Não disponível em português

Gianluigi Lopardo

International & European Relations

Division: International Policy Analysis
Current Position: Research Analyst
Fields of interest: Mathematical and Quantitative Methods,Financial Economics
Email: Gianluigi.Lopardo@ecb.europa.eu

Biography

Education: 2021-2024
PhD in Applied Mathematics, Inria & Université Côte d'Azur, France; 2019-2021
MSc in Mathematical Engineering, Politecnico di Torino, Italy; 2016-2019
BSc in Mathematics for Engineering, Politecnico di Torino, Italy
Professional experience: 2024-2025
PhD Trainee - International Policy Analysis Division, Directorate General International & European Relations, European Central Bank; 2021-2024
Doctoral Researcher - Inria & Université Côte d'Azur, France; 2021-2024
Teaching Assistant - Université Côte d'Azur, France; 2020-2021
Machine Learning Engineer Intern - Alten, France; 2021
AI Research Intern - Maasai team, Inria Sophia Antipolis, France
Awards: 2025
Young Researcher Prize (Victoires de la Recherche - Prix Jeune Chercheur), Ville de Nice
Teaching experience: 2023-2024
Mathematical Statistics - Mathematical Engineering MSc, Université Côte d'Azur, France; 2023-2024
Statistics 2 - Economics BSc, Université Côte d'Azur, France; 2023-2024
Probability and Introduction to Statistics - Economics BSc, Université Côte d'Azur, France; 2022-2023
Mathematical Methods - BSc, Université Côte d'Azur, France; 2022-2023
Introduction to Mathematics - BSc, Université Côte d'Azur, France; 2021-2022
Mathematical Statistics - Mathematical Engineering MSc, Université Côte d'Azur, France; 2021-2022
Fundamentals of Mathematics - BSc, Université Côte d'Azur, France

ECB publications

12 August 2025

WORKING PAPER SERIES - No. 3093

Verba volant, transcripta manent: what corporate earnings calls reveal about the AI stock rally

English

Abstract: This paper investigates the economic impact of technological innovation, focusing on generative AI (GenAI) following ChatGPT’s release in November 2022. We propose a novel framework leveraging large language models to analyze earnings call transcripts. Our method quantifies firms’ GenAI exposure and classifies sentiment as opportunity, adoption, or risk. Using panel econometric techniques, we assess GenAI exposure’s impact on S&P 500 firms’ financial performance over 2014-2023. We find two main results. First, GenAI exposure rose sharply after ChatGPT’s release, particularly in IT, Consumer Services, and Consumer Discretionary sectors, coinciding with sentiment shifts toward adoption. Second, GenAI exposure significantly influenced stock market performance. Firms with early and high GenAI exposure saw stronger returns, though earnings expectations improved modestly. Panel regressions show a 1 percentage point increase in GenAI exposure led to 0.26% rise in quarterly excess returns. Difference-in-Difference estimates indicate 2.4% average quarterly stock price increases following ChatGPT’s release.
JEL Code: C80 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→General
G14 : Financial Economics→General Financial Markets→Information and Market Efficiency, Event Studies, Insider Trading
G30 : Financial Economics→Corporate Finance and Governance→General
L25 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Performance: Size, Diversification, and Scope
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes

External publications

2025

VoxEU column

What Corporate Earnings Calls Reveal About the AI Stock Rally