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Mathematics
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Economic Model Analysis and Application
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Economic Model Analysis and Application

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 28870

Special Issue Editor

Dr. Yuanbo Qiao

E-Mail Website
Guest Editor
Institute for Studies in County Development, Shandong University, Jinan, China
Interests: econometric modeling; macroeconomics; economic growth; structural analysis; policy evaluation; environmental economics; sustainability

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the field of applied mathematics, economic dynamics and econometric analysis to this Special Issue entitled “Economic Model Analysis and Application”. The aim of the Special Issue is to extend the analysis of economic models and their applications in economics, business, management and other social sciences. Economic models involved in macroeconomics, microeconomics, managerial economics and other economics branches are suitable for this Special Issue. Econometric models that are widely used in empirical research are also included. Both theoretical and empirical research papers are welcome. Dynamic stochastic general equilibrium, computable general equilibrium, input–output analysis, game theory models and econometric analysis are some examples that are suitable for this Special Issue. Moreover, research using newly developed methods, such as big data and machine learning, is also welcome. Review papers that summarize the development and applications of specific economic models are also highly appreciated. We hope this Special Issue can help researchers to reach a better understanding of economic models and their wide applications. Most importantly, we hope you are interested in this Special Issue and can contribute (submit your latest research) to it.

Dr. Yuanbo Qiao
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. 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

  • dynamic stochastic general equilibrium
  • computable general equilibrium
  • input–output analysis
  • time series analysis
  • cross-section data analysis
  • panel data analysis
  • data envelope analysis
  • economic dynamics
  • economic policy analysis
  • environmental policy analysis
  • difference in differences
  • regression discontinuity design
  • machine learning
  • decision theory
  • game theory
  • information economics
  • mathematical economics

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Published Papers (6 papers)

