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Applications of Quantitative Methods in Business and Economics Research
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Applications of Quantitative Methods in Business and Economics Research

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 73863

Special Issue Editor

Prof. Dr. Vicente Coll-Serrano

E-Mail Website
Guest Editor
Department of Applied Economics, University of Valencia, 46021 Valencia, Spain
Interests: statistics applied to business and economics; data envelopment analysis

Special Issue Information

Dear colleges,

Data we traditionally use in our empirical research comes from sources such as surveys, polls, questionnaires, databases, etc. However, the development of information and communication technologies and the increasing connection of things with the Internet generate an amazingly huge amount of both structured and unstructured data every minute. But the data does not say anything by itself, it does not add value. Raw data needs to be processed, treated, and analyzed using mathematical, statistical, econometric, and/or computational methods to extract the real value, knowledge. This will allow researchers and professionals to obtain substantial improvements in the processes they are engaged in. In summary, the use of quantitative methods in economics and business research helps to understand the socio-economic and business systems, either by creating new models or improving existing ones. In this sense, for example, prediction models (based on conventional or new techniques) can be used to support decision-making processes and improve the results of companies and institutions.

This Special Issue publishes original research articles that apply quantitative methods in fields such as economics, management, marketing, banking and finance, actuarial, accounting, cultural and creative industries, tourism, operations research, and so on. Research articles describing new R software packages that implement novel quantitative techniques are also welcome.

Only high-quality manuscripts that represent real progress in knowledge within their field will be accepted.

Prof. Dr. Vicente Coll-Serrano
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

  • Big data
  • Boosting
  • Causal and predictive modeling
  • Data envelopment analysis
  • Data mining
  • Deep learning
  • Dynamic factor models
  • Multicriteria decision making
  • Multivariate analysis
  • Neuronal network
  • R programming
  • Random forest
  • Recommendation Systems
  • Sentiment analysis
  • Statistical learning
  • Structural equation models
  • Support vector machine
  • Wavelet analysis

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Published Papers (20 papers)

