As discussed in Section 5.3.2, successive orders placed by the same trader type and traded at the same price can bias the permanent price effect measures. Consider the following timeline (Figure 5.6), with seven transactions executed at prices P1 to P7, with P3, P4, P5 and P6 being the same amount.
Figure 5.6 Timeline of trades where the third, fourth, fifth and sixth trades are transacted at the same price.
Assume that P3, P4, P5 and P6 are from the same trader type and are on the same side. The PPE1 for transactions 4 and 5 will be zero as the transaction prices are the same for transactions 3 to 6. Assuming the trades are executed for information reasons, the method used in the earlier results will understate the price effect. To overcome this difficulty, the temporary price effect (TPE1 and TPE2) and the permanent price effect (PPE1 and PPE2) are recomputed using the average price of successive orders if they are from the same trader type and same side of the market. In the above example, the price series used is thus re-computed as in Figure 5.7
Figure 5.7 Timeline of trades where the trades transacted at the same transaction price are amalgamated.
Table 5.8 shows the temporary and permanent price effects of orders after making the above-mentioned adjustments. The results are consistent with the previous
(
3 1)
1 1 PPE = P −P P 1 P P2 P3 P4 P5 P6 3 4 5 6 P =P =P =P 7 P(
3 1)
1 1 PPE = P −P P 1 P P2 P3 P4 P5 P6 3 4 5 6 P =P =P =P 7 P 1 P P2 PA P7(
3 4 5 6)
where 4PA= P +P + +P P(
1)
1 1 A PPE = P −P P 1 P P2 PA P7(
3 4 5 6)
where 4PA= P +P + +P P(
1)
1 1 A PPE = P −P P 1 P P2 PA P7(
3 4 5 6)
where 4PA= P +P + +P P 1 P P2 PA P7(
3 4 5 6)
where 4PA= P +P + +P P(
1)
1 1 A PPE = P −P P110
discussion. Panel A shows the results for the heavily traded stocks (Decile 1) where the permanent price effect (both PPE1 and PPE2) for retail trades is less than for institutional trades for both bid and ask orders examined. This provides evidence that retail trades convey less information than the other trade types, giving further support for hypothesis H1. The total price effect is greater for retail trades, resulting in a greater temporary price effect. This is again consistent with the previous results and hypothesis H2. The larger temporary price movements suggest retail trades are placed in inferior market positions.
Panel B shows the results for lightly traded stocks (Decile 10). The results are again consistent with the previous analysis. Contrary to hypothesis H1, retail trades convey more information than institutional trades while the temporary price effect of retail trades is larger than for institutional trades.
Table 5.8 Price effect of orders where successive orders on the same side and of the same broker type are amalgamated
Side Type N TPE1 TPE2 PPE1 PPE2
Panel A: Heavily Traded Stocks
Ask Institutional 437,169 0.031 0.024 0.019 0.019 Others 320,618 0.049 0.041 0.013 0.011 Retail 182,247 0.060 0.053 0.008 0.004 Bid Institutional 459,620 0.031 0.027 0.018 0.020 Others 313,413 0.050 0.043 0.015 0.015 Retail 180,004 0.060 0.053 0.009 0.005
Panel B: Lightly Traded Stocks
Ask Institutional 13,074 0.298 0.352 0.141 0.208 Others 21,149 0.452 0.435 0.201 0.255 Retail 13,065 0.544 0.627 0.221 0.329 Bid Institutional 11,140 0.265 0.259 0.134 0.159 Others 21,648 0.434 0.416 0.197 0.287 Retail 13,983 0.551 0.537 0.216 0.297 5.6 Summary
The results from this chapter show that the identity of the trader is related to the price effect of the order. However, the expected relationship between trader identity and price effect is evident only in the most heavily traded stocks. In these stocks, orders placed by institutional traders have larger permanent price effects and this relationship remains, after controlling for order size. Based on the information
hypothesis, I conclude that institutional traders are better informed. In contrast, orders placed by retail traders are associated with a smaller permanent price effect, which lends support for the hypothesis that retail traders are less informed (H1). Institutional trades are associated with a smaller total price effect when compared to retail trades, suggesting that the inventory cost or price-pressure effect is smaller for institutional traders. This provides support for hypothesis (H2), that retail traders are less experienced in their order placement and incur higher transaction costs when executing their trades.
