Exploring the Challenges Facing the Sell-Side

1. Driving down costs while still improving technology and automation is key for the sell-side.
   
   
2. The move to remote trading has opened up interesting new channels for how the sell-side can cooperate with its clients.
   
   
3. The partnership between Quod and Refinitiv brings together a complete suite of technologies and data for the sell-side.

Quod: Cost, Cost, Cost. Every decision made by sell-side firms recently has been related to the “do more with less” methodology. Driving down costs while still improving technology and automation through innovation has become key. From fees to headcount, the true challenge is to meet increasing transparency and performance demands with a smaller pot of money.

The leading disrupting technology that is needed to break the spiral many sell-sides are facing is automation. Automation improves the trading outcomes for clients by providing greater efficiency across the trade execution workflow and reduces the load on individual traders — allowing them to focus more on complex trades and more systematically (and fairly) manage clients.

The technology solutions required today, and in the future, are about simplifying the trading desk through clever uses of automation and minimizing the need for simple, repetitive tasks to be done by traders.

Refinitiv: COVID-19 has provided an interesting perspective for much of the sell-side. On the one hand, the increased volatility and related volumes have proven to be gold dust at a time when many brokers have struggled with increased costs due to changes in regulation, such as MiFID II, while seeing both volume and commission compression.

On the other hand, the challenges of providing the technology necessary for remote trading have pushed greater demands around system deployment and the tools required to administer them, bringing to the fore some of the shortcomings of the traditional ways to manage order management systems (OMS) and related systems.

One of the largest costs associated with all trading businesses in recent times has been market data. In Europe, the increased fragmentation caused by firstly MiFID I and subsequently MiFID II, along with the lack of consolidated tape, has seen costs rise exponentially.

These costs are often unnecessarily duplicated when dealing with disparate systems within an organization, with exchange fees alone rising in the millions of dollars. A change of tack is now taking place on the sell-side, with the pressure on vendors to utilize a single data feed across systems where possible.

Quod: The primary, and often overlooked, challenge in transaction cost analysis (TCA) is understanding how to convert the output of a TCA report into practical changes that need to be made in a complex execution management system (EMS)/OMS technology solution.

The responsibility of interpreting TCA/benchmarks and taking action is often pushed to the trader. However, the true challenge is to understand how specific settings in the execution policies and algos can influence order outcome for a specific stock in specific market conditions.

Artificial intelligence (AI) and machine learning (ML) can add significant value if they are provided in a holistic service from the technology vendor. By combining all three of these technologies (EMS, TCA, AI/ML), you can use machine learning to make real-time recommendations on EMS configurations based on the TCA output provided to the traders.

It also enables the monitoring of many trends, which are mostly time-series trend analyses (e.g. Hit ratios or Fill ratios over time) and can detect an event or shift in trend earlier than currently possible. This empowers monitoring executions with actionable intelligence to then select the execution mechanism most appropriate for a given execution strategy, and improve trading decisions for traders.

Refinitiv: For the sell-side agency business, post-trade tends to be something offered to customers but provides minimal influence on its own trading desk. However, both pre and in trade are essential tools in assisting the trading with providing a premium service to the broker and its clients.

At a time when many front-office roles are being reduced, the ability to bring machine learning techniques can only help brokers handle increasing workloads in incredibly complex markets. The ability to automatically spot either erroneous or under/over performed orders in the deck real-time will allow the trader more time to focus on value-add services and less time staring at the screen looking to spot problems before they occur.

Using ML within pre-trade is really the holy grail for traders assisting in making decisions around finding liquidity or the best practice in executing orders. By learning from previous methods of execution, over time the system will be able to present the traders with several options based on historical performance but tailored for current market conditions.

While order automation already exists to a certain extent, this hybrid model combines the talent and domain knowledge of the trader with tools simplifying the analysis required to make informed trading decisions.

Quod: As brokers and traders look to differentiate from their competitors, the need for more in-house technology has significantly increased. The goal is to empower trading desks to build their own automation workflows and improve automated decisions in execution to improve trading outcomes.

