The Future of Trading
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Calculating Cost First, Trading Second
November 4, 2010
Although the volume weighted average price (VWAP) will tell a trader how he or she did relative to the average price for the day, it won’t say anything about market impact. As is the case with IS, average price performance can be improved at the expense of higher opportunity costs when some trades are left unfinished, so the use of limit prices makes it very difficult to interpret both implementation shortfall and VWAP performance.
“The trading speed, the effect of limit prices and extraneous market conditions must be taken into account to devise forward-looking strategies,” says Waelbroeck. “Trading schedules and limit prices if used appropriately can enhance alpha but if used incorrectly can exacerbate trading costs.”
Pipeline has come up with an advance in TCA that it says can decompose implementation shortfall into its root components. Those are short-term alpha loss, algorithmic impact, adverse selection, opportunistic savings and the tradeoff between the selected speed and impact prices.
Adverse selection refers to the problem where an algorithm delivers a worse average price than if you had simply participated with the market at a constant rate. This tends to happen when an algo doesn't get enough fills ahead of an adverse price move, or vice versa, when the algorithm speeds up just before the price moves in your favor. Opportunistic savings is the mirror image of adverse selection; an algorithm can anticipate an adverse price move and pick up liquidity before it happens, or vice versa, apply a maximum amount of trading to avoid moving too fast before the price improves. The adverse selection of one trader is the opportunistic savings of another; the trader with superior predictive technology will come out ahead.
Waelbroeck says that Pipeline’s TCA framework builds subsets of trades that profile a particular trader or manager’s trade arrivals and measures the number of basis points that are being gained or could be gained through decisions such as the choice of trading speed or limit prices, and do so separately for each trade profile.
“Armed with this kind of analysis the trading desk can separately assess the value added by the trader’s decisions from the underlying quality of the algorithmic trading tools provided by each broker,” says Waelbroeck. “The trader’s strategy can then be customized to specific situations.
For example, for a particular portfolio manager, when competitive order flow in the first hour of a trade is predictive of a continuing trend in the coming weeks, the trader will want to accept blocks of liquidity when they appear. They also will avoid the use of limits, since these are more likely to lead to delays and opportunity costs. By contrast, for another portfolio manager the same competitive order flow environment might be associated with overshoot-and-reversion after the trade is done; in this case the use of limits is critical to avoid buying on peaks and delays are more likely to lead to better prices.
Quantitative Services Group (GSG) says that its Sync service can calculate what it calls a Liquidity Charge. That “charge” is the difference in price that results from the execution of all the individual trades made in accumulating or selling a position, in a day. If buying 500,000 shares of stock in 1,000 500-share trades pushed the stock up from $48 to $50, the charge is $2 times 500,000 or $1 million. The same in reverse, if selling, broadly speaking.