Hedge Funds Variables and SEO Volatility

By Rosemary Walker, Rob Hull, and Sungkyu Kwak Presentation by Rosemary Walker, April 5, 2011 Washburn University Kaw Valley Seminar 1

Introduction

•

Hedge fund researchers often study either (i) the actual performance of hedge funds or (ii) the economic or market impact of hedge funds.

–

We focus on the market impact of hedge funds

–

Hedge funds receive bad press

• 1998 hedge fund troubles led to fears that it would – • cripple the financial system 2008 financial crisis is remembered for the huge profits made by some hedge funds from the collapse of subprime mortgages

Our paper shows a positive impact: reduced volatility in stock returns around SEOs

2

CHOICES MADE

Choices we make to investigate the impact of hedge funds on stock return volatility:



We choose the most common corporate event: seasoned equity offerings (SEOs).

 SEOs are known to be associated with definite stock return behavior surrounding their initial announcement dates.

 Huge price run-ups prior to SEO announcement  Initial negative market reaction followed by short-run gains  Long-run poor post-SEO performance 

We choose an SEO sample of smaller firms with huge insider ownership levels and changes

 Institutional impact can be larger for smaller stocks. Gompers and Metrick ( 2001 the demand for smaller stocks compared to only a 4.5% increase for larger stocks ) find that large investors produce a 29.1% decrease in 

We choose a time covering bubble and non-bubble years

 Where differences in volatility should occur 3

4

Four Major Hypotheses

 Hypothesis One (H

surrounding SEOs.

 1):

A greater amount of assets under management by the hedge fund industry (or a greater number of hedge funds) will be associated with less volatility in SEO stock returns for periods

 Hypothesis Two (H  2):

The volatility in stock returns around SEOs can be diminished when hedge funds increase their use of leverage and a

relative value (arbitrage) strategy.

 Hypothesis Three (H  3):

Strategies linked to SEOs, such as an event driven strategy or an equity hedge strategy, can cause greater volatility in SEO stock returns for periods surrounding SEOs.

 Hypothesis Four (H  4):

Stock return volatility will increase when greater hedge fund returns are obtained during pre-SEO periods where hedge funds are riding the pre-SEO stock price run-up. Otherwise, greater hedge fund returns will lower volatility as this will indicate that hedge funds are taking advantage of misvalued situations so as to enhance their profit-taking.

5

Other Hypotheses

• • •

We will also test to see if inside ownership levels and the change in these levels influence stock return volatility.

We will also seek to determine if either financial liquidity (the relative amount of cash and cash equivalence) and trading liquidity (NASDAQ versus NYSE/AMEX influence volatility.

Dummy variables tested include internet technology bubble time period and purpose of the offering.

6

Our Regression Model

VOL

 

0



h

 

H



h HFV h



n

 

N



n HFV n

  

VOL

= Daily Excess Stock Return Volatility (we use idiosyncratic volatility)  ΔVOL = Change or Shift in

VOL

(we use ΔIVOL ) 

HFV



AUM

= Hedge Fund Variables include nine variables described below.

= Hedge Fund Assets under Management during month 0 

NUM



PUL

= Number of Hedge Funds = Proportion of Hedge Funds Using Leverage 

PED



PRV



PEH

= Proportion of Hedge Funds with an Event-Driven Strategy = Proportion of Hedge Funds with a Relative Value (Arbitrage)

Strategy

= Proportion of Hedge Funds with a Equity Hedge Strategy 

CHR Return

=Average Equal-Weighted Compounded Monthly Hedge Fund  ΔCHR = Change in the Average Equal-Weighted Compounded Monthly Hedge Fund Return (Computed as Post-SEO CHR – Pre-SEO CHR) 

PCHR

= Average Equal-Weighted Compounded Monthly Hedge Fund Return for months  3,  2, and  1 7

The Regression Model



NFV

= Non-Hedge Fund Variables include nine variables described below.