This requires advanced SOR and automation technology to be built directly into the OMS. Multi-vendor solutions are a challenge when facing the need to drive down costs. SOR was historically the advanced technology only available to the Tier 1 brokers for direct market executions, but the same routing and execution intelligence can now be used in mono-markets.

Multi-broker decision making now includes selecting the lowest cost + price execution route for a specific stock or predicting dark pool liquidity and market impact. SOR and automation is the key tool in improving best execution and TCA outcomes. The most advanced SORs are already making use of a wealth of information in the post-trade to generate intelligent data to scale the whole decision-making process.

With ML prioritizing innovation in the context of the SOR, this provides a low-cost and efficient mechanism to extract data patterns and implicitly find a new trading workflow. It means that the trade data derived from the internal EMS, best execution reporting and external data providers can be used to refine some of the behavior of the underlying SOR algorithm(s).

Smart Order Router (SOR) - Manage and seek liquidity with 470+ configurable parameters

Quod: Quod Financial with Refinitiv brings together the complete suite of technologies and data, globally for the sell-side. Every aspect of the sell-side technology requirements in all asset classes can be met in this single partnership, helping to improve efficiency, providing best-in-breed technology, and lowering the cost of ownership.

As the sell-side market consolidates and monopolies of technologies emerge threatening sell-side with huge cost increases, this partnership offers a safe-haven for clients combined with cutting-edge functionality.

Refinitiv: We at Refinitiv began a project over 18 months ago seeking a way to make our offerings more attractive to traders on the sell-side. We realized we had many relationships for data, connectivity, and other offerings with brokers but had yet to really penetrate the trading desk itself. It was clear that without providing traders with the tools to actually trade, we were unlikely to really be considered a viable alternative to the incumbents in the space.

In choosing a partner we wanted to fully integrate the offering into our workflow, and this meant integration not only from a technical standpoint, but also a cultural one. We found the right partner in Quod, who had over 20 years’ experience working with our products, but also fully understood how we could cooperate and on presenting the appropriate solution to clients and prospective clients.

We found we had a number of mutual clients in existence, where the use of our data in particular was prevalent in powering Quod’s OMS, Algo Engine and Smart Order Router, and we also found that the vast majority of Quod clients had existing connectivity with Refinitiv Autex Trade Route.

For this reason, choosing Quod as our partner in this space made the decision so much easier, as our go-to market timing could be day one, rather than having to spend time on a complex integration program. We’ve already taken our first client live as part of the co-sell and have developed a very healthy pipeline together without putting any type of marketing campaign together, which gives testament to the solid nature of our joint venture.

Read more about the Refinitiv and Quod partnership

Quod: Exciting! Every form of change is an opportunity and many of the challenges faced by the sell-side are a result of the wrong tools for the wrong era. The regulatory, TCA, execution challenges seem burdensome to many institutions because the technology they are using to address these problems is too inflexible to offer proper solutions.

Our experience with clients pertains to requests for new product features, automation, and AI/ML processes that can revolutionize how they interact with their clients and the services they offer. Change is always stigmatized in financial markets, but approaching this new future with market-leading innovation creates more opportunities than challenges.

Refinitiv: The sell-side is undergoing a transformation in a similar way to the buy-side has already seen over the last few years and is ripe for disruption. As contracts for incumbent providers begin to expire, the sell-side will begin to look outside of these providers. Many who are struggling with legacy technology hangovers and others that have been caught up in internal and external mergers causing chaos in retaining both key staff and customers.

This opens an opportunity for next generation system providers to enter the space and up the game in terms of the both functionality available alongside the methods that these can be delivered. Bringing in new techniques in the analysis of trading data should bring a change in the role that the trading desk plays in the workflow with a move to higher quality, higher touch services, with more menial tasks becoming semi- or fully automated.

The partnership of Quod’s OMS suite alongside Refinitiv’s data and connectivity offerings means we are fully equipped to meet the requirement of this changing landscape in bringing the next generation of trading tools to the sell-side.