TCA: It’s All About the Data03.19.2013
Transaction cost analysis is rising to the top of the agenda as buy side traders cope with continuing fragmentation and turbulence in the financial markets.
“Best execution is at the top of our list of priorities and we monitor it closely with the vast venue choice and data available,” said Craig Jensen, principal and co-head of trading at Armstrong Shaw Associates, which manages $2.5 billion. “We look at four benchmarks each time we trade.”
As marketplace uncertainty becomes the new normal, the withering volumes and volatility in 2012 will likely continue throughout 2013.
“Economic turmoil and regulatory overhang are recurring themes, and firms must look to the analysis of data for in-depth understanding of market behaviors,” said Louis Lovas, director of solutions at OneMarketData, provider of the OneTick database. “The key to success is challenging assumptions and analyzing data to conquer the unpredictable market.”
Effective market data management “is about linking disparate data sets under some common thread to tease out an intelligible answer,” Lovas said. “It’s about finding the cross asset correlations or understanding how to best hedge a position to offset risk, these are the underpinnings for profitable trade models, portfolio management and TCA.”
The need for high quality of data cuts across all aspects of the trade lifecycle. Those trade-related solutions, model back-testing, portfolio mark-to-market and compliance depend on a high-degree of data quality, where accuracy is vital to determining outcomes.
“As a tick database, OneTick is about capture and analysis of data for back testing quantitative models and TCA, both of which are hugely dependent on quality of data,” said Lovas.
To ensure strategies will perform in today’s markets, users need the ability to test strategies on historical and real-time data.
A complex event processing (CEP) engine enables analysts to filter, correlate and aggregate real-time event data in a low latency environment.
“When combined with a historical database and data management solution, CEP engines help users test strategies on historical and real-time data – empowering them to determine whether their strategies will perform as predicted once deployed,” said Lovas. “When you want to build pattern detection logic for quant trading strategies or TCA, you need to incorporate historical data.”
The determinants of price discovery, volume, and trading patterns define the structure unique to a market, an asset class and geography influenced by participants and current regulation.
“Tick data is often derived from many sources; there are 13 major exchanges in the U.S. alone,” said Lovas. “That’s a natural barrier to algorithmic trading and it creates unique challenges for efficiently trading across them – recognizing their differences in both market microstructure and regulation.”
The collection and analysis of data demands sophisticated tools and systems as firms look to discover the unique determinants of transaction costs, prices, and trading behavior to devise smarter trading models.
“Everybody uses TCA, but that’s not necessarily indicative of performance depending on how you trade,” said Jensen of Armstrong Shaw. “We have formal TCA capabilities through Bloomberg, but we also track things manually. At the end of the day, when I collect reports, I will look for a trend and then plug that into our strategy.”
Measuring data quality for TCA is about bringing together disparate data sources.
“TCA measures broker performance; identifies outliers and measures trades against benchmarks,” said Lovas. “TCA is looking at intra-day execution efficiencies by measuring and monitoring executions against those benchmarks – arrival price and market price. The ability to accurately derive these benchmark prices at the precise time of trade execution is a cornerstone for understanding trade performance. And it demands a high quality of data, its reliable capture and storage.”
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