An Exploration of GAN Applications in Stock Prediction through Bibliometric Analysis and Co-occurrence Networks

Predicting the stock market has become a global problem in recent years, requiring massive scientific study. In this study, bibliometric investigation was performed to comprehend a essential outlook and help academics recognize the features of stock market prediction. In light of the aforementioned research, this article concludes with some discussions regarding the upcoming challenges of stock market prediction. With a focus on its many approaches, this article provides a detailed introduction to bibliometric methodology. Additionally, it offers a set of trustworthy, detailed directions for carrying out bibliometric analysis with assurance. Additionally, this study looks into the appropriate application of bibliometric analysis as a substitute for systematic literature reviews. Concretely, it provides a thorough overview of the field of stock market prediction using GAN in terms of the number of regular publications produced, the primary focus of stock market prediction researchers, the most influential nations (authors, institutions, and sources), and intriguing research avenues in this area. The evolution of stock market prediction using GAN is objectively understood in this study, which also serves as a useful reference for academics and researchers in the fields of technology, management, and economics.