Social Media Communication and Its Impact on Stock Price Volatility During Product Launches

Abstract

This thesis investigates the influence of social media communication on stock price volatility, focusing on events related to well-known companies like Tesla, Apple, TSMC, Meta, Amazon, Microsoft, Nio Inc., and P&G. By analyzing tweets and YouTube comments surrounding key corporate events, sentiment analysis will be conducted to assess correlations with stock price fluctuations during these periods. The research adopts a quantitative data analysis with qualitative sentiment evaluation. Sentiment scores will be calculated based on a set of positive and negative keywords, facilitating a deeper understanding of the relationship between public sentiment and market reactions. The findings reveal that patterns in social media sentiment are not only associated with short-term stock price volatility but also provide predictive insights into market trends. A machine learning model is employed to analyze the relationship between sentiment dynamics and stock price volatility, helping to predict how stock prices may fluctuate in response to changes in sentiment. This study aims to provide actionable insights for corporations and investors, enabling informed decisions based on the impact of social media on financial markets. The findings contribute to existing literature on behavioural finance and social media's role in investor sentiment, with potential implications for future corporate communication strategies.This thesis investigates the influence of social media communication on stock price volatility, focusing on events related to well-known companies like Tesla, Apple, TSMC, Meta, Amazon, Microsoft, Nio Inc., and P&G. By analyzing tweets and YouTube comments surrounding key corporate events, sentiment analysis will be conducted to assess correlations with stock price fluctuations during these periods. The research adopts a quantitative data analysis with qualitative sentiment evaluation. Sentiment scores will be calculated based on a set of positive and negative keywords, facilitating a deeper understanding of the relationship between public sentiment and market reactions. The findings reveal that patterns in social media sentiment are not only associated with short-term stock price volatility but also provide predictive insights into market trends. A machine learning model is employed to analyze the relationship between sentiment dynamics and stock price volatility, helping to predict how stock prices may fluctuate in response to changes in sentiment. This study aims to provide actionable insights for corporations and investors, enabling informed decisions based on the impact of social media on financial markets. The findings contribute to existing literature on behavioural finance and social media's role in investor sentiment, with potential implications for future corporate communication strategies