Optimal asset allocation using visual programming techniques: A quantitative analysis based on an ESG portfolio

Pier Giuseppe Giribone, Damiano Verda, Francesco Mantovani, Federico Milanesio and Alessio Tissone
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Pier Giuseppe Giribone: Department of Economics, University of Genoa Via F. Vivaldi, 5, 16126 Genova, Italy2Financial Engineering, BPER Banca Via Cassa di Risparmio 15, 16123 Genova, Italy
Damiano Verda: Rulex Innovation Labs Via Felice Romani 9, 16122 Genova GE, Italy
Francesco Mantovani: Rulex Innovation Labs Via Felice Romani 9, 16122 Genova GE, Italy4PhD Engineer, Huawei Paris Lab 18, Quai du Pont du Jour, 92100 Boulogne-Billancourt, France
Federico Milanesio: Financial Engineering Intern, BPER Banca Via Cassa di Risparmio 15, 16123 Genova, Italy
Alessio Tissone: Department of Economics, University of Genoa Via F. Vivaldi, 5, 16126 Genova, Italy6Portfolio Management, AXPO Via XII Ottobre 1, 16121 Genova, Italy

International Journal of Financial Engineering (IJFE), 2025, vol. 12, issue 03, 1-34

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.

Keywords: Asset allocation; visual programming; ESG portfolio; Hierarchical Risk Parity (HRP); k-means clustering (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S2424786325500045

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