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Axioms
Special Issues
Big Data Analytics and Mathematical Methods in Digital Economy
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Big Data Analytics and Mathematical Methods in Digital Economy

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2941

Special Issue Editors

Dr. Chunjia Han

E-Mail Website
Guest Editor
Business Innovation at Birkbeck, University of London, London, UK
Interests: the application, development, and impact of digital technologies including big data analytics, artificial intelligence, and social media in influencing practices in business and economy
Prof. Dr. Fei Zhou

E-Mail Website
Guest Editor
School of Business Administration, Huaqiao University, Quanzhou, China
Interests: social media marketing; business model innovation; big data marketing
Dr. Lin Wang

E-Mail Website
Guest Editor
School of Management, Chongqing University of Technology, Chongqing 400054, China
Interests: digital service innovation; service innovation in manufacturing companies
Dr. Zaoli Yang

E-Mail Website
Guest Editor
School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Interests: intelligence decision support systems; expert systems and decision support; information management; artificial intelligence; fuzzy set theory; data mining; their application in various fields
Special Issues, Collections and Topics in MDPI journals
Dr. Lu Tong

E-Mail Website
Guest Editor
Research Institute for Frontier Science, Beihang University, Beijing 100191, China
Interests: multi-model transportation network planning; space–time accessibility analysis; transportation system simulation and optimization

Special Issue Information

Dear Colleagues, 

The development of the digital economy has continuously boosted the growth of the global economy. The digital economy has played an important role in areas of daily life such as education, shopping, investment, and entertainment. A huge volume of data are generated daily in the digital world from numerous computerized channels, including the Internet, social media, smart phones, and other smart devices. The data are invaluable, but their exploration can be challenging due to their mega-size and diverse formats (including structured, unstructured, and semi-structured). Thanks to the development of big data analytics and mathematical methods, it is possible and increasingly efficient to leverage the value of the data in the digital economy. The application of big data analytics and mathematical methods has attracted much attention in multiple fields, which has fueled investigations to improve our understanding and increase the application of big data analytics and mathematical methods in the digital economy.

This Special Issue seeks high-quality articles from industry-focused scholars and researchers who are passionate about the study of big data analytics and mathematical methods in the digital economy. We are interested in publishing articles that aim to solve real-world business problems through the application of mathematical methods and digital techniques, such as big data analytics and artificial intelligence. Submissions that present methodological contributions on big data analytics and mathematical methods in the digital economy are also welcome. Potential topics include, but are not limited to, the use of big data analytics and mathematical methods to research digital consumer behaviour, user-generated content, digital innovation, fintech, and the digital supply chain.

Dr. Chunjia Han
Prof. Dr. Fei Zhou
Dr. Lin Wang
Dr. Zaoli Yang
Dr. Lu Tong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data analytics
  • mathematical modeling
  • artificial intelligence
  • digital economy
  • user-generated content
  • digital consumer behaviour
  • digital supply chain
  • fintech

Published Papers (1 paper)

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Research

22 pages, 3141 KiB  
Article
Default Prediction with Industry-Specific Default Heterogeneity Indicators Based on the Forward Intensity Model
by Zhengfang Ni, Minghui Jiang and Wentao Zhan
Axioms 2023, 12(4), 402; https://doi.org/10.3390/axioms12040402 - 21 Apr 2023
Viewed by 1122
Abstract
When predicting the defaults of a large number of samples in a region, this will be affected by industry default heterogeneity. To build a credit risk model that is more suitable for Chinese-listed firms, which have highly industry-specific default heterogeneity, we extend the [...] Read more.
When predicting the defaults of a large number of samples in a region, this will be affected by industry default heterogeneity. To build a credit risk model that is more suitable for Chinese-listed firms, which have highly industry-specific default heterogeneity, we extend the forward intensity model to predict the defaults of Chinese-listed firms with information about the default heterogeneity of industries. Compared with the original model, we combine the Bayes approach with the forward intensity model to generate time-varying industry-specific default heterogeneity indicators. Our model can capture co-movements of different industries that cannot be observed based on the original forward intensity model so that the model can flexibly adjust the firm’s PD according to the industry. In addition, we also consider the impact of default heterogeneity in other industries by studying the influence of the level and trends of other industries’ default heterogeneity on a firm’s credit risk. Finally, we compute PDs for 4476 firms from January 2001 to December 2019 for 36 prediction horizons. The extended model improves the prediction accuracy ratios both for the in-sample and out-of-sample firm’s PDs for all 36 horizons. Almost all the accuracy ratios of the prediction horizons’ PDs are increased by more than 6%. In addition, our model also reduces the gap between the aggregated PDs and the realized number of defaults. Our industry-specific default heterogeneity indicator is helpful to improve the model’s performance, especially for predicting defaults in a large portfolio, which is of significance for credit risk management in China and other regions. Full article
(This article belongs to the Special Issue Big Data Analytics and Mathematical Methods in Digital Economy)
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