Accounting Disclosure Quality and Information Asymmetry in Capital Markets

Abstract

This research introduces a novel methodological framework that reconceptualizes the relationship between accounting disclosure quality and information asymmetry in capital markets through the lens of quantum-inspired information theory
and computational linguistics. Traditional approaches have predominantly relied on
econometric models using conventional disclosure indices and market microstructure proxies, which often fail to capture the multidimensional, dynamic nature
of information flow between corporate entities and heterogeneous investor groups.
Our study proposes an unconventional hybrid methodology that integrates quantum
probability amplitudes to model investor belief states, natural language processing
techniques to extract semantic richness from narrative disclosures, and network
analysis to map information diffusion pathways. We formulate the problem as one
of quantum information transmission through noisy channels, where accounting
disclosures represent quantum states that undergo decoherence as they propagate
through market participants with varying interpretive capabilities. This approach
allows us to quantify not just the amount but the quality of information transmission, capturing how semantic precision, contextual framing, and temporal consistency affect the collapse of investor uncertainty distributions. Our analysis of a
unique dataset comprising 15,000 corporate disclosures from technology and pharmaceutical sectors between 2018-2023 reveals previously undocumented nonlinear
relationships: disclosure quality exhibits threshold effects on information asymmetry reduction, with diminishing returns beyond optimal semantic density levels.
Furthermore, we identify paradoxical regions where increased disclosure quantity
actually amplifies asymmetry when accompanied by decreased semantic coherence,
challenging conventional wisdom that more disclosure universally reduces information gaps. The findings demonstrate that the structural properties of disclosure
networks—specifically the centrality of financial statement items within the semantic network—moderate the quality-asymmetry relationship more significantly than
previously recognized. This research contributes original theoretical insights by
bridging quantum information concepts with accounting disclosure theory, offers a
novel methodological toolkit for disclosure analysis, and provides practical implications for standard-setters seeking to optimize disclosure frameworks that genuinely
enhance market transparency rather than merely increasing information volume