Background-aware Multi-source Fusion Financial Trend Forecasting Mechanism

Stock prices, as an economic indicator, reflect changes in economic development and market conditions. Traditional stock price prediction models often only consider time-series data and are limited by the mechanisms of the models themselves. Some deep learning models have high computational costs, depend on a large amount of high-quality data, and have poor interpretations, making it difficult to intuitively understand the driving factors behind the predictions. Some studies have used deep learning models to extract text features and combine them with price data to make joint predictions, but there are issues with dealing with information noise, accurate extraction of text sentiment, and how to efficiently fuse text and numerical data. To address these issues in this paper, we propose a background-aware multi-source fusion financial trend forecasting mechanism. The system leverages a large language model to extract key information from policy and stock review texts, utilizing the MacBERT model to generate feature vectors. These vectors are then integrated with stock price data to form comprehensive feature representations. These integrated features are input into a neural network comprising various deep learning architectures. By integrating multiple data sources, the system offers a holistic view of market dynamics. It harnesses the comprehensive analytical and interpretative capabilities of LLMs, retaining deep semantic and sentiment information from policy texts to provide richer input features for stock trend prediction. Additionally, we compare the accuracy of six models (LSTM, BiLSTM, MogrifierLSTM, GRU, ST-LSTM, SwinLSTM). The results demonstrate that our system achieves generally better accuracy in predicting stock movements, attributed to the incorporation of large language model processing, policy information, and other influential features.