Special Issue "Application of Quantitative Methods in Modelling Sustainability in Economics and Finance"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Marek Durica
E-Mail Website1 Website2
Guest Editor
Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 010 26 Zilina, Slovakia
Interests: quantitative methods in economics; data mining; predictive modeling; applied multivariate statistics; econometrics
Dr. Lucia Svabova
E-Mail Website1 Website2
Guest Editor
Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 010 26 Zilina, Slovak Republic
Interests: data analysis; statistical analysis; econometrics; counterfactual evaluations; financial derivatives
Special Issues and Collections in MDPI journals
Dr. Jaroslav Frnda
E-Mail Website1 Website2 Website3
Guest Editor
Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 010 26 Zilina, Slovakia
Interests: modeling; machine learning; data analysis; service quality prediction
Dr. Katarina Kramarova
E-Mail Website1 Website2
Guest Editor
Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 010 26 Zilina, Slovakia
Interests: financial analysis and accounting; risk management and insurance; business administration

Special Issue Information

Dear Colleagues,

We would like to invite you to participate in the Special Issue on “Application of Quantitative Methods in Modeling Sustainability in Economics and Finance” in the Sustainability journal .

Since the middle of the 20th century, there has been a visible effort to develop new mathematical models, quantitative methods, and data-mining tools for application in economics and finance. In recent years, the application of these approaches in economics and finance has attracted much attention. A wide range of quantitative methods, based on mathematical, probabilistic, statistical, and econometric principles are being applied and developed in various economic phenomena and processes. In addition, the huge development of computer technology has brought forth more recent methods of artificial intelligence, big data, and machine learning techniques such as decision trees, and back-propagation neural networks or self-organizing maps have been and are still being created and applied. However, it is still necessary to continue developing new quantitative tools and applying already developed methods to better understand and analyze economic and financial processes and problems. Achieving stability of the economic environment in terms of financial and social sustainability of economic entities is the goal of almost all research.

The purpose of this Special Issue is to bring together a collection of articles reflecting recent developments in various areas of economy and finance, in which quantitative methods and data mining techniques going hand in hand with the practical side of solved issues play an important role. This Special Issue aims to be a collection of studies that develop new knowledge and/or apply approaches based on quantitative methods, mathematical–statistical and econometrics models, probabilistic models, and various artificial intelligence-based approaches. Contributions focused on the application of valuation financial instruments are of interest. Studies dealing with the application of current methods of artificial networks in evaluation and forecasting in economics, insurance, and finance, which support decision-making processes in companies or management of processes in them, are also welcome.

We invite both theoretical and empirical contributions of authors who would be interested in contributing to the development of the subject issue and who would appreciate this Special Issue as an opportunity to discuss their research results whether with theorists or practitioners interested in the topic.

Dr. Marek Durica
Dr. Lucia Svabova
Dr. Jaroslav Frnda
Dr. Katarina Kramarova
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 papers will be 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. Sustainability 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 1900 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

  • modeling of economic and finance processes
  • quantitative methods in economics and finance
  • financial derivatives
  • insurance models
  • company evaluation
  • predictive models
  • neural networks
  • data mining
  • machine learning
  • applied statistical models
  • econometrics models

Published Papers (8 papers)

