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Mathematics
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Research and Application of Data Optimization Model in Finance
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Research and Application of Data Optimization Model in Finance

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 8940

Special Issue Editors

Prof. Dr. Georgeta Șoavă

E-Mail Website
Guest Editor
Department of Statistics and Economic Informatics, University of Craiova, A.I. Cuza 13, 200585 Craiova, Romania
Interests: e-business; financial market modeling and simulation; decision and strategies making; big data; modeling and optimization algorithms of business; risk analysis; forecasting; neural network
Dr. Anca Mehedintu

E-Mail Website
Guest Editor
Department of Statistics and Economic Informatics, University of Craiova, 200585 Craiova, Romania
Interests: database management systems; statistical analysis of stationary and non-stationary time-series data; big data; structural equation models; time-series modelling and forecasting; neuronal network
Dr. Mihaela Sterpu

E-Mail Website
Guest Editor
Department of Mathematics, University of Craiova, 200585 Craiova, Romania
Interests: dynamical systems; business mathematics; causal and predictive modelling; computational mathematics; financial mathematics; mathematical modelling

Special Issue Information

Dear Colleagues,

We are pleased to announce the launch of a new Special Issue of Mathematics entitled "Research and Application of Data Optimization Model in Finance". In the era of digital technology, advanced mathematical tools and methods are becoming increasingly necessary in finance to provide a better characterization of the complex relationships in this field. Advances in optimization models, algorithms and software can be applied to solve practical problems specific to finance. This Special Issue will provide researchers with the opportunity to present research that illustrates the applicability of new mathematical tools and methods to a wide range of topics in finance to generate optimal solutions for complex economic phenomena and new insights into theoretical and practical concepts. Submissions may address one or more of the following problems: neural network; mathematical modelling; financial market; multicriteria decision making; financial risk; financial accounting; financial mathematics; time-series modelling and forecasting; portfolio management and optimization.

We welcome high-quality original theoretical and empirical articles involving developed mathematical and statistical methodology, such as: dynamical systems; computational mathematics; causal and predictive modelling; statistical programming, econometrics (modelling, forecasting, simulation), operational research (programming, simulation, optimization of business processes and management challenges, multicriteria decision making) and mathematical statistics (statistical inferences, distributions, hypothesis testing). Topics of interest include but are not limited to the issues presented.

Prof. Dr. Georgeta Soava
Dr. Anca Mehedintu
Dr. Mihaela Sterpu
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 process modelling
  • financial market modelling and simulation
  • financial risk management
  • accounting financial
  • financial time-series
  • cryptocurrencies
  • big data
  • causal and predictive modelling
  • exchange rates and currency fluctuations
  • forecasting and uncertainty
  • computational mathematics
  • asset pricing models
  • derivative pricing
  • corporate finance
  • investments

Published Papers (4 papers)

