International Journal of Science, Engineering and TechnologyAuthors: Research Scholar Govind Kumar Verma, Associate Professor Dr. Richa Pareek
Abstract: Gold has long been considered one of the most important investment assets and safe-haven instruments in global financial markets. Rising market volatility, inflationary pressure, geopolitical uncertainty and shifting economic conditions have made accurate gold price forecasting essential for investors, financial institutions and policymakers. This study proposes a multi-factor predictive analytics approach to gold price forecasting that supports investment decision-making and risk management. The framework integrates machine learning, deep learning, econometric modelling and managerial analytics techniques to analyse the impact of macroeconomic indicators such as interest rates, exchange rates, inflation, commodity prices, climate risks and market volatility on gold price movements. Twelve studies published between 2017 and 2026 are reviewed and organised into three themes covering deep learning approaches, econometric and multi-factor models, and data mining with investment decision support. The reviewed evidence is consolidated into comparative tables that map each study to its data context, method, principal finding and contribution to the proposed design, and seven research gaps are identified and mapped to corresponding research directions. The framework is specified through five macroeconomic factor blocks and five forecasting components including Long Short-Term Memory (LSTM) networks, hybrid neural networks, ARIMA, Support Vector Machines and adaptive learning mechanisms. The findings indicate that multi-factor predictive analytics frameworks can improve forecasting accuracy, support strategic investment planning and enhance investor decision-making in uncertain financial environments.
