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Large Investment Model

Jian Guo, Heung-Yeung Shum

TL;DR

The paper introduces the Large Investment Model (LIM) to overcome diminishing returns and rising costs in quantitative investment by coupling an upstream foundation model trained on diverse, multi-exchange data with a downstream task model fine-tuned for specific strategies. It emphasizes end-to-end modeling and universal modeling to enable direct strategy generation and cross-task transfer, supported by a patch-based, Transformer-inspired upstream architecture and a data-aligned downstream workflow. The authors detail LIM's system architecture, upstream/downstream design, and automated strategy generation, and they present numerical experiments on cross-instrument commodity futures prediction that leverage insights from stock markets. They also discuss practical challenges (e.g., retraining costs, data selection, integration of alternative data) and outline future research directions, including risk modeling, world simulation, multi-granularity backbones, and agent-enabled scalability. Overall, LIM is positioned as a scalable, knowledge-accelerating paradigm for quantitative finance that aims to unify data augmentation, transfer learning, and automated strategy development across markets and speeds.

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.

Large Investment Model

TL;DR

The paper introduces the Large Investment Model (LIM) to overcome diminishing returns and rising costs in quantitative investment by coupling an upstream foundation model trained on diverse, multi-exchange data with a downstream task model fine-tuned for specific strategies. It emphasizes end-to-end modeling and universal modeling to enable direct strategy generation and cross-task transfer, supported by a patch-based, Transformer-inspired upstream architecture and a data-aligned downstream workflow. The authors detail LIM's system architecture, upstream/downstream design, and automated strategy generation, and they present numerical experiments on cross-instrument commodity futures prediction that leverage insights from stock markets. They also discuss practical challenges (e.g., retraining costs, data selection, integration of alternative data) and outline future research directions, including risk modeling, world simulation, multi-granularity backbones, and agent-enabled scalability. Overall, LIM is positioned as a scalable, knowledge-accelerating paradigm for quantitative finance that aims to unify data augmentation, transfer learning, and automated strategy development across markets and speeds.

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.
Paper Structure (24 sections, 2 equations, 9 figures)

This paper contains 24 sections, 2 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Review on Quantitative Investment
  3. Quant Strategies
  4. Data Diversity in Quant Modeling
  5. Multifactor Quant Modeling
  6. Large Investment Models
  7. End-to-End Modeling for LIM
  8. Universal Modeling for LIM
  9. Upstream Foundation Model
  10. Problem Formulation
  11. Modeling Principle
  12. Design of Foundation Model
  13. Downstream Task Model
  14. Data Alignment and Standardization
  15. Model Fine-tuning
  16. ...and 9 more sections

Figures (9)

  • Figure 1: Three quant research paradigms: multi-factor model, end-to-end model and large investment model.
  • Figure 2: A strategy matrix for quantitative investment.
  • Figure 3: Spectrum of various financial data along depth and breadth.
  • Figure 4: Quant research pipeline and various types of end-to-end quant modeling.
  • Figure 5: Workflow of large investment model.
  • ...and 4 more figures