Research project at HEC Lausanne, funded by the Sandoz Foundation, Monique de Meuron Programme (Project funding 2022-2026)
AI & Finance
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
Head of AI & Digital Economy Lab
Assistant Professor of Finance
HEC Lausanne & SFI & CEPR
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 Risk, Firm Growth and Innovation, Roxana Mihet with Orlando Gomes and Kumar Rishabh (SSRN working paper)
Cybercrime risk prompts AI-intensive companies to pursue digital innovation, enhancing productivity in other domains. Notably, in-house cybersecurity innovation sustains this cycle, while third-party cybersecurity delegation lacks the same innovation benefits.
Finance Crowd Analysis: Non-Standard Errors, Roxana Mihet with #FinCap (JF, forthcoming)
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.
Household Belief Formation in Uncertain Times, Roxana Mihet with Luca Gemmi (SSRN working paper)
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 (RCFS, 2023)
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 (in progress)
How does regulation of payment for order flow impact market data pricing?
Consumer Privacy and the Value of Consumer Data, Roxana Mihet with Mehmet Canayaz and Ilja Kantorovitch (SSRN working paper)
When the introduction of the CCPA increases the cost of trading data, firms with a low ability to collect in-house data and high reliance on data suffer the most as they cannot adequately substitute the previously externally purchased data.
The Economics of Digital Regulations, Roxana Mihet (in progress)
What are the welfare consequences of digital regulations?
Recent Discussions
The Hidden Costs of Fairness in Platform Markets, 2024, by Annamaria Conti and Juan Santalo
Fundraising and governance of sustainability-oriented ventures: Evidence from equity crowdfunding, 2023, by Silvio Vismara and Peter Wirtz
Customer Data Access and Fintech Entry: Early Evidence from Open Banking, 2022, by Tania Babina, Greg Buchak, and Will Gornall
Temporal Focus in Earnings Conference Calls, 2022, by Ming Deng, Michal Dzielinski, and Alexander Wagner
Man + Machine: The Art & AI of Stock Analysis by Cao, Sean S. and Jiang, Wei et al. and Workplace Automation and Corporate Financial Policies, by Bates, Thomas et al.
The AI Economist: Improving Inequality & Productivity with AI-Driven Tax Policies, 2022, by Stephan Zheng et al.
