Abstract
We quantitatively investigate the ideas behind the often-expressed adage “it takes volume to move stock prices,” and study the statistical properties of the number of shares traded for a given stock in a fixed time interval We analyze transaction data for the largest 1000 stocks for the two-year period 1994–95, using a database that records every transaction for all securities in three major US stock markets. We find that the distribution displays a power-law decay, and that the time correlations in display long-range persistence. Further, we investigate the relation between and the number of transactions in a time interval and find that the long-range correlations in are largely due to those of Our results are consistent with the interpretation that the large equal-time correlation previously found between and the absolute value of price change (related to volatility) are largely due to
- Received 1 May 2000
DOI:https://doi.org/10.1103/PhysRevE.62.R4493
©2000 American Physical Society