Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Advances in Economics, Management and Political Sciences
Series Vol. 76 , 18 April 2024
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Predictive Analysis of Tesla Inc. Stock with Machine Learning
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Abstract
Tesla currently stands among the world's most prominent corporations, especially in the development of new energy sources and the automotive industry. With the company's growth rising, the value of its stock has become a major focus for investors. There are many ways to predict stock prices, but most of them are more subjective and based on personal experience, so the value of the reference is not great and varies from person to person. Therefore, this paper aims to investigate whether it is possible to analyze the price trend of Tesla's stock from the perspective of data through machine learning. The research will examine the stock price of Tesla based on the ARIMA-LSTM combined model prediction method to determine if this method can be applied to predict the stock price trend of Tesla and similar technology companies. Finally, after testing Tesla's stock data in 2019, it can be concluded that the machine learning prediction method based on the ARIMA-LSTM combination model is highly accurate and can be used to predict the future stock price trends of similar companies.
Keywords
Tesla, ARIMA, LSTM, Machine Learning, Stock Prediction
References
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11. Data source. Model Whale (https://www.heywhale.com/mw/dataset/6374aadfb1d622f1cb68f2ca/file)
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Business and Policy Studies
- ISBN (Print)
- 978-1-83558-375-3
- ISBN (Online)
- 978-1-83558-376-0
- Published Date
- 18 April 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/76/20241760
- Copyright
- 18 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited