Unsupervised Machine Learning-Based Financial Anomalies, ESG, and Accounting Conservatism

This study empirically examines the joint effect of financial anomaly risk and ESG performance on accounting conservatism using accrual models, market models, and earnings time-series models. Financial anomaly scores are obtained using unsupervised machine learning to identify reporting anomalies for firms. Our findings suggest that higher financial anomaly risk is negatively related to accounting conservatism through delayed or reduced loss recognition. ESG engagement serves as a moderating variable to mitigate conditional conservatism losses partially for both accrual- and earnings-based models, conditional on financial anomaly risk; otherwise, ESG engagement has a weak or insignificant effect on market-based models. ESG practice is therefore a state-dependent conditional governor to complement traditional governance structures, depending on both levels of anomaly risk as well as accounting models used to derive conservatism measures. Our findings have practical implications for investors and government regulators, as well as managers, which emphasize that ESG practice is not universally beneficial to conservatism but can further improve reporting quality, conditional on certain risk levels.