Paper: arXiv 2408.15846

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.0/10 (capped from 9.0: transaction costs only partially modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (24)

  1. Detecting the lead–lag effect in stock markets: definition, patterns, and investment strategies (2022)
  2. Causal inference on time series using restricted structural equation models (2013)
  3. Survey and evaluation of causal discovery methods for time series (2022)
  4. Returns to buying winners and selling losers: Implications for stock market efficiency (1993)
  5. Algorithmic trading and quantitative strategies (2020)
  6. Momentum: what do we know 30 years after Jegadeesh and Titman’s seminal paper? (2023)
  7. Causal discovery from heterogeneous/nonstationary data (2020)
  8. Behind distribution shift: Mining driving forces of changes and causal arrows (2017)
and 16 more
  1. Do industries explain momentum? (1999)
  2. Time series momentum (2012)
  3. Quantitative equity portfolio management: An active approach to portfolio construction and management (2006)
  4. Value and momentum everywhere (2013)
  5. Testing for causality: A personal viewpoint (1980)
  6. Some recent development in a concept of causality (1988)
  7. On causal discovery from time series data using FCI (2010)
  8. Estimation of a structural vector autoregression model using non-gaussianity. (2010)
  9. A linear non-Gaussian acyclic model for causal discovery. (2006)
  10. Trading costs (2018)
  11. Deep learning with long short-term memory networks for financial market predictions (2018)
  12. Evaluating multiple classifiers for stock price direction prediction (2015)
  13. Elements of causal inference: foundations and learning algorithms (2017)
  14. Causation, prediction, and search (2001)
  15. GPU acceleration for information-theoretic constraint-based causal discovery (2022)
  16. Accelerating constraint-based causal discovery by shifting speed bottleneck (2022)

0 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.