Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market

Daniel Pyeong Kang Kim; Jongwhee Lee; Jungwoo Lee; Jeanne Suh

doi:doi:10.11648/j.ijdsa.20210701.14

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Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market

Daniel Pyeong Kang Kim, Jongwhee Lee, Jungwoo Lee, Jeanne Suh

Published in International Journal of Data Science and Analysis (Volume 7, Issue 1)

Received: 28 February 2021 Accepted: 16 March 2021 Published: 26 March 2021

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Abstract

Over the past few years, Twitter has rapidly grown into a prominent social media platform, and various research papers have attempted to prove the relationship between the stocks and the tweets made on Twitter. The purpose of this research paper is to investigate the specific connection between Elon Musk’s twitter and the stock value of Tesla. The primary form of analysis used was Exploratory Data Analysis to be able to more easily distinguish patterns within our dataset, which was preprocessed to exclude any stopwords. Utilizing various graphs and Machine Learning algorithms such as Logistic Regression and Support Vector Machine, we wrote this research paper that respectively analyzes the change in the close price of Tesla’s stock and Elon Musk’s Twitter engagement, including tweets, likes, and retweets dating from the start of 2015 up until July of 2020. Furthermore, the article illustrates the contents of Elon Musk’s tweets and allows a deeper understanding of other correlations that may exist through the use of Machine Learning to perform Sentiment Analysis. This was achieved by categorizing Elon’s tweets into three different tones (positive, negative, and neutral) and seeing how the underlying mood would correspondingly affect Tesla’s stock value. The combination of such techniques and factors allowed for a conclusive result in which a distinct correlation was apparent: an increase in the number of tweets/engagement would lead to an increase in the closing price of Tesla, as well as vice versa.

Published in	International Journal of Data Science and Analysis (Volume 7, Issue 1)
DOI	10.11648/j.ijdsa.20210701.14
Page(s)	13-19
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Science, Elon Musk, Stock Market, Machine Learning, Exploratory Data Analysis, Tesla

References

[1]	Pendleton, Devon. “Elon Musk Overtakes Bill Gates to Grab World’s Second-Richest Ranking.” Bloomberg.com, Bloomberg, 24 Nov. 2020.
[2]	“Elon Musk Biography.” Biography.com, A & amp; E Networks Television, 17 Nov. 2020.
[3]	Ross, Sean. “Elon Musk's Best Investments.” Investopedia, Investopedia, 8 Sept. 2020.
[4]	O. Vynakov, E. V. Sa, and A. I Skryn “Modern Electric Cars of Tesla Motors Company”, Automation Technological and Business Processes.
[5]	Schreiber, Barbara A. “Tesla, Inc.” Encyclopædia Britannica, Encyclopædia Britannica, Inc., www.britannica.com/topic/Tesla-Motors.
[6]	Tahir M. Nisar and Man Yeung, “Twitter as a tool for forecasting stock market movements: A short-window event study”, Science Direct, Feb 2018.
[7]	Emanuele Teti et al., “The relationship between twitter and stock prices. Evidence from the US technology industry”, Science Direct, Nov 2019.
[8]	“Exploratory Data Analysis.” Carnegie Mellon University Statistics & Data Science, www.stat.cmu.edu/~hseltman/309/Book/chapter4.pdf.
[9]	“Python - Tokenization.” Tutorialspoint, www.tutorialspoint.com/python_text_processing/python_tokenization.htm.
[10]	Pant, Ayush. “Introduction to Logistic Regression.” Medium, Towards Data Science, 22 Jan. 2019.
[11]	H. Patel and P. Pra “Study and Analysis of Decision Tree Based Classification Algorithm, International Journal of Computer Sciences and Engineering, Oct 2018.
[12]	F. Qin, X. Tan, Z. Cheng “Application and research of multi_label Naïve Bayes Classifier, Proceedings of the 10^th World Congress on Intelligent Control and Automation, July 2012.
[13]	“A. Wibawa, A. Kurn, D. Murti, R. Adi “Naïve Bayes Classifier for Journal Quartile Classification, June, 2019.
[14]	T. Koren, K. Jarv, J. Lau, M. Juh “Stemming and Lemmatiztion in the clustering of Finnish text documents”, January 2004.
[15]	S. Qaiser and R. Ali “Text Minig: Use of TF-IDF to Examine the Relevance of Words to Documents”, July 2018.

