JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

Large investment model

Author(s): Jian GUO, Heungyeung SHUM
Affiliation(s): IDEA Research, International Digital Economy Academy, Shenzhen 518045, China
Corresponding email(s): chenguojian@idea.edu.cn, hshum@idea.edu.cnwb@bistu.edu.cn
Key Words: Arti?cial general intelligence; End-to-end; Large investment model; Quantitative investment; Foundation model; Multimodal large language model

Share this article to： More <<< Previous Paper \|Next Paper >>>

Jian GUO, Heungyeung SHUM. Large investment model[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2500268

@article{title="Large investment model",
author="Jian GUO, Heungyeung SHUM",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2500268"
}

%0 Journal Article
%T Large investment model
%A Jian GUO
%A Heungyeung SHUM
%J Frontiers of Information Technology & Electronic Engineering
%P
%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2500268"

TY - JOUR
T1 - Large investment model
A1 - Jian GUO
A1 - Heungyeung SHUM
J0 - Frontiers of Information Technology & Electronic Engineering
SP -
EP -
%@ 2095-9184
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/FITEE.2500268"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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 the LIM, address the technical challenges inherent in this approach, and outline potential directions for future research.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

Open peer comments: Debate/Discuss/Question/Opinion

<1>

- Go to

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference