Anne Plested
Traders Magazine Online News

Bottlenecks Ahead

Anne Plested, head of Fidessa's EU Regulation Change programme, has written a short blog arguing that although we should be thankful that ESMA have taken a pragmatic approach to moving things along, more bottlenecks could appear in the future.

Traders Poll

Would you feel better if the Chicago Stock Exchange were purchased by U.S. firm or consortium rather than a foreign one?

Free Site Registration

June 6, 2006

Going Algo: When and If Be careful when the whip comes down

By Joseph Saluzzi

Also in this article

  • Going Algo: When and If Be careful when the whip comes down

While algorithmic trading systems are designed to provide a fast, anonymous and cost-effective means of executing block trades, traders are finding that algos often require careful oversight and a feel for when to use them and, even more importantly, when not. Most professionals know that algo systems, at least in the case of the "first-generation" products widely used today, work best for large-cap stocks with millions of shares in average daily volume. What is not commonly known though, but is a fact that more and more traders and asset managers are discovering, is that even some of the Street's most popular large caps, ones with seemingly high liquidity, are actually not good candidates for algo trading.

When preparing to execute an order and deciding whether to employ an algo system, traders need to do their homework and pay careful attention to five critical factors:

* Average trade size (in shares) of the equity

* Average intraday price volatility (the spread between the daily high and low)

* How many times during the day the stock typically spikes up and down

* Average day-to-day price volatility

* How your effectiveness is measured (commission cost, execution price, time, etc.)

We've found that, while a number of large caps that appear to be ideal algo candidates, they actually trade more like small caps. These include some well-known, very active names. In many cases, average daily volume is in excess of 10 million shares, and yet average trade size is less than 300 shares. These stocks also exhibit relatively high price volatility, both within the trading day and from one day to the next. That's even when there is low overall market activity and no company or industry news. Despite these factors, their big volume attracts high levels of algorithmic trading. Ironically, it actually may be the algos themselves that are making these and other stocks poor candidates for this kind of electronic trading.

We call these equities Whip Stocks because they demonstrate exaggerated, "whip-like," up-and-down trading patterns. If there is disproportionately heavy algorithmic trading in a particular stock, large numbers of small, automated block trades are fed into the market to match, for example, the day's volume weighted average price (VWAP). The latter is one of the most popular first-generation algorithmic trading methods. This is why a stock's average trade size is so important. Increased algo activity creates an abnormally high number of smaller trades, as large blocks are "chopped" into smaller lots, in an attempt to limit market impact. If there's too much algo activity, though, market impact becomes a problem.

The Problems of Algos