Working Papers
Mahnaz Paydarzarnaghi, David Rakowski, and Mahmut Yasar
This research is derived from Essay 2 of my dissertation. We explore how stock price reactions to Twitter (now known as X) posts are associated with the perceived credibility of social media users making the posts. We introduce new credibility metrics based on the sender and the content of Twitter posts. Less credible tweets influence prices through a transient and non-informational liquidity effect, while more credible tweets lead to a persistent information effect. Our results support 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).
Status: Submitted to the Journal of Business Finance & Accounting and under review.
Mahnaz Paydarzarnaghi, John David Diltz and Salil K Sarkar
This study applies natural language processing (NLP) techniques to assess the degree of accounting conservatism in a large sample of 10-K filings over the period from 1999 through 2023. Following an extensive body of research focused on analysis of accounting conservatism using objective financial statement data, we explore the possibility that affective meaning (i.e., the subjective content of 10-K text) may provide insight into the degree of, and motivation for, conservative accounting practices. Our NLP measures of accounting conservatism, which employed the “log odds ratio informative Dirichlet prior,” generally align with conventional measures regarding conservatism, text uncertainty, and tone, but they differ regarding 10-K readability and analyst forecast accuracy. We conclude that NLP techniques represent a useful tool for the analysis of accounting conservatism exhibited in SEC filings.
Status: Submitted to the Journal of Contemporary Accounting and Economics and under review.
Herding Behaviors in Financial Markets: The Influence of Social Media on Market Perceptions
Mahnaz Paydarzarnaghi, Mahyar Vaghefi, David Rakowski, and Mahmut Yasar
This research is derived from Essay 1 of my dissertation. Based on the herding theory and the Emotion as Social Information model (EASI), we examine herding behavior in financial markets, especially Bitcoin. In this study, we focus on the StockTwits platform. The findings show that 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.
Status: For submission to the Journal of the Association for Information Systems