Gaetan BAKALLI

Assistant Professor

PhD in Statistics

Quantitative Finance & Economics

Research topics

1. Machine Learning

2. Feature Selection

3. Financial Econometrics

4. Time Series Analysis

5. Applied statistical research in Finance, Medicine and Engineering

Publications

Expertises

Financial Econometrics, Machine Learning, Time series analysis, Computational Statistics.

Associations

European Finance Association

Academic articles

  • Miglioli, C., Bakalli, G., Guerrier, S., Orso, S., Molinari, R., Karemera, M. & Mili, N., Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks'', Scientific Report 12, 5166, 2022.
  • Parisi, N., Janier-Dubry, A., Ponzetto, E., Pavlopoulos, C., Bakalli, G., Molinari, R, Guerrier, S., & Mili, N., Non Applicability of Validated Predictive Models for Intensive Care Admission and Death of COVID-19 Patients in a Secondary Care Hospital in Belgium'', Journal of Emergency and Critical Care Medicine, 5: 22, 2021.
  • Guerrier, S., Jurado, J., Khaghani, M., Bakalli, G., Karemera, M., Molinari, R., Orso, S., Raquet, J, Schubert Kabban, C., Skaloud, J. Xu, H. & Zhang, Y., Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration'', IEEE Transactions on Instrumentation \& Measurement, 69(10), p.7542-7551, 2020.
  • Radi, A., Bakalli, G., Guerrier, S., El-Sheimy, N., Sesay, A. B., & Molinari, R., A Multisignal Wavelet Variance-based Framework for Inertial Sensor Stochastic Error Modeling''. IEEE Transactions on Instrumentation and Measurement, 68(12), p.~4924-4936, 2019.

Working papers

  • Bakalli, G., Guerrier, S., & Scaillet, O., A Penalized Two-pass Regression to Predict Stock Returns with Time-varying Risk Premia''. Full text: https://ssrn.com/abstract=3777215.
  • Bakalli, G., Cucci, D. A., Radi, A., El-Sheimy, N., Molinari, R., Scaillet, O., & Guerrier, S., Multi-signal Moment-based Approaches for Repeated Sampling Schemes''. Full text: https://doi.org/10.48550/arXiv.2105.06217.

Thesis (Doctoral Dissertation)

Domain-Tailored Approaches to Statistical Learning.