The application of artificial intelligence (AI) by hedge funds is accelerating and transforming the industry, particularly in investing cost models and recruitment. Managers must also explain innovative AI-based approaches to investors. Given that the strategies result from supercomputers processing billions of data points and learning how to respond to markets in real-time, articulating how returns are generated is beyond human comprehension.
In September 2018, BarclayHedge’s Hedge Fund Sentiment Survey found that more than half of hedge fund respondents (56%) used AI to help them make investment decisions. It is almost three times more than the 20% who said the same thing a year earlier. Around two-thirds of people who used AI did so to develop trading ideas and improve their portfolios. People who took the survey said that more than a quarter of them used it to make trades easier.
The preliminary findings are encouraging. For example, in 2017 and 2018, the Eurekahedge AI Hedge Fund Index1 surpassed the flagship Eurekahedge Hedge Fund Index. Furthermore, the Eurekahedge Hedge Fund Index fell by 4% in the fourth quarter of 2018, while the Eurekahedge AI Hedge Fund Index remained unchanged.
How Hedge Funds Use AI?
Several hedge funds use AI to analyze massive amounts of data, forecast supply and demand imbalances, and forecast market movements for tactical asset allocation. It could help a CIO’s team combine different strategies and tailor allocations.
AI is being used by many investment managers, from pure AI-driven specialists to large quant-driven shops to traditional fundamental investors looking for a competitive advantage. Many companies across the board are turning to artificial intelligence to improve efficiency in their operations, accounting, and investor relations functions.
Indeed, in recent years, a new class of AI pure-play hedge funds has emerged, entirely based on machine learning and AI algorithms. Aidyia Holdings, Cerebellum Capital, Taaffeite Capital Management, and Numerai are some examples. Numerai, a well-known AI hedge fund, pushes the hedge fund business model to its limits.
The firm discovers investment strategies by hosting competitions among external AI experts, mathematicians, and data scientists. Numerai recently expanded its business model by making elements of its platform available to the rest of the financial community through its product Erasure, a decentralized prediction marketplace based on blockchain technology.
Dwarfing the newcomer Large quant funds with household names in the hedge fund industry, such as Man AHL, Two Sigma, Citadel, Bridgewater, and D.E. Shaw, are AI pure plays. For many years, traders like these have used computer-driven models to discover new trading strategies and identify themes, factors, and trading signals.
These factors and signals will then be fed into trading systems by human “quants.” With constantly changing and shifting markets, quants must frequently monitor and reprogramme these pre-AI models. AI models differ in that, while humans create them, they can adapt to changing market conditions on their own with far less human supervision and intervention. Quant managers have developed algorithms that gather and fine-tune data before changing the investment course on their own when a new pattern is discovered.
Let’s walk through some points to understand how efficiency plays an important part for hedge funds in predicting stock movement.
- AI is also being used by hedge fund managers and their service providers to optimize middle and back-office operations.
- As teams transition away from spreadsheets and digital and cloud enterprise resource planning (ERP) solutions, AI can provide a competitive advantage.
- Obviously, not all fund processes can be completely automated, but AI can help to speed up reconciliation, reduce errors, and, ultimately, save money.
- AI is being used by software and service providers in the hedge fund space to help their hedge fund clients operate more efficiently and accurately.
For example, BNY Mellon’s hedge fund middle office and administration services use an artificial intelligence and machine learning platform to analyze historical trade break data and predict the root cause of current trade breaks with a high degree of certainty. This use of AI can significantly reduce costs and speed up the NAV production process in an industry that is still plagued by manually intensive reconciliation challenges.
Few doubt AI’s impact, but the immediate effect may be delayed due to a scarcity of talent. Although estimates vary, it is clear that there are only a few thousand people with advanced education and skills in AI. In practice, financial firms have recruited AI talent from tech companies such as Google and Facebook. The cascading of new ideas into the financial sector is a side benefit of bringing in talent from global tech firms.
The scarcity of talent is now colliding with the realization that artificial intelligence (AI) is critical for hedge funds to keep up with traditional rivals and tech-savvy new entrants. Recognizing this has resulted in significant new investments in academic programs and training capacity to attract millennials and address the issue of talent scarcity.
Investing & Partnering
MIT, for example, recently announced one of its most ambitious initiatives to date, the establishment of the $1 billion Stephen A. Schwarzman College of Computing. Unsurprisingly, funding comes from the CEO of Blackstone, one of the world’s largest alternative investment managers. It emphasizes the need for the alternative investment sector to expand its talent pool partly because the thriving tech sector is luring many top graduates away from finance.
Some of the industry’s most prominent players are experimenting with unconventional partnerships and methods to gain an AI advantage in the talent market. The Man Group and Oxford University founded the Oxford-Man Institute of Quantitative Finance. Engineers, statisticians, and coders share resources and collaborate with academics and researchers to study how algorithms, artificial intelligence, and other advances in technology can be applied to finance.
Another case in point is Two Sigma, which employs more technologists than traditional portfolio managers. Two Sigma, like Man, seeks an advantage by collaborating with elite academia, in this case, Cornell University. Two Sigma uses an AI programming challenge in its own game called ‘Halite®’ to recruit employees. The competition assesses applicants’ ability to control a bot in their preferred programming language.
Understanding the importance of talent and investing in its development is critical. However, investment managers must understand how to position themselves to attract tomorrow’s highly skilled AI specialists. What actions should hedge fund firms take to attract and retain talent?
Free snacks may help, but it is more important to emphasize the fiduciary responsibilities of this potential career and that millennials will have numerous opportunities to make a difference. It entails entrusting graduates with genuine responsibility for real-world issues such as pension fund management, portfolio construction, and investment idea generation.
The importance of human creativity cannot be overstated. Companies that combine AI and human talent will be the big winners. Data analysis by machines is already a requirement. To get the most out of AI, it is necessary to empower motivated and curious individuals who are encouraged to ask profound and creative questions about it.
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