IAS 39, income smoothing, and pro-cyclicality: evidence from Hong Kong banks

Purpose: The purpose of this study is to investigate the impact of International Accounting Standard 39 (IAS 39) on income-smoothing activities and pro-cyclical behavior through loan loss provisions using a sample of Hong Kong banks. Design/methodology/approach: Fixed effects estimator is used, and the analysis covers the period from 2000 to 2009.Findings: The results suggest that Hong Kong banks engage less in income-smoothing activity after they comply with the IAS 39. No evidence supports loan loss provisions of Hong Kong banks exhibiting more pro-cyclical behavior after IAS 39 adoption.Research limitations/implications: Compliance with IAS 39 should improve the quality of bank financial reporting. The reduction in income-smoothing activities among Hong Kong banks after IAS 39 adoption fairly supports the effectiveness of International Financial Reporting Standard (IFRS) and countries that have yet to comply with IFRS may take action to apply the standards. Bank regulators should take pro-active action in addressing the issue of pro-cyclicality of loan loss provisions, as IAS 39 focuses more on improving the financial information quality, while pro-cyclicality is associated with the economic cycles.Originality/value: Hong Kong banking industry is unique, as it was among the first IFRS adopters in the East Asia region and it has its own legal framework for developing accounting standards. The results of this study are expected to shed some light on the effects of IAS 39 adoption on income smoothing and pro-cyclicality of banks in the East Asia region, where the accounting cultural value dimensions and institutional structures are different than that of European countries

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)