1. Short Selling Bans and Institutional Investors' Herding Behavior: Evidence from the Global Financial Crisis Martin T. Bohl a, Arne C. Klein a and Pierre L. Siklos b a Department of Economics, Westphalian Wilhelminian University of Münster, Germany b Department of Economics, Wilfrid Laurier University and Viessmann European Research Centre, Canada

2. 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?

3. Contribution to the literature Do short sales constraints (SSC) have a significant impact on herding behavior?

4. 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

5. 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

6. 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

7. 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

8. 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 05/24/2009 Ban expires

9. Markets under consideration USUKGERFRROKAUS No. Stocks banned (N) 182910121644 No. Stock Control group 182910121644 T1783343347317127

10. Markets under consideration US & UK > 200% GER, FR, AUS > 100% ROK ≈ 90% In 2007 (Gonnard (2008))

11. Literature Review Miller (1977), Diamond and Verrecchia (1987) Short selling bans

12. Miller (1977) Divergence of opinion

13. Miller (1977) Divergence of opinion SSC deter pessimists from expressing their beliefs

14. Miller (1977) Divergence of opinion SSC deter pessimists from expressing their beliefs therefore, market prices are build upon optimists’ valuation

15. Miller (1977) Divergence of opinion SSC deter pessimists from expressing their beliefs therefore, market prices are build upon optimists’ valuation Overvaluation

16. Diamond and Verrecchia (1987) SSC reduce informational efficiency: new information is impounded into prices with a delay

17. 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

18. 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

19. 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

20. 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)

21. Beber and Pagano (2011) Their results for 30 countries underscore negative effects on market liquidity

22. 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

23. Empirical Approach We aim at identifying the impact of short sale constraints on herding behavior

24. Empirical Approach We aim at identifying the impact of short sale constraints on herding behavior 1.A measure of herding is needed

25. Empirical Approach We aim at identifying the impact of short sale constraints on herding behavior 1.A measure of herding is needed 2.Control for the effects of the crisis per se is needed

26. Empirical Approach We aim at identifying the impact of short sale constraints on herding behavior 1.A measure of herding is needed 2.Control for the effects of the crisis per se is needed 3.Robust inference based on small/medium size samples

27. Measure of Dispersion Measure of Dispersion as an input to evaluate Herding: details S t = dispersion: captures a key characteristics of herd behavior –N = number of stocks, –T = number of observations –r it = return, stock i, time t; –r mt = 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 an E(r) Christie and Huang (1995)

28. Rational Pricing

29. Herding

30. Adverse Herding

31. Methodology: Regression Form Autocorrelation: Schwert’s criterion From max to min, use a 10% criterion  0 means deviation from rational Asset pricing Proxies variance since  BANNED  CONTROL IMPLIES SSR have an effect (2)  > 0 under rational Asset pricing; {e.g., changing  may be one reason} Chang et al. (2000)

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

33. Matching Matching variables: Market capitalization, trading volume and market beta (all standardized) Matching metric: Sum of squared differences in those three variables (Euclidean distance)

34. Interpretation In general, evidence supporting an effect of short sale constraints is found if the estimate for significantly differs between test and control groups

35. 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.

36. 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.

37. Bootstrap A bootstrap algorithm enables us to draw reliable inference from small and medium samples

38. Bootstrap A bootstrap algorithm enables us to draw reliable inference from small and medium samples allows us to directly test the H 0 of Rational Asset Pricing (i.e., CAPM-type)

39. Bootstrap We generate data by the following processes 1.

40. Bootstrap We generate data by the following processes 1. 2.

41. Further empirical issues Persistently rising vs falling markets may make a difference: sort S t, by length of runs l  {1,2}

42. Further empirical issues Persistently rising vs falling markets may make a difference: sort S t, by length of runs l  {1,2} Threshold effects?

43. Further empirical issues Persistently rising vs falling markets may make a difference: sort S t, 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

44. Empirical Results Recall that we bootstrap deviations from Rational Asset Pricing

45. Empirical Results Recall that we bootstrap deviations from Rational Asset Pricing Significance does not mean significantly different from zero

46. 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

47. Empirical Results Adverse Herding!Herding

48. Empirical Results

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

50. 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

51. Interpretation It is well known in the literature that short sale constraints create uncertainty about fundamental asset values

52. 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

53. 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

54. Thank you for your attention!