The objective of this study is to conduct an in-depth and comprehensive analysis of optimal asset allocation by employing state-of-the-art visual programming technology that enables the intuitive implementation of Machine Learning methodologies. In particular, this paper shows how two unsupervised clustering methods, one splitting (k-means) and one agglomerative Hierarchical Risk Parity (HRP), aimed at the optimal choice of weights to be allocated within an ESG portfolio, can be programmed in a low-code platform.
Optimal asset allocation using visual programming techniques: A quantitative analysis based on an ESG portfolio
Pier Giuseppe Giribone;Damiano Verda;Alessio Tissone
2025-01-01
Abstract
The objective of this study is to conduct an in-depth and comprehensive analysis of optimal asset allocation by employing state-of-the-art visual programming technology that enables the intuitive implementation of Machine Learning methodologies. In particular, this paper shows how two unsupervised clustering methods, one splitting (k-means) and one agglomerative Hierarchical Risk Parity (HRP), aimed at the optimal choice of weights to be allocated within an ESG portfolio, can be programmed in a low-code platform.
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