Until recently, the majority of

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1 Identifying the real value of algorithms in a fragmented market Charlie Susi* Until recently, the majority of algorithmic trading strategies used in the global marketplace have been created to address very common problems or goals. Even though they are tailored at the margins, they have been built to be deployed across broad populations of clients. They have been solutions to common problems. As the industry grew, these strategies enabled people to deal with pervasive challenges, such as fragmentation. It was also a practical approach; to be able to fund these capabilities firms had to be able to realise meaningful ROI on their research and development. Industry participants have therefore been incentivised to build algorithms that would work for many clients and generate significant commission volume. Now, however, we are entering a new generation in electronic trading. We must challenge and rethink the way we address the delivery of execution services to clients. There is a desire for truly specific, one-to-one execution advisory and a holistic, more prescriptive service delivery in electronic trading. Rather than starting with the common solution, we should start with individual clients and their specific and unique sets of requirements. This means identifying a specific problem for one client at a time, then quickly and efficiently constructing a uniquely optimal solution or set of solutions with great precision, allowing clients to implement genuinely individualised trading programs. It is also necessary to rethink the way the technology works. The development environment has to evolve in a way that supports a whole new approach to strategy creation and client service. A return to simplicity On the client front, we see a demand for a simpler order interface and for simpler strategic decision processes. There are so many choices out there. Every broker offers a full suite of algorithms; there are various * Charlie Susi, global co-head, UBS Direct Execution 9

2 10 branded strategies, customisable strategies and flexible parameters. This makes the order entry process extremely complex for clients in some cases. Our clients tell us they want to reduce the need for unnecessary keystrokes. They want their decision process to be simpler or more intuitive and they want their workflow to be as streamlined as possible. Algorithms have come a long way from the fixed schedule, rigid structures of the past. They are now fully dynamic systems, using complex mathematical models to drive not only macro-level decisions of speed and urgency but also the micro-level order placement. The sophistication of these models will continue to adapt and evolve based on client demand and market structure change. To some degree, technology will continue to play its part as calculations or optimisations which were too slow to be useful in the past can now be performed quickly enough to be incorporated into the decision process. Have algorithms kept pace? Around the world, exchanges and alternative venues have transformed dramatically over the past several years. To keep up sufficiently with these market structure changes algorithms must be able to do the following: Process symbol and market activity on both a historical and real-time basis; Monitor performance on a venue-by-venue basis and determine whether their mechanics of interacting with the current flows are ideal; Have real-time protection to decrease participation as needed based on reversion or alpha views; Adapt their order placement logic based on nuances and the structure of each venue, including various order books and hidden order types; Use direct market data feeds and co-location facilities to avoid potential issues related to data latency or latency arbitrage. Many clients are concerned about stories in the media that suggest the majority of continuous flow in the markets is short-term in nature. We are seeing a much more exacting degree of analysis relating to the actual quality of execution in both dark and lit venues. A key to dealing with the prevalence of short-term liquidity is the amount of discretion an algorithm is given to achieve its objective. An algorithm with little discretion has no flexibility to react to changing market conditions or adapt to changes in volatility and volume. Often the benefit a human brings is in knowing when to deviate from the original plan based on new information.

