The Effect of Accounting Conservatism on Firm Value and Market-Based Performance Metrics

Posted: May 08, 2025

• Logan Scott
• Logan Thomas
• Logan Thompson

Abstract

The study of accounting conservatism has traditionally been confined to the boundaries of financial economics and accounting theory, with researchers employing conventional statistical methods to examine its relationship with firm value and market performance. This paper introduces a radical departure from established methodologies by proposing a computational framework that reconceptualizes accounting conservatism through the interdisciplinary integration of machine learning, quantum-inspired algorithms, and bio-inspired computing. Our approach treats accounting conservatism not as a static reporting principle but as a dynamic, multi-dimensional construct that interacts with market mechanisms in ways that conventional linear models cannot adequately capture. Traditional research has largely focused on measuring accounting conservatism through established metrics such as the Basu asymmetric timeliness measure or the book-to-market ratio, then correlating these measures with various performance indicators using regression analysis. While these approaches have yielded valuable insights, they suffer from inherent limitations in capturing the complex, non-linear relationships that characterize modern financial markets. Our research addresses these limitations by developing a quantum-bio hybrid neural network that models the emergent properties of accounting information in capital markets. We formulate three research questions that have not been adequately addressed in existing literature. First, how does accounting conservatism exhibit fractal-like properties in its impact on market-based performance metrics? Second, to what extent does the relationship between conservatism and firm valuation follow principles analogous to quantum superposition? Third, what are the network propagation characteristics of conservative accounting information through market systems? These questions represent a fundamental rethinking of how accounting information interacts with market mechanisms. Our contribution lies in developing a computational methodology that transcends traditional disciplinary boundaries. By integrating principles from quantum computing, where multiple states can coexist until observation collapses the system, with bio-inspired neural networks that mimic biological information