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Gold Futures Price Forecast Based on Artificial intelligence
- 1 Woodsworth College, University of Toronto, Toronto, Ontario, Canada
* Author to whom correspondence should be addressed.
Abstract
Gold futures prices are affected by many factors such as macroeconomics, market supply and demand, and financial markets. Their volatility is crucial to investment decisions and market stability. However, the complexity of the market makes it difficult for traditional forecasting methods to accurately capture the law of price changes. It is of great significance to study efficient forecasting methods. This paper systematically reviews the factors affecting gold futures prices and forecasting methods. The key driving factors are analyzed from the aspects of inflation rate, interest rate, crude oil price, and US dollar index, and three main forecasting methods are summarized: traditional time series models (ARIMA, GARCH, ARDL), hybrid models (MS-MIDAS-CJ, VMD-ICSS-BiGRU) and deep learning methods (SGRU-AM, DBN). Finally, this paper explores the optimization directions of model computational efficiency, generalization ability, and market sentiment integration. This study provides a systematic analysis of gold futures market forecasting. In the future, the combination of deep learning and market sentiment analysis is expected to improve forecasting accuracy and provide support for investors and market decision-making.
Keywords
Gold futures, Prices, Forecasts, Gold
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Cite this article
He,T. (2025). Gold Futures Price Forecast Based on Artificial intelligence. Advances in Economics, Management and Political Sciences,174,27-33.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Management Research and Economic Development
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