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Research

33 pages, 628 KiB  
Article
Affine Term Structure Models: Applications in Portfolio Optimization and Change Point Detection
by Konstantinos Bisiotis, Stelios Psarakis and Athanasios N. Yannacopoulos
Mathematics 2022, 10(21), 4094; https://doi.org/10.3390/math10214094 - 3 Nov 2022
Viewed by 2494
Abstract
Affine term structure models are widely used for studying the relationship between yields on assets of different maturities. However, it can be a helpful tool for the construction of fixed-income portfolios. The monitoring of these bond portfolios is of great importance for the [...] Read more.
Affine term structure models are widely used for studying the relationship between yields on assets of different maturities. However, it can be a helpful tool for the construction of fixed-income portfolios. The monitoring of these bond portfolios is of great importance for the investor. The purpose of this work is twofold. Firstly, we construct and optimize fixed-income portfolios using Markowitz’s portfolio approach to a multifactor Gaussian affine term structure model (ATSM) under no-arbitrage conditions estimated with the minimum chi square estimation method. The fixed-income portfolios based on the term structure model are compared with some benchmark portfolio strategies, and our findings show that our proposed approach performs well under the risk–return tradeoff. Secondly, we propose control chart procedures for monitoring the optimal weights of government bond portfolios in order to detect possible changes. The results indicate that control chart procedures can be useful in the detection of changes in the optimal asset allocation of fixed income portfolios. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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23 pages, 3285 KiB  
Article
Comparative Study on Lower-Middle-, Upper-Middle-, and Higher-Income Economies of ASEAN for Fiscal and Current Account Deficits: A Panel Econometric Analysis
by Maran Marimuthu, Hanana Khan and Romana Bangash
Mathematics 2022, 10(18), 3259; https://doi.org/10.3390/math10183259 - 7 Sep 2022
Viewed by 1812
Abstract
For the last three decades, ASEAN has been facing a persistent fiscal deficit. However, the impact of fiscal deficit on the current account deficit in the sub-groups of ASEAN is still unknown. This study aims to investigate the impact of fiscal deficit on [...] Read more.
For the last three decades, ASEAN has been facing a persistent fiscal deficit. However, the impact of fiscal deficit on the current account deficit in the sub-groups of ASEAN is still unknown. This study aims to investigate the impact of fiscal deficit on current account deficit and their relationship among the three sub-groups of ASEAN which are based on gross national income (GNI), i.e., lower-middle-, upper-middle-, and higher-income countries. The analysis covers the panel data collected over the span of the last three decades (1990–2020) for ten Southeast Asian nations (ASEAN). The analyses incorporate the panel methodology for data analysis such as panel unit root for checking data stationarity, cointegration testing, panel autoregressive distributed lag (PARDL) for short- and long-run analysis, cointegration regression (fully modified and dynamic ordinary least squares) for significance, the panel Dumitrescu and Hurlin Granger causality test for examining causal relationships in tested variables, and stability diagnostics and CUMSUM and CUSUMSQ techniques for structural breaks and coefficient stability in the model. In lower-middle-income economies (LMIE), results indicate the existence of a unidirectional causal relationship from the current account deficit (CAD) to the fiscal deficit (FD), suggesting a reverse causal relationship from CAD to FD. In the long run, FD does not significantly induce CAD, while real interest rate (RIR) and exchange rate (EXC) influence CAD. In upper-middle-income economies (UMIE), results specify that there is no causality between FD and CAD. The RIR, EXC, and FD are significant to CAD in the long run. In higher-income economies (HIE), RIR and FD have an influence on CAD in the long run period. Moreover, from CAD to FD, a unidirectional causal association exists, and likewise for LMIE. This is a reverse causal relationship from CAD to the FD, supporting the current account targeting hypothesis (CATH) in both the LMIE and HIE groups. This study recommends that the LMIE and HIE groups can use the fiscal deficit as a tool to eliminate the unfavorable current account position. Policymakers can target EXC and RIR to stabilize CAD in long run. In UMIE and HIE, policymakers must consider FD alarming, as it can induce CAD in the long run. The RIR can be the targeted factor in the sub-groups of ASEAN. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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22 pages, 403 KiB  
Article
Sparse Index Tracking Portfolio with Sector Neutrality
by Yuezhang Che, Shuyan Chen and Xin Liu
Mathematics 2022, 10(15), 2645; https://doi.org/10.3390/math10152645 - 28 Jul 2022
Cited by 2 | Viewed by 2088
Abstract
As a popular passive investment strategy, a sparse index tracking strategy has advantages over a full index replication strategy because of higher liquidity and lower transaction costs. Sparsity and nonnegativity constraints are usually assumed in the construction of portfolios in sparse index tracking. [...] Read more.
