标题： 基于Transformer和LLM生成的公式化阿尔法的情感感知股票价格预测 显示英文标题

标题： Sentiment-Aware Stock Price Prediction with Transformer and LLM-Generated Formulaic Alpha

摘要： 传统上，交易员和量化分析师通过利用领域专业知识和试错法手动构建公式化阿尔法，这些数学表达式用于识别金融数据中的模式或信号。这个过程通常耗时且难以扩展。随着大型语言模型（LLMs）的最新进展，现在可以通过利用LLMs的推理能力来自动化生成此类阿尔法。本文介绍了一个新颖的框架，该框架将基于提示的LLM与Transformer模型相结合，用于股票价格预测。LLM首先使用结构化输入（如历史股票特征（收盘价、开盘价、最高价、最低价、成交量）、技术指标、目标公司和相关公司的情感评分）生成多样且适应性强的阿尔法。这些阿尔法不直接用于交易，而是作为捕捉金融数据中复杂依赖关系的高级特征。为了评估这些LLM生成的公式化阿尔法的有效性，将阿尔法特征输入预测模型，如Transformer、LSTM、TCN、SVR和随机森林，以预测未来的股票价格。实验结果表明，LLM生成的阿尔法显著提高了预测准确性。此外，LLM提供的伴随自然语言推理增强了预测的可解释性和透明度，支持更明智的金融决策。 显示英文摘要

摘要： Traditionally, traders and quantitative analysts address alpha decay by manually crafting formulaic alphas, mathematical expressions that identify patterns or signals in financial data, through domain expertise and trial-and-error. This process is often time-consuming and difficult to scale. With recent advances in large language models (LLMs), it is now possible to automate the generation of such alphas by leveraging the reasoning capabilities of LLMs. This paper introduces a novel framework that integrates a prompt-based LLM with a Transformer model for stock price prediction. The LLM first generates diverse and adaptive alphas using structured inputs such as historical stock features (Close, Open, High, Low, Volume), technical indicators, sentiment scores of both target and related companies. These alphas, instead of being used directly for trading, are treated as high-level features that capture complex dependencies within the financial data. To evaluate the effectiveness of these LLM-generated formulaic alphas, the alpha features are then fed into prediction models such as Transformer, LSTM, TCN, SVR, and Random Forest to forecast future stock prices. Experimental results demonstrate that the LLM-generated alphas significantly improve predictive accuracy. Moreover, the accompanying natural language reasoning provided by the LLM enhances the interpretability and transparency of the predictions, supporting more informed financial decision-making.