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AI, Labor and Finance

Paper Session

Sunday, Jan. 4, 2026 8:00 AM - 10:00 AM (EST)

Loews Philadelphia Hotel, Lescaze
Hosted By: Association of Financial Economists
  • Chair: Rene Stulz, Ohio State University

The Impact of AI-Powered Trading on Financial Instability

Winston Wei Dou
,
University of Pennsylvania
Itay Goldstein
,
University of Pennsylvania
Yan Ji
,
Hong Kong University of Science and Technology

Abstract

The integration of algorithmic trading with reinforcement learning, termed AI-powered trading, is transforming financial markets and has profound consequences for financial instability. This article
constructs a theoretical and quantitative laboratory in which bubbles can emerge and persist due to arbitrageurs’ failure to coordinate in correcting mispricing, thus stabilizing the market, or their success in colluding to ride and exit bubbles, thereby destabilizing the market. We find that AI arbitrageurs with noisy trading signals can autonomously coordinate to profit from arbitrage opportunities and can also sustain collusive trading profits through bubble-riding strategies, all without agreement, communication, or intent. Which form of AI collusive behavior dominates, and thus whether AI stabilizes or destabilizes financial markets, depends critically on the market environment.

Artificial Intelligence and the Labor Market

Menaka Hampole
,
Yale University
Dimitris Papanikolaou
,
Northwestern University
Lawrence D.W. Schmidt
,
Massachusetts Institute of Technology
Bryan Seegmiller
,
Northwestern University

Abstract

We leverage recent advances in NLP to construct measures of workers’ task exposure to AI and machine learning technologies over the 2010 to 2023 period, varying across firms and time. Using a theoretical framework that allows labor-saving technology to affect worker productivity both directly and indirectly, we show that the impact on wage earnings and employment can be summarized by two statistics. First, labor demand decreases in the average exposure of workers’ tasks to AI technologies; second, holding the average exposure constant, labor demand increases in the dispersion of task exposures to AI as workers shift effort to tasks not displaced by AI. Exploiting exogenous variation in our measures based on pre-existing hiring practices across firms, we find empirical support for these predictions, together with a lower demand for skills affected by AI. Overall, we find muted effects of AI on employment due to offsetting effects: occupations high exposed to AI experience relatively lower demand compared to less exposed occupations, but the resulting increase in firm productivity increases overall employment across all occupations.

Artificial Intelligence and Firms’ Systematic Risk

Tania Babina
,
University of Maryland
Anastassia Fedyk
,
University of California-Berkeley
Alex Xi He
,
University of Maryland
James Hodson
,
AI for Good Foundation and Jožef Stefan Institute

Abstract

We provide direct evidence that firms' investments in new technologies affect the composition of firms' risk profiles. Leveraging comprehensive data on firm-level artificial intelligence (AI) investments, we document that firms that invest more in AI experience increases in their systematic risk, measured by market beta. This is unique to AI: robotics, IT, organizational capital, and R&D investments do not display similar effects. Our results are consistent with AI investments creating growth options: AI-investing firms become more growth-like, and the effect on market betas concentrates during market upswings and periods of increased news and attention around AI advances.

Global R&D, Local Knowledge: Evidence from Patent Text Similarity

Kose John
,
New York University
Kyeong Hun Lee
,
University of Alabama
Zhou Lu
,
Nankai University
Emma Q. Xu
,
University of Memphis

Abstract

This paper investigates knowledge spillovers between R&D laboratories within multinational firms. Using deep-learning-based patent text similarity measures, we document significant differences in innovation between R&D labs located in a firm’s home country and those abroad. We examine several channels underlying this heterogeneity. The evidence shows that firms limit the sharing of core technologies with foreign subsidiaries operating in countries with weak intellectual property protection. In addition, internal competition among divisional managers— who are incentivized to differentiate their innovation—contributes to within-firm differences in innovation. Our results are not driven by firms adapting their innovation to local market needs nor by cultural distance between countries.

Discussant(s)
Phil Dybvig
,
Washington University in St. Louis
Miao Ben Zhang
,
University of Southern California
Leland Bybee
,
University of Chicago
S. Abraham Ravid
,
Yeshiva University
JEL Classifications
  • G0 - General
  • J2 - Demand and Supply of Labor