Post on 23-Feb-2016
description
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!