Cierre de la venta

To investigate the causal relationship between volatility and trader activity, the concept of Granger (1969) causality is used to measure how much two variables

38 _{The placement of an order may result in the execution of a trade or the creation of a standing limit}

order. The placement of an aggressive order, such as a limit order that is in the market or a market order, will result in the change in the midpoint spread giving a non-zero r_{i t,} .

precede each another. The following bi-variate VAR model is used to estimate the relationship between volatility and retail trader activity. Battalio, Hatch and Jennings (1997) used a similar model to test the effect of Small Order Execution System (SOES) trades on stock price volatility on Nasdaq. As discussed in Chapters 5 and 6, the extent to which each trader type participates in a stock depends on the stock examined. Consequently, the following system is estimated separately for each of the 36 selected stocks: 5 3 1 1, 1, 1, 1, 1 1, 1 1 2 2 5 3 2 2, 2, 2, , 2, , 2 2, 1 1 2 2 − − = = = = − − = = = = = + + + + + + = + + + + + +

∑

∑

∑

∑

∑

∑

∑

∑

where R_t is the proportion of retail trade activity, V_t is the volatility measure, day is the dummy variable for day of the week, timei is the dummy variable for time of the day, and Tt is the total number of trades in the 15-minute intervals.

While the number of lags, n, for each of the variables will be determined by the data available, the relationship is not expected to last more than one trading day (i.e., 23 lags). Brown et al. (1997) found the bi-directional causality between order imbalance and return to not last beyond a single day. Although not discussed in their paper, the closing of the market at the end of the day appears to provide a break in the relationship examined. The Schwartz Bayesian Criterion (SBC) is used to estimate the optimal lag length. Other criteria such as the Akaike Information Criterion can be used, but it has been suggested that more parsimonious models are achieved by using the SBC (Lutkepohl, 1991, p.138).

Exogenous variables, day, time and Tt, are included in the model as they affect volatility (Jones et al., 1994b; Wood et al., 1985) and order placements by different trader types. The trading period is divided into three intervals as time-of-the-day effects are found for the earlier and later parts of the trading period: (1) 10:15am - 11:59am (2) 12:00pm - 1:59pm (3) 2:00pm to 4:00pm.

146 7.3 Results

7.3.1 Summary statistics

Table 7.2 presents summary statistics of trading by trader type and price volatility in the 23 15-minute intervals. Panel A shows, on average, 47% (24 of 51) of the orders placed in the heavily traded stocks during a 15-minute interval are by institutional traders and 18% (9 of 51) are by retail traders. The difference between institutional and retail order flow is more apparent when order flow is measured using the volume of shares placed. Institutional traders contributed 73% (283,828 of 391,310) of the total order flow during an average 15-minute interval and retail traders contributed 6% (22,684 of 391,310). The large contribution to the order flow by institutional traders is not found for the lightly traded stocks (see Panel B). Instead, the order flow from retail traders is larger than from institutional traders both in terms of frequency and volume of orders placed. The table shows that 31% (32,648 of 105,387) of the share order volume is placed by retail traders compared to 24% (25,614 of 105,387) by institutional traders.

Table 7.2 also shows the proportion of the orders placed by each trader type that are classified as aggressive. Aggressive orders include marketable limit orders, market orders and limit orders placed in the market. These orders result in trades being executed immediately and, generally, a change in the bid-ask spread.39 For the heavily traded stocks, the proportion of orders placed by institutional traders that are aggressive is similar to that placed by retail traders, at 54% and 52% respectively. However, the differences are more apparent in the lightly traded stocks where 57% of the orders placed by institutional traders are aggressive and 47% of the orders placed by retail traders. These results are similar to those presented in Table 6.5. The similarity in the aggressiveness measures suggests that the trading of the two different trader types should not have a significant impact on share price volatility in the heavily traded stocks. However, differences may be observed in the lightly traded stocks.

39 _{Some market or marketable limit orders result in trades being executed but do not affect the bid-ask}

spread. These are generally smaller orders that do not consume the entire depth offered at the best price on the opposing side of the market.

147 Table 7.2 Summary statistics of the average order activity in a 15-minute interval

The analysis uses data from the period 1 January 2001 to 31 December 2001 for the selected sample of stocks. The summary statistics are presented for two samples comprising heavily traded stocks (Panel A) and lightly traded stocks (Panel B), respectively. The “frequency of orders placed” shows the total number of orders placed by each trader type in a 15-minute interval. “Volume transacted” shows the number of shares in the orders placed. “Proportion of aggressive orders” shows the proportion of orders (calculated using order frequency) that are aggressive. The criterion for being classified as aggressive is that the price on the bid (ask) order is greater (less) than or equals to the best bid (ask). V1 is the

summation of the square midpoint return in the interval. V2, is computed by 100*log(PH/PL) where PH (PL) is the highest (lowest) midpoint spread in the 15-minute interval.

Frequency of orders placed Volume transacted Proportion of aggressive orders

All Instn Retail Others Instn Retail Others Instn Retail Others V1 V2

Panel A: Heavily traded stocks

N 98,486 98,486 98,486 98,486 97,482 93,458 97,138 97,482 93,458 97,138 98,486 98,486 Mean 51 24 9 17 283,828 22,684 84,798 54% 52% 56% 0.15 0.27 Median 38 18 6 12 128,292 7,603 38,979 54% 50% 56% 0.06 0.20 Max 1,276 347 461 586 191,350,000 40,019,038 100,060,000 100% 100% 100% 226.06 9.75 Min 1 0 0 0 1 1 1 0% 0% 0% 0.00 0.00 Std Dev 47 22 12 19 815,539 140,832 383,500 18% 27% 22% 1.31 0.26

Panel B: Lightly traded stocks

N 70,804 70,804 70,804 70,804 34,995 37,959 49,492 34,995 37,959 49,492 70,804 70,804 Mean 4 1 1 2 25,614 32,648 47,125 57% 47% 55% 1.47 0.40 Median 3 0 1 1 6,000 9,000 10,383 67% 50% 50% 0.00 0.00 Max 601 57 263 320 22,165,103 4,721,818 6,436,264 100% 100% 100% 7434.85 67.39 Min 1 0 0 0 1 1 1 0% 0% 0% 0.00 0.00 Std Dev 8 2 3 5 186,732 106,534 164,461 42% 43% 41% 30.27 0.89

148

The first volatility measure, V1, shows the heavily traded stocks (V1 = 0.149) are less volatile than the lightly traded stocks (V1 = 1.473). The standard deviation of V1, for the lightly traded stocks (30.27) is more than 20 times larger than for for heavily traded stocks (1.31). The alternative volatility measure, V2, has a much smaller standard deviation for both samples but yields a similar conclusion. That is, the heavily traded stocks are less volatile and there are larger variations in the measure for the lightly traded stocks. The higher volatility in smaller stocks could be driven by the tick size of the stocks examined in the two samples. The heavily traded stocks generally have a lower proportional spread so that percentage price changes between transactions are likely to be smaller.