Mehmet I. Canayaz

Assistant Professor of Finance
Carl H. Lindner College of Business
University of Cincinnati

Please find my CV here.
Please find my GitHub page here.
Please find my paper discussions here.

Academic Publications

[6] Small Business Judging Judges, Management Science. Forthcoming. (with Matthew Gustafson)

[5] Macro Sentiment and Hedge Fund Returns, Journal of Banking and Finance. Forthcoming. (with Mustafa Caglayan, Timothy T. Simin and Le Zhao)

[4] Financial Consequences of the Belt and Road Initiative, Journal of Financial and Quantitative Analysis. Forthcoming.

[3] Choose Your Battles Wisely: The Consequences of Protesting Government Procurement Contracts, Management Science. 2025, 71(10): 8526-8548. (with Jess Cornaggia and Kimberly Cornaggia)
Replication Files: https://github.com/mehmetcanayaz/bidprotests

[2] Country Reputation and Corporate Activity, Management Science. 2023; 70(3), 1483-1504. (with Alper Darendeli)
Replication Files: https://github.com/mehmetcanayaz/countryreputation
Awards: Oxford University Reputation Symposium Runner-Up Award, ranked among the Top 6 papers from over 1,000 publications (2022-2024).

[1] Fake Products, Real Effects: Evidence from Special 301 Actions, Journal of Financial and Quantitative Analysis. 2023; 58(5):2024-2063.
(with Umit Gurun)

Papers Undergoing Revision

Crafting an AI Compass

with Zhe Wang

(Revise & Resubmit, The Review of Financial Studies)

The adoption and commercialization of new technologies rely heavily on universally accepted principles, known as standards. Surprisingly, the role of standards in shaping business outcomes is largely unexplored. This paper provides a first examination of how standardization of artificial intelligence (AI) influences business outcomes. By leveraging hand-collected data on global AI standards, we provide insights into their creation, content, and shed light on the countries leading or lagging in global AI standardization efforts. Our findings reveal that establishing universal rules and guidelines for AI has a first order impact on corporate investment. Standardization of machine learning methods, programming languages, protocols for big data, guidelines for data interchange, and interoperability of industrial data fuel capital and R&D investments. Conversely, frameworks for ethical, societal, and privacy considerations in AI have distortionary effects. Overall, AI standards have a positive 'ripple effect' on firm value, gradually amplifying as the standards mature and their impact on firms deepens.
Presentations: Penn State University, University of Cincinnati, 2024 Next Generation of Antitrust, Data Privacy & Data Protection Scholars Conference, Asian Bureau of Finance and Economic Research 11th Annual Conference, 2024 Columbia & RFS AI in Finance Conference, AI & Platform Conference (Emory), 2025 SFS Cavalcade North America, 2025 AI/ML Conference (Columbia).

Working Papers

When Protectionism Kills Talent

with Isil Erel, Umit Gurun and Yufeng Wu

We study how protectionist policies reshape the US chip-manufacturing workforce and talent pipeline—from prospective student interest and degree completion to skill composition and innovation capacity. We show that science and engineering employment has declined in response to protectionist measures—particularly in entry-level roles and at firms exposed to tariffs and reliant on foreign talent. Patent output fell and recruitment shifted overseas, while fewer domestic students pursued chip-related degrees, reducing the US share of global chip-manufacturing expertise. Our conceptual framework shows that a high share of foreign workers and inelastic labor supply contribute to the adverse effects of protectionist policies.
Presentations: 2026 Jackson Hole Finance Group Conference, 2026 American Finance Association Meetings (Philadelphia), 2025 University of Kentucky Finance Conference, 2025 Western Finance Association Meeting, 2025 EFA Meeting, ABFER 12th Annual Conference, Labor and Finance Group Spring 2025 Conference, 2024 CSEF-RCFS Conference on Finance, Labor and Inequality*, 2024 Finance, Organizations and Markets (FOM) Conference, Second UIC Finance Conference, Midwest Finance Association 2025 Annual Meeting, University of Oregon*, Penn State University, University of Notre Dame*, University of Toronto*.* denotes presentation by co-author
Awards: Best Paper Award at 2024 CSEF-RCFS Conference on Finance, Labor and Inequality.Media: VoxEU, NPR’s Marketplace.

Signal Fragmentation in Cryptocurrency Markets

with Charles Cao, Zongbo Huang, Giang Nguyen, and Qiang Wang

We study how cryptocurrency markets process fragmented price-relevant information across media channels. We develop a cryptocurrency valuation model where social media complements traditional media through signal refinement and substitutes for it by providing unique information. Using a comprehensive dataset of 58 granular sentiment and attention measures spanning social media, news, and television coverage for 267 cryptocurrencies, we test our model's predictions. Our findings indicate that social media becomes price-informative primarily when accompanied by concurrent traditional media coverage, with stronger effects when sentiment alignment occurs across platforms. Social media, therefore, complements rather than substitutes traditional media in cryptocurrency price formation.

Presentations: 2024 WFA Meeting, 2025 MFA Meeting, Penn State University*, Conference on Advances in Time Series Analysis at University of Chicago, CMU-PSU-Pitt Finance Conference*, 2023 Early Career Women in Finance Conference*, 2023 FMA Annual Meeting*, 38th Australasian Finance and Banking Conference*.* denotes presentation by co-authorMedia: Les Affaires.

AI Agency

AI agents, or autonomous digital employees, introduce a new form of agency within firms. This paper quantifies the economic trade-offs of integrating AI agents into firms' organizational structures. By analyzing AI agents’ reasoning patterns, tool usage, interactions with confidential information, and inter-agent dialogues, the paper demonstrates that strategic AI workforce design and robust oversight are essential for firms to fully capture the financial benefits derived from deploying AI agents. Conversely, inadequate oversight leads to higher operational costs and greater agency risks, particularly when AI agents are equipped with more advanced technologies.
Presentations: Penn State University, 2025 CMU-PITT-PSU Finance Conference.

Trade Policy & Innovation

with Jess Cornaggia and Yuchen Li

This paper explores how U.S. firms adjust their green innovation strategies in response to international trade barriers. China, formerly the largest global recipient of solid waste, enacted a permanent ban in 2018 that disrupted U.S. firms’ long-standing dependence on exporting waste. Our analysis reveals that firms affected by the ban face higher operating costs, reduced profit margins, and greater involvement in environmental-related incidents. In response, they substantially increase their R&D spending and redirect their innovation efforts toward environmentally sustainable solutions.
Presentations: Penn State University

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