Nos derniers articles

“… we aim to provide a summary of the evolving landscape of AI applications in finance and accounting research and project future avenues of exploration.”

The paper explores convex risk measures with weak optimal transport penalties, demonstrating explicit representations via nonlinear transformations of loss functions. It delves into computational aspects, discussing approximations using neural networks and applies these concepts to diverse examples. Finally, it demonstrates practical applications in insurance and finance for worst-case losses and no-arbitrage pricing beyond quoted maturities.

Companies use #ai tools for #hr decisions, but they face a balance between benefits and #risks. With limited federal #regulation and complex state laws, employers seek guidance. The #model#riskmanagement#mrm framework, adapted from #finance, aids in managing #airisks for #employment choices. Proportionality lets employers adjust validation to risks and tech changes. Objective analysis and a competent MRM team ensure AI tools align with design and legal requirements, fostering trust and #compliance.

Traditional #statistical and #algorithm-based methods used to analyze #bigdata often overlook small but significant evidence. #bayesian #statistics, driven by #conditional #probability, offer a solution to this challenge. The review identifies two main applications of Bayesian statistics in #finance: prediction in financial markets and credit risk models. The findings aim to provide valuable insights for researchers aiming to incorporate Bayesian methods and address the sample size issue effectively in #financial #research.

"Our study extends the research on the determinants of corporate #climaterisk and impact of firm-level #politicalrisk on various corporate policies and decision-making processes. It further motivates future research on the interplay among these #risks and their effects on #finance."

"#systemicrisk measures have been shown to be predictive of #financialcrises and declines in real activity. Thus, forecasting them is of major importance in #finance and #economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (#mes)."

"... the new Climate Risk Division will integrate climate risks into its supervision of regulated entities, support the industry’s growth in managing climate risks, coordinate with international, national, and state regulators, develop internal capacity on climate-related financial risks, support the capacity-building of peer regulators on climate-related supervision, and ensure fair access to financial services for all communities, especially those most impacted by climate change. "

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.

"... we study the behavior of the asymptotic tail distribution of independent sums of heavy-tailed random vectors under the paradigm of multivariate regular variation. Assessment of such tail probabilities are of interest in risk management for many finance, insurance, queueing, and environmental applications. Multidimensional tail events are often characterized by at least one variable exceeding a high threshold, and the asymptotic probability of such events follow the so-called “one large jump” principle..."

"When developing large-sample statistical inference for quantiles, also known as Values-at-Risk in finance and insurance, the usual approach is to convert the task into sums of random variables. The conversion procedure requires that the underlying cumulative distribution function (cdf) would have a probability density function (pdf), plus some minor additional assumptions on the pdf. In view of this, and in conjunction with the classical continuous-mapping theorem, researchers also tend to impose the same pdf-based assumptions when investigating (functionals of) integrals of the quantiles, which are natural ingredients of many risk measures in finance and insurance. Interestingly, the pdf-based assumptions are not needed when working with integrals of quantiles, and in this paper we explain and illustrate this remarkable phenomenon."