About
I am a Ph.D. candidate in Accounting at the University of Maryland's Robert H. Smith School of Business, advised by Musa Subasi and Michael Kimbrough. My research studies how new technologies, especially artificial intelligence and generative AI, are changing the way financial information is produced and used in capital markets. I am also interested in financial intermediaries and behavioral finance. My research has been recognized as the best paper in Fintech at the 2024 SFA Annual Meeting, and I have received multiple awards, such as the Frank T. Paine Award for Academic Achievement and the Long Jiang Graduate Student Fellowship.
Before the Ph.D., I worked as an investment manager in Beijing and as an auditor at Ernst & Young. I hold an M.S. in Management from Peking University's HSBC Business School and a B.S. in Accounting from Huazhong University of Science and Technology.
I am on the 2026–27 academic job market.
Research
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Synthesizing the Consensus: Generative AI, LLM Visibility, and the Integration of Multi-Provider Consensus Forecasts
Status: Job market paper, draft May 2026.
Dissertation committee: Musa Subasi (chair), Michael Kimbrough (co-chair), Rebecca Hann, Nick Seybert, Jingyi Qian, Erkut Y. Ozbay.
Abstract
This paper studies how generative AI (GenAI) tools facilitate financial information processing in the setting of the market's integration and use of multi-provider analyst consensus forecasts. Using S&P 1500 firm-quarters over 2021–2025 covering all five major forecast data providers (FDPs), I first document a wider, more selective use of consensus after ChatGPT: prices respond more to more-accurate providers and reweight from I/B/E/S where it loses relative accuracy. I then construct an accuracy-weighted consensus, and announcement returns load on it increasingly over two heuristics: I/B/E/S and the equal-weighted average across the five FDPs. This shift strengthens with GenAI's retrieval capability and concentrates where providers disagree most, especially where that disagreement most likely reflects methodological differences across FDPs. Measuring the visibility of each firm's consensus forecasts to GenAI tools using the GPT-4o model, I find the synthesis benefit concentrates in high-visibility firms. The results show how GenAI powers investors' processing of multi-provider consensus.
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AI-Powered Analysts
Status: Revise & resubmit, The Accounting Review.
Best Paper in Fintech, SFA 2024.
Abstract
We examine how brokerage-level adoption of artificial intelligence (AI) influences analysts' information production. We observe substantial heterogeneity in AI adoption across brokerages, driven by factors including peer influence, organizational resources, and market pressure from institutional investors. Importantly, we find that analysts at brokerages with greater AI integration issue significantly more accurate earnings forecasts. The information advantages from AI are more pronounced when forecasts are issued earlier in the forecasting cycle, when there is less information asymmetry among analysts, when firm disclosures are more readable, and when analysts have less experience. We also find that AI adoption helps mitigate the adverse effects of decision fatigue and reduce analysts' heuristic forecasting. Finally, forecasts issued by AI-powered analysts are more informative to capital markets. Overall, our findings underscore the role of AI in augmenting analysts' performance by improving their information processing and forecasting accuracy.
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The Role of Generative AI in Democratizing Investment Research: Evidence from Crowdsourced Forecasts
Status: Ready to submit.
Abstract
We examine whether generative AI (GenAI) helps level the playing field between professional and non-professional forecasters. Using crowdsourced forecasts from Estimize, we find that the accuracy gap between these groups narrows significantly following the release of ChatGPT, driven primarily by improvements among non-professional contributors. Exploiting ChatGPT service outages as a quasi-natural experiment, we find that forecast accuracy declines during outages, particularly for non-professional contributors, consistent with these contributors relying on GenAI for earnings-related information processing. GenAI adoption is associated with more accurate and more independent forecasts, with the largest gains concentrated among non-professionals. Cross-sectional tests indicate that GenAI's benefits are more pronounced when disclosures are less readable but are attenuated when analyst forecast dispersion is high, suggesting that GenAI is less effective in settings with greater information asymmetry. Finally, earnings announcement returns are more strongly associated with earnings surprises derived from non-professional forecasts in the post-ChatGPT period. Overall, our evidence suggests that GenAI democratizes investment research by enhancing the financial information-processing capacity of non-professionals.
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Stepping Into the Spotlight: CFO Debuts in Conference Calls
Status: In final preparation for submission.
Abstract
This paper investigates whether CFOs who debut on their first earnings conference call exhibit a tendency to stay in their “comfort zone” by emphasizing numbers and accounting jargon rather than qualitative strategic information as a means of maintaining credibility in the face of potential public scrutiny, both because CFOs have more prior experience with accounting and quantitative information than with soft information, and because such information is verifiable and objective. Analyzing a large sample of earnings call transcripts, we find that debuting CFOs exhibit a significantly greater reliance on quantitative language and accounting jargon at the expense of softer strategic disclosures. These trends decline nearly monotonically over subsequent calls. The debut effect is particularly pronounced in settings where a new CFO is expected to experience greater pressure and uncertainty: when it is the CFO's first time in the C-Suite, for higher growth firms, and when the firm reports a loss during the first conference call. We also find that CFOs engaging in greater comfort zone behavior are less likely to be promoted to CEO at their current firm or to be hired into a C-Suite position at another firm. Finally, comfort zone behavior does not measurably impact market or analyst reaction to conference calls. Taken together, our results suggest that debut pressure encourages CFOs to focus on accounting and quantitative information over softer strategic information during early conference calls, and that this behavior worsens future career outcomes and does not benefit the firm with respect to investors or analysts.
Teaching
BMGT 220 — Principles of Accounting I: Financial Accounting. Instructor of record, University of Maryland, Robert H. Smith School of Business.
| Term | Course | Instructor |
|---|---|---|
| Summer 2025 | 3.82 / 4.0 | 3.89 / 4.0 |
| Winter 2025 | 3.77 / 4.0 | 3.85 / 4.0 |
Other Teaching Experience
Representative PhD student speaker, GenAI and Business Research, UMD Smith (scheduled, Sep 2026).
Guest speaker, Fundamentals of Business Research, UMD Smith (Sep 2025).
Teaching assistant, Peking University HSBC Business School (2018–2020).
Contact
- yliu337@umd.edu
- Office
- 3330F Van Munching Hall, Robert H. Smith School of Business, College Park, MD 20742