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Intraday stock prices, duration, and volume 267<br />

Seller-Initiated<br />

Trades<br />

Buyer-Initiated<br />

Trades<br />

a Current Duration 20th Percentile<br />

Lagged Duration 80th Percentile<br />

0.4<br />

0.2<br />

0<br />

-10<br />

change. The distributions here are also much tighter than the correspond<strong>in</strong>g distributions<br />

<strong>in</strong> Fig. 2. This suggests that our results reported earlier are more appropriate<br />

at median values <strong>of</strong> the lagged variables, or when current durations are short<br />

relative to lagged durations. These results are merely <strong>in</strong>dicative; a more extensive<br />

study <strong>of</strong> the dynamic relationships <strong>in</strong> the framework used here is left for future<br />

research.<br />

5 Conclud<strong>in</strong>g comments<br />

-5<br />

0<br />

Price Change<br />

c Current Duration 20th Percentile<br />

Lagged Duration 80th Percentile<br />

0.4<br />

0.2<br />

0<br />

-10<br />

-5<br />

0<br />

Price Change<br />

5<br />

5<br />

10<br />

10<br />

80<br />

80<br />

70<br />

70<br />

60<br />

60<br />

50<br />

40<br />

Volume (Perc.)<br />

50<br />

20 0<br />

30 -10<br />

70<br />

80<br />

We <strong>in</strong>vestigate the distribution, conditional on trad<strong>in</strong>g volume, duration between<br />

trades, and the sign <strong>of</strong> trades <strong>of</strong> <strong>high</strong>-<strong>frequency</strong> price changes on four stocks traded<br />

on the New York Stock Exchange. The conditional probabilities are estimated nonparametrically<br />

us<strong>in</strong>g local polynomial regression methods. We f<strong>in</strong>d substantial<br />

skewness <strong>in</strong> the distribution <strong>of</strong> price changes, with the direction <strong>of</strong> skewness<br />

dependent on trade sign. We also f<strong>in</strong>d that the probability <strong>of</strong> larger price changes<br />

<strong>in</strong>creases with volume, but only for trades that occur with longer durations. Durations<br />

affect prices, with a stronger effect when volume is <strong>high</strong>.<br />

The evidence suggests substantial <strong>in</strong>teraction effects between duration and<br />

volume with respect to their effect on prices; parametric analyses <strong>of</strong> the relationship<br />

between duration, volume, and prices should take these <strong>in</strong>to account, such as <strong>in</strong> Tay<br />

et al. (2004). The <strong>high</strong> degree <strong>of</strong> skewness <strong>in</strong> the distributions <strong>in</strong>dicate that it is also<br />

important to dist<strong>in</strong>guish between seller- and buyer-<strong>in</strong>itiated trades, and that care<br />

should be taken <strong>in</strong> <strong>in</strong>terpret<strong>in</strong>g results concern<strong>in</strong>g the volatility <strong>of</strong> price changes.<br />

The results are consistent with theoretical research. Our f<strong>in</strong>d<strong>in</strong>gs for seller-<strong>in</strong>itiated<br />

trades provide direct evidence <strong>in</strong> support <strong>of</strong> the Diamond and Verrecchia (1987)<br />

analysis, where larger probability <strong>of</strong> price falls is associated with a <strong>high</strong>er level<br />

<strong>of</strong> duration.<br />

0.4<br />

0.2<br />

0<br />

20<br />

-10<br />

30<br />

40<br />

Volume (Perc.)<br />

b Current Duration 80th Percentile<br />

Lagged Duration 20th Percentile<br />

-5<br />

0<br />

Price Change<br />

d Current Duration 80th Percentile<br />

Lagged Duration 20th Percentile<br />

0.4<br />

0.2<br />

-5<br />

0<br />

Price Change<br />

Fig. 3 Estimated conditional probabilities (GE with lagged duration)<br />

5<br />

5<br />

10<br />

10<br />

70<br />

80<br />

60<br />

60<br />

50<br />

40<br />

30<br />

Volume (Perc.)<br />

50<br />

40<br />

30<br />

Volume (Perc.)<br />

20<br />

20

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