Assessing the Correlation Between News Sentiment and Stock Price Movements: A Case Study of ‘WeWork’ Using Advanced NLP Techniques

Document Type

Conference Proceeding

Source of Publication

2024 International Conference on Computer and Applications (ICCA)

Publication Date

12-19-2024

Abstract

This research explores the intricate relationship between news article sentiment and stock price movements, with the company ‘WeWork’ serving as a case study. Leveraging advanced Natural Language Processing (NLP) techniques and Large Language Models (LLMs) such as Open AI, this study aims to transform unstructured textual data into actionable financial insights. Through rigorous data collection and preprocessing, sentiment scores were derived from a wide range of news sources and correlated with historical stock prices. Statistical analyses, including linear regression and correlation metrics, revealed a weak positive correlation of 16.47% between sentiment and stock prices. Although the correlation suggests that sentiment analysis can offer valuable insights into market trends, it is insufficient as a standalone predictor for investment decisions. The findings underscore the importance of integrating sentiment analysis with traditional financial metrics, and call for the development of more robust models incorporating diverse data sources in future research.

ISBN

979-8-3503-6756-0

Publisher

IEEE

Volume

00

First Page

1

Last Page

6

Disciplines

Business | Computer Sciences

Keywords

News Sentiment, Stock Price Movements, Natural Language Processing, WeWork, Financial Insights

Indexed in Scopus

no

Open Access

no

Link to Full Text

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