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

TL;DR

This paper investigates how stock market efficiency varies across different time scales by analyzing news-based uncertainty and systemic volatility, revealing predictability at daily but not monthly frequencies and identifying key policy-related drivers.

Contribution

It introduces a multiscale causal framework combining network analysis and the Financial Chaos Index to assess market efficiency and the influence of macro-financial uncertainties over 34 years.

Findings

Market inefficiency at daily frequency with predictable asset responses to news.

Market efficiency at monthly frequency with no significant predictive structure.

Fiscal and monetary policy uncertainties are key systemic volatility drivers.

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.