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Jasper Pan, PhD

I am a tenure-track Assistant Professor of Finance at The College of New Jersey. I received my PhD in Finance from Rutgers University, an MA in Economics from Vanderbilt University, and a Bachelor of Arts and Science in Applied Mathematics and Economics from the University of California, Los Angeles.

My research examines how regulatory frameworks, market structure, and technological change shape risk and capital allocation in modern financial systems. I study banking, financial innovation, and corporate finance through the lens of organizational design and incentives, with work on bank diversification, political alignment and risk taking, cryptocurrency markets, and generative artificial intelligence on corporate financing and investment decisions. Across these settings, I analyze how structural forces influence firm behavior and financial stability.

I teach Investments and FinTech at The College of New Jersey and FinTech, Business, Finance, and Economics at Columbia University.

Email: panja@tcnj.edu | CV | SSRN | Google Scholar | LinkedIn

Artificial Intelligence and Corporate Finance

Generative AI and the Cost of Debt (with Priyank Gandhi, Juntai Lu, Alberto Plazzi, and Jia Wei)

We examine how exposure to generative artificial intelligence affects corporate borrowing costs in public bond markets. Exploiting the release of ChatGPT as a technological shock, we show that firms with greater AI workforce exposure face wider bond spreads, particularly among lower-credit-quality and labor-intensive firms. The results suggest that bond markets price disruption risk associated with technological change.

Generative AI and Corporate Investment (with Juntai Lu and Jia Wei)

We examine how generative AI affects corporate investment decisions. Exploiting the release of ChatGPT as a technological shock, we find that firms with higher AI exposure reduce capital expenditures and acquisitions, consistent with AI operating as a capital-saving technology.

Banking and Financial Intermediation

Do 'MEASURES' of Bank Diversification Measure Up? (with Priyank Gandhi and Darius Palia)

We propose a correlation-adjusted entropy (CAE) measure of bank product diversification that incorporates cross-income correlations. Existing measures fail to capture the diversification effect predicted by portfolio theory. CAE responds to regulatory shocks and predicts higher profitability and lower tail and systemic risk.

Selected Presentations: American Finance Association Junior Faculty Mentorship Program (2026), Eastern Finance Association Conference (2026), Midwest Finance Association Conference (2026), Financial Management Association Conference (2025), Sydney Banking and Financial Stability Conference (2024), American Finance Association Committee on Racial Diversity Mentoring Session (2023).

Executive Political Alignment and Bank Risk-Taking (with Priyank Gandhi, Simi Kedia, and Juntai Lu)

We examine whether executive political alignment influences bank risk-taking. Using political contribution data, we find that shifts toward alignment are associated with increases in multiple measures of bank risk.

Selected Presentations: Eastern Finance Association Early Career Forum (New Ideas Session, 2026).

Bank Product Diversification and Lending (with Alan Chernoff and Juntai Lu)

We study how bank product diversification affects lending behavior. Using a correlation-adjusted entropy measure across business lines, we show that diversified banks exhibit stronger lending growth and greater resilience during downturns.

Selected Presentations: Southwestern Finance Association Conference (2025), Contemporary Issues in Financial Markets and Banking (2025) (Earlier version presented as “Bank Diversification and Tail Risk”).

Financial Market Structure and Digital Assets

Exchange Heterogeneity in Cryptocurrency Volatility and Price Jumps (with Alan Chernoff, Le "Tim" Dong, and Juntai Lu) - Under Review

We analyze cross-exchange differences in cryptocurrency volatility and extreme price movements. Using high-frequency data, we document substantial heterogeneity across exchanges and show how liquidity, market depth, and trading frictions shape digital asset risk.

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