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December 1, 2004

J.P. Morgan's Analytical Difference

By Peter Chapman

Also in this article

A New Kind of Algo Player

J.P. Morgan Securities has joined a growing list of brokers marketing algorithmic trading to institutions. But in an effort to stand out from the crowd, it is promoting software that helps traders decide how to trade a stock.

The investment banking arm of J.P. Morgan Chase & Co., seeking to distinguish its algorithmic trading services, is championing the nascent science of pre-trade analytics.

"We think clients will turn to our suite of algorithmic products instead of someone else's if they have some guidance," says Emily Portney, Morgan's chief operating officer for equities Americas. "This is the missing link."

Pre-trade analytics is software that purports to measure the potential trade-off between cost and risk associated with various trading strategies. The technology provides traders with a menu of execution options - both automated and manual - derived from an analysis of current and historical market data.

Morgan execs contend they are ahead of the curve in coupling decision-making technology with algorithmic trading tools. While most brokers are simply throwing bunches of trading models at the market, Morgan, the execs claim, is taking a more thoughtful approach. "We want to try to simplify and rationalize the marketplace," says Carl Carrie, head of product development in Morgan's electronic execution services group. "This is one approach."

Algorithmic trading models - those computer-managed execution strategies -are certainly proliferating. Most of the major equities houses are coming out with their own VWAP and arrival price strategies. However, despite Morgan's philosophy, it is not alone in cultivating clients with pre-trade decision-making. Other brokers, such as Investment Technology Group and Goldman Sachs, offer the service.

And unlike the more established post-trade cost analysis, pre-trade analysis doesn't get much respect.

Brokers may increasingly see pre-trade analysis as a key value-add', but some analysts find the practice wanting. Pre-trade analysis works best with baskets of stocks or highly-liquid stocks, according to Marie Konstance, director of sales and product management at Plexus Group, a cost measurement firm.

But the methodology fails when traders need it the most - trading less liquid names. "The very moment when they are really interested in pre-trade analytics is the moment they don't know how the stock will trade," she said. "But it's the very time a model is not going to be tremendously helpful."

The use of historical average volume data, Konstance notes, works fine for stocks such as IBM, but not so for smaller names. Those can trade 20,000 shares one day, a million the next, and nothing some days.

"The models tend to work better in conditions where history is a good predictor of the future," says Konstance. "And that is more true of big stocks that have consistent liquidity patterns and are less volatile. But those are not usually the ones for which people are dying for help."