Predicting Tesla Stock Return Using Twitter Data - An Intraday View on the Relation between Twitter Dimensions and the Tesla Stock Return
(2020) NEKP01 20201Department of Economics
- Abstract
- In this thesis, Twitter data is used to predict the intraday stock return for Tesla, Inc. We present two different methods to extract the tweets’ sentiment: A dictionary-based approach (VADER) and a machine learning approach (SVM).
Additionally, we control for other dimensions as the user and discussion dimension. Then a Granger causality test and a lasso regression are conducted on a one- and five-minute interval. The results suggest that there is no predictive power in the information of the tweets for the dictionary data set and the machine learning data set. Using a subset of the dictionary data set with only the cashtag does not alter the results. The reason for this may be that we employ two linear models on a
possible non-linear... (More) - In this thesis, Twitter data is used to predict the intraday stock return for Tesla, Inc. We present two different methods to extract the tweets’ sentiment: A dictionary-based approach (VADER) and a machine learning approach (SVM).
Additionally, we control for other dimensions as the user and discussion dimension. Then a Granger causality test and a lasso regression are conducted on a one- and five-minute interval. The results suggest that there is no predictive power in the information of the tweets for the dictionary data set and the machine learning data set. Using a subset of the dictionary data set with only the cashtag does not alter the results. The reason for this may be that we employ two linear models on a
possible non-linear problem. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9025587
- author
- Edman, Gustav LU and Weishaupt, Martin LU
- supervisor
- organization
- course
- NEKP01 20201
- year
- 2020
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Sentiment, Tesla, Twitter, Support Vector Machine, VADER.
- language
- English
- id
- 9025587
- date added to LUP
- 2020-08-29 10:39:12
- date last changed
- 2020-08-29 10:39:12
@misc{9025587, abstract = {{In this thesis, Twitter data is used to predict the intraday stock return for Tesla, Inc. We present two different methods to extract the tweets’ sentiment: A dictionary-based approach (VADER) and a machine learning approach (SVM). Additionally, we control for other dimensions as the user and discussion dimension. Then a Granger causality test and a lasso regression are conducted on a one- and five-minute interval. The results suggest that there is no predictive power in the information of the tweets for the dictionary data set and the machine learning data set. Using a subset of the dictionary data set with only the cashtag does not alter the results. The reason for this may be that we employ two linear models on a possible non-linear problem.}}, author = {{Edman, Gustav and Weishaupt, Martin}}, language = {{eng}}, note = {{Student Paper}}, title = {{Predicting Tesla Stock Return Using Twitter Data - An Intraday View on the Relation between Twitter Dimensions and the Tesla Stock Return}}, year = {{2020}}, }