Accounting Treatment of Leases and Its Impact on Financial Performance Indicators
Keywords:
lease accounting, financial performance indicators, complex adaptive systems, agent-based modeling, ASC 842, IFRS 16, network analysis, accounting standardsAbstract
This research introduces a novel computational framework for analyzing the
systemic 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 that
treats financial statements as a complex adaptive system, where changes in lease
classification (operating versus finance) propagate through liquidity, solvency, and
profitability metrics in non-linear ways. Our methodology integrates agent-based
modeling with network analysis to trace how accounting policy shifts under ASC
842 and IFRS 16 create ripple effects that alter investor perception, credit assessment, and managerial decision-making. We simulate 10,000 corporate entities
with varying lease portfolios across industries, introducing stochastic elements for
market reactions and management responses. Results reveal previously undocumented threshold effects where marginal changes in lease capitalization trigger
disproportionate impacts on debt covenants, with particular sensitivity in retail,
transportation, and telecommunications sectors. Our analysis demonstrates that
the 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 that
lease 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 findings
challenge linear assumptions in accounting policy analysis and offer regulators and
practitioners a more sophisticated tool for predicting second-order consequences of
standard-setting decisions.
