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Advances in Predictive Analytics and Systemic Risks in Finance and...
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Advances in Predictive Analytics and Systemic Risks in Finance and Insurance

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Risk".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 4763

Special Issue Editors

Prof. Dr. Lysa Porth

E-Mail Website
Guest Editor
Gordon S. Lang School of Business Economics, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: actuarial science; finance; risk mangement and insurance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ken Seng Tan

E-Mail Website
Guest Editor
Nanyang Business School, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
Interests: actuarial science; finance; risk mangement and insurance
Dr. Wenjun Zhu

E-Mail Website
Guest Editor
Nanyang Business School, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
Interests: actuarial science; finance; risk mangement and insurance

Special Issue Information

Dear Colleagues,

Our society is facing increasing pressure from various systemic risks. For example, financial systemic risks may disrupt all or parts of the financial system and may have severe adverse consequences for the real economy. Climate systemic risks increase concern of more frequent and severe natural hazards, leading to significant economic losses, potentially spanning larger areas, affecting more people, and even widening the protection gap. The ongoing coronavirus pandemic has created unprecedented challenges in managing both economic and health crises for individuals and businesses. Moreover, different systemic risk factors magnify and reinforce each other. The development of advanced predictive analytic models offers new perspectives on quantifying, predicting, and transferring systemic risks.

This Special Issue on “Advances in Predictive Analytics and Systemic Risks in Finance and Insurance” calls for original, innovative, and interdisciplinary research papers on topics covering systemic risks in finance, insurance, and state-of-the-art predictive technology. We especially welcome research with inter-/multi-disciplinary approaches. The potential topics include, but are not limited to, the following:

  • Innovative approaches to modeling systemic risk;
  • Systemic risk measurement;
  • Design of new financial facilities for systemic risk;
  • Catastrophic risk management;
  • Climate change, food security, and sustainability;
  • Pandemic implications for individuals, corporations, and governments;
  • Government intervention and public–private partnership (PPP);
  • Disaster risk financing.

Prof. Dr. Lysa Porth
Prof. Dr. Ken Seng Tan
Dr. Wenjun Zhu
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. Journal of Risk and Financial Management 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 1400 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

  • systemic risk
  • financial risk management
  • predictive analytics
  • climate change
  • catastrophic risk
  • sustainability

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

14 pages, 1562 KiB  
Article
Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models
by Manel Hamdi, Sami Mestiri and Adnène Arbi
J. Risk Financial Manag. 2024, 17(4), 132; https://doi.org/10.3390/jrfm17040132 - 22 Mar 2024
Cited by 4 | Viewed by 2214
Abstract
The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network [...] Read more.
The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network (DNN) model is also applied to conduct a prediction performance comparison with other statistical and machine learning algorithms. The data used for this empirical investigation covers 25 financial ratios for a large sample of 732 Tunisian companies from 2011–2017. To interpret the prediction results, three performance measures have been employed; the accuracy percentage, the F1 score, and the Area Under Curve (AUC). In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods. Full article
(This article belongs to the Special Issue Advances in Predictive Analytics and Systemic Risks in Finance and Insurance)
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15 pages, 1874 KiB  
Article
Evaluation of Weather Yield Index Insurance Exposed to Deluge Risk: The Case of Sugarcane in Thailand
by Thitipong Kanchai, Wuttichai Srisodaphol, Tippatai Pongsart and Watcharin Klongdee
J. Risk Financial Manag. 2024, 17(3), 107; https://doi.org/10.3390/jrfm17030107 - 7 Mar 2024
Viewed by 1760
Abstract
Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression [...] Read more.
Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and handling complex data by adjusting the complexity of the model through the addition or reduction of terms. Moreover, quantile generalized additive regression was deliberated to evaluate relationships with lower quantiles, such as low-yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as conditional tail expectation (CTE), certainty equivalence of revenue (CER), and mean root square loss (MRSL). The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R2 of 0.691. MRSL should be taken into consideration initially, as it is a farmer’s revenue assessment that distinguishes between those with and those without insurance. As a result, the GAM model indicates the least fluctuation in farmer income at the 90th percentile. Additionally, our study suggests that this type of insurance could apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production. Full article
(This article belongs to the Special Issue Advances in Predictive Analytics and Systemic Risks in Finance and Insurance)
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