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School of Business and Management

Dr Michel F. C. Haddad

Michel F. C.

Associate Professor in Business Statistics

Room Number: Room 3.44b, Francis Bancroft Building, Mile End Campus



  • Associate Professor in Business Statistics
  • Director of the Computational and Quantitative Methods (CQM) Research Cluster
  • Member of the Department of Business Analytics and Applied Economics


Dr Michel is an Associate 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.


Teaching prizes:

  • 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)


  • BUS135: Quantitative Analysis for Business
  • BUS164: Quantitative Methods


Research Interests:

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 quantitative established methods.

Research awards:

  • 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:

  • The two-component Beta-t-QVAR-M-lev: A new forecasting model; with Blazsek, S.; Arestis, P., Fuerst, F., Sheng, H.H.; Financial Markets and Portfolio Management (2023)
  • How are climate policies assessed in emerging economies? A study of ex-ante policy appraisal in Brazil, China, and India; with Qin, J., Lynch, C., Barbrook-Johnson, P., Salas, P., Yang, G., Nijsse, F., Pasqualino, R., Mercure, J-F. Climate Policy (2023)
  • Score-driven multi-regime Markov-switching EGARCH: Empirical evidence using the Meixner distribution; with Blazsek, S.; Studies in Nonlinear Dynamics & Econometrics (2022)
  • Crises and the development of economic institutions: A narrow replication of Rajan and Ramcharan (2016) through a multilevel econometric approach; with Fávero, L.P.L., Souza, R.F.; Springer Nature Business & Economics, 2, 42 (2022)
  • Multilevel evidence on how policymakers may reduce avoidable deaths due to Covid-19: The case of Brazil; with Souza, R.F., Fávero, L.P.L., Corrêa, H.L.; International Journal of Mathematics in Operational Research, 21 (3), 321-337 (2022)
  • 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)
  • A Kullback-Leibler divergence-based locally linear embedding method: A novel parametric approach for cluster analysis; with Levada A.L.M.; Brazilian Conference on Intelligent Systems, pp. 406-420, Springer, Cham (2021)
  • Entropic Laplacian eigenmaps for unsupervised metric learning; with Levada A.L.M.; 34th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 307-314, IEEE (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)
  • 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)
  • Is there an economic case for energy-efficient dwellings in the UK private rental market?; with Fuerst, F., 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)

Book chapters:

  • A Kullback-Leibler Divergence-Based Locally Linear Embedding Method: A Novel Parametric Approach for Cluster Analysis; with Levada, A.L.M.; In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science, vol 13073. Springer, Cham (2021)
  • The Impact of the Global Financial Crisis on the Brazilian Stock Market; with Arruda, B.P.; In The Brazilian Economy since the Great Financial Crisis of 2007/2008 (pp. 273-305). Cham: Springer International Publishing (2017)


Dr Michel Haddad is interested in supervising new PhD students who wish to follow an academic career, with a focus on machine learning or statistical methodological improvements.

Dr Michel is currently supervising the doctoral students José Medina-Reyes, Cagri Yuksel, and Yuanzhe Li.

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