(untitled) (gjstudies, 2020)
This research introduces a novel computational framework for analyzing thesystemic impact of lease accounting standards on financial performance indicators,moving beyond traditional ratio analysis to model the cascading effects across interconnected financial statements. We develop a dynamic simulation model thattreats financial statements as a complex adaptive system, where changes in leaseclassification (operating versus finance) propagate through liquidity, solvency, andprofitability metrics in non-linear ways. Our methodology integrates agent-basedmodeling with network analysis to trace how accounting policy shifts under ASC842 and IFRS 16 create ripple effects that alter investor perception, credit assessment, and managerial decision-making. We simulate 10,000 corporate entitieswith varying lease portfolios across industries, introducing stochastic elements formarket reactions and management responses. Results reveal previously undocumented threshold effects where marginal changes in lease capitalization triggerdisproportionate impacts on debt covenants, with particular sensitivity in retail,transportation, and telecommunications sectors. Our analysis demonstrates thatthe conventional focus on individual ratios (like debt-to-equity or return on assets) underestimates the systemic consequences of accounting changes by 37-42%,as it fails to capture feedback loops between financial metrics. The study contributes a new computational paradigm for accounting research, establishes thatlease accounting effects follow power-law distributions rather than normal distributions, and provides evidence that industry-specific factors moderate impact severity more significantly than firm size or lease portfolio magnitude. These findingschallenge linear assumptions in accounting policy analysis and offer regulators andpractitioners a more sophisticated tool for predicting second-order consequences ofstandard-setting decisions.