3,959 research outputs found
Decision support systems for large dam planning and operation in Africa
Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects
Mack-net model: Blending Mack's model with Recurrent Neural Networks
In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007?2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is an indicator of the risk assumed by the company. Thus, measures that relate profitability with risk are crucial in order to understand the financial position of insurance firms. Taking advantage of the increasing computational power, this paper introduces a stochastic reserving model whose aim is to improve the performance of the traditional Mack?s reserving model by applying an ensemble of Recurrent Neural Networks. The results demonstrate that blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities
Forecasting inflation with thick models and neural networks
This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models
Financial crises and bank failures: a review of prediction methods
In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises
Machine and deep learning applications for improving the measurement of key indicators for financial institutions: stock market volatility and general insurance reserving risk
Esta tesis trata de lograr mejoras en los modelos de estimaciĂłn de los riesgo financieros y actuariales a travĂ©s del uso de tĂ©cnicas punteras en el campo del aprendizaje automático y profundo (machine y deep learning), de manera que los modelos de riesgo generen resultados que den un mejor soporte al proceso de toma de decisiones de las instituciones financieras. Para ello, se fijan dos objetivos. En primer lugar, traer al campo financiero y actuarial los mecanismos más punteros del campo del aprendizaje automático y profundo. Los algoritmos más novedosos de este campo son de amplia aplicaciĂłn en robĂłtica, conducciĂłn autĂłnoma o reconocimiento facial, entre otros. En segundo lugar, se busca aprovechar la gran capacidad predictiva de los algoritmos anteriormente adaptados para construir modelos de riesgo más precisos y que, por tanto, sean capaces de generar resultados que puedan dar un mejor soporte a la toma de decisiones de las instituciones financieras. Dentro del universo de modelos de riesgos financieros, esta tesis se centra en los modelos de riesgo de renta variable y reservas de siniestros. Esta tesis introduce dos modelos de riesgo de renta variable y otros dos de reservas. Por lo que se refiere a la renta variable, el primero de los modelos apila algoritmos tales como redes neuronales, bosques aleatorios o regresiones aditivas mĂşltiples con árboles con el objetivo de mejorar la estimaciĂłn de la volatilidad y, por tanto, generar modelos de riesgo más precisos. El segundo de los modelos de riesgo adapta al mundo financiero y actuarial los Transformer, un tipo de red neuronal que, debido a su alta precisiĂłn, ha apartado al resto de algoritmos en el campo del procesamiento del lenguaje natural. Adicionalmente, se propone una extensiĂłn de esta arquitectura, llamada Multi-Transformer y cuyo objetivo es mejorar el rendimiento del algoritmo inicial mediante el ensamblaje y aleatorizaciĂłn de los mecanismos de atenciĂłn. En lo relativo a los dos modelos de reservas introducidos por esta tesis el primero de ellos trata de mejorar la estimaciĂłn de reservas y generar modelos de riesgo más precisos apilando algoritmos de aprendizaje automático con modelos de reservas basados en estadĂstica bayesiana y Chain Ladder. El segundo modelo de reservas trata de mejorar los resultados de un modelo de uso habitual, como es el modelo de Mack, a travĂ©s de la aplicaciĂłn de redes neuronales recurrentes y conexiones residuales
Financial crises and bank failures: a review of prediction methods
In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Multi-fidelity (gray-box) hyperparameter optimization techniques (HPO) have
recently emerged as a promising direction for tuning Deep Learning methods.
However, existing methods suffer from a sub-optimal allocation of the HPO
budget to the hyperparameter configurations. In this work, we introduce DyHPO,
a Bayesian Optimization method that learns to decide which hyperparameter
configuration to train further in a dynamic race among all feasible
configurations. We propose a new deep kernel for Gaussian Processes that embeds
the learning curve dynamics, and an acquisition function that incorporates
multi-budget information. We demonstrate the significant superiority of DyHPO
against state-of-the-art hyperparameter optimization methods through
large-scale experiments comprising 50 datasets (Tabular, Image, NLP) and
diverse architectures (MLP, CNN/NAS, RNN).Comment: Accepted at NeurIPS 202
Claim Models: Granular Forms and Machine Learning Forms
This collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and machine learning models. Others illustrate applications with real-world data. The examples include neural networks, which, though well known in some disciplines, have previously been limited in the actuarial literature. This volume expands on that literature, with specific attention to their application to loss reserving. For example, one of the articles introduces the application of neural networks of the gated recurrent unit form to the actuarial literature, whereas another uses a penalized neural network. Neural networks are not the only form of machine learning, and two other papers outline applications of gradient boosting and regression trees respectively. Both articles construct loss reserves at the individual claim level so that these models resemble granular models. One of these articles provides a practical application of the model to claim watching, the action of monitoring claim development and anticipating major features. Such watching can be used as an early warning system or for other administrative purposes. Overall, this volume is an extremely useful addition to the libraries of those working at the loss reserving frontier
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
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