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...

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...

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 use of the machine learning algorithm is examined in derivation of the determinants of price movements of stock indices. The Random Forest algorithm was selected as an ideal representative of the nonlinear algorithms based on decision trees. Various brokering and investment...

Banks are concerned with the assessment of the risk of financial distress before giving out a loan. Many researchers proposed the use of models based on the Neural Networks in order to help the banker better make a decision. The objective of this paper is to explore a new practical way based on...

Credit risk is defined as the risk that borrowers will fail to pay its loan obligations. In recent years, a large number of banks have developed sophisticated systems and models to help bankers in quantifying, aggregating and managing risk. The outputs of these models also play increasingly...