Working Papers

Inflation Narratives and Risk Premia (with Luís Fonseca, Giulia Martorana, and Fabian Schupp) [ECB Working Paper No 3288]

Abstract: Theory suggests that inflation risk premia are generally positive when investors believe supply shocks will outweigh demand shocks, and negative otherwise. We measure these beliefs using demand and supply narratives derived from inflation news through Causality Extraction, a tool that identifies causal relationships between inflation and its causes. We extract Euro Area demand and supply narratives from the Financial Times and Reuters inflation news, and United States demand and supply narratives from the Wall Street Journal’s inflation news. Our primary narrative variable, NetDemand, is the difference in the number of articles attributing inflation to demand versus supply factors. Consistent with theory, for both the Euro Area and United States, inflation risk premia are inversely related to NetDemand across maturities. NetDemand’s explanatory power remains strong even after controlling for the PMI and VIX, becomes stronger as risk aversion rises in the United States, and is not subsumed by the demand and supply contributions to inflation, views of professional forecasters, and narratives obtained from LLMs. 

Presentations: IAAE 2026

Keywords: Demand and Supply, News Media, Natural Language Processing, Dependency parsing, Causality extraction

Inflation Narratives and Expectations [ECB Working Paper No 3158]

Abstract: I study how demand-supply narrative disagreement between general and specialized newspapers can explain households’ absolute gap in inflation expectations with experts. I measure inflation narratives via a Causality Extraction algorithm that can identify causal relationships between events in a text and, hence, extract the perceived triggers of inflation. I then classify inflation narratives into demand and supply narratives based on their focus on demand and supply triggers. I measure narrative disagreement between general and specialized newspapers from their attention difference on demand and supply narratives. The absolute expectation gap widens when narrative disagreement increases, especially for non-college-educated and older households. Unlike specialized newspapers’ narratives, general newspapers’ narratives incorrectly align with experts’ demand-supply views.

Presentations: ifo Dresden Workshop on Macroeconomics and International Finance 2024 · 18th Belgian Financial Research Forum 2024 · 7th Dauphine Finance PhD Workshop · 30th Annual Meeting of the German Finance Association · NETSPAR Pension Day 2024

Posters: CEPR Paris Symposium 2024 · Finalist in the European Central Bank’s Young Economist Prize 2025

Keywords: News media, Natural Language Processing, Dependency parsing, Causality extraction

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What Triggers Flights to Safety? (with Lieven Baele and Frank de Jong) [SSRN]

Abstract: We apply causality extraction (CE) algorithms on more than 36,000 articles from 2 major financial newspapers (WSJ, FT) and three major newswires (DJ, MNI Market News, Reuters) over the extended period 1980-2020 to shed light on the fundamental triggers of flights to safety. Our CE algorithm searches for sentences that causally relate flight-to-safety terms to a “cause”, or candidate FTS trigger. Our method identifies nearly 3,000 unique triggers of FTS, which we subsequently allocate using a dictionary-based method to nine economically motivated primitive categories. While some primitives such as “Funding Liquidity,” “Financial Intermediaries,” or “Pandemic” are only mentioned during specific FTS spells, others such as Political, Geopolitical, Macro, and terms broadly associated with Risk Aversion, Sentiment, and Uncertainty are more generally mentioned as potential triggers of FTS. We additionally show that causal FTS articles are associated with larger market responses (compared to days with noncausal FTS articles), especially if they refer to current (instead of future) FTS events.

Presentations: 2023 ENTER Jamboree in Mannheim · 10th Asset Pricing Workshop at the University of York · 18th Belgian Financial Research Forum 2024 · 4th Annual Bristol Financial Markets Conference · 31st Finance Forum in Tenerife · 2024 China International Conference in Finance in Beijing

Keywords: Empirical asset pricing, Natural Language Processing, Dependency parsing, Causality extraction