Dr. Francesco Grossetti
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Dr. Francesco Grossetti

Assistant Professor of Accounting Analytics and Data Science

Bocconi
  • Department of Accounting, Bocconi University
    Via Roentgen 1, 20136 Milano, Italy
  • Bocconi Institute for Data Science and Analytics, Bocconi University
    Via Roentgen 1, 20136 Milano, Italy
  • francesco.grossetti@unibocconi.it

Short Bio

I am an Assistant Professor of Accounting Analytics and Data Science at the Department of Accounting at Bocconi University, a Fellow at the Bocconi Institute for Data Science and Analytics, and a member of the Bocconi Digital Transformation and AI Committee.

I study core questions in accounting and finance — corporate disclosure and its consequences for firm tax outcomes, investor reactions to multi-dimensional financial narratives, and the dynamics through which corporate information reaches the market — using Natural Language Processing, machine learning, and computational statistics on large-scale textual and multimodal financial data. Methodologically, I draw on statistical learning and stochastic processes for sequential data, which let me model how corporate information evolves over time (disclosure cascades, topic model identification, and more).

My work has appeared in the Review of Accounting Studies, the Journal of Machine Learning Research, the Journal of Accounting and Public Policy, and other outlets.

Positions

  • Assistant Professor, Department of Accounting, Bocconi University · 2018 – present
  • Fellow, Bocconi Institute for Data Science and Analytics · 2018 – present
  • Visiting Associate Professor (recurrent), NHH, Bergen · 2024 – 2025
  • Visiting Assistant Professor, IE University, Madrid · October 2022
  • Postdoctoral Fellow, Bocconi University · 2017 – 2018
  • Research Fellow, Politecnico di Milano · 2011 – 2013

Education

  • Ph.D. in Applied and Computational Statistics | Politecnico di Milano (MOX) · 2017
  • M.Sc. in Astrophysics and Space Physics | Università di Milano-Bicocca
  • B.Sc. in Physics | Università di Milano-Bicocca

Featured Publications

Investor Distraction and Multi-Dimensional Financial Narrative

Review of Accounting Studies (2026)

Mar 9, 2026

Using Narrative Disclosures to Predict Tax Outcomes

Review of Accounting Studies (2026)

Oct 27, 2025

A Statistical Approach for Optimal Topic Model Identification

Journal of Machine Learning Research (2022)

Feb 1, 2022
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Featured Code

NLPstudio

Scalable, reproducible NLP workflows in R — corpus preparation, dictionaries, readability, similarity, and a consistent topic-model API across backends. Built on…

OpTop

Parametric tests to identify the optimal number of topics from a pool of LDA models, plus diagnostics for topic stability and redundancy. Companion code to Lewis & Grossetti…

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© 2026 Francesco Grossetti