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Mathematics
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Mathematics and Economic Modeling
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Mathematics and Economic Modeling

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 13869

Special Issue Editors

Prof. Dr. Ioan Batrancea

E-Mail Website
Guest Editor
Faculty of Economics and Business Administration, Babes-Bolyai University, 400591 Cluj-Napoca, Romania
Interests: financial analysis; audit; banking; public finance
Special Issues, Collections and Topics in MDPI journals
Dr. Ramona-Anca Nichita

E-Mail Website
Guest Editor
Faculty of Economics, 1 Decembrie 1918 University, 510009 Alba Iulia, Romania
Interests: tax behavior; financial analysis; cognitive neuroscience
Dr. Lucian Gaban

E-Mail Website
Guest Editor
Faculty of Economics, 1 Decembrie 1918 University, 510009 Alba Iulia, Romania
Interests: auditing; banking; public finance
Dr. Mircea-Iosif Rus

E-Mail Website
Guest Editor
National Institute of Research of Development Urban INCERC, 400524 Cluj-Napoca, Romania
Interests: education research; innovation; R&D transfer

Special Issue Information

Dear Colleagues,

The global financial crisis, the sovereign debt crisis, and especially the health crisis have shown that the world economy is undergoing tests that can affect long-term economic stability.

We believe that the prevention of such crises can only be achieved by modeling economic phenomena at the micro- and macroeconomic level.

Through their studies, researchers can highlight the factors and establish the causes that can influence the financial balance of banks, companies, and local administrations.

Hence, studies in this field are important in order to emphasize the link between economic phenomena and their factors of influence in order to prevent new financial crises triggered by the current health crisis.

Prof. Dr. Ioan Batrancea
Dr. Ramona-Anca Nichita
Dr. Lucian Gaban
Dr. Mircea-Iosif Rus
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • financial analysis
  • regional growth
  • regionalism
  • financing
  • R&D
  • transfer innovations
  • tourism
  • hospitals
  • health crisis
  • banking
  • financial markets

Published Papers (6 papers)

