)

• Their results for 30 countries underscore negative effects on market liquidity

• In addition, they find increased autocorrelations in the residuals of market model regressions

Empirical Approach

• We aim at identifying the impact of short sale constraints on herding behavior

Empirical Approach

• We aim at identifying the impact of short sale constraints on herding behavior

1. A measure of herding is needed

Empirical Approach

• We aim at identifying the impact of short sale constraints on herding behavior

1. A measure of herding is needed2. Control for the effects of the crisis per se is

needed

Empirical Approach

• We aim at identifying the impact of short sale constraints on herding behavior

1. A measure of herding is needed2. Control for the effects of the crisis per se is

needed3. Robust inference based on small/medium size

samples

Measure of DispersionMeasure of Dispersion as an input to evaluate Herding: details

• St = dispersion: captures a key characteristics of herd behavior– N = number of stocks, – T = number of

observations– rit = return, stock i, time

t; – rmt = cross-sectional

weighted average of returns in a ‘portfolio’ of N stocks

Measure of Dispersion

Average deviation of a stock from the market proxies how investors discriminate between stocks

NOT anE(r)

Christie and Huang (1995)

Rational Pricing

Herding

Adverse Herding

Methodology: Regression Form

Autocorrelation:Schwert’s criterionFrom max to min,use a 10% criterion

¹ 0 meansdeviation from rationalAsset pricing

Proxies variance since2 2 2 2( ) ( ) ( )mt mt mt mtE r E r E r

BANNED ¹CONTROL

IMPLIES SSR havean effect

(2)

d> 0 under rationalAsset pricing;{e.g., changing may be onereason}

Chang et al. (2000)

Matching

• Matching variables: Market capitalization, trading volume and market beta (all standardized)

Matching

• Matching variables: Market capitalization, trading volume and market beta (all standardized)

• Matching metric: Sum of squared differences in those three variables (Euclidean distance)

Interpretation

In general, evidence supporting an effect of short sale constraints is found if the estimate for

significantly differs between test and control groups

Interpretation

In particular, support for regulators’ point of view is given in case of a dampening effect of SSC on

herding which, in turn, is found if is significantly negative for the control group while being equal to

zero for the banned stocks.

0Control 0Test

Interpretation

By contrast, evidence in line with a amplifying effect of SSC on herding, is found if is negative for the test group but equal to zero for the control

stocks.

0Control 0Test

Bootstrap

A bootstrap algorithm • enables us to draw reliable inference from small

and medium samples

Bootstrap

A bootstrap algorithm• enables us to draw reliable inference from small

and medium samples• allows us to directly test the H0 of Rational Asset

Pricing (i.e., CAPM-type)

Bootstrap

We generate data by the following processes

1.titmiiti

rr ,,,

Bootstrap

We generate data by the following processes

1.

2.

titmiitirr ,,,

tiiitmiiti HMLSMBrr ,,,

Further empirical issues• Persistently rising vs falling markets

may make a difference: sort St, by length of runs l {1,2}

Further empirical issues• Persistently rising vs falling markets

may make a difference: sort St, by length of runs l {1,2}

• Threshold effects?

Further empirical issues• Persistently rising vs falling markets

may make a difference: sort St, by length of runs l {1,2}

• Threshold effects?• Small cap versus large cap: former

exhibit more herding than latter; former lag latter in terms of correlation of returns

Empirical Results

Recall that we bootstrap deviations from Rational Asset Pricing

Empirical Results

Recall that we bootstrap deviations from Rational Asset Pricing

• Significance does not mean significantly different from zero

Empirical Results

Recall that we bootstrap deviations from Rational Asset Pricing

• Significance does not mean significantly different from zero

• but significantly different from the value implied by the asset pricing model

Empirical Results

Adverse Herding! Herding

Empirical Results

Empirical Results

• Almost no herding (either adverse or regular) in case of unbanned stocks

Empirical Results

• Almost no herding (either adverse or regular) in case of unbanned stocks

• strong evidence for adverse herding for the stocks subject to the constraints for some countries

Interpretation

• It is well known in the literature that short sale constraints create uncertainty about fundamental

asset values

Interpretation

• It is well known in the literature that short sale constraints create uncertainty about fundamental

asset values• The work of Hwang and Salmon (2004, 2009) suggests

that during such turmoils investors loose trust in the market consensus and come back to fundamental based

pricing

Interpretation

• It is well known in the literature that short sale constraints create uncertainty about fundamental

asset values• The work of Hwang and Salmon (2004, 2009) suggests

that during such turmoils investors loose trust in the market consensus and come back to fundamental based

pricing• This may show up in adverse herding, via an increased

dispersion of returns

Thank you for your attention!