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Hedge fund attributes, insider behavior, and IPO volatility

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Abstract

In this study, we use regression analysis to explore the influence of hedge fund and insider attributes on the volatility in the excess stock returns for 707 initial public offerings (IPOs). For these tests, we separate stock price volatility into systematic volatility (SVOL) and idiosyncratic volatility (IVOL). Our sample period is for 2004–2010 and thus encompasses the subprime mortgage crisis from 2007 to 2009. During this latter period, the number of hedge funds rose 36% despite the fact the overall size of hedge fund assets fell 38%. We find that hedge funds are more likely to influence systematic volatility and insider behavior is more likely to affect idiosyncratic volatility. SVOL is reduced when there are more hedge funds in the market, hedge fund returns are higher, hedge funds have more assets under management, and the proportion of hedge funds using the relative value (arbitrage) and event driven investment strategies are reduced. A reduction in IVOL is correlated with smaller insider ownership after the offering and greater declines in insider ownership due to the IPO. Our findings are valuable in helping investors and government regulators understand the role hedge funds and other insiders have on stock price volatility.

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Notes

  1. As mentioned in the concluding section, testing longer periods after IPOs is a task for future research.

  2. As will be seen in Table 1, our IPO sample is characterized by decreases in insider ownership proportions. In fact, 702 of our 707 IPOs have a decrease in their insider ownership proportion.

  3. Our periods are chosen to represent a variety of periods to track any change in volatility and how it might be impacted by our explanatory variables. As mentioned previously, we use monthly hedge fund data for the month of the offering such that going past 50 days poses problems as the hedge fund attributes are more apt to change as the period after the IPO lengthens.

  4. We use a period where the crisis affected the economy and the market as this period is deemed the best for our purposes.

  5. For this paper’s time period of study, we estimate the HFR database has about 2/3 of all hedge funds. Fung and Hsieh (2006) describe the potential biases in the commercial databases like HFR. By using the HFR database, this paper avoids survivorship bias as this data base keeps hedge funds that have ceased to exist. Malkiel and Saha (2005) discuss the biases in reported hedge fund returns.

  6. The standard deviation is computed as \( \sqrt{\sum {\left( Statistic- Meanof\kern0.5em Statistic\right)}^2/\left( n-1\right)} \). In this case, we have the “statistic” = (Raw Return – Fama-French residual) = R−ε with “mean of statistic” = \( \overline{R-\varepsilon}=\kern0.5em \overline{R} \) since the mean of ε is 0. Thus, we have SVOL as found in (6). Because we use the log in (3) for TVOL, the expression of TVOL = SVOL + IVOL does not hold.

  7. Jim Frost (2013) stated that doing nothing when you have multicollinearity can be a correct decision because all remedies have potential drawbacks. By doing nothing you have less precise coefficient estimates on the collinear regression coefficients.

  8. For what follows, the results expressed in term of percent change come (where applicable) from the formula: [exp(b/100)-1] 100% where “exp” means exponential function and “b” is the coefficient of interest.

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Authors and Affiliations

  1. Washburn University, School of Business, 1700 SW College Ave., Topeka, KS, 66621, USA

    Robert M. Hull, Sungkyu Kwak & Rosemary Walker

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  1. Robert M. Hull
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  2. Sungkyu Kwak
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  3. Rosemary Walker
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Correspondence to Rosemary Walker.

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Hull, R.M., Kwak, S. & Walker, R. Hedge fund attributes, insider behavior, and IPO volatility. J Econ Finan 42, 268–292 (2018). https://doi.org/10.1007/s12197-017-9396-8

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