A preliminary arXiv paper examines whether large language models can stand in for people as user simulators in financial settings. The authors ran a controlled paper-trading experiment with 120 volunteers to see how well model behavior tracks evolving individual investing choices. They conclude that current LLM simulation of such decisions is not yet reliable and call for further work.
A revised arXiv paper introduces GoAnt, a quality-diversity multi-agent search method for automatically discovering alpha factors from price-volume panels and order-book data. The authors frame the problem as symbolic program search under a fixed evaluation budget, where existing single- and multi-agent approaches can overfit predictive proxies that break down in live trading.
A working paper examines the 2023 integration of generative AI into the FactSet platform, treating the rollout as a natural experiment in AI access for finance professionals. The authors report that the technology measurably changed how equity analysts produce research and process information. The findings offer early evidence on how generative AI affects knowledge work in the financial industry.
Heurist Finance has created a conversational investment analysis tool using Amazon Bedrock AgentCore, as described in an AWS customer story. The workbench relies on AgentCore's payments, identity, memory, code interpreter, and observability components so a small team can purchase premium market data per query and run analysis inside an isolated sandbox. The account highlights how a lean organization can assemble AI-driven financial workflows from managed agent infrastructure.