Publicação

Forecasting S&P 500 sector ETFS returns : a machine learning approach to sector rotation

Detalhes bibliográficos
Resumo:	This thesis develops a machine learning-based dynamic sector rotation strategy, sector ETFs are used as a representation of the United States 11 sectors and of the benchmark S&P500. We use a variety of machine learning models, including LASSO, XGBoost, and Random Forest, to forecast sector returns by utilizing financial market data, market sentiment metrics, currencies and macroeconomic indicators. The top and bottom-performing sectors are chosen to create long, short, and long-short strategies for portfolio construction based on these forecasts. Results show limited predictive power overall, but the strategies built offer modest improvements over the benchmark in certain market conditions.
Autores principais:	Paulino, João Carlos Marques
Assunto:	Exchange-traded funds (ETFs) Feature importance Importância das variavéis Previsão de retornos Returns prediction Rotação de setores Sector rotations
Ano:	2025
País:	Portugal
Tipo de documento:	dissertação de mestrado
Tipo de acesso:	acesso aberto
Instituição associada:	Universidade Católica Portuguesa
Idioma:	inglês
Origem:	Veritati - Repositório Institucional da Universidade Católica Portuguesa

Descrição
Resumo:	This thesis develops a machine learning-based dynamic sector rotation strategy, sector ETFs are used as a representation of the United States 11 sectors and of the benchmark S&P500. We use a variety of machine learning models, including LASSO, XGBoost, and Random Forest, to forecast sector returns by utilizing financial market data, market sentiment metrics, currencies and macroeconomic indicators. The top and bottom-performing sectors are chosen to create long, short, and long-short strategies for portfolio construction based on these forecasts. Results show limited predictive power overall, but the strategies built offer modest improvements over the benchmark in certain market conditions.