Recent breakthroughs in machine learning offer powerful tools that can significantly transform systematic investment research. Within our quantitative research and development team, we leverage specialized ML techniques—including large language models (LLMs), retrieval-augmented generation (RAG), agent-based systems, variational autoencoders (VAEs), graph neural networks (GNNs), and multimodal signal processing—to curate large-scale datasets, automate feature extraction, construct robust trading signals, and systematically generate innovative investment hypotheses.

This talk will identify key opportunities where advanced ML methods, featured in ICLR 2025 papers, can substantially enhance systematic investment pipelines. Additionally, we propose ambitious directions for future research, such as building sophisticated ecosystems of interacting ML agents, creating a compelling landscape for ML researchers interested in translating research innovation into impactful real-world investment strategies.