3 We can give algorithms that same capability if we do not force them to trade in a fixed or linear way. When an algorithm is given discretion around a specific target, it can utilise that scope to make value-adding micro decisions throughout the life of the order. Those decisions can be anything from utilising proprietary alpha models that aid in short sensitivity to price movements to allowing orders to take advantage of unique liquidity opportunities in lit or dark markets. The inherent amount of discretion is different for every stock and changes on a real-time basis, but clients can also define the amount using volume ranges, for example. Algorithms can be configured to take discretion based on either mean reversion or momentum views of the market. Being smarter in the dark Dark pools once consisted purely of I would liquidity, passive natural order flow that was happy to trade but had a lower urgency. As high frequency trading has increased and brokers and vendors have integrated all algorithms and SORs into these pools, the nature of dark liquidity has changed. Now tactics ping in the dark before going to lit, searching for dark liquidity before having to go to the market and affect the displayed quote. Depending on the strategy being used, this can create a constant rate of market bound liquidity passing through the dark pools. What does this mean for algorithmic trading? It reveals a need to rethink sensitivity to market impact or signalling risk and require different selection decisions. The client/sales trader experience is key algorithmic logic can be tailored to suit one s view on urgency versus negative selection risks. Clients can exercise greater control over limits or increase an algorithm s discretion to react dynamically. The US has seen the biggest proliferation of non-displayed pools running the spectrum from exclusive to broad access. Most smart order routers (SORs) are optimising to access all levels of liquidity from dark to lit. UBS liquidity pools are optimised for quality and institutional algorithmic trading flow, striking a balance between both ends of the spectrum. We believe that urgent flow is not toxic, but that great care needs to be taken when interacting with it. UBS algorithms are designed to look for best execution and quality liquidity as a primary driver not to maximise crossing with all types of participants (see Figure 1). A trader who absorbs all the urgent market-bound liquidity creates an imbalance in what gets to the market. The quoted price will adjust based on the new supply and demand. So a dark order, while still unseen, is creating impact by the way in which it supplies or removes liquidity. 11

4 Figure 1: Impact from dark executions Most exclusive Broadest access Exchange/ECN/MTF displayed orders Exchange/ECN/MTF dark hidden orders SOR smart order router Broker ATSs Broker undisplayed liquidity Buy-side crossing network 12 Source: UBS Algorithms need to be smarter, and more nuanced, in the dark. The key is to create greater subtlety in algorithms. By expanding real-time discretion, for example, algorithms can dynamically control their participation in the dark to carefully optimise interaction with urgent liquidity. Using dark discretion in combination with dynamic minimum fill sizes, one can tailor a client s interaction in the dark to maximise interaction with natural liquidity, while still being sensitive to the large percentage of market volumes that more traditional, scheduled algorithms represent (such as VWAP and volumesensitive orders). This enables the optimisation of liquidity capture, while managing the order s footprint in the market on a holistic basis. Although this discretion can increase the duration of the order, it reduces the existence of outliers and can provide for a more consistent execution. We believe it is important to focus on the right kind of interaction with the right kind of liquidity, which is why we have always been particular about when and how to access certain venues and types of flow. That is not to say that we make black and white, connect or don t connect decisions quite the opposite. Our bias has been to make many choices available. The key is to always be hypersensitive about the way an order interacts

5 with certain kinds of participants and certain types of venues at the strategy and symbol level. In this new, short-term liquidityheavy environment, by opening up the degree of discretion an algorithm s logic uses when making order management decisions, we can be even more precise in our interactions in a real-time way. It is, however, important to note that not all clients want this level of discretion in their dark algorithms. If clients feel very strongly that they would prefer to treat all dark liquidity as if it were basically lit, it is critical to limit the algorithm s discretion and instead let clients put hard participation limits in place, calculating an aggregate participation rate between both displayed and non-displayed liquidity. The result: finely tailored dark strategies that suit an individual trader s preferences and trading characteristics, translating best execution to the client s own definitions of performance and value. Change is the only constant This brings us back full circle to the environment of rapid market structure change and fragmentation. It is a global phenomenon, as regulators across the Americas, Europe and Asia-Pacific are grappling with ways to evolve their rules and surveillance to keep pace with the global markets and the rapid development of advanced technology. This requires the architecture of an algorithmic trading platform to be respectful of core regulatory concerns, while always staying focused on serving our investor clients and keeping abreast of the constant dynamic change in flows. For us, that means ensuring everything we do is driven by our commitment to market efficiency, investor choice and transparency. At the end of the day, the best way to navigate fragmented markets is to stay focused on what your business model is all about and be sure that you are taking care of your clients evolving needs along the way. n 13

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