Loss firms' annual report narratives and share price anticipation of earnings

We extend prior research into the association between disclosure quality and share price anticipation of earnings by discriminating between firms that report profits and firms that report losses. As a measure of disclosure quality we count the number of forward-looking profit statements in annual report narratives. To measure the extent to which current share price movements anticipate future earnings changes we regress current stock returns on current and future earnings changes. The coefficients on the future earnings change variables are our measure of share price anticipation of earnings. Our regression results show that the association between annual report narratives and share price anticipation of earnings is not the same for profit and loss firms. For loss firms we find that the ability of stock returns to anticipate next period’s earnings change is significantly greater when the firm provides a large number of profit predictions in annual report narratives. We make no such observation for profit firms. In addition, once we control for variations in the intrinsic lead-lag relation between returns and earnings across industries, the observed difference between profit and loss firms becomes statistically significant. Overall, our results are consistent with annual report narratives being a particularly important source of information for loss-making firms

Does mandatory IFRS adoption improve the information environment?

Author's pre-print version. Final version published in Contemporary Accounting Research. Available online at http://onlinelibrary.wiley.com/We examine the effect of mandatory International Financial Reporting Standards (‘IFRS’) adoption on firms’ information environment. We find that after mandatory IFRS adoption consensus forecast errors decrease for firms that mandatorily adopt IFRS relative to forecast errors of other firms. We also find decreasing forecast errors for voluntary adopters, but this effect is smaller and not robust. Moreover, we show that the magnitude of the forecast errors decrease is associated with the firm-specific differences between local GAAP and IFRS. Exploiting individual analyst level data and isolating settings where investors would benefit more from either increased comparability or higher quality information, we document that the improvement in the information environment is driven both by information and comparability effects. These results are robust to variations in the measurement of information environment quality, forecast horizon, sample composition and tests of earnings management

Convergence, divergence and hybridity: a regulatory governance perspective on health technology assessment HTA) in England and Germany

Countries adopt different methods and processes to evaluate the benefits and costs of health technologies. It is important to identify and analyse the factors that influence the uptake and use of these methods and processes across countries. In this paper, we introduce a regulatory governance approach to the analysis of convergence, divergence and hybridity in HTA methods, discussing and critically analysing national processes for HTA in two major European Union (EU) Member States: England and Germany. We argue that any reasonably sophisticated account of national approaches to HTA must recognise that globalisation and the emergence of advanced industrial society involves the potential for widely varying processes, methods and evidential requirements. We suggest that this potentiality also confronts health policy analysts with the challenge of constructing analytical frameworks capable of identifying the diverse institutional, domestic and other factors that shape national approaches to HTA

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

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A chance constrained optimization model for risk

Choice among risky investments has been described using a chance constrained programming model with a finite number of states of nature. This paper presents a simple combinational algorithm for solving this model which, at worst, requires solving a number of linear programming problems.