By Ed Gouldstone, COO Northern Europe, Linedata Asset Management
The prevailing narrative is that active managers are becoming obsolete amidst the seemingly inexorable rise of passive investing. Indeed, this existential threat has grounds. The record-breaking bull run and period of low or negative interest rates have meant managers have struggled to outperform market indices – stock-focused hedge funds on average gained 13.7% last year, while the S&P 500 climbed 29%, according to Hedge Fund Research. Signs active managers are losing their edge have spurred investors to pile vast sums into passive investment vehicles, with assets in US index-based equity mutual funds and ETFs outstripping active stock funds for the first time in August last year.
However, active managers are responding to the overriding pressure to increase returns and realise that the front-office must evolve. It’s becoming ever clearer that deploying technology in this area will be a decisive factor in being able to continue to deliver value to investors.
Algorithms and adoption of automation are nothing new in the hedge fund industry. A Morgan Stanley poll at one of their recent investor events showed that only 13% of firms were not employing or looking to employ machine learning in their investment strategy. Looking at in-flight data, hiring in-house meteorologists and building powerful machine learning models are all part and parcel of how hedge funds are evolving to market pressures.
However, beyond pursuing a more data-led investment strategy, upgrading technology across front office operations can deliver greater efficiencies and unlock new opportunities, helping managers to wring every last drop of alpha out of each investment idea.
In my view, there are three key areas where active managers can deploy technology in their front-offices to help achieve a market edge:
- Trade and compliance automation
Automation is fast becoming an inextricable strategy for hedge fund managers and the front office is no exception. Trade automation through machine learning enables firms to automatically match execution strategies to order conditions and even tweak orders in mid-flight, if necessary.
Furthermore, monitoring and other support services are also becoming increasingly automated. This not only reduces the burden of reporting but can also aid risk management and prevent failures. Last month, the FCA outlined its concerns that the uptake in algo trading had led to widespread market failures and flash crashes, like the ones seen in 2010 and 2018. However, this may be a rare instance where it is possible to fight fire with fire, and applying AI to compliance and risk management may help improve the health of financial markets.
- Data visualisation
Trade automation, while maximising potential, has also led to far greater complexity in the daily lives of active managers. It has enabled managers to hold drastically more positions at any given time and across a wider range of asset classes. This far outweighs any single manager’s ability to hold all this detail in their head and to understand the potential outcomes of each investment.
Data visualisation tools and software will therefore be vital for managers to continue to operate successfully. These distil investment data into a clear and digestible user interface, giving managers a holistic understanding of their portfolios. This enables managers to make informed decisions with confidence, quickly change investment strategy where necessary and identify untapped opportunities.
- Cloud migration
Many sectors are already experiencing the benefits and flexibility that comes from moving to the public cloud. While financial services in general, and asset managers in particular, have been slower to adopt cloud technology, the industry is starting to make the jump in full force.
The front-office of the future needs to be far more agile and be able to integrate third-party capabilities which more often than not are cloud-hosted. External data inputs, research and other applications need to be added or integrated in modular fashion, often using APIs. By adopting this approach, the front office can be quickly adapted to traders’ needs, which may shift suddenly according to market developments. Digitalisation will bring greater customisation and optimisation to increasingly tech-savvy traders, who want to integrate the best software and data into their investment decision-making.
The halcyon days of the high fee hedge fund are gone, and most probably forever. The unrelenting search for efficiency to drive down fees and increase returns have changed or are rapidly changing the modus operandi of hedge funds. The future is uncertain, but hedge fund front offices will be cloud-enabled, data-led hotspots for machine learning applications. The only remaining question is who will change with the times and who will be left behind.