Experience

 
 
 
 
 
August 2021 – Present
Sherbrooke, Canada

Assistant Professor of Finance

Université de Sherbrooke

 
 
 
 
 
January 2020 – June 2021
Ghent, Belgium

Post-Doctoral Researcher

Ghent University & HEC Montreal

SNSF PostDoctoral Researcher
 
 
 
 
 
January 2016 – December 2019
Neuchâtel, Switzerland

Teaching Assistant

University of Neuchâtel

Support students and professor for the Fixed Income, Derivatives, and Portoflio Management Course. Assist in supervising master student level thesis.
 
 
 
 
 
August 2015 – December 2015
Québec, Canada

Quantitative Analyst

Lakeroad asset management

Developed and maintained quantitative financial models using Excel, VBA, R, and Matlab. Analyzed and developed various portfolio strategies.

Selected Publications

A novel token-distance-based triple approach is proposed for identifying EPU mentions in textual documents. The method is applied to a corpus of French-language news to construct a century-long historical EPU index for the Canadian province of Quebec. The relevance of the index is shown in a macroeconomic nowcasting experiment.
Economics Letters, Vol. 205, 2021
PDF DOI arxiv

We empirically test the prediction of Pastor, Stambaugh, and Taylor (2020) that “green” firms tend to outperform “brown” firms when concerns about climate change increase, using data for S&P 500 companies from January 2010 to June 2018. To capture unexpected increases in climate change concerns, we construct a Media Climate Change Concerns index using news about climate change published by major U.S. newspapers. We find that when concerns about climate change increase unexpectedly, green firms’ stock prices increase, while brown firms’ decrease. Further, using topic modeling, we conclude that climate change concerns affect returns both through investors updating their expectations about firms’ future cash flows and through changes in investors’ preferences for sustainability.
Working Paper, 2020
DOI SSRN

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics , which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software.
Journal of Economic Surveys, Vol. 34, Issue 3, pp. 512-547, 2020
PDF DOI SSRN

Recent Publications

More Publications

We conduct a tone-based event study to examine the aggregate abnormal tone dynamics in media articles around earnings announcements. We …
SSRN

A novel token-distance-based triple approach is proposed for identifying EPU mentions in textual documents. The method is applied to a …
PDF DOI arxiv

We empirically test the prediction of Pastor, Stambaugh, and Taylor (2020) that “green” firms tend to outperform …
DOI SSRN

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform …
PDF DOI SSRN

The modern calculation of textual sentiment involves a myriad of choices as to the actual calibration. We introduce a general sentiment …
PDF DOI SSRN

We test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log-returns using Markov-switching GARCH (MSGARCH) …
PDF DOI SSRN

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) …
PDF DOI SSRN

Numerical standard error (NSE) is an estimate of the standard deviation of a simulation result if the simulation experiment were to be …
PDF DOI SSRN

We describe the package MSGARCH, which implements Markov-switching GARCH models in R with efficient C object-oriented programming. …
PDF DOI SSRN CRAN Github

We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment …
PDF DOI SSRN CRAN Github

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