Back

Enhancing Economic Stability with Innovative Crude Oil Price Prediction and Policy Uncertainty Mitigation in USD Energy Stock Markets.

  • Published In: Fluctuation & Noise Letters, 2024, v. 23, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Islam, Umar; Awwad, Emad Mahrous; Sarhan, Nadia Mohamed; Fattah Sharaf, Mohamed Abdel; Ali, Ijaz; Khan, Inayat; Ahmad, Shehzad; Khan, Faheem 3 of 3

Abstract

In today's globalized economic landscape, the assurance of economic stability is of paramount importance, necessitating precise financial decision-making and policy formulation. This assurance is significantly augmented by innovative approaches to predicting crude oil prices, particularly in the context of energy stock markets denominated in USD. This paper delves into the transformative effect of accurate crude oil price prediction on economic policy stability. It underscores the challenges and limitations posed by policy uncertainties and emphasizes the pivotal role of innovative solutions in mitigating these challenges. Moreover, it recognizes the imperative need for secure data storage to facilitate the application of machine learning in this domain. Furthermore, effective management and regulation of power grid systems are explored as indispensable strategies for tempering the volatility introduced by fluctuations in energy stock markets. As we work to address these gaps in knowledge, the potential for sustainable power systems to supersede fossil fuels emerges as a driving force behind the maintenance of stable economic policies. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Fluctuation & Noise Letters. 2024/04, Vol. 23, Issue 2, p1
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:0219-4775
  • DOI:10.1142/S0219477524400212
  • Accession Number:177219040
  • Copyright Statement:Copyright of Fluctuation & Noise Letters is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.