In a piece written by Alvin Chia, senior vice-president – head of digital assets innovation at Northern Trust, and Krishan Dave, head of investment risk and analytical services at Northern Trust, the pair said that while ChatGPT and generative AI is new to the public consciousness, the world is rapidly changing to understand the implications of the technology.
“It has the power to change the face of every industry on the planet. But we believe that in the short term, the role of generative AI will be limited to supporting existing research and data gathering and not the primary driver of decisions,” the pair said.
“Our current knowledge of generative AI does not allow us to discern false positives with actual data, resulting in additional risk for portfolio managers. But that does not deter us from taking a longer-term view of how generative AI can shape the investment management industry.”
In terms of how exactly AI could impact investment management in the future, Mr Chia and Mr Dave said that real-time dashboards powered by generative AI could help managers key into trends.
“Alongside market data, generative AI could combine esoteric data sets such as sentiment analysis or keywords searches that generative AI has deemed to be most relevant,” they said.
“It could unearth hidden trends or ‘black swan’ events that were previously unseen, providing a unique view of the investment horizon faster than ever before.”
The pair added that the technology could also be used to improve and refine portfolio optimisation, which they said is often over-reliant on historical data and focuses too heavily on comparing returns maximisation with the level of investment risk.
“Using the learned knowledge of the manager’s style and investment philosophy, generative AI could create unique optimisation strategies, helping to create bespoke stock allocation suggestions and overlay the client’s investment and ethical policies to assist with further review,” Mr Chia and Mr Dave said.
“AI has the potential to leave a footprint in the investment analytics space, too. Traditional ex-ante risk models or performance attribution may be consigned to the analytical scrapheap.
“Instead, the models could follow continuous and iterative paths of improvement as generative AI seeks to tweak and optimise models based on investment style and market events, making the models more relevant to the portfolio manager and the client.”
Other areas that the pair noted could be impacted by generative AI include helping market makers forecast liquidity demands and reading market conditions, as well as supporting the investment decision-making process to allow investors to shift from being intuition-based to data-driven.
“The power of generative AI would not be limited to the direct investment process, but swathes of operational efficiencies could be made, too,” they said.
Mr Chia and Mr Dave added: “The actual integration of generative AI has also yet to fully materialise today. An organisation would have to consider where they are comfortable introducing AI and the areas that are ‘out of bounds’.
“Having a clear vision detailing what needs to be achieved and what success looks like could help drive the development while of course being cognisant of the potential bias and arising ethical issues based in the data sets.”