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Forecasting, Volume 6, Issue 4 (December 2024) – 7 articles

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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 268
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
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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 467
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
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 796
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
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16 pages, 10311 KiB  
Article
Climate Risks and Real Gold Returns over 750 Years
by Rangan Gupta, Anandamayee Majumdar, Christian Pierdzioch and Onur Polat
Forecasting 2024, 6(4), 952-967; https://doi.org/10.3390/forecast6040047 - 25 Oct 2024
Viewed by 458
Abstract
Using data that cover the annual period from 1258 to 2023, we studied the link between real gold returns and climate risks. We documented a positive contemporaneous link and a negative predictive link. Our findings further show that the predictive link historically gave [...] Read more.
Using data that cover the annual period from 1258 to 2023, we studied the link between real gold returns and climate risks. We documented a positive contemporaneous link and a negative predictive link. Our findings further show that the predictive link historically gave rise to significant out-of-sample forecasting gains. The positive contemporaneous link is consistent with the view that investors viewed gold as a safe haven in times of elevated climate risks. The negative predictive link, in turn, is consistent with an overshooting scenario in which the real gold price overshot in response to climate risks, only to return subsequently to a lower value. Our findings should provide important implications for investors and policymakers, given that our analysis covered the longest possible data sample involving the gold market, and hence, was independent of any sample selection bias. Full article
(This article belongs to the Special Issue Green Finance: Trends and Forecasting)
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27 pages, 1808 KiB  
Review
Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization
by Sambandh Bhusan Dhal and Debashish Kar
Forecasting 2024, 6(4), 925-951; https://doi.org/10.3390/forecast6040046 - 19 Oct 2024
Viewed by 1402
Abstract
Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food [...] Read more.
Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts. Full article
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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 538
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
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23 pages, 1046 KiB  
Article
Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory
by Kgothatso Makubyane and Daniel Maposa
Forecasting 2024, 6(4), 885-907; https://doi.org/10.3390/forecast6040044 - 4 Oct 2024
Viewed by 1024
Abstract
This study investigates wind speed prediction using advanced machine learning techniques, comparing the performance of Vanilla long short-term memory (LSTM) and convolutional neural network (CNN) models, alongside the application of extreme value theory (EVT) using the r-largest order generalised extreme value distribution ( [...] Read more.
This study investigates wind speed prediction using advanced machine learning techniques, comparing the performance of Vanilla long short-term memory (LSTM) and convolutional neural network (CNN) models, alongside the application of extreme value theory (EVT) using the r-largest order generalised extreme value distribution (GEVDr). Over the past couple of decades, the academic literature has transitioned from conventional statistical time series models to embracing EVT and machine learning algorithms for the modelling of environmental variables. This study adds value to the literature and knowledge of modelling wind speed using both EVT and machine learning. The primary aim of this study is to forecast wind speed in the Limpopo province of South Africa to showcase the dependability and potential of wind power generation. The application of CNN showcased considerable predictive accuracy compared to the Vanilla LSTM, achieving 88.66% accuracy with monthly time steps. The CNN predictions for the next five years, in m/s, were 9.91 (2024), 7.64 (2025), 7.81 (2026), 7.13 (2027), and 9.59 (2028), slightly outperforming the Vanilla LSTM, which predicted 9.43 (2024), 7.75 (2025), 7.85 (2026), 6.87 (2027), and 9.43 (2028). This highlights CNN’s superior ability to capture complex patterns in wind speed dynamics over time. Concurrently, the analysis of the GEVDr across various order statistics identified GEVDr=2 as the optimal model, supported by its favourable evaluation metrics in terms of Akaike information criteria (AIC) and Bayesian information criteria (BIC). The 300-year return level for GEVDr=2 was found to be 22.89 m/s, indicating a rare wind speed event. Seasonal wind speed analysis revealed distinct patterns, with winter emerging as the most efficient season for wind, featuring a median wind speed of 7.96 m/s. Future research could focus on enhancing prediction accuracy through hybrid algorithms and incorporating additional meteorological variables. To the best of our knowledge, this is the first study to successfully combine EVT and machine learning for short- and long-term wind speed forecasting, providing a novel framework for reliable wind energy planning. Full article
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