The financial market has been developing rapidly in recent years, and the issue of credit risk concerning listed companies has become increasingly prominent. Therefore, predicting the credit risk of listed companies is an urgent concern for banks, regulators and investors. The commonly used models are the Z-score, Logit (logistic regression model), the kernel-based virtual machine (KVM) and neural network models. However, the results achieved could be more satisfactory. This paper proposes a credit-risk-prediction model for listed companies based on a CNN-LSTM and an attention mechanism, Our approach is based on the benefits of the long short-term memory network (LSTM) model for long-term time-series prediction combined with a convolutional neural network (CNN) model. Furthermore, the advantages of being integrated into a CNN-LSTM model include reducing the complexity of the data, improving the calculation speed and training speed of the model and solving the possible lack of historical data in the long-term sequence prediction of the LSTM model, resulting in prediction accuracy. To reduce problems, we introduced an attention mechanism to assign weights independently and optimize the model. The results show that our model has distinct advantages compared with other CNNs, LSTMs, CNN-LSTMs and other models. The research on the credit-risk prediction of the listing formula has significant meaning.

/pdf/credit-risk-prediction-model-for-listed-companies-based-on-gujz3aus.pdf

Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism

Profitability of Turkish banking sector gained importance after national and international financial crisis happened in the last decade, which revealed the need to make a research on profitability and the factors determining profitability. In recent years, new techniques of soft computing (SC) like genetic algorithms (GAs), fuzzy logic (FL) and especially artificial neural networks (ANNs) have been applied into the financial domain to solve the domain issues because of their successful applications in nonlinear multivariate situations. An adaptive system was needed due to the fact that insufficient use of application software programs for SC and the fact that single software is only applicable for specific model. Furthermore, even though ANNs have been applied to many areas; little attention has been paid to estimation of bank profitability with ANNs. This article is intended to analyze and estimate the profitability of deposit banks in Turkey with an adaptive software model of ANNs which have not been previously applied for this context, comprehensively. The results from the software model, which processes the factors affecting profitability, indicate that all of the variables used have significant impacts in varying proportions on profitability and that obtained estimations achieved the targeted and acceptable performance of success. This software model is expected to provide easiness on estimating bank profitability, since giving successful estimations and not being affected by user differences. Additionally, it is aimed to construct a software model for being used in different fields of study and financial domain.

An intelligent software model design for estimating deposit banks profitability with soft computing techniques

In order to overcome the insufficiencies of weak extensive ability and the problem of easily trapping into the local extreme value BP Neural Networks (BPNN), a new hybrid algorithm-LMBR, which uses the Levenberg-Marquardt(LM) Bayesian Regularization Algorithm to optimize BPNN, is proposed. The network model, which met the requirements after being examined by the test samples, was applied to predict first-grade overheating desuperheater A side desuperheating water flow. By Simulation Experiments analysis, the LMBR network model was trained until getting the converged network only by 13 steps, while the BP network model didn't achieve the same desired network by 1000 steps, which obviously shows that the LMBR algorithm has faster convergence rate. The average relative forecasting error (ARFE) of the predictive results obtained by the LMBR algorithm is 1.77%, while the ARFE of the predictive results obtained by LM Algorithm and BP is respectively 2.47% and 67.81%. From above comparison, this clearly indicates that the forecast precision of the LMBR algorithm is higher.

An Application Based on Levenberg-Marquardt Bayesian Regulation Algorithm in Power Plate

This study proposed a FTIR spectroscopy combing with quaternion parallel feature extraction methodology for the first time to address the qualitative and quantitative analysis of microplastics. For the international available microplastic FTIR dataset, the original, the first derivative and the second derivative FTIR spectra of polyethylene(PE), PE + fouling and polypropylene(PP) samples were first formed as an integral representation by a pure quaternion matrix. Then quaternion principal component analysis(QPCA) was conducted to obtain quaternion feature vectors and support vector machine(SVM) was followed to establish the qualitative model. Meanwhile, we experimentally mixed four types of microplastics of polyvinyl chloride(PVC), polystyrene (PS), PP and PE into sand to prepare simulated samples and collected their FTIR spectra. The concentrations of microplastics were 0.5%, 1%, 1.5%, 3%, 5%, 10%, respectively. The QPCA based parallel feature extraction combing with SVR was conducted to establish the quantitative model. The results show that QPCA has transcended traditional PCA and serial feature extraction methods. Furthermore, compared with traditional algorithms of PLS, BPNN, RF, and 1D-CNN, QPCA-SVM has achieved the highest values of accuracy, precision, recall and F1-score, which were 0.98571, 1, 0.99412 and 0.99697, respectively. QPCA-SVR has achieved the smallest RMSEP for four kinds of microplastics, which were 0.26361, 0.80558, 0.37099, and 0.16246, respectively. This work demonstrates QPCA fuses more spectral characteristics and reduces the redundancy of the complex vector, which is beneficial to improve the recognition and regression ability of SVM. The “QPCA enabled FTIR spectroscopy technique” in this study provides a new idea to promote the rapid and accurate determination of microplastics. 

Determination of microplastics by FTIR spectroscopy based on quaternion parallel feature fusion and support vector machine

This paper will make research on the credit rating problem in small and mediumsized enterprises, including deep understanding of asymmetry information problem among banks as well as selecting prope...

Research and application of credit rating model in small and micro enterprises based on fuzzy neural network

Credit risk analysis of enterprise is an important topic in financial field, this paper employs BP neural network to solve this problem. Indexes system of company and three different BP neural networks have been built. The neural networks are trained using financial data from different industry. We use Matlab program and neural network to get the results in seven learning schemes with different training-to-validation data ratios. Experimental results will suggest which neural network model, and under which learning scheme can deliver optimum performance.

https://www.ccsenet.org/journal/index.php/ijbm/article/download/9254/6822

Investigation of Listed Companies Credit Risk Assessment Based on Different Learning Schemes of BP Neural Network

Near-infrared spectroscopy analysis of compound fertilizer based on GAF and quaternion convolution neural network

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Investigation of Listed Companies Credit Risk Assessment Based on Different Learning Schemes of BP Neural Network

Near-infrared spectroscopy analysis of compound fertilizer based on GAF and quaternion convolution neural network