How Long-Only Fund Managers Can Optimise Execution

In this blog, the second in our series focusing on trading system requirements of long only fund managers. We take a closer look at how long only funds can benefit from specific order and execution management functionality. In particular, how real-time transaction cost analysis (TCA) can boost effectiveness, optimise execution and enhance algo wheels. Real-time TCA is arguably the most important and powerful aspect of the algo wheel, however, few algo wheels in the industry are able to provide such functionality.

Algo wheels and their limitations

The use of algo wheels as a mechanism for not only optimising trade execution, but also minimising market impact and reducing trading costs, has become relatively commonplace among buy side traders in recent years.

There are several reasons for their growing popularity. Firstly, algo wheels can reduce the impact of human bias and emotion on trading decisions. To avoid becoming too attached to a particular trading strategy or system, traders can opt for the wheel to choose from a select number of algos. Secondly, because algo wheels typically include a range of different algorithms that are designed to take advantage of different market conditions and trading strategies, they can help improve execution quality. Thirdly, they can often be customized to meet the specific needs and preferences of individual traders or desks. Traders can choose which algos to include in the wheel, for example, and set parameters for the types of trades they want to execute.

Algo wheels do have their limitations, however. They are typically pre-programmed with a limited set of algos, which may not be suitable for all market conditions or trading strategies. This means that traders may miss out on opportunities or face losses if the selected algorithm is not appropriate for the current market conditions. Also, they often rely on a random selection process, providing limited control over the selected algo. Another limitation is that during times of market volatility or unexpected events, algo wheels can struggle to adapt to sudden changes in market conditions, which can also lead to unexpected losses.

Ideally, to be most effective as an execution tool for long only fund managers, the algo wheel should contain three key components. The first is a mechanism for automatically routing orders to brokers based on predefined rules, without any human intervention. Most algo wheels have that. The second is a randomiser, which selects brokers at random to ensure that they all receive an equal amount of flow. Most algo wheels have that too, and some even provide weighting options, for taking fees and commissions into account, for example. The third component, which is a lot less common, but probably the most essential, is the ability to work with real-time TCA data.

Incorporating real-time TCA

Long only fund managers typically run transaction cost analysis on a post-trade or T+1 basis, and they use it for several purposes: to assess the effectiveness of their trading strategies and execution methods; to better understand their trading costs; to make more informed decisions; and ultimately to improve overall trading performance. TCA has also become an important tool to help firms meet their regulatory obligations around best execution.

The TCA process itself usually involves gathering trade execution data (including the time, size and price at which orders were traded), analysis of direct and indirect costs (such as slippage, for example), and evaluation against various benchmarks such as VWAP (volume-weighted average price) or TWAP (time-weighted average price).

While this kind of post-trade TCA is certainly a useful tool, an increasing number of forward-looking long only fund managers are now starting to make use of real-time TCA metrics, so that they can determine the optimal venue, broker/counterparty and algorithm for each order on a pre-trade basis.

Where this approach becomes really powerful is when the real-time TCA output is integrated into an ‘intelligent’ algo wheel, i.e. one that can ingest that data and, using both pre-defined and adjustable parameters, make the necessary routing decisions based upon how the various venues, brokers and algos are likely to perform under similar conditions to those under which the order is being executed. This means taking into account factors such as expected liquidity, participation, spreads, live market volatility, and volume consumption, for example. Of course, the algo wheel should also provide the capability for traders to either accept those instructions or override them.

A key point here is that this sort of execution functionality should be customisable. Every fund is different, so it should be possible to tailor the TCA and the algo wheel to meet the needs of both the firm and the individual trading desk. Particularly as the algorithmic execution workflow around equities, fixed income, FX and exchange-traded derivatives is markedly different.

Conclusion

By incorporating real-time, actionable TCA into the algorithmic execution process in this way, long-only fund managers can improve their execution performance by ensuring they are always using the optimal algo, broker and venue combination for the current market conditions. An additional benefit is that this type of functionality can lead to traders’ workflow being greatly simplified, thus freeing up their time to focus on more challenging tasks.

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