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) find that large investors produce a 29.1% decrease in
the demand for smaller stocks compared to only a 4.5% increase for
larger stocks
 We choose a time covering bubble and non-bubble years
Where differences in volatility should occur
3
4
Four Major Hypotheses
 Hypothesis One (H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
surrounding SEOs.
 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 eventdriven 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 internettechnology bubble time period and purpose of the
offering.
6
Our Regression Model
VOL   0 
 h HFV h    n HFV n  

n N
h H
 VOL = Daily Excess Stock Return Volatility (we use idiosyncratic volatility)
ΔVOL = Change or Shift in VOL (we use ΔIVOL )
 HFV = Hedge Fund Variables include nine variables described below.
AUM = Hedge Fund Assets under Management during month 0
NUM = Number of Hedge Funds
PUL = Proportion of Hedge Funds Using Leverage
PED = Proportion of Hedge Funds with an Event-Driven Strategy
PRV = Proportion of Hedge Funds with a Relative Value (Arbitrage)
Strategy
PEH = Proportion of Hedge Funds with a Equity Hedge Strategy
CHR =Average Equal-Weighted Compounded Monthly Hedge Fund
Return
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 =Inside Ownership Proportion after SEO
 CIL =Change in Inside Ownership Proportion
 PRI =Primary Shares as a Proportion of Total Shares Offered
 DIS = Discounting: log of (Estimated Price) / (Offer Price)
 ITB = 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 
  i2,
 t
nt  1
Where εi,τ is the Fama and French (2009) residual for day τ. εi,τ is
calculated from the following regression:
ri,τ – rf,t = αt + β1i,t(MKTτ – ) + β2i,t(HMLτ) + β3i,t(SMBτ) + εi,τ
where ri,τ is the raw return on stock i for day τ; rf,t is the risk-free return
for day τ given by the one-month T-bill; MKTτ is the return on the valueweighted CRSP index for day τ; HMLτ is the average return for day τ for the
value portfolios minus the average return for day τ for growth portfolios;
and, SMBτ 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: ΔIVOLi,Δt = IVOLi,t − IVOLi,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
MEAN
$760B
2,538
$366M
$79M
0.595
0.084
Proportion of Hedge Funds with an Relative Value
(Arbitrage) Strategy
Proportion of Hedge Funds with an Equity Hedge Strategy
0.104
Average Hedge Fund Return for Month 0 (where month 0
contains the announcement date)
1.19%
0.321
12
Average Equal-Weight Compounded Monthly
Hedge Fund Return (CHR)
MEAN
PCHR for months –3 to –1 (pre-SEO three-month compounded return)
0.0392
CHR for months –2 to –1 (pre-SEO two-month compounded return)
0.0247
CHR for months +1 to +2 (post-SEO two-month compounded return)
0.0222
CHR for months –2 to +2 (five-month compounded return around SEO
announcement)
0.0599
ΔCHR for months +1 to +2 minus months –2 to –1 (difference in postSEO and pre-SEO returns)
–0.0025
CHR for months –24 to –1 (pre-SEO 24-month compounded return)
0.2870
CHR for months +1 to +24 (post-SEO 24-month compounded return)
0.2535
CHR for months –24 to +24 (49-month compounded return around
SEO announcement)
0.6272
ΔCHR for months +1 to +24 minus months –24 to –1 (difference in
post-SEO and pre-SEO returns)
–0.0335
13
Descriptive Statistics
MEAN
Common Value: (Estimated Price) × (Shares Outstanding before SEO) where
$2.05B
“B” stands for billions
Inside Ownership Proportion Before:
0.490
(Insider Shares before SEO) / (Shares Outstanding before SEO)
Inside Ownership Proportion After:
0.384
(Insider Shares after SEO) / (Shares Outstanding after SEO)
Change in Inside Ownership Proportion:
