Concepts-Based Accounting Standards

While comparability across firms and consistency over time are generally held to be fundamental goals of financial reporting, I provide an analytic representation of a concept that explains why concepts-based accounting standards cannot assure comparability and why their induced consistency may not always be desirable. While the term, concepts-based accounting standards, has not caught on in the academic and professional literatures, its use here emphasizes the foundational role that language-based concepts play in constructing accounting standards. I appeal to the academic literature in machine learning, neural networks and especially cognitive science – all of which may represent concepts by S-curve (sigmoid) signatures. I then show how S-curves can explain an accounting standard’s (1) precision, (2) comparability across firms, (3) demands placed on judgment, and (4) consistency across time. Accordingly, an S-curve formulation may guide both analytical modelling of accounting standards and add structure to empirical research designs