On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors

ArXiv ID: 2401.05414 “View on arXiv”

Authors: Unknown

Abstract

Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution - nonstationarity, and causal factors that are important but unknown/unobserved. In this paper, we follow a causal perspective to systematically look into these three demons in finance. Specifically, we reexamine these issues in the context of causality, which gives rise to a novel and inspiring understanding of how the issues can be addressed. Following this perspective, we provide systematic solutions to these problems, which hopefully would serve as a foundation for future research in the area.

Keywords: causality, nonstationarity, time-varying distribution, financial time series, General Financial Markets

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper presents advanced mathematical derivations and causal graph formalisms (SCM, Markov conditions, faithfulness) alongside empirical validation on S&P 100 data with proposed methods like CD-NOD, striking a balance between theoretical depth and data-driven implementation.

  flowchart TD
    A["Research Goal<br>Address Three Demons in Finance<br>(Time Resolution, Nonstationarity, Latent Factors)"] --> B["Causal Perspective<br>Framework Selection"]
    B --> C["Data Input<br>Financial Time Series Data"]
    C --> D["Methodology<br>Causal Analysis of Demons"]
    D --> E["Computational Process<br>Addressing Time Resolution Mismatch"]
    D --> F["Computational Process<br>Handling Nonstationarity & Latent Factors"]
    E --> G["Key Findings<br>Systematic Causal Solutions"]
    F --> G
    G --> H["Outcome<br>Foundation for Future Research<br>in General Financial Markets"]