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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

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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

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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 stocks2001 We choose a time covering bubble and non-bubble years Where differences in volatility should occur 3

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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 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

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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

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Our Regression Model VOL = Daily Excess Stock Return Volatility (we use idiosyncratic volatility) VOL ΔVOL = Change or Shift in VOL (we use ΔIVOL ) ΔVOL ΔIVOL HFV = Hedge Fund Variables include nine variables described below. HFV AUM = Hedge Fund Assets under Management during month 0 AUM NUM = Number of Hedge Funds NUM PUL = Proportion of Hedge Funds Using Leverage PUL PED = Proportion of Hedge Funds with an Event-Driven Strategy PED PRV = Proportion of Hedge Funds with a Relative Value (Arbitrage) Strategy PRV PEH = Proportion of Hedge Funds with a Equity Hedge Strategy PEH CHR =Average Equal-Weighted Compounded Monthly Hedge Fund Return CHR 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

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The Regression Model NFV = Non-Hedge Fund Variables include nine variables described below. NFV ILA =Inside Ownership Proportion after SEO ILA CIL =Change in Inside Ownership Proportion CIL PRI =Primary Shares as a Proportion of Total Shares Offered PRI DIS = Discounting: log of (Estimated Price) / (Offer Price) ITB = Internet-Technology Bubble Period (dummy variable = 1 if before 1/1/02) ITB POP =Purpose of Proceeds (dummy variable = 1 if purpose expansionary) POP CLS =Class of Common Shares (dummy variable = 1 if more than one class) CLS TLQ =Trading Liquidity (dummy variable = 1 if NASDAQ) TLQ FLQ =Financial Liquidity Ratio (Cash and Cash Equivalents / BVE) FLQ 8

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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 This period covers the tail-end of the internet-technology bubble that had ended by 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

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Volatility Measures 10 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.

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Idiosyncratic Volatility 11 Idiosyncratic Volatility (IVOL): Where ε i,τ is the Fama and French (2009) residual for day τ. ε i,τ is calculated from the following regression:2009 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 is the raw return on stock i for day τ; r f,t is the risk-free return for day τ given by the one-month T-bill; MKT τ 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

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Hedge Fund Variables MEAN Hedge Fund Assets under Management where “B” stands for billions $760B Number of Hedge Funds 2,538 Average Hedge Fund Size where “M” for millions $366M Median Hedge Fund Size where “M” for millions $79M 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) 1.19% 12

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Average Equal-Weight Compounded Monthly Hedge Fund Return (CHR) MEAN 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) 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) – CHR for months –24 to –1 (pre-SEO 24-month compounded return) 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) –

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Descriptive Statistics MEAN Common Value: (Estimated Price) × (Shares Outstanding before SEO) where “B” stands for billions $2.05B 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 –0.106 Primary Shares as a Proportion of Total Shares Offered0.604 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

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Time FrameIVOL Mean Days –50 to Days +1 to Days –50 to to +50 minus –50 to 0– Days –520 to Days –520 to Days +1 to Days –520 to +520– Day +1 to +520 minus Days –520 to

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Test for Differences in Volatilities around SEOs Period Total VolatilityIdiosyncratic VolatilitySystematic Volatility Differencet (z)Differencet (z)Differencet (z) 21 days – –6.30 (–8.48)– –5.89 (–8.20)– –0.07 (1.81) 41 days – –5.80 (–7.88)– –5.69 (–7.77) (2.23) 61 days – –4.16 (–6.48)– –4.38 (–6.64) (4.67) 81 days – –3.22 (–5.63)– –3.92 (–5.86) (2.16) 101 days – –1.64 (–4.45)– –2.54 (–4.98) (1.60) 2 Years – –6.96 (–8.06)– –6.96 (–8.06)– –44.3 (–22.8) 4 Years – –6.96 (–8.06)– –6.96 (–8.06) (–5.92) 6 Years – –6.96 (–8.06)– –6.96 (–8.06) (–7.04) 16

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Hedge Variables by Year YearnAUMNNUMPULPED PRV PEHP CHR $448B1, $553B1, $654B1, $772B2, $919B3, $1,110B4, $1,310B5, 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.AUMNUM AUMNUM 18

