What does this research mean for the field?

The hybrid CNN-QRLSTM model improves the accuracy of gold price prediction by integrating media sentiment analysis from online news. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.SUPPORTS_CONSENSUS.

What question did this study set out to answer?

To enhance the accuracy of gold price prediction by integrating media sentiment analysis and quantifying uncertainty.

March 3, 2026Open Access

Forecasting the Price of Gold with Integrated Media Sentiment—A Prediction Framework Based on Online News Sentiment Mining with CNN-QRLSTM

Puntos clave

To enhance the accuracy of gold price prediction by integrating media sentiment analysis and quantifying uncertainty.
Developed a hybrid CNN-QRLSTM model to process news sentiment data.
Utilized EEMD and Hurst index to remove noise from gold price data.
Employed entropy measurement to quantify uncertainty and information density of sentiment indicators.
Constructed a financial sentiment lexicon and calculated sentiment scores based on news texts.
Performed empirical analysis on London gold spot prices and related news from 2022 to 2025.
The hybrid model improved prediction accuracy of gold prices compared to traditional methods.
Quantifying media sentiment revealed significant impacts on gold price fluctuations.
Multi-source data fusion enhanced the interpretability of financial risks and uncertainties.

Resumen

Accurate gold price forecasting is crucial for economic stability and investment decision-making. In order to improve the accuracy of gold price prediction and quantify the uncertainty of gold price fluctuation, this paper proposes a hybrid model (CNN-QRLSTM) that integrates convolutional neural network (CNN) and quantile regression long- and short-term memory network (QRLSTM) and innovatively introduces news text data to quantify the media sentiment. We combine EEMD with the Hurst index to remove white noise from the original signal, and the processed data is used as the input layer of the prediction model. Furthermore, to demonstrate the impact of news sentiment on gold prices, this paper employs entropy measurement methods based on information theory to quantify the uncertainty and information content embedded within processed gold price sequences and derived sentiment indicators. The mutual information (MI) algorithm, based on information entropy, captures the nonlinear correlations between financial keywords and market sentiment. It constructs a financial sentiment lexicon (covering keywords such as economic policies and geopolitical conflicts), combines semantic rules with context-weighted strategies, calculates sentiment scores for news texts, and generates daily aggregated media sentiment indicators. This entropy-based perception method not only enhances the interpretability of emotion-driven fluctuations but also provides a theoretical foundation for reducing prediction uncertainty through multi-source data fusion. The experiment uses 2022–2025 daily London gold spot price data, Shanghai Gold Exchange gold price data, and the same period of Gold Investment Network gold market news to carry out the study. The empirical study shows that the synergy of multi-source data fusion and the quantile regression mechanism can improve the accuracy of gold price prediction and the new paradigm of risk interpretation while providing theoretical support for the formulation of quantitative investment strategies.

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