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Research

22 pages, 4735 KiB  
Article
Selecting and Weighting Mechanisms in Stock Portfolio Design Based on Clustering Algorithm and Price Movement Analysis
by Titi Purwandari, Riaman, Yuyun Hidayat, Sukono, Riza Andrian Ibrahim and Rizki Apriva Hidayana
Mathematics 2023, 11(19), 4151; https://doi.org/10.3390/math11194151 - 2 Oct 2023
Cited by 5 | Viewed by 2669
Abstract
The fundamental stages in designing a stock portfolio are each stock’s selection and capital weighting. Selection and weighting must be conducted through diversification and price movement analysis to maximize profits and minimize losses. The problem is how the technical implementations of both are [...] Read more.
The fundamental stages in designing a stock portfolio are each stock’s selection and capital weighting. Selection and weighting must be conducted through diversification and price movement analysis to maximize profits and minimize losses. The problem is how the technical implementations of both are carried out. Based on this problem, this study aims to design these selection and weighting mechanisms. Stock selection is based on clusters and price movement trends. The optimal stock clusters are formed using the K-Means algorithm, and price movement analyses are carried out using the moving average indicator. The selected stocks are those whose prices have increasing trends with the most significant Sharpe ratio in each cluster. Then, the capital weighting for each preferred stock is carried out using the mean-variance model with transaction cost and income tax. After designing the mechanism, it is applied to Indonesia’s 80 index stock data. In addition, a comparison is conducted between the estimated portfolio return and the actual one day ahead. Finally, the sensitivity of investors’ courage in taking risks to their profits and losses is also analyzed. This research is expected to assist investors in diversification and price movement analysis of the stocks in the portfolios they form. Full article
(This article belongs to the Special Issue Economic Model Analysis and Application)
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19 pages, 5517 KiB  
Article
The Impact of Investments in Physical Capital, Labor, and Knowledge Capital on Enterprise Market Value: Estimation and Optimization
by Yuanbo Qiao, Xiaoyan Shao, Zhuolin Han and Hao Duan
Mathematics 2023, 11(18), 4016; https://doi.org/10.3390/math11184016 - 21 Sep 2023
Cited by 1 | Viewed by 2624
Abstract
This study analyzes the market value of listed companies in Mainland China across different industries, including capital-intensive, labor-intensive, technology-intensive, and other industries. A generalized neoclassical investment model that considers physical capital, labor, and knowledge capital as input variables is built to theoretically decompose [...] Read more.
This study analyzes the market value of listed companies in Mainland China across different industries, including capital-intensive, labor-intensive, technology-intensive, and other industries. A generalized neoclassical investment model that considers physical capital, labor, and knowledge capital as input variables is built to theoretically decompose firm value. The empirical results indicate that knowledge capital accounts for an increasing proportion of the market value of companies, rising sharply from 21.5% in 2009 to 37.9% in 2018. In contrast, the share of labor in enterprise market value has been decreasing year by year, dropping from 56.5% in 2009 to 36.4% in 2018. The share of physical capital in enterprise market value remains relatively stable. Based on these findings, the study simulates the optimal investment behaviors and their influence on the firm value of various types of enterprises, providing valuable insights for investment decision-making for managers in different industries. Full article
(This article belongs to the Special Issue Economic Model Analysis and Application)
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18 pages, 3533 KiB  
Article
Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction
by Yoonjae Noh, Jong-Min Kim, Soongoo Hong and Sangjin Kim
Mathematics 2023, 11(16), 3603; https://doi.org/10.3390/math11163603 - 20 Aug 2023
Cited by 7 | Viewed by 4525
Abstract
The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid [...] Read more.
The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid development of data engineering, a situation has arisen wherein extensive amounts of information must be processed at finer time intervals. Addressing the prevalent issues of difficulty in handling multivariate high-frequency time-series data owing to multicollinearity, resource problems in computing hardware, and the gradient vanishing problem due to the layer stacking in recurrent neural network (RNN) series, a novel algorithm is developed in this study. For financial market index prediction with these highly complex data, the algorithm combines ResNet and a variable-wise attention mechanism. To verify the superior performance of the proposed model, RNN, long short-term memory, and ResNet18 models were designed and compared with and without the attention mechanism. As per the results, the proposed model demonstrated a suitable synergistic effect with the time-series data and excellent classification performance, in addition to overcoming the data structure constraints that the other models exhibit. Having successfully presented multivariate high-frequency time-series data analysis, this study enables effective investment decision making based on the market signals. Full article
(This article belongs to the Special Issue Economic Model Analysis and Application)
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24 pages, 2679 KiB  
Article
Advances on External Machine Computing Power Focusing on Internal Personal Value: A Case Study on the New Digital Currency
by Yiming Qian, Hao Zhang, Jiahao Liu, Hanran Ma, Xinyu Li and Xi Xi
Mathematics 2023, 11(11), 2425; https://doi.org/10.3390/math11112425 - 24 May 2023
Viewed by 1638
Abstract
As global inflation escalates and geopolitical conflicts exacerbate, the world’s economy confronts an intensified degree of instability. In this volatile environment, blockchain currencies emerge as a potential bulwark, offering both value preservation and liquidity benefits. However, the conventional “mining” process introduces significant challenges, [...] Read more.
