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Forecasting and Foresight in Business and Economics in the Turbulent...
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Forecasting and Foresight in Business and Economics in the Turbulent and Uncertain New Normal

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Economics and Management".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 18425

Special Issue Editors

Prof. Dr. Konstantinos Nikolopoulos

E-Mail Website
Guest Editor
Business Information Systems & Analytics, Durham University, Durham DH1 3LE, UK
Interests: forecasting; business analytics; information systems; operations; economics
Dr. Vasileios Bougioukos

E-Mail Website
Guest Editor
Institute of Hazard, Risk and Resilience Forecasting Lab, University of Notre Dame, London SW1Y 4HG, UK
Interests: social investments; labour markets; judgmental forecasting

Special Issue Information

Dear Colleagues,

We live in the aftermath of a very turbulent period—the COVID-19 pandemic—and during severe geopolitical tensions, which create a very uncertain environment. As such, we are in need of accurate and robust forecasting models for near-, short-, and mid-term periods as well as foresight models for the longer term. This is true for both business applications as well as applications in finance and economics. Valuable research can range from technical results and contributions on models and methods up to methodological contributions and case studies of the successful application of such models during and after the pandemic.

The Special Issue aims to gather a series of state-of-the-art contributions on forecasting and foresight in business and economics under turbulent and uncertain conditions; given the overall aim of the journal (to advance forecasting studies), this is an extremely relevant and timely Special Issue.

  • Forecasting studies in business in uncertain and turbulent environments;
  • Foresight studies in business in uncertain and turbulent environments;
  • Forecasting studies in finance in uncertain and turbulent environments;
  • Forecasting studies in economics in uncertain and turbulent environments;
  • Foresight studies in economics in uncertain and turbulent environments;
  • Forecasting methodological contributions;
  • Empirical studies;
  • Forecasting studies in regional contexts – especially in BRICS, The Gulf, and South East Asia;
  • Comparative international studies of performance of forecasting models.

Prof. Dr. Konstantinos Nikolopoulos
Dr. Vasileios Bougioukos
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 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. Forecasting is an international peer-reviewed open access quarterly 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 1800 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

  • forecasting
  • foresight
  • business
  • economics
  • turbulence
  • disruptions
  • uncertainty

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Published Papers (7 papers)

