Adding alpha through agility

How will active managers generate alpha in 2030? Many are already wondering where they will find excess returns this year, let alone the end of the decade. The search will need to go far and wide, but must start close to home, with a more flexible and efficient front office.

In the wake of the COVID-19 pandemic, an eleven-year bull run has come to a crashing end, with all leading indices suffering precipitous collapses and extreme volatility. Few are brave enough to predict what comes next, due to the manifold sources of uncertainty and disruption. Many central banks have responded to the coronavirus crisis with the resumption of quantitative easing, which had a been a driving force behind the bull run and a key reason for the popularity of passive vehicles. In August 2019, assets in US index-based equity mutual funds and ETFs outstripped active stock funds for the first time, confirming a clear trend and adding to pressure on active managers to cut fees and raise their game.

Outperform or be outsourced?

Today, however, there’s little clarity on the ‘new normal’, nor where outperformance will come from. We don’t yet know the full impact of recent market movements on all active investors in 2020, however the pain for those in passive vehicles is clear. Unpredictability will give actively managed funds an opportunity to re-establish their worth to clients, but they will need to evolve. Already, portfolio managers, analysts and traders are innovating, using new alternative data sources and analytics to identify and implement investment ideas across increasingly diverse asset classes and geographies. Trading desks in particular have seen rapid automation, but much more remains. To avoid being outsourced, fast-shrinking trading desks must contribute alpha and demonstrate best execution, while expanding the volume and range of trades executed.

Equities traders were early adopters of algorithms, but market structure reforms and increasingly granular execution data (e.g. FIX tags) are constantly pushing the boundaries of automation. FX and rates are not far behind, albeit using different protocols, venues and platforms. Less-liquid OTC markets are also evolving, for example using pre-defined parameters for automated requests for quote.

Across asset classes, automation remains a work in progress, intensifying demands on technology infrastructure. Traders are often supported by quantitative execution teams who deploy analytics tools – often driven by machine learning – to refine trading strategies further. Sophisticated data analytics are helping to evaluate the contributions of inputs (ranging from third-party research to market signals), decisions and processes to effective pre- and post-trade execution.

Technology is also helping the front office wring every last drop of alpha out of each investment idea, matching the execution strategy to the order and the conditions. This can involve tweaking in mid-flight if necessary, but also monitoring and streamlining supporting processes. Pre-trade analysis is growing in importance and sophistication but must be allied to a trading infrastructure that can support high speeds, high volume and high levels of trading automation, whilst offering traders optionality across a variety of liquidity destinations. Firms are also relying on specialist third-party capabilities across research, risk and portfolio analysis, and on wider business consultancy services.

These initiatives enable front-office staff to seek differentiated returns beyond the major indices, markets and asset classes. With listed securities subject to increasing uncertainty on volatility, liquidity and price, active managers are turning to alternatives, from precious metals to private market assets and insurance-linked securities. In parallel, more sophisticated hedging strategies are being developed to preserve investor capital.

To add value in the decade ahead, front-office platforms must become more agile and adept at integrating cloud-hosted third-party capabilities quickly and seamlessly. Ensuring high performance at speed and over time will mean delivering application upgrades continuously over the cloud, while offering flexibility through modular design. Increasingly tech-savvy traders must be able to make minor tweaks to suit their preferences without waiting for vendors or in-house staff. By 2030, trading solutions may well be more app-like in functionality, offering ever-greater levels of customisation.

Avoiding overload

As well as supporting agility across asset classes, the buy-side trading technology infrastructure of the future must also support vigilance and insight. Trades might be automated, but they must be calibrated, then monitored then analysed. As traders operate across multiple protocols and asset classes, they risk information overload. This calls for greater use of data visualisation techniques to present potential outcomes from alternative scenarios. Tailored dashboards can present the trader and the portfolio manager with the data they need – fast, accurate and easily digested – to make the right decisions, then switch plans quickly and painlessly.

The reforms of the last decade have reset the relationship between asset managers and investors, with the aim of rebuilding trust. Regulatory change also imposed cost at a time when margins were being squeezed by undifferentiated returns. In the 2020s, the line between active and passive may blur, but the priority for all managers will be to optimise opportunity, while minimising risk. As trust rises, so should inflows, meaning the growing size of the cake will compensate for the lower margins. Whether meeting niche needs precisely or reaching out to the mass markets, the front office will stay in the front line.