ILA



CIL



PRI



DIS



ITB

= =

Change in Inside Ownership Proportion

= = =

Inside Ownership Proportion after SEO Primary Shares as a Proportion of Total Shares Offered Discounting

: log of (Estimated Price) / (Offer Price)

Internet-Technology Bubble Period

(dummy variable = 1 if before 1/1/02) 

POP

=

Purpose of Proceeds

(dummy variable = 1 if purpose expansionary) 

CLS

=

Class of Common Shares

(dummy variable = 1 if more than one class) 

TLQ

=

Trading Liquidity

(dummy variable = 1 if NASDAQ) 

FLQ

=

Financial Liquidity Ratio

(Cash and Cash Equivalents / BVE) 8

• •

Sample and Data

Our initial sample of 2,371 SEOs was identified from the Investment Dealer’s Digest for the period from January 1999 to December 2005. This period covers the tail-end of the internet-technology bubble that had ended by 2001.

After applying our criteria (CRSP data, Compustat data, insider information), we have 705 SEOs for testing purposes.

– Insiders include (i) the directors and officers as a group, and (ii) all five percent owners of outstanding common stock.

While some studies use ten percent, prospectuses claim that five percent ownership is the “magic” percentage worthy of a warning that these beneficial owners can impact share value by their trading.

– While all 705 SEOs had Compustat data, this data was not always complete for all Compustat variables used in our empirical tests.

9

Volatility Measures



Total volatility measures the total volatility of the excess return during the period in question.



Idiosyncratic volatility measure the volatility in the firm-specific component of the excess return during the time in question .



Systematic volatility measures the portion of the volatility that is inherent in the market and outside the firm’s control during the time in question.



This paper’s focuses on idiosyncratic volatility.

10

Idiosyncratic Volatility

 Idiosyncratic Volatility (IVOL):

IVOL i

,

t

  

t n t

 

i

2 ,  1 Where ε

i,τ

is the Fama and French ( 2009 ) residual for day τ. ε

i,τ

calculated from the following regression: is

r i,τ

– r f,t = α

t

+ β 1i,t (MKT

τ

– ) + β 2i,t (HML

τ

) + β 3i,t (SMBτ) + ε

i,τ

where r

i,τ

for day

τ

is the raw return on stock i for day τ; given by the one-month T-bill;

MKT τ

r

f

,

t

is the risk-free return is the return on the value weighted CRSP index for day τ; HML

τ

is the average return for day τ for the value portfolios minus the average return for day τ for growth portfolios; and, SMB

τ

is the average return for day τ for small portfolios minus the average return for day τ for the large portfolios. We also look at the change in volatility: ΔIVOL

i,Δt

= IVOL

i,t

− IVOL i,t−1 11

Hedge Fund Variables

Hedge Fund Assets under Management where “B” stands for billions Number of Hedge Funds Average Hedge Fund Size where “M” for millions Median Hedge Fund Size where “M” for millions Proportion of Hedge Funds Using Leverage Proportion of Hedge Funds with an Event-Driven Strategy Proportion of Hedge Funds with an Relative Value (Arbitrage) Strategy Proportion of Hedge Funds with an Equity Hedge Strategy Average Hedge Fund Return for Month 0 (where month 0 contains the announcement date)

MEAN

$760B 2,538 $366M $79M 0.595

0.084

0.104

0.321

1.19% 12

Average Equal-Weight Compounded Monthly Hedge Fund Return (CHR)

P CHR

for months –3 to –1

(pre-SEO three-month compounded return)

CHR for months –2 to –1

(pre-SEO two-month compounded return)

MEAN 0.0392

0.0247

CHR for months +1 to +2

(post-SEO two-month compounded return)

CHR for months –2 to +2

(five-month compounded return around SEO announcement)

ΔCHR for months +1 to +2 minus months –2 to –1

(difference in post SEO and pre-SEO returns)

0.0599

–0.0025

CHR for months –24 to –1

(pre-SEO 24-month compounded return)

0.0222

0.2870

CHR for months +1 to +24

(post-SEO 24-month compounded return)

CHR for months –24 to +24

(49-month compounded return around SEO announcement)

ΔCHR for months +1 to +24 minus months –24 to –1

(difference in post-SEO and pre-SEO returns)

0.2535

0.6272

–0.0335

13

Descriptive Statistics

Common Value:

(Estimated Price)

×

“B” stands for billions (Shares Outstanding before SEO) where

Inside Ownership Proportion Before:

(Insider Shares before SEO)

/

(Shares Outstanding before SEO)

Inside Ownership Proportion After:

(Insider Shares after SEO)

/

(Shares Outstanding after SEO)

Change in Inside Ownership Proportion:

Inside Ownership Proportion After

–

Inside Ownership Proportion Before

Primary Shares as a Proportion of Total Shares Offered

Discounting:

Logarithm of (Estimated Price

/

Offer Price) where Estimated Price is given by the

Investment Dealer’s Digest

.