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Research

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27 pages, 2690 KiB  
Article
The MECOVMA Framework: Implementing Machine Learning Under Macroeconomic Volatility for Marketing Predictions
by Manuel Muth
Forecasting 2025, 7(1), 3; https://doi.org/10.3390/forecast7010003 - 7 Jan 2025
Viewed by 529
Abstract
The methodological framework introduced in this paper, MECOVMA, is a novel framework that guides the application of Machine Learning specifically for marketing predictions within volatile macroeconomic environments. MECOVMA has been developed in response to the identified gaps displayed by existing frameworks—when it comes [...] Read more.
The methodological framework introduced in this paper, MECOVMA, is a novel framework that guides the application of Machine Learning specifically for marketing predictions within volatile macroeconomic environments. MECOVMA has been developed in response to the identified gaps displayed by existing frameworks—when it comes to consolidation, relevance, interdisciplinarity, and individuality—and in light of the polycrises occurring in the current decade. The methodology to develop MECOVMA comprises three phases: firstly, synthesizing existing frameworks based on their thematic relevance to select MECOVMA’s process steps; secondly, integrating the evidence provided by a systematic literature review to design the content of these process steps; and thirdly, using an expert evaluation, structured through a qualitative content analysis, to validate MECOVMA’s applicability. This leads to the final framework with four overarching PMECOVMA process steps, guiding the Machine Learning application process in this context with specific tasks. These include, for example, the processing of multidimensional data inputs, complexity reduction in a dynamic environment, and training methods adapted to particular macro-conditions. In addition, features are provided on how Machine Learning can be put into marketing practice, incorporating both narrower statistical- and broader business-oriented evaluations, and iterative feedback loops to mitigate limitations. Full article
(This article belongs to the Special Issue Forecasting and Foresight in Business and Economics in the Turbulent and Uncertain New Normal)
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20 pages, 4455 KiB  
Article
Forecasting Raw Material Yield in the Tanning Industry: A Machine Learning Approach
by Ismael Cristofer Baierle, Leandro Haupt, João Carlos Furtado, Eluza Toledo Pinheiro and Miguel Afonso Sellitto
Forecasting 2024, 6(4), 1078-1097; https://doi.org/10.3390/forecast6040054 - 20 Nov 2024
Viewed by 1071
Abstract
This study presents an innovative machine learning (ML) approach to predicting raw material yield in the leather tanning industry, addressing a critical challenge in production efficiency. Conducted at a tannery in southern Brazil, the research leverages historical production data to develop a predictive [...] Read more.
This study presents an innovative machine learning (ML) approach to predicting raw material yield in the leather tanning industry, addressing a critical challenge in production efficiency. Conducted at a tannery in southern Brazil, the research leverages historical production data to develop a predictive model. The methodology encompasses four key stages: data collection, processing, prediction, and evaluation. After rigorous analysis and refinement, the dataset was reduced from 16,046 to 555 high-quality records. Eight ML models were implemented and evaluated using Orange Data Mining software, version 3.38.0, including advanced algorithms such as Random Forest, Gradient Boosting, and neural networks. Model performance was assessed through cross-validation and comprehensive metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Coefficient of Determination (R2). The AdaBoost algorithm emerged as the most accurate predictor, achieving impressive results with an MAE of 0.042, MSE of 0.003, RMSE of 0.057, and R2 of 0.331. This research demonstrates the significant potential of ML techniques in enhancing raw material yield forecasting within the tanning industry. The findings contribute to more efficient forecasting processes, aligning with Industry 4.0 principles and paving the way for data-driven decision-making in manufacturing. Full article
(This article belongs to the Special Issue Forecasting and Foresight in Business and Economics in the Turbulent and Uncertain New Normal)
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25 pages, 1530 KiB  
Article
Synergy of Modern Analytics and Innovative Managerial Decision-Making in the Turbulent and Uncertain New Normal
by Maria Kovacova, Eva Kalinova, Pavol Durana and Katarina Frajtova Michalikova
Forecasting 2024, 6(4), 1001-1025; https://doi.org/10.3390/forecast6040050 - 7 Nov 2024
Viewed by 890
Abstract
This paper focuses on analyzing the relationship between the financial performance of companies and their ability to utilize modern business methods. Financial analysis was conducted using the example of the automobile manufacturer Škoda Auto, with the results providing deeper insights into the company’s [...] Read more.
This paper focuses on analyzing the relationship between the financial performance of companies and their ability to utilize modern business methods. Financial analysis was conducted using the example of the automobile manufacturer Škoda Auto, with the results providing deeper insights into the company’s financial situation. The companies examined in this study were scored and underwent regression and cluster analyses. A questionnaire focusing on the modernity of advertising in selected companies was answered by 276 respondents. Based on the findings, a model for evaluating the modernity and stability of companies was developed, combining various factors including financial indicators and the adoption of modern technologies. The results indicate that there is a relationship between financial performance and the modernization of companies, although this relationship is not always straightforward. In particular, the operating profit and current ratio emerged as important factors influencing modernization. Overall, it can be concluded that the financial performance and modernization of companies are interconnected, but their relationship is complex and requires further investigation. This paper is an important contribution to understanding company modernization and sets the stage for further studies on this issue. Full article
(This article belongs to the Special Issue Forecasting and Foresight in Business and Economics in the Turbulent and Uncertain New Normal)
