Abstract
The aim of this study is to examine and synthesize the literature produced by AI-enabled gold bullion market investor’s behaviour. This study explored the influential authors, sources and themes in the research of AI-led investment decisions. The research methodology adopted was a hybrid methodology which includes biblioshiny and systematic review from the Scopus database from 2012-2024. The results showed that Resource Policy is a major outlet for academics and plays a major part in the diffusion of research followed by Expert Systems with Applications and emerging theme is forecasting gold prices in financial markets. China tops the world in research output and the average citation rate is 24.40, showing both volume and highly influential work. Gold has the highest frequency of 20, followed by financial markets, gold prices, forecasting, and commerce at a frequency of 15. The review outcomes show that machine learning, neural networks and artificial intelligence tools are capable of handling complex datasets in predicting the investors’ behavior in the gold bullion market. Most of the studies used algorithms like Fuzzy Rough Quick Reduct, Extreme Learning Machines and Neural Networks. The results paved that the GRU, CNN, RNN and NLP methods will be adopted for further research studies.
Keywords: Artificial Intelligence, Bibliometrix, Gold, Investors Behaviour, R-studio, Systematic Review.