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Predicting bank distress in Europe: Using machine learning and a novel definition of distress

Malikkidou, Despo; Strohbach, Wolfgang - 2025

This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

Persistent link: https://www.econbiz.de/10015199522

A novel model structured on predictive churn methods in a banking organization

Silveira, Leonardo José; Pinheiro, Plácido Rogério; … - In: Journal of Risk and Financial Management 14 (2021) 10, pp. 1-24

A constant in the business world is the frequent movement of customers joining or abandoning companies' services and products. The customer is one of the company's most important assets. Reducing the customer abandonment rate has become a matter of survival and, at the same time, the most...

Persistent link: https://www.econbiz.de/10013201165

Pricing vanilla options using artificial neural networks: Application to the South African market

Du Plooy, Ryno; Venter, Pierre J. - In: Cogent Economics & Finance 9 (2021) 1, pp. 1-15

In this paper, a feed-forward artificial neural network (ANN) is used to price Johannesburg Stock Exchange (JSE) Top 40 … European call options using a constructed implied volatility surface. The prices generated by the ANN were compared to the … prices obtained using the Black-Scholes (BS) model. It was found that the pricing performance of the ANN significantly …

Persistent link: https://www.econbiz.de/10014001524

A bibliometric analysis of machine learning econometrics in asset pricing

Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of Risk and Financial Management 15 (2022) 11, pp. 1-17

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

Persistent link: https://www.econbiz.de/10014332691

The effectiveness of central bank purchases of long-term treasury securities: A neural network approach

Tänzer, Alina - 2024

Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. This paper introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables...

Persistent link: https://www.econbiz.de/10014533855

Multivariate macroeconomic forecasting: From DSGE and BVAR to artificial neural networks

Tänzer, Alina - 2024

. Specifically, a fully connected feed forward nonlinear autoregressive neural network (ANN) is contrasted to a well established … compared to the DSGE model, however there is evidence for an overall improvement of predictions when relying on ANN, or …

Persistent link: https://www.econbiz.de/10014534021

Exploring neural network models in understanding bilateral trade in APEC: A review of history and concepts

Quimba, Francis Mark A.; Barral, Mark Anthony A. - 2018

Machine Learning algorithms, such as the artificial neural networks, are acknowledged to outperform several econometric procedures in predictions. Machine learning becomes popular for doing operations that practically require more efficiency and accuracy, derived basically from the algorithm's...

Persistent link: https://www.econbiz.de/10012145918

Application of machine learning algorithms to predict hotel occupancy

Kozlovskis, Konstantins; Liu, Yuanyuan; Lace, Natalja; … - In: Journal of Business Economics and Management (JBEM) 24 (2023) 3, pp. 594-613

(bagged CART, bagged MARS, XGBoost, random forest, SVM) were optimized and applied for predicting occupancy. All models are …

Persistent link: https://www.econbiz.de/10015401218

Der Einfluss von menschlichen Denkmustern auf künstliche Intelligenz – Eine strukturierte Untersuchung von kognitiven Verzerrungen

Herm, Lukas-Valentin; Janiesch, Christian; Fuchs, Patrick - In: HMD Praxis der Wirtschaftsinformatik 59 (2022) 2, pp. 556-571

Künstliche Intelligenz (KI) dringt vermehrt in sensible Bereiche des alltäglichen menschlichen Lebens ein. Es werden nicht mehr nur noch einfache Entscheidungen durch intelligente Systeme getroffen, sondern zunehmend auch komplexe Entscheidungen. So entscheiden z. B. intelligente Systeme, ob...

Persistent link: https://www.econbiz.de/10015210326

Machine Learning im Logistikmanagement: Entwicklung eines Gestaltungsansatzes zum Einsatz von ML-Anwendungen in logistischen Entscheidungsprozessen

Weinke, Manuel - 2023

Als ein Teilbereich der Künstlichen Intelligenz stellt Machine Learning (ML) eine Schüsseltechnologie des 21. Jahrhunderts dar. Unter Nutzung der mathematisch-statistischen Verfahren sind technische Systeme realisierbar, die anhand von Daten eigenständig empirische Zusammenhänge erkennen und...

Persistent link: https://www.econbiz.de/10014433975