Research

Out of the (Black)Box: AI as Conditional Probability (2024), with H. Chen and A. Didisheim

We explore the economic significance and interpretability of the distribution of conditional probabilities behind LLM's text generation. Using a dataset of news and returns, we find that conditional probabilities are interpretable and correlate with model accuracy. Conversely, measures of declared confidence used in the literature are opaque, structurally biased, unstable, and more model-dependent, indicating that LLMs cannot assess their own confidence. Using conditional probabilities, we analyze LLM biases and provide insights into the internal mechanisms driving model decisions. Our results indicate that conditional probabilities provide a reliable and transparent reflection of LLM beliefs, particularly for economic 

Presentations:  WFA 2025

AI in Finance and Information Overload (2025), with A.Balogh, A. Didisheim and Hanquing Tian

Artificial intelligence is reshaping financial markets, yet the limits to its rationality remain underexplored. This paper documents information overload in Large Language Models applied to financial analysis. Using earnings forecasts from corporate calls and market reaction predictions from news, we show that predictive accuracy follows an inverted U-shaped pattern, where excessive context degrades performance. Larger LLMs mitigate this effect, increasing the optimal context length. Our findings underscore a fundamental limitation of AI-driven finance: more data is not always better, necessitating empirical tuning to determine the right amount of context for each task. 

CBDC and Banks: Threat or Opportunity? (2023), with M. Fraschini

We study how banks react to the introduction of a Central Bank Digital Currency (CBDC) when households have heterogeneous preferences. We find that banks increase their deposit interest rates in response to a CBDC, even when the CBDC pays no interest rate. However, when the central bank provides funding to offset the loss in deposits, banks optimally push households towards the CBDC by reducing deposit interest rates. This allows them to liquidate reserves, reduce their cost of funding, and increase their profits. We calibrate the model to provide quantitative estimates of these mechanisms.

Presentations:  CEPR Fintech and Digital Currencies RPN Workshop 2024, BS Gillmore Centre Conference on DeFi & Digital Currencies 2024, 4th Sailing the Macro Workshop 2024, EEA 2024, ArmEA 2024, AFA 2024, Bank of England, Swiss Finance Institute Research Days 2021.

The Monetary Entanglement between CBDC and Central Bank Policies (2023),  with M. Fraschini and T. Terracciano

Using a banking model, we show that the effects of introducing a Central Bank Digital Currency (CBDC) depend on the ongoing monetary policy. We derive the conditions for a neutral introduction without central bank pass-through funding and find that they do not always hold with quantitative easing, as bank lending shrinks if demand for CBDC is above a certain threshold. Moreover, we find that commercial banks optimally liquidate their excess reserves to accommodate households’ demand for CBDC. Consequently, households will replace banks on the liability side of the central bank balance sheet, making quantitative tightening difficult to implement.

This paper previously circulated as "Central Bank Digital Currency and Quantitative Easing"

Honors: Finalist for the 2022 ECB Young Economist Prize

Presentations: EEA 2023, Bank of England, ECB Forum on Central Banking (Poster Session, June 2022), The Future.s of Money - Paris (June 2022), 26th Spring Meeting of Young Economists (SMYE, May 2022), ASSA 2022 Virtual Annual Meeting (AEA Poster Session, January 2022), Day-Ahead Workshop on Financial Regulation at the University of Zurich (October 2021), Swiss Finance Institute Research Days (June 2021), 14th Financial Risks International Forum (March 2021), Finance BB seminar at the University of Geneva (January 2021)

Media: Financial Times (see Outreach)

The End of the Crypto-Diversification Myth (2022), with A.Didisheim and M. Fraschini

Cryptocurrencies and equities have exhibited a high and positive correlation since March 2020. Without obvious fundamental drivers, we theoretically show that trading flows by retail investors can drive this correlation. Using a unique dataset of investor-level holdings from a bank offering trading accounts and cryptocurrency wallets, we show that retail investors tend to trade equities and cryptocurrencies simultaneously in the same direction. This behavior became prominent in March 2020. We provide suggestive evidence showing that stocks preferred by crypto-traders exhibit a stronger correlation with Bitcoin, especially when the cross-asset retail volume is high.

Presentations: AFA 2024, MFA Annual meeting 2023, ToDeFi 2023 (Best PhD paper award), 5th UWA Blockchain and Cryptocurrency conference, New Zealand Finance Meeting 2022, CB&DC Job Market Candidates Workshop, NYU Stern (PhD brownbag), Swiss Finance Institute, HEC Lausanne.

Media: Financial Times, VoxEU (see Outreach)

Are Green Funds for Real? (2023), with C. Jaunin, T. Terracciano

We empirically study green funds’ investments following earning calls where firms discuss the climate transition. To do so, we use an unsupervised machine learning algorithm to extract a measure of climate transition talk during earning conference calls and look at how mutual funds respond to it. We find that after starting to discuss the climate transition, firms see their green fund ownership increase by twice as much as matched firms with similar characteristics but that did not discuss the topic. As expected, we do not find any differences in non-green ownership. Importantly, we also show that firms discussing the climate transition limit their carbon emissions compared to the market portfolio. Contrary to claims of generalized greenwashing, our results indicate the existence of a channel for firms to communicate their climate stance to green investors effectively.

Presentations: CEBRA 2023, GoingGreen 2023, Wharton PhD seminar, SFI Research Days 2022, UNIL & EPFL brown bag seminar

Network and Risk in Venture Capital Investing (2023), with M. Fraschini

Does the network of venture capital firms (VC) affect their risk-taking? We use data from Crunchbase to build a network of VCs based on syndications. Consistent with the literature, better-connected VCs have a higher probability of success. Additionally, we find that these funds take deals that are riskier ex-ante. We build a theoretical model of the VC investment process to rationalize these findings. The representative fund decides whether to invest in a startup and whether to syndicate. The results suggest that if the VC is able to internalize the value-added effect of its network, it selects riskier deals.