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Smart Automation: How Technology is Redefining the Trading Business

Data Science and Technology

By Manu Sharma  |  February 11, 2020

Over the past few years, emerging technologies have become an integral part of global financial markets. Companies across all industries are leveraging the latest innovations in technology to reduce overall costs, improve workflow efficiency, and increase employee productivity.

Trading is no exception as the workload of a trader increases. Between the steady asset growth of funds and constant mandates to trade in new markets, firms must look for smarter ways to achieve alpha. For this reason, traders are looking to automation to optimize their processes and do more with less.

How Are Traders Approaching Automation?

Earlier last year, researchers at Greenwich Associates interviewed more than 100 capital markets professionals worldwide to conduct a study about technology trends and the benefits that an advanced execution management system (EMS) could pose for trading desks. 

Researchers learned that traders regularly playing offense believe an EMS is “the single most critical tool needed to succeed in that seat.” EMSs upgraded with access to the latest algorithmic trading tools and analytics could produce a return on investment in just a few weeks. “The wave of M&A in the order and execution management world over the past several years and the $1.4 billion the buy-side spent on those technologies in 2018 help to quantify the importance of these systems going forward.” 

Some trading desks are looking for technology to do the busy work for them so traders can focus on the more complex orders that require the experience and expertise of a human. Others, specifically trading desks with low-touch teams covering multiple asset classes, are looking for automation that lets them establish complex decision-making criteria at scale—without being limited by the automation capabilities of their incumbent system. More specifically, they need a single EMS that can help to maximize the number of multi-asset trades going out to market.

SmartAutomation_Trading1With this information as a backdrop, automating the trading desk may seem daunting. However, generally speaking, it is  considered to be a three-step process: 
  1. Performing a data-driven assessment of which orders to execute via automation
  2.  Selecting optimal execution destinations (e.g., brokers, algorithms, dark pools, electronic communication networks)
  3.  Defining and distributing orders to those execution destinations

Once these steps are complete, traders will be able to monitor trades in real time and disable the automation if a tail events happens. Setting up an advanced broker algorithm wheel helps funds identify the best performing execution strategies while removing selection bias. This leads to increased execution performance. 

The Future of Automated Trading Systems

As we head into the future, it’s worth reflecting on how trading workflows will change as the structure of automation evolves.

John Adam, Senior Vice President of Trading Solutions at FactSet, also believes automation will become an important driver for advancing the trading workflow. “I think that 2020 is the year we will see automation and data science enter the mainstream as foundational components of the portfolio lifecycle. Specifically, we will see a marked uptick in multi-asset trade automation,” Adam says. “Most exciting of all, the trader can rely on automation and data science to scale their productivity and assure consistent execution for low-touch and no-touch trading.” 

With a sophisticated and customizable automation tool, traders will become unburdened by the mundane tasks that occupy them today. Their expertise can be directed where it is most needed—whether that be SmartAutomation_Trading4on large or illiquid orders or on updating their portfolio managers with market color and real-time analysis. To ensure precision, orders should be routed out based on a single or combination of data points in the automation system. These include value, price, security type, percentage of volume, market impact analysis, and even a user-defined field or calculation. Simple examples include routing or prohibiting trades based on availability, the advice of portfolio managers, ADV or market-capitalization thresholds, bond rating, and an indication of interest. 

Rules-based automation for OTC markets should be integrated with in-house or third-party algorithm trading tools that offer smarter execution strategies for automated price improvement. Decision support features would help traders determine when alternative strategies will cause better execution performance than a single-counterparty request for quotation. The strategies could then be modified to reflect the desired behavior.

As for asset managers and hedge funds, researchers at Greenwich Associates found that investing in artificial intelligence from a third-party provider is more sensible. “There is significant upfront cost and technology risk inherent in building cutting-edge solutions; cost and risk the buy-side would have to pass on to their clients, which is less than ideal. The ability to easily tap into new, fully vetted tools through a third-party provider alleviates this problem considerably.”

 

Click here to learn more about FactSet Trading Solutions.

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Manu Sharma

Head of Trading Sales, APAC

Mr. Manu Sharma is the Trading Solutions Head of APAC Sales at FactSet. In this role, he is responsible for growing the Electronic Trading franchise for FactSet throughout the region. Mr. Sharma brings 12 years of industry experience to the role, which promoted the best practices of multi-asset electronic trading for Bloomberg. During his tenure, he successfully advanced the electronification of complex trading workflows across numerous markets in the region, capturing market share for their multi-asset class trading solutions. Mr. Sharma earned a Master of Science in Global Finance from New York University.

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