study, I propose a novel entity-specific sentiment index to examine how massive
general knowledge can be quantified and used to extract better financial
inferences from media outlets following a similar reasoning process as human
news readers. With the advent of graph representation techniques, external
knowledge can be represented by knowledge graphs, and then quantified through
graph embedding processes. By modifying traditional sentiment analysis using
quantified knowledge, I find that the introduction of external knowledge
significantly and consistently improves the predictive power of sentiment
indexes as indicators of stock market activity.
Table of ContentsChapter 1: Introduction -- Chapter 2: Literature Review -- Chapter 3: Data Source -- Chapter 4: Methodology -- Chapter 5: In-Sample Regression Tests -- Chapter 6: Out-of-Sample Regression Tests -- Chapter 7: Conclusion – References -- Appendix
NotesA thesis presented for the degree of Master of Research.
18 May 2020.
Includes bibliographical references (pages 60-63).
Awarding InstitutionMacquarie University
Degree TypeThesis MRes
DegreeThesis MRes, Macquarie University, Macquarie Business School, 2020
Department, Centre or SchoolDepartment of Applied Finance
Year of Award2020
Principal SupervisorJing Shi
Additional Supervisor 1Terry Pan
RightsCopyright disclaimer: https://www.mq.edu.au/copyright-disclaimer
Copyright Tiancheng Wang 2020.
Extent1 online resource (66 pages)