Predictive Analysis of Tesla Inc. Stock with Machine Learning

Zhicheng Jiang

doi:10.54254/2754-1169/76/20241760

Research Article

Open access

Published on 18 April 2024

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Jiang,Z. (2024). Predictive Analysis of Tesla Inc. Stock with Machine Learning. Advances in Economics, Management and Political Sciences,76,8-14.

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Predictive Analysis of Tesla Inc. Stock with Machine Learning

Zhicheng Jiang *^,1,

¹ University of Illinois at Urbana-Champaign, United States, 61820

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/76/20241760

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

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References

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Cite this article

Jiang,Z. (2024). Predictive Analysis of Tesla Inc. Stock with Machine Learning. Advances in Economics, Management and Political Sciences,76,8-14.

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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About volume

Volume title: Proceedings of the 3rd International Conference on Business and Policy Studies

Conference website: https://www.confbps.org/

ISBN：978-1-83558-375-3(Print) / 978-1-83558-376-0(Online)

Conference date: 27 February 2024

Editor：Arman Eshraghi

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.76

ISSN：2754-1169(Print) / 2754-1177(Online)

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