As the buy side continues to grow, so does its reliance on technology to manage trading activities. The long-only section of that buy-side spectrum should not be ignored as it too looks for new technologies and vendors to help address its needs.
- Buy-side firms’ reliance on new technologies continues to grow.
- Long-only funds have trading system requirements different from other types of funds.
- Use of algorithmic trading is growing amongst long-only fund managers.
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People often talk about the ‘buy side’ in generic terms but buy-side investment firms come in all shapes and sizes, from small teams of quants running systematic trading strategies, to multinational asset management corporations with trillions of dollars in AUM. It’s a vast spectrum. What they all have in common, however, is an increasing reliance on technology to manage their trading activities.
In previous articles, we’ve focused on the hedge fund end of the buy-side spectrum, looking at how trading technology can help address the challenges and opportunities that hedge funds face: when first launching; when running various types of strategies across asset classes; managing their post-trade processes; conducting systematic and quantitative trading using APIs; and more.
In this article however, we’ll look more closely at the long-only end of the market where trading system requirements can be markedly different and will highlight the implications of those differences from a trading systems perspective. In future articles, we will look at how long-only funds can benefit from specific areas of functionality around order and execution management, drilling down into how real-time transaction cost analysis (TCA) can help boost the effectiveness of tools like algo wheels, for example.
But first of all, let’s go back to basics. What do we mean by “long-only”? Unlike hedge funds that can run a wide variety of strategies and are generally only available to accredited investors, long-only fund managers – as the name suggests – focus on buying and holding assets for the long term to achieve capital growth.
At the risk of generalising, long-only funds typically run higher levels of AUM than hedge funds, and because they are responsible for managing the investment assets of retirement and pension fund holders and other non-accredited investors, they are more heavily regulated. From a trading systems perspective, this means that full transparency is essential across the entire trade flow, pre-trade to post-trade, and that regulatory reporting and compliance functions need to be extensive.
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Global, multinational long-only funds that operate trading desks in multiple regions face various challenges from an execution management perspective. Given that each desk is likely to have its unique workflow around how it trades through regional brokers and how it connects to local data centres for accessing liquidity, the execution management system (EMS) must be flexible enough to accommodate those workflows and that connectivity. But it also needs to be balanced with an ability to unify all the trading activity – across regions, desks, brokers, asset classes and data centres – under a single view. That way, the fund can gain an accurate and consolidated overview of portfolio performance and risk, while still retaining the ability to break everything down at a more granular level from a trade execution standpoint.
Long-only funds often maintain more broker relationships than hedge funds too, so having a means of readily identifying the ideal broker/algo combination for orders they’re generating and sending into the market is highly beneficial. Algo wheels have become a popular tool in this regard, but they are often limited in their functionality and capabilities, so – as we will explore in our next article in this series – it is important for firms to be able to bring in metrics that can measure the performance of algos in real-time, and to have the ability to change routing paths on the fly.
The ability to trade multiple asset classes is also a key requirement for many long-only funds, and this is where a true multi-asset trading platform offers advantages over those that focus on a single asset class such as equities. If the fund is trading fixed income for example, it needs a platform that can efficiently execute and manage cash bonds in addition to bond and interest rate futures, options and ETFs, with links to the various liquidity centres, counterparties and trading venues.
From an FX perspective, whether hedging FX exposures or trading FX as an asset class in its own right, long-only firms need to be able to access FX aggregators and counterparty banks and to have a similar level of algo trading functionality for FX as that offered in equities.
For any fund trading fixed income, the system should be able to seamlessly accommodate RFQ (request for quote) workflow and display axe and inventory and other pre-trade data.
Another key requirement is the ability to handle more complex or advanced trading strategies, such as lists, baskets and program trades, which long-only funds use to buy or sell a large number of securities simultaneously while minimizing market impact and transaction costs.
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Regarding trade execution, given that such funds are generally moving large size when entering or exiting a position, market impact is always a concern. This is undoubtedly one of the reasons why the use of algorithmic trading is growing amongst long-only fund managers. According to a recent industry survey, long-only funds now use a range of algorithms to execute large orders, including VWAP/TWAP, % volume participation, dark liquidity seeking, implementation shortfall, and target close/auction algos, amongst others. And they are increasingly using algorithmic trading not just for straight equities, but also for equity baskets and other asset classes such as ETFs, listed derivatives, FX and fixed income.
Reducing market impact is just one of the reasons for the growing popularity of algo trading amongst long-only fund managers. Other factors, such as lower costs, ease of use, speed of execution, access to multiple liquidity sources, anonymity, firm/desk-specific customisation, and flexibility around how orders are routed into the market, as well as increased trader productivity and consistency of execution performance, are all contributing to that growth.
Some of the more advanced long-only funds are now using order data from the various execution venues to closely monitor and analyse the performance of their execution algos on a real-time basis, and incorporating that data into their order routing logic, setting different participation rates based upon market conditions, for example. They are also using algorithms for portfolio optimisation, identifying the optimal mix of assets to maximise returns while minimising risk, based on factors like historical performance, volatility, and correlation between assets. And algorithms are increasingly being used by long-only funds to monitor and manage portfolio risk, assessing exposure to market, sector, or currency risks, and making the necessary adjustments to maintain the desired risk profile.
Of course, to fully realise all the above benefits that algo trading can offer, the fund needs to have access to a comprehensive suite of algo trading functionality in its own right, and not be purely dependent on what is on offer from its brokers. This is why forward-looking long-only firms are deploying independent execution management systems (EMSs) that can offer such functionality and can be easily integrated with their existing technology stack.
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Long-only funds are increasingly making use of modern trading technology. And as their adoption of algorithmic trading grows, forward-looking firms that deploy multi-asset, functionally rich order and execution management systems that provide data integration, portfolio optimisation, risk management, regulatory compliance, performance attribution, customisable workflows, and scalability, are reaping the benefits.
In the next article in this series, we will explore how long-only funds can benefit from specific areas of functionality around order and execution management, particularly real-time transaction cost analysis.