Machine learning in financial forecasting: A U.S. review: Exploring the advancements, challenges, and implications of AI-driven predictions in financial markets

Abstract

This study delves into the integration of Artificial Intelligence (AI) and Machine Learning (ML) in financial forecasting within the United States, aiming to uncover the advancements, challenges, and broader implications for stakeholders in the financial markets. Employing a systematic literature review and content analysis, the research meticulously examines peer-reviewed journals, conference proceedings, and reputable institutional reports from 2010 to 2024. The methodology focuses on identifying empirical evidence that highlights the role of AI and ML technologies in enhancing the accuracy and efficiency of financial predictions, while also considering the ethical and regulatory challenges posed by these advancements. Key findings indicate that AI and ML have significantly revolutionized financial forecasting, offering improved precision in market trend analysis and asset price predictions through innovations in deep learning, reinforcement learning, and hybrid models. Despite these advancements, challenges related to data quality, model interpretability, and ethical considerations persist, underscoring the need for robust regulatory frameworks to ensure the responsible use of AI in finance. The study concludes that while AI and ML present substantial opportunities for transforming financial forecasting and decision-making processes, addressing the associated challenges is crucial for their ethical and effective integration. Strategic recommendations for financial leaders and policymakers emphasize the importance of fostering innovation, enhancing AI literacy, and developing international standards for AI use in finance. Future research directions include exploring the impact of emerging technologies on financial forecasting and developing adaptive regulatory frameworks to accommodate technological advancements.