  We study the daily trading volume volatility of 17,197 stocks in the U.S.
stock markets during the period 1989--2008 and analyze the time return
intervals $\tau$ between volume volatilities above a given threshold q. For
different thresholds q, the probability density function P_q(\tau) scales with
mean interval <\tau> as P_q(\tau)=<\tau>^{-1}f(\tau/<\tau>) and the tails of
the scaling function can be well approximated by a power-law f(x)~x^{-\gamma}.
We also study the relation between the form of the distribution function
P_q(\tau) and several financial factors: stock lifetime, market capitalization,
volume, and trading value. We find a systematic tendency of P_q(\tau)
associated with these factors, suggesting a multi-scaling feature in the volume
return intervals. We analyze the conditional probability P_q(\tau|\tau_0) for
$\tau$ following a certain interval \tau_0, and find that P_q(\tau|\tau_0)
depends on \tau_0 such that immediately following a short/long return interval
a second short/long return interval tends to occur. We also find indications
that there is a long-term correlation in the daily volume volatility. We
compare our results to those found earlier for price volatility.
