By Emily Chasan
NEW YORK (Reuters) - Volume figures touted by more than 40 off-exchange stock trading venues to attract customers may be too easily inflated because of cut-throat competition and flawed accounting processes, a top Morgan Stanley trading executive warned on Monday.
Some market analysts believe anywhere from 10 to 20 percent of U.S. equity trades could be occurring in dark pools, the anonymous venues where stock traders can match large orders while concealing details about price and volume, instead of on traditional exchanges such as the New York Stock Exchange (NYX.N: Quote, Profile, Research, Stock Buzz) or Nasdaq (NDAQ.O: Quote, Profile, Research, Stock Buzz).
But the volume figures pools report can be easily misstated and there is no standardization in the way pools account for volume, according to Andrew Silverman, managing director of electronic trading at Morgan Stanley (MS.N: Quote, Profile, Research, Stock Buzz).
"The volume in the dark pools is very overstated," he said at the Reuters Exchanges & Trading Summit in New York. "There are a lot of issues with people claiming volume where there isn't."
Dark pool providers have every incentive to show large volumes in their pools because such figures are used by traders to figure out how easy it would be to match orders in a particular venue.
"Volume could attract liquidity ... and people want to show they're the largest," Silverman said.
Silverman explained that one transaction of 10,000 shares could be counted four times in dark pools as orders are quickly routed in and out of various venues before they are matched.
"I think there should be standards on volume reporting with absolute transparency," Silverman said.
Instead of the 10 or 20 percent volume figure, Morgan Stanley estimates the figure is closer to 5 to 9 percent and not higher.
He said his firm separates volume data on trades routed through its pool to other pools from the data on how many trades it matches within its dark pools.
(For summit blog: summitnotebook.reuters.com/)
(Editing by Richard Chang)
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