–0.106
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
0.604
0.041
0.259
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
0.059
0.250
0.228
6.807
14
Time Frame
IVOL Mean
Days –50 to 0
0.0423
Days +1 to +50
0.0408
Days –50 to +50
0.0421
+1 to +50 minus –50 to 0
–0.0015
Days –520 to 0
0.0459
Days –520 to 0
0.0417
Days +1 to +520
0.0446
Days –520 to +520
Day +1 to +520 minus Days –520 to 0
–0.0042
0.0459
15
Test for Differences in Volatilities around SEOs
Total Volatility
Idiosyncratic Volatility
Systematic Volatility
Period
Difference
t (z)
Difference
t (z)
Difference
t (z)
21 days
–0.005805
–6.30 (–8.48)
–0.005484
–5.89 (–8.20)
–0.000004
–0.07 (1.81)
41 days
–0.004208
–5.80 (–7.88)
–0.004157
–5.69 (–7.77)
0.000109
2.59 (2.23)
61 days
–0.002677
–4.16 (–6.48)
–0.002834
–4.38 (–6.64)
0.000120
2.97 (4.67)
81 days
–0.002002
–3.22 (–5.63)
–0.002422
–3.92 (–5.86)
0.000096
2.29 (2.16)
101 days
–0.001015
–1.64 (–4.45)
–0.001532
–2.54 (–4.98)
0.000066
1.58 (1.60)
2 Years
–0.001778
–6.96 (–8.06)
–0.002263
–6.96 (–8.06)
–0.002652
–44.3 (–22.8)
4 Years
–0.002826
–6.96 (–8.06)
–0.004208
–6.96 (–8.06)
0.000342
4.13 (–5.92)
6 Years
–0.005058
–6.96 (–8.06)
–0.006134
–6.96 (–8.06)
0.000650
6.08 (–7.04)
16
17
Hedge Variables by Year
Year
n
AUM
NUM
PUL
PED
PRV
PEH
PCHR
1999
140
$448B
1,304
0.579
0.093
0.097
0.312
0.0470
2000
143
$553B
1,591
0.584
0.092
0.098
0.324
0.0666
2001
101
$654B
1,982
0.596
0.086
0.103
0.329
0.0233
2002
82
$772B
2,517
0.592
0.084
0.104
0.326
0.0201
2003
75
$919B
3,221
0.592
0.077
0.108
0.321
0.0431
2004
94
$1,110B
4,029
0.611
0.074
0.111
0.317
0.0262
2005
70
$1,310B
5,030
0.629
0.070
0.115
0.327
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
Short-Run Volatility Means by Year
Days –50 to 0
Days +1 to +50
IVOL
SVOL
IVOL
1999 140
0.0491
0.0023 0.0458 0.0024 0.0480 0.0024 –0.0034
0.0001
2000* 143
0.0630
0.0037 0.0674 0.0043 0.0661 0.0042
0.0006
2001 101
0.0432
0.0027 0.0410 0.0023 0.0426 0.0030 –0.0023 –0.0004
2002
82
0.0330
0.0017 0.0325 0.0018 0.0332 0.0018 –0.0005
2003
75
0.0322
0.0015 0.0271 0.0014 0.0301 0.0015 –0.0051 –0.0001
2004
94
0.0278
0.0015 0.0247 0.0015 0.0266 0.0016 –0.0031
0.0000
2005
70
0.0263
0.0018 0.0222 0.0018 0.0246 0.0019 –0.0041
0.0000
Year
n
101 days
SVOL IVOL
SVOL
Difference
ΔIVOL ΔSVOL
0.0044
0.0001
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.
Long-Run Volatility Means by Year
Days –520 to 0 Days +1 to +520 Days –520 to 520 2-Year Difference
Year
n
IVOL
SVOL
IVOL
SVOL
IVOL
SVOL
ΔIVOL ΔSVOL
1999 140
0.0511 0.0030 0.0581 0.0040 0.0556 0.0039 0.0070
0.0010
2000 143
0.0595 0.0038 0.0589 0.0062 0.0594 0.0057 –0.0006 0.0024
2001 101
0.0464 0.0045 0.0409 0.0039 0.0441 0.0074 –0.0054 –0.0006
2002
82
0.0411 0.0056 0.0303 0.0020 0.0365 0.0055 –0.0108 –0.0035
2003
75
0.0415 0.0024 0.0268 0.0018 0.0351 0.0024 –0.0147 –0.0006
2004
94
0.0356 0.0019 0.0263 0.0035 0.0317 0.0038 –0.0093 0.0016
2005
70
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 lefthand half of the table. As seen below hedge fund variables are highly correlated.
AUM
AUM
NUM
PUL
0.99
0.90
0.89
PRV
PED
PEH PCHR
0.97
-0.97
0.33
-0.35
NUM
0.99
0.97
-0.98
0.29
-0.34
PUL
0.89
0.89
0.91
-0.90
0.33
-0.38
PRV
0.95
0.95
0.88
-0.98
0.32
-0.39
PED
-0.96
-0.96
-0.90
-0.97
-0.25
0.39
PEH
0.36
0.36
0.42
0.30
-0.31
PCHR
-0.33
-0.33
-0.38
-0.36
0.36
-0.28
-0.31
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 R2 values in the last column are adjusted.
AUM R PUL PRV R PEDR PEH
CHR
ILA
ΔCHR
CIL PRI R DIS ITBR POP CLS TLQ R FLQ R2 /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 R2 values in the last column are adjusted.
AUM R PUL PRV R PEDR PEH
CHR
ILA
ΔCHR
