Nos derniers articles

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.

Effective #riskmanagement, including #operationalriskmanagement, is crucial for minimizing #financialrisks posed by #operationalrisk. Risk evaluation, which includes assessing potential risks and their #probabilities, is also vital. #bibliometric analysis using #metrics such as citations, networks, co-authorship, and region-based #publications can provide insights into the quality of #research on operational risk and identify gaps. Such analysis reveals a growing interest in the study of operational risk, but also highlights research gaps that need to be addressed for effective risk management.

"... since 2015 investors have increased their visibility through their climate pledges and ESG performance. However, they have mostly failed to divest from fossil fuels. We draw on this evidence to propose a novel international climate finance research agenda."