112 CHAPTER SIX
PROVISION OF LIQUIDITY AND ORDER PLACEMENT
“I’m concerned about the great influx of new and relatively inexperienced investors who may be so seduced by the ease and speed of internet trading that they may be trading in a way that does not match their specific goals and risk tolerances.” (Levitt, 1999)
6.1 Introduction
Share markets are generally grouped into two types: (1) quote driven and (2) order driven. A market is quote driven if dealers announce the prices at which other market participants can trade. Examples of such markets include National Association of Securities Dealers Automated Quotations (NASDAQ) in the United States and the Stock Exchange Automated Quotation System (SEAQ) in London. A market is classified as order driven if investors (or brokers acting as principals), by placing limit orders, establish the prices at which other participants can buy and sell. Examples of order driven markets include the Tokyo Stock Exchange, Paris Bourse and the Australian Stock Exchange (ASX). Some exchanges are a hybrid of the two and rely, at least partially, upon limit orders for the provision of liquidity. An example of this is the New York Stock Exchange (NYSE). Traders can place limit orders while the specialist is obliged to supply liquidity when the need arises.
The frequent use of order driven markets and the reliance of these markets on limit orders as a major source of liquidity makes it useful to understand the placement strategies of different traders. It was discussed in Chapter Five that traders who trade through different brokers can be classified into three types: (1) institutional traders, (2) retail traders and (3) others. Using this classification, the statistics presented in Chapter Four showed that the level of trading by retail traders has increased substantially over the period examined. Furthermore, the contrast between the change in trade frequency and trading volume confirms our intuition that institutional
traders and retail traders differ in the size of their orders. While the frequency of trades by retail traders has increased substantially over the period examined, aggregate trading volume has not increased by the same proportion. Given the growth of the prominence of retail trading, there is an interesting question: Does order placement strategy differ by trader type?
Several theoretical studies have addressed the mix of limit and market orders in an order driven market (Foucault, 1999; Foucault et al., 2001; Parlour, 1998). Parlour (1998) developed a one-tick model where the trader’s choice between a limit and market order depends on the state of the limit order book, in particular the depth available at the best bid and ask. Parlour’s model assumes each trader chooses his order by evaluating its execution probability and how the order would affect the order placement of other traders who follow. Foucault (1999) analyses a model in which limit order traders face the risk of non execution and are exposed to their risk of the order being executed at a loss when the limit order becomes mispriced. He finds the volatility of the asset to be a major determinant of the mix between market and limit orders. As (information based) volatility increases, limit order traders have a greater tendency to price their orders further from the market to compensate themselves for the higher probability of being picked off by informed traders. This results in higher execution costs for market order traders thus decreasing the proportion of market orders used. Foucault et al. (2001) model the limit order book as a market for liquidity provision and consumption. Their model comprises discretionary liquidity traders who trade off the cost of waiting against the cost of obtaining immediacy. They show tick size, cost of time and the proportion of patient traders determine the market equilibrium.
Other empirical papers have examined the choice of limit versus market orders (Ahn et al., 2001; Al-Suhaibani and Kryzanowski, 2001; Harris and Hasbrouck, 1996; Ranaldo, 2004; Verhoeven et al., 2004). Verhoeven et al. (2004) examine the mix of limit and market orders in two liquid stocks traded on the ASX. Using a logit model, they find the choice of order type depends on the bid-ask spread, depth at the best price, price change in the last five minutes and order imbalance. Their results are similar to those in Al-Suhaibani and Kryzanowski (2001) for the Saudi stock market. In both studies, market orders are associated with greater volatility. This finding is
114
contrary to the predictions of theoretical models such as Foucault (1999). Ronaldo (2004) also finds similar results but argues his findings could be due to statistical issues such as “collinearity and multivariate biases” (Ranaldo, 2004, p. 61).
While the empirical studies have examined the mix of limit and market orders and have attributed the choice of order type to the condition of the market, none has examined its relationship with the traders’ intention or motivation for trading. Ronaldo (2004) argues no inference can be drawn about how informed a trader is from observing his usage of market versus limit orders. Instead, the only inference possible from a trader’s choice of market or limit order is his eagerness to trade. In an earlier paper, Glosten (1994) defines eager and patient traders as market and limit order submitters, respectively. Ronaldo (2004) argues that an eager trader does not equate to an informed trader because, according to Chakravarty and Holden (1995), an informed trader may optimally choose any combination of market and limit orders.
This chapter extends the analysis of the order placement of traders by examining the aggressiveness of all market and limit orders placed and the use of limit orders in the provision of liquidity to the market. The main research question is: Does the trader type help determine the order placement strategy used? This question is addressed by examining the type of order used conditioned on the state of the market.
Limit orders provide important liquidity to traders who wish to trade immediately (Handa et al., 1998). The increased number of retail traders raises a second question: Do retail traders contribute to the depth of the limit order book? Although the analysis of the order flow in addressing the first question provides some indication of the contribution of different trader types to market depth, a more thorough analysis involves examining the bid-ask schedule over the normal trading phase.