Analyzing Causality between Actual Stock Prices and User-weighted Sentiment in Social Media for Stock Market Prediction

In this thesis, an improved sentiment analysis algorithm is proposed which reflects the impact of user, and to analyze whether public sentiment calculated by the proposed algorithm can contribute to stock prediction. The proposed sentiment analysis algorithm reflects the factors of Twitter which are relevant to users’ authority to calculate sentiment weight of each message that is different from existing sentiment analysis algorithms. Linear and nonlinear prediction models are constructed to forecast future stock prices of selected companies. The proposed algorithm is applied to both linear and nonlinear prediction models and comparisons of prediction accuracy with the existing sentiment analysis algorithm are performed. To support the approach of the proposed algorithm that the authoritative users affect the other users, causal relationship between them is figured out through Granger Causality analysis. Further analysis is also provided to find causal relationship between public sentiment and the actual changes of the stock prices.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2016

Author dc:creator

• Park, Jin-Tak

Advisor dc:contributor.advisor

• Leung, Henry

Committee members dc:contributor.committeemember

• Far, Behrouz
• Ruhe, Guenther

• Engineering--Electronics and Electrical

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/24828

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:11023/3374