Jasper Pan

Working Papers 

1. Equity Prices the Opportunity, Debt Prices the Risk: Generative AI and Corporate Credit Spreads (with Priyank Gandhi, Juntai Lu, Alberto Plazzi, and Jia Wei).

How do corporate bond markets price workforce exposure to generative AI? We find that firms with greater workforce exposure to generative AI experience credit spreads nearly 8 basis points wider per standard deviation of exposure following the release of ChatGPT, well exceeding the 4 – 6 basis point effects for policy uncertainty shocks documented in the literature. This finding appears paradoxical: high-exposure firms simultaneously experience rising equity valuations and wider credit spreads. We argue that ChatGPT’s release created a real option to restructure labor whose fundamental parameters — adoption costs, success probabilities, and competitive consequences — were unknown at the outset. This parameter uncertainty, typically absent from prior models of labor-technology substitution, simultaneously raises expected asset value and generates relatively wider credit spreads for high-exposure firms. A model with parameter uncertainty rationalizes this pattern, and cross-sectional tests confirm its predictions: the effect is larger for firms with greater credit fragility, labor intensity, and weaker governance.

Conference Presentations: AFA Junior Faculty Mentoring Program (2027), Wharton-Chicago-Harvard Insolvency and Restructuring Conference (2026), Ohio State Fisher AI in Business Conference (2026)*, Indian School of Business Summer Research Conference (2026)*, Financial Management Association Emerging Scholar Initiative (2026), Eastern Finance Association Early Career Forum (2026), Concordia University AI in Finance Conference (2026)*, RCF-ECGI Corporate Finance and Governance Conference (2026), C.r.e.d.i.t. Conference (2026)*, Southern Finance Association (2026).

Invited Seminars: Drexel University (2026), Rutgers University Camden (2026), California State University Fresno (2026).

2. Do Measures of Bank Diversification “Measure Up”? (with Priyank Gandhi and Darius Palia).   

We analyze the effectiveness of several widely used measures of bank business line diversification in capturing the diversification effect (i.e., the idea that diversification reduces the idiosyncratic volatility of a portfolio of financial products). Contrary to the implications of the modern portfolio theory, current measures of bank business line diversification are poorly or positively correlated with idiosyncratic volatility. We instead propose the correlation-adjusted entropy (CAE) measure which accounts for number of business lines, income shares, and the imperfect correlations among business line incomes. CAE accurately captures the diversification effect, and variation in CAE coincides with exogenous shocks to bank business line diversification (the passage of Dodd-Frank and Economic Growth Acts), providing an important external validation for our measure. When we use CAE to revisit the question of how bank business line diversification impacts performance and risk, we find that diversified banks are more profitable, have lower probability of bankruptcy, and lower tail and systemic risk, in contrast to findings in the literature that document mixed results.

Conference Presentations: AFA Junior Faculty Mentoring Program (2026), FDIC Bank Research Conference (2026), Midwest Finance Association (2026), Financial Engineering and Banking Society Conference (2026), Contemporary Issues in Financial Markets and Banking (2026), Financial Management Association (2025), Eastern Finance Association (2025), Financial Markets and Liquidity Conference (2025), Southwestern Finance Association (2025), RCF-ECGI Corporate Finance and Governance Conference (2025), International Moscow Finance Conference (2025), Research Symposium on Finance and Economics (2025), Friends of Women in Finance Symposium (2025), Contemporary Issues in Financial Markets and Banking (2025), Sydney Banking and Financial Stability Conference (2024)*, Pacific Basin Finance, Economics, Accounting, and Management Conference (2024), Northeast Business and Economics Association (2024), Inter-Finance PhD Seminar (2024), AFA Committee on Racial Diversity Mentoring Program (2023).

Invited Seminars: Central European University (2025)*, University of Melbourne (2024)*, University of Sydney (2024)*, University of New South Wales (2024)*, Monash University (2024)*, Auckland University of Technology (2024)*, Victoria University of Wellington (2024)*.

3. Awaiting the Prompt? Generative AI Uncertainty and Firm-Level Investment (with Priyank Gandhi, Simi Kedia, Juntai Lu, and Jia Wei).

We document that firms with relatively greater workforce exposure to GenAI experience a decline in investment in the period following the release of ChatGPT on November 30, 2022. A one standard deviation increase in workforce exposure lowers the ratio of firm-level investment to lagged total assets by 0.57 percentage points, or nearly 6% of the sample mean. We argue that ChatGPT's release created a real option for firms to reorganize production around GenAI whose adoption cost, success probability, and competitive consequences were unknown at the outset. This parameter uncertainty, which scales with the firm's workforce exposure to GenAI, raises the option value of waiting and depresses current investment, when investments are irreversible. A real option model of firm investment with parameter uncertainty calibrated to bond market data (Gandhi, Lu, Pan, Plazzi, and Wei, 2026) reproduces the empirical investment response. Cross-sectional tests confirm the model's prediction that the decline in investment is concentrated in physical capital, persists through fiscal year 2025 without reversal, and is smaller for firms that have more redeployable assets. Additionally, we rule out that our results are driven by productivity substitution, talent hoarding, payout reallocation, an asymmetric-information flip, or managerial agency frictions.

Conference Presentations: Financial Management Association (2026)*, Southern Finance Association (2026).

4. Data Readiness and Firm Adjustment to Generative AI (with Tim Dong). Revise and Resubmit, Review of Corporate Finance.

Workforce exposure to generative AI is a common shock, but exposed firms adjust very differently depending on their capacity to use the technology. Among firms with high workforce exposure to generative AI, those entering the ChatGPT era with above-median GPT data assets become substantially more profitable than otherwise-exposed peers, with return on assets rising by 2.5 percentage points and gross margin by 7.7 percentage points. We argue that data readiness converts exposure into usable production capacity and, by lowering the option value of scale expansion, also disciplines empire-building behavior. Consistent with this mechanism, pre-existing prepared-firm premiums in acquisitions and goodwill disappear after ChatGPT, and the profitability gains concentrate in firms most exposed to free-cash-flow agency frictions. The paper introduces readiness as a first-order source of cross-sectional heterogeneity in how exposed firms translate generative AI into real corporate outcomes.

5. Accounting Restatements and Employment in the AI-Labor Era (with Tim Dong). Revise and Resubmit, Review of Corporate Finance.

The direction of the real-activity response to accounting restatements depends on which operating choices signal high productivity in the prevailing labor market. Kedia and Philippon (2009) show that restating firms in the 1997–2002 GAO sample expanded employment during affected periods and contracted after disclosure, and this pattern has become the benchmark for the real effects of financial misreporting. In a modern panel, the sign reverses: the affected-period employment coefficient changes from modestly positive before April 27, 2023 to significantly negative afterward, and the reversal is stable across a 21-specification sensitivity grid. We argue that when workforce reduction becomes a credible efficiency signal, the operating choice consistent with a high-productivity report can shift from expansion to restraint. Matched restater-control tests locate the reversal among restaters with workforce-cut language or nearby labor-cut events, and demand and distress account for the remainder.

*  Presented by coauthor.