The Future of Trading
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Calculating Cost First, Trading Second
November 4, 2010
“This analysis is done on a tick by tick basis and allows the trader to separate the impact of his or her strategy from what other market participants did in the market on the same stock,” says Alex Hagmeyer, manager of trading analytics at QSG, a global equity research and transaction cost analysis firm in Naperville, Illinois.
The empirical decomposition of implementation shortfall allows the trader to uncover brokers and algorithms that unnecessarily and excessively pay for liquidity, while illuminating the strategies that are truly able to stay under the radar and keep a small footprint in the market. Over time, choosing the trading strategies that limit their footprint in the market reduces the magnitude and uncertainty of costs in the implementation process, says Hagmeyer.
QSG’s SYNC platform also allows for pretrade analysis that uses forecasted costs and client-specific historical post-trade data for like trades to help the trader understand which brokers or algorithms have performed the best and with the least amount of uncertainty for any given benchmark or cost measure.
SJ Levinson and Sons, a New York-based agency broker-dealer which specializes in quantitative analytics, has just released its Trade Analysis Program (TAP), which allows traders and portfolio managers to compare execution results against hundreds of benchmarks historically on large data sets and in real-time via their desktops. SJLS takes into account the past performance of trading for a particular manager, security, sector, fund, trade size and liquidity.
“The goal is not to micromanage each order but to maximize the return to investors,” says Matthew Celebuski, senior managing director and head of quantitative research at SJ Levinson. “Eighty percent of the gain from TCA is to find a strategy that matches the trading style of each portfolio manager and consistently improves the returns of the fund. The remaining 20 percent of the gain is from the selection of algorithms, brokers and trying to access liquidity in the market in the most efficient way possible.”
Case in point: a trader executing an order for a value investment manager may need to execute more slowly than for a growth investment manager because growth managers typically have short term momentum in their orders.
Taking its analysis to the next level, SJ Levinson plans before year end to combine the results from its transaction cost analysis to conduct pre-trade analysis on the same platform. That means allowing the trader to set an optimal trading schedule based on the history in the type of trade. “The trader wouldn’t have to rely solely on his or her own instinct but could simply push a button to generate quantitative recommendations automatically,” says Celebuski.
As real-time analytics gains particular popularity one particular tack known as predictive analytics may become the wave of the future, according to Celent’s Habbal. Pipeline, for one, has already developed what it calls an Algorithmic Switching Engine that predicts the performance of algorithms under real-time conditions based on information from the post-trade analysis. “It takes a trader's instructions about how hard the trader would like to drive an order into the market and translates that minute-by-minute into a choice of an algorithm style, price limit for an algo, the control parameters on that algo and the size of the orders sent to that algorithm, and then the algorithms execute it," says Waelbroeck. Currently, Pipeline's switching engine can accommodate more than 100 algorithms and is adding about 10 more each month.