Auditor industry specialization, political economy and earnings quality: some cross-country evidence

Using cross-country data, we evaluate the impact of investor protection on the association between earnings quality and audits by industry specialists. Our findings show that the positive association between industry specialist auditors and earnings quality as documented in the literature is affected by the political electoral system, which reflects investor protection rights in a country. We document that audits by industry specialists are associated with higher earnings quality in countries with the proportional electoral system, reflecting weak investor protection. Our results also confirm Kwon et al.\u27s findings that overall there is a positive association between earnings quality and audits by industry specialists in countries with weak legal enforcement. Our findings, however, indicate that Kwon et al.\u27s results are valid only for countries with weak investor protection reflected by the proportional electoral system and not for countries with strong investor protection reflected by the majoritarian electoral system. These findings thus suggest that higher earnings quality of firms audited by industry specialists across countries can especially be expected when investor protection is low and legal enforcement is also weak. In addition, our research suggests that future cross-country studies could explicitly consider the role of the political electoral system of a country in evaluating corporate governance, management and accounting issues

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting

ObjectivesTo develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.MethodsPatients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).ResultsA total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV.ConclusionTwo simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation