Financial Reporting Timeliness and Its Effect on Market Information Efficiency

Abstract

This research introduces a novel computational framework that re-conceptualizes
financial reporting timeliness not merely as a regulatory compliance metric, but as
a dynamic information flow variable that directly modulates market microstructure
efficiency. Departing from traditional event-study methodologies that treat reporting
dates as binary events, we model the entire financial reporting preparation and dissemination lifecycle as a continuous-time information diffusion process. Our methodology
integrates three unconventional components: (1) a quantum-inspired annealing algorithm to model the simultaneous, non-linear processing of financial data points within
a firm, simulating how delayed reporting creates superposition-like states of unresolved
financial information; (2) a neuromorphic computing model of market participant networks that learns and adapts to firm-specific reporting patterns, where delayed reports trigger synaptic re-weighting that degrades information processing fidelity; and
(3) a bio-inspired optimization approach, drawing from slime mold path-finding algorithms, to model how information asymmetries propagate through trading networks
when reports are untimely. We apply this framework to a unique dataset comprising millisecond-level market microstructure data, internal reporting process metadata
from corporate filing systems, and natural language processing outputs of earnings
announcement calls. Our findings reveal several non-intuitive phenomena: first, that
the marginal efficiency loss from reporting delay follows a power-law rather than linear
relationship, with critical thresholds where small additional delays trigger disproportionate market inefficiency; second, that market participants develop ’compensatory
inefficiencies’—systematic mispricing patterns that correlate with a firm’s historical
reporting timeliness profile; and third, that the information entropy of a financial report, measured through a novel application of Kolmogorov complexity to accounting
statements, interacts with timeliness to produce previously undocumented market effects. This research contributes to both accounting information systems and market
microstructure theory by providing a first-principles, computationally-grounded model
of how the temporal dimension of financial information flow fundamentally reshapes
market efficiency landscapes, with implications for real-time reporting systems, algorithmic trading regulation, and the design of next-generation continuous auditing
frameworks.