Trading with Time Series Causal Discovery: An Empirical Study

ArXiv ID: 2408.15846 “View on arXiv”

Authors: Unknown

Abstract

This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to build investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms. The performance of the strategy is evaluated based on the profitability and effectiveness in stock markets. The results indicate that causal discovery algorithms can successfully uncover actionable causal relationships in large markets, leading to profitable investment outcomes. However, the research also identifies a critical challenge: the computational complexity and scalability of these algorithms when dealing with large datasets. This challenge presents practical limitations for their application in real-world market analysis.

Keywords: Causal discovery, Investment strategy, Equity markets, Computational complexity, Causal inference

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical models (VarLiNGAM, TiMINo) with substantial mathematical formulation and proofs, meeting the high math complexity threshold; it also demonstrates high empirical rigor by using real-world multi-market data, implementing a full backtesting workflow, and providing open-source code for reproducibility.

  flowchart TD
    A["Research Goal: Apply Causal Discovery to Equity Markets for Profitable Strategies"] --> B["Data Input: Time Series of Equity Market Returns"]
    B --> C["Methodology: Causal Discovery Algorithms"]
    C --> D{"Computational Process: Scalability & Complexity"}
    D -- Issues with Large Datasets --> C
    D -- Successful Identification --> E["Outcome: Identified Causal Structures"]
    E --> F["Application: Investment Strategy Construction"]
    F --> G{"Evaluation: Profitability & Effectiveness"}
    G -- Yes --> H["Key Finding: Actionable Causal Relationships & Profits"]
    G -- No/Partial --> I["Key Finding: Limitations in Scalability"]