mlsanityData to 7 Oct 2026

Large Investment Model

arXiv 2408.10255 ↗First posted 12 Aug 2024q-fin.ST cs.AI q-fin.CPcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 95 references that are in the map. First posted on arXiv 12 Aug 2024.

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27 of 95 references.

References · 27

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15 more
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