Guest Blogger Louis Lovas: Leveraging Technology for Customized Transaction Cost Analysis

Headshot of Louis Lovas(Editor’s note:  Very happy to have Louis Lovas contributing his TCA insights to TradingSmarter. Louis is the primary contributor to the insightful OneTick blog. Click on the link to check it out.)

Technology is making a sweeping transformation in trading styles as algorithms create a more competitive environment and exponential growth in data volumes. As the number of firms deploying algorithms increases they will be chasing after a diminishing pot. The days of easy money are over as reported by IBISWorld.  Eighty percent of funds are expected to be fully algorithmic within the next three years. Regulators are ever watchful as this technological change blankets the industry.  The fear, uncertainty and doubt factor pervades market integrity as witnessed by falling volumes and the market’s sharp volatile moves.  Regulators wrestle with the techno-fall out manifested in the flash crash, BATS failed IPO and Knight Capital’s rogue code debacle by deploying circuit breakers, banning naked access and proposing a consolidated audit trail to reduce systemic risk, improve market transparency and accountability and control.

Yet while high-speed trading has been grabbing the headlines there has been another technology evolution occurring, one that leverages the same super-smart and super-fast computers used in co-location. As a corollary to high frequency trader’s low-latency objectives, asset managers and institutional investors are focused on overall trade performance. This has essentially pushed trade execution management beyond best price to include cost control.

A lower risk appetite and widespread liquidity across lit pools and dark has pushed firms to expand their hunt for alpha across brokers and borders. With this ever widening search for best execution comes the immediacy of measuring trade performance. TCA is intended to provide a number of measurable objectives, first to measure and compare broker performance – looking at intra-day execution efficiencies by measuring and monitoring executions against benchmarks – arrival price and market price.  Second to identify outliers and highlight the impact of implicit costs or slippage – measured as implementation shortfall and opportunity costs such as crossing the spread.

Institutional investors are leveraging the same technologies that enable algo-trading to build custom TCA to attain the cross-broker, cross-asset visibility into their trade performance. These low-latency engines encapsulate the core set of capabilities necessary for measuring trade performance both in real-time and historically. TCA relies are three fundamental components; data management, analytics and visualization. These are needed for both traditional post-trade TCA and real-time cost analysis.

Data management for TCA is about bringing together disparate data sources. It starts with consuming market data – trades and quotes which comes in many shapes, sizes and encodings. Whether discovering new alpha or measuring trade performance it demands a confidence in the accuracy of pricing data. Tick data management has to deal with cancelations and corrections, consolidating order books across exchanges and applying corporation action price and symbol changes. The creation of accurate and reliable price analytics for measuring trade performance – average trade price, VWAP, and arrival price are only possible with this scrubbing.  By the same token, capturing individual orders and their corresponding fills is equally vital. The ability to accurately derive these benchmark prices at the precise time of trade execution is a cornerstone for understanding trade performance.

Deeper analysis can show individual order and fill performance against benchmarks (arrival & VWAP) by broker, broker algo, industry and venue. Book analytics such as an order’s percentage of market volume and percentage of market value can show market impact as fills dig deeper into the book for liquidity.  The analysis can provide insight to determine the best broker algo for a particular order based on past performance.

There is an ever increasing requirement to measure trade cost performance as it happens, where analytics become actionable information at the point of trade. Execution strategies and routing logic can be adjusted intelligently in response to outlier conditions; either aggressively or passively in reaction to market conditions and can level the playing field between institutional investors and high frequency traders.

The third component, visualization fashions the analytical metrics into a human readable consumable. And it’s a well-known fact that 80 percent of information processing is attained visually.  So having visual representations that plot executions fill rates and performance metrics to benchmarks will pinpoint order exceptions, those that have become a problem.

Technology is reshaping the trading landscape as algorithms and low-latency analytics continue to dominate. But such is not the sole domain of high-speed traders. The weapons in this war can easily be recast, and molded into a different form for unearthing trade performance, a goal that is central in the investment process as liquidity continues to be fragmented and fleeting. Investors are discovering a critical component to profitability is uncovering the performance of their trading behavior through customized, personalized transaction cost analysis.

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