The impact of immunotherapies on COVID-19 case fatality rates during the US vaccination campaign: a multidisciplinary open data analysis using FDA Adverse Event Reporting System and Our World in Data.

Introduction: Patients under immunotherapies were excluded from the pivotal trials of vaccinations against the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), and no population-level data on disease outcomes such as case fatality rates in relation to vaccination coverage exist. Our study aims to fill this gap by investigating whether CFRs in patients with immunotherapies decrease with increasing vaccination coverage in the total population. Methods: We combined aggregated open source data on COVID-19 vaccination coverage from "Our World in Data" with publicly available anonymized COVID-19 case reports from the FDA Adverse Event Reporting System to compute COVID-19 CFRs for patients under immunotherapy at different vaccination coverage levels in the total population. CFRs at different vaccination coverage levels were then compared to CFRs before vaccination campaign start. Results: While we found an overall decrease in CFRs on population level with increasing vaccination coverage, we found no decrease in people using anti-CD20 or glucocorticoids. Discussion: Risk-mitigation strategies on an individual- and population-level are thus still needed to lower the probability of fatal SARS-CoV2 infection for these vulnerable populations

Does stakeholder pressure influence corporate GHG emissions reporting? Empirical evidence from Europe

Purpose – The purpose of this paper is to seek to shed light on the practice of incomplete corporate disclosure of quantitative Greenhouse gas (GHG) emissions and investigates whether external stakeholder pressure influences the existence, and separately, the completeness of voluntary GHG emissions disclosures by 431 European companies. Design/methodology/approach – A classification of reporting completeness is developed with respect to the scope, type and reporting boundary of GHG emissions based on the guidelines of the GHG Protocol, Global Reporting Initiative and the Carbon Disclosure Project. Logistic regression analysis is applied to examine whether proxies for exposure to climate change concerns from different stakeholder groups influence the existence and/or completeness of quantitative GHG emissions disclosure. Findings – From 2005 to 2009, on average only 15 percent of companies that disclose GHG emissions report them in a manner that the authors consider complete. Results of regression analyses suggest that external stakeholder pressure is a determinant of the existence but not the completeness of emissions disclosure. Findings are consistent with stakeholder theory arguments that companies respond to external stakeholder pressure to report GHG emissions, but also with legitimacy theory claims that firms can use carbon disclosure, in this case the incomplete reporting of emissions, as a symbolic act to address legitimacy exposures. Practical implications – Bringing corporate GHG emissions disclosure in line with recommended guidelines will require either more direct stakeholder pressure or, perhaps, a mandated disclosure regime. In the meantime, users of the data will need to carefully consider the relevance of the reported data and develop the necessary competencies to detect and control for its incompleteness. A more troubling concern is that stakeholders may instead grow to accept less than complete disclosure. Originality/value – The paper represents the first large-scale empirical study into the completeness of companies’ disclosure of quantitative GHG emissions and is the first to analyze these disclosures in the context of stakeholder pressure and its relation to legitimation

Climate change and asset prices: are corporate carbon disclosure and performance priced appropriately?

This paper empirically assesses the value relevance of information on corporate climate change disclosure and performance to asset prices, and discusses whether this information is priced appropriately. Findings indicate that corporate disclosures of quantitative GHG emissions and, to a lesser extent, carbon performance are value relevant. We use hand-collected information on quantitative greenhouse gas (GHG) emissions for 433 European companies and build portfolios based on GHG disclosure and performance. We regress portfolios on Carhart (1997) four factor models extended for industry effects over the years 2005 to 2009. Results show that investors achieved abnormal risk-adjusted returns of up to 13.05% annually by exploiting inefficiently priced positive effects of (complete) GHG emissions disclosure and good corporate climate change performance in terms of GHG efficiency. Results imply that, firstly, information costs involved in carbon disclosure and management do not present a burden on corporate financial resources. Secondly, investors should not neglect carbon disclosure and performance when making investment decisions. Thirdly, during the period analysed financial markets were inefficient in pricing publicly available information on carbon disclosure and performance. Mandatory and standardised information on carbon performance would consequently not only increase market efficiency but result in better allocation of capital within the real economy

Multiple Sclerosis immunotherapies and COVID-19 mortality: an analysis of the FDA Adverse Event Reporting System.

