Simone Cenci is an Advanced Research Fellow and Data Scientist at the Leonardo Centre on Business for Society at Imperial College Business School.
His research lies at the intersection of management science, finance, and applied statistics. It primarily focuses on understanding what corporations do to lower their environmental impact and how they can change their management practices to contribute to sustainable development. Broadly speaking, the main objective of his research is to use data, statistics and causal reasoning to help facilitate the transition towards a more sustainable economy. In this context, he develops a comprehensive dataset and empirical tools to characterise firms’ sustainability behaviour within a systematic and quantitative framework.
More information about his research can be found on his personal website.
Before joining Imperial College, Simone was a postdoctoral research associate at DCI, LLC, an independent asset management firm now acquired by Blackstone Credit. At DCI, he worked on statistical and causal inference methods to explain and predict firms' capital structure dynamics. Simone obtained his Ph.D. at the Massachusetts Institute of Technology, working on nonparametric statistical inference from nonlinear time series data to understand how populations respond and adapt to environmental changes. His PhD thesis received the Best Doctoral Thesis Award.
Cenci S, Kealhofer S, 2022, A causal approach to test empirical capital structure regularities, Journal of Finance and Data Science, Vol:8, Pages:214-232
et al., 2020, Assessing the predictability of nonlinear dynamics under smooth parameter changes, Journal of the Royal Society Interface, Vol:17, ISSN:1742-5689
Cenci S, Saavedra S, 2019, Non-parametric estimation of the structural stability of non-equilibrium community dynamics, Nature Ecology & Evolution, Vol:3, ISSN:2397-334X, Pages:912-918
Cenci S, Saavedra S, 2018, Structural stability of nonlinear population dynamics, Physical Review E, Vol:97, ISSN:2470-0045