Multiscale Causal Analysis of Market Efficiency via News Uncertainty Networks and the Financial Chaos Index

ArXiv ID: 2505.01543 “View on arXiv”

Authors: Masoud Ataei

Abstract

This study evaluates the scale-dependent informational efficiency of stock markets using the Financial Chaos Index, a tensor-eigenvalue-based measure of realized volatility. Incorporating Granger causality and network-theoretic analysis across a range of economic, policy, and news-based uncertainty indices, we assess whether public information is efficiently incorporated into asset price fluctuations. Based on a 34-year time period from 1990 to 2023, at the daily frequency, the semi-strong form of the Efficient Market Hypothesis is rejected at the 1% level of significance, indicating that asset price changes respond predictably to lagged news-based uncertainty. In contrast, at the monthly frequency, such predictive structure largely vanishes, supporting informational efficiency at coarser temporal resolutions. A structural analysis of the Granger causality network reveals that fiscal and monetary policy uncertainties act as core initiators of systemic volatility, while peripheral indices, such as those related to healthcare and consumer prices, serve as latent bridges that become activated under crisis conditions. These findings underscore the role of time-scale decomposition and structural asymmetries in diagnosing market inefficiencies and mapping the propagation of macro-financial uncertainty.

Keywords: Informational Efficiency, Granger Causality, Network Theory, Volatility, Efficient Market Hypothesis, Equities

Complexity vs Empirical Score

• Math Complexity: 9.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced multilinear algebra (tensor CP decomposition, eigenvalue theory) and rigorous Granger causality networks, fitting high math complexity, while backing it with 34 years of daily data (CRSP/WRDS), statistical significance (1% level), and structural network analysis, requiring substantial empirical implementation.

  flowchart TD
    A["Research Goal:<br>Test Scale-Dependent Market Efficiency<br>using News Uncertainty & Financial Chaos"] --> B
    subgraph B ["Data & Inputs"]
        B1["Daily & Monthly Data<br>1990-2023"]
        B2["Financial Chaos Index<br>Tensor-Eigenvalue Volatility"]
        B3["Uncertainty Indices<br>Economic, Policy, News"]
    end
    B --> C
    subgraph C ["Methodology"]
        C1["Granger Causality Tests"]
        C2["Network-Theoretic Analysis"]
    end
    C --> D
    subgraph D ["Key Findings"]
        D1["Daily: Market Inefficiency<br>Semi-strong EMH rejected"]
        D2["Monthly: Market Efficiency<br>Predictive structure vanishes"]
        D3["Network Structure:<br>Policy indices = Systemic triggers"]
    end