Dr Michel F. C. Haddad
Lecturer in Business Statistics
Email: email@example.comRoom Number: Room 3.44b, Francis Bancroft Building, Mile End Campus
- Lecturer in Business Statistics
- Member of the Department of Business Analytics and Applied Economics
Dr Michel is Lecturer (Assistant Professor) in Business Statistics. His research interests are divided into two streams. Primarily, he proposes novel statistical and machine learning methodological innovation, notably within time series and unsupervised learning. The second research stream focuses on applied econometric modelling and machine learning to extract insights exploring relevant real-world data (e.g. financial time series, Covid-19 data).
Before joining the Queen Mary University of London, he did two postdocs, working on artificial intelligence explainability in the financial sector (The Alan Turing Institute) and modelling economics/ systems transition (University of Cambridge).
His PhD is from University of Cambridge (thesis title: “Harnessing the power of intersection for data disaggregation: A novel similarity measure and unsupervised data-driven classification method applied to financial contagion”).
- BUS160: Introduction to Statistics
- BUS159: Fundamentals of Quantitative Research Methods and Data analytics
- 2019 Cambridge Student-Led Teaching Awards: Best Undergraduate Supervisor (Shortlisted)
- 2018 Cambridge Student-Led Teaching Awards: Best Lecturer (Shortlisted)
- 2017 Cambridge Student-Led Teaching Awards: Best Lecturer (Shortlisted)
Michel’s research focuses on the theory and application of statistics/ econometrics and machine learning, particularly exploring time series and unsupervised learning methods. His main research projects seek to propose methodological improvements to established methods.
- Honourable Mention for Best Research among 1st and 2nd year PhD Students - Early Career Researchers Conference, University of Cambridge (2017)
- CAPES and Cambridge Commonwealth, European & International Trust: Full-time PhD scholarship (2015-2019)
- Honourable mention for the “Excellence in the Master of Economics Dissertation”, School of Economics of Sao Paulo - FGV EESP (2013)
Peer-reviewed Journals and Conferences:
- Harnessing the power of intersection for pattern recognition: A novel unsupervised learning method and its application to financial engineering. Engineering Reports, 3(4), e12329 (2021)
- The effect of temperature anomaly and macroeconomic fundamentals on agricultural commodity futures returns; with Makkonen, A., Vallström, D., Uddin, G. S., & Rahman, M. L. Energy Economics, 105377 (2021)
- Entropic Laplacian eigenmaps for unsupervised metric learning; with Levada A. Conference on Graphics, Patterns and Images - SIBGRAPI (2021)
- A Kullback-Leibler divergence-based locally linear embedding method: A novel parametric approach for cluster analysis; with Levada A. Brazilian Conference on Intelligent System - BRACIS (2021)
- Count data regression analysis: Concepts, overdispersion detection, zero-inflation identification, and applications with R; with Fávero, L. P. L., Souza, R. F, Belfiore, P., Corrêa, H. L. Practical Assessment, Research, and Evaluation, Vol. 26 , Article 13 (2021)
- Hierarchical linear modelling evidence on how policymakers may reduce avoidable deaths due to Covid-19: The case of Brazil; with Fávero, L. P. L., Souza, R. F., Corrêa, H. L. International Journal of Mathematics in Operational Research (2021)
- Is there an economic case for energy-efficient dwellings in the UK private rental market?; with Fuerst, F., and Adan, H. Journal of Cleaner Production, 245, 118642 (2020)
- Real estate data to analyse the relationship between property prices, sustainability levels and socio-economic indicators; with Fuerst, F. Data in Brief, 106359 (2020)
- Sphere-sphere intersection for investment portfolio diversification - a new data-driven cluster analysis. MethodsX, 6, 1261-1278 (2019)
- Variation analysis on national standard interest rate. Future Studies Research Journal: Trends and Strategies, 4(1), 140-158 (2012)
- The impact of the global financial crisis on the Brazilian stock market. The Brazilian Economy since the Great Financial Crisis of 2007/2008 (pp. 273-305); with Arruda, B. P. Palgrave Macmillan, Cham (2017)
- Beyond the Minkowski metrics: A novel spherical intersection distance measure for machine learning
- K-ISOMAP: A differential geometry based isometric feature mapping for unsupervised metric learning; with Levada A.
- Entropic locally linear embedding for unsupervised metric learning; with Levada A.
- Non-path-dependent Markov regime-switching score-driven EGARCH models: Statistical inference and forecasting; with Szabolcs, B.
- Are dynamic conditional score-driven models the state-of-the-art of volatility estimation? Empirical evidence from G20 stock markets
- Time- and frequency-based dependence between real estate and sustainable investments; with Stenvall, D., Uddin, G. S.
- Climate policy under uncertainty: A quantile regression with fixed effects approach; with Linnér, B., Uddin, G. S., Stenvall D.
- Are sustainable investments affected by pandemic uncertainty? A mixed-frequency vector autoregressive modelling framework; with Hedström, A., Park, D., Taghizadeh-Hesary, F., Uddin, G. S., Yahya, M.
- Crises and the development of economic institutions: Replicating the Rajan and Ramcharan (2016) results; with Fávero, L. P. L., Souza, R. F.
Michel is interested in supervising new PhD students in statistics/ econometrics and machine learning, either with a focus on methodological innovation or applied (preferably, involving big data) research.
Michel has over a decade of professional experience in consultancy projects on business risk and corporate transactions (mergers, acquisitions, divestitures), covering a wide range of industries (e.g. banking, media, automotive) and S&P500 companies.
He served as vice-president (2016 - 2017) of the Cambridge University Brazilian Society.