Tactical asset allocation and machine learning: empirical findings on weights portfolio optimization with elastic net regularization

• Autor(a) principal: Gigli, Pierfrancesco
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/108603
• topic: Machine learning; Elastic net; Portfolio optimization; Tactical allocation; Investment strategy; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

This paper studies how a machine learning algorithm can generate tactical allocation which out performs returns fora pre-defined benchmark. We use three distinct and diverse data sets to implement the model which tries to forecast the next month’ sa selected equity index price. The algorithm used to accomplish this task is Elastic Net.Once the predictions are generated from an out-of-sample subset, we elaborate a tactical portfolio allocation aiming to maximize the return of a different combination of classical allocation between bonds and equity,and a risk parity strategy. Finally, we evaluate those returns by comparing them to the benchmark.