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Research

Article
An Analysis of the Impact of Market Segmentation on Energy Efficiency: A Spatial Econometric Model Applied in China
Sustainability 2021, 13(14), 7659; https://doi.org/10.3390/su13147659 - 08 Jul 2021
Viewed by 473
Abstract
China’s recent development has been nothing short of remarkable, but energy-saving, and environmental protection is still a serious problem. The improvement of energy efficiency (EE) is an important factor for China to better follow the path of energy conservation, sustainable development, and environmental [...] Read more.
China’s recent development has been nothing short of remarkable, but energy-saving, and environmental protection is still a serious problem. The improvement of energy efficiency (EE) is an important factor for China to better follow the path of energy conservation, sustainable development, and environmental protection. Meanwhile, market segmentation is a unique phenomenon in the process of China’s economic development. Hence, studying market segmentation on energy efficiency has positive significance for improving energy efficiency. The major objective of this study is to investigate the relationship between EE and market segmentation. This paper measures market segmentation by the Price-Based Approach, calculating EE by super slack-based measure (super-SBM), and integrated spatial Durbin model and geographically weighted regression model. Based on the panel data of 30 provinces in China from 1995 to 2018, this paper finds that: (1) Regional market segmentation has a significant negative effect on EE. Moreover, in terms of spatial effect, market segmentation has a positive spatial spillover on EE estimated by 0-1 matrix suggesting that market segmentation in the surrounding area has a positive impact on local EE. (2) The negative effect of Market segmentation on EE demonstrates the obvious regional difference: Eastern region > central region > western region. In addition, geographically weighted regression results show that the impact of market segmentation on EE shows that in regional spatial distribution, Shanghai, Jiangsu, Zhejiang, and Anhui have the strongest negative effect, second in Fujian, Jiangxi, Shandong, Henan, Hubei, Beijing, Tianjin, and Hebei. (3) This paper confirms that market segmentation can affect EE through local protectionism, technological difference, and scale effect. Finally, through the above research basis, put forward the corresponding policy suggestions. Full article
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Article
Green Strategy Effect on Financial and Environmental Performance: A Mediation Analysis of Product Quality
Sustainability 2021, 13(4), 2115; https://doi.org/10.3390/su13042115 - 16 Feb 2021
Cited by 1 | Viewed by 706
Abstract
The dilemma of firms in developing economies was the crux of this study. In probing whether the adoption of organization-wide green strategy would enhance the product quality and the firm’s financial lifeline, while also improving the environment, we developed a mediation model. The [...] Read more.
The dilemma of firms in developing economies was the crux of this study. In probing whether the adoption of organization-wide green strategy would enhance the product quality and the firm’s financial lifeline, while also improving the environment, we developed a mediation model. The specific objectives were to ascertain the direct effect of green strategy on both environmental and financial performance and its total effect on both environmental and financial performance through product quality. With data collated and analyzed from 648 respondents, using the Hayes mediation approach, results show that while environmental performance is strongly predicted by green strategy and product quality (as a mediator), financial performance is also positively predicted, but by a smaller effect. The import of the findings of this study is that the adoption of green strategy mediated by product quality improves both environmental and financial performance, implying that firms can remain financially viable while adopting product-focused green strategy. Full article
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Article
Directional Distance Function Technical Efficiency of Chili Production in Thailand
Sustainability 2021, 13(2), 741; https://doi.org/10.3390/su13020741 - 14 Jan 2021
Cited by 1 | Viewed by 495
Abstract
To overcome the challenging food safety and security problem, in 2003, the Thai government initiated ‘Good Agricultural Practices’ (GAP) technology. This paper used a sample of 107 small chili farms from the Chiyaphoom province for the 2012 crop year, and data envelopment analysis [...] Read more.
To overcome the challenging food safety and security problem, in 2003, the Thai government initiated ‘Good Agricultural Practices’ (GAP) technology. This paper used a sample of 107 small chili farms from the Chiyaphoom province for the 2012 crop year, and data envelopment analysis (DEA) meta-frontier directional distance function technique to answer two questions: (1) Are GAP-adopting farms, on average, more efficient than conventional farms? (2) Does access to GAP technology affect farmers’ decisions to adopt GAP technology? We also developed an ‘indirect’ approach to reduce the potential sample selection bias for small samples. For the dry-season subsample, GAP farms were more technically efficient when compared with non-GAP farms. These dry-season non-GAP farms may not adopt the GAP method because they have limited access to GAP technology. For the rainy-season subsample, on average, GAP farms were more efficient than non-GAP farms at the 5% level. Access to the GAP technology is not a possible reason for non-GAP rainy season farms to not adopt the GAP technology. To enable sustainable development, government agencies and nongovernmental organizations (NGOs) must develop and implement appropriate educational and training workshops to promote and assist GAP technology adoption for chili farms in Thailand. Full article
Article
Impact of Sustainability on Firm Value and Financial Performance in the Air Transport Industry
Sustainability 2020, 12(23), 9957; https://doi.org/10.3390/su12239957 - 28 Nov 2020
Cited by 2 | Viewed by 913
Abstract
In this study, we examine the extent to which the implementation of environmental, social, and governance (ESG) disclosures influence the firm value and financial performance of airlines. The panel data analysis is applied to the set of collected data from the Thomson Reuters [...] Read more.
In this study, we examine the extent to which the implementation of environmental, social, and governance (ESG) disclosures influence the firm value and financial performance of airlines. The panel data analysis is applied to the set of collected data from the Thomson Reuters Eikon database for the sample of 27 airlines worldwide from 2013 to 2019. Findings of this study support the positive relationship between the environmental pillar score (Env) and governance pillar score (Gov), with market-to-book ratio and Tobin’s Q as proxies for firm value and financial performance, respectively. This finding implies that an increase in both pillars leads to higher market value and financial efficiency for investigated airlines. Therefore, an airline’s effort to improve Env and Gov dimensions will lead to higher market value and return on invested funds. In contrast, the social pillar disclosure in both models is found to have a significant negative association with the dependent variables, showing that airlines’ social activities result in lower value as well as level of performance. Full article
Article
