Research

Abstract: We analyze the links between advances in financial technology, investors’ sophistication, and the composition and returns of their financial portfolios. We develop a portfolio choice model under asymmetric information and derive some theoretical predictions. Using detailed microdata from Banca d’Italia, we test these predictions for Italian households over the period 2004-2020. In general, heterogeneity in portfolio composition and in returns between sophisticated and unsophisticated investors grows with improvements in financial technology. This heterogeneity is reduced only if financial technology is accessible to everyone and if investors have a similar capacity to use it.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022

Prepared for the RCFS 2022 Winter Conference and RCFS Special Issue on "Finance for the Greater Good"

Abstract: In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Abstract: The tech revolution is transforming firm and industry dynamics, yet the roots of firm growth in the modern economy remain unclear. Is firm growth driven by compute capabilities (AI), access to data, or the interaction between them? We develop a dynamic model in which firms gain knowledge from raw data using AI, but face ``informational entropy'': without sufficient AI, more raw data leads to information overload and has negative returns. The model predicts two key dynamics: (1) improvements in AI (compute) disproportionately benefit data-rich firms; and (2) access to processed data substitutes for compute, increasing industry dynamism. We confirm these predictions using novel data from 2000–2023 and two exogenous shocks: the 2006 launch of Amazon Web Services (AWS) and the 2017 introduction of transformer-based architectures. Our findings suggest that regulating data usability, not just AI models, is essential to preserving firm dynamism in the modern economy.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022. 

Selected Presentations: Carnegie-Rochester-NYU 2025 Conference on Public Policy “The Consequences of AI use on Society and Policy”.

Abstract: Does AI make firms vulnerable or resilient to cyber risk? To answer this question, we develop a novel measure identifying AI-intensive U.S. public firms using publicly available patents and business-description data. While cyber threats typically suppress innovation, AI-intensive firms neutralize this effect. This protective effect strengthens with greater AI experience. Moreover, firms combining AI innovation and implementation exhibit a stronger buffer protecting their innovation and financial outcomes under cyber stress, whereas firms merely implementing AI without internal innovation gain no such resilience. Our results emphasize internal AI innovation as fundamental in enabling firms to effectively withstand cyber threats.

Supported by ArmaSuisse Cyberdefense Campus Grant 2024

Prepared for the RCFS 2025 Winter Conference and RCFS Special Issue on “Understanding Firms, Households and Financing in the Age of AI”

Abstract: New financial information technologies were expected to democratize finance and narrow wealth gaps. I show they can do the opposite. In a  noisy-rational expectations general equilibrium model of the stock-market, technology works through two distinct frictions: participation and information. Lowering participation frictions broadens entry, improves risk sharing, and reduces wealth and return inequality. Lowering information costs reallocates surplus to informed traders, concentrates intermediation, and increases cross-sectional inequality. The model yields clear distributional predictions consistent with recent data. Broadly shared gains require targeting participation frictions; subsidizing information acquisition alone can exacerbate dispersion.

Winner of the ECB Young Economists' Competition, ECB 

Winner of the Cubist Strategies Prize for Outstanding Research, WFA 

Winner of the 2021 Outstanding Dissertation Award for Inequality Category, NYU

Supported by the Macro-Financial Modelling Grant and the Internet Association Grant

Selected Presentations: WFA, EFA, AFA, Barcelona Summer Forum, Brussels Economic Forum, ESADE

Abstract: Data security is often viewed as a defensive cost. But in today’s data-driven economy, can it also create value? We propose a novel measure of "data innovation complementarity", capturing the extent to which firms have structurally integrated data-security expertise into their broader innovation. We show that such integration unlocks cross-domain knowledge spillovers, fueling firm-wide innovation and growth. Exploiting U.S. Data Breach Notification Laws as a quasi-exogenous increase in the perceived importance of data security, we find that only high-complementarity firms realize these gains. A calibrated real-options model shows that integrated data protection is a significant driver of innovation-led growth.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022

Selected presentations: Wharton AI and the Future of Labor 2025*, AFA 2025*, Future of Financial Information 2024*, Toulouse Digital Economics 2024, WEIS 2024, GRETA 2024*, EEA 2024, University of Zurich, ETH Risk Center, Boston University.

