Large Investment Model
This addresses efficiency and performance challenges for quantitative investment researchers, but it appears incremental as it builds on existing foundation model concepts applied to a new domain.
The paper tackles the problem of diminishing returns and high costs in quantitative investment by introducing the Large Investment Model (LIM), a novel research paradigm that uses an upstream foundation model to learn global patterns from diverse financial data and transfer them to downstream strategies, demonstrating advantages through numerical experiments on cross-instrument prediction for commodity futures trading.
Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs. To overcome these challenges, we introduce the Large Investment Model (LIM), a novel research paradigm designed to enhance both performance and efficiency at scale. LIM employs end-to-end learning and universal modeling to create an upstream foundation model capable of autonomously learning comprehensive signal patterns from diverse financial data spanning multiple exchanges, instruments, and frequencies. These "global patterns" are subsequently transferred to downstream strategy modeling, optimizing performance for specific tasks. We detail the system architecture design of LIM, address the technical challenges inherent in this approach, and outline potential directions for future research. The advantages of LIM are demonstrated through a series of numerical experiments on cross-instrument prediction for commodity futures trading, leveraging insights from stock markets.