Financial Liquidity Ratio:

(Cash and Other Short-Term Investments)

/

Total Assets

Growth Ratio:

Capital Expenditures

/

Total Assets

Leverage Ratio:

(Total Liabilities)

/

(Common Value

+

Total Liabilities).

Tangible Assets Ratio:

Net Plant and Equipment

/

Total Assets

Tobin’s Q Ratio:

(Common Value + Total Liabilities)

/

Total Assets

MEAN $2.05B

0.490

0.384

–0.106

0.604

0.041

0.259

0.059

0.250

0.228

6.807

14

Days –50 to 0

Time Frame

Days +1 to +50 Days –50 to +50 +1 to +50 minus –50 to 0 Days –520 to 0 Days –520 to 0 Days +1 to +520 Days –520 to +520 Day +1 to +520 minus Days –520 to 0

IVOL Mean

0.0423

0.0408

0.0421

–0.0015

0.0459

0.0417

0.0446

–0.0042

0.0459

15

Test for Differences in Volatilities around SEOs

Period 21 days 41 days 61 days 81 days 101 days 2 Years 4 Years 6 Years Total Volatility Difference –0.005805

t (z)

–6.30 (–8.48) Idiosyncratic Volatility Systematic Volatility Difference –0.005484

t (z)

–5.89 (–8.20) Difference –0.000004

t (z)

–0.07 (1.81) –0.004208

–0.002677

–0.002002

–0.001015

–0.001778

–0.002826

–0.005058

–5.80 (–7.88) –4.16 (–6.48) –3.22 (–5.63) –1.64 (–4.45) –6.96 (–8.06) –6.96 (–8.06) –6.96 (–8.06) –0.004157

–5.69 (–7.77) –0.002834

–4.38 (–6.64) –0.002422

–3.92 (–5.86) –0.001532

–2.54 (–4.98) –0.002263

–6.96 (–8.06) –0.002652

–44.3 (–22.8) –0.004208

–6.96 (–8.06) –0.006134

–6.96 (–8.06) 0.000109

0.000120

0.000096

0.000066

0.000342

0.000650

2.59 (2.23) 2.97 (4.67) 2.29 (2.16) 1.58 (1.60) 4.13 (–5.92) 6.08 (–7.04)

16

17

Year

1999 2000 2001 2002 2003 2004 2005

n

140 143 101 82 75 94 70

AUM

Hedge Variables by Year

NUM PUL PED PRV

$448B 1,304 0.579

0.093

0.097

$553B $654B $772B $919B $1,110B $1,310B 1,591 1,982 2,517 3,221 4,029 5,030 0.584

0.596

0.592

0.592

0.611

0.629

0.092

0.086

0.084

0.077

0.074

0.070

0.098

0.103

0.104

0.108

0.111

0.115

PEH

0.312

0.324

0.329

0.326

0.321

0.317

0.327

P CHR

0.0470

0.0666

0.0233

0.0201

0.0431

0.0262

0.0264

To illustrate, the consistent percentage changes consider the two key size variables of

AUM

and

NUM

. From 1999 through 2005, the respective changes for

AUM

are 23%, 18%, 18%, 19%, 21%, and 18%, and those for

NUM

are 22%, 25%, 27%, 28%, 25%, and 25%. It can be noted that hedge fund return variables do not show this patterns.

18

Year n

1999 140

2000* 143

Short-Run Volatility Means by Year

Days –50 to 0 Days +1 to +50 101 days Difference

IVOL SVOL IVOL SVOL IVOL SVOL

Δ

IVOL

Δ

SVOL

0.0491

0.0630

0.0023 0.0458 0.0024 0.0480 0.0024 –0.0034

0.0037 0.0674 0.0043 0.0661 0.0042

0.0044

0.0001

0.0006

2001 101 2002 2003 2004 82 75 94 0.0432

0.0330

0.0322

0.0278

0.0027 0.0410 0.0023 0.0426 0.0030 –0.0023 –0.0004

0.0017 0.0325 0.0018 0.0332 0.0018 –0.0005

0.0015 0.0271 0.0014 0.0301 0.0015 –0.0051 –0.0001

0.0015 0.0247 0.0015 0.0266 0.0016 –0.0031

0.0001

0.0000

2005 70 0.0263

0.0018 0.0222 0.0018 0.0246 0.0019 –0.0041

0.0000

Unlike the constant and same directional change of hedge fund variables the changes in volatility, while typically falling, are not constant or of the same direction. For example, IVOL for 101 days have respective percentage changes of 38%, –36%, –22%, –9%, –12%, and –7% for years 1999 through 2005.

