融合卷积LSTM与大模型，从价格和技术指标+新闻社交媒体全面预测股票走势。
A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting
- 分两层：先用卷积LSTM提取股价与技术指标的时空特征。
- 再用大模型分析金融新闻等文本的情感与上下文信息。
- 适合关注多源数据融合与智能投研的从业者。
金融市场具有复杂的波动性，受多种数据源影响。传统模型通常仅使用卷积神经网络（CNN）提取空间特征或长短期记忆网络（LSTM）捕捉时间依赖性，且缺乏对外部文本数据的整合。本文提出一种两级卷积LSTM与大语言模型（LLM）集成的综合股票预测模型。第一层通过卷积层识别历史股价与技术指标中的局部模式，再由LSTM层捕获时间动态；第二层将时序输出与大语言模型结合，分析来自金融新闻、社交媒体和报告的文本情感与上下文信息，实现对市场状况的全局理解。该方法旨在提升预测准确性并提供语境丰富的投资建议。
原文摘要 · Abstract (English)
The financial domain presents a complex environment for stock market prediction, characterized by volatile patterns and the influence of multifaceted data sources. Traditional models have leveraged either Convolutional Neural Networks (CNN) for spatial feature extraction or Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, with limited integration of external textual data. This paper proposes a novel Two-Level Conv-LSTM Neural Network integrated with a Large Language Model (LLM) for comprehensive stock advising. The model harnesses the strengths of Conv-LSTM for analyzing time-series data and LLM for processing and understanding textual information from financial news, social media, and reports. In the first level, convolutional layers are employed to identify local patterns in historical stock prices and technical indicators, followed by LSTM layers to capture the temporal dynamics. The second level integrates the output with an LLM that analyzes sentiment and contextual information from textual data, providing a holistic view of market conditions. The combined approach aims to improve prediction accuracy and provide contextually rich stock advising.
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