Sentiment Analysis and Its Impact on Stock Market Volatility: Leveraging Natural Language Processing Techniques

Abstract

Sentiment analysis has emerged as a novel method in financial markets, utilizing textual data such as news and social media posts to predict stock price volatility. This technique helps investors and financial institutions make more informed decisions by extracting positive and negative sentiments and analyzing their impact on financial markets. To examine the impact of sentiment analysis on stock market volatility and evaluate the advantages, limitations, and opportunities of using this technique for market behavior prediction. This paper provides a comprehensive review of past studies and analyses on the relationship between sentiment analysis and market volatility. The reviewed data includes results from natural language processing (NLP)-based analysis, machine learning algorithms, and social media data. The findings indicate that sentiment analysis, particularly during economic crises, can serve as a strong predictive indicator. Positive sentiments lead to stock price increases, while negative sentiments cause price declines. Additionally, NLP-based tools have improved prediction accuracy. However, challenges such as modeling complexity and data quality remain significant. Sentiment analysis is recognized as an effective tool for short- and mid-term volatility predictions, especially in dynamic financial markets. To enhance its accuracy and effectiveness, the use of more advanced models and improvements in data quality are recommended.