As financial markets, new asset classes, and regulations become ever more complex, so does managing the trade lifecycle. Hedge funds are increasingly trading more sophisticated derivatives and working to respond faster to capture market opportunities, and are looking for the right technologies to support faster and smarter investment decisions. 

From Beacon’s 2024 Hedge Fund research, 51% of hedge funds surveyed consider seamless integration with existing systems as a very important source of competitive advantage, and 45% think that broad market coverage of asset classes across global markets is very important to be able to capitalize on diverse opportunities.

Amazon AWS recently hosted a discussion with Beacon Platform CEO and Co-Founder Kirat Singh on this evolving technology landscape for hedge funds. They explored the benefits, challenges, and outlook for the proactive, data-driven technology changes that hedge funds need to effectively navigate current and future markets.

Technology-driven changes and benefits of the past few years

One of the notable changes that Kirat commented on is the shift towards multi-strategy funds and the impact this is having on technology infrastructure. Having quants and developers at the trading desk has changed from a competitive advantage to table stakes. Monolithic or “black box” systems are not flexible enough to handle multiple strategies, but using different systems leads to inconsistent risk and PnL metrics across the desks. The growing capability of integration platforms with open interfaces and pre-built plugins is enabling funds to complement and augment legacy systems with best-of-breed technologies for different processes and strategies.

Another significant shift is the use of cloud computing. The flexibility of cloud environments to scale infrastructure in line with business demand is encouraging funds to move high-volume or high-performance quant processes like pre-trade analysis, strategy backtesting, signal generation, risk analytics, and PnL calculations to the cloud. Typical use cases include:

• Pre-trade research, using the cloud for portfolio simulation and strategy backtesting with notebooks and large volumes of data
• Intraday pricing and risk, streaming live market data into real-time pricing and risk calculations for complex assets and customizable risk factors
• Multi-asset assessment, generating risk, PnL, and VaR reports across multiple pods, asset classes, and risk factors

Current technology usage and challenges

Recent market structure changes and the current market environment are continuing to put pressure on technology usage and choices. Kirat noted that hedge funds need a very flexible technology and quant stack to be able to spin up a new trading strategy or react quickly to geopolitical events and market shifts. Real-time or intraday risk reporting across portfolios and asset classes is essential to ensuring that the fund does not become too concentrated or over-exposed at the book, portfolio, or firm level. This may require quickly ingesting new data sources, customizing risk analytics and pricing models, and modifying the set of risk factors.

Commodities, renewables trading, interest rates and inflation, and private credit are driving some of the biggest new funds. One of Beacon’s hedge fund clients runs thousands of calls in their cloud platform to generate intraday real-time risk measures for their equity dispersion strategies. A credit desk looking to take SRT transactions and buy loans off a bank’s balance sheet has data scientists using the platform to calibrate credit spreads and then plug them into the fund’s VaR models to define how those spreads will float on top of rates. Running applications like these on the cloud creates some data movement challenges, as the desk needs access to a complete set of market data, trading activity, pricing models, and risk analytics. But modern portfolio analysis and risk management platforms make this faster and easier, with integrated data warehouse tools, development environments, and extensible financial frameworks.

Outlook and advice on future technology developments

Whether running on old-school spreadsheets or trying to leverage modern AI/ML capabilities, the core of the hedge fund business model is still generating and communicating a clearer understanding and better visibility of risks. AI/ML is helping by becoming a productivity enhancer, improving or accelerating processes like code review, pre-trade research, model training, and workflow automation. 

From Beacon’s 2024 Hedge Fund research, most respondents thought that their risk visibility had improved slightly (75%) or dramatically (18%) over the last two years. The surveyed group ranked investment in technology as the primary reason for this improvement, and using a platform for easy integration as the secondary reason.

The job of technology providers in this industry is to give funds and their investors a set of fundamental building blocks that they can use to ingest data, customize their pricing models and risk analytics, and map those onto compute infrastructure to get the results they need when they need them. Having a single system that can do 80% of the work just isn’t good enough. So funds are looking to technology leaders to deliver best-of-breed solutions that work together to take advantage of each team’s specialized expertise.

Kirat concluded that “Our job is to make sure that hedge funds can, in a timely manner, figure out where their edge is, trade the risks that they want to take, and manage the portfolio lifecycle. Because if you don’t understand your risks, you can’t take the right risks.”