TL;DR
A bibliometric analysis of machine learning based gold price forecasting research from 2016 to March 2026 provides a roadmap for advancing gold price forecasting through more sophisticated, validated machine learning approaches.
Financial volatility forecasting has undergone a rapid methodological transition from parametric econometric models towards machine and deep learning, hybrid architectures, and ensemble techniques. Despite this expansion, the literature lacks a synthesis combining bibliometric mapping with a granular methodological tax...
View the results as a methodological contribution rather than direct evidence of practical investment value, given the modest trend-classification accuracy and the lack of trading back testing, transaction costs, or risk-adjusted performance measures.
This study investigates the performance of four forecasting models—ARIMA, XGBoost, Long Short-Term Memory (LSTM), and Transformer)—for Apple Inc. (AAPL) stock price prediction using 20 years of historical daily data from Yahoo Finance.
This paper provides a comprehensive review of stock market prediction techniques from two main perspectives: methods based solely on historical stock data, and approaches that incorporate both numerical and textual information.
The rapid convergence of artificial intelligence, machine learning, and financial and risk analytics has produced a large and fragmented body of scientific literature, making it difficult to identify its thematic structure and evolutionary trends. This study addresses that gap by proposing a bibliometric-computational...
People commonly invest in gold to protect their assets because of its role as a stable, safe-haven asset against economic volatility. However, the uncertainty of future gold price movements creates challenges, making the ability to predict gold prices beneficial for analysis. This study proposes a comparative approach...
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