MoF: A Background-Aware Multi-source Fusion Financial Trend Forecasting Mechanism

With the rapid growth of economic globalization and digital economics, accurately predicting stock price fluctuations has become crucial yet challenging due to high volatility and market noise. Existing forecasting methods, relying primarily on time-series data, technical indicators, and sentiment analysis, often fail to capture the semantic depth of background knowledge, particularly the influence of real-time events. To address this limitation, we propose MoF, a background-aware multi-source fusion mechanism for financial trend forecasting. MoF integrates stock price data with background knowledge on the impact of real-time events on stock trends, which includes key information from policy documents and stock commentaries, and subsequently leverages the MacBERT model to generate feature vectors for stock prediction. Our results show that MoF, by incorporating the influence of real-time events, improves accuracy and interpretability in stock trend forecasting, surpassing LSTM-based models with over 90% accuracy in predicting market fluctuations and providing reliable directional predictions.