Developing a multi stage predicting system for corporate credit rating in emerging markets

2014 ◽  
Vol 27 (4) ◽  
pp. 475-487 ◽  
Author(s):  
Dana Al-Najjar ◽  
Basil Al-Najjar
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Credit Rating ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Content Type ◽  
Corporate Credit ◽  
Multi Stage ◽  
Financial Characteristics ◽  
Corporate Credit Rating

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.

Applying Back-Propagation Neural Network for Estimating the Slump of Concrete

2013 ◽  
Vol 651 ◽  
pp. 986-989
Author(s):  
Chin Ming Kao ◽  
Li Chen ◽  
Chang Huan Kou ◽  
Shih Wei Ma
Keyword(s):  
Neural Network ◽  
High Performance ◽  
High Performance Concrete ◽  
Linear Regression Analysis ◽  
Back Propagation ◽  
Nonlinear Function ◽  
Multiple Linear Regression Analysis ◽  
Back Propagation Neural Network ◽  
Fine Aggregate ◽  
Furnace Slag

This paper proposes the back-propagation neural network (BPN) and applies it to estimate the slump of high-performance concrete (HPC). It is known that HPC is a highly complex material whose behaviour is difficult to model, especially for slump. To estimate the slump, it is a nonlinear function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. Therefore, slump estimation is set as a function of the content of these seven concrete ingredients and additional four important ratios. The results show that BPN obtains a more accurate mathematical equation through learning procedures which outperforms the traditional multiple linear regression analysis (RA), with lower estimating errors for predicting the HPC slump.

scholarly journals A Pattern Recognition Model of Python Programming using Artificial Neural Network via NeMo

2020 ◽  
Vol 8 (5) ◽  
pp. 109-113
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Programming Languages ◽  
Programming Language ◽  
High Performance ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Simulator ◽  
Source Codes ◽  
Python Programming

Background/Objectives: In the field of software development, the diversity of programming languages increases dramatically with the increase in their complexity. This leads both programmers and researchers to develop and investigate automated tools to distinguish these programming languages. Different efforts were conducted to achieve this task using keywords of source codes of these programming languages. Therefore, instead of using keywords classification for recognition, this work is conducted to investigate the ability to detect the pattern of a programming language characteristic by using NeMo(High-performance spiking neural network simulator) of neural network and testing the ability of this toolkit to provide detailed analyzable results. Methods/Statistical analysis: the method of achieving these objectives is by using a back propagation neural network via NeMo based on pattern recognition methodology. Findings: The results show that the NeMo neural network of pattern recognition can identify and recognize the pattern of python programming language with high accuracy. It also shows the ability of the NeMo toolkit to represent the analyzable results through a percentage of certainty. Improvements/Applications: it can be noticed from the results the ability of NeMo simulator to provide beneficial platform for studying and analyzing the complexity of the backpropagation neural network model.

A prediction model for the secure issuance scale of Chinese local government bonds

Kybernetes ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bowen Jia ◽  
Jiaying Wu ◽  
Juan Du ◽  
Yun Ji ◽  
Lina Zhu
Keyword(s):  
Neural Network ◽  
Local Government ◽  
Local Governments ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Government Bonds ◽  
Content Type ◽  
Kmv Model ◽  
Fiscal Revenue ◽  
Chinese Local Government

Purpose The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018 with the artificial neural network (ANN). Design/methodology/approach The principal components analysis (PCA), particle swarm optimization (PSO) and extreme learning machine (ELM) model was designed to produce the inputs of KMV model. Then the KMV model was used for obtaining the default probabilities under different issuance scales. Data were collected from Wind Database. MATLAB 2018b and SPSS 22 were used in the field of modeling and results analysis. Findings This study’s findings show that PCA–PSO–ELM proposed in this research has the highest accuracy in terms of the prediction compared with ELM, back propagation neural network and auto regression. And PCA–PSO–ELM–KMV model can calculate the secure issuance scale of local government bonds effectively. Practical implications The sustainability forecast in this study can help local governments effectively control the scale of debt issuance, strengthen the budget management of local debt and establish the corresponding risk warning mechanism, which could make local governments maintain good credit ratings. Originality/value This study sheds new light on helping local governments avoid financial risks effectively, and it is conducive to establish a debt repayment reserve system for local governments and the proper arrangement for stock debt.

An automatic inspection system for segment-type display module defects

Sensor Review ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 396-403 ◽  
Author(s):  
Chern Sheng Lin ◽  
Pei-Feng Yang ◽  
Chi-Chin Lin ◽  
Yuen-Chang Hsu
Keyword(s):  
Neural Network ◽  
Data Acquisition ◽  
Detection System ◽  
Back Propagation ◽  
Video Camera ◽  
Back Propagation Neural Network ◽  
Inspection System ◽  
Content Type ◽  
Display Module ◽  
Data Acquisition Card

Purpose – This study aimed to developed a defect detection system for a segment-type display module panel. Design/methodology/approach – The system included a data acquisition card, a video camera, a computer and a display module on a testing table. The video camera captured the display pattern of the display module and transferred it to the computer through the data acquisition card. The dynamic multi-thresholding method and analysis as well as back propagation neural network classification was used to classify the detected defects. Findings – The threshold values for the brightness at different positions in the display module image were obtained using the neural network and then stored in the look-up table, using two to six matrixes. Originality/value – The recognition speed was faster and the system was more flexible in comparison to the previous system. The proposed method, using unsophisticated and economical equipment, was also verified as providing highly accurate results with a low error rate.

