(untitled) (gjstudies, 2024)
This research investigates the nuanced relationship between accounting estimatesand earnings volatility through a novel computational framework that diverges fromtraditional econometric approaches. While prior literature has examined earnings management and estimation bias, this study introduces a bio-inspired optimization algorithm—specifically, a quantum-enhanced genetic algorithm—to model the complex,non-linear interdependencies between estimation choices and resulting earnings patterns. We conceptualize accounting estimates not as isolated managerial decisions butas elements within a dynamic financial ecosystem, where interactions between differentestimate categories (such as allowance for doubtful accounts, depreciation methods,inventory valuation, and warranty liabilities) create emergent volatility patterns. Ourmethodology applies principles from complex systems theory and computational biology to financial statement data from 500 publicly traded companies over a ten-yearperiod. The findings reveal that earnings volatility exhibits fractal-like properties whenexamined through this lens, with small variations in interdependent estimates producing disproportionate effects on reported earnings stability. Furthermore, we identifyestimation ’resonance points’—specific combinations of estimate methodologies thateither amplify or dampen volatility—which traditional linear models fail to capture.This research contributes original insights by reframing accounting estimates as a complex adaptive system, demonstrating how computational techniques from seeminglyunrelated disciplines can illuminate persistent challenges in financial reporting. Theimplications extend to audit planning, financial analysis, and standard-setting, suggesting that volatility management requires holistic consideration of estimate ecosystemsrather than piecemeal evaluation of individual accounts.