Graduation Semester and Year
Spring 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Finance
Department
Finance
First Advisor
David Rakowski
Second Advisor
Mahyar Sharif Vaghefi
Third Advisor
Mahmut Yasar
Fourth Advisor
John Adams
Fifth Advisor
Sanjiv Sabherwal
Abstract
This dissertation comprises three essays on information diffusion in financial markets.
Essay 1: Social media is pivotal for spreading information in the financial sector, influencing market perceptions through user-generated content. While previous studies have focused on public market perception for financial predictions, the impact of herding behavior—noninformative social forces affecting opinion formation—has been neglected. Based on the herding theory and the Emotion as Social Information model (EASI), this study examines herding behavior in cryptocurrency markets, especially Bitcoin. Findings show herding behavior significantly influences market perceptions, especially in volatile conditions, with unexpected interactions between crowd market trajectory and emotional content, offering new insights into online financial discussions.
Essay 2: This research explores how the stock price reactions to Twitter (now known as X) posts are associated with the perceived credibility of the social media users making the posts. We introduce new credibility metrics based on both the sender and the content of Twitter posts. Less credible tweets primarily influence prices through a transient liquidity effect, while more credible tweets lead to a persistent informational effect. Our results offer support to the Elaboration Likelihood Model by demonstrating that the direct route of persuasion (represented by post credibility) is larger in magnitude and more persistent over time than the peripheral route of persuasion (represented by sender credibility).
Essay 3: This research explores the link between information diffusion and user demographic characteristics by analyzing social media posts and stock returns. We analyzed around 22 million posts specific to S&P 500 companies made by 511,458 Twitter users between 2019 and 2022. We test how stock prices react to information associated with the demographic characteristics of social media users who post that information. Firms with more posts generated by older Twitter users and males are associated with a sustained information effect. In contrast, posts by female and younger individuals are associated with a temporary liquidity effect.
Keywords
Cryptocurrency, Emotional Content, Herding Behavior, Online Social Networks, Investor Behavior, Information Diffusion, Twitter Users, Text Credibility, Behavioral Finance, Social Media Demographics
Disciplines
Finance and Financial Management
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Paydarzarnaghi, Mahnaz, "THE ROLE OF HERDING BEHAVIOR, CREDIBILITY, DEMOGRAPHICS, AND INFORMATION DIFFUSION IN SOCIAL MEDIA: INSIGHTS FROM STOCK AND CRYPTO-CURRENCY MARKETS - THREE ESSAYS" (2025). Finance and Real Estate Dissertations. 64.
https://mavmatrix.uta.edu/financerealestate_dissertations/64
Comments
First and foremost, I would like to express my deepest appreciation to Dr. David Rakowski, who profoundly impacted my experience at UTA as my dissertation chair and mentor during my Ph.D. studies. He was always available to answer my questions, offer thoughtful guidance, and provide insightful feedback. His consistent support and encouragement made my doctoral journey much smoother, and I am sincerely grateful for his mentorship.
I sincerely thank my dissertation committee members, Dr. Sanjiv Sabherwal, Dr. John Adams, Dr. Mahmut Yasar, and Dr. Mahyar Sharif Vaghefi, for their invaluable contributions and support.
I am especially thankful to Dr. Mahyar Sharif Vaghefi, who significantly shaped my academic experience at UTA. As both a committee member and an instructor, he was always generous with his time and knowledge, whether answering questions or offering encouragement. His guidance helped deepen my understanding and skills in big data and data analysis, and I appreciate his mentorship.
I also extend my gratitude to Dr. Sanjiv Sabherwal, who supported me as a committee member and the department chair. I am also deeply grateful to Dr. Mahmut Yasar for his invaluable support throughout my Ph.D. journey. As my instructor, dissertation committee member, and research co-author, he is pivotal in shaping my academic and research development. I appreciate the time and effort he devoted to mentoring me.
I also sincerely thank Dr. Sima Jannati for generously sharing her knowledge and expertise. Her guidance and support, particularly in teaching me the necessary skills to prepare my dissertation manuscript, were invaluable and deeply appreciated.
I would also like to thank the following faculty members in the College of Business at UTA for their support, mentorship, and collaboration during my doctoral studies: Dr. Sriram Villupuram, Dr. Trang Thai, Dr. Salil Sarkar, Dr. David Diltz, Dr. Andy Hansz, Dr. Grace Hao, and Dr. Ramya Aroul. I worked with many of them as a research assistant, teaching assistant, co-author, or student in their courses. Their guidance and encouragement have had a lasting impact on my academic and professional development.
I want to thank the department’s administrative staff, Melanie Bacci and Teresa Sexton, whose consistent assistance and resourcefulness were invaluable throughout my time at UTA. I also want to thank William Wright, Ph.D. Program Advisor in the College of Business, for his guidance and support during my studies.
Finally, I want to acknowledge my family’s unwavering support and motivation. Completing this dissertation would have been impossible without their mental and emotional support during this time. Thank you, everyone, for being integral to my success on this Ph.D. journey.