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16 pages, 278 KiB  
Article
A Foresight Framework for the Labor Market with Special Reference to Managerial Roles—Toward Diversified Skill Portfolios
by Anna-Maria Kanzola and Panagiotis E. Petrakis
Forecasting 2024, 6(4), 985-1000; https://doi.org/10.3390/forecast6040049 - 29 Oct 2024
Viewed by 1200
Abstract
This study introduces a methodology for labor market foresight through alternative futures. It discusses three alternative scenarios for managerial roles, each exploring varying levels of technological advancement and economic growth, to provide insights into the evolving demands for managerial roles. By drafting a [...] Read more.
This study introduces a methodology for labor market foresight through alternative futures. It discusses three alternative scenarios for managerial roles, each exploring varying levels of technological advancement and economic growth, to provide insights into the evolving demands for managerial roles. By drafting a diversified skill portfolio, it is argued that employability skills for managers concern providing education in a combination of areas, such as new technologies, trend analysis, and strategic foresight based on the sector in which the firm operates, negotiation skills and human resources management, contemporary sales techniques, entrepreneurship, and personal growth, including time management, creativity, public speaking skills, and foresight skills. Utilizing responses obtained through an online survey administered in Greece during 2024 to managers and employing principal component analysis (PCA), we establish correlations between skill portfolio composition preferences, foresight analysis, and design of diversified skill portfolios. Diversified skill portfolios are a holistic approach to training, reskilling, and upskilling, including an optimum combination of foundational, complex, digital, green, and always case-fit per occupation or sector of economic activity. Consequently, the insights derived from this study offer a microeconomic perspective regarding the optimal combination of skills for managerial occupations and a macroeconomic perspective concerning the formulation of future training policies for human capital development. Full article
(This article belongs to the Special Issue Forecasting and Foresight in Business and Economics in the Turbulent and Uncertain New Normal)
17 pages, 8320 KiB  
Article
Using Machine Deep Learning AI to Improve Forecasting of Tax Payments for Corporations
by Charles Swenson
Forecasting 2024, 6(4), 968-984; https://doi.org/10.3390/forecast6040048 - 25 Oct 2024
Viewed by 2136
Abstract
This paper aims to demonstrate how machine deep learning techniques lead to relatively accurate forecasts of quarterly corporate income tax payments. Using quarterly data from Compustat for all U.S. publicly traded corporations from 2000 to 2024, I show that neural nets, the tree [...] Read more.
This paper aims to demonstrate how machine deep learning techniques lead to relatively accurate forecasts of quarterly corporate income tax payments. Using quarterly data from Compustat for all U.S. publicly traded corporations from 2000 to 2024, I show that neural nets, the tree method, and random forest models provide robust forecasts despite their encompassing COVID-19 pandemic time periods. The results should be of interest to corporate tax planners, stock analysts, and governments. Full article
(This article belongs to the Special Issue Forecasting and Foresight in Business and Economics in the Turbulent and Uncertain New Normal)
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17 pages, 1913 KiB  
Article
Does Google Analytics Improve the Prediction of Tourism Demand Recovery?
by Ilsé Botha and Andrea Saayman
Forecasting 2024, 6(4), 908-924; https://doi.org/10.3390/forecast6040045 - 18 Oct 2024
Viewed by 935
Abstract
Research shows that Google Trend indices can improve tourism-demand forecasts. Given the impact of the recent pandemic, this may prove to be an important predictor of tourism recovery in countries that are still struggling to recover, including South Africa. The purpose of this [...] Read more.
Research shows that Google Trend indices can improve tourism-demand forecasts. Given the impact of the recent pandemic, this may prove to be an important predictor of tourism recovery in countries that are still struggling to recover, including South Africa. The purpose of this paper is firstly, to build on previous research that indicates that Google Trends improves tourism-demand forecasting by testing this within the context of tourism recovery. Secondly, this paper extends previous research by not only including Google Trends in time-series forecasting models but also typical tourism-demand covariates in an econometric specification. Finally, we test the performance of Google Trends in forecasting over a longer time period, because the destination country is a long-haul destination where more lead time may be required in decision-making. Additionally, this research contributes to the body of knowledge by including lower frequency data (quarterly) instead of the higher frequency data commonly used in current research, while also focusing on an important destination country in Africa. Due to the differing data frequencies, the MIDAS modelling approach is used. The MIDAS models are compared to typical time-series and naïve benchmarks. The findings show that monthly Google Trends improve forecasts on lower frequency data. Furthermore, forecasts that include Google Trends are more effective in forecasting one to two quarters ahead, pre-COVID. This trend changed after COVID, when Google Trends led to improved recovery forecasts even over a longer term. Full article
(This article belongs to the Special Issue Forecasting and Foresight in Business and Economics in the Turbulent and Uncertain New Normal)
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Review

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35 pages, 1703 KiB  
Review
Cryptocurrency Price Prediction Algorithms: A Survey and Future Directions
by David L. John, Sebastian Binnewies and Bela Stantic
Forecasting 2024, 6(3), 637-671; https://doi.org/10.3390/forecast6030034 - 15 Aug 2024
Viewed by 10837
Abstract
In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced [...] Read more.
In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable. Full article
(This article belongs to the Special Issue Forecasting and Foresight in Business and Economics in the Turbulent and Uncertain New Normal)
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