Using Language Models to Augment Data in Stock Prediction

Abstract

This paper delves into the innovative application of language models as a means of enhancing data augmentation techniques in the context of Sentiment Analysis for Event-Driven Stock Prediction. In recent years, the integration of natural language processing and machine learning has led to significant advancements in sentiment analysis, enabling the extraction of valuable insights from textual data for enhancing stock prediction accuracy. In this work, we incorporate T-5 language model to enrich the training dataset with semantically diverse and contextually relevant textual variations. By conducting extensive experimental results, we demonstrate the effectiveness of using T-5 for data augmentation in the task of Sentiment Analysis for Event-Driven Stock Prediction..