Cogent Economics & Finance

Leveraging the Power of AI in Building the Best for Our World

Artificial intelligence (AI) is rapidly transforming how the world engages in finance and economic activities. The pace of innovation creates opportunities for efficiency and performance but also introduces unprecedented risks. Thus, knowledge regarding how stakeholders are subject to the concerns, consequences, and impacts of AI usage is utmost critical in promoting more responsible use and safer development. This Article Collection invites rigorous, applicable, and replicable financial economics and business scholarship that advances our understanding of the ongoing rapid growth of AI innovation and employment. We welcome theoretical, empirical, and methodological contributions. Work that strengthens real-world decision-making for investors, institutions, policymakers, researchers, or educators is particularly welcomed. The Collection emphasizes the importance of research that (i) advances practical insights into the evolving role that AI plays in businesses and financial markets or (ii) applies reliable AI methods to contribute to new knowledge in the fields.

As a relatively new form of democratization, AI is already reshaping, in many ways, how we approach tasks. In the business world, we have witnessed the use of AI to classify regulatory filings, flag suspicious items, read financial statements, and propose investment strategies. With such innovation, the nature of many jobs that we have known for decades, especially those in the middle office, has changed materially. In business and economics research, AI is directly applicable to text analyses, such as assessing the sentiment of earnings calls and analyzing variations in the texts of annual reports. Tones and attitudes can now be more easily captured. AI also makes assessing readability and consistency less burdensome. While these rapid developments have improved work efficiency, they have also introduced significant risks. The downsides often fall along concerns over bias, errors, fairness, overreliance, transparency, etc., as well as their long-term consequences, all of which can exacerbate when safety measures and ethical considerations fail to keep pace. This Article Collection seeks studies on how AI transforms tasks and jobs, the reliability and fairness of such transformations, and the consequences of their use.

This Article Collection calls for research that (i) showcases novel AI-related findings or (ii) demonstrates research methodologies that significantly incorporate the use of AI in answering important questions in business and economics research.

Articles that consider theoretical or empirical studies that advance practical insights into the evolving role that AI plays in businesses and financial markets are welcome. These may include, but are not limited to, those that examine the outcomes of employing AI, concerns associated with AI employment, and the long-term consequences of AI usage. Below are some examples:

Outcomes:

• Firms: The efficiency, performance, and dynamics of work environments, the production of goods and services, etc., as a result of employing AI.
• Investments: Improvements/deterioration in the modeling of returns and volatility of firms and the allocation of assets, price discovery, etc.
• Financial markets and institutions: Alleviation/exacerbation of agency and information asymmetry problems through AI incorporation by financial institutions or individual investors.
• General: The impact of AI usage on workforce readiness and education.

Concerns: Studies that examine aspects such as bias (e.g., gender, population segments, etc.), misinformation, cybersecurity/privacy threats, environmental harms (e.g., carbon emissions and water consumption for NLP training, etc.), legal issues (e.g., property rights infringement, ownership of generated content, etc.), and reliability (accountability, transparency)

Longer-term consequences: Studies that investigate the potential effects on human capital over time, such as overreliance, sense of purpose, slack, etc., as well as the creation of new jobs, markets, and industries (e.g., AI infrastructure), and their effects on markets and the economy.

Studies that consider AI in research methodologies are also welcome, such as those that demonstrate the applications of reliable and replicable AI methods to contribute to new knowledge in business and economics research. These may include, but are not limited to:

• AI-assisted textual analyses on sentiment, tone, uncertainty, overconfidence/hubris, disclosure quality, etc.
• Examinations of the robustness and consistency of AI methods and training processes.
• Critiques of AI-assisted research methods and designs for implementable improvement techniques

Dr. Kershen Huang's research interests primarily lie in empirical corporate finance, with a current focus on the interactions between investor heterogeneity, externalities, and firm policies, as well as their relations with agency issues and capital prices. At NSU, he offers UG/MBA/MS level finance, economics, econometrics, and special topics courses.

For more information about this Collection, please contact Dr. Molly Cole, commissioning editor, at [email protected].

Dr. Huang has disclosed utilizing both Julius and ChatGPT 5 for language and editing this call for papers text; all original ideas are his own.