Firms are exploring numerous use cases
Buyside firms are increasingly using AI for tasks such as coding, analyzing documents, and writing marketing material/email prompts with the goal of improving internal efficiencies. At the same time, experiments are underway to advance more tailored generative AI applications. Buy-side firms have been developing numerous generative AI applications and exploring how to use it for asset management or investment research, said one panelist.
The most common and most promising use cases, thus far, are:
• Summarization: AI can help summarize and list critical points in a recent 10-K filing or other document to help portfolio managers or analysts scan through reams of information.
• Opinion mining, or sentiment analysis: Using machine learning and large language models, AI can perform text analysis on various sources — web chats, case studies, news articles, social media, and more — to produce a snapshot of overall market sentiment.
• Case scenario analysis: Imagine being able to ask questions such as: “If interest rates were raised by 25 basis points in 2018, how would it impact the economy in 2020?” Portfolio managers could explore different theories or hypotheses, like how a certain CEO candidate might change a company or industry or the case for or against a certain deal.
As a new technology, generative AI’s capabilities and limitations are still evolving. Gary Kazantsev, Bloomberg’s Global Head of Quant Technology Strategy, noted that customers are still struggling with finding real use cases. Models, he says, are undertrained and — because the models are so large — not yet suitable for real-time workflows.
Citability, hallucination and other challenges
Though generative AI opens the possibilities to many exciting new applications, there are challenges to overcome first. The possibilities are potentially groundbreaking, but panelists emphasized caution. Asset managers need to be able to see citations of any given data’s source and made-up data, or “hallucinations,” are still common.
Hallucinations can be minimized by ensuring citations and references are in place, so the source of information is traceable. But this solution is not foolproof. Sometimes, even when it’s traceable, there are still hallucinations. A panelist noted that the important question is if investment analysts can accept that.
Bloomberg’s Kazantsev added that technical questions remain about how to design architectures or train the models. This aspect is evolving rapidly. Over the course of the last 12 months, for example, almost all the models that have been built up until say, three months ago, have been severely under trained.
There are also challenges in creating a sense of memory to allow for more detailed follow-up questions. Further, the application would have to be able to do that in real-time with enormous, specialized, continually updated data sets.
Bloomberg
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