In the realm of financial decision-making, predicting stock prices is pivotal. Artificial intelligence techniques such as long short-term memory networks (LSTMs), support-vector machines (SVMs), and natural language processing (NLP) models are commonly employed to predict said prices. This paper utilizes stock percentage change as training data, in contrast to the traditional use of raw currency values, with a focus on analyzing publicly released news articles. The choice of percentage change aims to provide models with context regarding the significance of price fluctuations and overall price change impact on a given stock. The study employs specialized BERT natural language processing models to predict stock price trends, with a particular emphasis on various data modalities. The results showcase the capabilities of such strategies with a small natural language processing model to accurately predict overall stock trends, and highlight the effectiveness of certain data features and sector-specific data.

翻译：在金融决策领域中，预测股票价格至关重要。长短期记忆网络（LSTM）、支持向量机（SVM）及自然语言处理（NLP）模型等人工智能技术常被用于此类预测。本文采用股票百分比变化作为训练数据，区别于传统使用的原始货币数值，并重点分析公开发布的新闻文章。选择百分比变化旨在为模型提供关于价格波动显著性及整体价格变化对特定股票影响的背景信息。本研究运用专门的BERT自然语言处理模型预测股票价格趋势，特别关注多种数据模态。结果显示，这类策略借助小型自然语言处理模型即可准确预测整体股票趋势，同时突显了特定数据特征与行业细分数据的有效性。