* The year 2000 was a roller coaster ride as prices peaked, started falling, then went

19

up, and then the crash solidified itself.

Year n

1999 140 2000 143 2001 101 2002 82 2003 2004 2005 75 94 70

Long-Run Volatility Means by Year

Days –520 to 0 Days +1 to +520 Days –520 to 520 2-Year Difference

IVOL SVOL IVOL SVOL IVOL SVOL

Δ

IVOL

Δ

SVOL

0.0511 0.0030 0.0581 0.0040 0.0556 0.0039 0.0070

0.0595

0.0010

0.0038 0.0589 0.0062 0.0594 0.0057 –0.0006 0.0024

0.0464 0.0045 0.0409 0.0039 0.0441 0.0074 –0.0054 –0.0006

0.0411 0.0056 0.0303 0.0020 0.0365 0.0055 –0.0108 –0.0035

0.0415 0.0024

0.0268 0.0018 0.0351 0.0024 –0.0147 –0.0006

0.0356

0.0019

0.0263 0.0035 0.0317 0.0038 –0.0093 0.0016

0.0318 0.0031 0.0253 0.0031 0.0298 0.0054 –0.0065 0.0000

Unlike the constant and same directional change of hedge fund variables the changes in volatility, while typically falling, are not constant or of the same direction. For example, IVOL for 521 days before have respective percentage changes of 17%, –22%, –11%, 1%, –14%, and –11% for years 1999 through 2005.

20

Pearson correlations coefficients are presented in the upper right-hand half of the table, while the Spearman correlation coefficients are reported in the lower left hand half of the table. As seen below hedge fund variables are highly correlated.

AUM AUM NUM

0.99

PUL

0.90

PRV

0.97

PED

-0.97

PEH

0.33

PCHR

-0.35

NUM PUL PRV PED

0.99

0.89

0.95

-0.96

0.89

0.95

-0.96

0.89

0.88

-0.90

0.97

0.91

-0.97

-0.98

-0.90

-0.98

0.29

0.33

0.32

-0.25

-0.34

-0.38

-0.39

0.39

PEH P CHR

0.36

-0.33

0.36

-0.33

0.42

-0.38

0.30

-0.36

-0.31

0.36

-0.31

-0.28

Non-hedge fund do not experience the same degree of correlation and so concern about collinearity is less of a concern. Possible exceptions are some compounded hedge fund return variables and PRI with POP and TLQ with FLQ for a few tests.

21

SHORT-RUN REGRESSSION RESULTS:

The first row for each test gives coefficients and the second row reports t statistics. We indicate significance at the 1% and 5% levels by ** and *, respectively, for the one-tailed t test (where applicable). The R

2

values in the last column are adjusted.

AUM R PUL PRV R PED R PEH CHR

Δ

CHR ILA CIL PRI R DIS ITB R POP CLS TLQ R FLQ R 2

/

F

Pre-SEO Short-Run Volatility: Days –50 to 0 (CHR for months –2 & –1) -0.598 -13.88 -63.41 25.40 7.429 1.012 0.145 -0.365 0.104 1.125 0.213 0.069 -0.130 0.366 0.584 0.62

-5.05** -13.4** -6.52** 2.53** 3.07** 1.72* 2.42** -2.16* 2.88** 6.68** 2.75** 2.30** -2.94** 11.7** 12.7** 76.3** Post-SEO Short-Run Volatility: Days +1 to +50 (CHR for months +1 & +2) -0.738

-17.83 -45.30 48.53

8.045

-1.485 0.123 -0.114 0.049

1.182

0.156 0.073

-0.135 0.349

0.704

0.63

-5.68** -15.7** -3.86** 4.52** 2.99** -2.49** 1.84* -0.61

1.23

6.31** 1.84* 2.17* -2.73** 10.0** 13.9** 77.1** Around-SEO Short-Run Volatility: Days –50 to +50 (CHR for months –2 to +2) -0.660