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Research

20 pages, 729 KiB  
Article
A Forgotten Effects Approach to the Analysis of Complex Economic Systems: Identifying Indirect Effects on Trade Networks
by Felipe Chávez-Bustamante, Elliott Mardones-Arias, Julio Rojas-Mora and Jaime Tijmes-Ihl
Mathematics 2023, 11(3), 531; https://doi.org/10.3390/math11030531 - 18 Jan 2023
Viewed by 2029
Abstract
The purpose of this paper is to identify the emergence of indirect trade flows prompted by the export interaction of the world’s economies. Using data on exports from the United Nations Conference on Trade and Development (UNCTAD) for the period 2016–2021, we construct [...] Read more.
The purpose of this paper is to identify the emergence of indirect trade flows prompted by the export interaction of the world’s economies. Using data on exports from the United Nations Conference on Trade and Development (UNCTAD) for the period 2016–2021, we construct an international trade network which is analyzed through the “forgotten effects theory” that identifies tuples of countries with an origin, intermediary countries, and a destination. This approach intends to spotlight something beyond the analysis of the direct trade network by the identification of second and third-order paths. The analysis using both network analyses, as well as the forgotten effect approaches, which show that the international trade network presents a hub-and-spoke behavior in contrast to most extant research finding a core-periphery structure. The structure is then comprised of three almost separated trade networks and a hub country that bridges commerce between those networks. The contribution of this article is to move the analysis forward from other works that utilize trade networks, including those of econometric nature—such as the ones based on gravity models—by incorporating indirect relationships between countries, which could provide distinctive and novel insights into the study of economic networks. Full article
(This article belongs to the Special Issue Mathematics and Economic Modeling)
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14 pages, 316 KiB  
Article
The Impact of Intangible Assets on the Market Value of Companies: Cross-Sector Evidence
by Darya Dancaková, Jakub Sopko, Jozef Glova and Alena Andrejovská
Mathematics 2022, 10(20), 3819; https://doi.org/10.3390/math10203819 - 16 Oct 2022
Cited by 9 | Viewed by 4004
Abstract
The impact of corporate intangibles on a company’s market value has been a widely debated topic. A large body of literature has separately examined the industry’s effect- or firm-specific attributes, such as industry type, company size, company age, or indebtedness and profitability, on [...] Read more.
The impact of corporate intangibles on a company’s market value has been a widely debated topic. A large body of literature has separately examined the industry’s effect- or firm-specific attributes, such as industry type, company size, company age, or indebtedness and profitability, on the motivation to disclose information on intangible assets, but without considering a comprehensive view. This paper examines the role intangible assets play in a firm’s market valuation besides other firm-specific characteristics. The reducted dataset we use in this study comprises 250 publicly traded companies operating in four different business sectors in France, Germany, and Switzerland for the ten years from 2009 to 2018. Based on the panel data regression models, the study provides an extension of previous knowledge about the effect intangible assets may have on the investors’ view of a company’s value, where the value added of this paper is the empirical evidence of a possible link between the intangible assets’ disclosure and the market value of German, French, and Swiss enterprises. The importance of our contribution lies in a comparative analysis carried out to reveal substantial differences in the impact of intangible assets and innovation activity on the market value firms in three European countries and across four industry sectors. Although the results show the positive impact of intangible assets on the companies’ market value, we suggest that investors still assess companies based on their profitability rather than considering the information on intangible assets the enterprises disclose in their financial statements. Full article
(This article belongs to the Special Issue Mathematics and Economic Modeling)
13 pages, 681 KiB  
Article
A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform
by Dong-Her Shih, Ting-Wei Wu, Po-Yuan Shih, Nai-An Lu and Ming-Hung Shih
Mathematics 2022, 10(13), 2282; https://doi.org/10.3390/math10132282 - 29 Jun 2022
Cited by 1 | Viewed by 1609
Abstract
A great challenge for credit-scoring models in online peer-to-peer (P2P) lending platforms is that credit-scoring models simply discard rejected applicants. This selective discard can lead to an inability to increase the number of potentially qualified applicants, ultimately affecting the revenue of the lending [...] Read more.
A great challenge for credit-scoring models in online peer-to-peer (P2P) lending platforms is that credit-scoring models simply discard rejected applicants. This selective discard can lead to an inability to increase the number of potentially qualified applicants, ultimately affecting the revenue of the lending platform. One way to deal with this is to employ reject inference, a technique that infers the state of a rejected sample and incorporates the results into a credit-scoring model. The most popular approach to reject inference is to use a credit-scoring model built only on accepted samples to directly predict the status of rejected samples. However, the distribution of accepted samples in online P2P lending is different from the distribution of rejected samples, and the credit-scoring model on the original accepted sample may no longer apply. In addition, the acceptance sample may also include applicants who cannot repay the loan. If these applicants can be filtered out, the losses to the lending platform can also be reduced. Therefore, we propose a global credit-scoring model framework that combines multiple feature selection methods and classifiers to better evaluate the model after adding rejected samples. In addition, this study uses outlier detection methods to explore the internal relationships of all samples, which can delete outlier applicants in accepted samples or increase outlier applicants in rejected samples. Finally, this study uses four data samples and reject inference to construct four different credit-scoring models. The experimental results show that the credit-scoring model combining Pearson and random forest proposed in this study has significantly better accuracy and AUC than other scholars. Compared with previous studies, using outlier detection to remove outliers in loan acceptance samples and identify potentially creditworthy loan applicants from loan rejection samples is a good strategy. Furthermore, this study not only improves the accuracy of the credit-scoring model but also increases the number of lenders, which in turn increases the profitability of the lending platform. Full article
