scholarly journals Research on the Effectiveness of KMV Model in China's Bond Credit Rating Market

2020 ◽  
Vol 4 (1) ◽  
pp. 59 ◽  
Author(s):  
Jifeng Sun ◽  
Tingwei Sun
Keyword(s):  
Big Data ◽  
Credit Risk ◽  
Credit Rating ◽  
Stock Exchange ◽  
Rapid Development ◽  
Bond Market ◽  
Theoretical Research ◽  
Kmv Model ◽  
Domestic Credit ◽  
Risk Supervision

In recent years, China's bond market has experienced rapid development, but the pace of credit risk supervision has not kept up. Since 2014, the number of domestic credit bond defaults has increased. In 2016, there were 79 domestic default bonds, with a default amount of up to 40.3 billion Yuan. From the perspective of domestic bond market credit risk supervision and early warning mechanism, rating is not objective, and tracking is not timely also rating methods are backward. Therefore, with the development of big data and other technologies, it is urgent to study credit risk supervision methods suitable for the domestic bond market. On the basis of combing the development of domestic bond market and analyzing the current situation of domestic credit rating, this paper combines the results of theoretical research at home and abroad, the information available in the domestic market, big data mining and automation technology, based on the financial and stock exchange information of listed companies, combined with BS option pricing theory, constructs KMV model.

scholarly journals Research on Credit Risk Measurement of Small and Micro Enterprises Based on the Integrated Algorithm of Improved GSO and ELM

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Li Jingming ◽  
Li Xuhui ◽  
Dai Daoming ◽  
Ruan Sumei ◽  
Zhu Xuhui
Keyword(s):  
Big Data ◽  
Credit Risk ◽  
Extreme Learning Machine ◽  
Risk Measurement ◽  
Theoretical Research ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Learning Machine ◽  
Credit Risk Measurement ◽  
Micro Enterprises

Small and micro enterprises play a very important role in economic growth, technological innovation, employment and social stability etc. Due to the lack of credible financial statements and reliable business records of small and micro enterprises, they are facing financing difficulties, which has become an important factor hindering the development of small and micro enterprises. Therefore, a credit risk measurement model based on the integrated algorithm of improved GSO (Glowworm Swarm Optimization) and ELM (Extreme Learning Machine) is proposed in this paper. First of all, according to the growth and development characteristics of small and micro enterprises in the big data environment, the formation mechanism of credit risk of small and micro enterprises is analyzed from the perspective of granularity scaling, cross-border association and global view driven by big data, and the index system of credit comprehensive measurement is established by summarizing and analyzing the factors that affect the credit evaluation index. Secondly, a new algorithm based on the parallel integration of the good point set adaptive glowworm swarm optimization algorithm and the Extreme learning machine is built. Finally, the integrated algorithm based on improved GSO and ELM is applied to the credit risk measurement modeling of small and micro enterprises, and some sample data of small and micro enterprises in China are collected, and simulation experiments are carried out with the help of MATLAB software tools. The experimental results show that the model is effective, feasible, and accurate. The research results of this paper provide a reference for solving the credit risk measurement problem of small and micro enterprises and also lay a solid foundation for the theoretical research of credit risk management.

The Relation between Two Kinds of Par Yield Curves: Benchmark Par Yield Curve and Company Par Yield Curve

2012 ◽  
Vol 20 (3) ◽  
pp. 325-346
Author(s):  
Seung Hyun Oh
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Yield Curve ◽  
Bond Market ◽  
Corporate Bond ◽  
Spot Rate ◽  
Yield Curves ◽  
Transaction Data ◽  
Absolute Value ◽  
Bootstrapping Method

This study investigates the relation between two kinds of par yield curves estimated in Korean bond market: benchmark par yield curve and company par yield curve. The former is published as a benchmark for corporate bonds with a given credit rating and the latter is utilized for valuing a specific corporate bond. Spot rate curves are extracted from the par yield curves by applying bootstrapping method. The spreads between the two spot rate curves are analyzed for 7 years (2005~2012) of corporate bond transaction data. Six results are obtained from various sub-samples classified by credit rating and maturity. 1) Most of the sample means of the spreads are above zero. 2) Negative average spreads are found mainly from the sample of BBB rated bonds. 3) Average spreads from the sample with credit greater than or equal to A tend to positively related with credit risk. 4) Absolute value of the average spreads are positively related with credit risk. 5) The average spreads are increased rapidly after the year of 2009. 6) The proportion of sub-samples having negative average spreads are decreased as the average maturity of the sample is shortened.