Nuclear Hazard and Asset Prices: Implications of Nuclear Disasters in the Cross-Sectional Behavior of Stock Returns
Sustainability 2020, 12(22), 9721; https://doi.org/10.3390/su12229721 - 21 Nov 2020
Viewed by 559
Abstract
Using stock return data for the Japanese equity market, for the period from July 1983 to June 2018, we analyze the effect of major nuclear disasters worldwide on Japanese discount rates. For that purpose, we compare the performance of the capital asset pricing [...] Read more.
Using stock return data for the Japanese equity market, for the period from July 1983 to June 2018, we analyze the effect of major nuclear disasters worldwide on Japanese discount rates. For that purpose, we compare the performance of the capital asset pricing model (CAPM) conditional on the event of nuclear disasters with that of the classic CAPM and the Fama–French three- and five-factor models. In order to control for nuclear disasters, we use an instrument that allows us to parameterize the linear stochastic discount factor of the conditional CAPM and transform the classic CAPM into a three-factor model. In this regard, the use of nuclear disasters as an explanatory variable for the cross-sectional behavior of stock returns is a novel contribution of this research. Our results suggest that nuclear disasters account for a large fraction of the variation of stock returns, allowing the CAPM to perform similarly to the Fama–French three- and five-factor models. Furthermore, our results show that, in general, nuclear disasters are positively related to the expected returns of a large number of assets under study. Our results have important implications for the task of estimating the cost of equity and constitute a step forward in understanding the relationship between equity risk premiums and nuclear disasters. Full article
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Article
Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk
Sustainability 2020, 12(21), 8849; https://doi.org/10.3390/su12218849 - 25 Oct 2020
Viewed by 757
Abstract
Extreme financial events usually lead to sharp jumps in stock prices and volatilities. In addition, jump clustering and stock price correlations contribute to the risk amplification acceleration mechanism during the crisis. In this paper, four Jump-GARCH models are used to forecast the jump [...] Read more.
Extreme financial events usually lead to sharp jumps in stock prices and volatilities. In addition, jump clustering and stock price correlations contribute to the risk amplification acceleration mechanism during the crisis. In this paper, four Jump-GARCH models are used to forecast the jump diffusion volatility, which is used as the risk factor. The linear and asymmetric nonlinear effects are considered, and the value at risk of banks is estimated by support vector quantile regression. There are three main findings. First, in terms of the volatility process of bank stock price, the Jump Diffusion GARCH model is better than the Continuous Diffusion GARCH model, and the discrete jump volatility is significant. Secondly, due to the difference of the sensitivity of abnormal information shock, the jump behavior of bank stock price is heterogeneous. Moreover, CJ-GARCH models are suitable for most banks, while ARJI-R2-GARCH models are more suitable for small and medium sized banks. Thirdly, based on the jump diffusion volatility information, the performance of the support vector quantile regression is better than that of the parametric quantile regression and nonparametric quantile regression. Full article
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Article
Between Sustainable and Temporary Competitive Advantages in the Unstable Business Environment
Sustainability 2020, 12(21), 8832; https://doi.org/10.3390/su12218832 - 24 Oct 2020
Cited by 5 | Viewed by 1517
Abstract
Gaining a competitive advantage assumes that a company should build a unique position, but this concept is related to a relatively stable environment. However, it is difficult to predict the consequences of the enterprises’ changes, leading to changes both in the business and [...] Read more.
Gaining a competitive advantage assumes that a company should build a unique position, but this concept is related to a relatively stable environment. However, it is difficult to predict the consequences of the enterprises’ changes, leading to changes both in the business and natural environment. Therefore, this study’s authors asked a research question: Is it possible to restore a balance between durability and variability of the organization in terms of strategy? The answer to such a question was drawn upon the literature review and survey research. This paper presents a qualitative and quantitative model of competitive advantage in a changing business environment. This article uses an inductive inference method supported by a literature study and a deduction method supported by statistical calculations, based on a survey conducted among 150 Polish companies in different economic sectors. As a result of the research methods, a dualistic competitive advantage model in a changing environment was proposed and discussed. The article’s aim was achieved in the model combining a sustainable (SCA) and temporary competitive advantage (TCA). Understanding the conditions for gaining competitive advantage allowed to formulate general conditions under which sustainable strategic management can be built to consider sustainability objectives and contribute to the green economy. This research has confirmed that building a competitive advantage in unstable conditions requires finding a balance between implementing the planned development strategy and using new opportunities. Full article
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Article
Relationship between International Reserves and FX Rate Movements
Sustainability 2020, 12(17), 6961; https://doi.org/10.3390/su12176961 - 26 Aug 2020
Viewed by 640
Abstract
This paper investigates the relationship between international reserves changes and foreign exchange rate movements for five Far Eastern countries (China, Japan, Taiwan, Hong Kong, and Korea) from January 1997 to May 2020. We use the quantile Granger causality test and the quantile autoregressive [...] Read more.
This paper investigates the relationship between international reserves changes and foreign exchange rate movements for five Far Eastern countries (China, Japan, Taiwan, Hong Kong, and Korea) from January 1997 to May 2020. We use the quantile Granger causality test and the quantile autoregressive model to capture the monetary authorities’ motivations for intervention. The primary results of this study are as follows. First, in China and Hong Kong, we capture the mercantilists’ motive of accumulating their international reserves for the purpose of responding to the appreciation of currencies. Relatively speaking, the monetary authorities’ motivation for precautionary stabilizing their currencies is high in Korea and Japan. Second, we identify the asymmetric causal relationship between the variables. Considering the causal relationship with significant regression coefficients, these characteristics are found to be more evident in all countries. Last, we confirm the properties of the quantile- and tail-dependent relationship between the variables. In particular, Korea has a relatively stronger tail-dependence than other countries. That is, the causal relationship between the Korean foreign exchange reserves and the exchange rate is stronger at the rapid fluctuations of the variables, and this relationship is weakened at the moderate fluctuations of them. Full article
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