As a popular passive investment strategy, a sparse index tracking strategy has advantages over a full index replication strategy because of higher liquidity and lower transaction costs. Sparsity and nonnegativity constraints are usually assumed in the construction of portfolios in sparse index tracking. However, none of the existing studies considered sector risk exposure of the portfolios that prices of stocks in one sector may fall at the same time due to sudden changes in policy or unexpected events that may affect the whole sector. Therefore, sector neutrality appeals to be critical when building a sparse index tracking portfolio if not using full replication. The statistical approach to sparse index tracking is a constrained variable selection problem. However, the constrained variable selection procedure using Lasso fails to produce a sparse portfolio under sector neutrality constraints. In this paper, we propose a high-dimensional constrained variable selection method using TLP for building index tracking portfolios under sparsity, sector neutrality and nonnegativity constraints. Selection consistency is established for the proposed method, and the asymptotic distribution is obtained for the sparse portfolio weights estimator. We also develop an efficient iteration algorithm for the weight estimation. We examine the performance of the proposed methodology through simulations and an application to the CSI 300 index of China. The results demonstrate the validity and advantages of our methodology. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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19 pages, 474 KiB  
Article
Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions
by Shengkun Xie and Rebecca Luo
Mathematics 2022, 10(10), 1630; https://doi.org/10.3390/math10101630 - 11 May 2022
Cited by 4 | Viewed by 3511
Abstract
Predictive modeling is a critical technique in many real-world applications, including auto insurance rate-making and the decision making of rate filings review for regulation purposes. It is also important in predicting financial and economic risk in business and economics. Unlike testing hypotheses in [...] Read more.
Predictive modeling is a critical technique in many real-world applications, including auto insurance rate-making and the decision making of rate filings review for regulation purposes. It is also important in predicting financial and economic risk in business and economics. Unlike testing hypotheses in statistical inference, results obtained from predictive modeling serve as statistical evidence for the decision making of the underlying problem and discovering the functional relationship between the response variable and the predictors. As a result of this, the variable importance measures become an essential aspect of helping to better understand the contributions of predictors to the built model. In this work, we focus on the study of using generalized linear models (GLM) for the size of loss distributions. In addition, we address the problem of measuring the importance of the variables used in the GLM to further evaluate their potential impact on insurance pricing. In this regard, we propose to shift the focus from variable importance measures of factor levels to factors themselves and to develop variable importance measures for factors included in the model. Therefore, this work is exclusively for modeling with categorical variables as predictors. This work contributes to the further development of GLM modeling to make it even more practical due to this added value. This study also aims to provide benchmark estimates to allow for the regulation of insurance rates using GLM from the variable importance aspect. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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11 pages, 1780 KiB  
Article
Research on Multicriteria Decision-Making Scheme of High-Speed Railway Express Product Pricing and Slot Allocation under Competitive Conditions
by Yuxuan Fang, Xiaodong Zhang and Yueyi Li
Mathematics 2022, 10(9), 1610; https://doi.org/10.3390/math10091610 - 9 May 2022
Cited by 8 | Viewed by 1779
Abstract
Scientifically and reasonably pricing products and allocating slots are key to improving the profitability and competitiveness of high-speed railway expresses. In this research, we focus on the freight transportation pricing and slot allocation of high-speed railway express companies in a competitive environment. The [...] Read more.
Scientifically and reasonably pricing products and allocating slots are key to improving the profitability and competitiveness of high-speed railway expresses. In this research, we focus on the freight transportation pricing and slot allocation of high-speed railway express companies in a competitive environment. The goals of this research are to make decisions on the pricing and slot allocation schemes of the high-speed railway express between sections through the method of multicriteria decision making, and then to test these changes in reality. This research innovatively takes the high-speed freight Electric Multiple Unit (EMU) train as its research object and innovatively applies the revenue management theory to high-speed railway express research in a competitive environment, proposing a comprehensive decision-making model based on the sharing rate model. The results show that, by adopting the scheme proposed in this research, the income of high-speed railway express companies can be increased by 13.6%. In addition, the method proposed in this research also enriches the current theory on high-speed railway freight transportation, providing strategies for companies to expand their market and increase profit. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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18 pages, 865 KiB  
Article
Machinery Lean Manufacturing Tools for Improved Sustainability: The Mexican Maquiladora Industry Experience
by Jorge Luis García Alcaraz, Adrián Salvador Morales García, José Roberto Díaz Reza, Julio Blanco Fernández, Emilio Jiménez Macías and Rita Puig i Vidal
Mathematics 2022, 10(9), 1468; https://doi.org/10.3390/math10091468 - 27 Apr 2022
Cited by 16 | Viewed by 3578
Abstract
