Natural Language Processing and Multimodal Stock Price Prediction

TL;DR

This paper explores the use of NLP models, particularly BERT, combined with stock percentage change data and multimodal inputs, to improve stock trend prediction accuracy.

Contribution

It introduces a novel approach using percentage change data and specialized BERT models for multimodal stock trend prediction, emphasizing sector-specific features.

Findings

Small NLP models can accurately predict stock trends

Certain data features significantly enhance prediction accuracy

Sector-specific data improves model performance

Abstract

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.