The Experts below are selected from a list of 58578 Experts worldwide ranked by ideXlab platform
Linda Lai - One of the best experts on this subject based on the ideXlab platform.
-
The Norwegian Banking Crisis: Managerial escalation of decline and Crisis
Scandinavian Journal of Management, 1994Co-Authors: Linda LaiAbstract:This paper examines the impact and nature of managerial contribution to the Norwegian Banking Crisis. Numerous findings are reviewed which suggest that many bank managers produced inappropriate responses to the initial decline and subsequent Crisis, and thus contributed to the Crisis, due to a set of common managerial misrepresentations of the situation. These include: external attribution of failure, overoptimism and overconfidence, the confirmation trap, the illusion of control, irrational escalation of commitment and insufficient adjustment.
Johan P R De Villiers - One of the best experts on this subject based on the ideXlab platform.
-
systemic Banking Crisis early warning systems using dynamic bayesian networks
Expert Systems With Applications, 2016Co-Authors: Joel Janek Dabrowski, Conrad Beyers, Johan P R De VilliersAbstract:Dynamic Bayesian networks are applied as early warning systems in Banking crises.A comparison to the traditional logit and signal extraction methods is provided.A unique approach is used to measure the ability of a method to predict a Crisis.Results indicate that dynamic Bayesian networks are superior at predicting crises. For decades, the literature on Banking Crisis early-warning systems has been dominated by two methods, namely, the signal extraction and the logit model methods. However, these methods, do not model the dynamics of the systemic Banking system. In this study, dynamic Bayesian networks are applied as systemic Banking Crisis early-warning systems. In particular, the hidden Markov model, the switching linear dynamic system and the naive Bayes switching linear dynamic system models are considered. These dynamic Bayesian networks provide the means to model system dynamics using the Markovian framework. Given the dynamics, the probability of an impending Crisis can be calculated. A unique approach to measuring the ability of a model to predict a Crisis is utilised. The results indicate that the dynamic Bayesian network models can provide precise early-warnings compared with the signal extraction and the logit methods.
Hans Petter Wilse - One of the best experts on this subject based on the ideXlab platform.
-
The Norwegian Banking Crisis
2004Co-Authors: Karsten R. Gerdrup, Thorvald Grung Moe, Harald Moen, Knut Sandal, Christoph Schwierz, Jon A. Solheim, Erling Steigum, Bent Vale, Hans Petter WilseAbstract:It has been ten years since the Norwegian Banking Crisis ended. Although many papers have been written about the Norwegian Banking Crisis, it may be time to consider the Crisis in retrospect. Actually, it is our impression that a comprehensive, but reasonably compact description in English of the Norwegian Banking Crisis is lacking. With this publication, we try to fill this gap.
Joel Janek Dabrowski - One of the best experts on this subject based on the ideXlab platform.
-
systemic Banking Crisis early warning systems using dynamic bayesian networks
Expert Systems With Applications, 2016Co-Authors: Joel Janek Dabrowski, Conrad Beyers, Johan P R De VilliersAbstract:Dynamic Bayesian networks are applied as early warning systems in Banking crises.A comparison to the traditional logit and signal extraction methods is provided.A unique approach is used to measure the ability of a method to predict a Crisis.Results indicate that dynamic Bayesian networks are superior at predicting crises. For decades, the literature on Banking Crisis early-warning systems has been dominated by two methods, namely, the signal extraction and the logit model methods. However, these methods, do not model the dynamics of the systemic Banking system. In this study, dynamic Bayesian networks are applied as systemic Banking Crisis early-warning systems. In particular, the hidden Markov model, the switching linear dynamic system and the naive Bayes switching linear dynamic system models are considered. These dynamic Bayesian networks provide the means to model system dynamics using the Markovian framework. Given the dynamics, the probability of an impending Crisis can be calculated. A unique approach to measuring the ability of a model to predict a Crisis is utilised. The results indicate that the dynamic Bayesian network models can provide precise early-warnings compared with the signal extraction and the logit methods.
Philipp J. Süss - One of the best experts on this subject based on the ideXlab platform.
-
The Outbreak of the Russian Banking Crisis
The Czech Economic Review, 2011Co-Authors: Jarko Fidrmuc, Philipp J. SüssAbstract:Owing to a combination of domestic, regional and international factors, Russian banks have been strongly influenced by the worldwide financial Crisis which started in the second half of 2008. In this paper, we estimate an early warning model for the Russian Banking Crisis. In a first step, we identify 47 Russian banks which failed after September 2008. Using the Bankscope dataset, we then show that balance sheet indicators were informative as early as in 2006 and 2007 about possible failures of these banks. Especially equity, net interest revenues, return on average equity, net loans, and loan loss reserves are identified as the early indicators with high predictive power.
