Generative AI in systematic investing

Even though it is the capacity of generative AI models to produce new realistic content that garners both wonder and concern, their significance and potential impact extend far beyond their purely generative use cases.

The source of generative AI's power is also its limitation. The vast amounts of data such models require for training may not exist for many investment problems. Knowing where, when, and how to successfully apply generative AI to systematic investing will be a source of competitive edge.

Generative AI: Hardly out of Nowhere

While the barcode was invented in the late 1940s, most people were unaware of the technology's existence prior to its widespread adoption in supermarkets decades later.

Checkout scanners and their ubiquitous beeps rapidly proceeded to become so engrained in daily life that President George H. W. Bush's apparent, but misreported, unfamiliarity with the technology at a campaign-stop in Florida fuelled a narrative that he was disconnected from ordinary voters, likely contributing to his defeat by the up-and-coming challenger, Bill Clinton.

The idea of interconnected computer networks had been brewing in academic and research circles for decades, with notable developments like the Advanced Research Projects Agency Network (ARPANET) in the late 1960s and Tim Berners-Lee's proposal for the World Wide Web in 1989. However, it was the release of Netscape Navigator in December 1994 that made the internet accessible to the public, leading to a rapid expansion of online services and the birth of the dot-com boom.

These are not isolated phenomena. A technology often emerges and makes strides for years largely out of public view before capturing mass attention with a prominent milestone or product release, leading to the misconception that it was completely new. Generative AI, a branch of artificial intelligence so named for models that are trained to 'generate' new data, has followed a similar path to mainstream consciousness