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Application of Time Series Analyses in Business Sustainability

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: closed (28 February 2022) | Viewed by 7976

Special Issue Editor


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Guest Editor
Thompson-Hill Chair of Excellence and Professor of Accounting, Fogelman College of Business and Economics, the University of Memphis, Memphis, TN 38152, USA
Interests: auditing; business sustainability; corporate governance; data analytics; financial and managerial accounting; forensic accounting; professional ethics
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Special Issue Information

Dear Colleagues,

The application of and time series analyses in business is currently at an early stage. This Special Issue of the Sustainability Journal is devoted to the publication of high-quality papers in time series analyses. This themed issue welcomes all views on the application of time series analyses in business with all types of papers (emperical, theoretical, & analytical) and case studies. Using time series models and analyses, millions of transactions can be searched to spot patterns and detect abnormalities and irregularities. The emergence of Big Data creates an opportunity to further investigate the application of time series models in business, accounting, and auditing using financial performance information and non-financial sustainability information on environmental, social, and governance performance. The ever-increasing business complexity, corporate governance reforms, risk management, and globalization, along with the growing demand for high-quality financial and non-financial information, necessitate the use of time series analyses to modernize the financial reporting and audit processes. Information and insight that once were not publicly available now extend far beyond traditional financial transactions and reports and expand into data from social media, e-mail, audio, video, and text files.

Submissions should be original work that investigate an aspect of time series and its application in business. The submitted manuscripts for this Special Issue are expected to address the following topics of interest but they are not intended to be exhaustive:

  • The relevance and use of time series analyses for Big Data and business analytics.
  • How time series models can be efficiently and effectively applied in business, accounting, and auditing.
  • Presentation of policy, practical, educational, and research implications of time series analyses.
  • The use of time series analyses by businesses and management in their predictive models of managerial strategies, decisions, and actions.
  • Integration of time series analyses into the curricula of business schools and accounting programs.
  • The use of time series models and data analytics in the age of Big Data by businesses to enable them to make more informed strategic and operational decisions.
  • The use of time series models to transform unstructured and semi-structured data into structured information in improving the quality of financial and non-financial information.
  • Application of time series analyses in advancing business sustainability by presenting an example of the integrated Big Data and time series analyses into environmental, social and governance dimensions of business sustainability.
  • The use of time series analyses in detecting patterns in unstructured data and generates testable research hypotheses in future business, accounting, and auditing research
  • The application of time series in evaluating the feasibility, cost efficiency, and effectiveness of new rules, regulations, as well as accounting and auditing standards.
  • The use of time series in data science algorithms to capture all relevant information for decision making.

Prof. Zabihollah Rezaee
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. 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 2400 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
  • time series
  • data analytics
  • business analytics
  • business sustainability
  • financial information
  • non-financial information
  • corporate governance
  • organizational ethics

Published Papers (3 papers)

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Research

21 pages, 1940 KiB  
Article
Hybrid Stochastic-Grey Model to Forecast the Behavior of Metal Price in the Mining Industry
by Zoran Gligorić, Miloš Gligorić, Dževdet Halilović, Čedomir Beljić and Katarina Urošević
Sustainability 2020, 12(16), 6533; https://0-doi-org.brum.beds.ac.uk/10.3390/su12166533 - 13 Aug 2020
Cited by 8 | Viewed by 1978
Abstract
Accurate metal price forecasting is the precondition for optimal and sustainable mine production planning. This paper combined two methods for time series analysis. The developed model represents the combination of the Grey System Theory and a Stochastic differential equation. More precisely, we added [...] Read more.
Accurate metal price forecasting is the precondition for optimal and sustainable mine production planning. This paper combined two methods for time series analysis. The developed model represents the combination of the Grey System Theory and a Stochastic differential equation. More precisely, we added stochastic term to the first-order whitenization differential equation. Solution of this equation represents the time response function which is capable of creating artificial evolving paths of the metal price. The simulation process resulted in a distribution and adequate expected value at every single point. Further, model efficiency was increased by adding residuals modeled by the Singular Spectrum Analysis method. The model was tested on the monthly lead metal price series. Mean absolute percentage error is 4.37% and the model can be classified as a high-performance model. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business Sustainability)
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24 pages, 8569 KiB  
Article
Multiscale Quantile Correlation Coefficient: Measuring Tail Dependence of Financial Time Series
by Chao Xu, Jinchuan Ke, Xiaojun Zhao and Xiaofang Zhao
Sustainability 2020, 12(12), 4908; https://0-doi-org.brum.beds.ac.uk/10.3390/su12124908 - 16 Jun 2020
Cited by 7 | Viewed by 2364
Abstract
In the context of the frequent occurrence of extreme events, measuring the tail dependence of financial time series is essential for maintaining the sustainable development of financial markets. In this paper, a multiscale quantile correlation coefficient (MQCC) is proposed to measure the tail [...] Read more.
In the context of the frequent occurrence of extreme events, measuring the tail dependence of financial time series is essential for maintaining the sustainable development of financial markets. In this paper, a multiscale quantile correlation coefficient (MQCC) is proposed to measure the tail dependence of financial time series. The new MQCC method consists of two parts: the multiscale analysis and the correlation analysis. In the multiscale analysis, the coarse graining approach is used to study the financial time series on multiple temporal scales. In the correlation analysis, the quantile correlation coefficient is applied to quantify the correlation strength of different data quantiles, especially regarding the difference and the symmetry of tails. One reason to adopt this method is that the conditional distribution of the explanatory variables can be characterized by the quantile regression, rather than simply by the conditional expectation analysis in the traditional regression. By applying the MQCC method in the financial markets of different regions, many interesting results can be obtained. It is worth noting that there are significant differences in tail dependence between different types of financial markets. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business Sustainability)
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17 pages, 1052 KiB  
Article
Application of Time Series Models in Business Research: Correlation, Association, Causation
by Zabihollah Rezaee, Sara Aliabadi, Alireza Dorestani and Nick J. Rezaee
Sustainability 2020, 12(12), 4833; https://0-doi-org.brum.beds.ac.uk/10.3390/su12124833 - 12 Jun 2020
Cited by 8 | Viewed by 3098
Abstract
Time series models are used to determine relationships, spot patterns, and detect abnormalities and irregularities among data. We explore the application of time series analyses in business research by discussing the differences among correlation, association, and Granger causality and providing insight into their [...] Read more.
Time series models are used to determine relationships, spot patterns, and detect abnormalities and irregularities among data. We explore the application of time series analyses in business research by discussing the differences among correlation, association, and Granger causality and providing insight into their proper use in the sustainability literature. In statistics, two correlation coefficients are typically calculated. The first one is the Pearson correlation coefficient and the second is the Spearman correlation coefficient. In the commonly used correlation analysis (the Pearson and the Spearman correlation coefficients), the focus is primarily on the changes in two variables regardless of the effects of other variables. On the contrary, in association analyses, the researcher examines the relationship between two variables while holding the effects of other related variables constant (ceteris paribus). In the study of the causation, or the cause–effect relationship between two variables, researchers are concerned about the effect of variable X on variable Y. The difficulty of achieving the third condition of causation is believed to be the main reason that in business literature causations are rarely used. The difficulty of achieving a causal relationship between two variables has moved researchers toward a special form of causation called “Granger causality”. We offer practical examples for correlation, association, causation, and the Granger causality and discuss their main differences and show how the use of a linear regression is inappropriate when the true relationship is non-linear. Finally, we discuss the policy, practical, and educational implications of our study. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business Sustainability)
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