Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 45 , 01 December 2023
Open
Access | Article
Using Machine Learning Methods to Predict Tesla Stock
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Abstract
Electric vehicles are now a common mode of transportation due to the aggressive promotion of new energy. Tesla currently has the largest market share among all brands, as well as its techniques and patents may ensure the benefits of future development. And also, Elon Musk is a powerful and ambitious businessman who can steer a technological company toward a brighter future. Yet, due to some uncontrollable factors like Covid-19, disaster incidents, technical staff resignations, etc., might influence the prospects of Tesla stock. So, in this study, machine learning techniques will be primarily employed to forecast Tesla stock prices' trajectory over the next 30 days. The two primary methods used to forecast and evaluate accuracy to determine which model is more appropriate are linear regression and random forest. Before the model is trained, all of the stock data is divided into a training side and a test side. According to the research, linear regression model performs better in predicting the direction of Tesla stock than a random forest model. Based on the search results, it can be said that machine learning methods are likely to unearth patterns and insights that humans haven’t seen before and can be used to make accurate and unmistakable stock predictions.
Keywords
Machine Learning Methods, Tesla Stock, Linear Regression, Random Forest
References
1. Yahoo!: Tesla, Inc. (TSLA) Stock Price, News, Quote & History. Retrieved April 3, 2023, from https://finance.yahoo.com/quote/TSLA/.
2. Srivastava P., Mishra P, K.: Stock Market Prediction Using RNN LSTM.In 2021 2nd Global Conference for Advancement in Technology (GCAT), pp. 1-5. IEEE, Bangalore, India (2021).
3. Marchai F. L., Martin W., Suhartono D.: Stock Prices Prediction Using Machine Learning. In: 8th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), pp. 79-84. IEEE, Semarang, Indonesia (2021).
4. Mottaghi N., Farhangdoost S.: Stock Price Forecasting in Presence of Covid-19 Pandemic and Evaluating Performances of Machine Learning Models for Time-Series Forecasting, Papers 2105.02785, arXiv.org. (2021).
5. Chatterjee A., Bhowmik H., Sen J.: Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models. In: 2021 IEEE Mysore Sub Section International Conference (MysuruCon), pp. 289-296.IEEE (2021).
6. Bhuriya D., Kaushal G., Sharma A., Singh U.: Stock Market Prediction Using A Linear Regression. In: International conference of Electronics, Communication and Aerospace Technology (ICECA), pp. 510-513.IEEE (2017).
7. Hastie, T., Tibshirani, R., Friedman, Jerome, F.: The Elements of Statis-tical Learning Data Mining, Inference, and Prediction. 2nd edn. Springer, Heidelberg (2009).
8. Piryonesi S, M., El-Diraby T, E.: Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems. Journal of Transportation Engineering, Part B: Pavements. 146 (2), (2020).
9. Piryonesi S., M., El-Diraby T, E.: Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling. Journal of Infrastructure Systems. 27 (2), (2021).
10. Gareth J., Daniela W., Trevor H., et al.: An Introduction to Statistical Learning with applications in R. P343-345, Springer New York, NY (2021).
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 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-137-7
- ISBN (Online)
- 978-1-83558-138-4
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/45/20230263
- Copyright
- 01 December 2023
- 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