-15.13 -70.81 29.82

11.06

0.868

0.142 -0.279 0.084

1.133

0.187 0.069

-0.129 0.363

0.635

0.67

-5.96** -14.6** -7.30** 2.97** 4.18** 1.92* 2.51** -1.75* 2.46** 7.13** 2.56** 2.44** -3.08** 12.34**14.7** 95.8** Short-Run ΔIVOL: +1 to +50 minus –50 to 0 (ΔCHR months +1 & +2 minus months –2 & –1) -0.207 -2.965 20.10 11.97

3.668

-1.011

-0.012 0.264

-0.054 0.081 -0.100 -0.004 -0.006 -0.019 0.100 0.06

-1.80

-3.02** 2.02* 1.27

1.63

-3.26** -0.21

1.61

-1.53

0.49

-1.33

-0.14

-0.14

-0.64

2.25* 3.73** 22

LONG-RUN REGRESSSION RESULTS:

The first row for each test gives coefficients and the second row reports t statistics. We indicate significance at the 1% and 5% levels by ** and *, respectively, for the one-tailed t test (where applicable). The R

2

values in the last column are adjusted.

AUM R PUL PRV R PED R PEH CHR

Δ

CHR ILA CIL PRI R DIS ITB R POP CLS TLQ R FLQ R 2

/

F

Pre-SEO Long-Run Volatility: Days –520 to 0 (CHR for months –24 & –1) -0.180 -10.85 1.094 2.853 6.073 0.748 0.246 -0.247 0.091 0.974 -0.055 0.031 -0.104 0.365 0.683 0.59

-1.73* -11.6** 0.12

0.33

2.67** 3.62** 4.60** -1.64* 2.82** 6.48** -0.76

1.16

-2.62** 13.1** 16.5** 68.2** Post-SEO Long-Run Volatility: Days +1 to +520 (CHR for months +1 & +24) -1.066 -14.81 -15.47 26.39 -.369

-0.883 0.116 -0.240 0.094 0.668 0.038 0.083 -0.047 0.351 0.584 0.66

-8.69** -15.5** -1.54

2.74** -0.16

-3.08**2.07* -1.51

2.76** 4.22** 0.46

2.96** -1.13

11.9** 13.5** 92.2** Around-SEO Long-Run Volatility: Days –520 to +520 (CHR for months –24 to +24) -0.438 -12.36 -17.99 23.92 3.583 0.291 0.176 -0.276 0.090 0.828 0.049 0.061 -0.052 0.360 0.628 0.63

-4.07** -13.5** -2.14* 2.88** 1.76* 1.24

3.46** -1.93* 2.93** 5.81** 0.70

2.41** -1.39

13.6** 16.2** 82.4** Long-Run ΔIVOL: +1 to +520 minus –520 to 0 (ΔCHR for months +1 & +24 minus months –24 & –1) -0.745 -5.321 -22.43 32.203 -11.90

0.019

-0.125 0.023

0.008 -0.322 0.143 0.055 0.053 -0.015 -0.109 0.33

-7.55** -5.99** -2.59** 3.89** -5.24** 0.15

-2.48**0.16

0.28

-2.27* 1.79

2.17* 1.42

-0.56

-2.79** 23.9** 23

COMPARISON TESTS FOR SHORT-RUN REGRESSSIONS:

hedge fund variables used by themselves The green print is the regression with just and red print is for when just the non-hedge fund variables are used by themselves.

We indicate significance at the 1% and 5% levels by ** and *, respectively, for the one-tailed t test (where applicable). The R

2

values are adjusted.

