COMPARISON OF MODELS WITH NEURAL NETWORK AND OLS-REGRESSION IN CONSTRUCTING THE RISK MANAGEMENT STRATE GY AGAINST THE INCOME ACCORDING TO INDEX

2017 ◽  
Vol 27 (1) ◽  
pp. 12-20 ◽  
Author(s):  
Vladimir N. Shchennikov ◽  
◽  
Yelena V. Shchennikova ◽  
Sergey A. Sannikov
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Ols Regression

A Confidence-Based RBF Neural Network Ensemble Learning Paradigm with Application to Delinquent Prediction for Credit Risk Management

2010 ◽  
pp. 105-117
Author(s):  
Lean Yu ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Credit Risk ◽  
Ensemble Learning ◽  
Rbf Neural Network ◽  
Network Models ◽  
Neural Network Ensemble ◽  
Credit Risk Management ◽  
Learning Paradigm ◽  
Neural Network Models

In this study, a multistage confidence-based radial basis function (RBF) neural network ensemble learning model is proposed to design a reliable delinquent prediction system for credit risk management. In the first stage, a bagging sampling approach is used to generate different training datasets. In the second stage, the RBF neural network models are trained using various training datasets from the previous stage. In the third stage, the trained RBF neural network models are applied to the testing dataset and some prediction results and confidence values can be obtained. In the fourth stage, the confidence values are scaled into a unit interval by logistic transformation. In the final stage, the multiple different RBF neural network models are fused to obtain the final prediction results by means of confidence measure. For illustration purpose, two publicly available credit datasets are used to verify the effectiveness of the proposed confidence-based RBF neural network ensemble learning paradigm.

Establishment and Application of Risk Forecast Model of Construction Project Based upon BP Neural Network

2014 ◽  
Vol 945-949 ◽  
pp. 3056-3059 ◽  
Author(s):  
Xin Xin Li
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Minimal Cost ◽  
Input And Output ◽  
Sample Data ◽  
Risk Forecast ◽  
The Relationship ◽  
And Training

Risk management is a kind of activity by economic unit to obtain the maximal safety guarantee at the minimal cost through the identification and measuring of risk, in which reasonable economic and technical means are defined to cope with the risk, and it is also a process of estimating, evaluating and preventing the risk. Based upon the collection and normalization of sample data, determination and training of network structure, by identifying the relationship between input and output, BP neural network establishes risk forecast model of project, then the sample is tested and risk forecast model is validated.

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