Eric-Woo/Tesla-stock-price-prediction-
This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.