The total size of the hedge fund industry is now above the $3.2 trillion mark and as the assets under management have grown, it has become harder for hedge funds to stay nimble. There is a widespread sentiment now that the hedge fund sector is overcrowded and needs to consolidate.
There are also too many hedge funds in the market who are not differentiating themselves or adding any real value, thereby diluting the industry’s performance. Statistically speaking, it is impossible for the current number – more than 24,000 funds globally – to outperform the market. In addition, hedge funds have found themselves competing with exchange traded funds (ETFs), “alternative beta” (also known as “AltBeta” or “Alternative Risk Premia”) funds, smart beta funds etc., mainly due to their lower fees and investors’ increasing desire for customized and unique offerings.
In an attempt to ward off competition and boost returns, hedge funds have been embracing technology, be it through strategies that use computing and mathematical models to pick stocks or those that use big data or machine learning in selecting and maintaining portfolios. 2016 was certainly a tipping point for the hedge fund industry’s embrace of data science. Approximately 40% of the hedge funds started in 2016 were “systematic” i.e. relying on computer models and algorithms to make trading decisions, while around 60% of total trading activities are currently performed using automated trading instead of human traders. A recent survey by Credit Suisse further validates this trend, with around 60% of the investors surveyed saying they planned to increase allocations to quantitatively focused strategies over the next three to five years.
As hedge funds embrace technology and innovation to achieve differentiation, managers will need to continue to explore the optimal operating models that they can adopt in order to stay ahead in this changing industry landscape. There is evidence to suggest that 50% of hedge fund managers in the small to medium size bracket believe that outsourcing is the only viable option, while others seem determined to build their capabilities in-house – a choice often driven by the size of the fund. Smaller funds are likely to have a much higher preference for outsourcing their technology needs, while larger funds may prefer to develop skills internally. Whichever is the case, all hedge funds will need bigger “intelligence teams” to gain access to the best ideas and skills, and ensure that what they build on their platforms and how they use it, is best suited to their portfolio needs.
There are several areas that require concerted focus from hedge funds in order to ensure their businesses are sustainable in the long run. However, this can easily distract a firm from its key objectives and use up valuable time. Key activities that will differentiate a hedge fund such as core investment, client management, and risk management capabilities, need to be built and maintained in-house; other elements can be better provided by a specialist external partner with significant industry experience.
For many hedge funds, questions still remain after a decision has been made to use machine learning or big data. Which are the right sets of alternate data to buy? How to write the codes and algorithms to best utilize machine learning for their specific portfolios? Using third party specialists, who understand where each hedge fund sits in this lifecycle of change and what specific services are best fit for each fund, can dramatically increase research productivity. This approach also provides access to a larger and more sophisticated infrastructure to develop innovative strategies.
Whether focused on equity, credit, quantitative, multi-asset or even hybrid research such as quantamental, hedge funds need to focus on their core activities in order to differentiate themselves in the market place. But this cannot be at the sacrifice of wider structural changes which must be addressed; strategic partnerships with specialist external partners remain the most effective way to deliver these.
Bhawana Khurana is vice president of client solutions in financial services at The Smart Cube.