Timely Financial Reporting and Stock Market Information Processing Efficiency

Abstract

This research introduces a novel methodological framework for examining the relationship between financial reporting timeliness and stock market information processing
efficiency, departing from conventional event-study approaches by integrating concepts
from computational linguistics, network theory, and behavioral finance. We propose
that market efficiency should be evaluated not merely through price adjustment speed
but through the structural properties of information diffusion networks that emerge
during earnings announcement periods. Our methodology constructs temporal information networks from trading data, analyst reports, and news media coverage, applying
graph-theoretic measures to quantify market responsiveness. We develop a multidimensional timeliness metric that captures not only reporting lag but also information
clarity, comparability, and accessibility. Through analysis of a unique dataset spanning
1998-2004, we demonstrate that conventional measures of timeliness explain only 31%
of variation in market efficiency metrics, while our integrated framework explains 74%.
We identify three distinct market response patterns—coherent absorption, fragmented
processing, and cascading adjustment—each associated with different reporting characteristics. Most significantly, we find that markets process information most efficiently
when reports exhibit what we term ’structured transparency’: moderate timeliness
combined with high information organization. This challenges the prevailing assumption that faster reporting always enhances efficiency. Our findings suggest regulatory
and corporate reporting policies should prioritize information architecture alongside
speed, offering a fundamentally new perspective on financial disclosure effectiveness.