Timely Financial Reporting and Stock Market Information Processing Efficiency (gjstudies, 2021)
This research introduces a novel methodological framework for examining the relationship between financial reporting timeliness and stock market information processingefficiency, departing from conventional event-study approaches by integrating conceptsfrom computational linguistics, network theory, and behavioral finance. We proposethat market efficiency should be evaluated not merely through price adjustment speedbut through the structural properties of information diffusion networks that emergeduring earnings announcement periods. Our methodology constructs temporal information networks from trading data, analyst reports, and news media coverage, applyinggraph-theoretic measures to quantify market responsiveness. We develop a multidimensional timeliness metric that captures not only reporting lag but also informationclarity, comparability, and accessibility. Through analysis of a unique dataset spanning1998-2004, 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, fragmentedprocessing, and cascading adjustment—each associated with different reporting characteristics. Most significantly, we find that markets process information most efficientlywhen reports exhibit what we term ’structured transparency’: moderate timelinesscombined with high information organization. This challenges the prevailing assumption that faster reporting always enhances efficiency. Our findings suggest regulatoryand corporate reporting policies should prioritize information architecture alongsidespeed, offering a fundamentally new perspective on financial disclosure effectiveness.