(This article belongs to the Special Issue Mathematics and Economic Modeling)
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16 pages, 2846 KiB  
Article
Coarse Graining on Financial Correlation Networks
by Mehmet Ali Balcı, Larissa M. Batrancea, Ömer Akgüller and Anca Nichita
Mathematics 2022, 10(12), 2118; https://doi.org/10.3390/math10122118 - 17 Jun 2022
Cited by 10 | Viewed by 1378
Abstract
Community structure detection is an important and valuable task in financial network studies as it forms the basis of many statistical applications such as prediction, risk analysis, and recommendation. Financial networks have a natural multi-grained structure that leads to different community structures at [...] Read more.
Community structure detection is an important and valuable task in financial network studies as it forms the basis of many statistical applications such as prediction, risk analysis, and recommendation. Financial networks have a natural multi-grained structure that leads to different community structures at different levels. However, few studies pay attention to these multi-part features of financial networks. In this study, we present a geometric coarse graining method based on Voronoi regions of a financial network. Rather than studying the dense structure of the network, we perform our analysis on the triangular maximally filtering of a financial network. Such filtered topology emerges as an efficient approach because it keeps local clustering coefficients steady and it underlies the network geometry. Moreover, in order to capture changes in coarse grains geometry throughout a financial stress, we study Haantjes curvatures of paths that are the farthest from the center in each of the Voronoi regions. We performed our analysis on a network representation comprising the stock market indices BIST (Borsa Istanbul), FTSE100 (London Stock Exchange), and Nasdaq-100 Index (NASDAQ), across three financial crisis periods. Our results indicate that there are remarkable changes in the geometry of coarse grains. Full article
(This article belongs to the Special Issue Mathematics and Economic Modeling)
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28 pages, 4343 KiB  
Article
Stacking Machine Learning Model for the Assessment of R&D Product’s Readiness and Method for Its Cost Estimation
by Nataliya Chukhray, Nataliya Shakhovska, Oleksandra Mrykhina, Lidiya Lisovska and Ivan Izonin
Mathematics 2022, 10(9), 1466; https://doi.org/10.3390/math10091466 - 27 Apr 2022
Cited by 3 | Viewed by 1651
Abstract
The modern technology universities have the necessary resource and material base for developing and transferring R&D products. However, the cost estimation process is not formalized. There are many methods of estimating the cost of R&D products’ commercialization processes. However, in some cases, we [...] Read more.
The modern technology universities have the necessary resource and material base for developing and transferring R&D products. However, the cost estimation process is not formalized. There are many methods of estimating the cost of R&D products’ commercialization processes. However, in some cases, we cannot consider any single technique to be the best one as each of them has advantages and disadvantages. In such conditions, all efforts should be made to use a combination of the estimation techniques to arrive at a better cost and quality estimate. The effectiveness of the valuation of R&D products is of particular importance in today’s economy and due to the need to analyze large data sets prepared for transfer from universities to the business environment. This paper presents the model, two methods, and general information technology for R&D products’ readiness level assessment and R&D products’ cost estimation. The article presents the complex method for determining the cost of R&D products, which will allow: increasing the efficiency of the transfer, commercialization, and market launch of R&D products, and promoting the interaction of all components of the national innovation infrastructure, innovations, etc. The need to consider many different indicators when evaluating R&D products has determined the need to use machine learning algorithms. We have designed a new machine learning-based model for the readiness assessment of R&D products, which is based on the principle of “crowd wisdom” and uses a stacking strategy to integrate machine learning methods. It is experimentally established that the new stacking model based on machine learning algorithms that use random forest as a meta-algorithm provides a minimum of a 1.03 times higher RMSE compared to other ensemble strategies. Full article
(This article belongs to the Special Issue Mathematics and Economic Modeling)
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13 pages, 2859 KiB  
Article
Optimal Reinforcement Learning-Based Control Algorithm for a Class of Nonlinear Macroeconomic Systems
by Qing Ding, Hadi Jahanshahi, Ye Wang, Stelios Bekiros and Madini O. Alassafi
Mathematics 2022, 10(3), 499; https://doi.org/10.3390/math10030499 - 03 Feb 2022
Cited by 7 | Viewed by 1949
Abstract
Due to the vital role of financial systems in today’s sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a [...] Read more.
Due to the vital role of financial systems in today’s sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a reinforcement learning algorithm. Using the Q-learning algorithm, the best optimal action for the system is obtained, and the behavior of the system is controlled. We illustrate that it is possible to control the nonlinear dynamics of the macroeconomic systems using restricted actuation. The highly effective performance of the proposed controller for uncertain systems is demonstrated. The simulation results evidently confirm that the proposed controller satisfies the expected performance. In addition, the numerical simulations clearly confirm that even when we confined the control actions, the proposed controller effectively finds optimal actions for the nonlinear macroeconomic system. Full article
(This article belongs to the Special Issue Mathematics and Economic Modeling)
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