Despite the wide availability of powerful AI models, a sustainable edge in the zero-sum game of investing comes from a combination of unique, curated data sets, bespoke technology for scale, and the experienced human context to ask the right questions of the models.
Widespread use of similar AI models by average investors will likely lead to herd behavior and crowding in certain securities. This pushes prices away from fundamental value, creating predictable inefficiencies and new alpha opportunities for sophisticated investors who can model these effects.
A Goldman Sachs quant head reveals that over half of a stock's performance is attributable to non-fundamental factors. These include market sentiment, themes, and trends, which can now be captured with unprecedented accuracy using fine-tuned language models on unstructured data.
Contrary to the narrative of AI leading to smaller teams, Goldman Sachs' 100-person quantitative strategies group has remained about the same size after adopting new AI technologies. This suggests AI automates difficult work but doesn't replace the need for human experts to provide oversight, context, and strategic direction.