Paper: arXiv 2408.10255

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.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper presents a high-level conceptual framework for a Large Investment Model with minimal mathematical formalism, focusing on system architecture and paradigm shifts rather than new derivations. While it mentions numerical experiments, the excerpt lacks specific backtest metrics, datasets, or implementation details needed for high empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Enhance Investment Research<br>Performance & Efficiency"] --> B["Methodology: Large Investment Model LIM<br>Universal Modeling & End-to-End Learning"]
  
  B --> C["Data Inputs:<br>Multi-Exchange, Multi-Instrument, Multi-Frequency"]
  
  C --> D["Process: Upstream Foundation Model<br>Autonomous Learning of Global Signal Patterns"]
  
  D --> E["Process: Downstream Transfer & Optimization<br>Task-Specific Strategy Modeling"]
  
  E --> F["Outcomes:<br>Cross-Instrument Prediction<br>Performance Demonstration"]
  
  F --> G["Future: Technical Challenges &<br>Research Directions"]

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5 of 122 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.