The latest #ai-#cybersecurity-#knowledgemanagement practices advance the future of #riskmanagement practices. The article highlights the importance of risk management and #cyberresilience in a dynamic world characterized by #uncertainty and complexity.
"#risksharing is one way to pool risks without the need for a #thirdparty. To ensure the attractiveness of such a system, the rule should be accepted and understood by all participants. A desirable risk-sharing rule should fulfill #actuarial fairness and #pareto optimality while being easy to compute. This paper establishes a one-to-one correspondence between an actuarially fair #paretooptimal (AFPO) risk-sharing rule and a fixed point of a specific function."
Academic research in #alternativedata has become increasingly popular recently, with more research needed to explore new investment strategies and theories in finance innovation and #businessmanagement. This #chinese Beijing Normal University (BNU) paper provides an overview of more than 100 papers published from 2015 to 2021 on the emerging applications of alternative data, categorizing the data types and their applications in #finance and business. It also analyzes the roles of alternative data in finance theory and business management, arguing that alternative data can act as a bridge leading to more efficient financial markets.
This paper proposes a #credit#portfolio approach for evaluating #systemicrisk and attributing it across #financialinstitutions. The proposed model can be estimated from high-frequency credit default swap (#cds) data and captures risks from publicly traded #banks, privately held institutions, and coöperative banks. The approach overcomes limitations of earlier studies by accounting for correlated losses between institutions and also offers a modeling extension to account for #fattails and #skewness of #assetreturns. The model is applied to a universe of banks in #europe, highlighting discrepancies between the #capitaldequacy of the largest contributors to systemic risk and less systemically important banks.
By analyzing the content of #rating#reports, the study identifies seven different mechanisms that rating analysts are concerned about, with the most significant being the #misstatement-related violations of #debt covenants that increase #liquidityrisk and #compliancerisk. The study finds that #creditratings and #reputationalrisk of #misreporting firms are adversely affected for up to seven years after an intentional misstatement becomes publicly known. The impact of an intentional misstatement on a firm's credit rating is most pronounced when rating analysts express concerns about covenant violations.
This study leverages prospective memory theory to examine how encouraging #auditors to have implementation intentions can improve their attention to #fraud. Results suggest that encouraging implementation intentions, can increase auditors’ attention to fraud and make them more likely to take effective #antifraud actions. However, even in high fraud risk settings, auditors may still devote insufficient attention to fraud when performing planned #audit procedures, raising concerns for #regulators.
In #corporategovernance, where boards are being held liable for #misconduct based on #operationalrisk. Operational misconduct is a critical source of #director#liability and should be given the same attention as #financial#mismanagement. Operational risk marks a fundamental shift in the way boards monitor the firm. Judicial doctrine is changing the way boards manage operational risk, avoid liability, and protect stakeholders' lives and the society at large.
The paper discusses the proposed #climatechange #disclosure rules by the #sec, which would mandate companies to provide detailed disclosures on the impact of climate change on their financial performance and policies. The authors conducted a study using hand-collected data from 99 annual reports of 34 S&P 500 companies from 2019 to 2021, finding that 91% of the #annualreports included some disclosures on climate-related risks. The study found a positive relationship between climate-related disclosures and firms’ financial performance.
This paper focuses on the development of #bayesian classification and regression tree (#cart) models for claims frequency modeling in non-life #insurance pricing. The authors propose the use of the zero-inflated #poisson distribution to address the issue of imbalanced claims data and introduce a general MCMC algorithm for posterior tree exploration. Additionally, the deviance information criterion (DIC) is used for model selection. The paper discusses the applicability of these models through simulations and real insurance data.
This paper advocates for the use of #sandbox#regulation to complement a strict #liabilityregime in regulating #artificialintelligence (#ai). The #eu#regulators have already embraced this concept.