This guide is a summary of the use of artificial intelligence and machine learning approaches to hedge funds. It is not intended to be a substitute for specific legal or tax advise.
Using Artificial Intelligence In Hedge Funds
The integration of artificial intelligence (“AI”) and the financial industry has always been a match made in heaven. High volumes, the quantitative aspect of finance, the need for expediency and accuracy are ideal for the unique skill-set of AI. However, can it impact the high-risk, high-return world of hedge funds? Several companies think so.
What is a hedge fund?
Today, there are more than 10,000 hedge funds that manage approximately $3 trillion in assets. Hedge fund managers can use trading techniques where they “hedge” themselves by going long (if they predict that the market will rise) or shorting stocks when they believe the market will drop. Hedge funds are generally considered riskier investments.
Hedge fund companies use humans to build and train the original system but soon the system can use artificial intelligence to trade stocks entirely without any more human intervention. Companies trading or working toward trading with AI systems include San Francisco startup Sentient Technologies, Renaissance Technologies, and Bridgewater.
Market research firm Preqin estimates that 1,360 hedge funds use computer models to make the majority of their trades. While hedge funds have used computer models to help make trades, it’s the innovation of an AI machine having full autonomy and not relying on a data scientist for an assist that is revolutionary. While models are helpful, unless they get updated as quickly as mark conditions change, they will diminish in performance over time without being updated to reflect new market intelligence.
How AI is used
Machines analyze inordinate amounts of data at extraordinary speeds that is impossible for humans to do. They learn from the information they analyze to improve their trading acumen. This information includes market prices to corporate financial reports and accounting documents to social media, news trends, political developments, changing interest rates, and macroeconomic data. Once the information is analyzed by thousands of machines, the machines then “vote” on what action to take and the best trades to make.
Deep learning helps train large neural networks to recognize patterns in the data and be able to analyze data in a variety of forms such as audio, images, and text. Today, unstructured data is a crucial piece of the puzzle and allows the AI systems used for trading to review news articles, social media posts, and other unstructured data to help inform their strategy.
Even though “past performance does not predict future returns” AI systems also use historical stock data to test their performance and learn from how the market reacted in the past. In a form of “evolutionary computation,” the AI determines the winners and uses their “genes” to create the next generation of trades. This process continues indefinitely, and the result is a smarter trader population. Eventually, this technology can be used to improve today’s deep learning capabilities.
One of the reasons hedge funds tend to use a variety of AI technologies in their trading systems is to avoid another company imitating their methods. If everyone would be able to use the same smart systems to trade and the unique recipe for success was realized, it would undermine the competitive advantage companies get by using AI to stay a step ahead of their competition.
A number of hedge funds are using AI to analyze masses of data, predict corrections in supply and demand imbalances, and forecast market movements for tactical asset allocation. This has the potential to assist a CIO’s team to combine different strategies and tailor allocations.
Use of AI is playing out across a wide spectrum of investment managers from pure AI-driven specialists, to large quant-driven shops, to traditional fundamental investors looking for an edge. A growing number of firms across the spectrum are also turning to AI to improve efficiency in their operations, accounting and investor relations functions.
Indeed, a class of AI pure play hedge funds has emerged in recent years that are based entirely on machine learning and AI algorithms. Examples include Aidiyia Holdings, Cerebellum Capital, Taaffeite Capital Management and Numerai. Numerai, a recognized AI hedge fund, is pushing the boundaries of the hedge fund business model. The firm uncovers investment strategies by hosting competitions among external AI experts, mathematicians and data scientists. Recently, Numerai expanded its business model by making elements of its platform available to the rest of financial community with its product Erasure, which is a decentralized prediction marketplace using blockchain technology.
Dwarfing the upstart AI pure plays are the large quant funds that are household names in the hedge fund industry such as Man AHL, Two Sigma, Citadel, Bridgewater and D.E. Shaw. For years, players like these have used computer-driven models to uncover new trading strategies and identify themes, factors and trading signals. Human “quants” will then feed these factors and signals into trading systems. With markets continually changing and shifting, these pre-AI models often need frequent monitoring and reprogramming by the quants. AI models are different because while initially crafted by humans, they are able to adapt to changing market circumstances on their own with far less human supervision and intervention. Quant managers have developed algorithms that gather and fine tune data, then autonomously change the investment course when a new pattern is identified.
