David Ardia

David Ardia

About

I am a Full IVADO professor in the Department of Decision Sciences at HEC Montréal and Director of the MSc in Financial Engineering program. My current research centers on artificial intelligence, machine learning, and natural language processing for finance — building methods that turn data, including unstructured text, into signals for risk forecasting, asset allocation, and economic prediction. Grounded in financial econometrics, my work and the projects I supervise span risk modeling, textual sentiment, hedge-fund and portfolio performance, climate finance, credit risk, and market microstructure.

Together with my team, I develop open-source tools and methodologies in R and Python and share them with the broader community to foster real-world impact. I am also active in Sentometrics Research, an open-source initiative bridging text mining, sentiment analysis, and econometrics, and FAME, a joint Paris Dauphine–PSL and HEC Montréal initiative on generative AI and large language models in financial markets.

I also serve as Associate Editor for the International Journal of Forecasting, the Journal of Statistical Software, and the R Journal. I am a Fellow of the Institut Louis Bachelier, an elected member of the ISI, a regular member or researcher at CIRANO, CIREQ, CRM, Fin-ML, GERAD, and Quantact, an associate researcher at OBVIA and the Penner Institute, and an instructor at DataCamp.

Research

Selected publications

Working papers

Other publications

Software

Open-source R packages developed with collaborators and distributed on CRAN.

  • bidask — Efficient estimation of bid-ask spreads from open, high, low, and close prices.
  • sentometrics — Computation, aggregation, and prediction with textual sentiment.
  • MSGARCH — Markov-switching GARCH models.
  • GAS — Generalized autoregressive score models.
  • RiskPortfolios — Construction of risk-based portfolios.
  • PeerPerformance — Luck-corrected peer performance analysis.
  • spantest — Mean-variance spanning tests.
  • RSDC — Regime-switching dynamic correlation models.
  • DEoptim — Global optimization by differential evolution.
  • nse — Computation of numerical standard errors.
  • AdMit — Adaptive mixtures of Student-t distributions.
  • bayesGARCH — Bayesian estimation of the GARCH(1,1) model with Student-t innovations.

Impact

Awards

Media

Teaching

I teach graduate courses on statistical and machine-learning methods for financial data in the MSc in Financial Engineering at HEC Montréal, in both French and English sections.

  • Winter 2026 — Statistical Methods for Financial Data (MATH 60633 / 60633A)
  • Fall 2025 — Machine Learning Applied to Financial Data (MATH 60610 / 60610A)
  • Winter 2025 — Statistical Methods for Financial Data (MATH 60633 / 60633A)
  • Fall 2024 — Machine Learning Applied to Financial Data (MATH 60610 / 60610A)

Students

PhD students: I am always looking for strong candidates in finance, financial engineering, and data science to join the team (see the PhD in Financial Engineering) — feel free to reach out. MSc students: I supervise theses and supervised projects only for students enrolled at HEC Montréal (including projects with industry partners); if that's you and you'd like to work with me, get in touch with a short note on your interests and timeline.

Postdoctoral researchers

PhD students

MSc theses

Supervised projects

Beyond theses, my 60+ MSc supervised projects (many with industry partners) span:

Contact

david.ardia [at] hec.ca

Department of Decision Sciences, HEC Montréal, 3000 Côte-Sainte-Catherine Road, Montréal (Québec) H3T 2A7, Canada