The Relationship Between Accounting Estimates and Earnings Volatility

Abstract

This research investigates the nuanced relationship between accounting estimates
and earnings volatility through a novel computational framework that diverges from
traditional 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 but
as elements within a dynamic financial ecosystem, where interactions between different
estimate categories (such as allowance for doubtful accounts, depreciation methods,
inventory valuation, and warranty liabilities) create emergent volatility patterns. Our
methodology applies principles from complex systems theory and computational biology to financial statement data from 500 publicly traded companies over a ten-year
period. The findings reveal that earnings volatility exhibits fractal-like properties when
examined through this lens, with small variations in interdependent estimates producing disproportionate effects on reported earnings stability. Furthermore, we identify
estimation ’resonance points’—specific combinations of estimate methodologies that
either 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 seemingly
unrelated disciplines can illuminate persistent challenges in financial reporting. The
implications extend to audit planning, financial analysis, and standard-setting, suggesting that volatility management requires holistic consideration of estimate ecosystems
rather than piecemeal evaluation of individual accounts.