Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
2.3
Journals
Mathematics
Special Issues
Mathematical Developments in Modeling Current Financial Phenomena
share announcement

Mathematical Developments in Modeling Current Financial Phenomena

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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 23696

Special Issue Editors

Prof. Dr. Camelia Oprean-Stan

E-Mail Website
Guest Editor
Faculty of Economic Sciences, Department of Finance and Accounting, Lucian Blaga University of Sibiu, 550324 Sibiu, Romania
Interests: corporate finance; portfolio administration; financial markets
Special Issues, Collections and Topics in MDPI journals
Dr. Radu Voichita Adriana

E-Mail Website
Guest Editor
Department of Statistics, Forecasting and Mathematics, Faculty of Economics and Business Administration, Babes Bolyai University of Cluj-Napoca, 400591 Cluj Napoca, Romania
Interests: approximation theory; linear positive operators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to submit your most recent research to this Special Issue, “Mathematical Developments in Modeling Current Financial Phenomena”, in the journal Mathematics.

This Special Issue aims to bridge the gap between advanced mathematical models and financial and economic research by providing a collection of articles illustrating the applicability of new mathematical tools and methods to a wide range of current financial challenges, such as, but not limited to, the mathematical modeling of financial market behavior; recession forecasting; behavioral finance models; asset allocation and portfolio theory; modeling in corporate finance; bankruptcy and failure prediction; sustainability issues, e.g., sustainable finance, sustainable growth, green finance, ESG, socially responsible investments; and fintech and digital finance, e.g., cryptocurrency, machine learning, Web 3.0, and financial big data analytics.

We encourage the submission of research papers presenting novel results from modeling current financial phenomena using a logical, behavioral, institutional, and quantitative approach.

Prof. Dr. Camelia Oprean-Stan
Dr. Radu Voichita Adriana
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 market modeling
  • behavioral finance
  • economic preferences
  • adaptive market hypothesis (AMH)
  • efficient market hypothesis (EMH)
  • fractal market hypothesis (FMH)
  • portfolio theory
  • corporate finance modeling
  • prediction of bankruptcy and failure
  • sustainable finance
  • fintech and digital finance

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 1817 KiB  
Article
Modeling Risk Sharing and Impact on Systemic Risk
by Walter Farkas and Patrick Lucescu
Mathematics 2024, 12(13), 2083; https://doi.org/10.3390/math12132083 - 2 Jul 2024
Viewed by 666
Abstract
This paper develops a simplified agent-based model to investigate the dynamics of risk transfer and its implications for systemic risk within financial networks, focusing specifically on credit default swaps (CDSs) as instruments of risk allocation among banks and firms. Unlike broader models that [...] Read more.
This paper develops a simplified agent-based model to investigate the dynamics of risk transfer and its implications for systemic risk within financial networks, focusing specifically on credit default swaps (CDSs) as instruments of risk allocation among banks and firms. Unlike broader models that incorporate multiple types of economic agents, our approach explicitly targets the interactions between banks and firms across three markets: credit, interbank loans, and CDSs. This model diverges from the frameworks established by prior researchers by simplifying the agent structure, which allows for more focused calibration to empirical data—specifically, a sample of Swiss banks—and enhances interpretability for regulatory use. Our analysis centers around two control variables, CDSc and CDSn, which control the likelihood of institutions participating in covered and naked CDS transactions, respectively. This approach allows us to explore the network’s behavior under varying levels of interconnectedness and differing magnitudes of deposit shocks. Our results indicate that the network can withstand minor shocks, but higher levels of CDS engagement significantly increase variance and kurtosis in equity returns, signaling heightened instability. This effect is amplified during severe shocks, suggesting that CDSs, instead of mitigating risk, propagate systemic risk, particularly in highly interconnected networks. These findings underscore the need for regulatory oversight to manage risk concentration and ensure financial stability. Full article
(This article belongs to the Special Issue Mathematical Developments in Modeling Current Financial Phenomena)
►▼ Show Figures

Figure 1

20 pages, 5398 KiB  
Article
Analyzing the Impact of Financial News Sentiments on Stock Prices—A Wavelet Correlation
by Marian Pompiliu Cristescu, Dumitru Alexandru Mara, Raluca Andreea Nerișanu, Lia Cornelia Culda and Ionela Maniu
Mathematics 2023, 11(23), 4830; https://doi.org/10.3390/math11234830 - 30 Nov 2023
Cited by 1 | Viewed by 8551
Abstract
This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression [...] Read more.
This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression analysis, this research probes the degree to which stock-price fluctuations can be attributed to the polarity of media sentiment. The methodology combines statistical techniques to assess sentiment’s predictive power for stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships. The results demonstrate a significant correlation between sentiment polarity and stock prices, with description polarity affecting Microsoft’s opening prices, title polarity influencing Tesla’s opening prices, and a positive impact of title polarity on Apple’s closing prices. However, Tesla’s stock showed no significant coherence, indicating a potential divergence in how sentiment affects stock behavior across companies. The study highlights the importance of sentiment analysis in forecasting stock-market trends, revealing not only direct correlations but also lagged influences on stock prices. Despite its focus on large-cap tech firms, this research provides a foundational understanding of sentiment’s financial implications, suggesting further investigation into smaller firms and other market sectors. Full article
(This article belongs to the Special Issue Mathematical Developments in Modeling Current Financial Phenomena)
►▼ Show Figures