This paper reports a structural equation model (SEM) to quantify the relationship between Lean Manufacturing (LM) tools associated with machinery and sustainability. The LM tools are independent variables and include Total Productive Maintenance (TPM), Jidoka, and overall equipment effectiveness (OEE), whereas dependent sustainability [...] Read more.
This paper reports a structural equation model (SEM) to quantify the relationship between Lean Manufacturing (LM) tools associated with machinery and sustainability. The LM tools are independent variables and include Total Productive Maintenance (TPM), Jidoka, and overall equipment effectiveness (OEE), whereas dependent sustainability variables comprise environmental, social, and economic sustainability. The SEM proposes ten hypotheses, tested statistically using information from 239 responses to a questionnaire applied to the Mexican maquiladora industry and the Partial Least Squares (PLS) technique for quantifying relationships among variables. Additionally, we discuss conditional probabilities to explain how low and high levels of TPM, Jidoka, and OEE impact sustainability. Findings reveal that TPM, Jidoka, and OEE directly impact social, environmental, and economic sustainability, thus indicating that safe workplaces improve employee commitment, safety, delivery time, and morale. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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17 pages, 2448 KiB  
Article
The Impact of Interregional Collaboration on Multistage R&D Productivity and Their Interregional Gaps in Chinese Provinces
by Xionghe Qin
Mathematics 2022, 10(8), 1310; https://doi.org/10.3390/math10081310 - 14 Apr 2022
Cited by 2 | Viewed by 1621
Abstract
Interregional collaboration is a core element of Chinese innovation policy, as it accelerates the knowledge recombination across geographic boundaries and promotes regional R&D performance. This study emphasizes interregional collaboration and investigates its effect on R&D productivity using 2009–2017 panel data for 30 Chinese [...] Read more.
Interregional collaboration is a core element of Chinese innovation policy, as it accelerates the knowledge recombination across geographic boundaries and promotes regional R&D performance. This study emphasizes interregional collaboration and investigates its effect on R&D productivity using 2009–2017 panel data for 30 Chinese provinces. Furthermore, it examines the relationship between interregional research collaboration and interregional gaps of R&D productivity based on a multistage perspective. Our findings reveal that although interregional collaboration and R&D productivity in China constantly improved during the study period, there is to some extent a mismatch in their spatial distribution. We find that interregional collaboration is required to support overall R&D productivity. We also emphasize that interregional collaboration contributes more to narrowing the interregional gaps of knowledge productivity (rather than technology transfer productivity). Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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26 pages, 3010 KiB  
Article
Outcomes of Industry–University Collaboration in Open Innovation: An Exploratory Investigation of Their Antecedents’ Impact Based on a PLS-SEM and Soft Computing Approach
by Călin Florin Băban, Marius Băban and Adalberto Rangone
Mathematics 2022, 10(6), 931; https://doi.org/10.3390/math10060931 - 14 Mar 2022
Cited by 7 | Viewed by 3224
Abstract
The outcomes of industry–university collaboration, in an open innovation context, may be of great support to firms, in their response to the challenges of today’s highly competitive environment. However, there is no empirical evidence on how these outcomes are influenced by their antecedents. [...] Read more.
The outcomes of industry–university collaboration, in an open innovation context, may be of great support to firms, in their response to the challenges of today’s highly competitive environment. However, there is no empirical evidence on how these outcomes are influenced by their antecedents. Aiming to fill this gap, a research model to investigate the impact of the major antecedents, identified in the literature as motives, barriers and knowledge transfer channels on the beneficial outcomes and drawbacks of open innovation between the two organizations was developed in this study. The research model was empirically assessed, using a dual-stage predictive approach, based on PLS-SEM and soft computing constituents (artificial neural networks and adaptive neuro-fuzzy inference systems). PLS-SEM was successfully used to test the hypotheses of the research model, while the soft computing approach was employed to predict the complex dependencies between the outcomes and their antecedents. Insights into the relative importance of the antecedents, in shaping the open innovation outcomes, were provided through the importance–performance map analysis. The findings revealed the antecedents that had a significant positive impact on both the beneficial outcomes and drawbacks of industry–university collaboration, in open innovation. The results also highlighted the predictor importance in the research model, as well as the relative importance of the antecedent constructs, based on their effects on the two analyzed outcomes. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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25 pages, 1176 KiB  
Article
Predicting the Entrepreneurial Behaviour of Starting Up a New Company: A Regional Study Using PLS-SEM and Data from the Global Entrepreneurship Monitor
by José Alberto Martínez-González, Carmen Dolores Álvarez-Albelo, Javier Mendoza-Jiménez and Urszula Kobylinska
Mathematics 2022, 10(5), 704; https://doi.org/10.3390/math10050704 - 23 Feb 2022
Cited by 5 | Viewed by 3474
Abstract
It is essential to understand the variables that explain and predict the behaviour of starting up a new company in a regional context. This study aims to analyse the theoretical basis and predictive potential of the Global Entrepreneurship Monitor (GEM) data, considering the [...] Read more.