As global inflation escalates and geopolitical conflicts exacerbate, the world’s economy confronts an intensified degree of instability. In this volatile environment, blockchain currencies emerge as a potential bulwark, offering both value preservation and liquidity benefits. However, the conventional “mining” process introduces significant challenges, such as high energy consumption, data security risks, and detachment from the real economy, which potentially facilitate financial capital manipulation. This research endeavors to mitigate these issues, constructing an innovative blockchain cryptocurrency framework that integrates mining and distribution with intelligent big data. It also incorporates social contributions from individuals in domains such as health, knowledge, and ecological conservation. Consequently, the efficiency of cryptocurrency production and distribution correlates with the individual’s societal contribution. The more substantial the contribution, the higher the intrinsic value of the individual and the more efficient the access. Utilizing a comprehensive framework of mathematical modeling, computer numerical simulation, and fuzzy integrated evaluation, we propose a novel endogenous-value blockchain cryptocurrency system. We quantify and optimize variables such as individual intrinsic value, community efficiency, redistribution weights, and total monetary potential. We introduce an innovative method for accumulating time-decaying values such as knowledge contribution and establish an anti-cheating framework. Our results indicate that this pioneering approach can significantly enhance mining efficiency and optimize cryptocurrency distribution. This counters traditional criticisms of blockchain currencies and paves the way for a more sustainable, fair, and efficient model for future blockchain currency systems. Full article
(This article belongs to the Special Issue Economic Model Analysis and Application)
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26 pages, 5411 KiB  
Article
Random “Decision and Experienced Utility”, Adaptive “Consumer Memory and Choice”: The Impact of Mind Fluctuations and Cognitive Biases on Consumption and Classification
by Runze Yuan, Xi Xi and Zhentao Liu
Mathematics 2023, 11(6), 1470; https://doi.org/10.3390/math11061470 - 17 Mar 2023
Viewed by 1786
Abstract
In the study of consumer behavior, we believe that a distinction should be made between the subjective mental activity of consumption and the objective process of consumption experience, and that the deviation and fluctuation of “decision utility” and the randomness of “experience utility” [...] Read more.
In the study of consumer behavior, we believe that a distinction should be made between the subjective mental activity of consumption and the objective process of consumption experience, and that the deviation and fluctuation of “decision utility” and the randomness of “experience utility” have essential effects on consumer behavior. For one thing, purchase often precedes experience, and consumers cannot precisely predict utility and its distribution but can only make decisions based on adaptive expectations. Secondly, there is often uncertainty in the experience of goods, making predictions more difficult. Thirdly, the revision of consumer decision utility is carried out through memory in an adaptive process. We introduce two stochastic forms of decision and experience utility functions and build a two-period model. In the model, consumers make decisions based on a pre-determined decision utility function in the first period and a randomly realized experience utility function in the second period. The analysis shows that the volatility of “decision utility” and “experience utility” affects consumers in opposite directions; the former may trigger expansionary consumption, while the latter makes consumers more cautious. Finally, the consumption behaviors in the model can be divided into 24 categories based on the dimensions of chance, systematicity, luck, and deviance, corresponding to various scenarios. The total number of consumer behavior categories is a full ranking of the size relationship of the four factors mentioned above, thus 24 categories. For example, when good luck is accompanied by chance underestimation versus systematic underestimation, it leads to a better process experience for the consumer. Full article
(This article belongs to the Special Issue Economic Model Analysis and Application)
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13 pages, 1782 KiB  
Article
Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction
by Cheng Zhao, Ping Hu, Xiaohui Liu, Xuefeng Lan and Haiming Zhang
Mathematics 2023, 11(5), 1130; https://doi.org/10.3390/math11051130 - 24 Feb 2023
Cited by 33 | Viewed by 14023
Abstract
The ability to predict stock prices is essential for informing investment decisions in the stock market. However, the complexity of various factors influencing stock prices has been widely studied. Traditional methods, which rely on time-series information for a single stock, are incomplete as [...] Read more.
The ability to predict stock prices is essential for informing investment decisions in the stock market. However, the complexity of various factors influencing stock prices has been widely studied. Traditional methods, which rely on time-series information for a single stock, are incomplete as they lack a holistic perspective. The linkage effect in the stock market, where stock prices are influenced by those of associated stocks, necessitates the use of more comprehensive data. Currently, stock relationship information is mainly obtained through industry classification data from third-party platforms, but these data are often approximate and subject to time lag. To address this, this paper proposes a time series relational model (TSRM) that integrates time and relationship information. The TSRM utilizes transaction data of stocks to automatically obtain stock classification through a K-means model and derives stock relationships. The time series information, extracted using long short-term memory (LSTM), and relationship information, extracted with a graph convolutional network (GCN), are integrated to predict stock prices. The TSRM was tested in the Chinese Shanghai and Shenzhen stock markets, with results showing an improvement in cumulative returns by 44% and 41%, respectively, compared to the baseline, and a reduction in maximum drawdown by 4.9% and 6.6%, respectively. Full article
(This article belongs to the Special Issue Economic Model Analysis and Application)
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