Building a winning multi-asset execution strategy: what is needed in terms of trading, intelligence, and technology to succeed

Solve for the future: what the street can learn from the valley

The road to digitization is paved with promises. Great stuff – like cost efficiencies, speed, smart automation, decision-ready intelligence, and real-time optimization. With payoffs so attractive, our industry has been pursuing greater digitization for quite some time. Despite our obsession with speed however, we’ve largely overlooked the more straightforward route-learning from the native digital companies who paved the way in the first place. If the buy-side wants to make the most of digital’s next level possibilities, we should take a page from these companies’ innovation roadmap. Instead of reacting to the acceleration of technology and the proliferation of venues, we can proactively transform to not only keep up with but capitalize on, the pace of change.

Valley talk

Native digital companies like Apple, Amazon, Facebook, and Google operate on a product-led architecture-an organizational methodology that puts making innovative software front and center. Most product-led organizations function through an engineering-inspired flat management structure that is anti-silo and synergistic. Data sharing allows inspired hypotheses to surface from all corners of the organization, encourages a culture of constant learning and breadth and depth of knowledge among team members, and opens the door to greater automation-all of which increase productivity. This helps explain why product-led teams can make it work with less capital than traditional companies- while operating at scale. Ideally, the product-led methodology leads to a culture of creativity, innovation, flexibility and growth and the ongoing ability to adapt and pivot as new opportunities appear.

Street smarts

For today’s asset management firms, the creation and management of intelligent algorithmic trading tools have become increasingly more vital. Trends point to a future where increasingly intelligent and customized algorithms carry out multi-asset strategies and even govern fully intelligent portfolios – with both asset allocation and trading strategy dynamically tied to multi-factor models. Around the bend, machine learning and automation play a bigger and bigger role, across progressively blurred boundaries of asset classes and an ever-proliferating number of venues. Institutional traders must navigate the immediate opportunity of multi-asset trading and–beyond that-the ever-accelerating and digitally-driven unknown. In other words, they’ve found themselves in the ‘product development’ business. So borrowing and stealing from a tried and true innovation framework-in terms of organizational structure, technology, and intelligence leveraged-makes a lot of sense. Not only that, it will set them up for success.

Consolidate to innovate

Automation, done right, simplifies, economizes and optimizes-better and faster. The possibilities of technology allow firms to rethink the traditional separation of desks in terms of asset class. They can now interconnect formerly-siloed components, which simplifies the trading process and creates new insights traders can leverage in real-time. For instance, since asset class makes little difference in terms of trading strategy at the highly liquid end of the spectrum, organizations can streamline and trade multiple low-touch instruments from a single desk. This boosts the number of orders traders can manage during the daily trading window and allows traders to focus on more complex transactions. Not to mention, automation creates the architecture for tomorrow: as data science and machine learning facilitate more cognitive models, many of today’s ‘high touch’ transactions will move into ‘low touch’ routing.

BlackRock’s Global Head of Trading, Supurna Vedbrat, recently testified to her firm’s own structural makeover in this consolidated and interconnected vein. Through the adoption of a more dynamic organizational architecture, BlackRock has sought to aggregate fragmented liquidity, as well as to build in the flexibility and agility required to respond quickly when conditions change. Additionally, the firm now rotates traders among asset classes to broaden and deepen their understanding, build their capacity to work in multiple areas, and foster a culture of continual learning and skill development. Since making these changes, Supurna reports trading capacity management has increased and traders have enjoyed the chance to become ‘specialists’ in multiple asset classes.

Choose technology that conducts the flow of insight

When it comes to innovation, connectivity is the name of the game. The free flow of data makes meaningful real-time and historical insight possible. It grounds trading strategy including multi-asset high-touch/low-touch frameworks; provides the testing ground for algorithms before release into the wild; enables the collection and analysis of post-trade feedback; and facilitates unfettered access to analytics throughout an organization to maximize collaboration, reveal new angles and spark great ideas.

These technology components work hard to connect the analytical dots:

  • Automated multi-asset execution relies on a seamless connection between OMS and EMS. This is essential to separate orders between low-touch and high-touch execution styles and route to the correct desks without manual intervention. The OMS filters and routes the order internally, while the EMS carries out the trade-in accordance with a defined strategy and relays the execution insight to hone and influence future performance.
  • Open APIs interconnect the OEMS with its component parts and facilitate the free flow of insight throughout an organization and with its partners. The more interconnection between both internal divisions and external allies, the more advanced an organization’s capability to capitalize on real-time opportunities and innovate at speed.
  • Trading technology must also allow for fast and accessible customization. This enables traders to action both in-flight and post-trade data, while that data still matters. As the application of machine learning continues to evolve, traders will rely on increasingly intelligent algorithms to optimize for multi-asset, multi-strategy approaches in real time-making this feature even more indispensable.

Make the most of your human resources

And more good news: all of this consolidation, aggregation, and collaboration makes traders even smarter. Building in efficiencies like automating low-touch deals across asset classes gives gifted traders time to focus on complex deals, and put those skills to good use. Then there’s the positive impact of operational change. Electronic connectivity breaks down the tribalism traditionally dividing FX, futures, fixed income and equity. Once systems can connect with one another and insights get shared, specialists start learning from one another. They discuss market conditions, share trends, cross-pollinate ideas and make big picture connections invaluable for developing and coordinating original and competitive multi-asset strategies.

And, of course, traders can learn from the data itself and constantly improve performance helping best execution live up to its name. In a recent interview with The Desk, Josh Grodin of Wellington Management expressed his own enthusiasm for multi-asset low-touch trading, in great part because of its contribution to the feedback loop. He cited, “We think that flow trading and the concept of ‘low touch’ which has been prevalent in equities for so many years is consistent and transferable across rates, FX, and credit. From a high level, if we can segment the flow correctly, then we can introduce data-driven decision-making to optimally route orders, and then measure how we did from a cost standpoint. Then the lifecycle of a trade comes into play, wherefrom a pre-trade standpoint we have an idea of how we should route a trade, we dynamically watch and measure how we are trading the security during the trade, and then post-trade we can measure how we did and feed the findings back into the pre-trade model.”

Set up a culture of learning, get set for the future

To succeed in an increasingly fast-moving and digitized trading environment, buy-side firms would be well-served to borrow structurally and technologically from their Silicon Valley compatriots. Product-led organizations feature interconnections that fast-track collaboration, and agile feedback loops that deliver vital insights. Having these operational advantages would enable buy-side firms to act on Next Gen opportunities, such as low-touch multi-asset trading. In addition, they would set firms and their team members up with the architectural, technological, and intellectual agility to stay ahead as technology accelerates. As we move forward into this future of continual reinvention, it will not be the strongest or the smartest companies that survive (although those qualities can definitely be helpful), it will be those most adaptable to change.

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