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Forecasting, Predictive Analytics and Econometrics in Business Research
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Forecasting, Predictive Analytics and Econometrics in Business Research

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".

Deadline for manuscript submissions: closed (27 December 2024) | Viewed by 2920

Special Issue Editor

Dr. Yongchen Zhao

E-Mail Website
Guest Editor
Department of Economics, Towson University, Towson, MD 21252, USA
Interests: forecasting; econometrics; survey analytics; finance

Special Issue Information

Dear Colleagues, 

This Special Issue focuses on the use of forecasting, predictive analytics, and other novel econometric/statistical techniques in business research, including, but not limited to, risk and financial management. 

The Special Issue highlights both theoretical and empirical studies and invites contributions related to the forecasting behavior and decision making of individuals, businesses, and government agencies/policymakers. Research on the broader fields of accounting, economics, management, marketing, finance, business analytics and supply chain management, as well as cyber security and health care management , are all welcome.

The submission of articles featuring predictive analytics in risk and financial management and the sentiments and/or expectations of businesses and consumers are especially appreciated. 

Dr. Yongchen Zhao
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. Journal of Risk and Financial Management is an international peer-reviewed open access monthly 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 1400 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

  • business analytics
  • forecasting
  • predictive analytics
  • business sentiment
  • survey expectations
  • econometrics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 496 KiB  
Article
Unethical Conduct Under Uncertainty: A Fear-Based Perspective
by Sasha Pustovit, Andrea L. Hetrick and Tanja R. Darden
J. Risk Financial Manag. 2025, 18(2), 103; https://doi.org/10.3390/jrfm18020103 - 18 Feb 2025
Viewed by 540
Abstract
Rising uncertainty in the business environment has coincided with a significant increase in unethical behaviors within organizations, posing substantial financial and reputational risks. Unethical conduct is estimated to cost organizations around the world more than USD 4.5 trillion per year, impacting corporate financial [...] Read more.
Rising uncertainty in the business environment has coincided with a significant increase in unethical behaviors within organizations, posing substantial financial and reputational risks. Unethical conduct is estimated to cost organizations around the world more than USD 4.5 trillion per year, impacting corporate financial stability, investor confidence, and market integrity. Traditional risk assessment and predictive models, which rely on historical data, often fail to account for behavioral responses to uncertainty, creating blind spots in financial risk management and economic forecasting. This paper advances the literature by applying experimental methodologies to investigate the underlying emotional, fear-based mechanisms (namely short-term focus and self-concern) impacting decision-making under uncertainty. By utilizing two distinct types of experimental studies (comprising three studies in total), we empirically examine how uncertainty influences the types of unethical behaviors that are prevalent in today’s organizations. Our findings contribute to the fields of financial risk management and behavioral economics by offering evidence-based insights into the psychological drivers of unethical decision-making. We conclude with managerial implications, outlining proactive strategies to mitigate the financial and operational risks associated with individuals’ responses to uncertainty. Full article
(This article belongs to the Special Issue Forecasting, Predictive Analytics and Econometrics in Business Research)
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25 pages, 1642 KiB  
Article
Forecasting Follies: Machine Learning from Human Errors
by Li Sun and Yongchen Zhao
J. Risk Financial Manag. 2025, 18(2), 60; https://doi.org/10.3390/jrfm18020060 - 28 Jan 2025
Viewed by 708
Abstract
Reliable inflation forecasts are essential for both business operations and macroeconomic policy making. This study explores the potential of using machine learning (ML) techniques to improve the accuracy of human forecasts of inflation. Specifically, we develop and examine ML-centered forecast adjustment procedures where [...] Read more.
Reliable inflation forecasts are essential for both business operations and macroeconomic policy making. This study explores the potential of using machine learning (ML) techniques to improve the accuracy of human forecasts of inflation. Specifically, we develop and examine ML-centered forecast adjustment procedures where advanced ML techniques are employed to predict and thus mitigate the errors of human forecasts, akin to how an AI-powered spell and grammar checker helps to prevent mistakes in human writing. Our empirical exercises demonstrate the benefits of several popular ML techniques, such as the elastic net, LASSO, and ridge regressions, and provide evidence of their ability to improve both our own benchmark inflation forecasts and those reported by the frequent participants in the US Survey of Professional Forecasters. The forecast adjustment procedures proposed in this paper are conceptually appealing, widely applicable, and empirically effective in reducing forecast bias and improving forecast accuracy. Full article
(This article belongs to the Special Issue Forecasting, Predictive Analytics and Econometrics in Business Research)
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29 pages, 1411 KiB  
Article
Optimizing Energy Storage Profits: A New Metric for Evaluating Price Forecasting Models
by Simone Sbaraglia, Alessandro Fiori Maccioni and Stefano Zedda
J. Risk Financial Manag. 2024, 17(12), 538; https://doi.org/10.3390/jrfm17120538 - 26 Nov 2024
Viewed by 910
Abstract
Storage profit maximization is based on buying energy at the lowest prices and selling it at the highest prices. The best strategy must thus be based on both accurately predicting the price peak hours and on rightly choosing when to buy and when [...] Read more.
Storage profit maximization is based on buying energy at the lowest prices and selling it at the highest prices. The best strategy must thus be based on both accurately predicting the price peak hours and on rightly choosing when to buy and when to sell the stored energy. In this aim, price prediction is crucial, but choosing the prediction model by means of the usual metrics, as the lowest mean squared error, is not an effective solution as the mean squared error computation equally weights the prediction error of all prices, while the focus must be on the higher and lower prices. In this paper, we propose a new metric focused on the correct forecasting of high and low prices so as to allow for a more effective choice among price forecasting models. Results show that the new metric outperforms the standard metrics, allowing for a more accurate estimation of the possible profit for storage (or other trading) activities. Full article
(This article belongs to the Special Issue Forecasting, Predictive Analytics and Econometrics in Business Research)
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