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Research

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28 pages, 2949 KiB  
Article
Study on Economic Data Forecasting Based on Hybrid Intelligent Model of Artificial Neural Network Optimized by Harris Hawks Optimization
by Renbo Liu, Yuhui Ge and Peng Zuo
Mathematics 2023, 11(21), 4557; https://doi.org/10.3390/math11214557 - 6 Nov 2023
Cited by 1 | Viewed by 950
Abstract
To use different models for forecasting economic data suitably, three main basic models (the grey system model, time series analysis model, and artificial neural network (ANN) model) are analyzed and compared comprehensively. Based on the analysis results of forecasting models, one new hybrid [...] Read more.
To use different models for forecasting economic data suitably, three main basic models (the grey system model, time series analysis model, and artificial neural network (ANN) model) are analyzed and compared comprehensively. Based on the analysis results of forecasting models, one new hybrid intelligent model based on the ANN model and Harris hawks optimization (HHO) has been proposed. In this hybrid model, HHO is used to select the hyperparameters of the ANN and also to optimize the linking weights and thresholds of the ANN. At last, by using four economic data cases including two simple data sets and two complex ones, the analysis of the basic models and the proposed hybrid model have been verified comprehensively. The results show that the grey system model can suitably analyze exponential data sequences, the time series analysis model can analyze random sequences, and the ANN model can be applied to any kind of data sequence. Moreover, when compared with the basic models, the new hybrid model can be suitably applied for both simple data sets and complex ones, and its forecasting performance is always very suitable. In comparison with other hybrid models, not only for computing accuracy but also for computing efficiency, the performance of the new hybrid model is the best. For the least initial parameters used in the new hybrid model, which can be determined easily and simply, the application of the new hybrid model is the most convenient too. Full article
(This article belongs to the Special Issue Research and Application of Data Optimization Model in Finance)
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23 pages, 652 KiB  
Article
A Generalization of the Grey Lotka–Volterra Model and Application to GDP, Export, Import and Investment for the European Union
by Mihaela Sterpu, Carmen Rocșoreanu, Georgeta Soava and Anca Mehedintu
Mathematics 2023, 11(15), 3351; https://doi.org/10.3390/math11153351 - 31 Jul 2023
Cited by 1 | Viewed by 947
Abstract
This study proposes a generalized grey Lotka–Volterra model with a finite number of variables. The model is obtained by applying the grey modelling method to estimate the parameters of a finite dimensional quadratic Lotka–Volterra system. Subsequently, the model is used to analyze the [...] Read more.
This study proposes a generalized grey Lotka–Volterra model with a finite number of variables. The model is obtained by applying the grey modelling method to estimate the parameters of a finite dimensional quadratic Lotka–Volterra system. Subsequently, the model is used to analyze the competition and cooperation relationship between four macroeconomic indicators, namely Gross Domestic Product, Export, Import and Investment, and to obtain short-time forecasting for them. The data used in the empirical investigation cover the time periods 2005–2022 and 2011–2022, for the European Union. The empirical results are compared to the ones obtained by using the grey model GM(1,1) and the two-dimensional grey Lotka–Volterra model. Finally, economic interpretations of the empirical findings are formulated. Full article
(This article belongs to the Special Issue Research and Application of Data Optimization Model in Finance)
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18 pages, 1638 KiB  
Article
Antecedents of Risky Financial Investment Intention among Higher Education Students: A Mediating Moderating Model Using Structural Equation Modeling
by Ibrahim A. Elshaer and Abu Elnasr E. Sobaih
Mathematics 2023, 11(2), 353; https://doi.org/10.3390/math11020353 - 9 Jan 2023
Cited by 3 | Viewed by 2247
Abstract
The current study examines the direct effect of investment awareness and university education support on students’ risky financial investment intentions. Additionally, it examines the mediating effect of social influence and the moderating effect of self-control on these relationships. For this purpose, we directed [...] Read more.
The current study examines the direct effect of investment awareness and university education support on students’ risky financial investment intentions. Additionally, it examines the mediating effect of social influence and the moderating effect of self-control on these relationships. For this purpose, we directed an online questionnaire to senior students at three public universities in Saudi Arabia. The results of SmartPLS showed positive significant effects of investment awareness and university education support on social influence towards investment. The results also showed direct positive significant effects of investment awareness and university education support on students’ risky financial investment intentions. The results confirmed a partial mediating effect of social influence on the link between investment awareness and university education support on students’ risky financial investment intentions. Moreover, self-control was found to have a moderating effect on the link between investment awareness, university education support and social influence. Self-control failed to confirm the other moderating effects; i.e., the link between university education support and investment awareness, nor the link between investment awareness, university education support and risky financial investment intention. Implications of these findings for academics and policymakers to stimulate investment intention among higher education graduates in the Kingdom of Saudi Arabia (KSA) are discussed. Full article
(This article belongs to the Special Issue Research and Application of Data Optimization Model in Finance)
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Review

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34 pages, 2768 KiB  
Review
A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets
by Longsheng Cheng, Mahboubeh Shadabfar and Arash Sioofy Khoojine
Mathematics 2023, 11(5), 1148; https://doi.org/10.3390/math11051148 - 25 Feb 2023
Cited by 5 | Viewed by 4087
Abstract
Portfolio management has long been one of the most significant challenges in large- and small-scale investments alike. The primary objective of portfolio management is to make investments with the most favorable rate of return and the lowest amount of risk. On the other [...] Read more.
Portfolio management has long been one of the most significant challenges in large- and small-scale investments alike. The primary objective of portfolio management is to make investments with the most favorable rate of return and the lowest amount of risk. On the other hand, time series prediction has garnered significant attention in recent years for predicting the trend of stock prices in the future. The combination of these two approaches, i.e., predicting the future stock price and adopting portfolio management methods in the forecasted time series, has turned out to be a novel research line in the past few years. That is, to have a better understanding of the future, various researchers have attempted to predict the future behavior of stocks and subsequently implement portfolio management techniques on them. However, due to the uncertainty in predicting the future, the reliability of these methodologies is in question, and it is unclear to what extent their results can be relied upon. Therefore, probabilistic approaches have also entered the research arena, and attempts have been made to incorporate uncertainty into future forecasting and portfolio management. This issue has led to the development of probabilistic portfolio management for future data. This review paper begins with a discussion of various time-series prediction methods for stock market data. Next, a classification and evaluation of portfolio management approaches are provided. Afterwards, the Monte Carlo sampling method is discussed as the most prevalent technique for probabilistic analysis of stock market data. The probabilistic portfolio management method is applied to future Shanghai Stock Exchange data in the form of a case study to measure the applicability of this method to real-world projects. The results of this research can serve as a benchmark example for the analysis of other stock market data. Full article
(This article belongs to the Special Issue Research and Application of Data Optimization Model in Finance)
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