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A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search
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| 1.38 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
The curiosity of investors regarding Environmental, Social and Governance (ESG) factors has seen a growth in the last few years (Alcoforado, 2016), as the world faces some of its biggest problems to date, such as Climate Change and Ecological Collapse. As these issues are not to be taken lightly, individuals have started to act, in the hopes of creating a ‘greener’ world. As individuals hope to align with principles such as Sustainability and Veganism, the proposed project hopes to build a Vegan and Sustainable Index Fund, as “An investment is not an investment if it is destroying our planet.” (Shiva, 2017).
The aim of the proposed work is, consequently, to build and optimize an Industry and Geographical diversified Index Fund, using a Genetic Algorithm (GA), demonstrating this through the incorporation of Vegan and Sustainable companies, in addition to the global top-50 ESG ranked firms. Index Funds, which are mutual or Exchange-Traded Funds (ETF), are known to be passively managed portfolios, which have been broadly used in hedge trading (Orito, Inoguchi, & Yamamoto, 2008).
This study uses historical data from Vegan, Sustainable and ESG-ranked companies as sample data, replacing traditional optimization methods using a Genetic Algorithm.
The GA method was applied to a sample of 61 assets, regarding vegan and sustainable companies, further obtaining a well-diversified and non-centred asset allocation. The obtained results confirm the possible efficiency of genetic algorithms, given their high-speed convergence towards a better solution. A few functions were presented in the algorithm, for example the penalty function method, to perform portfolio optimization which expects to maximize profits and minimize risks. Some flaws have been identified in regard to the method applied.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
Palavras-chave
Index fund Vegan Genetic Algorithm Heuristic Local Search ESG