Large investment model

Jian GUO , Heung-Yeung SHUM

Front. Inform. Technol. Electron. Eng ›› 2025, Vol. 26 ›› Issue (10) : 1771 -1792.

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Front. Inform. Technol. Electron. Eng ›› 2025, Vol. 26 ›› Issue (10) : 1771 -1792. DOI: 10.1631/FITEE.2500268
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Large investment model

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Abstract

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, which is 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.

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Artificial general intelligence / End-to-end / Large investment model / Quantitative investment / Foundation model / Multimodal large language model

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Jian GUO, Heung-Yeung SHUM. Large investment model. Front. Inform. Technol. Electron. Eng, 2025, 26(10): 1771-1792 DOI:10.1631/FITEE.2500268

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