Two Sigma is a global quantitative investment management and trading firm that combines scientific inquiry, advanced technology, and deep domain expertise. Our collaborative research environment connects ML researchers with specialists across datasets and methods, making proprietary domain knowledge a shared resource for tackling complex financial prediction problems.

What can a model learn from watching financial television—and what does it take to turn that understanding into a forecast? A single broadcast contains multiple layers of information, unfolding at different cadences and carrying meanings that depend on context. Using financial TV recordings as a starting point, we will explore the challenges of aligning asynchronous evidence, incorporating data-specific knowledge into training, enabling richer architectures through efficient inference, and adapting to changing market conditions without forgetting useful history. We will discuss how collaboration between domain specialists and ML researchers shapes these questions, connecting financial forecasting to frontier problems in multimodal representation learning, temporal reasoning, continual adaptation and agentic fine-tuning.