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Individual Investors’ Trading Activities and Price Volatility

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Market Microstructure and Nonlinear Dynamics

Abstract

We investigate the volume-volatility relation and the effect of the number of trades and average trade size, institutional and individual trading, and order imbalance on price volatility. We document a positive relation between trading volume and volatility for stocks traded on the Australian Securities Exchange (ASX). We further show that the number of trades has a more significant effect on price volatility than average trade size. When the number of trades is decomposed into the number of trades of different sizes, the number of trades in the medium size category often has the most significant impact on volatility. The trading activity of both institutions and individuals are positively related to volatility, with individual trading has a more significant role in explaining price volatility than institutional trading. Finally, we document that on the ASX (a pure limit order book market) order imbalance—however it is important in explaining the volume-volatility dynamics—it is not the main factor driving this relation.

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Notes

  1. 1.

    Karpoff (1987) provides a review of the early work. For more recent empirical studies on the volume-volatility relation, see, among other, Lamoureaux and Lastrapes (1990), Gallant et al. (1992), Bessembinder and Seguin (1992, 1993), Jones et al. (1994), Andersen (1996), Daigler and Wiley (1999), Chan and Fong (2000), Chen et al. (2001), Huang and Masulis (2003), Darrat et al. (2003, 2007), Kalev et al. (2004), Chan and Fong (2006), Naes and Skjeltorp (2006), Chen and Daigler (2008), and Giot et al. (2010).

  2. 2.

    Prior studies (see, for example, Sias 1996; Dennis and Strickland 2002; Bohl and Brzeszczynski 2006; Chiyachantana et al. 2006) often rely on the use of changes in institutional ownership or trades executed by a subset of institutions when testing the relation between institutional trading and price volatility.

  3. 3.

    Glosten (1994) provides the theoretical background for the importance of order-driven markets. Jain (2003) documents that at the end of 1999, 26 of the 51 stock markets in his study were limit order markets. Virtually all of the stock markets in Europe are also organized as limit order markets (Handa et al. 2003). For an extensive review of the research on limit order markets, see Parlour and Seppi (2008).

  4. 4.

    The use of bid-ask mid-point instead of transaction prices to calculate price volatility is motivated by Roll (1984), where returns calculated from transaction prices are influenced by the bid-ask bounce, which results in spurious volatility in the observed return series.

  5. 5.

    These classification criteria for large cap and mid cap stocks are consistent with those of the ASX.

  6. 6.

    This classification scheme is consistent with that applied by Barclay and Warner (1993) for stocks traded on the NYSE. Walsh (1998) investigates three alternative proxies to approximate the trade size classes on the ASX and shows that the distribution of these trade sizes is indeed consistent with the classifications employed by Barclay and Warner (1993) and in this study.

  7. 7.

    See, for example, Andersen et al. (2001, 2003).

  8. 8.

    Note that trading volume is the same regardless of whether “Disaggregate Measurement” or “Aggregate Measurement” is used when performing regressions. Therefore, in Panel A of Table 2, only the results using “Disaggregate Measurement” are reported.

  9. 9.

    Szewczyk et al. (1992), Alangar et al. (1999), Chakravarty (2001) and Anand et al. (2005) provide empirical evidence that institutional investors are better informed than individual investors.

  10. 10.

    Andersen and Bollerslev (1998) document that utilizing the sum of 5-min returns to measure daily volatility for the two exchange rates DM-$ and ¥-$ produces measurement errors from the latent volatility of 0.004 and 0.003, respectively. In contrast, when measuring daily volatility with daily returns, the measurement errors from the latent volatility increase to 1.138 and 0.842, respectively.

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Author information

Authors and Affiliations

  1. Department of Banking and Finance, Monash University, Melbourne, Australia

    Huu Nhan Duong

  2. Centre for Applied Financial Studies, School of Commerce, UniSA Business School, University of South Australia, City West Campus, Adelaide, SA, 5000, Australia

    Petko S. Kalev

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  1. Huu Nhan Duong
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Correspondence to Petko S. Kalev .

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Editors and Affiliations

  1. GREQAM/DEFI, Aix-Marseille University, Les Milles, France

    Gilles Dufrénot

  2. University of Evry, Evry, France

    Fredj Jawadi

  3. ESSCA School of Management, Boulogne-Billancourt, France

    Waël Louhichi

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Duong, H.N., Kalev, P.S. (2014). Individual Investors’ Trading Activities and Price Volatility. In: Dufrénot, G., Jawadi, F., Louhichi, W. (eds) Market Microstructure and Nonlinear Dynamics. Springer, Cham. https://doi.org/10.1007/978-3-319-05212-0_6

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