The escalating significance of data in the digital economy cannot be overstated. Data serves as a fundamental input in driving advancements in Artificial Intelligence (AI), Machine Learning (ML), and Financial Information (FinTech) technologies, offering the potential to foster substantial innovation, enhance efficiency, lower inequality, inform policies and strategies, enlarge current markets, or penetrate novel markets. At the same time, the rise of the data economy is changing sources of revenue and sources of risk. Our research is designed to investigate the strategic utilization of data for growth and innovation, for transforming financial inclusion and democratizing financial markets, and for changing the competition landscape, and to undertake a comprehensive evaluation of policy regulations pertaining to data use and privacy.
Team
Core academic members
Affiliated academic members
Private sector project partners
Projects
Data and the Aggregate Economy
What do we know about attitudes towards privacy and the willingness of individuals or businesses to provide sensitive data?
What is the value of data for firms and consumers?
How is big data changing risk and uncertainty?
Who should own the data? How much should individuals be compensated for their data?
What is the relationship between Big Tech and big data?
Is the primary mechanism for addressing societal risks and potential harms by AI a solution that targets technical progress, regulations, consumer trust or something else?
Welfare and Inequality in the Digital Era
How do differences in access to digital services affect the way households save?
What are the implications of the rise of big data in finance?
How is inequality impacted by advances in financial information technologies?
What is the value of data for investors?
What are the implications of open-banking initiatives?
How can organizations quantify risks to individuals when risks and technologies are constantly changing?
Regulating the Data Economy
When developing best practices to audit their AI models, how should organizations effectively govern algorithmic models, data sets and algorithmic parameters?
How does digital regulation affect data collection and use?
What is the impact of digital regulation on firms and consumers?
What are the consequences of regulating BigTech?
What do we know about the value of research done using sensitive or confidential data, and of the costs associated with reducing the precision of the outputs of that research?
How will regulation of payment for order flow impact market data pricing?
Output
Data Innovation Complementarity and Firm Growth, Roxana Mihet with Anastassia Fedyk, Orlando Gomes and Kumar Rishabh
Data security is considered a compliance cost. But in today’s data-driven economy, can it also be an opportunity? We use inventor-level patent data to introduce a novel measure of \textit{``data innovation complementarity''} --- the extent to which firms integrate data-security expertise into broader innovation. Using the staggered adoption of U.S. Data Breach Notification Laws as quasi-exogenous variation, we find that only high-complementarity firms increase innovation output and profitability. To explain this, we develop a real-options model where integration is costly, but enables innovation spillovers. Integrated data protection, we show, can be a driver of innovation-led growth.
Finance Crowd Analysis: Non-Standard Errors, Roxana Mihet with #FinCap
Evidence-generated process variation across researchers adds uncertainty: Non-standard errors (NSEs), which turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. This type of uncertainty is underestimated by participants.
How do households form expectations in non-standard economic settings, when they are highly unsure about economic fundamentals and when they face uncertain information?
FinTech, Investor Sophistication and Financial Portfolio Choices, Roxana Mihet with Romina Gambacorta and Leonardo Gambacorta
The advancement of financial technology widens the gap in portfolio diversity and returns between sophisticated and unsophisticated investors, a divide that diminishes only when technology is universally accessible and investors possess comparable proficiency in its utilization.
The emergence of managed funds, in which investors can pool resources and share the information costs of investing, alters the landscape of inequality, benefiting certain investors, yet it does not resolve income inequality.
The Value of Investor Trading Data: Evidence From an Experiment, Roxana Mihet with Francois Longin and Ziwei Zhao
How does regulation of payment for order flow impact market data pricing?
AI vs Data for Market Power, with Orlando Gomes and Kumar Rishabh
The rise of AI has drawn considerable regulatory attention, with agencies like the FTC focusing on AI's potential to consolidate market power. However, this approach overlooks a critical driver: the data that fuels AI systems. Current regulatory frameworks miss an important dimension by focusing solely on AI, risking the reinforcement of market power not only through AI sophistication but also through unequal data access. We address these gaps by dissecting the distinct roles of AI and data feedback loops in market concentration.
AI Innovation and Climate Policy, with Emilia Maria Garcia-Appendini and Adam Feher
The rapid rise of artificial intelligence (AI) is transforming the global economy by driving unprecedented productivity gains, yet its escalating energy demands pose significant challenges to environmental sustainability. In this paper, we examine the interplay between AI-driven growth and climate policy, with a particular focus on the role of renewable energy in enabling sustainable technological progress.
Reviewer or Program Committee for AI & Finance-Related Conferences
