Abstract

The stock market is a very volatile component of the financial domain. Accurate predictions of various stocks are a highly active area of research and analysis. Following the previous ML prediction techniques using Artificial Neural networks and fuzzy-based techniques, this research aims to extend the accurate prediction results. Since multiple qualitative factors go into the decision-making of a buy-sell of stock, a blend of algorithmic trading is at the cornerstone of the research. This research work aims to look into the unique relationship between Elon Musk's Tweets and Tesla's stock value. Exploratory Data Analysis was employed as the primary analysis method to better differentiate patterns within our dataset, which had been pre-processed to remove any stop words. Combining these methodologies and elements yielded a decisive conclusion with a clear correlation: an increase in the number of tweets/engagements corresponded to an increase in Tesla's closing price and vice versa.

Keywords: Tesla, Elon Musk, Stock Market, Sentiment Analysis, Machine Learning

How to Cite:

Bhadkamar, A. & Bhattacharya, S., (2022) “Tesla Inc. Stock Prediction using Sentiment Analysis”, Australasian Accounting, Business and Finance Journal 16(5), 52-66. doi: https://doi.org/10.14453/aabfj.v16i5.05