Seit der globalen Finanzkrise 2007/2008 liegt aufgrund ihrer Schlüsselrolle für ein funktionierendes Finanzsystem ein besonderer Fokus auf den so genannten systemrelevanten Finanzinstitutionen (systemically important financial institutions, SIFIs). Neben der Größe von Finanzinstitutionen ist...

The aim of this manuscript is to provide the mathematical and statistical foundations of actuarial learning. This is key to most actuarial tasks like insurance pricing, product development, claims reserving and risk management. The basic approach to these tasks is regression modeling. This...

We introduce a novel application of machine learning to compare the Pooling and Servicing Agreements (PSA) that govern asset-backed securities. The PSA is often viewed as mostly boilerplate legal text and thus may appear similar across deals despite heterogeneity in the underlying collateral and...

Seit der globalen Finanzkrise 2007/2008 liegt aufgrund ihrer Schlüsselrolle für ein funktionierendes Finanzsystem ein besonderer Fokus auf den so genannten systemrelevanten Finanzinstitutionen (systemically important financial institutions, SIFIs). Neben der Größe von Finanzinstitutionen ist...

Using machine learning methods, we develop a new measure of aggregate analyst sentiment. We first train analyst-specific neural network (NN) models that capture each analyst's common biases across firms. Using NN model outputs, we decompose the forecast errors of individual analysts into...

Spanish abstract:Recientes avances tecnológicos, como el almacenamiento masivo de datos y la computación en la nube, están dando lugar a un mayor uso de la inteligencia artificial en la economía y las finanzas, y modificando aspectos fundamentales tanto para entidades como para supervisores....

Financial authorities' use of technology has evolved over the years, leading to different generations of technology that culminate in what the paper considers as suptech. Suptech refers to the application of big data or artificial intelligence (AI) to tools used by financial authorities. There...

A large body of literature has tried to identify the relative information contributions of different characteristics - jointly or in isolation - to the cross-section of stock returns. These characteristics generally cover three data sources: market, fundamentals, and analyst recommendations. In...

In this paper the relatively new technique of neural nets is integrated in a traditional model of portfolio choice. On the basis of Arrow’s State Preference Model the investment decision depends on the expectation building process which consists of two components. The individual information...

We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the...