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Short-Run Volatility Means by Year Days –50 to 0 Days +1 to daysDifference YearnIVOLSVOL IVOL SVOLIVOLSVOLΔIVOLΔSVOL – * –0.0023– – –0.0051– – – 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 * The year 2000 was a roller coaster ride as prices peaked, started falling, then went up, and then the crash solidified itself. 19

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Long-Run Volatility Means by Year Days –520 to 0 Days +1 to +520 Days –520 to Year Difference YearnIVOLSVOL IVOL SVOLIVOLSVOLΔIVOLΔSVOL – –0.0054– –0.0108– –0.0147– – – 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

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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. AUMNUMPULPRVPEDPEHPCHR AUM NUM PUL PRV PED PEH P CHR 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

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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 PULPRV R PED R PEH CHR ILACILPRI R DISITB R POPCLSTLQ R FLQR2/FR2/F ΔCHR Pre-SEO Short-Run Volatility: Days –50 to 0 (CHR for months –2 & –1) **-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) **-15.7**-3.86**4.52**2.99**-2.49**1.84* **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) **-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) **2.02* ** *3.73** 22

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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 PULPRV R PED R PEH CHR ILACILPRI R DISITB R POPCLSTLQ R FLQR2/FR2/F ΔCHR Pre-SEO Long-Run Volatility: Days –520 to 0 (CHR for months –24 & –1) *-11.6** **3.62**4.60**-1.64*2.82**6.48** **13.1**16.5**68.2** Post-SEO Long-Run Volatility: Days +1 to +520 (CHR for months +1 & +24) **-15.5** ** **2.07* **4.22** ** **13.5**92.2** Around-SEO Long-Run Volatility: Days –520 to +520 (CHR for months –24 to +24) **-13.5**-2.14*2.88**1.76* **-1.93*2.93**5.81** ** **16.2**82.4** Long-Run ΔIVOL: +1 to +520 minus –520 to 0 (ΔCHR for months +1 & +24 minus months –24 & –1) **-5.99**-2.59**3.89**-5.24** ** * * **23.9** 23

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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 R 2 values are adjusted. AUM R PULPRV R PED R PEH P CHR ILACILPRI R DISITB R POPCLSTLQ R FLQR2/FR2/F Pre-SEO Short-Run Volatility: Days –50 to ** -8.74**-11.4**-3.69**1.66*5.01**5.48**4.16** **4.97**3.49**3.66** **13.2** ** Post-SEO Short-Run Volatility: Days +1 to ** -8.80**-12.7 ** -2.06*2.42**5.82**7.40**3.98**1.77* **2.97**3.30** **13.8** ** Around-SEO Short-Run Volatility: Days –50 to ** -9.47**-13.0**-3.25**2.19*5.97**6.91**4.39** *4.91**3.56**3.74** **14.2** ** Short-Run ΔIVOL: +1 to +50 minus –50 to ** **1.97* ** * ** * 24

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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 R 2 values are adjusted. AUM R PULPRV R PED R PEH P CHR ILACILPRI R DISITB R POPCLSTLQ R FLQR2/FR2/F Pre-SEO Long-Run Volatility: Days –520 to ** -3.10**-9.53** **4.11**5.71** **5.48** * **17.6** ** Post-SEO Long-Run Volatility: Days +1 to ** -11.3**-13.3** ** **3.97** *2.51**3.16**4.23** **13.5** ** Around-SEO Long-Run Volatility: Days –520 to ** -6.93**-11.5** *2.22**5.11**5.01** **4.33**1.63*3.52** **16.3** ** Long-Run ΔIVOL: +1 to +520 minus –520 to ** -11.2**-4.72**-2.02*3.56**-6.45** * * ** 25

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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.CHRIVOL Short-Run Volatility PeriodsLong-Run Volatility Periods VariablePredicted 50 to 0 +1 to +50 50 to 50 520 to 0 0 to +520 520 to +520 520 +520 AUM ** NUM ** PUL ** PRV ** +*+*+ ** PED++** ++ PEH++** + +*+* ** CHR + / +*+* ** +*+*+** ** + ΔCHR ** + P CHR ++** + ILA++**+*+* +*+* ** CIL ** ** + ** ** + PRI++**+ + DIS++** + ** ITB++**+*+* +++*+* POP++**+*+* + +*+* CLS ** + TLQ++** FLQ++** +*+* ** 26

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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

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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

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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

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