In this paper, we propose a novel approach in stock clustering with the purpose of the construction of a portfolio optimization strategy. The idea is to exploit hierarchical Neural Network Principal Component Analysis and Adaptive LASSO in combination with the Arbitrage Pricing Theory in order to group stocks whose returns are affected by the same risk factors, and then eliminate such dependence through an appropriately constructed portfolio. We test our proposal on the Italian stock market.

A Machine Learning Approach in Stock Risk Management

Salvatore Cuomo
;
Federico Gatta;Fabio Giampaolo;Carmela Iorio;Francesco Piccialli
2021

Abstract

In this paper, we propose a novel approach in stock clustering with the purpose of the construction of a portfolio optimization strategy. The idea is to exploit hierarchical Neural Network Principal Component Analysis and Adaptive LASSO in combination with the Arbitrage Pricing Theory in order to group stocks whose returns are affected by the same risk factors, and then eliminate such dependence through an appropriately constructed portfolio. We test our proposal on the Italian stock market.
978-88-5518-340-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/858152
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