Background Evidence on mortality risks associated with MS-immunotherapies during the SARS-CoV2 pandemic derived thus far mainly from single country experiences. Objective In this analysis, we aim to determine the frequency of COVID-19 associated fatality reports of patients receiving an MS-immunotherapy as reported to the international Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from February 2020 to March 2021. Methods In all, 1071 cases for this cross-sectional analysis were retrieved from FAERS and a multivariable logistic regression was performed. We adjusted for sex, age, region, month of report to FDA, immunotherapy-class and additionally for healthcare-system and pandemic-related metrics. Result Anti-CD20 therapies (60%) followed by sphingosine-1 phosphate modulators (12%) and dimethylfumarat (10%) were reported most frequently. In 50% of the cases, MS-phenotype is not reported, relapsing MS in 35% and progressive MS in 15%. Besides older age (odds ratio [OR]: 1.1; 95% confidence interval [CI]: 1.07-1.13; p < 0.01), anti-CD20 therapies were significantly associated with a higher risk of death (OR: 4.1; 95% CI: 1.17-14.46; p = 0.03), whereas female sex was associated with a reduced mortality risk (OR: 0.4, 95% CI: 0.22-0.72; p < 0.01). Conclusion Using international open access data and a multidisciplinary approach for risk prediction, we identified an increased mortality risk associated with anti-CD20 therapies, which is in line with national and multi-national cohort studies

ESG Shareholder Engagement and Downside Risk

We show that engagement on environmental, social, and governance issues can benefit shareholders by reducing firms’ downside risks. We find that the risk reductions (measured using value at risk and lower partial moments) vary across engagement types and success rates. Engagement is most effective in lowering downside risk when addressing environmental topics (primarily climate change). Further, targets with large downside risk reductions exhibit a decrease in environmental incidents after the engagement. We estimate that the value at risk of engagement targets decreases by 9% of the standard deviation after successful engagements, relative to control firms

Transformer-based Entity Legal Form Classification

We propose the application of Transformer-based language models for classifying entity legal forms from raw legal entity names. Specifically, we employ various BERT variants and compare their performance against multiple traditional baselines. Our evaluation encompasses a substantial subset of freely available Legal Entity Identifier (LEI) data, comprising over 1.1 million legal entities from 30 different legal jurisdictions. The ground truth labels for classification per jurisdiction are taken from the Entity Legal Form (ELF) code standard (ISO 20275). Our findings demonstrate that pre-trained BERT variants outperform traditional text classification approaches in terms of F1 score, while also performing comparably well in the Macro F1 Score. Moreover, the validity of our proposal is supported by the outcome of third-party expert reviews conducted in ten selected jurisdictions. This study highlights the significant potential of Transformer-based models in advancing data standardization and data integration. The presented approaches can greatly benefit financial institutions, corporations, governments and other organizations in assessing business relationships, understanding risk exposure, and promoting effective governance

Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective

Although machine learning is frequently associated with neural networks, it also comprises econometric regression approaches and other statistical techniques whose accuracy enhances with increasing observation. What constitutes high quality machine learning is yet unclear though. Proponents of deep learning (i.e. neural networks) value computational efficiency over human interpretability and tolerate the ‘black box’ appeal of their algorithms, whereas proponents of explainable artificial intelligence (xai) employ traceable ‘white box’ methods (e.g. regressions) to enhance explainability to human decision makers. We extend Brooks et al.’s [2019. ‘Financial Data Science: The Birth of a New Financial Research Paradigm Complementing Econometrics?’ European Journal of Finance 25 (17): 1627–36.] work on significance and relevance as assessment critieria in econometrics and financial data science to contribute to this debate. Specifically, we identify explainability as the Achilles heel of classic machine learning approaches such as neural networks, which are not fully replicable, lack transparency and traceability and therefore do not permit any attempts to establish causal inference. We conclude by suggesting routes for future research to advance the design and efficiency of ‘white box’ algorithms

On the price of morals in markets: an empirical study of the Swedish AP-Funds and the Norwegian Government Pension Fund

This study empirically analyses the exclusion of companies from investors’ investment universe due to a company’s business model (sector-based exclusion) or due to a company’s violations of international norms (normbased exclusion). We conduct a time-series analysis of the performance implications of the exclusion decisions of two leading Nordic investors, Norway’s Government Pension Fund-Global (GPFG) and Sweden’s AP-funds. We find that their portfolios of excluded companies do not generate an abnormal return relative to the funds’ benchmark index. While the exclusion portfolios show higher risk than the respective benchmark, this difference is only statistically significant for the case of GPFG. These findings suggest that the exclusion of the companies generally does not harm funds’ performance. We interpret these findings as indicative that with exclusionary screening, as practiced by the sample funds, asset owners can meet the ethical objectives of their beneficiaries without compromising financial returns

State pension funds and corporate social responsibility: do beneficiaries’ political values influence funds’ investment decisions?

This study explores the underlying drivers of US public pension funds’ tendency to tilt their portfolios towards companies with stronger corporate social responsibility (CSR). Studying the equity holdings of large, internally-managed US state pension funds, we find evidence that the political leaning of their beneficiaries and political pressures by state politicians affect funds’ investment decisions. State pension funds from states with Democratic-leaning beneficiaries tilt their portfolios more strongly towards companies that perform well on CSR issues, and this tendency is intensified when the state government is dominated by Democratic state politicians. Moreover, we find that funds which tilt their portfolios towards companies with superior CSR scores generate a slightly higher return compared with their counterparts. Overall, our findings indicate that funds align their investment choices with the financial and non-financial interests of their beneficiaries when deciding whether to incorporate CSR into their equity allocations