Emanuele Guidotti

Emanuele Guidotti

PhD Student

University of Neuchâtel


I am PhD student at the University of Neuchâtel, with a focus on financial markets. Specifically, I study how microstructure variables impact asset prices and participate in price formation. In my working paper, I propose a theory where prices are formed in a purely mechanical manner through trading and derive testable predictions about trading volume, price impact, trading frequency, bid-ask spreads, order imbalance, market depth, and volatility.

I am also passionate about interdisciplinary projects and computational methods. In particular, I am the developer of COVID-19 Data Hub (Scientific Data), I have authored a classification algorithm inspired by Born’s rule (NeurIPS), and I maintain several R and Python packages including a package for high dimensional numerical and symbolic calculus in R (JSS). For my contributions, I have received grants and awards from IVADO, the R Consortium, and Google Cloud.

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Recent Publications

(2022). calculus: High Dimensional Numerical and Symbolic Calculus in R. Journal of Statistical Software, vol. 104(5), pag. 1–37.

PDF DOI Cite Website GitHub CRAN

(2022). A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution. Scientific Data, vol. 9(1), pag. 1-7. Nature Publishing Group.

PDF DOI Cite Data Website GitHub CRAN PyPI

(2020). COVID-19 Data Hub. Journal of Open Source Software, vol. 5(51), pag. 2376.

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Recent & Upcoming Talks

Grants & Awards

Awarded to enable performance tests on GPU for the paper Text Classification with Born’s Rule
Awarded to support the maintainance of COVID-19 Data Hub
Awarded to support the development of COVID-19 Data Hub