Figure 1

17 pages, 11206 KiB  
Article
A Mathematical Model of Financial Bubbles: A Behavioral Approach
by Andrei Afilipoaei and Gustavo Carrero
Mathematics 2023, 11(19), 4102; https://doi.org/10.3390/math11194102 - 28 Sep 2023
Viewed by 5468
Abstract
In this work, we propose a mathematical model to describe the price trends of unsustainable growth, abrupt collapse, and eventual stabilization characteristic of financial bubbles. The proposed model uses a set of ordinary differential equations to depict the role played by social contagion [...] Read more.
In this work, we propose a mathematical model to describe the price trends of unsustainable growth, abrupt collapse, and eventual stabilization characteristic of financial bubbles. The proposed model uses a set of ordinary differential equations to depict the role played by social contagion and herd behavior in the formation of financial bubbles from a behavioral standpoint, in which the market population is divided into neutral, bull (optimistic), bear (pessimistic), and quitter subgroups. The market demand is taken to be a function of both price and bull population, and the market supply is taken to be a function of both price and bear population. In such a manner, the spread of optimism and pessimism controls the supply and demand dynamics of the market and offers a dynamical characterization of the asset price behavior of a financial bubble. Full article
(This article belongs to the Special Issue Mathematical Developments in Modeling Current Financial Phenomena)
►▼ Show Figures

Figure 1

23 pages, 5540 KiB  
Article
Credibilistic Multi-Period Mean-Entropy Rolling Portfolio Optimization Problem Based on Multi-Stage Scenario Tree
by Pejman Peykani, Mojtaba Nouri, Mir Saman Pishvaee, Camelia Oprean-Stan and Emran Mohammadi
Mathematics 2023, 11(18), 3889; https://doi.org/10.3390/math11183889 - 12 Sep 2023
Viewed by 1462
Abstract
This study considers a time-consistent multi-period rolling portfolio optimization issue in the context of a fuzzy situation. Rolling optimization with a risk aversion component attempts to separate the time periods and psychological effects of one’s investment in a mathematical model. Furthermore, a resilient [...] Read more.
This study considers a time-consistent multi-period rolling portfolio optimization issue in the context of a fuzzy situation. Rolling optimization with a risk aversion component attempts to separate the time periods and psychological effects of one’s investment in a mathematical model. Furthermore, a resilient portfolio selection may be attained by taking into account fuzzy scenarios. Credibilistic entropy of fuzzy returns is used to measure portfolio risk because entropy, as a measure of risk, is not dependent on any certain sort of symmetric membership function of stock returns and may be estimated using nonmetric data. Mathematical modeling is performed to compare the Rolling Model (RM) and the Unified Model (UM). Two empirical studies from the Tehran stock market (10 stocks from April 2017 to April 2019) and the global stock market (20 stocks from April 2021 to April 2023) are utilized to illustrate the applicability of the suggested strategy. The findings reveal that RM can limit the risk of the portfolio at each time, but the portfolio’s return is smaller than that of UM. Furthermore, the suggested models outperform the standard deterministic model. Full article
(This article belongs to the Special Issue Mathematical Developments in Modeling Current Financial Phenomena)
►▼ Show Figures

Figure 1

26 pages, 18610 KiB  
Article
The Impact of Sentiment Indices on the Stock Exchange—The Connections between Quantitative Sentiment Indicators, Technical Analysis, and Stock Market
by Florin Cornel Dumiter, Florin Turcaș, Ștefania Amalia Nicoară, Cristian Bențe and Marius Boiță
Mathematics 2023, 11(14), 3128; https://doi.org/10.3390/math11143128 - 15 Jul 2023
Cited by 4 | Viewed by 5563
Abstract
The stock market represents one of the most complex mechanisms in the financial world. It can be seen as a living being with complex ways to enact, interact, evolve, defend, and respond to various stimuli. Technical analysis is one of the most complex [...] Read more.
The stock market represents one of the most complex mechanisms in the financial world. It can be seen as a living being with complex ways to enact, interact, evolve, defend, and respond to various stimuli. Technical analysis is one of the most complex techniques based on financial data’s graphical aspects. News sentiment indices are very complex and highlight another important part of behavioral finance. In this study, we propose an integrated approach in order to determine the correlation between news sentiment indices, the stock market, and technical analysis. The research methodology focuses on the stock market’s practical and quantitative aspects. In this sense, we have used the graphical representation of technical analysis and econometric modeling techniques such as VAR and Bayesian VAR. The results of the empirical modeling techniques and analysis reveal some important connections between the stock market and news sentiment indices on the US stock market. The conclusions of this study highlight a strong connection between news sentiment indices, technical analysis, and the stock market which suggests that the behavioral finance aspect is a very important aspect in the analysis of the stock market. Full article
(This article belongs to the Special Issue Mathematical Developments in Modeling Current Financial Phenomena)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop