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nettey.boevi.gilles.koumou@usherbrooke.ca
I am a post doc researcher at Chaire Desjardins en Finance Responsable, École de Gestion, Université de Sherbrooke . Before that, I spent 28 months at Faculty of Governance, Economic and Social Sciences at Mohammed VI Polytechnic University as assistant professor, 24 months at the Canada Research Chair in Risk Management in HEC Montreal as a post doc, and 18 months at Université du Québec à Chicoutimi as a lecturer.
My research interest is Financial Economics. Specifically, I focus on Theory of Financial Decision making (with focus on portfolio selection, Asset Pricing and Financial Machine Learning).
I hold a PhD in Financial Economics from Université Laval, a M.Sc. in Mathematics from Université de Lomé, and an Engineering degree in applied statistics and economics from ISSEA (Yaoundé).
Publications
Weight bound constraints in mean-variance models: a robust control theory foundation via machine learning Quantitative Finance. 2024
Hierarchical Risk Budgeting Journal of Financial Data Science. 2024
Unifying Portfolio Diversification Measures Using Rao’s Quadratic Entropy (with Benoît Carmichael et Kevin Moran). Journal of Quantitative Economics. 2023
Risk budgeting using a generalized diversity index. Journal of Asset Management. 2023
Coherent Diversification Measures in Portfolio Theory : An Axiomatic Foundation (with Georges Dionne) Risks. 2022
Diversification and Portfolio Theory : A Review. Financial Markets and Portfolio Management. 2020
Mean-Variance Model And Investors’ Diversification Attitude : A Theoretical Revisit. Finance Research Letters. 2020
Rao’s Quadratic Entropy and Maximum Diversification Indexation (with Benoît Carmichael and Kevin Moran). Quantitative Finance. 2018
Working Papers
The RQE-CAPM: New insights about the pricing of idiosyncratic risk (with Benoît Carmichael et Kevin Moran)
Abstract: We use an equivalent form of Markowitz's mean-variance utility function, based on Rao's Quadratic Entropy (RQE), to update the standard capital asset pricing model (CAPM), both in the presence and in the absence of a risk-free asset. The resulting new equilibrium, which we denote RQE-CAPM, offers important new insights about the pricing of risk. Notably, it reveals that the reason for which the standard CAPM does not price idiosyncratic risk is not only because the market portfolio is law of large numbers diversified but also because the model implicitly assumes agents' total risk aversion and their correlation diversification risk preference balance each other exactly. We then demonstrate that idiosyncratic risk is priced in a general RQE-CAPM where agents' total risk aversion and their correlation diversification risk preference coefficients are not necessary equal. Our general RQE-CAPM therefore offers a unifying way of thinking about the pricing of idiosyncratic risk, including cases where such risk is negatively priced, and is relevant for the literature assessing the idiosyncratic risk puzzle. It also provides a natural theoretical underpinning for the empirical tests of the CAPM or the pricing of idiosyncratic risk performed in some existence studies.
Abstract: We propose a new, highly effective and easy-to-implement algorithm for solving large-scale mean-variance optimization problems --- with weight upper bound constraints and without short sales --- when the size of mean-variance portfolios is much smaller than the number of assets, which is almost always the case. Our novel algorithm is built on the novel representation of mean-variance models in terms of the support vector data description --- an unsupervised machine learning algorithm designed for a one-class classification problem --- and the chunking algorithm, a decomposition algorithm for support vector machine.
Enhancing ESG News Annotation: Leveraging GPT for the analysis of ESG News and Events (with Keven Bluteau and Frank Coggins)
Abstract: This paper introduces a novel methodology that integrates GPT-3.5 into the analysis of Environmental, Social, and Governance (ESG) news. Using a comprehensive dataset of news articles sourced from the Wall Street Journal via the ProQuest database (2000-2022), we develop an automated framework to classify and analyze ESG news items and events based on their novelty, relevance, materiality, and severity within the ESG context. The effectiveness of this AI-driven approach is validated through comparisons with manual annotation, established metadata, and interpretable classification models. Furthermore, we demonstrate that novel (news originating a new event) and severe material news, as classified by GPT-3.5, are negatively associated with S&P 500 firms' daily returns, while non-novel, non-severe, and non-material news exhibit no significant association with firm returns.
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