{"title":"Large Investment Model","authors":"Jian Guo, Heung-Yeung Shum","doi":"arxiv-2408.10255","DOIUrl":null,"url":null,"abstract":"Traditional quantitative investment research is encountering diminishing\nreturns alongside rising labor and time costs. To overcome these challenges, we\nintroduce the Large Investment Model (LIM), a novel research paradigm designed\nto enhance both performance and efficiency at scale. LIM employs end-to-end\nlearning and universal modeling to create an upstream foundation model capable\nof autonomously learning comprehensive signal patterns from diverse financial\ndata spanning multiple exchanges, instruments, and frequencies. These \"global\npatterns\" are subsequently transferred to downstream strategy modeling,\noptimizing performance for specific tasks. We detail the system architecture\ndesign of LIM, address the technical challenges inherent in this approach, and\noutline potential directions for future research. The advantages of LIM are\ndemonstrated through a series of numerical experiments on cross-instrument\nprediction for commodity futures trading, leveraging insights from stock\nmarkets.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.10255","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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 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.