  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.