Abstract: We leverage a novel dataset on U.S. data center energy loads, utility electricity prices, and establishment-level revenues, employment, and carbon emissions from 2010 to 2023 to examine whether rising data center demand affects local retail energy prices or other spillovers. For identification, we employ an instrumental variables continuous difference-in-differences design, exploiting exogenous variation in data center location attractiveness. We find no detectable local spillover effects from data center energy growth. A regional model calibrated to these null results suggests that shocks larger than those observed through 2023 could still result in noticeable increases in household utility bills if not offset by regulation or external supply.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022

Selected Presentations: MRS -MIT AI and Climate Risk*, CEBRA*, EEA*.

Abstract: How does uncertainty shape the way households form expectations? Using U.S. survey data, we show that households revise their beliefs less when uncertainty stems from noisy new information, and more when it reflects uncertainty about prior beliefs. We then estimate a belief-updating model to disentangle the post-2020 surge in uncertainty. During the initial pandemic, higher macroeconomic volatility raised prior uncertainty, prompting households to seek information. In contrast, during the high-inflation period, households perceived information as noisier and relied more on their priors. Our findings provide a simple belief-stickiness statistic that diagnoses in real time whether uncertainty is fundamentals- or noise-driven.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022

Selected Presentations: Salento Macro Meetings 2024, CEPR Monetary and Economic Fluctuations 2024, EEA-ESEM 2024, JME-SNB-SCG Conference* 2024, Sailing the Macro 2024*, 14th IFO Macro and Survey Data*.

Abstract: We study a model where firms accumulate data as a valuable intangible asset. Data accumulation affects firms' dynamics. It increases the skewness of the firm size distribution as large firms generate more data and invest more in active experimentation. On the other hand, small data-savvy firms can overtake more traditional incumbents, provided they can finance their initial money-losing growth. Our model can be used to estimate the market and social value of data.

Abstract: We analyze the expansion of Big Data and artificial intelligence technologies from the perspective of economic theory. We argue that these technologies can be viewed from three perspectives: (1) as an intangible asset; (2) as a search and matching technology; and (3) as a forecasting technology. These points of view shed light on how new technologies are likely to affect matching between firms and consumers, productivity growth, price discrimination, competition, inequality among firms, and inequality among workers.

Abstract: Macro-prudential policies aimed at mitigating systemic financial risks have become part of the policy toolkit in many emerging markets and some advanced countries. Their effectiveness and efficacy are not well-known, however. Using panel data regressions, we analyze how changes in balance sheets of some 2800 banks in 48 countries over 2000–2010 respond to specific policies. Controlling for endogeneity, we find that measures aimed at borrowers – caps on debt-to-income and loan-to-value ratios, and limits on credit growth and foreign currency lending – are effective in reducing leverage, asset and noncore to core liabilities growth during boom times. While countercyclical buffers (such as reserve requirements, limits on profit distribution, and dynamic provisioning) also help mitigate increases in bank leverage and assets, few policies help stop declines in adverse times, consistent with the ex-ante nature of macro-prudential tools.

Abstract: This paper investigates the effects of national culture on firm risk-taking, using a comprehensive dataset covering 50,000 firms in 400 industries in 51 countries. Risk-taking is found to be higher for domestic firms in countries with low uncertainty aversion, low tolerance for hierarchical relationships, and high individualism. Domestic firms in such countries tend to take substantially more risk in industries which are more informationally opaque (e.g., finance, mining, oil refinery, IT). Risk-taking by foreign firms is best explained by the cultural norms of their country of origin. These results hold even after controlling for legal constraints, insurance safety nets, and economic development.

Winner of the President's Prize by ACEI