A single-step firm-level identification of classification shifting

Abstract

PurposeThis study proposes a new model to measure unexpected core earnings, using Bayesian dynamic latent method.Design/methodology/approachThe Bayesian dynamic latent modeling approach identifies the effects that stem from complex, multidimensional variables related to culture and legal framework, on unexpected core earnings. It also allows testing whether there is persistence over time in unexpected core earnings. We use sequential Bayesian Monte Carlo methods, also known as particle filtering, that simplify estimations.FindingsIn an international empirical application, we find evidence of persistence in unexpected core earnings as well as classification shifting. The impact of the legal framework on classification shifting shows variability across samples. Religion reduces classification shifting, whereas the cultural variables of power distance, masculinity and uncertainty avoidance enhances it. Interestingly, the persistence in unexpected core earnings is strong and moderates the ability of legal framework and religion in abating classification shifting.Research limitations/implicationsIn terms of policy implications, we show that strengthening legal framework would improve financial reporting and reduce the scope for manipulation. This could involve stricter enforcement mechanisms, increased penalties for non-compliance and regular audits to detect and deter classification shifting practices. Given that religion plays a role in moderating classification shifting, policymakers may explore partnerships or collaborations with religious institutions to promote ethical financial practices. Engaging religious leaders and organizations can help emphasize the importance of integrity and ethical behavior in financial reporting, potentially influencing the behavior of individuals and organizations.Originality/valueTo the best of our knowledge this is the first study that opts for Bayesian dynamic latent model for an international sample.