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Met oog op de komst van DORA heeft DNB een nieuwe Good Practice Informatiebeveiliging uitgebracht. Wat zijn de belangrijkste wijzigingen en waar kan je op letten?
In this paper, I give a bird’s-eye view of Large Language Models and outline the most significant issues related to their applications in financial services. I will discuss potential use cases, LLMs’ limitations, and the challenges associated with their applications. The objective is to provide the reader with understanding of various aspects of LLMs, placing them in the context of financial institutions. Additionally, I will discuss ways of implementing LLMs in finance-related areas, outline potential dangers and pitfalls, and explore emerging strategies of overcoming these challenges.
Large Language Models are reshaping Financial Services.
This paper investigates the relationship between news sentiment and corporate bond yield spreads, including assymmetry and sentiment-based bond investing.
In this paper, we investigate the interaction between Refinitiv ESG scores of firms and the performance of corporate bonds issued by these firms. We provide a rather straight-forward analysis of the relationship between ESG scores and corporate bond yields.
In November 2021, we organized a roundtable regarding the application of expert judgment and overrides in behavioral modeling of prepayments and savings, including interest rates, COVID-19, and elasticity of savings.
The goal of this paper is to show how sustainability considerations can be combined with multifactor investment strategies, and to demonstrate that adding sustainability considerations to these strategies does not diminish their performance.
In this paper, we explore sentiment in the framework of multifactor investment strategies. We investigate the relationships between sentiment-based and more traditional factor strategies and show that sentiment offers an additional signal that can contain information that is not incorporated in traditional investment factors.
This white paper addresses whether and how it is possible to measure a company’s or a portfolio’s contribution to specific goals (SDGs).
In this paper we describe AI fairness from a quantitative perspective, on the example of credit decision making – which candidates should receive a loan and which not – but the principles we will describe hold more generally in financial services. We explore the roots of bias in AI systems and present popular definitions of fairness adopted by the industry.