Explore the vast range of titles available.

Early warnings of enterprise credit risk based on supply chain scenarios are helpful for preventing enterprise credit deterioration and resolving systemic risk. Enterprise credit risk data in the supply chain are characterized by higher‐dimension information and class imbalance. The class imbalance influences the feature selection effect, and the feature subset is closely related to the predictive performance of subsequent learning algorithms. Therefore, ensuring the adaptivity of feature selection and the subsequent class imbalance–oriented classification model is a key issue. We propose an AdaBoost ensemble model with fast nondominated feature selection (AdaFNDFS). AdaFNDFS uses the FNDFS method in the AdaBoost algorithm to iteratively select features and uses the classifier to evaluate the performance of feature subsets to train the class imbalance–oriented classifier and the best‐matched feature subset, ensuring the adaptivity of feature selection and subsequent classifiers. The further use of the differential sampling rate (DSR) method enables AdaFNDFS to integrate more training models with different knowledge and to obtain higher accuracy and better generalization ability for prediction tasks facing high‐dimensional information and class imbalance. A test using credit risk data from Chinese listed enterprises containing supply chain information demonstrates that the prediction scoring indicators, such as AUC, KS, AP, and accuracy, of the AdaFNDFS are better than those of basic models such as LR, LDA, DT, and SVM and multiple hybrid models that use SMOTE, feature selection, and ensemble methods. AdaFNDFS outperforms the basic models by at least 0.0073 (0.0344, 0.0349, and 0.0071) in terms of the AUC (KS, AP, and accuracy). AdaFNDFS has outstanding advantages in predicting enterprise credit risk in the supply chain and can support interested decision‐makers.

Big data has made it necessary for business administration to be more intelligent and informationized. This paper introduces data mining technology, explains the specific steps of data mining, and analyzes commonly used data mining algorithms. The rule mining of the C4.5 algorithm is illustrated by using information entropy, the XGBoost model is used as the base learner of Stacking integrated learning and model fusion is carried out. The regional economic prediction model was constructed using the C4.5 rule mining algorithm, while the enterprise credit rating classification model was established using the Stacking algorithm. The empirical evidence shows that the regional economy will be affected by the main body of the enterprise, the industrial structure and the development of the enterprise, in which the industrial structure and the development of the enterprise showed exponential growth in 2007-2018, and their growth rates are all around 30%. Using the Stacking algorithm for enterprise credit rating classification, the recall rate of the weighted fusion model with GRU network as a meta-learner has improved by 2.4%. By analyzing the application of big data technology in business administration data, we illustrate its role in business administration decision-making so as to provide a certain reference for the construction of the business administration informatization model.

PurposeSocial media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.Design/methodology/approachThe prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.FindingsThe prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.Practical implicationsThe prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.Originality/valueThe prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.

PurposeThis paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.Design/methodology/approachThis paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.FindingsThe machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.Originality/valueThe main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

A small enterprise’s credit rating is employed to measure its probability of defaulting on a debt, but, for small enterprises, financial data are insufficient or even unreliable. Thus, building a multi criteria credit rating model based on the qualitative and quantitative criteria is of importance to finance small enterprises’ activities. Till now, there has not been a multicriteria credit risk model based on the rank sum test and entropy weighting method. In this paper, we try to fill this gap by offering three innovative contributions. First, the rank sum test shows significant differences in the average ranks associated with index data for the default and entire sample, ensuring that an index makes an effective differentiation between the default and non-default sample. Second, the rating equation’s capacity is tested to identify the potential defaults by verifying a clear difference between the average ranks of samples with default ratings (i.e., not index values) and the entire sample. Third, in our nonparametric test, the rank sum test is used with rank correlation analysis made to screen for indices, thereby avoiding the assumption of normality associated with more common credit rating methods.

A more precise and rigorous assessment of the impact of environmental, social, and governance (ESG) performance in business necessitates evaluating various firm characteristics. This study, focused on the ESG impact on enterprise credit risk, employed logistic models that incorporated the ESG rating index alongside other financial-related factors, including organizational structure, risk, and performance. The data were selected from all related listing companies in the Shanghai and Shenzhen stock exchanges. The results affirmed that (1) the risk of default decreased with improved ESG performance; (2) the return on assets, asset turnover ratio, leverage ratio, and operating income growth rate were the main financial factors affecting the default probability of enterprises; and (3) including ESG variables in the prediction model significantly improved the prediction accuracy of the model. The potential policy implications are presented in three perspectives. Businesses should prioritize developing good governance, fulfilling social obligations, and protecting the environment. Second, investors should integrate ESG ratings when making investment strategies. Third, the regulatory authorities are recommended to rapidly harmonize the ESG rating criteria and gradually develop the enterprise ESG information disclosure framework.

