I am a tenure-track Assistant Professor of Finance at The College of New Jersey, where I teach courses in investments and FinTech. I hold a PhD in Finance from Rutgers University, an MA in Economics from Vanderbilt University, and a Bachelor of Arts and Science with double majors in Applied Mathematics and Economics from the University of California, Los Angeles.
My research focuses on banking, financial regulation, and FinTech, with particular emphasis on financial institutions, regulatory frameworks, and the risk and performance implications of financial innovation. My work examines how regulation, market structure, and capital allocation affect firm behavior, risk-taking, and value creation, issues central to corporate finance and strategic financial decision-making. I have taught courses in FinTech, business, finance, and economics at Columbia University.
I am also a founder and managing director of the Academic FinTech Project, an initiative that promotes collaboration between academia and the FinTech industry to advance rigorous, policy-relevant research.
Email: panja@tcnj.edu | CV | SSRN | Google Scholar | LinkedIn
Upcoming Talks:
Midwest Finance Association Conference, March 12-14, 2026, Chicago, IL, USA
Eastern Finance Association Conference & Early Career Forum, March 25-28, 2026, Asheville, NC, USA
1. Do 'MEASURES' of Bank Diversification Measure Up? (with Priyank Gandhi and Darius Palia)
We analyze the effectiveness of several widely-used measures of bank product market diversification in capturing the diversification effect (i.e., the implication that diversification reduces idiosyncratic volatility of a portfolio of financial products). Contrary to the implications of modern portfolio theory, existing measures of bank product market diversification are poorly or positively correlated with idiosyncratic volatility. We instead propose the Correlation-Adjusted Entropy (CAE) measure that accounts for the number of product categories, category income shares, and the imperfect correlations among category incomes. CAE accurately captures the diversification effect and variation in CAE coincides with exogenous shocks to bank product market diversification (the passage of Dodd-Frank and Economic Growth Acts of 2013 and 2018, respectively), providing an important external validation for our measure. We use CAE to revisit the question of how bank product market diversification impacts performance and find that diversified banks are more profitable and have lower probability of bankruptcy, and lower tail and systemic risk, which is in stark contrast to findings in the literature that document mixed results.
Selected Presentations: Midwest Finance Association Conference, Eastern Finance Association Conference, American Finance Association Junior Faculty Mentorship Program, Financial Management Association Conference, Annual Financial Markets and Liquidity Conference, Sydney Banking and Financial Stability Conference, American Finance Association Committee on Racial Diversity, Auckland University of Technology, Central European University, University of Sydney, University of Melbourne, University of New South Wales, Victoria University
2. Exchange Heterogeneity in Cryptocurrency Volatility and Price Jumps (with Alan Chernoff, Le "Tim" Dong, and Juntai Lu) - Under Review
Despite debate over cryptocurrencies as a medium of exchange, they remain primarily speculative assets, largely due to their high volatility. While prior research documents cryptocurrency volatility, less is known about whether it differs across trading venues. This paper examines cross-exchange variation in realized volatility and extreme price jumps using high-frequency transaction data. We find significant heterogeneity across international exchanges, though such differences are minimal among the largest and most liquid venues. The results suggest that exchange-specific microstructure features, including liquidity, market depth, and trading frictions, shape observed volatility and digital asset risk.
The papers will be posted on SSRN shortly. Please feel free to contact me if you would like to receive preliminary drafts.
3. Bank Product Market Diversification and Lending (with Alan Chernoff and Juntai Lu)
We examine the relationship between bank product market diversification and lending by measuring diversification across 16 business lines using a novel correlation-adjusted entropy metric. We find that more diversified banks exhibit greater growth in loan supply and stronger lending resilience during the Great Recession. The diversification effect differs depending on whether expansion occurs across interest income business lines or noninterest income activities. We further characterize the optimal level of diversification and discuss policy implications for financial stability.
4. Executive Political Alignment and Bank Risk-Taking (with Priyank Gandhi and Juntai Lu)
This paper examines whether executive political alignment affects bank risk-taking. We construct a novel measure of executive political alignment using political contribution data for senior executives of U.S. bank holding companies from 1992 to 2021. Exploiting within-bank changes in alignment, we study the relation between executive political alignment and multiple measures of bank risk, including tail risk, market-implied risk, downside risk, and non-performing loans. We find that shifts toward political alignment are followed by economically meaningful increases in bank risk-taking, consistent with political alignment relaxing perceived regulatory constraints or increasing expectations of government support.
5. Generative AI and the Cost of Debt (with Priyank Gandhi, Juntai Lu, and Jia Wei)
Firms with greater workforce exposure to generative artificial intelligence experience wider corporate bond credit spreads after ChatGPT’s release. Using a difference-in-differences design with 94,731 bond-month observations over 2021–2024, we find that a one-standard-deviation increase in generative AI exposure widens credit spreads by 5.9 basis points, or 4.7% of the sample mean. A quarterly event study confirms no pre-trend and an immediate, persistent break at the event quarter. Cross-sectional tests reveal that the effect concentrates in firms with lower credit quality, greater labor intensity, and weaker governance, consistent with bondholders pricing the disruption risk that generative AI poses to labor-dependent business models.
6. Generative AI and Corporate Investment (with Priyank Gandhi, Juntai Lu, and Jia Wei)
We study how exposure to generative artificial intelligence affects corporate investment. Exploiting the November 2022 release of ChatGPT as a technological shock, we implement a difference-in-differences design comparing firms with high versus low pre-existing generative AI exposure. Firms more exposed to generative AI reduce total investment following the shock; a one-standard-deviation increase in exposure is associated with a 6.6% decline relative to the sample mean. The contraction is driven by lower capital expenditures and acquisitions, while R&D remains unchanged. The evidence is consistent with generative AI operating as a capital-saving technology that reduces tangible investment without crowding out innovation inputs.