Wei Wei, Assistant Professor of Finance

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Assistant Professor Wei Wei’s research focuses on financial technology, corporate finance, and financial accounting, notably with investigations into the application of AI and machine learning to financial analysis.

Business portrait of Wei Wei, indoor setting.
Assistant Professor of Finance Wei Wei is based at the College of Business and Economics, UH Hilo. (Courtesy photo/COBE)

Posted Jan. 29, 2026.

Wei Wei is an assistant professor of finance at the University of Hawaiʻi at Hilo. Her research interests are in financial technology, corporate finance, and financial accounting, notably with investigations into the application of AI and machine learning to financial analysis.

Wei received her master of business in finance from the University of Minnesota’s Carlson School of Management, and her doctoral degree in finance from University of North Carolina at Charlotte. She is a certified Financial Risk Manager (FRM) and pursued the Chartered Financial Analyst (CFA) program, strengthening her expertise in risk management and investments.

Before coming to UH Hilo in 2025, she was a lecturer at UNC’s Charlotte Belk College of Business. Now at UH Hilo, Wei appreciates the university’s unique setting that allows her to connect business education with the cultural and social context of Hawaiʻi.

Two of Wei’s recent publications include investigations into audits of Chinese accounting firms and inadequate disclosures practices in relation to erosion in shareholder wealth.

Among her current interests is inquiry into how markets process information and how large language models, or LLMs, can detect fraud and predict market reactions. She is researching the application of AI and machine learning to financial analysis — “specifically, improving how we detect risks and extract insights from complex financial data,” she says.

Current working papers

Here are Wei’s descriptions of her current research:

1. How to Write Returns: A Counterfactual Editor for Earnings Guidance and Press Releases

Abstract: In AI era, more and more investors read and analyze text with Large Language Models (LLMs). This paper moves beyond predictive text analytics to a prescriptive question: how should managers write earnings disclosures to improve investor understanding? I develop a series of counterfactual CAR editor that proposes fact-preserving edits to earnings press releases and guidance updates, and reports the predicted change in abnormal returns (CAR) using widely-used LLMs including ChatGPT, Claude, and Gemini with uniform prompts. The results prove LLMs can better capture the underlying information from text by fact-preserving edits. The artifact reframes disclosure research from measuring what moves markets to advising how to write, and further provide guidance to each market participants.

2. Audio Context: The Causal Effect of CEO Communication

With Patrick S. Smith.

Abstract: Whether CEOs have a causal effect on the firms they run remains unclear. On the one hand, there is evidence that CEOs have particular management styles that affect firm performance. On the other hand, CEO-firm matches are highly endogenous in that boards hire CEOs with management styles that best match the firm’s needs. We explore this open question by examining one channel through which CEOs can affect firm performance: communication. We examine how CEOs’ ability to communicate their style, not the style itself, affects analyst forecasts and future firm performance. Implicit in our analysis is the idea that CEOs can have similar styles yet produce different results. Using Natural Language Processing (NLP), we abstract CEOs’ arousal level from Q&A session of earnings calls, and find communication is one channel through which CEOs have a causal effect on the firms they run.

3. Racial Diversity and Firm Commitments

With Al (Aloke) Ghosh.

  • Accounting and Business Research (Under Review)
  • Presented at SWFA 2026

Abstract: I analyze key factors driving corporate commitment towards enhancing racial diversity following the death of George Floyd and their effects on diversity performance. Using textual analysis of earnings calls and 10-K reports (corporate communications), my findings reveal a significant increase in race-related discussions for the post-event period. Key drivers of the increase in racial diversity discussions in corporate communications include corporate integrity, strong ESG performance, and the strength of pre-existing diversity policies. I also examine whether elevated corporate diversity communications coincide with efforts to improve DEI commitments, or whether firms engage in “diversity washing.” Based on a difference-in-differences research design, firms discussing race-related matters demonstrate a marked improvement in DEI performance metrics. Further, my results indicate that analysts and rating agencies view these changes favorably and some evidence that investors respond positively to DEI improvements. My research provides valuable insights into the socio economic drivers behind corporate diversity policies.

4. Short Interest and Financial Reporting Misstatements

  • Journal of Finance Reporting (Under Review)
  • Presented at SFA 2024, FMA 2024, Fox and Haskayne Accounting Conference 2024 at Temple University, University of Gothenburg Seminar, UNC Charlotte Seminar

Abstract: Based on an inverted U-shaped short interest curve around the announcement of misstatements, prior studies conclude that short sellers can predict financial reporting misstatements. My re-examination using a comprehensive sample fails to find convincing evidence of a pronounced inverted U-shaped curve around most reporting misstatements. However, I find heightened short-selling activity both before and after misstatement announcements. I test whether this result is attributable to short sellers’ superior ability to analyze the release of fundamental adverse corporate news. I find that most large increases in short interest levels leading up to misstatement announcements coincide with the disclosures of poor performance, legal troubles, stock downgrade, potential delisting, impending bankruptcy, stock sale, departure of key employees, the closure of businesses, and greater financial challenges. I infer that the elevated short interest before the announcement of misstatements is linked to short sellers’ ability to process public information and is not based entirely on non-public information.

Instilling curiosity in her students along with a strong sense of ethics and social justice

Wei says she actively integrates her empirical research into her teaching, both to expose students to cutting-edge tools and methods and to involve them directly in the research process.

“This gives students hands-on experience with real data, analytical tools, and collaborative problem-solving, helping prepare them for careers in finance, analytics, and related fields,” she says.

She also inspires her students to pursue this line of inquiry into their own research projects and internships.

“This type of work helps automate routine analysis while enhancing accuracy and transparency,” she says. “For graduates, understanding how AI can support financial decision-making is becoming an essential skill. Employers increasingly look for people who can interpret data, work with analytical tools, and use technology responsibly.”

Wei says this includes those in finance having a strong sense of ethics and social justice, values she instills in her students.

Fundamentally, through learning and scientific inquiry in both the classroom and through internships, Wei hopes her students will develop the essential skills of critical thinking, resilience, and ethical awareness — along with the belief that they can master complex topics and use financial knowledge to make a positive difference in their communities.

By Susan Enright, public information specialist for the Office of the Chancellor and editor of Keaohou and UH Hilo Stories. She received her bachelor of arts in English and certificate in women’s studies from UH Hilo.