DASF-Net: A Multimodal Framework for Stock Price Forecasting with Diffusion-Based Graph Learning and Optimized Sentiment Fusion

Nhat-Hai Nguyen, Thi-Thu Nguyen and Quan T. Ngo ()
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Nhat-Hai Nguyen: Department of Computer Science, School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
Thi-Thu Nguyen: Department of Computer Science, School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
Quan T. Ngo: Department of Artificial Intelligence, FPT University, Da Nang 550000, Vietnam

JRFM, 2025, vol. 18, issue 8, 1-25

Abstract: Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting.

Keywords: financial forecasting; stock price prediction; sentiment analysis; diffusion-based graph learning; multi-modal deep learning; FinBERT (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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