FINANCIAL FORECASTING AND MACHINE LEARNING: A BIBLIOMETRIC ANALYSIS OF GLOBAL RESEARCH TRENDS
DOI:
https://doi.org/10.29303/akurasi.v9i1.839Keywords:
bibliometric, financial forecasting, machine learning, deep learning, stock marketAbstract
The rapid growth of artificial intelligence applications in finance has generated a large body of literature. However, a comprehensive overview of its intellectual structure remains limited. To address this gap, this study aims to map and analyze global research trends in financial forecasting and stock price prediction using machine learning between 2015 and 2025. Using a bibliometric approach, 197 Scopus-indexed articles were analyzed through the Bibliometrix package in R Studio following the PRISMA framework. The analysis includes publication performance, co-authorship collaboration networks, and thematic evolution. The results indicate an annual publication growth rate of 13.98% with an average of 16.16 citations per document. “Forecasting” emerges as the central research theme, closely connected with “machine learning,” “financial markets,” and “LSTM.” International collaboration accounts for 32.99%, with China, India, and the United States as the leading contributors. Thematic evolution shows a shift from traditional econometric approaches toward artificial intelligence and deep learning–based prediction models. This study contributes by providing a comprehensive intellectual map of AI-driven financial forecasting research and identifying future research directions for scholars, practitioners, and policymakers.
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