Forecasting the Price of Gold with Integrated Media Sentiment—A Prediction Framework Based on Online News Sentiment Mining with CNN-QRLSTM
Yu Ji, Xinyue Lei, Lining Zhang, Jiani Heng, Jianwei Fan
TL;DR
This paper introduces a new model for predicting gold prices by combining machine learning with media sentiment analysis from news articles.
Contribution
The novel CNN-QRLSTM model integrates media sentiment from news with financial data to improve gold price prediction accuracy and quantify uncertainty.
Findings
The CNN-QRLSTM model improves gold price prediction accuracy by incorporating media sentiment.
Entropy-based methods enhance interpretability of emotion-driven price fluctuations.
Multi-source data fusion reduces prediction uncertainty and supports quantitative investment strategies.
Abstract
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.…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMarket Dynamics and Volatility · Stock Market Forecasting Methods · Blockchain Technology Applications and Security