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Risks
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Computational Methods and Models in the Financial Risk Management Process
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Computational Methods and Models in the Financial Risk Management Process

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 10 September 2025 | Viewed by 1189

Special Issue Editor

Dr. Marina Resta

E-Mail Website
Guest Editor
Department of Economics, University of Genoa, 16126 Genoa, Italy
Interests: machine learning; portfolio optimization; Bayesian networks; network analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing complexity of the financial context poses challenging issues that are particularly well suited to be managed using machine learning algorithms.

This Special Issue focuses on state-of-the-art applications in financial markets, with particular attention to all aspects of the risk management process. We therefore welcome and encourage high-quality contributions focused on these areas, including (but not limited to) the following:

  • Asset pricing;
  • Big data analytics;
  • Financial data mining;
  • Commodity markets;
  • Term structure models;
  • Trading systems;
  • Hedging strategies;
  • Actuarial mathematics;
  • Deep learning and artificial neural networks;
  • Fuzzy sets, rough sets, and granular computing;
  • Hybrid systems;
  • Support vector machines.

Dr. Marina Resta
Guest Editor

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. Risks 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 1800 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

  • machine learning
  • risk management
  • data analytics

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Published Papers (2 papers)

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Research

12 pages, 2274 KiB  
Article
A New Approach on Country Risk Monitoring
by Christos E. Kountzakis and Christos Floros
Risks 2025, 13(5), 80; https://doi.org/10.3390/risks13050080 - 22 Apr 2025
Viewed by 209
Abstract
Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product [...] Read more.
Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product in terms of a spot value. Another index is the spot value of the percentage of the annual interest rate payments of the state to the owners of sovereign debt. These indexes provide an inefficient evaluation of the national debt and moreover they are sensitive in their calculative aspect. Hence, we propose another index of national debt evaluation, which is more realistic, since public debt is a part of the balance sheet of the state itself. Moreover, this index may be translated into growth variables of the national economy. Since Gross National Product relies on consumption of the Economy, more consumption implies an ’illusion’ about sovereign debt. On the other hand, this index has limits to its credibility because it depends on the size of the annual investments. Full article
(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)
18 pages, 586 KiB  
Article
A Bivariate Model for Correlated and Mixed Outcomes: A Case Study on the Simultaneous Prediction of Credit Risk and Profitability of Peer-to-Peer (P2P) Loans
by Yan Wang, Xuelei Sherry Ni, Huan Ni and Sanad Biswas
Risks 2025, 13(2), 33; https://doi.org/10.3390/risks13020033 - 12 Feb 2025
Viewed by 670
Abstract
In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However, relying on a [...] Read more.
In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However, relying on a single scoring approach may bias the loan evaluation conclusion. In this paper, we propose a bivariate model based on the integration of two scoring approaches. We first formulate the loan evaluation task as a multi-target problem, in which loan_status (i.e., default or not default) is used as the discrete outcome for the credit risk measure while the annualized rate of return (ARR) is used as the continuous outcome for the profitability measure. Then to solve the multi-target problem, we design a novel loss function based on the assumption that the discrete outcome follows a Bernoulli distribution, and the continuous outcome is normally distributed conditional on the discrete output. The effectiveness of the proposed model is examined using the real-world P2P data from the Lending Club. Results indicate that our approach outperforms the sole scoring methods by identifying loans with higher profit and lower default risk. Therefore, the proposed method can serve as an alternative for loan evaluation. Full article
(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)
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