Research

Advisors

• Yen Tong (Nanyang Technological University)
• Kalin Kolev (Baruch College)

Dissertation Committee

• Edward Li (Baruch College)
• Lin Peng (Baruch College)
• Svenja Dube (Baruch College)

Working Papers

• Strategic Control of Facial Expressions by the Fed Chair
  • Presented at Baruch College Workshop
  • Abstract: This article investigates whether the Federal Reserve Chair strategically controls facial expressions during FOMC press conferences and how these nonverbal cues affect financial markets. I use facial recognition technology on videos of press conferences from April 2011 to December 2020 to quantify changes in the Chair’s nonverbal signals. Results show that facial expressions serve as a separate public signal, distinct from verbal content. Using deepfakes, I find that the same facial expressions expressed by different Fed Chairs are interpreted differentially. As their tenure increases, negative expressions become more frequent, eliciting adverse market reactions. Furthermore, the markets interpretation of these expressions evolves over time, suggesting that investors process facial cues with dual-processing finite-state Markov memory. In line with the Fed’s goals of transparency and non-volatility, I find that Fed Chairs do not strategically control their expressions.
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• Is Cryptocurrency Wash Trading Strategic?
  • This paper investigates the phenomenon of wash trading in cryptocurrencies, where traders simultaneously place buy and sell orders to inflate trading volumes artificially. Using high-frequency transaction data from the Mt.Gox Bitcoin exchange, I find that wash trades are strategic in response to low natural trading volume. When wash-trading in an exchange is high, the exchange’s activity also mirrors on-chain transactions and other markets’ transactions more closely. Wash-trading also takes place more when other asset classes trade less. Additionally, when the information environment is weak and there is more information asymmetry, wash trading’s effect is slower but more sticky in the market. Wash traders also take advantage of contemporary events and induce more wash trading to boost volume. The results highlight the finding that wash trades are strategic in nature and this has implications for the financial and legal literature in allowing a better characterization of how wash trades take place, and thus, how they can be detected.
• BigBird for ESG Reports - A Sparse Attention Model For Differences Between Languages In Textual Analysis
  • Coauthored with Shuai Xu (Shanghai University of Finance and Economics, Singapore Management University)
  • This paper introduces BigBird-ESG, a domain-specific transformer architecture pre-trained on manually classified paragraphs from Chinese ESG reports between 2016 to 2020. Our results show that BigBird-ESG, with its sparse attention mechanisms, more efficiently processes Chinese ESG reports due to innate qualities of the Chinese language and its difference from the English language. We show that BigBird-ESG outperforms BERT and FinBERT under specific conditions in both sentiment and category classification tasks. The findings suggest that language specificity affects the accuracy of LLM models in parsing textual data. Our findings affect the future advancement of multi-modal LLMs and transfer learning, which should consider language-specific qualities when interpreting sources of financial information. We also use state-of-the-art OCR to coherently extract paragraphs from the ESG reports, which preserves the meaning of the textual data and we use this new methodology to show that tone in Chinese ESG reports is correlated with ESG ratings.
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Conferences and Presentations

• Hawaii Doctoral Institute Summer 2025
• 2024 The Chinese Finance Association TCFA 30th Annual Conference
  • Participant
• NYU Accounting Theory Summer School 2024
  • Taught by Ilan Guttman, Judson Caskey, Jeremy Bertomeu
• Duke University Accounting Theory Summer School 2024
  • Presented paper - “Cybersecurity Disclosures”.
  • Taught by Itay Goldstein, Qi Chen, Chandra Kanodia, Thomas Hemmer

Paper Review

• 2023 Management Science Reproducibility Project
  • Reproduced a published paper on text-mining