دانلود مقالهپیش بینی ریسک اعتباری SMEs در تامین مالی زنجیره تامین بر اساس تکنیک یادگیری ماشین
خلاصه
1. مقدمه
2 آثار مرتبط
3 روش شناسی
4 اطلاعات مجموعه داده و پیش بینی کننده ها
5 نتیجه
6 بحث
7 نتیجه گیری
پیوست A: شبه کد طبقه بندی کننده مبتنی بر SVM
پیوست B: شبه کد طبقه بندی کننده مبتنی بر ANN
پیوست C: شبه کد C4.5 DT
پیوست D: شبه کد RF
ضمیمه E: کد شبه کیسه
پیوست F: شبه کد GB
پیوست ز: متغیرها و تعریف
منابع
Abstract
1 Introduction
2 Related works
3 Methodology
4 Dataset information and predictors
5 Results
6 Discussions
7 Conclusions
Appendix A: The pseudocode of SVM-based classifier
Appendix B: The pseudocode of ANN-based classifier
Appendix C: The pseudocode of C4.5 DT
Appendix D: The pseudocode of RF
Appedix E: The pseudocode of bagging
Appendix F: The pseudocode of GB
Appendix G: Variables and definition
References
چکیده
بررسی ارزش ادغام اطلاعات چند منبعی برای پیش‌بینی ریسک اعتباری شرکت‌های کوچک و متوسط (SMEs) در تامین مالی زنجیره تامین (SCF) یک کار محبوب و در عین حال چالش‌برانگیز است، زیرا باید به دو موضوع انتخاب متغیر کلیدی و کلاس نامتعادل پرداخته شود. همزمان. برای این منظور، مدل‌های پیش‌بینی جدیدی را با اتخاذ یک استراتژی نمونه‌گیری عدم تعادل بر اساس تکنیک‌های یادگیری ماشین توسعه می‌دهیم و این مدل‌های جدید را برای پیش‌بینی ریسک اعتباری SMEها در چین، با استفاده از اطلاعات مالی، اطلاعات عملیات، اطلاعات نوآوری و رویدادهای منفی به عنوان پیش‌بینی‌کننده، به کار می‌بریم. نتایج تجربی نشان می‌دهد که اطلاعات مبتنی بر مالی، مانند TOC، NIR، در پیش‌بینی ریسک اعتباری SMEها در SCF مفید هستند و ادغام اطلاعات چند منبعی در پیش‌بینی بهتر ریسک اعتباری معنادار است. علاوه بر این، بر اساس مدل ترجیحی CSL-RF، که یادگیری حساس به هزینه را به یک جنگل تصادفی گسترش می‌دهد، ما همچنین مکانیسم‌های مختلف پیش‌بینی‌کننده‌های کلیدی برای ریسک اعتباری SME را با استفاده از تحلیل وابستگی جزئی ارائه می‌کنیم. بینش استراتژیک به‌دست‌آمده ممکن است برای فعالان بازار، مانند مدیران شرکت‌های کوچک و متوسط، سرمایه‌گذاران و تنظیم‌کننده‌های بازار مفید باشد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs’ credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.
Introduction
The development of small and medium-sized enterprises (SMEs) has attracted attention from scholars and practitioners over the globe. However, because of the tightening of credit criteria for corporate loans, SMEs are facing significant challenges, mainly including capital constraints, high operational costs, and ambiguous information (Yan & He, 2020). As a major component of the economy in China, SMEs contribute almost 90% of the number of enterprises, 80% of urban employment, 70% of GDP, 60% of technological innovation, and 50% of tax revenue (see https://www.ndrc.gov.cn/ for more detail). SMEs in China also face problems mainly including high financial distress, high financing costs, high operational risks, tightening financing channels, high fraud risks, and asymmetric financing information (Weng et al., 2016; Zhu et al., 2019).
As a popular financing channel, supply chain finance (SCF) defined by Hofmann (2005) as the inter-firms optimization of financing and the integration of financing processes with customers, suppliers, and service providers to increase the value of all participating firms, has attracted attention from both practitioners and scholars alike. The Chinese government has developed some new financial policies to ease the financing pressure on SMEs, e.g., Promoting SME Development Plan (2016–2020), which seek to “promote more supply chains to join the financing service platform of SMEs”. Similar initiatives are underway in other countries and regions, such as the United States, United Kingdom, Japan, Canada, South Korea, Europe, and Mexico. SCF is also being used to promote the development of SMEs. For example, the Office of the United States Trade Representative (USTR) is implementing a series of initiatives to address the financing problems of SMEs, including SCF
Conclusions
To our best knowledge, this is the first study to consider the value of multi-source information fusion to predict the SMEs’ credit risk in SCF, and presents some managerial implications. We predict the credit risk of SMEs in SCF with an imbalance sampling strategy on machine learning techniques. Considering the value of multi-source information fusion in the big data era, we construct a broader knowledge base, including financial information, operation information, innovation information, and negative events, to predict the credit risk of SMEs in China, and develop new models to simultaneously solve for key predictor selection and imbalance classes. We then adopt six evaluation criteria to compare the prediction performances of the six machine learning techniques—SVM, NN, DT, RF, bagging, and GB—based on the data of VA and VS, respectively. We compare the results of new models via a re-sampling strategy for baseline models on VA and VS; the results indicate that the proposed CSL-RF model is optimal in terms of accuracy and robustness. The empirical results indicate that the financial-based information is the main source to predict SEMs’ credit risk in SCF, and the multi-source information fusion is meaningful. In addition, based on the preferred CSL-RF model, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. Finally, we generate strategic insights for market participants, such as regulators, investors, and managers.