High-frequency traders (HFTs) have been blamed for many things on Earth, including global warming (seriously, it was Al Gore). One of the frequently repeated mantras of late is a suggestion that the HFTs use “fleeting” orders that are surely designed to create problems for non-HFTs. The U.K. Financial Services Authority (FSA) even issued injunctions earlier today [Sept. 7] against HFTs who allegedly artificially moved market prices by placing orders, only to cancel them seconds later. The same traders were then thought to have taken advantage of the market jiggling by trading at a later point.
The FSA accusations, while undoubtedly serious, cast a dark shadow over the entire HFT community. Research shows that it is indeed possible to move the markets just by placing limit orders. According to a brand-new study of NASDAQ high-frequency data by Nicolaus Hautsch and Ruihong Huang, limit orders placed at or 1 tick behind the market price do move markets by reflecting other traders’ anticipation of the imminent change in supply and demand after a trade takes place. The price changes induced by the at-the-market or tick behind the market limit orders, however, amount to only 25% of the price changes caused by the fully executed orders, per researchers’ findings. Furthermore, once the orders disappear from the limit order book, rational market practitioners would adjust their supply-demand expectations to reasonable market price levels. Profiting from market moves by follow-up trades at that point appears to be difficult at best.
If not to unfairly move the markets, why else would anyone create “fleeting” orders? Hautsch and Ruihong suggest that 95% of fleeting orders are placed between the best bid and the best ask to infer the hidden liquidity between the spreads. However, their own statistics contradict this result: only 8% of orders are placed between the best bid and best ask, and thus cannot account for 95% of orders.
A more likely explanation is based on activity of order execution algorithms. Order execution algos are used by many traders to conceal their trades among other trades present in the markets. The idea of hiding the order is based on a simple principle: money talks. The confidence to place a large order requires hours of diligent research and experience, which are pricey in the large scheme of things. When a large dollar order crosses the ticker tape, passive market observers are likely to “piggyback” on the large order, placing the orders in the same direction and benefitting from it, yet having invested nothing in research or experience. These “piggyback” orders not only earn an undeserved return to the passive ticker-tape observers, they also shift supply and demand equations to worsen prices incurred by the trader placing the well-researched large order.
Most algos help traders of large orders execute the orders at or below a certain cost. For example, the Volume-Weighted Average Price (VWAP) algorithm executes the large order either at or at a better price than, well, the Volume-Weighted Average Price for the specified period of timeâ€”say, a trading day. Due to the natural market volatility, the target price may appear several times in the course of the day. If the order is not executed instantaneously, and the market is trending in the opposite direction, then the algo needs to recalibrate its activity. Since limit orders passively sitting in the order book do move markets, albeit with small intensity, it often makes sense to cancel a limit order placed just behind the market if the order is not executed right away, and wait for subsequent, more advantageous market conditions. The “fleeting” limit orders generated in the process are just proxies for market orders, which are canceled when the trader’s algo is unable to trade at a desired price.
As the average trade sizes in most securities markets decrease to smaller and smaller quantities, traders moving large positions are seeking increasingly nimble ways to blend in. Canceling limit orders representing chunks of a large order is one way to conceal a large trader’s trading activity. Such activity is geared to protect large traders, not to attack trades of others.
Irene Aldridge is a quantitative portfolio manager at ABLE Alpha Trading, LTD., where she supervises creation and production of quantitative and high-frequency trading strategies. Aldridge is the author of High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems (Wiley, 2009). Her latest research on high-frequency trading is forthcoming in Equity Valuation and Portfolio Management (Frank Fabozzi and Harry Markowitz, eds., Wiley 2011). She will be speaking on the subject of high-frequency trading in New York on Oct. 13, 2011. If you would like to attend, click here. She can be reached at firstname.lastname@example.org