Abstract
Contemporarily, Investors spend plenty of time to speculate and predict the growing trend of the stock price in order to gain extra return from the stock market. Nowadays, the problem of natural resources and global warming has put oil-fueled automotive into controversial dispute. Therefore, the importance of environment-friendly automotive is remarkable in global scale. Tesla (TSLA), as one of the leading electric automotive builders, widely attracted the attention of investors around the world. In this article, we will adopt several state-of-art models in machine learning to predict the stock price of Tesla including ARIMA, LSTM, Linear Regression to analyze the stock price of TSLA. 80% of data is used to be the training set and 20% as the contrast group to verify the accuracy of the prediction. According to the analysis, the outcome of ARIMA model is quite accurate, and LSTM model is better than linear regression model. These results shed light on guiding further exploration of electric vehicle, the new blood of automobile industry.
Reference10 articles.
1. Nikou M, Mansourfar G, Bagherzadeh J. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 2019, 26(4): 164-174.
2. Ho M K, Darman H, Musa S. Stock Price Prediction Using ARIMA, Neural Network and LSTM Models. Journal of Physics: Conference Series. IOP Publishing, 2021, 1988(1): 012041.
3. King Ciaran. Cars and global warming. The Irish Times, May 21, 1991, pp. 13. ProQuest, Retrieved from: https://ezaccess.libraries.psu.edu/login?qurl=https%3A%2F%2Fwww.proquest.com%2Fhistorical-newspapers%2Fcars-global-warming%2Fdocview%2F530057143%2Fse-2%3Faccountid%3D13158.
4. Nick S, Thoenes S. What drives natural gas prices?—A structural VAR approach. Energy Economics, 2014, 45: 517-527.
5. Business This Week. The Economist, The Economist Newspaper, Retrieve from: https://www.economist.com/the-world-this-week/2021/02/11/business-this-week.