It is essential to understand the variables that explain and predict the behaviour of starting up a new company in a regional context. This study aims to analyse the theoretical basis and predictive potential of the Global Entrepreneurship Monitor (GEM) data, considering the concerns and suggestions of other authors. In addition to an extensive literature review, a PLS-SEM methodology and data on variables and countries from the latest GEM report are used in this study. The results show that GEM reports have a sufficient theoretical foundation for quality studies in this field. In addition, a valid and reliable causal model is designed that includes all personal and contextual GEM variables. The hypotheses of the proposed model are based on the existing causal relationships in the literature, using GEM data in its formulation. The model is comprehensive and practical because it significantly predicts entrepreneurial behaviour, particularly entrepreneurial intention and action. The usefulness of this study is high, both for researchers, practitioners and institutions wishing to understand better and further promote entrepreneurial behaviour at a regional (country) level. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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18 pages, 1096 KiB  
Article
Deterministic Chaos Detection and Simplicial Local Predictions Applied to Strawberry Production Time Series
by Juan D. Borrero and Jesus Mariscal
Mathematics 2021, 9(23), 3034; https://doi.org/10.3390/math9233034 - 26 Nov 2021
Cited by 2 | Viewed by 1667
Abstract
In this work, we attempted to find a non-linear dependency in the time series of strawberry production in Huelva (Spain) using a procedure based on metric tests measuring chaos. This study aims to develop a novel method for yield prediction. To do this, [...] Read more.
In this work, we attempted to find a non-linear dependency in the time series of strawberry production in Huelva (Spain) using a procedure based on metric tests measuring chaos. This study aims to develop a novel method for yield prediction. To do this, we study the system’s sensitivity to initial conditions (exponential growth of the errors) using the maximal Lyapunov exponent. To check the soundness of its computation on non-stationary and not excessively long time series, we employed the method of over-embedding, apart from repeating the computation with parts of the transformed time series. We determine the existence of deterministic chaos, and we conclude that non-linear techniques from chaos theory are better suited to describe the data than linear techniques such as the ARIMA (autoregressive integrated moving average) or SARIMA (seasonal autoregressive moving average) models. We proceed to predict short-term strawberry production using Lorenz’s Analog Method. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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20 pages, 1823 KiB  
Article
Multi-Criteria Analysis for Business Location Decisions
by Virginia Perez-Benitez, German Gemar and Mónica Hernández
Mathematics 2021, 9(20), 2615; https://doi.org/10.3390/math9202615 - 17 Oct 2021
Cited by 6 | Viewed by 3795
Abstract
Choosing the physical place in which to locate a company or make investments is a strategic decision that managers must make when their business activities begin and as they expand. These decisions are key to firms’ survival. This study sought to shed light [...] Read more.
Choosing the physical place in which to locate a company or make investments is a strategic decision that managers must make when their business activities begin and as they expand. These decisions are key to firms’ survival. This study sought to shed light on this decision problem and assist managers in making these decisions. The first research objective was to examine the different dimensions that decision makers should consider regarding locations. The second objective was to test the efficacy of multi-criteria analysis methods regarding this decision problem. More specifically, this study applied a combination of the preference ranking organization method for enrichment of evaluations and the geometric analysis for interactive aid method, complemented by the analytical hierarchy process. The last objective was to rank major European cities on their suitability as business locations. The results include a preferential ranking of 66 European cities. London is the best positioned in all dimensions, followed by Paris and Barcelona. The findings’ originality comes from the inclusion of dimensions such as climate, security, and technology, which are given little weight in other similar indices, as well as the fresh approach to this decision problem from a business perspective and the combination of methodologies. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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21 pages, 8601 KiB  
Article
Sustainability, Big Data and Mathematical Techniques: A Bibliometric Review
by Matilde Lafuente-Lechuga, Javier Cifuentes-Faura and Ursula Faura-Martínez
Mathematics 2021, 9(20), 2557; https://doi.org/10.3390/math9202557 - 13 Oct 2021
Cited by 7 | Viewed by 2995
Abstract
This article has reviewed international research, up to the first half of 2021, focused on sustainability, big data and the mathematical techniques used for its analysis. In addition, a study of the spatial component (city, region, nation and beyond) of the works has [...] Read more.
