Machine Learning for Financial Data Forecasting: Deep Learning-Powered Scientometric Analysis

Recent studies underscore the growing application of machine learning (ML) in finance, as revealed through bibliometric analyses. Kureljusic and Karger (2024) reviewed AI-based forecasting in financial accounting, identifying gaps and proposing future research agendas. Similarly, Biju et al. (2023) explored the taxonomy of AI, deep learning, and ML in finance, highlighting the rise in publications and the need for empirical studies on algorithmic financial technologies. Building on this foundation, this study presents a scientometric analysis of ML in financial data forecasting from 1996 to 2024. Using data from Scopus and Web of Science, we examine key themes, collaboration networks, and influential contributors. Employing deep learning tools like Sentence-BERT, BerTopic, BERT, ChatGPT, and PEGASUS, we offer insights into how ML has reshaped financial forecasting. This analysis provides a basis for future research, guiding scholars and practitioners towards impactful areas in finance.