Mathematical Modeling and Machine Learning with Application to Economics and Finance

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 14488

Special Issue Editor


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Guest Editor
Department of Economics and Management, Ufa State Aviation Technical University, 450008 Ufa, Russia
Interests: mathematical methods in economics, business and finance; machine learning; data science and big data analytics; applied statistics and operational research in economics and finance; 4IR; digital economy and data-driven approach; digital footprint; digital twins; system design and engineering; game theory and applications; AI and intelligence based systems; differential equation and maps; decision support systems; sustainable management; resourses productivity and company performance; human capital; innovation-based development

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue is a collection of articles devoted to the development and implementation of advanced mathematical and computational methods in the field of economics, finance and social science. These methods can be based both on traditional statistical modeling methods and on new tools associated with the development of intelligent digital technologies and data science.

The focus of this Special Issue is mathematical modeling and machine learning for facing of such problems as digital economy, financial risks and social development under Industry 5.0. In the age of big data, statistics, machine learning and advanced analytics become highly important in the decision-making process in different fields of economics, social development, and finance. We are looking for new and innovative approaches to mathematical statistics and data science in multidisciplinary models. High-quality papers are solicited to address both theoretical and practical issues—advancements in statistical learning, high-dimensional approaches, complex data analysis, and causal inference, among others—will be highly welcome.

Prof. Dr. Ekaterina Orlova
Guest Editor

Manuscript Submission Information

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Keywords

  • modeling of economic and social processes
  • financial data analysis
  • R&D
  • digital economy and data-driven approach
  • business statistics
  • economic modeling
  • statistical modeling
  • predictive models
  • data analytics
  • machine leaning algorithms
  • artificial intelligence and intelligence based systems
  • risk management
  • applied statistics and operational research in economics and finance
  • decision making
  • decision support systems
  • optimization algorithms

Published Papers (4 papers)

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Research

19 pages, 4310 KiB  
Article
The Fama–French Five-Factor Model with Hurst Exponents Compared with Machine Learning Methods
by Yicun Li and Yuanyang Teng
Mathematics 2023, 11(13), 2988; https://doi.org/10.3390/math11132988 - 04 Jul 2023
Viewed by 1168
Abstract
Scholars and investors have been interested in factor models for a long time. This paper builds models using the monthly data of the A-share market. We construct a seven-factor model by adding the Hurst exponent factor and the momentum factor to a Fama–French [...] Read more.
Scholars and investors have been interested in factor models for a long time. This paper builds models using the monthly data of the A-share market. We construct a seven-factor model by adding the Hurst exponent factor and the momentum factor to a Fama–French five-factor model and find that there is a 7% improvement in the average R–squared. Then, we compare five machine learning algorithms with ordinary least squares (OLS) in one representative stock and all A-Share stocks. We find that regularization algorithms, such as lasso and ridge, have worse performance than OLS. SVM and random forests have a good improvement in fitting power, while the neural network is not always better than OLS, depending on the data, frequency, period, etc. Full article
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17 pages, 572 KiB  
Article
Capital Structure Theory in the Transport Sector: Evidence from Visegrad Group
by Jaroslav Mazanec
Mathematics 2023, 11(6), 1343; https://doi.org/10.3390/math11061343 - 09 Mar 2023
Viewed by 1489
Abstract
Capital structure plays an important role in corporate finance, especially in the period of restrictive monetary policy in many developed countries. This paper aims to estimate the debt ratio based on five selected financial indicators: tangibility, return on assets, size of total assets, [...] Read more.
Capital structure plays an important role in corporate finance, especially in the period of restrictive monetary policy in many developed countries. This paper aims to estimate the debt ratio based on five selected financial indicators: tangibility, return on assets, size of total assets, current ratio, and size of total sales using multiple linear regression for four countries, such as the Czech Republic, Hungary, Poland, and Slovakia, as well as the V4 region. The total sample consists of 3828 small- and medium-sized enterprises from the transport sector in the Central European area. These data are drawn from Amadeus by Bureau van Dijk from 2019. The results show that three of the five variables are statistically significant in all models. These findings indicate that transport companies prefer the pecking order theory. We find that the increase in tangibility, return on assets, as well as current ratio, reduce the debt ratio. The outputs provide new theoretical and empirical knowledge regarding transport companies in V4. Full article
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15 pages, 1107 KiB  
Article
Financial Time Series Forecasting with the Deep Learning Ensemble Model
by Kaijian He, Qian Yang, Lei Ji, Jingcheng Pan and Yingchao Zou
Mathematics 2023, 11(4), 1054; https://doi.org/10.3390/math11041054 - 20 Feb 2023
Cited by 18 | Viewed by 9873
Abstract
With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial market operation and management. In this paper, we propose [...] Read more.
With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial market operation and management. In this paper, we propose a new financial time series forecasting model based on the deep learning ensemble model. The model is constructed by taking advantage of a convolutional neural network (CNN), long short-term memory (LSTM) network, and the autoregressive moving average (ARMA) model. The CNN-LSTM model is introduced to model the spatiotemporal data feature, while the ARMA model is used to model the autocorrelation data feature. These models are combined in the ensemble framework to model the mixture of linear and nonlinear data features in the financial time series. The empirical results using financial time series data show that the proposed deep learning ensemble-based financial time series forecasting model achieved superior performance in terms of forecasting accuracy and robustness compared with the benchmark individual models. Full article
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22 pages, 1817 KiB  
Article
Inference of Factors for Labor Productivity Growth Used Randomized Experiment and Statistical Causality
by Ekaterina V. Orlova
Mathematics 2023, 11(4), 863; https://doi.org/10.3390/math11040863 - 08 Feb 2023
Cited by 2 | Viewed by 1117
Abstract
The study of causal dependencies in economics is fraught with great difficulties, that it is required to consider not only the object structure, but also take into account a huge number of factors acting on the object, about which nothing is either known [...] Read more.
The study of causal dependencies in economics is fraught with great difficulties, that it is required to consider not only the object structure, but also take into account a huge number of factors acting on the object, about which nothing is either known or difficult to measure. In this paper, we attempt to overcome this problem and apply the theory of statistical causality for labor productivity management. We suggest new technology that provides the inference of causal relations between the special programs implemented in the company’s and employee’s labor productivity. The novelty of the proposed technology is that it is based on a hybrid object model, combines two models: 1—the structural object model about its functioning and development to provide a causal inference and prediction the effect of explicit factors; 2—the model based on observed data to clarify causality and to test it empirically. The technology provides integration of the theory of causal Bayesian networks, methods of randomized controlled experiments and statistical methods, allows under nonlinearity, dynamism, stochasticity and non-stationarity of the initial data, to evaluate the effect of programs on the labor effeciency. The difference between the proposed technology and others is that it ensures determination the synergistic effect of the action of the cause (program) on the effect—labor productivity in condition of hidden factors. The practical significance of the research is the results of its testing the proposed theoretical provisions, methods and technologies on actual data about food service company. The results obtained could contribute to the labor productivity growth over uncertainty of the external and internal factors and provide the companies sustainable development and its profitability growth. Full article
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