Using Neural Networks to Support Early Warning System for Financial Crisis Forecasting

2005 ◽  
pp. 284-296 ◽  
Author(s):  
Kyong Joo Oh ◽  
Tae Yoon Kim ◽  
Hyoung Yong Lee ◽  
Hakbae Lee
Keyword(s):  
Neural Networks ◽  
Financial Crisis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System

scholarly journals Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Three Decision Tree Models

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gang Wang ◽  
Keming Wang ◽  
Yingying Zhou ◽  
Xiaoyan Mo ◽  
Weilin Xiao
Keyword(s):  
Financial Crisis ◽  
Decision Tree ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Comprehensive Evaluation ◽  
Warning System ◽  
Predictive Ability ◽  
Tree Models ◽  
The Impact

The financial crisis is a realistic problem that the general enterprise must encounter in the process of financial management. Due to the impact of the COVID-19 and the Sino-US trade war, domestic companies with unsound financial conditions are at risk of shutdowns and bankruptcies. Therefore, it is urgently needed to study the financial warning of enterprises. In this study, three decision tree models are used to establish the financial crisis early warning system. These three decision tree models include C50, CART, and random forest decision trees. In addition, the ROC curve was used for comprehensive evaluation of the accuracy analysis of the model to confirm the predictive ability of each model. This result can provide reference for domestic financial departments and provide financial management basis for the investing public.

scholarly journals Establishment of urban waterlogging pre-warning system based on coupling RBF-NARX neural networks

2020 ◽  
Vol 82 (9) ◽  
pp. 1921-1931
Author(s):  
Ming Wei ◽  
Lin She ◽  
Xue-yi You
Keyword(s):  
Neural Networks ◽  
Early Warning ◽  
Early Warning System ◽  
Low Impact Development ◽  
Warning System ◽  
Extreme Rainfall ◽  
Area At Risk ◽  
Risk Area ◽  
Rainfall Process ◽  
Flooding Duration

Abstract The optimal layout of low-impact development (LID) facilities satisfying annual runoff control for low rainfall expectation is not effective under extreme rainfall conditions and urban waterlogging may occur. In order to avoid the losses of urban waterlogging, it is particularly significant to establish a waterlogging early warning system. In this study, based on coupling RBF-NARX neural networks, we establish an early warning system that can predict the whole rainfall process according to the rainfall curve of the first 20 minutes. Using the predicted rainfall process curve as rainfall input to the rainfall-runoff calculation engine, the area at risk of waterlogging can be located. The results indicate that the coupled neural networks perform well in the prediction of the hypothetical verification rainfall process. Under the studied extreme rainfall conditions, the location of 25 flooding areas and flooding duration are well predicted by the early warning system. The maximum of average flooding depth and flooding duration is 16.5 cm and 99 minutes, respectively. By predicting the risk area and the corresponding flooding time, the early warning system is quite effective in avoiding and reducing the losses from waterlogging.

Machine learning as an early warning system to predict financial crisis

2020 ◽  
Vol 71 ◽  
pp. 101507 ◽  
Author(s):  
Aristeidis Samitas ◽  
Elias Kampouris ◽  
Dimitris Kenourgios
Keyword(s):  
Machine Learning ◽  
Financial Crisis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System
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