Bibliometric Analysis of Forecasting Gold Prices using Neural Network Models: A Review and Future Research Agenda

The surge in gold prices following the COVID-19 crisis has sparked significant interest among investors, portfolio managers, and speculators regarding investment in gold. This research paper employs bibliometric analysis to examine 202 Scopus-indexed journal articles published in English that focus on forecasting gold prices using neural network models. The study covers the period from 1993 to 2023, aiming to provide a comprehensive overview of the academic literature in this field. Through bibliometric analysis, we identify the most influential articles, prominent journals, leading authors, prestigious institutes, and noteworthy countries contributing to the research on gold price forecasting. This analysis sheds light on the key trends and research hotspots within this domain. Furthermore, this paper puts forth a future research agenda to guide future investigations in this domain. Overall, this study offers valuable insights into the existing literature on forecasting gold prices with neural network models and presents a roadmap for future research endeavors in this captivating field.