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Outsourced vs. In-House Trading: A False Choice

Data Science and Technology

By Manu Sharma  |  August 21, 2020

This article originally appeared in Traders Magazine.

The financial crisis ushered in by the COVID-19 pandemic has, like other black swan events before it, increased fee pressures on asset managers, forcing them to cut costs where they can. The resultant murmurs of buy-side firms looking to outsource trade execution is understandable against this backdrop, but it does not mean that firms face a binary choice between keeping in-house trading desks or offshoring them entirely.

Savvy firms that put their trading technology first can achieve the best of both worlds.

First, it is clear an in-house trading team can provide great value to a firm, allowing it to maintain control over a mission-critical operation and ensure best-in-class execution. A well-oiled trader-portfolio manager relationship can leverage unique insight into executing the firm’s order in line with mandates. And, while it is true that trading desks cost money and heightened volatility can swell the cost of execution for large and complex orders, research indicates that having traders with performance goals linked to achieving the best price execution brings costs down below the industry average. Assuming the standard industry average, execution cost of 10 basis points (bps) with a standard deviation of 4 bps could therefore deliver up to 40% cost savings.

In an ideal scenario, best execution is reached when a trader has deep knowledge of the instrument being traded, prevailing market conditions, and a clear understanding of the intentions of the portfolio manager.

Trading teams who are unaware of this may turn to outsourcingwhich may be the right answer for firms eager to remove these costs entirely. However, total outsourcing of trading neglects the benefits of seasoned in-house oversight and expertise and increases the chances of having to accept sub-optimal trading performance. In an ideal scenario, best execution is reached when a trader has deep knowledge of the instrument being traded, prevailing market conditions, and a clear understanding of the intentions of the portfolio manager. Perhaps the “price” of mass outsourcing is worth payingagain, every firm must weigh the pros and cons for itselfbut is there a way to optimize costs and talent while maintaining the best execution possible?

The answer, as is often the case, lies in technology. As traders operate from home offices and vacation rentals in the seemingly perpetual age of COVID-19, the need to execute on a real-time basis in fast-moving markets can be the difference between profit and loss.

As traders operate from home offices and vacation rentals in the seemingly perpetual age of COVID-19, the need to execute on a real-time basis in fast-moving markets can be the difference between profit and loss.

Traders can be supported by sophisticated and customizable automation tools that “sit on the blotter,” acting as a Smart Trader Assistant. Orders on the blotter can be routed based on simple or complex criteria across a broad range of data points including user-defined fields or calculation coming from systems up- or down-stream. Simple examples include auto-routing orders based on portfolio managers’ instructions, ADV or market-capitalization thresholds, bond ratings, and indications of interest. Automation tools can also be integrated with in-house or third-party algorithms that offer advanced execution strategies for achieving price improvement.

Augmenting automation provides timely and accurate decision-making support to traders, allowing them to focus on improving execution performance while leveraging automation for routine tasks. It also crucially removes the need to scale a trading desk to scale performance and therefore, helps keep costs low. 

We are at the tip of the iceberg when it comes to finding ways to apply Artificial Intelligence (AI), Machine Learning (ML), and Naturaloutsource_trading_diagram Language Processing (NLP) to a trading operation. As shown below, a compelling vision for the future could imagine a world where pre-trade market impact models inform broker and algorithm strategy selection, as well as auto-route eligible orders based on pre-defined rulesets. Traders, with the help of ML decision support, would monitor trade execution in real time and make decisions to improve the performance of executing orders in flight. Also, transaction cost analysis (TCA) post-trade would capture relevant performance details, which in turn are used as inputs for pre-trade decision support the following day.

Supplementing traders with technology is a future-proof way of scaling and cost-optimizing operations. This allows firms to maintain control over a mission-critical and ensure best execution while keeping costs in check. Technology offers enticing possibilities, but it’s only with the addition of human insights and expertise that it can produce optimal performance outcomes on a consistent and repeatable basis.

COVID-19 is not the first black swan event and certainly won’t be the last, but this doesn’t mean having to choose between lowering costs and maintaining alpha-generating execution.

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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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