AUM R PUL PRV R PED R PEH P CHR ILA CIL PRI R DIS ITB R POP CLS TLQ R FLQ R 2

/

F

Pre-SEO Short-Run Volatility: Days –50 to 0 -0.750

-14.49 -53.10 20.47 12.96 2.817

0.307 0.094

-8.74** -11.4** -3.69**1.66* 5.01** 5.48** 4.16** 0.45

0.110

1.040 0.244 0.135 -0.078 0.423 0.734

2.45** 4.97** 3.49** 3.66** -1.42

10.9** 13.2** 0.38

68.0** 0.40

53.8** Post-SEO Short-Run Volatility: Days +1 to +50 -0.805 -17.17 -31.61 31.78

16.06

4.063

-8.80** -12.7

** -2.06* 2.42** 5.82** 7.40** 0.334 0.418 0.054 1.049 0.237 0.139 -0.062 0.421 0.873

3.98** 1.77* 1.06

4.40** 2.97** 3.30** -0.99

0.42

9.56** 13.8** 85.8** 0.38

48.9** Around-SEO Short-Run Volatility: Days –50 to +50 -0.783 -15.87 -45.12 25.95 14.88 3.427

0.324 0.234

-9.47** -13.0** -3.25**2.19* 5.97** 6.91** 4.39** 1.12

0.094 1.028 0.250 0.138 -0.069 0.424 0.792

2.10* 4.91** 3.56** 3.74** -1.25

11.0** 14.2** 0.43

88.8** 0.42

58.0** Short-Run ΔIVOL: +1 to +50 minus –50 to 0 -0.055 -2.680 21.50 11.31

-0.85

-2.79** 1.97* 1.21

3.103 1.246

1.58

0.028 0.324

3.19** 0.47

1.96* -0.055 0.009 -0.008 0.004 0.016 -0.002 0.139

-1.55

0.05

-0.14

0.12

0.37

-0.05

3.14** 0.05

6.97** 0.01

1.96* 24

COMPARISON TESTS LONG-RUN REGRESSSION RESULTS:

hedge fund variables used by themselves The green print is the regression with just and red print is for when just the non-hedge fund variables are used by themselves.

We indicate significance at the 1% and 5% levels by ** and *, respectively, for the one-tailed t test (where applicable). The R

2

values are adjusted.

AUM R PUL PRV R PED R PEH P CHR ILA CIL PRI R DIS ITB R POP CLS TLQ R FLQ R 2

/

F

Pre-SEO Long-Run Volatility: Days –520 to 0 -0.256 -11.66 6.183 4.360 11.58

-3.10** -9.53** 0.45

0.37

2.039

4.64** 4.11** 0.336 -0.039 0.082 0.914 0.010 0.057 -0.063 0.403 0.780

5.71** -0.23

2.31** 5.48** 0.18

1.94* -1.45

13.1** 17.6** 0.21

31.3** 0.49

76.3** Post-SEO Long-Run Volatility: Days +1 to +520 -0.895 -15.65 -13.21 33.63 0.437 2.686

-11.3** -13.3** -0.99

2.95** 0.18

0.297 0.195

5.63** 3.97** 0.92

0.077 0.534 0.225 0.159 0.035 0.429 0.764

1.68* 2.51** 3.16** 4.23** 0.62

10.9** 13.5** 0.45

98.1** 0.38

49.8** Around-SEO Long-Run Volatility: Days –520 to +520 -0.529 -13.04 -1.014 20.27 5.126 2.343

-6.93** -11.5** -0.08

0.304 0.036

1.85* 2.22** 5.11** 5.01** 0.21

0.085

0.745

0.094 0.107

0.004

2.31** 4.33** 1.63* 3.52** 0.10

0.412

0.746

12.9** 16.3** 0.33

59.2** 0.47

68.9** Long-Run ΔIVOL: +1 to +520 minus –520 to 0 -0.639 -3.998 -19.39 29.27 -11.14 0.647

-11.2** -4.72** -2.02* 3.56** -6.45**1.88

-0.039 0.234

-0.65

1.40

-0.006 -0.380 0.215 0.102 0.098 0.026 -0.016

-0.16

-2.26* 3.81

3.42

2.22* 0.84

-0.35

0.30

51.5** 0.05

4.95** 25

The “variable” column gives the independent variable tested.

CHR

is the compounded hedge fund return used so as to best match the volatility period. The “predicted” column gives the predicted sign for a coefficient with purple print indicating nothing predicted . The subsequent columns give the actual sign found for each volatility period tested as well as if it is significant at the 5% level (*) or one 1% level (**) for the eight idiosyncratic volatility (

IVOL

) tests. Yellow background indicates not as predicted.