scholarly journals Value of big data to finance: observations on an internet credit Service Company in China

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Shaofeng Zhang ◽  
Wei Xiong ◽  
Wancheng Ni ◽  
Xin Li
Keyword(s):  
Big Data ◽  
Credit Risk ◽  
Credit Rating ◽  
Large Data ◽  
Future Research ◽  
Business Practices ◽  
Data Volume ◽  
Credit Risk Analysis ◽  
Service Businesses

Abstract Background his paper presents a case study on 100Credit, an Internet credit service provider in China. 100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business. The company makes use of Big Data on multiple aspects of individuals’ online activities to infer their potential credit risk. Methods Based on 100Credit’s business practices, this paper summarizes four aspects related to the value of Big Data in Internet credit services. Results 1) value from large data volume that provides access to more borrowers; 2) value from prediction correctness in reducing lenders’ operational cost; 3) value from the variety of services catering to different needs of lenders; and 4) value from information protection to sustain credit service businesses. Conclusion The paper also discusses the opportunities and challenges of Big Data-based credit risk analysis, which needs to be improved in future research and practice.

Asset Securitizations and Credit Risk

2011 ◽  
Vol 87 (2) ◽  
pp. 423-448 ◽  
Author(s):  
Mary E. Barth ◽  
Gaizka Ormazabal ◽  
Daniel J. Taylor
Keyword(s):  
Credit Risk ◽  
Financial Reporting ◽  
Credit Rating ◽  
Credit Ratings ◽  
Bond Market ◽  
Credit Rating Agencies ◽  
Rating Agencies ◽  
Bond Spreads ◽  
Insight Into

ABSTRACT This study examines the sources of credit risk associated with asset securitizations and whether credit-rating agencies and the bond market differ in their assessment of this risk. Measuring credit risk using credit ratings, we find the securitizing firm's credit risk is positively related to the firm's retained interest in the securitized assets and unrelated to the portion of the securitized assets not retained by the firm. Measuring credit risk using bond spreads, we find the securitizing firm's credit risk is positively related to both the firm's retained interest in the assets and the portion of the securitized assets not retained by the firm. Additionally, our findings indicate the bond market does not distinguish between the retained and non-retained portions of the securitized assets when assessing the credit risk of the securitizing firm. These different assessments of sources of credit risk associated with asset securitizations provide insight into ongoing controversies surrounding the financial reporting for asset securitizations and the efficacy of credit ratings.

Risk and Return of Retail Sukuk and Retail Bond in Indonesia Period 2008-2017 (Comparative Study)

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Rahma Yudi Astuti ◽  
Asad Arsya Brilliant Fani
Keyword(s):  
Standard Deviation ◽  
Comparative Study ◽  
Stock Exchange ◽  
Secondary Data ◽  
Bond Market ◽  
Bond Pricing ◽  
Risk And Return ◽  
Underlying Asset ◽  
The Real ◽  
The World

Sukuk and Bonds has differences and similarities. Fundamental differences between sukuk and bonds are first, underlying asset in every sukuk issuance, concept of profit loss sharing and the use of Islamic contracts. Whereas conducted research in practice of differences between sukuk and bonds are still an on-going discussion. This study aims to add the evidence in the discussion regarding whether there is differences between sukuk and bonds in the world of practice, provide investment preferences as well as educating investors in choosing sukuk or bonds as a sustainable and smooth instrument. The method used is Mann Whitney U-Test to test whether there is a different between yield to maturity (return) and standard deviation (risk) of both instruments. Using secondary data of Retail Sukuk (SR) and Retail Bonds (ORI) period 2008-2017 obtained from Indonesia Stock Exchange, Indonesia Bond Market Directory and Indonesia Bond Pricing Agency. The result shows that there is no significance difference of retail sukuk return and risk with retail bonds in Indonesia. Besides retail bonds are show higher return than retail sukuk because of higher coupon and longest mature date. While, retail sukuk is more stable rather than bonds as it backed up by the real underlying asset. Keywords: Retail Sukuk (SR), Retail Bonds (ORI), Yield to Maturity

An empirical study on big data stock volatility forecasting algorithm based on multivariate hybrid criterion fuzzy model

2021 ◽  
pp. 002072092098399
Author(s):  
Zhang Xiao-Wen ◽  
Zeng Min
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Fuzzy Model ◽  
Reference Value ◽  
Developed Countries ◽  
Theoretical Research ◽  
Prediction Algorithm ◽  
Real Economy ◽  
Stock Volatility ◽  
The Us

