A Comparative Study with Quantile Regression and Back Propagation Neural Network for Credit Rating

2016 ◽  
Vol 4 (2) ◽  
pp. 46-57 ◽  
Author(s):  
Shin-Yun Wang ◽  
◽  
He-Shun Syu ◽  
Author(s):  
Bo Huang

This study analyzed three prediction models: ID model, GM (1,1) model and back-propagation neural network (BPNN) model. Firstly, the principles of the three models were introduced, and the prediction methods of the three models were analyzed. Then, taking enterprise A as an example, the demand for human resources was predicted, and the prediction results of the three models were compared. The results showed that the maximum and minimum errors were 240 people and 12 people respectively in the prediction results of the ID3 model and 64 people and 37 people respectively in the prediction results of the GM (1, 1) model; the errors of the BPNN model were smaller than ten people, and the minimum value of the BPNN model was three people, which was in good agreement with the actual value. The prediction of the human resource demand of enterprise A in the future five years with the BPNN model suggested that the demand for employees would growing rapidly. The results show that the BPNN model has better reliability and can be popularized and applied in practice.


2014 ◽  
Vol 27 (4) ◽  
pp. 475-487 ◽  
Author(s):  
Dana Al-Najjar ◽  
Basil Al-Najjar

Purpose – The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance. Design/methodology/approach – The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007. Findings – BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007. Originality/value – This study is the first study to investigate credit rating in Jordan using Neural Network technique.


2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods


Sign in / Sign up

Export Citation Format

Share Document