Forecasting US Stock Prices Through Sentiment Analysis and Machine Learning–A Case Study of Tesla Inc.

The financial sector is characterized by high volatility, and the accurate forecasting of stock prices is an actively pursued area of research and analysis. This study extends the scope of machine learning techniques, such as Artificial Neural Networks and fuzzy-based techniques, to enhance the precision of stock price predictions. The central focus of this research is algorithmic trading, which combines various qualitative factors in stock buying and selling decisions. More specifically, this study delves into the unique relationship between Elon Musk’s tweets and Tesla’s stock value. To identify patterns in the pre-processed dataset, which has had stop words removed, exploratory data analysis is used as the primary research methodology. The study conclusively demonstrates that a positive correlation exists between the number of tweets/engagements and Tesla’s closing price, and this correlation holds true in reverse: a decrease in tweets/engagements corresponds with a decrease in Tesla’s closing price. This research has broader implications for macroeconomic analysis of the US stock market by highlighting the role of technology and innovation in financial markets, as well as the importance of data-driven approaches in economic