This article has reviewed international research, up to the first half of 2021, focused on sustainability, big data and the mathematical techniques used for its analysis. In addition, a study of the spatial component (city, region, nation and beyond) of the works has been carried out and an analysis has been made of which Sustainable Development Goals (SDGs) have received the most attention. A bibliometric analysis and a fractal cluster analysis were performed on the papers published in the Web of Science. The results show a continuous increase in the number of published articles and citations over the whole period, demonstrating a growing interest in this topic. China, the United States and India are the most productive countries and there are more papers at the regional level. It has been found that the environmental dimension is the most studied and the least studied is the social dimension. The mathematical techniques used in the empirical work are mainly regression analysis, neural networks and multi-criteria decision methods. SDG9 and SDG11 are the most worked on. The trend shows a convergence in recent years towards big data applied to supply chains, Industry 4.0 and the achievement of sustainable cities. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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19 pages, 668 KiB  
Article
An Academic Performance Indicator Using Flexible Multi-Criteria Methods
by Olga Blasco-Blasco, Marina Liern-García, Aarón López-García and Sandra E. Parada-Rico
Mathematics 2021, 9(19), 2396; https://doi.org/10.3390/math9192396 - 26 Sep 2021
Cited by 4 | Viewed by 2340
Abstract
Composite indicators are a very useful tool for conveying summary information on the overall performance of institutions and facilitating decision-making. Increasingly, there is a demand for indicators that allow performance to be assessed after the implementation of a strategy. This has several difficulties, [...] Read more.
Composite indicators are a very useful tool for conveying summary information on the overall performance of institutions and facilitating decision-making. Increasingly, there is a demand for indicators that allow performance to be assessed after the implementation of a strategy. This has several difficulties, and in this paper, we address three of them: how to evaluate at different points in time, how to estimate the weighting of the criteria and how to normalize the data. Our proposal is based on multicriteria techniques, using a recent method, uwTOPSIS, and is applied to data collected from 2975 students enrolled in the first year of science and engineering at the Industrial University of Santander (Colombia) from the first semester of 2016 to the first semester of 2019. In the paper, we show that our proposal makes it possible to measure and evaluate the academic performance of students at two points in time, and this allows the University to know whether its student support policy has been successful and to what degree it has been effective. Due to the large amount of data handled, data management has been done using R programming language, and model implementation has been done with Python. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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27 pages, 1956 KiB  
Article
Seasonality in Tourism: Do Senior Programs Mitigate It?
by Paz Rico, Bernardí Cabrer-Borrás and Francisco Morillas-Jurado
Mathematics 2021, 9(16), 2003; https://doi.org/10.3390/math9162003 - 21 Aug 2021
Cited by 7 | Viewed by 2707
Abstract
Seasonality is a widely recognised and accredited phenomenon known to cause an imbalance in tourism activity throughout the year, prompting tourist destinations, both public and private, to consider how best to plan the use of their resources. One way of mitigating the economic [...] Read more.
Seasonality is a widely recognised and accredited phenomenon known to cause an imbalance in tourism activity throughout the year, prompting tourist destinations, both public and private, to consider how best to plan the use of their resources. One way of mitigating the economic imbalances that seasonality can cause is to find strategies for seasonal adjustment, such as travel programmes aimed at the elderly. This paper analyses the seasonality of tourism activity in some EU countries, and in particular in Spain. Different indicators are used to compare the results and carry out a sensitivity analysis. The study then focuses on tourism programmes for the elderly in Spain to see whether this type of programme helps to alleviate the seasonality of tourism activity. To corroborate this, an econometric model is specified and estimated, which enables the scope of these programmes to be compared. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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9 pages, 449 KiB  
Article
Constructing an Adoption Model of Proactive Environmental Strategy: A Novel Quantitative Method of the Multi-Level Growth Curve Model
by Stanley Y. B. Huang, Shih-Chin Lee and Yue-Shi Lee
Mathematics 2021, 9(16), 1962; https://doi.org/10.3390/math9161962 - 17 Aug 2021
Cited by 11 | Viewed by 2688
Abstract
To fill in the literature flaws that have not been detected in previous studies, this research, therefore, examines the driving factors of proactive environmental strategy (PES). First, this research proposes how corporate social responsibility (CSR) predicts the agricultural company’s PES through the intermediary [...] Read more.
To fill in the literature flaws that have not been detected in previous studies, this research, therefore, examines the driving factors of proactive environmental strategy (PES). First, this research proposes how corporate social responsibility (CSR) predicts the agricultural company’s PES through the intermediary mechanism of green organization identification (GOI) of the top management team (TMT) according to symbolic context and theory of high-level echelon, to solve the first gap in exploring what factors can drive the PES. Second, this research proposes a multi-level growth curve model (MGCM) to solve how individuals adjust their behavioral intentions over time according to their translation and understanding of their use environment, because past studies consist of almost cross-sectional properties. Third, past research has also neglected the multi-level framework, leading to hierarchical reasoning bias. Therefore, this research believes that the MGCM can fill in the multi-level gap. Finally, this research collected 400 TMT employees from 100 different agricultural companies in Taiwan in three-stage time for six months. The results show that CSR will significantly lead to more growth in GOI, and more growth in GOI will lead to more growth in PES adoption. The research results can not only advance the agricultural sustainability literature but also serve as a guide for agricultural companies to implement PES. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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26 pages, 5979 KiB  
