Autor(es): Nimtz, Julius
Data: 2019
Identificador Persistente: http://hdl.handle.net/10362/73608
Origem: Repositório Institucional da UNL
Assunto(s): Support vector machine; Asset allocation; Risk parity; Volatility forecast
Detalhes do Documento
Asset allocation using machine learning
Descrição
This paper,Asset Allocation using Machine Learning, proposes a two step model, forecasting rst volatility through an GJR-GARCH model and using a Support Vector machine to do the investment decision between the market portfolio and a risk parity portfolio. Besides the volatility forecast, the Support Vector Machine is based on economic, price, fundamental and sentiment data. It manages to outperform both the market (S&P 500) and a risk parity portfolio in terms of returns and risk adjusted returns.
Tipo de Documento
Dissertação de mestrado
Idioma Inglês
Contribuidor(es) RUN; Lameira, Pedro
Idioma Inglês
Contribuidor(es) RUN; Lameira, Pedro