The fluctuation of the stock market has always been a matter of great concern to investors. People always hope to judge the trend of the stock market through the trend of the K line, so as to obtain the price difference through trading, Therefore, it is a theoretical research concerned by the academic circles to carry out empirical research through big data stock volatility prediction algorithm, so as to establish a model to predict the trend of the stock market. After decades of development, China's stock market has gradually matured in continuous exploration. However, compared with the stock market in developed countries, there are still imperfections. For example, the market value of China's stock market does not improve well with economic growth. Year-on-year growth and the development of the real economy. By studying the historical data from 2002 to 2017, we use the Multivariate Mixed Criterion Fuzzy Model (MMCFM) to predict the price changes in the stock market, and obtain the market in China through error statistical analysis. (SSE) is more unstable than the US stock market. Therefore, Multivariate Mixing Criterion (MMC) can be used as a reference indicator to visually measure market maturity. In this paper, we establish a multivariate mixed criteria fuzzy model, and use big data to predict the stock volatility. The algorithm verifies the reliability and accuracy of the model, which has a good reference value for investors.

Bibliometric analysis and critical review of the research on big data in the construction industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yusheng Lu ◽  
Jiantong Zhang
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Bibliometric Analysis ◽  
Critical Review ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
The Body ◽  
Future Research ◽  
Temporal And Spatial Distribution ◽  
Content Type

PurposeThe digital revolution and the use of big data (BD) in particular has important applications in the construction industry. In construction, massive amounts of heterogeneous data need to be analyzed to improve onsite efficiency. This article presents a systematic review and identifies future research directions, presenting valuable conclusions derived from rigorous bibliometric tools. The results of this study may provide guidelines for construction engineering and global policymaking to change the current low-efficiency of construction sites.Design/methodology/approachThis study identifies research trends from 1,253 peer-reviewed papers, using general statistics, keyword co-occurrence analysis, critical review, and qualitative-bibliometric techniques in two rounds of search.FindingsThe number of studies in this area rapidly increased from 2012 to 2020. A significant number of publications originated in the UK, China, the US, and Australia, and the smallest number from one of these countries is more than twice the largest number in the remaining countries. Keyword co-occurrence is divided into three clusters: BD application scenarios, emerging technology in BD, and BD management. Currently developing approaches in BD analytics include machine learning, data mining, and heuristic-optimization algorithms such as graph convolutional, recurrent neural networks and natural language processes (NLP). Studies have focused on safety management, energy reduction, and cost prediction. Blockchain integrated with BD is a promising means of managing construction contracts.Research limitations/implicationsThe study of BD is in a stage of rapid development, and this bibliometric analysis is only a part of the necessary practical analysis.Practical implicationsNational policies, temporal and spatial distribution, BD flow are interpreted, and the results of this may provide guidelines for policymakers. Overall, this work may develop the body of knowledge, producing a reference point and identifying future development.Originality/valueTo our knowledge, this is the first bibliometric review of BD in the construction industry. This study can also benefit construction practitioners by providing them a focused perspective of BD for emerging practices in the construction industry.

Bank Credit Risk Analysis and Bank Credit Rating

2020 ◽  
pp. 275-348
Author(s):  
Terence M. Yhip ◽  
Bijan M. D. Alagheband
Keyword(s):  
Risk Analysis ◽  
Credit Risk ◽  
Credit Rating ◽  
Bank Credit ◽  
Credit Risk Analysis

scholarly journals Big Data Analytics in E-commerce for the U.S. and China Through Literature Reviewing

2021 ◽  
Vol 9 (1) ◽  
pp. 16-44
Author(s):  
Weiqing Zhuang ◽  
Morgan C. Wang ◽  
Ichiro Nakamoto ◽  
Ming Jiang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Web Of Science ◽  
Big Data Analytics ◽  
Theoretical Research ◽  
Analysis Method ◽  
Theoretical Comparison ◽  
Statistical Analysis Method ◽  
And Performance ◽  
The U.S

Abstract Big data analytics (BDA) in e-commerce, which is an emerging field that started in 2006, deeply affects the development of global e-commerce, especially its layout and performance in the U.S. and China. This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S. and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases, Web of Science and CNKI, aimed at the U.S. and China. The results of this study help clarify doubts regarding the development of China’s e-commerce, which exceeds that of the U.S. today, in view of the theoretical comparison of BDA in e-commerce between them.

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