Shipping Enterprise Credit Named Entity Recognition (NER) aims to recognize shipping enterprise credit entities from unstructured shipping enterprise credit texts. Aiming at the problem of low entity recognition rate caused by complex and diverse entities and nesting phenomenon in the field of shipping enterprise credit, a deep learning method based on multi-feature fusion is proposed to improve the recognition effect of shipping enterprise credit entities. In this study, the shipping enterprise credit dataset is manually labeled using the BIO labeling model, combining the pre-trained model Bidirectional Encoder Representations from Transformers (BERT) and bidirectional gated recurrent unit (BiGRU) with conditional random field (CRF) to form the BERT-BiGRU-CRF model, and changing the input of the model from a single feature vector to a multi-feature vector (MF) after stitching character vector features, word vector features, word length features, and part-of-speech (pos) features; BiGRU is introduced to extract the contextual features of shipping enterprise credit texts. Finally, CRF completes the sequence annotation task. According to the experimental results, using the BERT-MF-BiGRU-CRF model for NER of shipping enterprise credit text data, the F1 Score (F1) reaches 91.7%, which is 8.37% higher than the traditional BERT-BiGRU-CRF model. The experimental results show that the BERT-MF-BiGRU-CRF model can effectively perform NER for shipping enterprise credit text data, which is helpful to construct a credit knowledge graph for shipping enterprises, while the research results can provide references for complex entities and nested entities recognition in other fields.

Credit identification is one of core issues of financing process. Enterprise credit involves a lot of financial and non-financial measures, among which entrepreneurship is an important but rarely mentioned variable. Good entrepreneur credit often leads to good enterprise credit. A comprehensive analysis of enterprise credit identification is important to avoid losses, foster excellent enterprise and make the optimal allocation of resources. The existing literature mainly studied the impact of entrepreneurship on enterprise credit from the perspective of historical information, which is about average and tendency. Hence, those models were unable to explain the function of complex human nature and, consequently, linear models are unable to well describe the relationship between enterprise credit and entrepreneur credit. Given the deficiency of parametric models when discussing the impact of entrepreneur credit, a non parametric approach are proposed to individually describe the impact path of different individuals. This paper established a decision tree based on nonparametric approach to verify the practicability of the model in the evaluation of enterprise credit recognition. In the end of this paper, we demonstrate the validity of the non parametric model and the validation method of it.

The paper discusses the role the financial sector can play in supporting growth in Japan. While overall credit conditions have been accommodative, credit growth has remained weak, especially for small and medium-sized enterprises (SMEs). Firm-level SME data and sectoral corporate balance sheets show that many SMEs have faced structural challenges of high leverage and low profitability. Moreover, the global financial crisis has weakened the financial position across SMEs, particularly for those with low credit worthiness. These challenges are closely related to low availability of riskcapital and the pervasiveness of credit support measures. This paper argues that to encourage the supply of risk-based capital, costly government support measures should be phased out and SME restructuring be accelerated. Efforts are also needed to deepen capital markets to enhance risk capitalavailability and address regulatory barriers to starting businesses. In that regard, addressing SMEweaknesses would improve private investment, enhance firm productivity, and lift growth.

The transmission effect of industrial policies on the quality of innovation of micro-enterprises is a central concern that attracts current academics and policy makers. Using the 2004–2019 data of A-shares of listed companies in Shanghai and Shenzhen, as well as the policies issued by Chinese ministries and departments at the ministry level and above, this paper empirically investigates the impact and mechanism of industrial policies on the innovation quality of wind power companies. The research results demonstrate that policies all play a significant role in promoting the quality of enterprise innovation of wind power. The intermediary role of credit financing of enterprises between different types of industrial guidelines and the quality of corporate innovation is different. Executive equity incentives positively moderate the relationship between different types of industrial policies and corporate credit financing and effectively weaken the adverse effects of regulatory policies, and it can also significantly enhance the role of policies in promoting the quality of corporate innovation. Finally, this paper gives targeted policy recommendations for the development of new energy industry innovation in China and similar countries and regions from the perspective of government and enterprise.