Accounting Estimates Uncertainty and Earnings Volatility Implications

Abstract

This research introduces a novel computational framework, the Probabilistic Estimate Convergence Algorithm (PECA), to model and quantify the inherent uncertainty
in accounting estimates and its direct implications for earnings volatility. Departing
from traditional deterministic accounting models, we conceptualize accounting estimates as probability distributions rather than point estimates, drawing upon principles from computational finance and stochastic processes. Our methodology employs
a Monte Carlo simulation engine integrated with a Bayesian inference layer to propagate estimation uncertainty through financial statements. We formulate three distinct
research questions: (1) How does the multi-dimensional uncertainty in key accounting
estimates (e.g., allowance for doubtful accounts, asset impairments, warranty liabilities) non-linearly amplify reported earnings volatility? (2) Can a convergence algorithm
identify stable estimation corridors that minimize volatility spillover? (3) Does the interaction between estimate uncertainty and operational leverage create systemic feedback loops observable in earnings time series? We test PECA on a synthetically generated dataset simulating 1,000 firms over a 20-quarter period, incorporating stochastic economic shocks. Our results reveal a previously under-characterized ’volatility
resonance’ effect, where small, correlated uncertainties across different estimates can
synchronize to produce earnings volatility magnitudes 2.3 to 4.1 times greater than predicted by standard sensitivity analysis. Furthermore, PECA identifies non-intuitive,
asymmetric corridors for estimate revision that enhance earnings stability. The algorithm successfully reduced simulated earnings volatility by 18.7% in high-uncertainty
regimes without compromising estimate accuracy. This work provides a foundational
computational model for moving beyond point-in-time disclosures towards a dynamic,
probabilistic reporting of financial health, offering auditors, preparers, and regulators a
novel toolkit for understanding and managing the volatility implications of accounting
judgment.