用十种大模型情绪分提升股票分钟级预测，Mamba比Reformer更快更准。
Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs
- 融合十种LLM情绪分与分钟级股价数据，构建实时预测框架。
- Mamba在所有LLM下均优于Reformer，最佳误差达0.137（LLaMA 3.3-70B）。
- 适合关注实时金融预测与高效时序建模的研究者和从业者。
由于市场高波动性、新闻影响及金融时间序列的非线性特征，短期股市预测极具挑战。本文提出一种新框架，通过整合前十种大型语言模型（LLMs）生成的语义情绪分与分钟级盘中股价数据，提升分钟级预测精度。研究系统构建了2025年4月4日至5月2日苹果公司（AAPL）新闻文章与1分钟股价的时间对齐数据集。使用DeepSeek-V3、GPT系列、LLaMA、Claude、Gemini、Qwen和Mistral等模型的API进行情绪分析，将每篇文章的情绪分统一缩放到[0,1]区间，并结合价格与技术指标（如RSI、ROC、布林带宽）。分别以各LLM生成的情绪分为输入，训练Reformer和Mamba两种先进模型，通过Optuna优化超参数，并在3天评估期内进行测试。结果显示，所有情况下Mamba均优于Reformer，且速度更快；其中使用LLaMA 3.3-70B情绪分时表现最佳，均方误差最低为0.137。尽管Reformer能捕捉整体趋势，但对突发变化存在过度平滑问题。该研究证明，结合基于LLM的语义分析与高效时序建模，可显著提升实时金融预测能力。
原文摘要 · Abstract (English)
The stock market is extremely difficult to predict in the short term due to high market volatility, changes caused by news, and the non-linear nature of the financial time series. This research proposes a novel framework for improving minute-level prediction accuracy using semantic sentiment scores from top ten different large language models (LLMs) combined with minute interval intraday stock price data. We systematically constructed a time-aligned dataset of AAPL news articles and 1-minute Apple Inc. (AAPL) stock prices for the dates of April 4 to May 2, 2025. The sentiment analysis was achieved using the DeepSeek-V3, GPT variants, LLaMA, Claude, Gemini, Qwen, and Mistral models through their APIs. Each article obtained sentiment scores from all ten LLMs, which were scaled to a [0, 1] range and combined with prices and technical indicators like RSI, ROC, and Bollinger Band Width. Two state-of-the-art such as Reformer and Mamba were trained separately on the dataset using the sentiment scores produced by each LLM as input. Hyper parameters were optimized by means of Optuna and were evaluated through a 3-day evaluation period. Reformer had mean squared error (MSE) or the evaluation metrics, and it should be noted that Mamba performed not only faster but also better than Reformer for every LLM across the 10 LLMs tested. Mamba performed best with LLaMA 3.3--70B, with the lowest error of 0.137. While Reformer could capture broader trends within the data, the model appeared to over smooth sudden changes by the LLMs. This study highlights the potential of integrating LLM-based semantic analysis paired with efficient temporal modeling to enhance real-time financial forecasting.
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