Research

Job Market Paper

Intensity Bursts in WallStreetBets Discussion and Stock Markets Trading (With Yoichi Otsubo and Ser-Huang Poon)

Abstract: Our study introduces the concept of WallStreetBets (WSB) submission intensity bursts (IB), offering a novel perspective by distinguishing varying levels of social media activity. Using a Difference-in-Differences approach, we establish a causal link between IBs and key market outcomes, including trading turnover, daily and overnight abnormal returns. We further find that IBs during regular trading hours (RTH) have a stronger and more lasting impact on stock returns compared to those outside RTH, with swift reversals observed on a half-day basis. Additionally, stocks targeted by WSB users typically exhibit a decrease in short interest ratios following an IB, suggesting the WSB users may anticipate short squeeze opportunities, adding strategic nuance to social media’s market influence.
Presentations (*by co-author): Financial Econometrics Conference to mark Stephen Taylor’s Retirement, Sofia University, 2023 Nippon Finance Association Annual Conference, 12th International Conference of the Financial Engineering and Banking Society (FEBS), 2024 British Accounting and Finance Association (BAFA) Annual Conference

Abstract: Our analysis of over 150,000 WallStreetBets comments reveals dominant phrases when members of the community experience collective excitement and solidarity (Collective Effervescence). Using the novel Vocabulary Herfindahl Index (VocaHIn), we find that stock returns volatility increases after CE, and vice versa.

LLM and Transparency in Supply Chains (with Ser-Huang Poon)

In this paper, we apply a state-of-art Large Language Model to evaluate Social reports related to business supply chains. We also explore a number of factors that are crucial for evaluating corporate disclosure quality.
Presentations: 2022 British Academy of Management (BAM)

User Patterns in Financial Social Media (with Yoichi Otsubo and Ser-Huang Poon)

In this paper, we implement Prompt Engineering, the process of structuring an instruction that can be interpreted and understood by a generative AI model, in specific financial social media text data. We also find an intriguing behavioural pattern that shows how the users on financial social media interact with each other.