The use of AI to trade stocks is certainly intriguing, but it hasn’t proven—yet—to be a resounding or guaranteed success. What is clear, however, is that the companies that are building AI systems to trade stocks have kick-started a new era on Wall Street.
Artificial intelligence and machine learning are prevalent in all aspects of everyday life and play an ever-increasing role in investing.
Machine learning investment strategies aim to deliver persistent, uncorrelated alpha streams while adapting to changes in market conditions—without the human input required in other quantitative investment approaches.
Applying machine learning techniques to financial markets is not easy. Diligent manager selection and portfolio construction, informed by deep industry expertise, are critical to capturing the potential benefits of machine learning strategies and improving portfolio outcomes.
Machine learning and the evolution of quant investing
Quantitative investing encompasses the universe of strategies in which managers use computer programs to trade systematically. Traditional quant investing relies on investment teams to identify pricing signals, constantly monitor their efficacy and actively intervene if the signals falter.
Early quant models based their forecasts on trading philosophies like “less expensive securities tend to outperform” (the value factor) or “markets exhibit trends” (the momentum factor). As more managers traded using these same factors, signals became crowded. This culminated in the “quant quake” of August 2007: A three-day period of dramatic losses occurring when managers had to sell similar positions in their quant books to cover margin calls from other portfolio losses.
Despite the events of 2007, some managers continued to trade based on these signals, which became a commoditized, low fee approach to gaining broad exposure to these market factors (or risk premia). Other managers developed more complex predictive rules using a greater variety of signals. Sophisticated players identified signals that were less known and thus slower to become crowded, enabling them to generate a higher quality return. These managers continue to deliver alpha but need to closely monitor their signals and use discretion to adjust predictive rules should their risk-adjusted returns begin to wane.
The machine learning difference
Machine learning (“ML”) algorithms digest reams of data to identify patterns and build a predictive rule that constantly evolves as it adapts to continuous feedback. ML’s application to quantitative investing took hold when quant managers realized that, just as their traditional models seek to systematize what fundamental active managers do, ML could systematize more of their own quant processes, including autonomously:
o finding predictive relationships and signals
o monitoring the environment for change and noticing when a source of return is fading or a new signal is emerging
o adapting to change on an ongoing basis—for example, by using more appropriate signals, reducing risk or shifting signal weights and allocations
Potential investment opportunities …
For investors, ML’s broader, deeper, faster analysis and, most importantly, its ability to continuously adapt investment processes give it the potential to deliver:
o More diversified alpha streams: The range of ML methods that quant investing can employ (from Bayesian processes to genetic algorithms and neural networks) and the varied quant strategies (e.g., directional strategies or market neutral strategies like statistical arbitrage), markets (equities, futures, fixed income, commodities, options) and investment horizons to which ML can be applied mean its alpha streams are likely to be less correlated to traditional equity, fixed income and quant strategies as well as to one another.
o More persistent alpha: Unlike traditional quant investing, in which signals are essentially fixed—i.e., unresponsive to changing market environments—ML systems decipher change and can even adapt the time frames of their measurements and price predictions to potentially enhance alpha generation across different market environments.
o Added value at multiple stages of the investment process.
Significant growth in the use of machine learning to trade financial markets is making ML difficult for investors to ignore, especially those looking for new sources of uncorrelated alpha. While risks and challenges remain, investors able to combine judicious manager selection with robust portfolio construction have the opportunity to tap into the full power of machine learning to improve portfolio outcomes.
We routinely work with all types of fund managers in the provision of fund formation advice, fund governance and other support services. We have worked on a range of AI and ML focused hedge fund strategies with managers based around the world. Such funds take a different approach to investment strategies and what drives performance and risk dependent on the data capture and core focus of each strategy. We continue to provide experienced independent directors to such funds. For further information on our fund services, please feel free to contact us: firstname.lastname@example.org