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APA Style

Daniel Pyeong Kang Kim, Jongwhee Lee, Jungwoo Lee, Jeanne Suh. (2021). Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market. International Journal of Data Science and Analysis, 7(1), 13-19. https://doi.org/10.11648/j.ijdsa.20210701.14

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ACS Style

Daniel Pyeong Kang Kim; Jongwhee Lee; Jungwoo Lee; Jeanne Suh. Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market. Int. J. Data Sci. Anal. 2021, 7(1), 13-19. doi: 10.11648/j.ijdsa.20210701.14

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AMA Style

Daniel Pyeong Kang Kim, Jongwhee Lee, Jungwoo Lee, Jeanne Suh. Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market. Int J Data Sci Anal. 2021;7(1):13-19. doi: 10.11648/j.ijdsa.20210701.14

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@article{10.11648/j.ijdsa.20210701.14,
  author = {Daniel Pyeong Kang Kim and Jongwhee Lee and Jungwoo Lee and Jeanne Suh},
  title = {Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {1},
  pages = {13-19},
  doi = {10.11648/j.ijdsa.20210701.14},
  url = {https://doi.org/10.11648/j.ijdsa.20210701.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210701.14},
  abstract = {Over the past few years, Twitter has rapidly grown into a prominent social media platform, and various research papers have attempted to prove the relationship between the stocks and the tweets made on Twitter. The purpose of this research paper is to investigate the specific connection between Elon Musk’s twitter and the stock value of Tesla. The primary form of analysis used was Exploratory Data Analysis to be able to more easily distinguish patterns within our dataset, which was preprocessed to exclude any stopwords. Utilizing various graphs and Machine Learning algorithms such as Logistic Regression and Support Vector Machine, we wrote this research paper that respectively analyzes the change in the close price of Tesla’s stock and Elon Musk’s Twitter engagement, including tweets, likes, and retweets dating from the start of 2015 up until July of 2020. Furthermore, the article illustrates the contents of Elon Musk’s tweets and allows a deeper understanding of other correlations that may exist through the use of Machine Learning to perform Sentiment Analysis. This was achieved by categorizing Elon’s tweets into three different tones (positive, negative, and neutral) and seeing how the underlying mood would correspondingly affect Tesla’s stock value. The combination of such techniques and factors allowed for a conclusive result in which a distinct correlation was apparent: an increase in the number of tweets/engagement would lead to an increase in the closing price of Tesla, as well as vice versa.},
 year = {2021}
}

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TY  - JOUR
T1  - Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market
AU  - Daniel Pyeong Kang Kim
AU  - Jongwhee Lee
AU  - Jungwoo Lee
AU  - Jeanne Suh
Y1  - 2021/03/26
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DO  - 10.11648/j.ijdsa.20210701.14
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 13
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PB  - Science Publishing Group
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UR  - https://doi.org/10.11648/j.ijdsa.20210701.14
AB  - Over the past few years, Twitter has rapidly grown into a prominent social media platform, and various research papers have attempted to prove the relationship between the stocks and the tweets made on Twitter. The purpose of this research paper is to investigate the specific connection between Elon Musk’s twitter and the stock value of Tesla. The primary form of analysis used was Exploratory Data Analysis to be able to more easily distinguish patterns within our dataset, which was preprocessed to exclude any stopwords. Utilizing various graphs and Machine Learning algorithms such as Logistic Regression and Support Vector Machine, we wrote this research paper that respectively analyzes the change in the close price of Tesla’s stock and Elon Musk’s Twitter engagement, including tweets, likes, and retweets dating from the start of 2015 up until July of 2020. Furthermore, the article illustrates the contents of Elon Musk’s tweets and allows a deeper understanding of other correlations that may exist through the use of Machine Learning to perform Sentiment Analysis. This was achieved by categorizing Elon’s tweets into three different tones (positive, negative, and neutral) and seeing how the underlying mood would correspondingly affect Tesla’s stock value. The combination of such techniques and factors allowed for a conclusive result in which a distinct correlation was apparent: an increase in the number of tweets/engagement would lead to an increase in the closing price of Tesla, as well as vice versa.
VL  - 7
IS  - 1
ER  -

