Everyone is talking about the new applications for AI. But hedge funds and private equity firms have been using data science and AI for years.
Alternatives firms strive to blend the best of human with the best of machine. Firms are looking for fresh perspectives and interesting questions. Machine learning can sort data quickly, transforming it into meaningful information, providing new insights. People can spend more time thinking.
HEDGE FUNDS
Let’s start with investing. Advances in technology, lower prices and the introduction of ChatGPT have made large language models (LLMs) popular, particularly in the quantitative hedge fund sector. As one example, the new technology isn’t considered a replacement but a complement to what Man Numeric is already doing, according to Greg Bond, the unit’s president. Specifically, advances in AI could supplement and amplify research work. “In addition to knowing what questions to ask, it’s also what data do you have under the hood you can point the technology at,” Bond said.
In the wider hedge fund market, there are already two dominant applications of LLMs to support investment analysis — financial report evaluation and sentiment (public opinion around a company) analysis.
Further out on the spectrum, hedge funds are also testing whether LLMs can enhance the predictive capabilities of current models. Daniel Leveau of SigTech- “the undeniable advantages of LLMs of efficiency, speed, and scalability empower hedge funds to process and analyze a virtually infinite volume of data. However, these benefits do not come without challenges. Funds often grapple with issues related to data quality and operational readiness.”
Beyond investing and risk management, managers are looking at other areas where the technology can be deployed, including marketing and legal.
PRIVATE EQUITY
Private equity leaders such as Blackstone
BX
have been using data science and AI for years. Today Blackstone has a team of 40 data scientists applying new technologies into the firm’s own operations and into portfolio companies. CEO Schwarzman said on his recent earnings call that data scientists are critical in its deals sourcing and vetting. “We use them on due diligence, interestingly, because when you accumulate large databases, sometimes it’s hard to figure out what’s going on with them”. He added, “Some of these large language models will be a little erratic for a while. But as this is applied, for example, individual businesses can set up their own generative AI over time.”
Some of the top AI concerns for alternatives firms
- Few firms are close to using LLM to invest without human input, i.e. what if the LLM gives the wrong answer?
- There is a risk of data and intellectual property leakage, especially when sending data to companies such as OpenAI. And there are related strategic questions around cybersecurity, such as where data lives and who owns the data.
- In response to safety and reliability issues, Microsoft
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and Salesforce
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are creating “trust tools” to manage data access and disinformation.
AI and data science provide great promise and some concern for early mover alternative investment firms.
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