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The effect of trading on volatility has been the focus of many previous studies, both empirical and theoretical. The theoretical models fall into two groups: competitive and strategic. In a competitive model with asymmetric information, the size of trade is positively related to the quality of the information possessed by informed traders (Easley and O'Hara, 1987; Holthausen and Verrecchia, 1990). In a strategic model, asymmetric information also leads to trading, but a monopolistic informed trader may camouflage his trading activity by making several small-sized trades rather than one large trade (Barclay and Warner, 1993; Kyle, 1985). This weakens the positive relation between the size of the transaction and the informed trader’s information.

However, Holden and Subrahmanyam (1992) show that, in a more realistic strategic model with multiple informed traders, the distinction between strategic and competitive models is blurred. In both models the trade size or trading volume of the informed agents’ trading increases with the quality of their information, resulting in a positive relation between volume and absolute price change. Subsequently, Jones et al. (1994b) find empirically that the number of transactions, rather than the volume,

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is more closely associated with volatility. Their results suggest that it is the “occurrence of the transactions per se” and not the size of the trades that generates volatility.

Chan and Fong argue that it may be “premature to conclude that the size of trades has no information content beyond that contained in the number of trades” (Chan and Fong, 2000, p.249). They suggest that if informed traders are to stealth trade by using medium sized orders as suggested by Barclay and Warner (1993), the volatility-trade size relation would not be detected using average trade size, as shown by Jones et al. (1994b). The literature on the volume-volatility relation suggests informed traders drive the volatility. That is, an increase in volume “per se” would not generate volatility but an increase in the number of informed traders would.

Recent papers on the volume-volatility relation have focused on the role of uninformed traders. Greene and Smart (1999) liken the trading by uninformed investors or “liquidity traders” to noise trading, where traders in fact have no private information to exploit. A large number of studies have emphasised the significance of noise traders in financial markets. Black (1986), for example, argues “noise” makes trading in financial markets possible as it is an important source of liquidity. Greene and Smart find that market liquidity increased modestly and the adverse selection component of the spread decreased significantly in response to noise trading stimulated by The Wall Street Journal’s “Investment Dartboard” column. Others have argued noise traders may be a source of risk that derives from their positive feedback trading behaviour (De Long et al., 1990b). In their analysis of SOES (Small Order Execution System) bandits, Battalio et al. (1997) find day traders who bought in “up-trending” and sold in “down-trending” markets exaggerated price movements, causing higher volatility in the short run.

Retail traders (also known as individual traders) are often described synonymously with noise traders. Hong and Kumar (2002) argue that, due to their relative lack of sophistication, small individual investors are likely to be a dominant source of noise trading in the market. Retail traders are predicted to be overconfident and uninformed and to engage in momentum trading. As a result, their trading causes volatility in the market, which leads to hypothesis H5.

H5: Periods with a greater proportion of orders from retail traders exhibit higher stock price volatility.

A number of factors may mitigate this effect. First, institutional traders may also engage in “noise” trading (Sias, 1996). For example, institutional traders could be more susceptible to herding behaviour than individual retail traders because of the close knit nature of the institutional investor community and the importance of benchmarking performance relative to other institutional traders. Thus, their herding behaviour may exacerbate price movements and increase volatility. A second argument arises from the clustering of informed trading with liquidity trades. Dupont (1998) argues that the equilibrium price is more volatile and less informative when there are more rational traders such as institutional traders. He suggests rational traders hide behind the noise created by liquidity traders and thereby keep more of the noise in the market in equilibrium than naïve traders, such as individual investors, would.

3.6 Summary

This chapter developed five hypotheses to be tested as part of my investigation into the role of different trader types. I hypothesise that institutional traders on the whole, are likely to be more informed and that their marketable orders would have larger permanent price effects than orders placed by retail traders (H1). On the other hand, retail traders are less experienced in order placement and their orders would have a larger temporary price effect (H2). Orders placed by institutional traders are expected to be more aggressive (H3) and the standing limit orders placed by retail traders are predicted to be further away from the market to compensate them for the adverse selection cost of providing liquidity (H4). The liquidity premiums charged by institutional and retail traders are likely to be different due to the information asymmetry that exists. The final hypothesis deals with the relationship between order volume and volatility. Due to a greater proportion of noise trading, other things being equal, orders from retail traders are predicted to be associated with greater volatility in transaction prices (H5).

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CHAPTER FOUR DATA

4.1 Introduction

This chapter outlines the data set used in this thesis and the investment environment over the period 1999 to 2001. It has three sections: the first discusses the sample period and the subset of stocks selected for analysis, the second section discusses the use of order and trade information and the third discusses the classification of orders using broker house information.