Predicting corporate credit rating based on qualitative information of MD&A transformed using document vectorization techniques

2020 ◽  
Vol 54 (2) ◽  
pp. 151-168
Author(s):  
Jinwook Choi ◽  
Yongmoo Suh ◽  
Namchul Jung
Keyword(s):  
Credit Rating ◽  
Prediction Models ◽  
Quantitative Information ◽  
Qualitative Information ◽  
Rating Data ◽  
Content Type ◽  
Financial Variables ◽  
Corporate Credit ◽  
Rating Prediction ◽  
Corporate Credit Rating

PurposeThe purpose of this study is to investigate the effectiveness of qualitative information extracted from firm’s annual report in predicting corporate credit rating. Qualitative information represented by published reports or management interview has been known as an important source in addition to quantitative information represented by financial values in assigning corporate credit rating in practice. Nevertheless, prior studies have room for further research in that they rarely employed qualitative information in developing prediction model of corporate credit rating.Design/methodology/approachThis study adopted three document vectorization methods, Bag-Of-Words (BOW), Word to Vector (Word2Vec) and Document to Vector (Doc2Vec), to transform an unstructured textual data into a numeric vector, so that Machine Learning (ML) algorithms accept it as an input. For the experiments, we used the corpus of Management’s Discussion and Analysis (MD&A) section in 10-K financial reports as well as financial variables and corporate credit rating data.FindingsExperimental results from a series of multi-class classification experiments show the predictive models trained by both financial variables and vectors extracted from MD&A data outperform the benchmark models trained only by traditional financial variables.Originality/valueThis study proposed a new approach for corporate credit rating prediction by using qualitative information extracted from MD&A documents as an input to ML-based prediction models. Also, this research adopted and compared three textual vectorization methods in the domain of corporate credit rating prediction and showed that BOW mostly outperformed Word2Vec and Doc2Vec.

3D printing optimization algorithm based on back-propagation neural network

2020 ◽  
Vol 18 (5) ◽  
pp. 1223-1230 ◽  
Author(s):  
Jinshun Yan
Keyword(s):  
Neural Network ◽  
3D Printing ◽  
Back Propagation ◽  
Three Dimensional ◽  
Pso Algorithm ◽  
Back Propagation Neural Network ◽  
Experimental Results ◽  
Average Error ◽  
Product Model ◽  
Content Type

Purpose To obtain a high-quality finished product model, three-dimensional (3D) printing needs to be optimized. Design/methodology/approach Based on back-propagation neural network (BPNN), the particle swarm optimization (PSO) algorithm was improved for optimizing the parameters of BPNN, and then the model precision was predicted with the improved PSO-BPNN (IPSO-BPNN) taking nozzle temperature, etc. as the influencing factors. Findings It was found from the experimental results that the prediction results of IPSO-BPNN were closer to the actual values than BPNN and PSO-BPNN, and the prediction error was smaller; the average error of dimensional precision and surface precision was 6.03% and 6.54%, respectively, which suggested that it could provide a reliable guidance for 3D printing optimization. Originality/value The experimental results verify the validity of IPSO-BPNN in 3D printing precision prediction and make some contributions to the improvement of the precision of finished products and the realization of 3D printing optimization.

A compounding-model comprising back propagation neural network and genetic algorithm for performance prediction of bio-based lubricant blending with functional additives

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tong Yu ◽  
Peng Yin ◽  
Wei Zhang ◽  
Yanliang Song ◽  
Xu Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Peer Review ◽  
High Efficiency ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wear Volume ◽  
Combined Model ◽  
Data Set ◽  
Content Type

Purpose The amount, type and addition conditions of additives of lubricants should be continuously adjusted to obtain appealing performance. To obtain the optimal pretreatment parameters and reduce the cost of time-consuming experiments, the purpose of this paper is to establish an optimal back propagation neural network (BPNN) model combined with genetic algorithm (GA) in this work. Design/methodology/approach Using trimethylolpropane trioleate as the base oil and three types of phosphorus compounds as additives, 25 sets of lubricant formulas were designed regarding lubricant performances of average friction coefficient, average spot diameter, disk wear volume and extreme pressure. The data set was used for training and learning of BPNN and then combined with GA to optimize BPNN with continuously optimization by adjusting various parameters. Findings Comparing prediction data of BPNN with actual test data, correlation coefficients were above 90%, indicating that the model could accurately predict the performance of lubricants. When combined with GA, all performance errors were less than 5%, indicating that BPNN could be optimized by GA to obtain an accurate combined model for prediction of lubricant performance. The best additive formula with excellent performances was obtained from the BPNN–GA model. Originality/value This work developed a new method to study lubricant compounding. The combined model was expected to provide a theoretical basis and guidance for the compounding optimization of lubricant additives with high efficiency and low cost and to expand the scope to practical applications. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2020-0165/

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

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

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