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Damian Bierman
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Further Automating the Trading Desk

Advanced decisions support that use predictive analytics to will soon become the norm.

Damian Bierman, Portware
Damian Bierman, Portware
In today’s high-speed electronic markets, the trader’s role is only becoming more challenging. From analyzing market conditions, selecting the best strategy for a particular order, monitoring the speed and quality of executions as they come in, and making appropriate adjustments as conditions change throughout the day, traders are ultimately responsible for the overall trade lifecycle. Unfortunately, strategy selection and optimization still remain largely a manual process with traditional execution management systems, placing yet another burden on the trader.

The more orders a trader has to handle, the more difficult it becomes to effectively monitor the factors impacting each one. Add to that the multitude of algorithmic strategies made available by the sell-side and the high degree of configurability that allows them to be tailored to so many different market conditions, and it’s easy to see how a trader can become overwhelmed. What’s really required today is a new type of thinking execution management system (EMS) that can bring measurable efficiencies and workflow automation to the trading desk, using artificial intelligence to give traders and portfolio managers the highest level of real-time color and Transaction Cost Analysis (TCA) on their orders.

Large global asset managers now realize they can gain significant value by intelligently automating this process. As more buy-side managers realize real money is being left on the table every month through non-optimal strategy selection, they will move out of their traditional comfort zone and reevaluate how they manage their algorithmic executions. Many have already begun to adopt tools and technologies to automate and optimize this process, and this trend is poised to grow steadily in the years to come.

The most successful of the technologies used to automate strategy selection and monitoring apply quantitative predictive analytics. This kind of solution diagnoses a trade at inception and generates an execution profile for it, constantly re-factoring against the initial hypothesis throughout the day and automatically adjusting to maximize alpha capture and minimize impact costs, adverse selection, information leakage, and the impact of high-frequency trading. This type of increased transparency empowers traders and portfolio managers by allowing them to see the decision-making process and the factors driving the decision for any trade at any given point in time. This type of advanced decision support gives the trader information and color around key factors impacting the execution of their trades in real time, providing them the confidence and support they need to ensure their selections are appropriate.

As traders and portfolio managers look for new ways to generate alpha, automated and intelligent solutions that use predictive analytics to enable better decision support will soon become the norm. Early adopters of the technology have an opportunity to gain a competitive edge by optimizing their strategy selection before the market for those strategies becomes too crowded. As more companies move toward this predictive analytics model to better manage algorithms, those first in line to automate the process will find themselves with a measurable advantage as they tap information and insights from other areas of the business, such as the portfolio manager, to usher in a new era in alpha profiling and intelligent decision-making.

Damian Bierman is Head of Asia Pacific Operations, Portware. He is based in Hong Kong, and oversees day-to-day operations in the region. Damian was formerly head of FIX Product Services at trading solutions provider NYFIX, in Hong Kong

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