CIL PRI R DIS ITBR POP CLS TLQ R FLQ R2 /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
-3.08**2.07* -1.51
2.74** -0.16
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: The green print is the regression with just
hedge fund variables used by themselves 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 R2 values are adjusted.
AUM R PUL PRV R PEDR PEH PCHR ILA
CIL PRI R DIS ITBR POP CLS TLQ R FLQ R2 /F
Pre-SEO Short-Run Volatility: Days –50 to 0
0.38
68.0**
0.40
10.9** 13.2**
53.8**
-0.750 -14.49 -53.10 20.47 12.96 2.817 0.307 0.094 0.110 1.040 0.244 0.135 -0.078 0.423 0.734
-8.74** -11.4** -3.69**1.66* 5.01** 5.48** 4.16** 0.45
2.45** 4.97** 3.49** 3.66** -1.42
Post-SEO Short-Run Volatility: Days +1 to +50
0.42
85.8**
0.38
4.40** 2.97** 3.30** -0.99 9.56** 13.8**
48.9**
-0.805 -17.17 -31.61 31.78 16.06 4.063 0.334 0.418 0.054 1.049 0.237 0.139 -0.062 0.421 0.873
-8.80** -12.7** -2.06* 2.42** 5.82** 7.40** 3.98** 1.77* 1.06
Around-SEO Short-Run Volatility: Days –50 to +50
0.43
88.8**
0.42
11.0** 14.2**
58.0**
-0.783 -15.87 -45.12 25.95 14.88 3.427 0.324 0.234 0.094 1.028 0.250 0.138 -0.069 0.424 0.792
-9.47** -13.0** -3.25**2.19* 5.97** 6.91** 4.39** 1.12
2.10* 4.91** 3.56** 3.74** -1.25
Short-Run ΔIVOL: +1 to +50 minus –50 to 0
0.05
6.97**
0.01
3.14**
1.96*24
-0.055 -2.680 21.50 11.31 3.103 1.246 0.028 0.324 -0.055 0.009 -0.008 0.004 0.016 -0.002 0.139
-0.85
-2.79** 1.97* 1.21
1.58
3.19** 0.47
1.96* -1.55
0.05
-0.14 0.12
0.37
-0.05
COMPARISON TESTS LONG-RUN REGRESSSION RESULTS: The green print is the regression with just
hedge fund variables used by themselves 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 R2 values are adjusted.
AUM R PUL PRV R PEDR PEH PCHR ILA
CIL PRI R DIS ITBR POP CLS TLQ R FLQ R2 /F
Pre-SEO Long-Run Volatility: Days –520 to 0
0.21
31.3**
0.49
13.1** 17.6**
76.3**
-0.256 -11.66 6.183 4.360 11.58 2.039 0.336 -0.039 0.082 0.914 0.010 0.057 -0.063 0.403 0.780
-3.10** -9.53** 0.45
0.37
4.64** 4.11** 5.71** -0.23
2.31** 5.48** 0.18
1.94* -1.45
Post-SEO Long-Run Volatility: Days +1 to +520
0.45
98.1**
0.38
10.9** 13.5**
49.8**
-0.895 -15.65 -13.21 33.63 0.437 2.686 0.297 0.195 0.077 0.534 0.225 0.159 0.035 0.429 0.764
-11.3** -13.3** -0.99
2.95** 0.18
5.63** 3.97** 0.92
1.68* 2.51** 3.16** 4.23** 0.62
Around-SEO Long-Run Volatility: Days –520 to +520
0.33
59.2**
0.47
12.9** 16.3**
68.9**
-0.529 -13.04 -1.014 20.27 5.126 2.343 0.304 0.036 0.085 0.745 0.094 0.107 0.004 0.412 0.746
-6.93** -11.5** -0.08
1.85* 2.22** 5.11** 5.01** 0.21
2.31** 4.33** 1.63* 3.52** 0.10
Long-Run ΔIVOL: +1 to +520 minus –520 to 0
-0.639 -3.998 -19.39 29.27 -11.14 0.647 -0.039 0.234 -0.006 -0.380 0.215 0.102 0.098 0.026 -0.016
-11.2** -4.72** -2.02* 3.56** -6.45**1.88
-0.65 1.40
-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.
Short-Run Volatility Periods
Long-Run Volatility Periods
Variable
Predicted
50 to
0
+1 to
+50
50 to
+50
+50 
50
520 to
0
0 to
+520
520 to
+520
520 
+520
AUM
NUM
PUL
PRV
PED
PEH
CHR




+
+
+/
 **
 **
 **
 **
+ **
+ **
+*
 **
 **
 **
 **
+ **
 **
 **
 **
 **
+ **
+ **
+*


 **
+*
+
+
 **
 **
 **
+
+
+ **
+ **
 **
 **
 **

+ **

 **
 **
 **
 **
*
+ **
+*
+
 **
 **
 **
 **
+ **
 **
+ **
 **
ΔCHR
PCHR
ILA
CIL
PRI
DIS
ITB
POP
CLS
TLQ
FLQ
+
+

+
+
+
+

+
+
+ **
+ **
*
+ **
+ **
+ **
+ **
 **
+ **
+ **
+ **
+*

+
+ **
+*
+*
 **
+ **
+ **
+ **
+ **
*
+ **
+ **
+ **
+ **
 **
+ **
+ **
 **
+ **

+

+




+*
+ **
+ **
*
+ **
+ **

+
 **
+ **
+ **
+ **
+*

+ **
+ **
+
+ **

+ **
+ **
+ **
+ **
*
+ **
+ **
+
+ **

+ **
+ **
+
+
 **
+
+
*
+*
+*
+

 **
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 nonbubble 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