Amalia Temneanu, Dae-Jin Rhee, Marcela Ulloa, Nicolas Bidaux
This project was a data challenge for the third week of the Data Science Bootcamp at SIT Academy.
Is there a basis for the so-called “Elon Effect”?
Taking some famous tweets from ELon Musk and Jack Dorsey and the Stock Price change for Tesla and Twitter as example, the group proposed this question.
https://twitter.com/jack/status/1465347002426867720?s=20 (TWTR Stock Price -4%)
https://twitter.com/elonmusk/status/1451015695106560000?s=20 (TSLA Stock Price +5%)
https://twitter.com/elonmusk/status/1457064697782489088?s=20 (TSLA Stock Price -5%)
- Twitter API: Fetching Tweets of company CEOs using Twython.
- Stock prices of publicly listed companies related to the CEOs (Source: Yahoo finance)
- Data cleaning
- TextBlob or VADER for Sentiment Analysis
- WordCloud
- Statistical analysis:
- T-test
- Correlation matrix: using Pearson correlation coefficient and Spearman correlation coefficient
- Simple Linear Regression model
- Multiple linear regression model
No correlation found when analysing CEO’s Twitter accounts and their companies’ stock prices evolution.
There is a higher correlation for tweets from Makert influencers in different sectors (commodities, real-estate, etc.).
Perform statistical analysis a larger amount of data from Market Influencers.
https://betterprogramming.pub/detecting-sentiment-from-elon-musks-tweets-using-python-ec7820469ac0 https://towardsdatascience.com/can-we-beat-the-stock-market-using-twitter-ef8465fd12e2 https://towardsdatascience.com/fine-grained-sentiment-analysis-in-python-part-1-2697bb111ed4