"The data giant interviewed 151 quants between April and November last year to determine how they've integrated generative AI tools into their investment research process. While this section of the investing world has used machine-learning techniques for years, generative AI has not yet broken through. Most of the respondents, 54%, do not incorporate it into their workflows, the survey found."
"Angana Jacob, the firm's global head of research data, said quants require their data to be cleaned and structured a specific way because of the complex systems their strategies run and the amount of capital at stake if there is an error. "They're working in a very controlled research environment, models need to be explainable, models need to be repeatable," said Jacob, in an interview with Business Insider."
A survey of 151 quant investors found 54% do not incorporate generative AI into their investment workflows. Quant teams have long used machine-learning techniques, but generative AI adoption remains limited and skeptical due to doubts about its ability to produce alpha. Data formatting and structure pose practical barriers: quants require cleaned, specifically structured datasets to feed complex systems and protect large amounts of capital from errors. Research environments demand explainable and repeatable models. Data-engineering work to prepare datasets is described as unglamorous but foundational. Building robust data products for quants could enable wider generative AI integration over time.
Read at Business Insider
Unable to calculate read time
Collection
[
|
...
]