A Data-Driven AI Methodology for Macroeconomic Assessment and Gold Price Forecasting

Abstract

This study delivers a comprehensive synthesis of the literature on AI-enhanced investor behavior in the gold bullion market, spotlighting key authors, publication channels, and thematic emphases. Combining bibliometric analysis via Biblioshiny with a systematic review of Scopus-indexed studies from 2012 to 2024, it identifies Resource Policy and Expert Systems with Applications as leading journals in the field—aligned with broader trends in neural-network-based gold price forecasting. The analysis also highlights an emerging research focus on forecasting gold prices, supported by frequent keywords like “gold,” “financial markets,” “gold prices,” “forecasting,” and “commerce.” China emerges as the top contributor in terms of volume, with an average of 24.4 citations per paper, underscoring its scholarly impact. The reviewed studies demonstrate that machine learning, neural networks, and AI techniques effectively process complex datasets to better understand investor behavior; commonly employed methods include Fuzzy Rough Quick Reduct, Extreme Learning Machines, and traditional neural network frameworks. Future research directions point toward advanced architectures—such as GRU, CNN, RNN, and NLP-based models—echoing the broader evolution seen in financial forecasting literature .