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Author Information

Daniel Pyeong Kang Kim

Stony Brook School, New York, USA
Jongwhee Lee

Yongsan International School of Seoul, Seoul, South Korea
Jungwoo Lee

Peddie School, New Jersey, USA
Jeanne Suh

Saint Paul Preparatory, Seoul, South Korea

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Plain Text BibTeX RIS

APA Style

Daniel Pyeong Kang Kim, Jongwhee Lee, Jungwoo Lee, Jeanne Suh. (2021). Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market. International Journal of Data Science and Analysis, 7(1), 13-19. https://doi.org/10.11648/j.ijdsa.20210701.14

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ACS Style

Daniel Pyeong Kang Kim; Jongwhee Lee; Jungwoo Lee; Jeanne Suh. Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market. Int. J. Data Sci. Anal. 2021, 7(1), 13-19. doi: 10.11648/j.ijdsa.20210701.14

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AMA Style

Daniel Pyeong Kang Kim, Jongwhee Lee, Jungwoo Lee, Jeanne Suh. Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market. Int J Data Sci Anal. 2021;7(1):13-19. doi: 10.11648/j.ijdsa.20210701.14

Copy | Download

@article{10.11648/j.ijdsa.20210701.14,
  author = {Daniel Pyeong Kang Kim and Jongwhee Lee and Jungwoo Lee and Jeanne Suh},
  title = {Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {1},
  pages = {13-19},
  doi = {10.11648/j.ijdsa.20210701.14},
  url = {https://doi.org/10.11648/j.ijdsa.20210701.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210701.14},
  abstract = {Over the past few years, Twitter has rapidly grown into a prominent social media platform, and various research papers have attempted to prove the relationship between the stocks and the tweets made on Twitter. The purpose of this research paper is to investigate the specific connection between Elon Musk’s twitter and the stock value of Tesla. The primary form of analysis used was Exploratory Data Analysis to be able to more easily distinguish patterns within our dataset, which was preprocessed to exclude any stopwords. Utilizing various graphs and Machine Learning algorithms such as Logistic Regression and Support Vector Machine, we wrote this research paper that respectively analyzes the change in the close price of Tesla’s stock and Elon Musk’s Twitter engagement, including tweets, likes, and retweets dating from the start of 2015 up until July of 2020. Furthermore, the article illustrates the contents of Elon Musk’s tweets and allows a deeper understanding of other correlations that may exist through the use of Machine Learning to perform Sentiment Analysis. This was achieved by categorizing Elon’s tweets into three different tones (positive, negative, and neutral) and seeing how the underlying mood would correspondingly affect Tesla’s stock value. The combination of such techniques and factors allowed for a conclusive result in which a distinct correlation was apparent: an increase in the number of tweets/engagement would lead to an increase in the closing price of Tesla, as well as vice versa.},
 year = {2021}
}

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TY  - JOUR
T1  - Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market
AU  - Daniel Pyeong Kang Kim
AU  - Jongwhee Lee
AU  - Jungwoo Lee
AU  - Jeanne Suh
Y1  - 2021/03/26
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210701.14
DO  - 10.11648/j.ijdsa.20210701.14
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 13
EP  - 19
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210701.14
AB  - Over the past few years, Twitter has rapidly grown into a prominent social media platform, and various research papers have attempted to prove the relationship between the stocks and the tweets made on Twitter. The purpose of this research paper is to investigate the specific connection between Elon Musk’s twitter and the stock value of Tesla. The primary form of analysis used was Exploratory Data Analysis to be able to more easily distinguish patterns within our dataset, which was preprocessed to exclude any stopwords. Utilizing various graphs and Machine Learning algorithms such as Logistic Regression and Support Vector Machine, we wrote this research paper that respectively analyzes the change in the close price of Tesla’s stock and Elon Musk’s Twitter engagement, including tweets, likes, and retweets dating from the start of 2015 up until July of 2020. Furthermore, the article illustrates the contents of Elon Musk’s tweets and allows a deeper understanding of other correlations that may exist through the use of Machine Learning to perform Sentiment Analysis. This was achieved by categorizing Elon’s tweets into three different tones (positive, negative, and neutral) and seeing how the underlying mood would correspondingly affect Tesla’s stock value. The combination of such techniques and factors allowed for a conclusive result in which a distinct correlation was apparent: an increase in the number of tweets/engagement would lead to an increase in the closing price of Tesla, as well as vice versa.
VL  - 7
IS  - 1
ER  -

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