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Insider trading and firm-specific return volatility

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Abstract

Roll (J Financ 43:541–566, 1988) argues that firm-specific stock return volatility may result either from informed trading or from noise trading that is unrelated to information. In this paper we provide evidence that insider purchases are inversely related to the idiosyncratic volatility of stocks. We also find that stock idiosyncratic volatilities are generally inversely related to future 6- and 12-month returns. Our results are primarily driven by the timing of insider sales rather than insider purchases. The results are consistent with an information-based explanation of firm-specific return volatility.

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Article Open access 05 November 2018

Clifford S. Asness, Andrea Frazzini & Lasse Heje Pedersen

Notes

  1. See Fama and French (1988), Poterba and Summers (1988), and Kim et al. (1991).

  2. Theoretical and empirical work by Glosten and Milgrom (1985), French and Roll (1986), and Ross (1989) also establish linkages between informed trading and return volatility.

  3. Jiang et al. (2009) find that IVOL is inversely related to future returns, and that this inverse relationship is induced by an inverse relationship between IVOL and future earnings.

  4. In a recent paper, Marin and Olivier (2008) provide theoretical and empirical evidence to validate the idea that insider sales are motivated by information. Firth et al. (2011) examine stocks listed on the Hong Kong Exchange and find that insider sales are more informative than insider purchases.

  5. Most of the trades were reported on form 4.

  6. Roll (1988) uses R2s from market model regressions and multi-factor regressions to distinguish between firm-specific return variation from systematic variation. For the market model, it is easy to show that \( {\text{IVOL}}_{\text{i}} = \left[ {\left( {{\text{N}} - 1/{\text{N}} - 2} \right)} \right]\left[ {{\text{var}}\left( {{\text{r}}_{\text{i}} } \right)\left( { 1- {\text{R}}^{ 2}_{\text{i}} } \right)} \right] \), where N is the time-series sample size for stock i, var(ri) is the sample variance of ri, and R 2i is the market model regression R2 for stock i.

  7. Each firm is counted once in a given six-monthly period. The same firm can appear in the sample in other six-monthly periods. For example, if insider trading data and IVOL for a given firm are available in all 30 six-monthly periods between 1994 and 2008, then we have 30 firm-half-years of observations.

  8. These are close to the average purchase ratios that have been reported in other studies. Rozeff and Zaman (1998) report an average PR ratio of 46.5 %. Piotroski and Roulstone (2005) report mean and median PR ratios of 40 and 13.7 % respectively.

  9. Ferreira and Laux (2007) report an average annualized IVOL of 44 percent between 1990 and 2001. Jiang et al. (2009) report mean and median IVOL numbers over different time periods. For the most recent time period in their study (2000–2002), the mean and median daily IVOLs are 2.87 and 2.53 % respectively. These are very close to the numbers that are reported in Table 1.

  10. As we discussed in the introductory section, the insider trading literature is ambivalent on the question of whether insider trading profits arise purely from contrarian trading, or whether trading profits also reflect the utilization of useful private information. The goal of this paper is to examine the information content of insider trades by linking insider trades to firm IVOLs. Since insiders are known to be contrarian traders, we need to control for contrarian trading in order to examine the incremental relationship between insider trades and IVOLs.

  11. Gider and Westheide (2011) argue that insiders take advantage of their superior information by purchasing stocks that have high IVOL. They assume that IVOL is positively correlated with the level of information asymmetry in a stock, even though they admit that “there is no consensus in the literature as to whether a high or a low level of idiosyncratic volatility indicates large information asymmetries” [p. 6].

  12. Piotroski and Roulstone (2004) do not test this hypothesis in their paper.

  13. Also, see Ang et al. (2006), and Ferreira and Laux (2007).

  14. Rtf6 and RTf12 are raw future six- and twelve-monthly returns. Ang et al. (2006) sort stocks into IVOL quintiles and report an average monthly difference of 1.06 percent in the raw returns between stocks in the bottom IVOL quintile and the top quintile. Jiang et al. (2009) report a quarterly differential of 3.28 percent for stocks in the bottom IVOL decile portfolio and the top IVOL decile portfolio.

  15. However, the coefficients of PR are small in Table 4. For the twelve-month horizon the coefficients for small, medium, and big firms are 6, 0.6, and 3 % respectively. Lakonishok and Lee (2001) report that insider trades are marginally profitable for small firms after controlling for contrarian trading, but are not profitable for other firms.

  16. This idea comes from Piotroski and Roulstone (2004), who argue that insider sales transactions that are motivated by the need for liquidity are positively related to IVOL because insiders diversify their holdings in response to high IVOL. As we mentioned earlier, Piotroski and Roulstone (2004) do not test this hypothesis in their paper. It is possible that the bulk of the insider trades in our sample of small firms are motivated by the demand for liquidity.

  17. In a more recent paper, Marin and Olivier (2008) examine insider trading patterns before large movements in stock prices. Marin and Olivier (2008) provide compelling theoretical and empirical evidence that “invalidate the mainstream view that insider sales are solely driven by liquidity needs” [p. 2444].

  18. PR and PRN are bounded between 0 and 1 by construction. Following Piotroski and Roulstone [2004, Eq. (2)], we take log transformations of PR and PRN to create “unbounded continuous” dependent variables that are more normally distributed, and replicate the results in Tables 2 and 3. Our conclusions and inferences do not change as a result of using the log transformations of PR and PRN as dependent variables in regressions (4) and (5).

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Authors and Affiliations

  1. Finance, Insurance, and Real Estate, Herberger College of Business, St. Cloud State University, St. Cloud, MN, 56301, USA

    Partha Gangopadhyay

  2. The Johns Hopkins Carey Business School, 100 International Drive, Baltimore, MD, 21202, USA

    Ken C. Yook

  3. Sellinger School of Business, Loyola University Maryland, 4501 N. Charles Street, Baltimore, MD, 21210, USA

    Yoon Shin

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  1. Partha Gangopadhyay
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  2. Ken C. Yook
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  3. Yoon Shin
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Correspondence to Partha Gangopadhyay.

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Gangopadhyay, P., Yook, K.C. & Shin, Y. Insider trading and firm-specific return volatility. Rev Quant Finan Acc 43, 1–19 (2014). https://doi.org/10.1007/s11156-013-0362-z

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