Natural Language Processing and Multimodal Stock Price Prediction
This work addresses stock price prediction for financial decision-makers, but it is incremental as it builds on existing AI techniques like LSTMs, SVMs, and NLP with a focus on data representation.
The paper tackled stock price prediction by using stock percentage change as training data and analyzing news articles with BERT models, achieving accurate trend predictions and highlighting effective data features and sector-specific data.
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.