Article
Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries
by Michal Pavlicko, Marek Durica and Jaroslav Mazanec
Mathematics 2021, 9(16), 1886; https://doi.org/10.3390/math9161886 - 8 Aug 2021
Cited by 21 | Viewed by 3341
Abstract
The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial [...] Read more.
The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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18 pages, 890 KiB  
Article
Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility
by Eduardo Ramos-Pérez, Pablo J. Alonso-González and José Javier Núñez-Velázquez
Mathematics 2021, 9(15), 1794; https://doi.org/10.3390/math9151794 - 28 Jul 2021
Cited by 25 | Viewed by 10122
Abstract
Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. [...] Read more.
Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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22 pages, 1004 KiB  
Article
Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company
by Mohammad Farhan Khan, Farnaz Haider, Ahmed Al-Hmouz and Mohammad Mursaleen
Mathematics 2021, 9(12), 1369; https://doi.org/10.3390/math9121369 - 12 Jun 2021
Cited by 1 | Viewed by 3324
Abstract
Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for [...] Read more.
Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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20 pages, 1753 KiB  
Article
The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach
by Maria-Carmen García-Centeno, Román Mínguez-Salido and Raúl del Pozo-Rubio
Mathematics 2021, 9(11), 1170; https://doi.org/10.3390/math9111170 - 22 May 2021
Cited by 1 | Viewed by 2271
Abstract
The financial catastrophe resulting from the out-of-pocket payments necessary to access and use healthcare systems has been widely studied in the literature. The aim of this work is to predict the impact of the financial catastrophe a household will face as a result [...] Read more.
The financial catastrophe resulting from the out-of-pocket payments necessary to access and use healthcare systems has been widely studied in the literature. The aim of this work is to predict the impact of the financial catastrophe a household will face as a result of out-of-pocket payments in long-term care in Spain. These predictions were made using machine learning techniques such as LASSO (Least Absolute Shrinkage and Selection Operator) penalized regression and elastic-net, as well as algorithms like k-nearest neighbors (KNN), MARS (Multivariate Adaptive Regression Splines), random forest, boosted trees and SVM (Support Vector Machine). The results reveal that all the classification methods performed well, with the complex models performing better than the simpler ones and showing no evidence of overfitting. Detecting and defining the profiles of individuals and families most likely to suffer from financial catastrophe is crucial in enabling the design of financial policies aimed at protecting vulnerable groups. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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25 pages, 6671 KiB  
Article
Multi-Attribute Online Decision-Making Driven by Opinion Mining
by Azra Shamim, Muhammad Ahsan Qureshi, Farhana Jabeen, Misbah Liaqat, Muhammad Bilal, Yalew Zelalem Jembre and Muhammad Attique
Mathematics 2021, 9(8), 833; https://doi.org/10.3390/math9080833 - 11 Apr 2021
Cited by 4 | Viewed by 2444
Abstract
With the evolution of data mining systems, the acquisition of timely insights from unstructured text is an organizational demand which is gradually increasing. The existing opinion mining systems have a variety of properties, such as the ranking of products’ features and feature level [...] Read more.
With the evolution of data mining systems, the acquisition of timely insights from unstructured text is an organizational demand which is gradually increasing. The existing opinion mining systems have a variety of properties, such as the ranking of products’ features and feature level visualizations; however, organizations require decision-making based upon customer feedback. Therefore, an opinion mining system is proposed in this work that ranks reviews and features based on novel ranking schemes with innovative opinion-strength-based feature-level visualization, which are tightly coupled to empower users to spot imperative product features and their ranking from enormous reviews. Enhancements are made at different phases of the opinion mining pipeline, such as innovative ways to evaluate review quality, rank product features and visualize opinion-strength-based feature-level summary. The target user groups of the proposed system are business analysts and customers who want to explore customer comments to gauge business strategies and purchase decisions. Finally, the proposed system is evaluated on a real dataset, and a usability study is conducted for the proposed visualization. The results demonstrate that the incorporation of review and feature ranking can improve the decision-making process. Full article
(This article belongs to the Special Issue Applications of Quantitative Methods in Business and Economics Research)
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