Variable

AUM NUM PUL PRV PED PEH CHR

Δ

CHR P CHR ILA CIL PRI DIS ITB POP CLS TLQ FLQ

Predicted + +



+ + + +



+ +

   

+ + +

/  +

**

+

**



*

+

**

+

**

+

**

+

**



** + ** + **

Short-Run Volatility Periods  50 to 0  ** 

**



**



**

+1 to +50 

**



**



**



**

 50 to +50 

**



**



**



**

+50   50   

**

+

*

+

**

+

**

+

**

+ +

**

+

*

+

**



**

+

**

+

*

+ +

**

+

*

 + +

**

+

*

+

*



** + ** + **

+

**

+

**



*

+

**

+

**

+

**

+

**



** + ** + **



**

+

**

 +  +    

+ *

+

**

+

**



*

+

**

+

**

 + 

** + ** + **

Long-Run Volatility Periods  520 to 0 to  520 to  520  0 

**



**



**

+ + +

**

+

**

+520 

**



**



**

 +

**

 

**

+520 

**



**



**



*

+

**

+

*

+ +520 

**



**



**



**

+

**



**

+

**

+

*

 +

**

+

**

+ +

**



+ ** + **

+

**

+

**



*

+

**

+

**

+ +

**



+ ** + **

+ + 

**

+ + 

*

+

*

+

*

+  

**

26

Hypotheses Confirmed

• • • • Hypothesis 1 (

H-1

) predicted that characteristics like greater amount of assets under management by the hedge fund industry (or any hedge fund characteristic correlated with this amount such as a greater number of hedge funds) will cause less volatility in SEO stock returns for periods surrounding SEOs. We found this to be true. We do not know if the relation between these hedge fund characteristics and volatility occurred by chance or if perhaps hedge funds just proxy for all large institutions that behave like hedge funds. The striking relation we find suggests that the relation should be further explored.

Hypothesis 2 (

H-2

) stated that the volatility will be further diminished when the hedge fund uses leverage and a relative value (arbitrage) strategy. We found this to be true except for the long-run pre-SEO test for the relative value strategy.

Hypothesis 3 (

H-3

) predicts that strategies linked to SEOs, such as event-driven and equity hedge strategies, will cause greater volatility in SEO stock returns for periods surrounding SEOs. We found this to be true except for the long-run post-SEO test for the equity hedge strategy.

Hypothesis 4 (

H-4

) predicts volatility will be further enhanced if greater hedge fund returns are obtained in the pre-SEO stock return period. We found this to be true.

H-4

also predicts that volatility will be diminished when there is not a bubble-like period and we found this to be true.

27

Conclusions

• With the common belief that hedge funds are playing havoc with the markets, we sought to empirically examine the impact of hedge funds on stock return volatility. In particular, we wanted to answer this question: “To what extent can hedge funds influence stock return volatility surrounding the announcements of major corporate events?” To answer this question, we examine one of the more common major corporate events: seasoned equity offerings (SEOs). In our examination, we tested the impact of hedge fund variables on idiosyncratic volatility for a variety of short-run and long-run periods around the initial announcement dates for SEOs. Periods tested included both a bubble period and a non bubble period.

• We found that stock return volatility decreased when (i) the total assets under management by the hedge fund industry increased, (ii) the number of hedge funds increased, (iii) leverage was more likely to be used by a hedge fund, (iv) a relative value strategy (as opposed to an event-driven or equity hedge) strategy was used, and (v) greater hedge fund returns were found for a post-SEO period. For a pre-SEO period, greater hedge fund returns increased volatility. We compared our hedge fund variables with non-hedge fund variables and found that the hedge fund variables tended to do a better job of explaining volatility and this was particularly true when accounting for the fall in volatility that occurred after SEOs.

• Finally, for all short-run and long-run tests, we found, on average, that a 10% increase in the assets under management by the hedge fund industry was associated with a reduction of around 6% in idiosyncratic (firm-specific) volatility. These results along with the impact of other hedge fund characteristics demonstrate that hedge funds are a major player in explaining volatility around noteworthy corporate event.

28

.

THE END -- APPLAUSE

-The School of Business was named an outstanding

business school by The Princeton Review.

-Washburn University is ranked 58th among Tier 1 Regional Universities (Midwest) by US News (2011).

- Washburn University has earned a top 10 rating in the 2010 America's Best Colleges rankings released today by U.S. News and World Report, rated 7th in the Midwest among public master's level universities.

-Overall it is placed 36th out of 146 public and private master's level institutions in the Midwest.

29